diff --git a/.gitignore b/.gitignore index b97f244a..7529509d 100644 --- a/.gitignore +++ b/.gitignore @@ -2,4 +2,3 @@ *.pyc large_files large_files/* -nlp_class2/chunking/* diff --git a/README.md b/README.md index e5075916..53b62543 100644 --- a/README.md +++ b/README.md @@ -3,189 +3,4 @@ machine_learning_examples A collection of machine learning examples and tutorials. -Find associated tutorials at https://lazyprogrammer.me - -Find associated courses at https://deeplearningcourses.com - -Please note that not all code from all courses will be found in this repository. Some newer code examples (e.g. most of Tensorflow 2.0) were done in Google Colab. Therefore, you should check the instructions given in the lectures for the course you are taking. - - -How to I find the code for a particular course? -=============================================== - -The code for each course is separated by folder. You can determine which folder corresponds with which course by watching the "Where to get the code" lecture inside the course (usually Lecture 2 or 3). - -Remember: one folder = one course. - - -Why you should not fork this repo -================================= - -I've noticed that many people have out-of-date forks. Thus, I recommend not forking this repository if you take one of my courses. I am constantly updating my courses, and your fork will soon become out-of-date. You should clone the repository instead to make it easy to get updates (i.e. just "git pull" randomly and frequently). - - -Where is the code for your latest courses? -========================================== - -Beginning with Tensorflow 2, I started to use Google Colab. For those courses, unless otherwise noted, the code will be on Google Colab. Links to the notebooks are provided in the course. See the lecture "Where to get the code" for further details. - - -VIP Course Links -=================== - -**Advanced AI: Deep Reinforcement Learning in PyTorch (v2)** - -https://deeplearningcourses.com/c/deep-reinforcement-learning-in-pytorch - - -**Data Science: Transformers for Natural Language Processing** - -https://deeplearningcourses.com/c/data-science-transformers-nlp - - -**Machine Learning: Natural Language Processing in Python (V2)** - -https://deeplearningcourses.com/c/natural-language-processing-in-python - - -**Time Series Analysis, Forecasting, and Machine Learning** - -https://deeplearningcourses.com/c/time-series-analysis - - -**Financial Engineering and Artificial Intelligence in Python** - -https://deeplearningcourses.com/c/ai-finance - - -**PyTorch: Deep Learning and Artificial Intelligence** - -https://deeplearningcourses.com/c/pytorch-deep-learning - - -**Tensorflow 2.0: Deep Learning and Artificial Intelligence** (VIP Version) - -https://deeplearningcourses.com/c/deep-learning-tensorflow-2 - - -**Math 0-1: Linear Algebra for Data Science & Machine Learning** - -https://deeplearningcourses.com/c/linear-algebra-data-science - -**Math 0-1: Probability for Data Science & Machine Learning** - -https://deeplearningcourses.com/c/probability-data-science-machine-learning - - -Deep Learning Courses Exclusives -================================ - -Data Science: Bayesian Linear Regression in Python -https://deeplearningcourses.com/c/bayesian-linear-regression-in-python - -Data Science: Bayesian Classification in Python -https://deeplearningcourses.com/c/bayesian-classification-in-python - -Classical Statistical Inference and A/B Testing in Python -https://deeplearningcourses.com/c/statistical-inference-in-python - -Linear Programming for Linear Regression in Python -https://deeplearningcourses.com/c/linear-programming-python - -MATLAB for Students, Engineers, and Professionals in STEM -https://deeplearningcourses.com/c/matlab - - - -Other Course Links -================== - -Generative AI: ChatGPT & OpenAI LLMs in Python -https://deeplearningcourses.com/c/genai-openai-chatgpt - -Math 0-1: Matrix Calculus for Data Science & Machine Learning -https://deeplearningcourses.com/c/matrix-calculus-machine-learning - -Machine Learning: Modern Computer Vision & Generative AI -https://deeplearningcourses.com/c/computer-vision-kerascv - -DeepFakes & Voice Cloning: Machine Learning The Easy Way -https://deeplearningcourses.com/c/deepfakes-voice-cloning - -Financial Analysis: Build a ChatGPT Pairs Trading Bot -https://deeplearningcourses.com/c/chatgpt-pairs-trading - -Math 0-1: Calculus for Data Science & Machine Learning -https://deeplearningcourses.com/c/calculus-data-science - -Data Science & Machine Learning: Naive Bayes in Python -https://deeplearningcourses.com/c/data-science-machine-learning-naive-bayes-in-python - -Cutting-Edge AI: Deep Reinforcement Learning in Python -https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence - -Recommender Systems and Deep Learning in Python -https://deeplearningcourses.com/c/recommender-systems - -Machine Learning and AI: Support Vector Machines in Python -https://deeplearningcourses.com/c/support-vector-machines-in-python - -Deep Learning: Advanced Computer Vision -https://deeplearningcourses.com/c/advanced-computer-vision - -Deep Learning: Advanced NLP and RNNs -https://deeplearningcourses.com/c/deep-learning-advanced-nlp - -Deep Learning: GANs and Variational Autoencoders -https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders - -Advanced AI: Deep Reinforcement Learning in Python -https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python - -Artificial Intelligence: Reinforcement Learning in Python -https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python - -Natural Language Processing with Deep Learning in Python -https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python - -Deep Learning: Recurrent Neural Networks in Python -https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python - -Unsupervised Machine Learning: Hidden Markov Models in Python -https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python - -Deep Learning Prerequisites: The Numpy Stack in Python -https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python - -Deep Learning Prerequisites: Linear Regression in Python -https://deeplearningcourses.com/c/data-science-linear-regression-in-python - -Deep Learning Prerequisites: Logistic Regression in Python -https://deeplearningcourses.com/c/data-science-logistic-regression-in-python - -Data Science: Deep Learning and Neural Networks in Python -https://deeplearningcourses.com/c/data-science-deep-learning-in-python - -Cluster Analysis and Unsupervised Machine Learning in Python -https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python - -Data Science: Supervised Machine Learning in Python -https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python - -Bayesian Machine Learning in Python: A/B Testing -https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing - -Data Science: Natural Language Processing in Python -https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python - -Modern Deep Learning in Python -https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow - -Ensemble Machine Learning in Python: Random Forest and AdaBoost -https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost - -Deep Learning: Convolutional Neural Networks in Python -https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow - -Unsupervised Deep Learning in Python 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-from scipy.stats import beta - - -# np.random.seed(2) -NUM_TRIALS = 2000 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - self.p = p - self.a = 1 - self.b = 1 - self.N = 0 # for information only - - def pull(self): - return np.random.random() < self.p - - def sample(self): - return np.random.beta(self.a, self.b) - - def update(self, x): - self.a += x - self.b += 1 - x - self.N += 1 - - -def plot(bandits, trial): - x = np.linspace(0, 1, 200) - for b in bandits: - y = beta.pdf(x, b.a, b.b) - plt.plot(x, y, label=f"real p: {b.p:.4f}, win rate = {b.a - 1}/{b.N}") - plt.title(f"Bandit distributions after {trial} trials") - plt.legend() - plt.show() - - -def experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - - sample_points = [5,10,20,50,100,200,500,1000,1500,1999] - rewards = np.zeros(NUM_TRIALS) - for i in range(NUM_TRIALS): - # Thompson sampling - j = np.argmax([b.sample() for b in bandits]) - - # plot the posteriors - if i in sample_points: - plot(bandits, i) - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update rewards - rewards[i] = x - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - # print total reward - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num times selected each bandit:", [b.N for b in bandits]) - - -if __name__ == "__main__": - experiment() diff --git a/ab_testing/bayesian_normal.py b/ab_testing/bayesian_normal.py deleted file mode 100644 index 07083d1b..00000000 --- a/ab_testing/bayesian_normal.py +++ /dev/null @@ -1,84 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt -from scipy.stats import norm - - -np.random.seed(1) -NUM_TRIALS = 2000 -BANDIT_MEANS = [1, 2, 3] - - -class Bandit: - def __init__(self, true_mean): - self.true_mean = true_mean - # parameters for mu - prior is N(0,1) - self.m = 0 - self.lambda_ = 1 - self.tau = 1 - self.N = 0 - - def pull(self): - return np.random.randn() / np.sqrt(self.tau) + self.true_mean - - def sample(self): - return np.random.randn() / np.sqrt(self.lambda_) + self.m - - def update(self, x): - self.m = (self.tau * x + self.lambda_ * self.m) / (self.tau + self.lambda_) - self.lambda_ += self.tau - self.N += 1 - - -def plot(bandits, trial): - x = np.linspace(-3, 6, 200) - for b in bandits: - y = norm.pdf(x, b.m, np.sqrt(1. / b.lambda_)) - plt.plot(x, y, label=f"real mean: {b.true_mean:.4f}, num plays: {b.N}") - plt.title(f"Bandit distributions after {trial} trials") - plt.legend() - plt.show() - - -def run_experiment(): - bandits = [Bandit(m) for m in BANDIT_MEANS] - - sample_points = [5,10,20,50,100,200,500,1000,1500,1999] - rewards = np.empty(NUM_TRIALS) - for i in range(NUM_TRIALS): - # Thompson sampling - j = np.argmax([b.sample() for b in bandits]) - - # plot the posteriors - if i in sample_points: - plot(bandits, i) - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - # update rewards - rewards[i] = x - - cumulative_average = np.cumsum(rewards) / (np.arange(NUM_TRIALS) + 1) - - # plot moving average ctr - plt.plot(cumulative_average) - for m in BANDIT_MEANS: - plt.plot(np.ones(NUM_TRIALS)*m) - plt.show() - - return cumulative_average - -if __name__ == '__main__': - run_experiment() - - diff --git a/ab_testing/bayesian_starter.py b/ab_testing/bayesian_starter.py deleted file mode 100644 index 68e12f75..00000000 --- a/ab_testing/bayesian_starter.py +++ /dev/null @@ -1,78 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import matplotlib.pyplot as plt -import numpy as np -from scipy.stats import beta - - -# np.random.seed(2) -NUM_TRIALS = 2000 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - self.p = p - self.a = # TODO - self.b = # TODO - self.N = 0 # for information only - - def pull(self): - return np.random.random() < self.p - - def sample(self): - return # TODO - draw a sample from Beta(a, b) - - def update(self, x): - self.a = # TODO - self.b = # TODO - self.N += 1 - - -def plot(bandits, trial): - x = np.linspace(0, 1, 200) - for b in bandits: - y = beta.pdf(x, b.a, b.b) - plt.plot(x, y, label=f"real p: {b.p:.4f}, win rate = {b.a - 1}/{b.N}") - plt.title(f"Bandit distributions after {trial} trials") - plt.legend() - plt.show() - - -def experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - - sample_points = [5,10,20,50,100,200,500,1000,1500,1999] - rewards = np.zeros(NUM_TRIALS) - for i in range(NUM_TRIALS): - # Thompson sampling - j = # TODO - - # plot the posteriors - if i in sample_points: - plot(bandits, i) - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update rewards - rewards[i] = x - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - # print total reward - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num times selected each bandit:", [b.N for b in bandits]) - - -if __name__ == "__main__": - experiment() diff --git a/ab_testing/cdfs_and_percentiles.py b/ab_testing/cdfs_and_percentiles.py deleted file mode 100644 index 124bc408..00000000 --- a/ab_testing/cdfs_and_percentiles.py +++ /dev/null @@ -1,35 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from scipy.stats import norm - - -mu = 170 -sd = 7 - - -# generate samples from our distribution -x = norm.rvs(loc=mu, scale=sd, size=100) - -# maximum likelihood mean -x.mean() - -# maximum likelihood variance -x.var() - -# maximum likelihood std -x.std() - -# unbiased variance -x.var(ddof=1) - -# unbiased std -x.std(ddof=1) - -# at what height are you in the 95th percentile? -norm.ppf(0.95, loc=mu, scale=sd) - -# you are 160 cm tall, what percentile are you in? -norm.cdf(160, loc=mu, scale=sd) - -# you are 180 cm tall, what is the probability that someone is taller than you? -1 - norm.cdf(180, loc=mu, scale=sd) \ No newline at end of file diff --git a/ab_testing/chisquare.py b/ab_testing/chisquare.py deleted file mode 100644 index 284dbcd3..00000000 --- a/ab_testing/chisquare.py +++ /dev/null @@ -1,69 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from scipy.stats import chi2, chi2_contingency - -# contingency table -# click no click -#------------------------------ -# ad A | a b -# ad B | c d -# -# chi^2 = (ad - bc)^2 (a + b + c + d) / [ (a + b)(c + d)(a + c)(b + d)] -# degrees of freedom = (#cols - 1) x (#rows - 1) = (2 - 1)(2 - 1) = 1 - -# short example - -# T = np.array([[36, 14], [30, 25]]) -# c2 = np.linalg.det(T)**2 * T.sum() / ( T[0].sum()*T[1].sum()*T[:,0].sum()*T[:,1].sum() ) -# p_value = 1 - chi2.cdf(x=c2, df=1) - -# equivalent: -# (36-31.429)**2/31.429+(14-18.571)**2/18.571 + (30-34.571)**2/34.571 + (25-20.429)**2/20.429 - - -class DataGenerator: - def __init__(self, p1, p2): - self.p1 = p1 - self.p2 = p2 - - def next(self): - click1 = 1 if (np.random.random() < self.p1) else 0 - click2 = 1 if (np.random.random() < self.p2) else 0 - return click1, click2 - - -def get_p_value(T): - # same as scipy.stats.chi2_contingency(T, correction=False) - det = T[0,0]*T[1,1] - T[0,1]*T[1,0] - c2 = float(det) / T[0].sum() * det / T[1].sum() * T.sum() / T[:,0].sum() / T[:,1].sum() - p = 1 - chi2.cdf(x=c2, df=1) - return p - - -def run_experiment(p1, p2, N): - data = DataGenerator(p1, p2) - p_values = np.empty(N) - T = np.zeros((2, 2)).astype(np.float32) - for i in range(N): - c1, c2 = data.next() - T[0,c1] += 1 - T[1,c2] += 1 - # ignore the first 10 values - if i < 10: - p_values[i] = None - else: - p_values[i] = get_p_value(T) - plt.plot(p_values) - plt.plot(np.ones(N)*0.05) - plt.show() - -run_experiment(0.1, 0.11, 20000) diff --git a/ab_testing/ci_comparison.py b/ab_testing/ci_comparison.py deleted file mode 100644 index 56394572..00000000 --- a/ab_testing/ci_comparison.py +++ /dev/null @@ -1,43 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import matplotlib.pyplot as plt -import numpy as np -from scipy.stats import beta, norm - -T = 501 # number of coin tosses -true_ctr = 0.5 -a, b = 1, 1 # beta priors -plot_indices = (10, 20, 30, 50, 100, 200, 500) -data = np.empty(T) -for i in range(T): - x = 1 if np.random.random() < true_ctr else 0 - data[i] = x - - # update a and b - a += x - b += 1 - x - - if i in plot_indices: - # maximum likelihood estimate of ctr - p = data[:i].mean() - n = i + 1 # number of samples collected so far - std = np.sqrt(p*(1-p)/n) - - # gaussian - x = np.linspace(0, 1, 200) - g = norm.pdf(x, loc=p, scale=std) - plt.plot(x, g, label='Gaussian Approximation') - - # beta - posterior = beta.pdf(x, a=a, b=b) - plt.plot(x, posterior, label='Beta Posterior') - plt.legend() - plt.title("N = %s" % n) - plt.show() diff --git a/ab_testing/client.py b/ab_testing/client.py deleted file mode 100644 index 1b130447..00000000 --- a/ab_testing/client.py +++ /dev/null @@ -1,51 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import requests -import numpy as np -import pandas as pd -from scipy import stats - -# get data -df = pd.read_csv('advertisement_clicks.csv') -a = df[df['advertisement_id'] == 'A'] -b = df[df['advertisement_id'] == 'B'] -a = a['action'].values -b = b['action'].values - -print("a.mean:", a.mean()) -print("b.mean:", b.mean()) - - -i = 0 -j = 0 -count = 0 -while i < len(a) and j < len(b): - # quit when there's no data left for either ad - r = requests.get('http://localhost:8888/get_ad') - # print(r.content) - r = r.json() - if r['advertisement_id'] == 'A': - action = a[i] - i += 1 - else: - action = b[j] - j += 1 - - if action == 1: - # only click the ad if our dataset determines that we should - requests.post( - 'http://localhost:8888/click_ad', - data={'advertisement_id': r['advertisement_id']} - ) - - # log some stats - count += 1 - if count % 50 == 0: - print("Seen %s ads, A: %s, B: %s" % (count, i, j)) diff --git a/ab_testing/comparing_epsilons.py b/ab_testing/comparing_epsilons.py deleted file mode 100755 index 8fe885c9..00000000 --- a/ab_testing/comparing_epsilons.py +++ /dev/null @@ -1,89 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - - -class BanditArm: - def __init__(self, m): - self.m = m - self.m_estimate = 0 - self.N = 0 - - def pull(self): - return np.random.randn() + self.m - - def update(self, x): - self.N += 1 - self.m_estimate = (1 - 1.0/self.N)*self.m_estimate + 1.0/self.N*x - - -def run_experiment(m1, m2, m3, eps, N): - bandits = [BanditArm(m1), BanditArm(m2), BanditArm(m3)] - - # count number of suboptimal choices - means = np.array([m1, m2, m3]) - true_best = np.argmax(means) - count_suboptimal = 0 - - data = np.empty(N) - - for i in range(N): - # epsilon greedy - p = np.random.random() - if p < eps: - j = np.random.choice(len(bandits)) - else: - j = np.argmax([b.m_estimate for b in bandits]) - x = bandits[j].pull() - bandits[j].update(x) - - if j != true_best: - count_suboptimal += 1 - - # for the plot - data[i] = x - cumulative_average = np.cumsum(data) / (np.arange(N) + 1) - - # plot moving average ctr - plt.plot(cumulative_average) - plt.plot(np.ones(N)*m1) - plt.plot(np.ones(N)*m2) - plt.plot(np.ones(N)*m3) - plt.xscale('log') - plt.show() - - for b in bandits: - print(b.m_estimate) - - print("percent suboptimal for epsilon = %s:" % eps, float(count_suboptimal) / N) - - return cumulative_average - -if __name__ == '__main__': - m1, m2, m3 = 1.5, 2.5, 3.5 - c_1 = run_experiment(m1, m2, m3, 0.1, 100000) - c_05 = run_experiment(m1, m2, m3, 0.05, 100000) - c_01 = run_experiment(m1, m2, m3, 0.01, 100000) - - # log scale plot - plt.plot(c_1, label='eps = 0.1') - plt.plot(c_05, label='eps = 0.05') - plt.plot(c_01, label='eps = 0.01') - plt.legend() - plt.xscale('log') - plt.show() - - - # linear plot - plt.plot(c_1, label='eps = 0.1') - plt.plot(c_05, label='eps = 0.05') - plt.plot(c_01, label='eps = 0.01') - plt.legend() - plt.show() - diff --git a/ab_testing/convergence.py b/ab_testing/convergence.py deleted file mode 100644 index 0752f19f..00000000 --- a/ab_testing/convergence.py +++ /dev/null @@ -1,40 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import matplotlib.pyplot as plt -import numpy as np -from bayesian_bandit import Bandit - - -def run_experiment(p1, p2, p3, N): - bandits = [Bandit(p1), Bandit(p2), Bandit(p3)] - - data = np.empty(N) - - for i in range(N): - # thompson sampling - j = np.argmax([b.sample() for b in bandits]) - x = bandits[j].pull() - bandits[j].update(x) - - # for the plot - data[i] = x - cumulative_average_ctr = np.cumsum(data) / (np.arange(N) + 1) - - # plot moving average ctr - plt.plot(cumulative_average_ctr) - plt.plot(np.ones(N)*p1) - plt.plot(np.ones(N)*p2) - plt.plot(np.ones(N)*p3) - plt.ylim((0,1)) - plt.xscale('log') - plt.show() - - -run_experiment(0.2, 0.25, 0.3, 100000) \ No newline at end of file diff --git a/ab_testing/demo.py b/ab_testing/demo.py deleted file mode 100644 index c8d03061..00000000 --- a/ab_testing/demo.py +++ /dev/null @@ -1,33 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from scipy.stats import beta - -def plot(a, b, trial, ctr): - x = np.linspace(0, 1, 200) - y = beta.pdf(x, a, b) - mean = float(a) / (a + b) - plt.plot(x, y) - plt.title("Distributions after %s trials, true rate = %.1f, mean = %.2f" % (trial, ctr, mean)) - plt.show() - -true_ctr = 0.3 -a, b = 1, 1 # beta parameters -show = [0, 5, 10, 25, 50, 100, 200, 300, 500, 700, 1000, 1500] -for t in range(1501): - coin_toss_result = (np.random.random() < true_ctr) - if coin_toss_result: - a += 1 - else: - b += 1 - - if t in show: - plot(a, b, t+1, true_ctr) diff --git a/ab_testing/epsilon_greedy.py b/ab_testing/epsilon_greedy.py deleted file mode 100755 index b6eeb067..00000000 --- a/ab_testing/epsilon_greedy.py +++ /dev/null @@ -1,93 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import matplotlib.pyplot as plt -import numpy as np - - -NUM_TRIALS = 10000 -EPS = 0.1 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class BanditArm: - def __init__(self, p): - # p: the win rate - self.p = p - self.p_estimate = 0. - self.N = 0. # num samples collected so far - - def pull(self): - # draw a 1 with probability p - return np.random.random() < self.p - - def update(self, x): - self.N += 1. - self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N - - -def choose_random_argmax(a): - idx = np.argwhere(np.amax(a) == a).flatten() - return np.random.choice(idx) - - -def experiment(): - bandits = [BanditArm(p) for p in BANDIT_PROBABILITIES] - - rewards = np.zeros(NUM_TRIALS) - num_times_explored = 0 - num_times_exploited = 0 - num_optimal = 0 - optimal_j = np.argmax([b.p for b in bandits]) - print("optimal j:", optimal_j) - - for i in range(NUM_TRIALS): - - # use epsilon-greedy to select the next bandit - if np.random.random() < EPS: - num_times_explored += 1 - j = np.random.randint(len(bandits)) - else: - num_times_exploited += 1 - j = choose_random_argmax([b.p_estimate for b in bandits]) - - if j == optimal_j: - num_optimal += 1 - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update rewards log - rewards[i] = x - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - - - # print mean estimates for each bandit - for b in bandits: - print("mean estimate:", b.p_estimate) - - # print total reward - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num_times_explored:", num_times_explored) - print("num_times_exploited:", num_times_exploited) - print("num times selected optimal bandit:", num_optimal) - - # plot the results - cumulative_rewards = np.cumsum(rewards) - win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1) - plt.plot(win_rates) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.show() - -if __name__ == "__main__": - experiment() diff --git a/ab_testing/epsilon_greedy_starter.py b/ab_testing/epsilon_greedy_starter.py deleted file mode 100755 index 4b2a77d1..00000000 --- a/ab_testing/epsilon_greedy_starter.py +++ /dev/null @@ -1,88 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import matplotlib.pyplot as plt -import numpy as np - - -NUM_TRIALS = 10000 -EPS = 0.1 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class BanditArm: - def __init__(self, p): - # p: the win rate - self.p = p - self.p_estimate = # TODO - self.N = # TODO - - def pull(self): - # draw a 1 with probability p - return np.random.random() < self.p - - def update(self, x): - self.N = # TODO - self.p_estimate = # TODO - - -def experiment(): - bandits = [BanditArm(p) for p in BANDIT_PROBABILITIES] - - rewards = np.zeros(NUM_TRIALS) - num_times_explored = 0 - num_times_exploited = 0 - num_optimal = 0 - optimal_j = np.argmax([b.p for b in bandits]) - print("optimal j:", optimal_j) - - for i in range(NUM_TRIALS): - - # use epsilon-greedy to select the next bandit - if np.random.random() < EPS: - num_times_explored += 1 - j = # TODO - else: - num_times_exploited += 1 - j = # TODO - - if j == optimal_j: - num_optimal += 1 - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update rewards log - rewards[i] = x - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - - - # print mean estimates for each bandit - for b in bandits: - print("mean estimate:", b.p_estimate) - - # print total reward - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num_times_explored:", num_times_explored) - print("num_times_exploited:", num_times_exploited) - print("num times selected optimal bandit:", num_optimal) - - # plot the results - cumulative_rewards = np.cumsum(rewards) - win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1) - plt.plot(win_rates) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.show() - -if __name__ == "__main__": - experiment() diff --git a/ab_testing/ex_chisq.py b/ab_testing/ex_chisq.py deleted file mode 100644 index b1e38e36..00000000 --- a/ab_testing/ex_chisq.py +++ /dev/null @@ -1,42 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import pandas as pd -from scipy.stats import chi2, chi2_contingency - -# contingency table -# click no click -#------------------------------ -# ad A | a b -# ad B | c d - - -def get_p_value(T): - # same as scipy.stats.chi2_contingency(T, correction=False) - det = T[0,0]*T[1,1] - T[0,1]*T[1,0] - c2 = float(det) / T[0].sum() * det / T[1].sum() * T.sum() / T[:,0].sum() / T[:,1].sum() - p = 1 - chi2.cdf(x=c2, df=1) - return p - -# get data -df = pd.read_csv('advertisement_clicks.csv') -a = df[df['advertisement_id'] == 'A'] -b = df[df['advertisement_id'] == 'B'] -a = a['action'] -b = b['action'] - -A_clk = a.sum() -A_noclk = a.size - a.sum() -B_clk = b.sum() -B_noclk = b.size - b.sum() - -T = np.array([[A_clk, A_noclk], [B_clk, B_noclk]]) - -print(get_p_value(T)) \ No newline at end of file diff --git a/ab_testing/ex_ttest.py b/ab_testing/ex_ttest.py deleted file mode 100644 index 4afd2e54..00000000 --- a/ab_testing/ex_ttest.py +++ /dev/null @@ -1,45 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import pandas as pd -from scipy import stats - -# get data -df = pd.read_csv('advertisement_clicks.csv') -a = df[df['advertisement_id'] == 'A'] -b = df[df['advertisement_id'] == 'B'] -a = a['action'] -b = b['action'] - -print("a.mean:", a.mean()) -print("b.mean:", b.mean()) - -# built-in t-test: -t, p = stats.ttest_ind(a, b) -print("t:\t", t, "p:\t", p) - -# welch's t-test: -t, p = stats.ttest_ind(a, b, equal_var=False) -print("Welch's t-test:") -print("t:\t", t, "p:\t", p) - -# welch's t-test manual: -N1 = len(a) -s1_sq = a.var() -N2 = len(b) -s2_sq = b.var() -t = (a.mean() - b.mean()) / np.sqrt(s1_sq / N1 + s2_sq / N2) - -nu1 = N1 - 1 -nu2 = N2 - 1 -df = (s1_sq / N1 + s2_sq / N2)**2 / ( (s1_sq*s1_sq) / (N1*N1 * nu1) + (s2_sq*s2_sq) / (N2*N2 * nu2) ) -p = (1 - stats.t.cdf(np.abs(t), df=df))*2 -print("Manual Welch t-test") -print("t:\t", t, "p:\t", p) \ No newline at end of file diff --git a/ab_testing/extra_reading.txt b/ab_testing/extra_reading.txt deleted file mode 100644 index 71360a2e..00000000 --- a/ab_testing/extra_reading.txt +++ /dev/null @@ -1,20 +0,0 @@ -The Unbiased Estimate of the Covariance Matrix -https://lazyprogrammer.me/covariance-matrix-divide-by-n-or-n-1/ - -Algorithms for the multi-armed bandit problem -https://www.cs.mcgill.ca/~vkules/bandits.pdf - -UCB REVISITED: IMPROVED REGRET BOUNDS FOR THE STOCHASTIC MULTI-ARMED BANDIT PROBLEM -http://personal.unileoben.ac.at/rortner/Pubs/UCBRev.pdf - -Finite-time Analysis of the Multiarmed Bandit Problem -https://link.springer.com/article/10.1023/A:1013689704352 - -A Tutorial on Thompson Sampling -https://web.stanford.edu/~bvr/pubs/TS_Tutorial.pdf - -An Empirical Evaluation of Thompson Sampling -https://papers.nips.cc/paper/4321-an-empirical-evaluation-of-thompson-sampling.pdf - -Analysis of Thompson Sampling for the Multi-armed Bandit Problem -http://proceedings.mlr.press/v23/agrawal12/agrawal12.pdf \ No newline at end of file diff --git a/ab_testing/optimistic.py b/ab_testing/optimistic.py deleted file mode 100644 index 1d024fef..00000000 --- a/ab_testing/optimistic.py +++ /dev/null @@ -1,71 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import matplotlib.pyplot as plt -import numpy as np - - -NUM_TRIALS = 10000 -EPS = 0.1 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - # p: the win rate - self.p = p - self.p_estimate = 5. - self.N = 1. # num samples collected so far - - def pull(self): - # draw a 1 with probability p - return np.random.random() < self.p - - def update(self, x): - self.N += 1. - self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N - - -def experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - - rewards = np.zeros(NUM_TRIALS) - for i in range(NUM_TRIALS): - # use optimistic initial values to select the next bandit - j = np.argmax([b.p_estimate for b in bandits]) - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update rewards log - rewards[i] = x - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - - # print mean estimates for each bandit - for b in bandits: - print("mean estimate:", b.p_estimate) - - # print total reward - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num times selected each bandit:", [b.N for b in bandits]) - - # plot the results - cumulative_rewards = np.cumsum(rewards) - win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1) - plt.ylim([0, 1]) - plt.plot(win_rates) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.show() - -if __name__ == "__main__": - experiment() diff --git a/ab_testing/optimistic_starter.py b/ab_testing/optimistic_starter.py deleted file mode 100644 index 56b4e5c9..00000000 --- a/ab_testing/optimistic_starter.py +++ /dev/null @@ -1,71 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import matplotlib.pyplot as plt -import numpy as np - - -NUM_TRIALS = 10000 -EPS = 0.1 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - # p: the win rate - self.p = p - self.p_estimate = # TODO - self.N = # TODO - - def pull(self): - # draw a 1 with probability p - return np.random.random() < self.p - - def update(self, x): - # TODO - self.p_estimate = # TODO - - -def experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - - rewards = np.zeros(NUM_TRIALS) - for i in range(NUM_TRIALS): - # use optimistic initial values to select the next bandit - j = # TODO - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update rewards log - rewards[i] = x - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - - # print mean estimates for each bandit - for b in bandits: - print("mean estimate:", b.p_estimate) - - # print total reward - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num times selected each bandit:", [b.N for b in bandits]) - - # plot the results - cumulative_rewards = np.cumsum(rewards) - win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1) - plt.ylim([0, 1]) - plt.plot(win_rates) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.show() - -if __name__ == "__main__": - experiment() diff --git a/ab_testing/server_solution.py b/ab_testing/server_solution.py deleted file mode 100644 index 491949ae..00000000 --- a/ab_testing/server_solution.py +++ /dev/null @@ -1,76 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from flask import Flask, jsonify, request -from scipy.stats import beta - -# create an app -app = Flask(__name__) - - -# define bandit -# there's no "pull arm" here -# since that's technically now the user/client -class Bandit: - def __init__(self, name): - self.clks = 0 - self.views = 0 - self.name = name - - def sample(self): - # Beta(1, 1) is the prior - a = 1 + self.clks - b = 1 + self.views - self.clks - return np.random.beta(a, b) - - def add_click(self): - self.clks += 1 - - def add_view(self): - self.views += 1 - - # print some helpful stats - if self.views % 50 == 0: - print("%s: clks=%s, views=%s" % (self.name, self.clks, self.views)) - - -# initialize bandits -banditA = Bandit('A') -banditB = Bandit('B') - - - -@app.route('/get_ad') -def get_ad(): - if banditA.sample() > banditB.sample(): - ad = 'A' - banditA.add_view() - else: - ad = 'B' - banditB.add_view() - return jsonify({'advertisement_id': ad}) - - -@app.route('/click_ad', methods=['POST']) -def click_ad(): - result = 'OK' - if request.form['advertisement_id'] == 'A': - banditA.add_click() - elif request.form['advertisement_id'] == 'B': - banditB.add_click() - else: - result = 'Invalid Input.' - - # nothing to return really - return jsonify({'result': result}) - - -if __name__ == '__main__': - app.run(host='127.0.0.1', port='8888') \ No newline at end of file diff --git a/ab_testing/server_starter.py b/ab_testing/server_starter.py deleted file mode 100644 index 6ecadb8d..00000000 --- a/ab_testing/server_starter.py +++ /dev/null @@ -1,61 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from flask import Flask, jsonify, request -from scipy.stats import beta - -# create an app -app = Flask(__name__) - - -# define bandits -# there's no "pull arm" here -# since that's technically now the user/client -class Bandit: - def __init__(self, name): - self.name = name - - def sample(self): - # TODO - return 1 - - # TODO - what else does the Bandit need to do? - - -# initialize bandits -banditA = Bandit('A') -banditB = Bandit('B') - - - -@app.route('/get_ad') -def get_ad(): - # TODO - return jsonify({'advertisement_id': 'A'}) - - -@app.route('/click_ad', methods=['POST']) -def click_ad(): - result = 'OK' - if request.form['advertisement_id'] == 'A': - # TODO - pass - elif request.form['advertisement_id'] == 'B': - # TODO - pass - else: - result = 'Invalid Input.' - - # nothing to return really - return jsonify({'result': result}) - - -if __name__ == '__main__': - app.run(host='127.0.0.1', port='8888') \ No newline at end of file diff --git a/ab_testing/ttest.py b/ab_testing/ttest.py deleted file mode 100644 index e3369a11..00000000 --- a/ab_testing/ttest.py +++ /dev/null @@ -1,29 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from scipy import stats - -# generate data -N = 10 -a = np.random.randn(N) + 2 # mean 2, variance 1 -b = np.random.randn(N) # mean 0, variance 1 - -# roll your own t-test: -var_a = a.var(ddof=1) # unbiased estimator, divide by N-1 instead of N -var_b = b.var(ddof=1) -s = np.sqrt( (var_a + var_b) / 2 ) # balanced standard deviation -t = (a.mean() - b.mean()) / (s * np.sqrt(2.0/N)) # t-statistic -df = 2*N - 2 # degrees of freedom -p = 1 - stats.t.cdf(np.abs(t), df=df) # one-sided test p-value -print("t:\t", t, "p:\t", 2*p) # two-sided test p-value - -# built-in t-test: -t2, p2 = stats.ttest_ind(a, b) -print("t2:\t", t2, "p2:\t", p2) \ No newline at end of file diff --git a/ab_testing/ucb1.py b/ab_testing/ucb1.py deleted file mode 100644 index 5779b654..00000000 --- a/ab_testing/ucb1.py +++ /dev/null @@ -1,81 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -# https://books.google.ca/books?id=_ATpBwAAQBAJ&lpg=PA201&ots=rinZM8jQ6s&dq=hoeffding%20bound%20gives%20probability%20%22greater%20than%201%22&pg=PA201#v=onepage&q&f=false -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - - -NUM_TRIALS = 100000 -EPS = 0.1 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - # p: the win rate - self.p = p - self.p_estimate = 0. - self.N = 0. # num samples collected so far - - def pull(self): - # draw a 1 with probability p - return np.random.random() < self.p - - def update(self, x): - self.N += 1. - self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N - - -def ucb(mean, n, nj): - return mean + np.sqrt(2*np.log(n) / nj) - - -def run_experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - rewards = np.empty(NUM_TRIALS) - total_plays = 0 - - # initialization: play each bandit once - for j in range(len(bandits)): - x = bandits[j].pull() - total_plays += 1 - bandits[j].update(x) - - for i in range(NUM_TRIALS): - j = np.argmax([ucb(b.p_estimate, total_plays, b.N) for b in bandits]) - x = bandits[j].pull() - total_plays += 1 - bandits[j].update(x) - - # for the plot - rewards[i] = x - cumulative_average = np.cumsum(rewards) / (np.arange(NUM_TRIALS) + 1) - - # plot moving average ctr - plt.plot(cumulative_average) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.xscale('log') - plt.show() - - # plot moving average ctr linear - plt.plot(cumulative_average) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.show() - - for b in bandits: - print(b.p_estimate) - - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num times selected each bandit:", [b.N for b in bandits]) - - return cumulative_average - -if __name__ == '__main__': - run_experiment() - diff --git a/ab_testing/ucb1_starter.py b/ab_testing/ucb1_starter.py deleted file mode 100644 index 9e9c3106..00000000 --- a/ab_testing/ucb1_starter.py +++ /dev/null @@ -1,81 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -# https://books.google.ca/books?id=_ATpBwAAQBAJ&lpg=PA201&ots=rinZM8jQ6s&dq=hoeffding%20bound%20gives%20probability%20%22greater%20than%201%22&pg=PA201#v=onepage&q&f=false -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - - -NUM_TRIALS = 100000 -EPS = 0.1 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - # p: the win rate - self.p = p - self.p_estimate = 0. - self.N = 0. # num samples collected so far - - def pull(self): - # draw a 1 with probability p - return np.random.random() < self.p - - def update(self, x): - self.N += 1. - self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N - - -def ucb(mean, n, nj): - return # TODO - - -def run_experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - rewards = np.empty(NUM_TRIALS) - total_plays = 0 - - # initialization: play each bandit once - for j in range(len(bandits)): - x = bandits[j].pull() - total_plays += 1 - bandits[j].update(x) - - for i in range(NUM_TRIALS): - j = # TODO - x = bandits[j].pull() - total_plays += 1 - bandits[j].update(x) - - # for the plot - rewards[i] = x - cumulative_average = np.cumsum(rewards) / (np.arange(NUM_TRIALS) + 1) - - # plot moving average ctr - plt.plot(cumulative_average) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.xscale('log') - plt.show() - - # plot moving average ctr linear - plt.plot(cumulative_average) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.show() - - for b in bandits: - print(b.p_estimate) - - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num times selected each bandit:", [b.N for b in bandits]) - - return cumulative_average - -if __name__ == '__main__': - run_experiment() - diff --git a/airline/ann.py b/airline/ann.py deleted file mode 100644 index a5cad526..00000000 --- a/airline/ann.py +++ /dev/null @@ -1,184 +0,0 @@ -# The corresponding tutorial for this code was released EXCLUSIVELY as a bonus -# If you want to learn about future bonuses, please sign up for my newsletter at: -# https://lazyprogrammer.me - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import theano -import theano.tensor as T -from sklearn.utils import shuffle - -def init_weight(M1, M2): - return np.random.randn(M1, M2) / np.sqrt(M1 + M2) - -def myr2(T, Y): - Ym = T.mean() - sse = (T - Y).dot(T - Y) - sst = (T - Ym).dot(T - Ym) - return 1 - sse / sst - -class HiddenLayer(object): - def __init__(self, M1, M2, f, an_id): - self.id = an_id - self.M1 = M1 - self.M2 = M2 - self.f = f - W = init_weight(M1, M2) - b = np.zeros(M2) - self.W = theano.shared(W, 'W_%s' % self.id) - self.b = theano.shared(b, 'b_%s' % self.id) - self.params = [self.W, self.b] - - def forward(self, X): - return self.f(X.dot(self.W) + self.b) - - -class ANN(object): - def __init__(self, hidden_layer_sizes): - self.hidden_layer_sizes = hidden_layer_sizes - - def fit(self, X, Y, activation=T.tanh, learning_rate=1e-3, mu=0.5, reg=0, epochs=5000, batch_sz=None, print_period=100, show_fig=True): - X = X.astype(np.float32) - Y = Y.astype(np.float32) - - # initialize hidden layers - N, D = X.shape - self.hidden_layers = [] - M1 = D - count = 0 - for M2 in self.hidden_layer_sizes: - h = HiddenLayer(M1, M2, activation, count) - self.hidden_layers.append(h) - M1 = M2 - count += 1 - W = np.random.randn(M1) / np.sqrt(M1) - b = 0.0 - self.W = theano.shared(W, 'W_last') - self.b = theano.shared(b, 'b_last') - - if batch_sz is None: - batch_sz = N - - # collect params for later use - self.params = [self.W, self.b] - for h in self.hidden_layers: - self.params += h.params - - # for momentum - dparams = [theano.shared(np.zeros(p.get_value().shape)) for p in self.params] - - # set up theano functions and variables - thX = T.matrix('X') - thY = T.vector('Y') - Yhat = self.forward(thX) - - rcost = reg*T.mean([(p*p).sum() for p in self.params]) - cost = T.mean((thY - Yhat).dot(thY - Yhat)) + rcost - prediction = self.forward(thX) - grads = T.grad(cost, self.params) - - # momentum only - updates = [ - (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads) - ] + [ - (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads) - ] - - train_op = theano.function( - inputs=[thX, thY], - outputs=[cost, prediction], - updates=updates, - ) - - self.predict_op = theano.function( - inputs=[thX], - outputs=prediction, - ) - - n_batches = N / batch_sz - # print "N:", N, "batch_sz:", batch_sz - # print "n_batches:", n_batches - costs = [] - for i in xrange(epochs): - X, Y = shuffle(X, Y) - for j in xrange(n_batches): - Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)] - Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)] - - c, p = train_op(Xbatch, Ybatch) - costs.append(c) - if (j+1) % print_period == 0: - print "i:", i, "j:", j, "nb:", n_batches, "cost:", c - - if show_fig: - plt.plot(costs) - plt.show() - - def forward(self, X): - Z = X - for h in self.hidden_layers: - Z = h.forward(Z) - return Z.dot(self.W) + self.b - - def score(self, X, Y): - Yhat = self.predict_op(X) - return myr2(Y, Yhat) - - def predict(self, X): - return self.predict_op(X) - -# we need to skip the 3 footer rows -# skipfooter does not work with the default engine, 'c' -# so we need to explicitly set it to 'python' -df = pd.read_csv('international-airline-passengers.csv', engine='python', skipfooter=3) - -# rename the columns because they are ridiculous -df.columns = ['month', 'num_passengers'] - -# plot the data so we know what it looks like -# plt.plot(df.num_passengers) -# plt.show() - -# let's try with only the time series itself -series = df.num_passengers.as_matrix() -# series = (series - series.mean()) / series.std() # normalize the values so they have mean 0 and variance 1 -series = series.astype(np.float32) -series = series - series.min() -series = series / series.max() - -# let's see if we can use D past values to predict the next value -N = len(series) -for D in (2,3,4,5,6,7): - n = N - D - X = np.zeros((n, D)) - for d in xrange(D): - X[:,d] = series[d:d+n] - Y = series[D:D+n] - - print "series length:", n - Xtrain = X[:n/2] - Ytrain = Y[:n/2] - Xtest = X[n/2:] - Ytest = Y[n/2:] - - model = ANN([200]) - model.fit(Xtrain, Ytrain, activation=T.tanh) - print "train score:", model.score(Xtrain, Ytrain) - print "test score:", model.score(Xtest, Ytest) - - # plot the prediction with true values - plt.plot(series) - - train_series = np.empty(n) - train_series[:n/2] = model.predict(Xtrain) - train_series[n/2:] = np.nan - # prepend d nan's since the train series is only of size N - D - plt.plot(np.concatenate([np.full(d, np.nan), train_series])) - - test_series = np.empty(n) - test_series[:n/2] = np.nan - test_series[n/2:] = model.predict(Xtest) - plt.plot(np.concatenate([np.full(d, np.nan), test_series])) - - plt.show() diff --git a/airline/international-airline-passengers.csv b/airline/international-airline-passengers.csv deleted file mode 100644 index bf7171de..00000000 --- a/airline/international-airline-passengers.csv +++ /dev/null @@ -1,148 +0,0 @@ -"Month","International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60" -"1949-01",112 -"1949-02",118 -"1949-03",132 -"1949-04",129 -"1949-05",121 -"1949-06",135 -"1949-07",148 -"1949-08",148 -"1949-09",136 -"1949-10",119 -"1949-11",104 -"1949-12",118 -"1950-01",115 -"1950-02",126 -"1950-03",141 -"1950-04",135 -"1950-05",125 -"1950-06",149 -"1950-07",170 -"1950-08",170 -"1950-09",158 -"1950-10",133 -"1950-11",114 -"1950-12",140 -"1951-01",145 -"1951-02",150 -"1951-03",178 -"1951-04",163 -"1951-05",172 -"1951-06",178 -"1951-07",199 -"1951-08",199 -"1951-09",184 -"1951-10",162 -"1951-11",146 -"1951-12",166 -"1952-01",171 -"1952-02",180 -"1952-03",193 -"1952-04",181 -"1952-05",183 -"1952-06",218 -"1952-07",230 -"1952-08",242 -"1952-09",209 -"1952-10",191 -"1952-11",172 -"1952-12",194 -"1953-01",196 -"1953-02",196 -"1953-03",236 -"1953-04",235 -"1953-05",229 -"1953-06",243 -"1953-07",264 -"1953-08",272 -"1953-09",237 -"1953-10",211 -"1953-11",180 -"1953-12",201 -"1954-01",204 -"1954-02",188 -"1954-03",235 -"1954-04",227 -"1954-05",234 -"1954-06",264 -"1954-07",302 -"1954-08",293 -"1954-09",259 -"1954-10",229 -"1954-11",203 -"1954-12",229 -"1955-01",242 -"1955-02",233 -"1955-03",267 -"1955-04",269 -"1955-05",270 -"1955-06",315 -"1955-07",364 -"1955-08",347 -"1955-09",312 -"1955-10",274 -"1955-11",237 -"1955-12",278 -"1956-01",284 -"1956-02",277 -"1956-03",317 -"1956-04",313 -"1956-05",318 -"1956-06",374 -"1956-07",413 -"1956-08",405 -"1956-09",355 -"1956-10",306 -"1956-11",271 -"1956-12",306 -"1957-01",315 -"1957-02",301 -"1957-03",356 -"1957-04",348 -"1957-05",355 -"1957-06",422 -"1957-07",465 -"1957-08",467 -"1957-09",404 -"1957-10",347 -"1957-11",305 -"1957-12",336 -"1958-01",340 -"1958-02",318 -"1958-03",362 -"1958-04",348 -"1958-05",363 -"1958-06",435 -"1958-07",491 -"1958-08",505 -"1958-09",404 -"1958-10",359 -"1958-11",310 -"1958-12",337 -"1959-01",360 -"1959-02",342 -"1959-03",406 -"1959-04",396 -"1959-05",420 -"1959-06",472 -"1959-07",548 -"1959-08",559 -"1959-09",463 -"1959-10",407 -"1959-11",362 -"1959-12",405 -"1960-01",417 -"1960-02",391 -"1960-03",419 -"1960-04",461 -"1960-05",472 -"1960-06",535 -"1960-07",622 -"1960-08",606 -"1960-09",508 -"1960-10",461 -"1960-11",390 -"1960-12",432 - -International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60 - diff --git a/airline/lr.py b/airline/lr.py deleted file mode 100644 index 175a4638..00000000 --- a/airline/lr.py +++ /dev/null @@ -1,59 +0,0 @@ -# The corresponding tutorial for this code was released EXCLUSIVELY as a bonus -# If you want to learn about future bonuses, please sign up for my newsletter at: -# https://lazyprogrammer.me - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.linear_model import LinearRegression - -# we need to skip the 3 footer rows -# skipfooter does not work with the default engine, 'c' -# so we need to explicitly set it to 'python' -df = pd.read_csv('international-airline-passengers.csv', engine='python', skipfooter=3) - -# rename the columns because they are ridiculous -df.columns = ['month', 'num_passengers'] - -# plot the data so we know what it looks like -plt.plot(df.num_passengers) -plt.show() - -# let's try with only the time series itself -series = df.num_passengers.as_matrix() - -# let's see if we can use D past values to predict the next value -N = len(series) -for D in (2,3,4,5,6,7): - n = N - D - X = np.zeros((n, D)) - for d in xrange(D): - X[:,d] = series[d:d+n] - Y = series[D:D+n] - - print "series length:", n - Xtrain = X[:n/2] - Ytrain = Y[:n/2] - Xtest = X[n/2:] - Ytest = Y[n/2:] - - model = LinearRegression() - model.fit(Xtrain, Ytrain) - print "train score:", model.score(Xtrain, Ytrain) - print "test score:", model.score(Xtest, Ytest) - - # plot the prediction with true values - plt.plot(series) - - train_series = np.empty(n) - train_series[:n/2] = model.predict(Xtrain) - train_series[n/2:] = np.nan - # prepend d nan's since the train series is only of size N - D - plt.plot(np.concatenate([np.full(d, np.nan), train_series])) - - test_series = np.empty(n) - test_series[:n/2] = np.nan - test_series[n/2:] = model.predict(Xtest) - plt.plot(np.concatenate([np.full(d, np.nan), test_series])) - - plt.show() diff --git a/airline/rnn.py b/airline/rnn.py deleted file mode 100644 index ff11b2d1..00000000 --- a/airline/rnn.py +++ /dev/null @@ -1,174 +0,0 @@ -# The corresponding tutorial for this code was released EXCLUSIVELY as a bonus -# If you want to learn about future bonuses, please sign up for my newsletter at: -# https://lazyprogrammer.me - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import theano -import theano.tensor as T -from sklearn.utils import shuffle -from datetime import datetime - -import os -import sys -sys.path.append(os.path.abspath('..')) -from rnn_class.lstm import LSTM -from rnn_class.gru import GRU - -def init_weight(M1, M2): - return np.random.randn(M1, M2) / np.sqrt(M1 + M2) - -def myr2(T, Y): - Ym = T.mean() - sse = (T - Y).dot(T - Y) - sst = (T - Ym).dot(T - Ym) - return 1 - sse / sst - -class RNN(object): - def __init__(self, hidden_layer_sizes): - self.hidden_layer_sizes = hidden_layer_sizes - - def fit(self, X, Y, activation=T.tanh, learning_rate=1e-1, mu=0.5, reg=0, epochs=2000, show_fig=False): - N, t, D = X.shape - - self.hidden_layers = [] - Mi = D - for Mo in self.hidden_layer_sizes: - ru = GRU(Mi, Mo, activation) - self.hidden_layers.append(ru) - Mi = Mo - - Wo = np.random.randn(Mi) / np.sqrt(Mi) - bo = 0.0 - self.Wo = theano.shared(Wo) - self.bo = theano.shared(bo) - self.params = [self.Wo, self.bo] - for ru in self.hidden_layers: - self.params += ru.params - - lr = T.scalar('lr') - thX = T.matrix('X') - thY = T.scalar('Y') - Yhat = self.forward(thX)[-1] - - # let's return py_x too so we can draw a sample instead - self.predict_op = theano.function( - inputs=[thX], - outputs=Yhat, - allow_input_downcast=True, - ) - - cost = T.mean((thY - Yhat)*(thY - Yhat)) - grads = T.grad(cost, self.params) - dparams = [theano.shared(p.get_value()*0) for p in self.params] - - updates = [ - (p, p + mu*dp - lr*g) for p, dp, g in zip(self.params, dparams, grads) - ] + [ - (dp, mu*dp - lr*g) for dp, g in zip(dparams, grads) - ] - - self.train_op = theano.function( - inputs=[lr, thX, thY], - outputs=cost, - updates=updates - ) - - costs = [] - for i in xrange(epochs): - t0 = datetime.now() - X, Y = shuffle(X, Y) - n_correct = 0 - n_total = 0 - cost = 0 - for j in xrange(N): - - c = self.train_op(learning_rate, X[j], Y[j]) - cost += c - if i % 10 == 0: - print "i:", i, "cost:", cost, "time for epoch:", (datetime.now() - t0) - if (i+1) % 500 == 0: - learning_rate /= 10 - costs.append(cost) - - if show_fig: - plt.plot(costs) - plt.show() - - def forward(self, X): - Z = X - for h in self.hidden_layers: - Z = h.output(Z) - return Z.dot(self.Wo) + self.bo - - def score(self, X, Y): - Yhat = self.predict(X) - return myr2(Y, Yhat) - - def predict(self, X): - N = len(X) - Yhat = np.empty(N) - for i in xrange(N): - Yhat[i] = self.predict_op(X[i]) - return Yhat - -# we need to skip the 3 footer rows -# skipfooter does not work with the default engine, 'c' -# so we need to explicitly set it to 'python' -df = pd.read_csv('international-airline-passengers.csv', engine='python', skipfooter=3) - -# rename the columns because they are ridiculous -df.columns = ['month', 'num_passengers'] - -# plot the data so we know what it looks like -# plt.plot(df.num_passengers) -# plt.show() - -# let's try with only the time series itself -series = df.num_passengers.as_matrix() -# series = (series - series.mean()) / series.std() # normalize the values so they have mean 0 and variance 1 -series = series.astype(np.float32) -series = series - series.min() -series = series / series.max() - -# let's see if we can use D past values to predict the next value -N = len(series) -for D in (2,3,4,5): - n = N - D - X = np.zeros((n, D)) - for d in xrange(D): - X[:,d] = series[d:d+n] - Y = series[D:D+n] - - print "series length:", n - Xtrain = X[:n/2] - Ytrain = Y[:n/2] - Xtest = X[n/2:] - Ytest = Y[n/2:] - - Ntrain = len(Xtrain) - Xtrain = Xtrain.reshape(Ntrain, D, 1) - Ntest = len(Xtest) - Xtest = Xtest.reshape(Ntest, D, 1) - - model = RNN([50]) - model.fit(Xtrain, Ytrain, activation=T.tanh) - print "train score:", model.score(Xtrain, Ytrain) - print "test score:", model.score(Xtest, Ytest) - - # plot the prediction with true values - plt.plot(series) - - train_series = np.empty(n) - train_series[:n/2] = model.predict(Xtrain) - train_series[n/2:] = np.nan - # prepend d nan's since the train series is only of size N - D - plt.plot(np.concatenate([np.full(d, np.nan), train_series])) - - test_series = np.empty(n) - test_series[:n/2] = np.nan - test_series[n/2:] = model.predict(Xtest) - plt.plot(np.concatenate([np.full(d, np.nan), test_series])) - - plt.show() diff --git a/ann_class/backprop.py b/ann_class/backprop.py index 62ce9e73..30ba6611 100644 --- a/ann_class/backprop.py +++ b/ann_class/backprop.py @@ -2,18 +2,11 @@ # with sigmoid activation # # the notes for this class can be found at: -# https://deeplearningcourses.com/c/data-science-deep-learning-in-python # https://www.udemy.com/data-science-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import numpy as np import matplotlib.pyplot as plt -np.random.seed(1) - def forward(X, W1, b1, W2, b2): Z = 1 / (1 + np.exp(-X.dot(W1) - b1)) A = Z.dot(W2) + b2 @@ -27,7 +20,7 @@ def forward(X, W1, b1, W2, b2): def classification_rate(Y, P): n_correct = 0 n_total = 0 - for i in range(len(Y)): + for i in xrange(len(Y)): n_total += 1 if Y[i] == P[i]: n_correct += 1 @@ -117,7 +110,7 @@ def main(): N = len(Y) # turn Y into an indicator matrix for training T = np.zeros((N, K)) - for i in range(N): + for i in xrange(N): T[i, Y[i]] = 1 # let's see what it looks like @@ -130,30 +123,23 @@ def main(): W2 = np.random.randn(M, K) b2 = np.random.randn(K) - learning_rate = 1e-3 + learning_rate = 10e-7 costs = [] - for epoch in range(1000): + for epoch in xrange(100000): output, hidden = forward(X, W1, b1, W2, b2) if epoch % 100 == 0: c = cost(T, output) P = np.argmax(output, axis=1) r = classification_rate(Y, P) - print("cost:", c, "classification_rate:", r) + print "cost:", c, "classification_rate:", r costs.append(c) # this is gradient ASCENT, not DESCENT # be comfortable with both! - # oldW2 = W2.copy() - - gW2 = derivative_w2(hidden, T, output) - gb2 = derivative_b2(T, output) - gW1 = derivative_w1(X, hidden, T, output, W2) - gb1 = derivative_b1(T, output, W2, hidden) - - W2 += learning_rate * gW2 - b2 += learning_rate * gb2 - W1 += learning_rate * gW1 - b1 += learning_rate * gb1 + W2 += learning_rate * derivative_w2(hidden, T, output) + b2 += learning_rate * derivative_b2(T, output) + W1 += learning_rate * derivative_w1(X, hidden, T, output, W2) + b1 += learning_rate * derivative_b1(T, output, W2, hidden) plt.plot(costs) plt.show() diff --git a/ann_class/batch_donut.py b/ann_class/batch_donut.py new file mode 100644 index 00000000..56a93a6f --- /dev/null +++ b/ann_class/batch_donut.py @@ -0,0 +1,110 @@ +# revisiting the XOR and donut problems to show how features +# can be learned automatically using neural networks. +# since full training didn't work so well with the donut +# problem, let's try learning from randomly selected batches. +# we can consistently get up to the high 90s with this method. +# +# the notes for this class can be found at: +# https://www.udemy.com/data-science-deep-learning-in-python + +import numpy as np +from sklearn.utils import shuffle +import matplotlib.pyplot as plt + +# for binary classification! no softmax here + +def forward(X, W1, b1, W2, b2): + # assume we will use tanh() on hidden + # and softmax on output + Z = 1 / (1 + np.exp( -(X.dot(W1) + b1) )) + activation = Z.dot(W2) + b2 + Y = 1 / (1 + np.exp(-activation)) + return Y, Z + + +def predict(X, W1, b1, W2, b2): + Y, _ = forward(X, W1, b1, W2, b2) + return np.round(Y) + + +def derivative_w2(Z, T, Y): + # Z is (N, M) + return (T - Y).dot(Z) + +def derivative_b2(T, Y): + return (T - Y).sum() + + +def derivative_w1(X, Z, T, Y, W2): + front = np.outer(T-Y, W2) * Z * (1 - Z) + return front.T.dot(X).T + + +def derivative_b1(Z, T, Y, W2): + front = np.outer(T-Y, W2) * Z * (1 - Z) + return front.sum(axis=0) + + +def cost(T, Y): + tot = 0 + for n in xrange(len(T)): + if T[n] == 1: + tot += np.log(Y[n]) + else: + tot += np.log(1 - Y[n]) + return tot + +def test_donut(): + # donut example + N = 1000 + R_inner = 5 + R_outer = 10 + + # distance from origin is radius + random normal + # angle theta is uniformly distributed between (0, 2pi) + R1 = np.random.randn(N/2) + R_inner + theta = 2*np.pi*np.random.random(N/2) + X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T + + R2 = np.random.randn(N/2) + R_outer + theta = 2*np.pi*np.random.random(N/2) + X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T + + X = np.concatenate([ X_inner, X_outer ]) + Y = np.array([0]*(N/2) + [1]*(N/2)) + + n_hidden = 8 + W1 = np.random.randn(2, n_hidden) + b1 = np.random.randn(n_hidden) + W2 = np.random.randn(n_hidden) + b2 = np.random.randn(1) + LL = [] # keep track of likelihoods + learning_rate = 0.0001 + regularization = 0.1 + + + # batch version + batch_size = 100 + for i in xrange(150000): + tmpX, tmpY = shuffle(X, Y) + + tmpX = tmpX[:batch_size] + tmpY = tmpY[:batch_size] + pY, Z = forward(tmpX, W1, b1, W2, b2) + ll = cost(tmpY, pY) + prediction = predict(X, W1, b1, W2, b2) + er = np.abs(prediction - Y).mean() + LL.append(ll) + W2 += learning_rate * (derivative_w2(Z, tmpY, pY) - regularization * W2) + b2 += learning_rate * (derivative_b2(tmpY, pY) - regularization * b2) + W1 += learning_rate * (derivative_w1(tmpX, Z, tmpY, pY, W2) - regularization * W1) + b1 += learning_rate * (derivative_b1(Z, tmpY, pY, W2) - regularization * b1) + if i % 100 == 0: + print "ll:", ll, "classification rate:", 1 - er + + plt.plot(LL) + plt.show() + + +if __name__ == '__main__': + test_donut() \ No newline at end of file diff --git a/ann_class/extra_reading.txt b/ann_class/extra_reading.txt deleted file mode 100644 index 3d4fde45..00000000 --- a/ann_class/extra_reading.txt +++ /dev/null @@ -1,11 +0,0 @@ -The Chain Rule of Calculus -http://tutorial.math.lamar.edu/Classes/CalcI/ChainRule.aspx - -Yes you should understand backprop by Andrej Karpathy -https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b - -The Matrix Cookbook -https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf - -Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986) Learning representations by back-propagating errors. -https://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf \ No newline at end of file diff --git a/ann_class/forwardprop.py b/ann_class/forwardprop.py index 2d5c7a8c..221faf28 100644 --- a/ann_class/forwardprop.py +++ b/ann_class/forwardprop.py @@ -1,16 +1,8 @@ # forward propagation example for deep learning in python class. # # the notes for this class can be found at: -# https://deeplearningcourses.com/c/data-science-deep-learning-in-python # https://www.udemy.com/data-science-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - - import numpy as np import matplotlib.pyplot as plt @@ -36,13 +28,8 @@ W2 = np.random.randn(M, K) b2 = np.random.randn(K) -def sigmoid(a): - return 1 / (1 + np.exp(-a)) - def forward(X, W1, b1, W2, b2): - Z = sigmoid(X.dot(W1) + b1) # sigmoid - # Z = np.tanh(X.dot(W1) + b1) # tanh - # Z = np.maximum(X.dot(W1) + b1, 0) # relu + Z = 1 / (1 + np.exp(-X.dot(W1) - b1)) A = Z.dot(W2) + b2 expA = np.exp(A) Y = expA / expA.sum(axis=1, keepdims=True) @@ -53,7 +40,7 @@ def forward(X, W1, b1, W2, b2): def classification_rate(Y, P): n_correct = 0 n_total = 0 - for i in range(len(Y)): + for i in xrange(len(Y)): n_total += 1 if Y[i] == P[i]: n_correct += 1 @@ -65,5 +52,5 @@ def classification_rate(Y, P): # verify we chose the correct axis assert(len(P) == len(Y)) -print("Classification rate for randomly chosen weights:", classification_rate(Y, P)) +print "Classification rate for randomly chosen weights:", classification_rate(Y, P) diff --git a/ann_class/regression.py b/ann_class/regression.py deleted file mode 100644 index d15e0fb0..00000000 --- a/ann_class/regression.py +++ /dev/null @@ -1,146 +0,0 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D - -# NOTE: some people using the default Python -# installation on Mac have had trouble with Axes3D -# Switching to Python 3 (brew install python3) or -# using Linux are both viable work-arounds - - - - - -# generate and plot the data -N = 500 -X = np.random.random((N, 2))*4 - 2 # in between (-2, +2) -Y = X[:,0]*X[:,1] # makes a saddle shape -# note: in this script "Y" will be the target, -# "Yhat" will be prediction - -fig = plt.figure() -ax = fig.add_subplot(111, projection='3d') -ax.scatter(X[:,0], X[:,1], Y) -plt.show() - - - - - -# make a neural network and train it -D = 2 -M = 100 # number of hidden units - -# layer 1 -W = np.random.randn(D, M) / np.sqrt(D) -b = np.zeros(M) - -# layer 2 -V = np.random.randn(M) / np.sqrt(M) -c = 0 - - - - -# how to get the output -# consider the params global -def forward(X): - Z = X.dot(W) + b - Z = Z * (Z > 0) # relu - # Z = np.tanh(Z) - - Yhat = Z.dot(V) + c - return Z, Yhat - - - - -# how to train the params -def derivative_V(Z, Y, Yhat): - return (Y - Yhat).dot(Z) - -def derivative_c(Y, Yhat): - return (Y - Yhat).sum() - -def derivative_W(X, Z, Y, Yhat, V): - # dZ = np.outer(Y - Yhat, V) * (1 - Z * Z) # this is for tanh activation - dZ = np.outer(Y - Yhat, V) * (Z > 0) # relu - return X.T.dot(dZ) - -def derivative_b(Z, Y, Yhat, V): - # dZ = np.outer(Y - Yhat, V) * (1 - Z * Z) # this is for tanh activation - dZ = np.outer(Y - Yhat, V) * (Z > 0) # this is for relu activation - return dZ.sum(axis=0) - -def update(X, Z, Y, Yhat, W, b, V, c, learning_rate=1e-4): - gV = derivative_V(Z, Y, Yhat) - gc = derivative_c(Y, Yhat) - gW = derivative_W(X, Z, Y, Yhat, V) - gb = derivative_b(Z, Y, Yhat, V) - - V += learning_rate*gV - c += learning_rate*gc - W += learning_rate*gW - b += learning_rate*gb - - return W, b, V, c - - - - -# so we can plot the costs later -def get_cost(Y, Yhat): - return ((Y - Yhat)**2).mean() - - - -# run a training loop -# plot the costs -# and plot the final result -costs = [] -for i in range(200): - Z, Yhat = forward(X) - W, b, V, c = update(X, Z, Y, Yhat, W, b, V, c) - cost = get_cost(Y, Yhat) - costs.append(cost) - if i % 25 == 0: - print(cost) - -# plot the costs -plt.plot(costs) -plt.show() - -# plot the prediction with the data -fig = plt.figure() -ax = fig.add_subplot(111, projection='3d') -ax.scatter(X[:,0], X[:,1], Y) - -# surface plot -line = np.linspace(-2, 2, 20) -xx, yy = np.meshgrid(line, line) -Xgrid = np.vstack((xx.flatten(), yy.flatten())).T -_, Yhat = forward(Xgrid) -ax.plot_trisurf(Xgrid[:,0], Xgrid[:,1], Yhat, linewidth=0.2, antialiased=True) -plt.show() - - - - -# plot magnitude of residuals -Ygrid = Xgrid[:,0]*Xgrid[:,1] -R = np.abs(Ygrid - Yhat) - -plt.scatter(Xgrid[:,0], Xgrid[:,1], c=R) -plt.show() - -fig = plt.figure() -ax = fig.add_subplot(111, projection='3d') -ax.plot_trisurf(Xgrid[:,0], Xgrid[:,1], R, linewidth=0.2, antialiased=True) -plt.show() - - diff --git a/ann_class/sklearn_ann.py b/ann_class/sklearn_ann.py deleted file mode 100644 index 5c4e5fa0..00000000 --- a/ann_class/sklearn_ann.py +++ /dev/null @@ -1,31 +0,0 @@ -# Train a neural network in just 3 lines of code! -# -# the notes for this class can be found at: -# https://deeplearningcourses.com/c/data-science-deep-learning-in-python -# https://www.udemy.com/data-science-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import sys -sys.path.append('../ann_logistic_extra') -from process import get_data - -from sklearn.neural_network import MLPClassifier -from sklearn.utils import shuffle - -# get the data -Xtrain, Ytrain, Xtest, Ytest = get_data() - -# create the neural network -model = MLPClassifier(hidden_layer_sizes=(20, 20), max_iter=2000) - -# train the neural network -model.fit(Xtrain, Ytrain) - -# print the train and test accuracy -train_accuracy = model.score(Xtrain, Ytrain) -test_accuracy = model.score(Xtest, Ytest) -print("train accuracy:", train_accuracy, "test accuracy:", test_accuracy) diff --git a/ann_class/tf_example.py b/ann_class/tf_example.py index 503aa9a7..e44e42ff 100644 --- a/ann_class/tf_example.py +++ b/ann_class/tf_example.py @@ -1,12 +1,7 @@ # neural network in TensorFlow very simple example. # # the notes for this class can be found at: -# https://deeplearningcourses.com/c/data-science-deep-learning-in-python # https://www.udemy.com/data-science-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import tensorflow as tf import numpy as np @@ -33,7 +28,7 @@ N = len(Y) # turn Y into an indicator matrix for training T = np.zeros((N, K)) -for i in range(N): +for i in xrange(N): T[i, Y[i]] = 1 @@ -55,14 +50,9 @@ def forward(X, W1, b1, W2, b2): W2 = init_weights([M, K]) b2 = init_weights([K]) -logits = forward(tfX, W1, b1, W2, b2) +py_x = forward(tfX, W1, b1, W2, b2) -cost = tf.reduce_mean( - tf.nn.softmax_cross_entropy_with_logits_v2( - labels=tfY, - logits=logits - ) -) # compute costs +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, T)) # compute costs # WARNING: This op expects unscaled logits, # since it performs a softmax on logits # internally for efficiency. @@ -72,17 +62,17 @@ def forward(X, W1, b1, W2, b2): train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) # construct an optimizer # input parameter is the learning rate -predict_op = tf.argmax(logits, 1) +predict_op = tf.argmax(py_x, 1) # input parameter is the axis on which to choose the max # just stuff that has to be done sess = tf.Session() -init = tf.global_variables_initializer() +init = tf.initialize_all_variables() sess.run(init) for i in range(1000): sess.run(train_op, feed_dict={tfX: X, tfY: T}) pred = sess.run(predict_op, feed_dict={tfX: X, tfY: T}) - if i % 100 == 0: - print("Accuracy:", np.mean(Y == pred)) + if i % 10 == 0: + print np.mean(Y == pred) diff --git a/ann_class/xor_donut.py b/ann_class/xor_donut.py index 48331337..a896616e 100644 --- a/ann_class/xor_donut.py +++ b/ann_class/xor_donut.py @@ -2,31 +2,18 @@ # can be learned automatically using neural networks. # # the notes for this class can be found at: -# https://deeplearningcourses.com/c/data-science-deep-learning-in-python # https://www.udemy.com/data-science-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import matplotlib.pyplot as plt # for binary classification! no softmax here def forward(X, W1, b1, W2, b2): - # sigmoid - # Z = 1 / (1 + np.exp( -(X.dot(W1) + b1) )) - - # tanh + # assume we will use tanh() on hidden + # and softmax on output + Z = 1 / (1 + np.exp( -(X.dot(W1) + b1) )) # Z = np.tanh(X.dot(W1) + b1) - - # relu - Z = X.dot(W1) + b1 - Z = Z * (Z > 0) - activation = Z.dot(W2) + b2 Y = 1 / (1 + np.exp(-activation)) return Y, Z @@ -46,20 +33,24 @@ def derivative_b2(T, Y): def derivative_w1(X, Z, T, Y, W2): - # dZ = np.outer(T-Y, W2) * Z * (1 - Z) # this is for sigmoid activation - # dZ = np.outer(T-Y, W2) * (1 - Z * Z) # this is for tanh activation - dZ = np.outer(T-Y, W2) * (Z > 0) # this is for relu activation + dZ = np.outer(T-Y, W2) * Z * (1 - Z) + # dZ = np.outer(T-Y, W2) * (1 - Z * Z) return X.T.dot(dZ) def derivative_b1(Z, T, Y, W2): - # dZ = np.outer(T-Y, W2) * Z * (1 - Z) # this is for sigmoid activation - # dZ = np.outer(T-Y, W2) * (1 - Z * Z) # this is for tanh activation - dZ = np.outer(T-Y, W2) * (Z > 0) # this is for relu activation + dZ = np.outer(T-Y, W2) * (1 - Z * Z) return dZ.sum(axis=0) -def get_log_likelihood(T, Y): +def cost(T, Y): + # tot = 0 + # for n in xrange(len(T)): + # if T[n] == 1: + # tot += np.log(Y[n]) + # else: + # tot += np.log(1 - Y[n]) + # return tot return np.sum(T*np.log(Y) + (1-T)*np.log(1-Y)) @@ -71,32 +62,32 @@ def test_xor(): b1 = np.zeros(5) W2 = np.random.randn(5) b2 = 0 - LL = [] # keep track of log-likelihoods - learning_rate = 1e-2 + LL = [] # keep track of likelihoods + learning_rate = 10e-3 regularization = 0. last_error_rate = None - for i in range(30000): + for i in xrange(30000): pY, Z = forward(X, W1, b1, W2, b2) - ll = get_log_likelihood(Y, pY) + ll = cost(Y, pY) prediction = predict(X, W1, b1, W2, b2) er = np.mean(prediction != Y) - + if er != last_error_rate: + last_error_rate = er + print "error rate:", er + print "true:", Y + print "pred:", prediction + # if LL and ll < LL[-1]: + # print "early exit" + # break LL.append(ll) - - # get gradients - gW2 = derivative_w2(Z, Y, pY) - gb2 = derivative_b2(Y, pY) - gW1 = derivative_w1(X, Z, Y, pY, W2) - gb1 = derivative_b1(Z, Y, pY, W2) - - W2 += learning_rate * (gW2 - regularization * W2) - b2 += learning_rate * (gb2 - regularization * b2) - W1 += learning_rate * (gW1 - regularization * W1) - b1 += learning_rate * (gb1 - regularization * b1) + W2 += learning_rate * (derivative_w2(Z, Y, pY) - regularization * W2) + b2 += learning_rate * (derivative_b2(Y, pY) - regularization * b2) + W1 += learning_rate * (derivative_w1(X, Z, Y, pY, W2) - regularization * W1) + b1 += learning_rate * (derivative_b1(Z, Y, pY, W2) - regularization * b1) if i % 1000 == 0: - print(ll) + print ll - print("final classification rate:", np.mean(prediction == Y)) + print "final classification rate:", np.mean(prediction == Y) plt.plot(LL) plt.show() @@ -109,52 +100,45 @@ def test_donut(): # distance from origin is radius + random normal # angle theta is uniformly distributed between (0, 2pi) - R1 = np.random.randn(N//2) + R_inner - theta = 2*np.pi*np.random.random(N//2) + R1 = np.random.randn(N/2) + R_inner + theta = 2*np.pi*np.random.random(N/2) X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T - R2 = np.random.randn(N//2) + R_outer - theta = 2*np.pi*np.random.random(N//2) + R2 = np.random.randn(N/2) + R_outer + theta = 2*np.pi*np.random.random(N/2) X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T X = np.concatenate([ X_inner, X_outer ]) - Y = np.array([0]*(N//2) + [1]*(N//2)) + Y = np.array([0]*(N/2) + [1]*(N/2)) n_hidden = 8 W1 = np.random.randn(2, n_hidden) b1 = np.random.randn(n_hidden) W2 = np.random.randn(n_hidden) b2 = np.random.randn(1) - LL = [] # keep track of log-likelihoods + LL = [] # keep track of likelihoods learning_rate = 0.00005 regularization = 0.2 last_error_rate = None - for i in range(3000): + for i in xrange(160000): pY, Z = forward(X, W1, b1, W2, b2) - ll = get_log_likelihood(Y, pY) + ll = cost(Y, pY) prediction = predict(X, W1, b1, W2, b2) er = np.abs(prediction - Y).mean() LL.append(ll) - - # get gradients - gW2 = derivative_w2(Z, Y, pY) - gb2 = derivative_b2(Y, pY) - gW1 = derivative_w1(X, Z, Y, pY, W2) - gb1 = derivative_b1(Z, Y, pY, W2) - - W2 += learning_rate * (gW2 - regularization * W2) - b2 += learning_rate * (gb2 - regularization * b2) - W1 += learning_rate * (gW1 - regularization * W1) - b1 += learning_rate * (gb1 - regularization * b1) - if i % 300 == 0: - print("i:", i, "ll:", ll, "classification rate:", 1 - er) + W2 += learning_rate * (derivative_w2(Z, Y, pY) - regularization * W2) + b2 += learning_rate * (derivative_b2(Y, pY) - regularization * b2) + W1 += learning_rate * (derivative_w1(X, Z, Y, pY, W2) - regularization * W1) + b1 += learning_rate * (derivative_b1(Z, Y, pY, W2) - regularization * b1) + if i % 100 == 0: + print "i:", i, "ll:", ll, "classification rate:", 1 - er plt.plot(LL) plt.show() if __name__ == '__main__': - test_xor() - # test_donut() + # test_xor() + test_donut() diff --git a/ann_class2/__init__.py b/ann_class2/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/ann_class2/adam.py b/ann_class2/adam.py deleted file mode 100644 index 3c0243de..00000000 --- a/ann_class2/adam.py +++ /dev/null @@ -1,204 +0,0 @@ -# Compare RMSprop with momentum vs. Adam -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -from sklearn.utils import shuffle -import matplotlib.pyplot as plt - -from util import get_normalized_data, error_rate, cost, y2indicator -from mlp import forward, derivative_w2, derivative_w1, derivative_b2, derivative_b1 - - -def main(): - max_iter = 10 - print_period = 10 - - Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - reg = 0.01 - - Ytrain_ind = y2indicator(Ytrain) - Ytest_ind = y2indicator(Ytest) - - N, D = Xtrain.shape - batch_sz = 500 - n_batches = N // batch_sz - - M = 300 - K = 10 - W1_0 = np.random.randn(D, M) / np.sqrt(D) - b1_0 = np.zeros(M) - W2_0 = np.random.randn(M, K) / np.sqrt(M) - b2_0 = np.zeros(K) - - W1 = W1_0.copy() - b1 = b1_0.copy() - W2 = W2_0.copy() - b2 = b2_0.copy() - - # 1st moment - mW1 = 0 - mb1 = 0 - mW2 = 0 - mb2 = 0 - - # 2nd moment - vW1 = 0 - vb1 = 0 - vW2 = 0 - vb2 = 0 - - # hyperparams - lr0 = 0.001 - beta1 = 0.9 - beta2 = 0.999 - eps = 1e-8 - - # 1. Adam - loss_adam = [] - err_adam = [] - t = 1 - for i in range(max_iter): - for j in range(n_batches): - Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] - Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] - pYbatch, Z = forward(Xbatch, W1, b1, W2, b2) - - # updates - # gradients - gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2 - gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2 - gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1 - gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1 - - # new m - mW1 = beta1 * mW1 + (1 - beta1) * gW1 - mb1 = beta1 * mb1 + (1 - beta1) * gb1 - mW2 = beta1 * mW2 + (1 - beta1) * gW2 - mb2 = beta1 * mb2 + (1 - beta1) * gb2 - - # new v - vW1 = beta2 * vW1 + (1 - beta2) * gW1 * gW1 - vb1 = beta2 * vb1 + (1 - beta2) * gb1 * gb1 - vW2 = beta2 * vW2 + (1 - beta2) * gW2 * gW2 - vb2 = beta2 * vb2 + (1 - beta2) * gb2 * gb2 - - # bias correction - correction1 = 1 - beta1 ** t - hat_mW1 = mW1 / correction1 - hat_mb1 = mb1 / correction1 - hat_mW2 = mW2 / correction1 - hat_mb2 = mb2 / correction1 - - correction2 = 1 - beta2 ** t - hat_vW1 = vW1 / correction2 - hat_vb1 = vb1 / correction2 - hat_vW2 = vW2 / correction2 - hat_vb2 = vb2 / correction2 - - # update t - t += 1 - - # apply updates to the params - W1 = W1 - lr0 * hat_mW1 / (np.sqrt(hat_vW1) + eps) - b1 = b1 - lr0 * hat_mb1 / (np.sqrt(hat_vb1) + eps) - W2 = W2 - lr0 * hat_mW2 / (np.sqrt(hat_vW2) + eps) - b2 = b2 - lr0 * hat_mb2 / (np.sqrt(hat_vb2) + eps) - - - if j % print_period == 0: - pY, _ = forward(Xtest, W1, b1, W2, b2) - l = cost(pY, Ytest_ind) - loss_adam.append(l) - print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l)) - - err = error_rate(pY, Ytest) - err_adam.append(err) - print("Error rate:", err) - - pY, _ = forward(Xtest, W1, b1, W2, b2) - print("Final error rate:", error_rate(pY, Ytest)) - - - # 2. RMSprop with momentum - W1 = W1_0.copy() - b1 = b1_0.copy() - W2 = W2_0.copy() - b2 = b2_0.copy() - loss_rms = [] - err_rms = [] - - # comparable hyperparameters for fair comparison - lr0 = 0.001 - mu = 0.9 - decay_rate = 0.999 - eps = 1e-8 - - # rmsprop cache - cache_W2 = 1 - cache_b2 = 1 - cache_W1 = 1 - cache_b1 = 1 - - # momentum - dW1 = 0 - db1 = 0 - dW2 = 0 - db2 = 0 - - for i in range(max_iter): - for j in range(n_batches): - Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] - Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] - pYbatch, Z = forward(Xbatch, W1, b1, W2, b2) - - # derivatives - gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2 - gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2 - gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1 - gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1 - - # caches - cache_W2 = decay_rate*cache_W2 + (1 - decay_rate)*gW2*gW2 - cache_b2 = decay_rate*cache_b2 + (1 - decay_rate)*gb2*gb2 - cache_W1 = decay_rate*cache_W1 + (1 - decay_rate)*gW1*gW1 - cache_b1 = decay_rate*cache_b1 + (1 - decay_rate)*gb1*gb1 - - # momentum - dW2 = mu * dW2 + (1 - mu) * lr0 * gW2 / (np.sqrt(cache_W2) + eps) - db2 = mu * db2 + (1 - mu) * lr0 * gb2 / (np.sqrt(cache_b2) + eps) - dW1 = mu * dW1 + (1 - mu) * lr0 * gW1 / (np.sqrt(cache_W1) + eps) - db1 = mu * db1 + (1 - mu) * lr0 * gb1 / (np.sqrt(cache_b1) + eps) - - # updates - W2 -= dW2 - b2 -= db2 - W1 -= dW1 - b1 -= db1 - - if j % print_period == 0: - pY, _ = forward(Xtest, W1, b1, W2, b2) - l = cost(pY, Ytest_ind) - loss_rms.append(l) - print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l)) - - err = error_rate(pY, Ytest) - err_rms.append(err) - print("Error rate:", err) - - pY, _ = forward(Xtest, W1, b1, W2, b2) - print("Final error rate:", error_rate(pY, Ytest)) - - plt.plot(loss_adam, label='adam') - plt.plot(loss_rms, label='rmsprop') - plt.legend() - plt.show() - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/ann_class2/batch_norm_tf.py b/ann_class2/batch_norm_tf.py deleted file mode 100644 index de25cd3b..00000000 --- a/ann_class2/batch_norm_tf.py +++ /dev/null @@ -1,200 +0,0 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import tensorflow as tf -from sklearn.utils import shuffle -from sklearn.model_selection import train_test_split -from util import get_normalized_data - - -def init_weight(M1, M2): - return np.random.randn(M1, M2) * np.sqrt(2.0 / M1) - - -class HiddenLayerBatchNorm(object): - def __init__(self, M1, M2, f): - self.M1 = M1 - self.M2 = M2 - self.f = f - - W = init_weight(M1, M2).astype(np.float32) - gamma = np.ones(M2).astype(np.float32) - beta = np.zeros(M2).astype(np.float32) - - self.W = tf.Variable(W) - self.gamma = tf.Variable(gamma) - self.beta = tf.Variable(beta) - - # for test time - self.running_mean = tf.Variable(np.zeros(M2).astype(np.float32), trainable=False) - self.running_var = tf.Variable(np.zeros(M2).astype(np.float32), trainable=False) - - def forward(self, X, is_training, decay=0.9): - activation = tf.matmul(X, self.W) - if is_training: - batch_mean, batch_var = tf.nn.moments(activation, [0]) - update_running_mean = tf.assign( - self.running_mean, - self.running_mean * decay + batch_mean * (1 - decay) - ) - update_running_var = tf.assign( - self.running_var, - self.running_var * decay + batch_var * (1 - decay) - ) - - with tf.control_dependencies([update_running_mean, update_running_var]): - out = tf.nn.batch_normalization( - activation, - batch_mean, - batch_var, - self.beta, - self.gamma, - 1e-4 - ) - else: - out = tf.nn.batch_normalization( - activation, - self.running_mean, - self.running_var, - self.beta, - self.gamma, - 1e-4 - ) - return self.f(out) - - -class HiddenLayer(object): - def __init__(self, M1, M2, f): - self.M1 = M1 - self.M2 = M2 - self.f = f - W = np.random.randn(M1, M2) * np.sqrt(2.0 / M1) - b = np.zeros(M2) - self.W = tf.Variable(W.astype(np.float32)) - self.b = tf.Variable(b.astype(np.float32)) - - def forward(self, X): - return self.f(tf.matmul(X, self.W) + self.b) - - -class ANN(object): - def __init__(self, hidden_layer_sizes): - self.hidden_layer_sizes = hidden_layer_sizes - - def set_session(self, session): - self.session = session - - def fit(self, X, Y, Xtest, Ytest, activation=tf.nn.relu, learning_rate=1e-2, epochs=15, batch_sz=100, print_period=100, show_fig=True): - X = X.astype(np.float32) - Y = Y.astype(np.int32) - - # initialize hidden layers - N, D = X.shape - self.layers = [] - M1 = D - for M2 in self.hidden_layer_sizes: - h = HiddenLayerBatchNorm(M1, M2, activation) - self.layers.append(h) - M1 = M2 - - # final layer - K = len(set(Y)) - h = HiddenLayer(M1, K, lambda x: x) - self.layers.append(h) - - if batch_sz is None: - batch_sz = N - - - # note! we will need to build the output differently - # for train and test (prediction) - - # set up theano functions and variables - tfX = tf.placeholder(tf.float32, shape=(None, D), name='X') - tfY = tf.placeholder(tf.int32, shape=(None,), name='Y') - - # for later use - self.tfX = tfX - - # for training - logits = self.forward(tfX, is_training=True) - cost = tf.reduce_mean( - tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=logits, - labels=tfY - ) - ) - # train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost) - # train_op = tf.train.RMSPropOptimizer(learning_rate, decay=0.99, momentum=0.9).minimize(cost) - train_op = tf.train.MomentumOptimizer(learning_rate, momentum=0.9, use_nesterov=True).minimize(cost) - # train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) - - # for testing - test_logits = self.forward(tfX, is_training=False) - self.predict_op = tf.argmax(test_logits, 1) - - # accuracy = tf.reduce_mean(1.0*(tfY == tf.argmax(logits, 1))) - - # init the variables - self.session.run(tf.global_variables_initializer()) - - n_batches = N // batch_sz - costs = [] - for i in range(epochs): - if n_batches > 1: - X, Y = shuffle(X, Y) - for j in range(n_batches): - Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)] - Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)] - - c, _, lgts = self.session.run([cost, train_op, logits], feed_dict={tfX: Xbatch, tfY: Ybatch}) - costs.append(c) - if (j+1) % print_period == 0: - acc = np.mean(Ybatch == np.argmax(lgts, axis=1)) - print("epoch:", i, "batch:", j, "n_batches:", n_batches, "cost:", c, "acc: %.2f" % acc) - # print('dbg:', self.session.run(self.layers[0].running_mean).sum()) - - print("Train acc:", self.score(X, Y), "Test acc:", self.score(Xtest, Ytest)) - - if show_fig: - plt.plot(costs) - plt.show() - - def forward(self, X, is_training): - out = X - for h in self.layers[:-1]: - out = h.forward(out, is_training) - out = self.layers[-1].forward(out) - return out - - def score(self, X, Y): - P = self.predict(X) - return np.mean(Y == P) - - def predict(self, X): - return self.session.run(self.predict_op, feed_dict={self.tfX: X}) - - - -def main(): - # step 1: get the data and define all the usual variables - Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - - ann = ANN([500, 300]) - - session = tf.InteractiveSession() - ann.set_session(session) - - ann.fit(Xtrain, Ytrain, Xtest, Ytest, show_fig=True) - - print("Train accuracy:", ann.score(Xtrain, Ytrain)) - print("Test accuracy:", ann.score(Xtest, Ytest)) - - -if __name__ == '__main__': - main() diff --git a/ann_class2/batch_norm_theano.py b/ann_class2/batch_norm_theano.py deleted file mode 100644 index c86c11a9..00000000 --- a/ann_class2/batch_norm_theano.py +++ /dev/null @@ -1,221 +0,0 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import theano -import theano.tensor as T -from theano.tensor.nnet.bn import batch_normalization_train, batch_normalization_test -from sklearn.utils import shuffle -from sklearn.model_selection import train_test_split -from util import get_normalized_data - - -def init_weight(M1, M2): - return np.random.randn(M1, M2) * np.sqrt(2.0 / M1) - - -class HiddenLayerBatchNorm(object): - def __init__(self, M1, M2, f): - self.M1 = M1 - self.M2 = M2 - self.f = f - - W = init_weight(M1, M2) - gamma = np.ones(M2) - beta = np.zeros(M2) - - self.W = theano.shared(W) - self.gamma = theano.shared(gamma) - self.beta = theano.shared(beta) - - self.params = [self.W, self.gamma, self.beta] - - # for test time - # self.running_mean = T.zeros(M2) - # self.running_var = T.zeros(M2) - self.running_mean = theano.shared(np.zeros(M2)) - self.running_var = theano.shared(np.zeros(M2)) - - def forward(self, X, is_training): - activation = X.dot(self.W) - if is_training: - # returns: - # batch-normalized output - # batch mean - # batch variance - # running mean (for later use as population mean estimate) - # running var (for later use as population var estimate) - out, batch_mean, batch_invstd, new_running_mean, new_running_var = batch_normalization_train( - activation, - self.gamma, - self.beta, - running_mean=self.running_mean, - running_var=self.running_var, - ) - - self.running_update = [ - (self.running_mean, new_running_mean), - (self.running_var, new_running_var), - ] - - # if you don't trust the built-in bn function - # batch_var = 1 / (batch_invstd * batch_invstd) - # self.running_update = [ - # (self.running_mean, 0.9*self.running_mean + 0.1*batch_mean), - # (self.running_var, 0.9*self.running_var + 0.1*batch_var), - # ] - - else: - out = batch_normalization_test( - activation, - self.gamma, - self.beta, - self.running_mean, - self.running_var - ) - return self.f(out) - - -class HiddenLayer(object): - def __init__(self, M1, M2, f): - self.M1 = M1 - self.M2 = M2 - self.f = f - W = init_weight(M1, M2) - b = np.zeros(M2) - self.W = theano.shared(W) - self.b = theano.shared(b) - self.params = [self.W, self.b] - - def forward(self, X): - return self.f(X.dot(self.W) + self.b) - - -def momentum_updates(cost, params, lr, mu): - grads = T.grad(cost, params) - updates = [] - - for p, g in zip(params, grads): - dp = theano.shared(p.get_value() * 0) - new_dp = mu*dp - lr*g - new_p = p + new_dp - updates.append((dp, new_dp)) - updates.append((p, new_p)) - return updates - - -class ANN(object): - def __init__(self, hidden_layer_sizes): - self.hidden_layer_sizes = hidden_layer_sizes - - def fit(self, X, Y, Xtest, Ytest, activation=T.nnet.relu, learning_rate=1e-2, mu=0.9, epochs=15, batch_sz=100, print_period=100, show_fig=True): - X = X.astype(np.float32) - Y = Y.astype(np.int32) - - # initialize hidden layers - N, D = X.shape - self.layers = [] - M1 = D - for M2 in self.hidden_layer_sizes: - h = HiddenLayerBatchNorm(M1, M2, activation) - self.layers.append(h) - M1 = M2 - - # final layer - K = len(set(Y)) - h = HiddenLayer(M1, K, T.nnet.softmax) - self.layers.append(h) - - if batch_sz is None: - batch_sz = N - - # collect params for later use - self.params = [] - for h in self.layers: - self.params += h.params - - # note! we will need to build the output differently - # for train and test (prediction) - - # set up theano functions and variables - thX = T.matrix('X') - thY = T.ivector('Y') - - # for training - p_y_given_x = self.forward(thX, is_training=True) - - cost = -T.mean(T.log(p_y_given_x[T.arange(thY.shape[0]), thY])) - prediction = T.argmax(p_y_given_x, axis=1) - grads = T.grad(cost, self.params) - - # momentum only - updates = momentum_updates(cost, self.params, learning_rate, mu) - for layer in self.layers[:-1]: - updates += layer.running_update - - train_op = theano.function( - inputs=[thX, thY], - outputs=[cost, prediction], - updates=updates, - ) - - # for testing - test_p_y_given_x = self.forward(thX, is_training=False) - test_prediction = T.argmax(test_p_y_given_x, axis=1) - - self.predict = theano.function( - inputs=[thX], - outputs=test_prediction, - ) - - n_batches = N // batch_sz - costs = [] - for i in range(epochs): - if n_batches > 1: - X, Y = shuffle(X, Y) - for j in range(n_batches): - Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)] - Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)] - - c, p = train_op(Xbatch, Ybatch) - costs.append(c) - if (j+1) % print_period == 0: - accuracy = np.mean(p == Ybatch) - print("epoch:", i, "batch:", j, "n_batches:", n_batches, "cost:", c, "accuracy:", accuracy) - - print("Train acc:", self.score(X, Y), "Test acc:", self.score(Xtest, Ytest)) - - if show_fig: - plt.plot(costs) - plt.show() - - def forward(self, X, is_training): - out = X - for h in self.layers[:-1]: - out = h.forward(out, is_training) - out = self.layers[-1].forward(out) - return out - - def score(self, X, Y): - P = self.predict(X) - return np.mean(Y == P) - - - -def main(): - # step 1: get the data and define all the usual variables - Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - - ann = ANN([500, 300]) - ann.fit(Xtrain, Ytrain, Xtest, Ytest, show_fig=True) - - print("Train accuracy:", ann.score(Xtrain, Ytrain)) - print("Test accuracy:", ann.score(Xtest, Ytest)) - - -if __name__ == '__main__': - main() diff --git a/ann_class2/cntk_example.py b/ann_class2/cntk_example.py deleted file mode 100644 index 29564592..00000000 --- a/ann_class2/cntk_example.py +++ /dev/null @@ -1,142 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -# simple installation with pip: -# -# just one line: pip install <....>.whl -# -# Linux -# https://docs.microsoft.com/en-us/cognitive-toolkit/setup-linux-python -# -# Windows -# https://docs.microsoft.com/en-us/cognitive-toolkit/setup-windows-python -# -# (No Mac) - - -# Note: useful to understand keras_example.py first - -import numpy as np -import matplotlib.pyplot as plt -from util import get_normalized_data, y2indicator - - -import cntk as C -from cntk.train import Trainer -from cntk.learners import adam -from cntk.ops import relu -from cntk.layers import Dense, Sequential -from cntk.losses import cross_entropy_with_softmax -from cntk.metrics import classification_error -from cntk.train.training_session import * - - -# get the data, same as Theano + Tensorflow examples -Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - -# get shapes -N, D = Xtrain.shape -K = len(set(Ytrain)) - -# we want one-hot encoded labels -Ytrain = y2indicator(Ytrain) -Ytest = y2indicator(Ytest) - -# split the data -X = X.astype(np.float32) -Y = Y.astype(np.float32) -Xtest = Xtest.astype(np.float32) -Ytest = Ytest.astype(np.float32) - - -# the model will be a sequence of layers -model = Sequential( - [ - Dense(500, activation=relu), - Dense(300, activation=relu), - Dense(K, activation=None), - ] -) - - -# define the inputs and labels -inputs = C.input_variable(D, np.float32, name='inputs') -labels = C.input_variable(K, np.float32, name='labels') - - -# get the output -logits = model(inputs) - - -# define loss / metrics -# like Tensorflow the softmax is done -# internally (if needed), so all we need are the logits -ce = cross_entropy_with_softmax(logits, labels) -pe = classification_error(logits, labels) - - - -# training config -batch_size = 32 -epochs = 15 -n_batches = len(Xtrain) // batch_size - - -# do the training - -# specify the training algorithm -trainer = Trainer(logits, (ce, pe), adam(logits.parameters, lr=1e-2, momentum=0.9)) - - -# helper function -def get_output(node, X, Y): - ret = node.forward(dict(inputs=X, labels=Y)) - return list(ret[1].values())[0].mean() - - -costs = [] -errors = [] -test_costs = [] -test_errors = [] -for i in range(epochs): - cost = 0 - err = 0 - for j in range(n_batches): - Xbatch = Xtrain[j*batch_size:(j+1)*batch_size] - Ybatch = Ytrain[j*batch_size:(j+1)*batch_size] - - ret = trainer.train_minibatch( - dict(inputs=Xbatch, labels=Ybatch), - outputs=(ce, pe) - ) - - cost += ret[1][ce].mean() - err += ret[1][pe].mean() - costs.append(cost / n_batches) - errors.append(err / n_batches) - - # test set calculations - test_cost = get_output(ce, Xtest, Ytest) - test_err = get_output(pe, Xtest, Ytest) - - test_costs.append(test_cost) - test_errors.append(test_err) - - print("epoch i:", i, "cost:", test_cost, "err:", test_err) - -plt.plot(costs, label='train cost') -plt.plot(test_costs, label='test cost') -plt.legend() -plt.title("cost") -plt.show() - -plt.plot(errors, label='train error') -plt.plot(test_errors, label='test error') -plt.legend() -plt.title("error") -plt.show() diff --git a/ann_class2/dropout_tensorflow.py b/ann_class2/dropout_tensorflow.py deleted file mode 100644 index b20c44fb..00000000 --- a/ann_class2/dropout_tensorflow.py +++ /dev/null @@ -1,148 +0,0 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt - -from util import get_normalized_data -from sklearn.utils import shuffle - - -class HiddenLayer(object): - def __init__(self, M1, M2): - self.M1 = M1 - self.M2 = M2 - W = np.random.randn(M1, M2) * np.sqrt(2.0 / M1) - b = np.zeros(M2) - self.W = tf.Variable(W.astype(np.float32)) - self.b = tf.Variable(b.astype(np.float32)) - self.params = [self.W, self.b] - - def forward(self, X): - return tf.nn.relu(tf.matmul(X, self.W) + self.b) - - -class ANN(object): - def __init__(self, hidden_layer_sizes, p_keep): - self.hidden_layer_sizes = hidden_layer_sizes - self.dropout_rates = p_keep - - def fit(self, X, Y, Xvalid, Yvalid, lr=1e-4, mu=0.9, decay=0.9, epochs=15, batch_sz=100, print_every=50): - X = X.astype(np.float32) - Y = Y.astype(np.int64) - Xvalid = Xvalid.astype(np.float32) - Yvalid = Yvalid.astype(np.int64) - - # initialize hidden layers - N, D = X.shape - K = len(set(Y)) - self.hidden_layers = [] - M1 = D - for M2 in self.hidden_layer_sizes: - h = HiddenLayer(M1, M2) - self.hidden_layers.append(h) - M1 = M2 - W = np.random.randn(M1, K) * np.sqrt(2.0 / M1) - b = np.zeros(K) - self.W = tf.Variable(W.astype(np.float32)) - self.b = tf.Variable(b.astype(np.float32)) - - # collect params for later use - self.params = [self.W, self.b] - for h in self.hidden_layers: - self.params += h.params - - # set up theano functions and variables - inputs = tf.placeholder(tf.float32, shape=(None, D), name='inputs') - labels = tf.placeholder(tf.int64, shape=(None,), name='labels') - logits = self.forward(inputs) - - cost = tf.reduce_mean( - tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=logits, - labels=labels - ) - ) - train_op = tf.train.RMSPropOptimizer(lr, decay=decay, momentum=mu).minimize(cost) - # train_op = tf.train.MomentumOptimizer(lr, momentum=mu).minimize(cost) - # train_op = tf.train.AdamOptimizer(lr).minimize(cost) - prediction = self.predict(inputs) - - # validation cost will be calculated separately since nothing will be dropped - test_logits = self.forward_test(inputs) - test_cost = tf.reduce_mean( - tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=test_logits, - labels=labels - ) - ) - - n_batches = N // batch_sz - costs = [] - init = tf.global_variables_initializer() - with tf.Session() as session: - session.run(init) - for i in range(epochs): - print("epoch:", i, "n_batches:", n_batches) - X, Y = shuffle(X, Y) - for j in range(n_batches): - Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)] - Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)] - - session.run(train_op, feed_dict={inputs: Xbatch, labels: Ybatch}) - - if j % print_every == 0: - c = session.run(test_cost, feed_dict={inputs: Xvalid, labels: Yvalid}) - p = session.run(prediction, feed_dict={inputs: Xvalid}) - costs.append(c) - e = error_rate(Yvalid, p) - print("i:", i, "j:", j, "nb:", n_batches, "cost:", c, "error rate:", e) - - plt.plot(costs) - plt.show() - - def forward(self, X): - # tf.nn.dropout scales inputs by 1/p_keep - # therefore, during test time, we don't have to scale anything - Z = X - Z = tf.nn.dropout(Z, self.dropout_rates[0]) - for h, p in zip(self.hidden_layers, self.dropout_rates[1:]): - Z = h.forward(Z) - Z = tf.nn.dropout(Z, p) - return tf.matmul(Z, self.W) + self.b - - def forward_test(self, X): - Z = X - for h in self.hidden_layers: - Z = h.forward(Z) - return tf.matmul(Z, self.W) + self.b - - def predict(self, X): - pY = self.forward_test(X) - return tf.argmax(pY, 1) - - -def error_rate(p, t): - return np.mean(p != t) - - -def relu(a): - return a * (a > 0) - - -def main(): - # step 1: get the data and define all the usual variables - Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - - ann = ANN([500, 300], [0.8, 0.5, 0.5]) - ann.fit(Xtrain, Ytrain, Xtest, Ytest) - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/ann_class2/dropout_theano.py b/ann_class2/dropout_theano.py deleted file mode 100644 index 61486626..00000000 --- a/ann_class2/dropout_theano.py +++ /dev/null @@ -1,162 +0,0 @@ -# A 1-hidden-layer neural network in Theano. -# This code is not optimized for speed. -# It's just to get something working, using the principles we know. - -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import theano -import theano.tensor as T -import matplotlib.pyplot as plt -from theano.tensor.shared_randomstreams import RandomStreams -from util import get_normalized_data -from sklearn.utils import shuffle - - -def momentum_updates(cost, params, lr, mu): - grads = T.grad(cost, params) - updates = [] - - for p, g in zip(params, grads): - dp = theano.shared(p.get_value() * 0) - new_dp = mu*dp - lr*g - new_p = p + new_dp - updates.append((dp, new_dp)) - updates.append((p, new_p)) - return updates - - -class HiddenLayer(object): - def __init__(self, M1, M2, an_id): - self.id = an_id - self.M1 = M1 - self.M2 = M2 - W = np.random.randn(M1, M2) * np.sqrt(2.0 / M1) - b = np.zeros(M2) - self.W = theano.shared(W, 'W_%s' % self.id) - self.b = theano.shared(b, 'b_%s' % self.id) - self.params = [self.W, self.b] - - def forward(self, X): - return T.nnet.relu(X.dot(self.W) + self.b) - - -class ANN(object): - def __init__(self, hidden_layer_sizes, p_keep): - self.hidden_layer_sizes = hidden_layer_sizes - self.dropout_rates = p_keep - - def fit(self, X, Y, Xvalid, Yvalid, learning_rate=1e-2, mu=0.9, decay=0.9, epochs=10, batch_sz=100, show_fig=False): - X = X.astype(np.float32) - Y = Y.astype(np.int32) - Xvalid = Xvalid.astype(np.float32) - Yvalid = Yvalid.astype(np.int32) - - self.rng = RandomStreams() - - # initialize hidden layers - N, D = X.shape - K = len(set(Y)) - self.hidden_layers = [] - M1 = D - count = 0 - for M2 in self.hidden_layer_sizes: - h = HiddenLayer(M1, M2, count) - self.hidden_layers.append(h) - M1 = M2 - count += 1 - W = np.random.randn(M1, K) * np.sqrt(2.0 / M1) - b = np.zeros(K) - self.W = theano.shared(W, 'W_logreg') - self.b = theano.shared(b, 'b_logreg') - - # collect params for later use - self.params = [self.W, self.b] - for h in self.hidden_layers: - self.params += h.params - - # set up theano functions and variables - thX = T.matrix('X') - thY = T.ivector('Y') - pY_train = self.forward_train(thX) - - # this cost is for training - cost = -T.mean(T.log(pY_train[T.arange(thY.shape[0]), thY])) - updates = momentum_updates(cost, self.params, learning_rate, mu) - - train_op = theano.function( - inputs=[thX, thY], - updates=updates - ) - - # for evaluation and prediction - pY_predict = self.forward_predict(thX) - cost_predict = -T.mean(T.log(pY_predict[T.arange(thY.shape[0]), thY])) - prediction = self.predict(thX) - cost_predict_op = theano.function(inputs=[thX, thY], outputs=[cost_predict, prediction]) - - n_batches = N // batch_sz - costs = [] - for i in range(epochs): - X, Y = shuffle(X, Y) - for j in range(n_batches): - Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)] - Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)] - - train_op(Xbatch, Ybatch) - - if j % 50 == 0: - c, p = cost_predict_op(Xvalid, Yvalid) - costs.append(c) - e = error_rate(Yvalid, p) - print("i:", i, "j:", j, "nb:", n_batches, "cost:", c, "error rate:", e) - - if show_fig: - plt.plot(costs) - plt.show() - - def forward_train(self, X): - Z = X - for h, p in zip(self.hidden_layers, self.dropout_rates[:-1]): - mask = self.rng.binomial(n=1, p=p, size=Z.shape) - Z = mask * Z - Z = h.forward(Z) - mask = self.rng.binomial(n=1, p=self.dropout_rates[-1], size=Z.shape) - Z = mask * Z - return T.nnet.softmax(Z.dot(self.W) + self.b) - - def forward_predict(self, X): - Z = X - for h, p in zip(self.hidden_layers, self.dropout_rates[:-1]): - Z = h.forward(p * Z) - return T.nnet.softmax((self.dropout_rates[-1] * Z).dot(self.W) + self.b) - - def predict(self, X): - pY = self.forward_predict(X) - return T.argmax(pY, axis=1) - - -def error_rate(p, t): - return np.mean(p != t) - - -def relu(a): - return a * (a > 0) - - -def main(): - # step 1: get the data and define all the usual variables - Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - - ann = ANN([500, 300], [0.8, 0.5, 0.5]) - ann.fit(Xtrain, Ytrain, Xtest, Ytest, show_fig=True) - - -if __name__ == '__main__': - main() diff --git a/ann_class2/extra_reading.txt b/ann_class2/extra_reading.txt deleted file mode 100644 index 64c17548..00000000 --- a/ann_class2/extra_reading.txt +++ /dev/null @@ -1,34 +0,0 @@ -The Marginal Value of Adaptive Gradient Methods in Machine Learning -https://arxiv.org/abs/1705.08292 - -Asynchronous Stochastic Gradient Descent with Delay Compensation for Distributed Deep Learning -https://arxiv.org/abs/1609.08326 - -Asynchronous Stochastic Gradient Descent with Variance Reduction for Non-Convex Optimization -https://arxiv.org/abs/1604.03584 - -Adam: A Method for Stochastic Optimization -https://arxiv.org/abs/1412.6980 - -Large Scale Distributed Deep Networks -https://static.googleusercontent.com/media/research.google.com/en//archive/large_deep_networks_nips2012.pdf - -Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift -Sergey Ioffe, Christian Szegedy -https://arxiv.org/abs/1502.03167 - -Xavier (Glorot) Normal Initializer -http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf - -He Normal Initializer -http://arxiv.org/abs/1502.01852 - -For understanding Nesterov Momentum: -Advances in optimizing Recurrent Networks by Yoshua Bengio, Section 3.5 -http://arxiv.org/pdf/1212.0901v2.pdf - -Dropout: A Simple Way to Prevent Neural Networks from Overfitting -https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf - -The Loss Surfaces of Multilayer Networks -https://arxiv.org/pdf/1412.0233.pdf \ No newline at end of file diff --git a/ann_class2/grid_search.py b/ann_class2/grid_search.py deleted file mode 100644 index 26534b1b..00000000 --- a/ann_class2/grid_search.py +++ /dev/null @@ -1,82 +0,0 @@ -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import theano.tensor as T -from theano_ann import ANN -from util import get_spiral, get_clouds -from sklearn.utils import shuffle -import matplotlib.pyplot as plt -import numpy as np - - -def grid_search(): - # get the data and split into train/test - X, Y = get_spiral() - # X, Y = get_clouds() - X, Y = shuffle(X, Y) - Ntrain = int(0.7*len(X)) - Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] - Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - - # hyperparameters to try - hidden_layer_sizes = [ - [300], - [100,100], - [50,50,50], - ] - learning_rates = [1e-4, 1e-3, 1e-2] - l2_penalties = [0., 0.1, 1.0] - - # loop through all possible hyperparameter settings - best_validation_rate = 0 - best_hls = None - best_lr = None - best_l2 = None - for hls in hidden_layer_sizes: - for lr in learning_rates: - for l2 in l2_penalties: - model = ANN(hls) - model.fit(Xtrain, Ytrain, learning_rate=lr, reg=l2, mu=0.99, epochs=3000, show_fig=False) - validation_accuracy = model.score(Xtest, Ytest) - train_accuracy = model.score(Xtrain, Ytrain) - print( - "validation_accuracy: %.3f, train_accuracy: %.3f, settings: %s, %s, %s" % - (validation_accuracy, train_accuracy, hls, lr, l2) - ) - if validation_accuracy > best_validation_rate: - best_validation_rate = validation_accuracy - best_hls = hls - best_lr = lr - best_l2 = l2 - print("Best validation_accuracy:", best_validation_rate) - print("Best settings:") - print("hidden_layer_sizes:", best_hls) - print("learning_rate:", best_lr) - print("l2:", best_l2) - - -# def one(): -# X, Y = get_spiral() -# X, Y = shuffle(X, Y) -# Ntrain = int(0.7*len(X)) -# Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] -# # plt.scatter(Xtrain[:,0], Xtrain[:,1], c=Ytrain) -# # plt.show() -# Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - -# model = ANN([100,100]) -# model.fit(Xtrain, Ytrain, activation=T.nnet.relu, learning_rate=1e-3, mu=0.99, reg=0., epochs=3000, show_fig=True) -# validation_accuracy = model.score(Xtest, Ytest) -# print("validation_accuracy:", validation_accuracy) -# print("train accuracy:", model.score(Xtrain, Ytrain)) - - -if __name__ == '__main__': - grid_search() - # one() diff --git a/ann_class2/keras_example.py b/ann_class2/keras_example.py deleted file mode 100644 index aa9a5e19..00000000 --- a/ann_class2/keras_example.py +++ /dev/null @@ -1,83 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.models import Sequential -from keras.layers import Dense, Activation -from util import get_normalized_data, y2indicator - -import matplotlib.pyplot as plt - -# NOTE: do NOT name your file keras.py because it will conflict -# with importing keras - -# installation is easy! just the usual "sudo pip(3) install keras" - - -# get the data, same as Theano + Tensorflow examples -# no need to split now, the fit() function will do it -Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - -# get shapes -N, D = Xtrain.shape -K = len(set(Ytrain)) - -# by default Keras wants one-hot encoded labels -# there's another cost function we can use -# where we can just pass in the integer labels directly -# just like Tensorflow / Theano -Ytrain = y2indicator(Ytrain) -Ytest = y2indicator(Ytest) - - -# the model will be a sequence of layers -model = Sequential() - - -# ANN with layers [784] -> [500] -> [300] -> [10] -model.add(Dense(units=500, input_dim=D)) -model.add(Activation('relu')) -model.add(Dense(units=300)) # don't need to specify input_dim -model.add(Activation('relu')) -model.add(Dense(units=K)) -model.add(Activation('softmax')) - - -# list of losses: https://keras.io/losses/ -# list of optimizers: https://keras.io/optimizers/ -# list of metrics: https://keras.io/metrics/ -model.compile( - loss='categorical_crossentropy', - optimizer='adam', - metrics=['accuracy'] -) - -# note: multiple ways to choose a backend -# either theano, tensorflow, or cntk -# https://keras.io/backend/ - - -# gives us back a -r = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32) -print("Returned:", r) - -# print the available keys -# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc']) -print(r.history.keys()) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='acc') -plt.plot(r.history['val_accuracy'], label='val_acc') -plt.legend() -plt.show() - - diff --git a/ann_class2/keras_functional.py b/ann_class2/keras_functional.py deleted file mode 100644 index 265d3f9b..00000000 --- a/ann_class2/keras_functional.py +++ /dev/null @@ -1,78 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.models import Model -from keras.layers import Dense, Input -from util import get_normalized_data, y2indicator - -import matplotlib.pyplot as plt - -# NOTE: do NOT name your file keras.py because it will conflict -# with importing keras - -# installation is easy! just the usual "sudo pip(3) install keras" - - -# get the data, same as Theano + Tensorflow examples -# no need to split now, the fit() function will do it -Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - -# get shapes -N, D = Xtrain.shape -K = len(set(Ytrain)) - -# by default Keras wants one-hot encoded labels -# there's another cost function we can use -# where we can just pass in the integer labels directly -# just like Tensorflow / Theano -Ytrain = y2indicator(Ytrain) -Ytest = y2indicator(Ytest) - - -# ANN with layers [784] -> [500] -> [300] -> [10] -i = Input(shape=(D,)) -x = Dense(500, activation='relu')(i) -x = Dense(300, activation='relu')(x) -x = Dense(K, activation='softmax')(x) -model = Model(inputs=i, outputs=x) - - -# list of losses: https://keras.io/losses/ -# list of optimizers: https://keras.io/optimizers/ -# list of metrics: https://keras.io/metrics/ -model.compile( - loss='categorical_crossentropy', - optimizer='adam', - metrics=['accuracy'] -) - -# note: multiple ways to choose a backend -# either theano, tensorflow, or cntk -# https://keras.io/backend/ - - -# gives us back a -r = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32) -print("Returned:", r) - -# print the available keys -# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc']) -print(r.history.keys()) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='acc') -plt.plot(r.history['val_accuracy'], label='val_acc') -plt.legend() -plt.show() - - diff --git a/ann_class2/mlp.py b/ann_class2/mlp.py index aeb1697d..a247b92e 100644 --- a/ann_class2/mlp.py +++ b/ann_class2/mlp.py @@ -1,25 +1,22 @@ # Simple multi-layer preceptron / neural network in Python and Numpy -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow +# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow # https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import numpy as np def forward(X, W1, b1, W2, b2): - # sigmoid # Z = 1 / (1 + np.exp(-( X.dot(W1) + b1 ))) - # relu + # rectifier Z = X.dot(W1) + b1 Z[Z < 0] = 0 + # print "Z:", Z A = Z.dot(W2) + b2 expA = np.exp(A) Y = expA / expA.sum(axis=1, keepdims=True) + # print "Y:", Y, "are any 0?", np.any(Y == 0), "are any nan?", np.any(np.isnan(Y)) + # exit() return Y, Z def derivative_w2(Z, T, Y): @@ -30,8 +27,8 @@ def derivative_b2(T, Y): def derivative_w1(X, Z, T, Y, W2): # return X.T.dot( ( ( Y-T ).dot(W2.T) * ( Z*(1 - Z) ) ) ) # for sigmoid - return X.T.dot( ( ( Y-T ).dot(W2.T) * (Z > 0) ) ) # for relu + return X.T.dot( ( ( Y-T ).dot(W2.T) * np.sign(Z) ) ) # for relu def derivative_b1(Z, T, Y, W2): # return (( Y-T ).dot(W2.T) * ( Z*(1 - Z) )).sum(axis=0) # for sigmoid - return (( Y-T ).dot(W2.T) * (Z > 0)).sum(axis=0) # for relu + return (( Y-T ).dot(W2.T) * np.sign(Z)).sum(axis=0) # for relu \ No newline at end of file diff --git a/ann_class2/momentum.py b/ann_class2/momentum.py index 8fb86962..f0b55f93 100644 --- a/ann_class2/momentum.py +++ b/ann_class2/momentum.py @@ -1,15 +1,10 @@ # Compare momentum with regular gradient descent -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow +# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow # https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow # NOTE: MUST restrict initial values of W by dividing by # # NOTE: sigmoid vs. rectifier for hiddens # We get 15% error rate with sigmoid, 3% error rate with ReLU -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import numpy as np from sklearn.utils import shuffle @@ -26,174 +21,152 @@ def main(): # 3. batch SGD with Nesterov momentum max_iter = 20 # make it 30 for sigmoid - print_period = 50 + print_period = 10 - Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() + X, Y = get_normalized_data() lr = 0.00004 reg = 0.01 + Xtrain = X[:-1000,] + Ytrain = Y[:-1000] + Xtest = X[-1000:,] + Ytest = Y[-1000:] Ytrain_ind = y2indicator(Ytrain) Ytest_ind = y2indicator(Ytest) N, D = Xtrain.shape batch_sz = 500 - n_batches = N // batch_sz + n_batches = N / batch_sz M = 300 K = 10 - W1 = np.random.randn(D, M) / np.sqrt(D) + W1 = np.random.randn(D, M) / 28 b1 = np.zeros(M) W2 = np.random.randn(M, K) / np.sqrt(M) b2 = np.zeros(K) - # save initial weights - W1_0 = W1.copy() - b1_0 = b1.copy() - W2_0 = W2.copy() - b2_0 = b2.copy() - # 1. batch - losses_batch = [] - errors_batch = [] - for i in range(max_iter): - Xtrain, Ytrain, Ytrain_ind = shuffle(Xtrain, Ytrain, Ytrain_ind) - for j in range(n_batches): + # cost = -16 + LL_batch = [] + CR_batch = [] + for i in xrange(max_iter): + for j in xrange(n_batches): Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] pYbatch, Z = forward(Xbatch, W1, b1, W2, b2) # print "first batch cost:", cost(pYbatch, Ybatch) - # gradients - gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2 - gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2 - gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1 - gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1 - # updates - W2 -= lr*gW2 - b2 -= lr*gb2 - W1 -= lr*gW1 - b1 -= lr*gb1 + W2 -= lr*(derivative_w2(Z, Ybatch, pYbatch) + reg*W2) + b2 -= lr*(derivative_b2(Ybatch, pYbatch) + reg*b2) + W1 -= lr*(derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1) + b1 -= lr*(derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1) if j % print_period == 0: + # calculate just for LL pY, _ = forward(Xtest, W1, b1, W2, b2) - l = cost(pY, Ytest_ind) - losses_batch.append(l) - print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l)) + # print "pY:", pY + ll = cost(pY, Ytest_ind) + LL_batch.append(ll) + print "Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll) - e = error_rate(pY, Ytest) - errors_batch.append(e) - print("Error rate:", e) + err = error_rate(pY, Ytest) + CR_batch.append(err) + print "Error rate:", err pY, _ = forward(Xtest, W1, b1, W2, b2) - print("Final error rate:", error_rate(pY, Ytest)) + print "Final error rate:", error_rate(pY, Ytest) # 2. batch with momentum - W1 = W1_0.copy() - b1 = b1_0.copy() - W2 = W2_0.copy() - b2 = b2_0.copy() - losses_momentum = [] - errors_momentum = [] + W1 = np.random.randn(D, M) / 28 + b1 = np.zeros(M) + W2 = np.random.randn(M, K) / np.sqrt(M) + b2 = np.zeros(K) + LL_momentum = [] + CR_momentum = [] mu = 0.9 dW2 = 0 db2 = 0 dW1 = 0 db1 = 0 - for i in range(max_iter): - Xtrain, Ytrain, Ytrain_ind = shuffle(Xtrain, Ytrain, Ytrain_ind) - for j in range(n_batches): + for i in xrange(max_iter): + for j in xrange(n_batches): Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] pYbatch, Z = forward(Xbatch, W1, b1, W2, b2) - # gradients - gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2 - gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2 - gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1 - gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1 - - # update velocities - dW2 = mu*dW2 - lr*gW2 - db2 = mu*db2 - lr*gb2 - dW1 = mu*dW1 - lr*gW1 - db1 = mu*db1 - lr*gb1 - # updates + dW2 = mu*dW2 - lr*(derivative_w2(Z, Ybatch, pYbatch) + reg*W2) W2 += dW2 + db2 = mu*db2 - lr*(derivative_b2(Ybatch, pYbatch) + reg*b2) b2 += db2 + dW1 = mu*dW1 - lr*(derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1) W1 += dW1 + db1 = mu*db1 - lr*(derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1) b1 += db1 if j % print_period == 0: + # calculate just for LL pY, _ = forward(Xtest, W1, b1, W2, b2) - l = cost(pY, Ytest_ind) - losses_momentum.append(l) - print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l)) - - e = error_rate(pY, Ytest) - errors_momentum.append(e) - print("Error rate:", e) + # print "pY:", pY + ll = cost(pY, Ytest_ind) + LL_momentum.append(ll) + print "Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll) + + err = error_rate(pY, Ytest) + CR_momentum.append(err) + print "Error rate:", err pY, _ = forward(Xtest, W1, b1, W2, b2) - print("Final error rate:", error_rate(pY, Ytest)) + print "Final error rate:", error_rate(pY, Ytest) # 3. batch with Nesterov momentum - W1 = W1_0.copy() - b1 = b1_0.copy() - W2 = W2_0.copy() - b2 = b2_0.copy() - - losses_nesterov = [] - errors_nesterov = [] - + W1 = np.random.randn(D, M) / 28 + b1 = np.zeros(M) + W2 = np.random.randn(M, K) / np.sqrt(M) + b2 = np.zeros(K) + LL_nest = [] + CR_nest = [] mu = 0.9 - vW2 = 0 - vb2 = 0 - vW1 = 0 - vb1 = 0 - for i in range(max_iter): - Xtrain, Ytrain, Ytrain_ind = shuffle(Xtrain, Ytrain, Ytrain_ind) - for j in range(n_batches): + dW2 = 0 + db2 = 0 + dW1 = 0 + db1 = 0 + for i in xrange(max_iter): + for j in xrange(n_batches): Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] pYbatch, Z = forward(Xbatch, W1, b1, W2, b2) # updates - gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2 - gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2 - gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1 - gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1 - - # v update - vW2 = mu*vW2 - lr*gW2 - vb2 = mu*vb2 - lr*gb2 - vW1 = mu*vW1 - lr*gW1 - vb1 = mu*vb1 - lr*gb1 - - # param update - W2 += mu*vW2 - lr*gW2 - b2 += mu*vb2 - lr*gb2 - W1 += mu*vW1 - lr*gW1 - b1 += mu*vb1 - lr*gb1 + dW2 = mu*mu*dW2 - (1 + mu)*lr*(derivative_w2(Z, Ybatch, pYbatch) + reg*W2) + W2 += dW2 + db2 = mu*mu*db2 - (1 + mu)*lr*(derivative_b2(Ybatch, pYbatch) + reg*b2) + b2 += db2 + dW1 = mu*mu*dW1 - (1 + mu)*lr*(derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1) + W1 += dW1 + db1 = mu*mu*db1 - (1 + mu)*lr*(derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1) + b1 += db1 if j % print_period == 0: + # calculate just for LL pY, _ = forward(Xtest, W1, b1, W2, b2) - l = cost(pY, Ytest_ind) - losses_nesterov.append(l) - print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l)) - - e = error_rate(pY, Ytest) - errors_nesterov.append(e) - print("Error rate:", e) + # print "pY:", pY + ll = cost(pY, Ytest_ind) + LL_nest.append(ll) + print "Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll) + + err = error_rate(pY, Ytest) + CR_nest.append(err) + print "Error rate:", err pY, _ = forward(Xtest, W1, b1, W2, b2) - print("Final error rate:", error_rate(pY, Ytest)) + print "Final error rate:", error_rate(pY, Ytest) - plt.plot(losses_batch, label="batch") - plt.plot(losses_momentum, label="momentum") - plt.plot(losses_nesterov, label="nesterov") + plt.plot(LL_batch, label="batch") + plt.plot(LL_momentum, label="momentum") + plt.plot(LL_nest, label="nesterov") plt.legend() plt.show() diff --git a/ann_class2/mxnet_example.py b/ann_class2/mxnet_example.py deleted file mode 100644 index 9ea745fc..00000000 --- a/ann_class2/mxnet_example.py +++ /dev/null @@ -1,99 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -# installation is just one line: -# https://mxnet.incubator.apache.org/get_started/install.html -# -# Mac: -# pip install mxnet -# -# Linux (GPU): -# pip install mxnet-cu80 -# -# Windows (a little more involved): -# https://mxnet.incubator.apache.org/get_started/windows_setup.html - -import mxnet as mx -import numpy as np -import matplotlib.pyplot as plt -from util import get_normalized_data, y2indicator - - - -# get the data, same as Theano + Tensorflow examples -# no need to split now, the fit() function will do it -Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - -# get shapes -N, D = Xtrain.shape -K = len(set(Ytrain)) - -# training config -batch_size = 32 -epochs = 15 - - -# convert the data into a format appropriate for input into mxnet -train_iterator = mx.io.NDArrayIter( - Xtrain, - Ytrain, - batch_size, - shuffle=True -) -test_iterator = mx.io.NDArrayIter(Xtest, Ytest, batch_size) - - - -# define a placeholder to represent the inputs -data = mx.sym.var('data') - - -# define the model architecture -a1 = mx.sym.FullyConnected(data=data, num_hidden=500) -z1 = mx.sym.Activation(data=a1, act_type="relu") -a2 = mx.sym.FullyConnected(data=z1, num_hidden = 300) -z2 = mx.sym.Activation(data=a2, act_type="relu") -a3 = mx.sym.FullyConnected(data=z2, num_hidden=K) -y = mx.sym.SoftmaxOutput(data=a3, name='softmax') - - - - -# train it - -# required in order for progress to be printed -import logging -logging.getLogger().setLevel(logging.DEBUG) - -# use mx.gpu() if you have gpu -model = mx.mod.Module(symbol=y, context=mx.cpu()) -model.fit( - train_iterator, # train data - eval_data=test_iterator, # validation data - optimizer=mx.optimizer.Adam(), - eval_metric='acc', # report accuracy during training - batch_end_callback = mx.callback.Speedometer(batch_size, 100), # output progress for each 100 data batches - num_epoch=epochs, -) -# no return value -# list of optimizers: https://mxnet.incubator.apache.org/api/python/optimization.html - - -# test it -# predict accuracy of mlp -acc = mx.metric.Accuracy() -model.score(test_iterator, acc) -print(acc) -print(acc.get()) - - -# currently, there is no good way to plot the training loss / accuracy history -# https://github.com/apache/incubator-mxnet/issues/2873 -# -# some have suggested parsing the logs -# https://github.com/apache/incubator-mxnet/blob/master/example/kaggle-ndsb1/training_curves.py diff --git a/ann_class2/pytorch_batchnorm.py b/ann_class2/pytorch_batchnorm.py deleted file mode 100644 index 766dc805..00000000 --- a/ann_class2/pytorch_batchnorm.py +++ /dev/null @@ -1,189 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -# Note: is helpful to look at keras_example.py first - - -import numpy as np -import matplotlib.pyplot as plt -from util import get_normalized_data - -import torch -from torch.autograd import Variable -from torch import optim - - - -# get the data, same as Theano + Tensorflow examples -# no need to split now, the fit() function will do it -Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - -# get shapes -_, D = Xtrain.shape -K = len(set(Ytrain)) - -# Note: no need to convert Y to indicator matrix - - -# the model will be a sequence of layers -model = torch.nn.Sequential() - - -# ANN with layers [784] -> [500] -> [300] -> [10] -model.add_module("dense1", torch.nn.Linear(D, 500)) -model.add_module("bn1", torch.nn.BatchNorm1d(500)) -model.add_module("relu1", torch.nn.ReLU()) -model.add_module("dense2", torch.nn.Linear(500, 300)) -model.add_module("bn2", torch.nn.BatchNorm1d(300)) -model.add_module("relu2", torch.nn.ReLU()) -model.add_module("dense3", torch.nn.Linear(300, K)) -# Note: no final softmax! -# just like Tensorflow, it's included in cross-entropy function - - -# define a loss function -# other loss functions can be found here: -# http://pytorch.org/docs/master/nn.html#loss-functions -loss = torch.nn.CrossEntropyLoss(size_average=True) -# Note: this returns a function! -# e.g. use it like: loss(logits, labels) - - -# define an optimizer -# other optimizers can be found here: -# http://pytorch.org/docs/master/optim.html -optimizer = optim.Adam(model.parameters(), lr=1e-4) - - - -# define the training procedure -# i.e. one step of gradient descent -# there are lots of steps -# so we encapsulate it in a function -# Note: inputs and labels are torch tensors -def train(model, loss, optimizer, inputs, labels): - # set the model to training mode - # because batch norm has 2 different modes! - model.train() - - inputs = Variable(inputs, requires_grad=False) - labels = Variable(labels, requires_grad=False) - - # Reset gradient - optimizer.zero_grad() - - # Forward - logits = model.forward(inputs) - output = loss.forward(logits, labels) - - # Backward - output.backward() - - # Update parameters - optimizer.step() - - # what's the difference between backward() and step()? - - return output.item() - - -# similar to train() but not doing the backprop step -def get_cost(model, loss, inputs, labels): - # set the model to testing mode - # because batch norm has 2 different modes! - model.eval() - - inputs = Variable(inputs, requires_grad=False) - labels = Variable(labels, requires_grad=False) - - # Forward - logits = model.forward(inputs) - output = loss.forward(logits, labels) - - return output.item() - - -# define the prediction procedure -# also encapsulate these steps -# Note: inputs is a torch tensor -def predict(model, inputs): - # set the model to testing mode - # because batch norm has 2 different modes! - model.eval() - - inputs = Variable(inputs, requires_grad=False) - logits = model.forward(inputs) - return logits.data.numpy().argmax(axis=1) - - -# return the accuracy -# labels is a torch tensor -# to get back the internal numpy data -# use the instance method .numpy() -def score(model, inputs, labels): - predictions = predict(model, inputs) - return np.mean(labels.numpy() == predictions) - - -### prepare for training loop ### - -# convert the data arrays into torch tensors -Xtrain = torch.from_numpy(Xtrain).float() -Ytrain = torch.from_numpy(Ytrain).long() -Xtest = torch.from_numpy(Xtest).float() -Ytest = torch.from_numpy(Ytest).long() - -# training parameters -epochs = 15 -batch_size = 32 -n_batches = Xtrain.size()[0] // batch_size - -# things to keep track of -train_costs = [] -test_costs = [] -train_accuracies = [] -test_accuracies = [] - -# main training loop -for i in range(epochs): - cost = 0 - test_cost = 0 - for j in range(n_batches): - Xbatch = Xtrain[j*batch_size:(j+1)*batch_size] - Ybatch = Ytrain[j*batch_size:(j+1)*batch_size] - cost += train(model, loss, optimizer, Xbatch, Ybatch) - - - # we could have also calculated the train cost here - # but I wanted to show you that we could also return it - # from the train function itself - train_acc = score(model, Xtrain, Ytrain) - test_acc = score(model, Xtest, Ytest) - test_cost = get_cost(model, loss, Xtest, Ytest) - - print("Epoch: %d, cost: %f, acc: %.2f" % (i, test_cost, test_acc)) - - # for plotting - train_costs.append(cost / n_batches) - train_accuracies.append(train_acc) - test_costs.append(test_cost) - test_accuracies.append(test_acc) - - - -# plot the results -plt.plot(train_costs, label='Train cost') -plt.plot(test_costs, label='Test cost') -plt.title('Cost') -plt.legend() -plt.show() - -plt.plot(train_accuracies, label='Train accuracy') -plt.plot(test_accuracies, label='Test accuracy') -plt.title('Accuracy') -plt.legend() -plt.show() diff --git a/ann_class2/pytorch_dropout.py b/ann_class2/pytorch_dropout.py deleted file mode 100644 index 58e76a49..00000000 --- a/ann_class2/pytorch_dropout.py +++ /dev/null @@ -1,190 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -# Note: is helpful to look at keras_example.py first - - -import numpy as np -import matplotlib.pyplot as plt -from util import get_normalized_data - -import torch -from torch.autograd import Variable -from torch import optim - - - -# get the data, same as Theano + Tensorflow examples -# no need to split now, the fit() function will do it -Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - -# get shapes -_, D = Xtrain.shape -K = len(set(Ytrain)) - -# Note: no need to convert Y to indicator matrix - - -# the model will be a sequence of layers -model = torch.nn.Sequential() - - -# ANN with layers [784] -> [500] -> [300] -> [10] -# NOTE: the "p" is p_drop, not p_keep -model.add_module("dropout1", torch.nn.Dropout(p=0.2)) -model.add_module("dense1", torch.nn.Linear(D, 500)) -model.add_module("relu1", torch.nn.ReLU()) -model.add_module("dropout2", torch.nn.Dropout(p=0.5)) -model.add_module("dense2", torch.nn.Linear(500, 300)) -model.add_module("relu2", torch.nn.ReLU()) -model.add_module("dropout3", torch.nn.Dropout(p=0.5)) -model.add_module("dense3", torch.nn.Linear(300, K)) -# Note: no final softmax! -# just like Tensorflow, it's included in cross-entropy function - - -# define a loss function -# other loss functions can be found here: -# http://pytorch.org/docs/master/nn.html#loss-functions -loss = torch.nn.CrossEntropyLoss(size_average=True) -# Note: this returns a function! -# e.g. use it like: loss(logits, labels) - - -# define an optimizer -# other optimizers can be found here: -# http://pytorch.org/docs/master/optim.html -optimizer = optim.Adam(model.parameters(), lr=1e-4) - - -# define the training procedure -# i.e. one step of gradient descent -# there are lots of steps -# so we encapsulate it in a function -# Note: inputs and labels are torch tensors -def train(model, loss, optimizer, inputs, labels): - # set the model to training mode - # because dropout has 2 different modes! - model.train() - - inputs = Variable(inputs, requires_grad=False) - labels = Variable(labels, requires_grad=False) - - # Reset gradient - optimizer.zero_grad() - - # Forward - logits = model.forward(inputs) - output = loss.forward(logits, labels) - - # Backward - output.backward() - - # Update parameters - optimizer.step() - - # what's the difference between backward() and step()? - - return output.item() - - -# similar to train() but not doing the backprop step -def get_cost(model, loss, inputs, labels): - # set the model to testing mode - # because dropout has 2 different modes! - model.eval() - - inputs = Variable(inputs, requires_grad=False) - labels = Variable(labels, requires_grad=False) - - # Forward - logits = model.forward(inputs) - output = loss.forward(logits, labels) - - return output.item() - - -# define the prediction procedure -# also encapsulate these steps -# Note: inputs is a torch tensor -def predict(model, inputs): - # set the model to testing mode - # because dropout has 2 different modes! - model.eval() - - inputs = Variable(inputs, requires_grad=False) - logits = model.forward(inputs) - return logits.data.numpy().argmax(axis=1) - - -# return the accuracy -# labels is a torch tensor -# to get back the internal numpy data -# use the instance method .numpy() -def score(model, inputs, labels): - predictions = predict(model, inputs) - return np.mean(labels.numpy() == predictions) - - -### prepare for training loop ### - -# convert the data arrays into torch tensors -Xtrain = torch.from_numpy(Xtrain).float() -Ytrain = torch.from_numpy(Ytrain).long() -Xtest = torch.from_numpy(Xtest).float() -Ytest = torch.from_numpy(Ytest).long() - -# training parameters -epochs = 15 -batch_size = 32 -n_batches = Xtrain.size()[0] // batch_size - -# things to keep track of -train_costs = [] -test_costs = [] -train_accuracies = [] -test_accuracies = [] - -# main training loop -for i in range(epochs): - cost = 0 - test_cost = 0 - for j in range(n_batches): - Xbatch = Xtrain[j*batch_size:(j+1)*batch_size] - Ybatch = Ytrain[j*batch_size:(j+1)*batch_size] - cost += train(model, loss, optimizer, Xbatch, Ybatch) - - - # we could have also calculated the train cost here - # but I wanted to show you that we could also return it - # from the train function itself - train_acc = score(model, Xtrain, Ytrain) - test_acc = score(model, Xtest, Ytest) - test_cost = get_cost(model, loss, Xtest, Ytest) - - print("Epoch: %d, cost: %f, acc: %.2f" % (i, test_cost, test_acc)) - - # for plotting - train_costs.append(cost / n_batches) - train_accuracies.append(train_acc) - test_costs.append(test_cost) - test_accuracies.append(test_acc) - - - -# plot the results -plt.plot(train_costs, label='Train cost') -plt.plot(test_costs, label='Test cost') -plt.title('Cost') -plt.legend() -plt.show() - -plt.plot(train_accuracies, label='Train accuracy') -plt.plot(test_accuracies, label='Test accuracy') -plt.title('Accuracy') -plt.legend() -plt.show() diff --git a/ann_class2/pytorch_example.py b/ann_class2/pytorch_example.py deleted file mode 100644 index 53a26d48..00000000 --- a/ann_class2/pytorch_example.py +++ /dev/null @@ -1,140 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -# Note: is helpful to look at keras_example.py first - - -import numpy as np -import matplotlib.pyplot as plt -from util import get_normalized_data - -import torch -from torch.autograd import Variable -from torch import optim - - - -# get the data, same as Theano + Tensorflow examples -# no need to split now, the fit() function will do it -Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - -# get shapes -_, D = Xtrain.shape -K = len(set(Ytrain)) - -# Note: no need to convert Y to indicator matrix - - -# the model will be a sequence of layers -model = torch.nn.Sequential() - - -# ANN with layers [784] -> [500] -> [300] -> [10] -model.add_module("dense1", torch.nn.Linear(D, 500)) -model.add_module("relu1", torch.nn.ReLU()) -model.add_module("dense2", torch.nn.Linear(500, 300)) -model.add_module("relu2", torch.nn.ReLU()) -model.add_module("dense3", torch.nn.Linear(300, K)) -# Note: no final softmax! -# just like Tensorflow, it's included in cross-entropy function - - - -# define a loss function -# other loss functions can be found here: -# http://pytorch.org/docs/master/nn.html#loss-functions -loss = torch.nn.CrossEntropyLoss(size_average=True) - - - -# define an optimizer -# other optimizers can be found here: -# http://pytorch.org/docs/master/optim.html -optimizer = optim.Adam(model.parameters()) - - - -# define the training procedure -# i.e. one step of gradient descent -# there are lots of steps -# so we encapsulate it in a function -# Note: inputs and labels are torch tensors -def train(model, loss, optimizer, inputs, labels): - # https://discuss.pytorch.org/t/why-is-it-recommended-to-wrap-your-data-with-variable-each-step-of-the-iterations-rather-than-before-training-starts/12683 - inputs = Variable(inputs, requires_grad=False) - labels = Variable(labels, requires_grad=False) - - # Reset gradient - # https://discuss.pytorch.org/t/why-do-we-need-to-set-the-gradients-manually-to-zero-in-pytorch/4903/7 - optimizer.zero_grad() - - # Forward - logits = model.forward(inputs) - output = loss.forward(logits, labels) - - # Backward - output.backward() - - # Update parameters - optimizer.step() - - # what's the difference between backward() and step()? - # https://discuss.pytorch.org/t/what-does-the-backward-function-do/9944 - return output.item() - - -# define the prediction procedure -# also encapsulate these steps -# Note: inputs is a torch tensor -def predict(model, inputs): - inputs = Variable(inputs, requires_grad=False) - logits = model.forward(inputs) - return logits.data.numpy().argmax(axis=1) - - - -### prepare for training loop ### - -# convert the data arrays into torch tensors -Xtrain = torch.from_numpy(Xtrain).float() -Ytrain = torch.from_numpy(Ytrain).long() -Xtest = torch.from_numpy(Xtest).float() - - - -epochs = 15 -batch_size = 32 -n_batches = Xtrain.size()[0] // batch_size - -costs = [] -test_accuracies = [] -for i in range(epochs): - cost = 0. - for j in range(n_batches): - Xbatch = Xtrain[j*batch_size:(j+1)*batch_size] - Ybatch = Ytrain[j*batch_size:(j+1)*batch_size] - cost += train(model, loss, optimizer, Xbatch, Ybatch) - - Ypred = predict(model, Xtest) - acc = np.mean(Ytest == Ypred) - print("Epoch: %d, cost: %f, acc: %.2f" % (i, cost / n_batches, acc)) - - # for plotting - costs.append(cost / n_batches) - test_accuracies.append(acc) - - -# EXERCISE: plot test cost + training accuracy too - -# plot the results -plt.plot(costs) -plt.title('Training cost') -plt.show() - -plt.plot(test_accuracies) -plt.title('Test accuracies') -plt.show() diff --git a/ann_class2/pytorch_example2.py b/ann_class2/pytorch_example2.py deleted file mode 100644 index a58c45b2..00000000 --- a/ann_class2/pytorch_example2.py +++ /dev/null @@ -1,175 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -# Note: is helpful to look at keras_example.py first - - -import numpy as np -import matplotlib.pyplot as plt -from util import get_normalized_data - -import torch -from torch.autograd import Variable -from torch import optim - - - -# get the data, same as Theano + Tensorflow examples -# no need to split now, the fit() function will do it -Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - -# get shapes -_, D = Xtrain.shape -K = len(set(Ytrain)) - -# Note: no need to convert Y to indicator matrix - - -# the model will be a sequence of layers -model = torch.nn.Sequential() - - -# ANN with layers [784] -> [500] -> [300] -> [10] -model.add_module("dense1", torch.nn.Linear(D, 500)) -model.add_module("relu1", torch.nn.ReLU()) -model.add_module("dense2", torch.nn.Linear(500, 300)) -model.add_module("relu2", torch.nn.ReLU()) -model.add_module("dense3", torch.nn.Linear(300, K)) -# Note: no final softmax! -# just like Tensorflow, it's included in cross-entropy function - - -# define a loss function -# other loss functions can be found here: -# http://pytorch.org/docs/master/nn.html#loss-functions -loss = torch.nn.CrossEntropyLoss(size_average=True) -# Note: this returns a function! -# e.g. use it like: loss(logits, labels) - - -# define an optimizer -# other optimizers can be found here: -# http://pytorch.org/docs/master/optim.html -optimizer = optim.Adam(model.parameters(), lr=1e-4) - - - -# define the training procedure -# i.e. one step of gradient descent -# there are lots of steps -# so we encapsulate it in a function -# Note: inputs and labels are torch tensors -def train(model, loss, optimizer, inputs, labels): - inputs = Variable(inputs, requires_grad=False) - labels = Variable(labels, requires_grad=False) - - # Reset gradient - optimizer.zero_grad() - - # Forward - logits = model.forward(inputs) - output = loss.forward(logits, labels) - - # Backward - output.backward() - - # Update parameters - optimizer.step() - - # what's the difference between backward() and step()? - - return output.item() - - -# similar to train() but not doing the backprop step -def get_cost(model, loss, inputs, labels): - inputs = Variable(inputs, requires_grad=False) - labels = Variable(labels, requires_grad=False) - - # Forward - logits = model.forward(inputs) - output = loss.forward(logits, labels) - - return output.item() - - -# define the prediction procedure -# also encapsulate these steps -# Note: inputs is a torch tensor -def predict(model, inputs): - inputs = Variable(inputs, requires_grad=False) - logits = model.forward(inputs) - return logits.data.numpy().argmax(axis=1) - - -# return the accuracy -# labels is a torch tensor -# to get back the internal numpy data -# use the instance method .numpy() -def score(model, inputs, labels): - predictions = predict(model, inputs) - return np.mean(labels.numpy() == predictions) - - -### prepare for training loop ### - -# convert the data arrays into torch tensors -Xtrain = torch.from_numpy(Xtrain).float() -Ytrain = torch.from_numpy(Ytrain).long() -Xtest = torch.from_numpy(Xtest).float() -Ytest = torch.from_numpy(Ytest).long() - -# training parameters -epochs = 15 -batch_size = 32 -n_batches = Xtrain.size()[0] // batch_size - -# things to keep track of -train_costs = [] -test_costs = [] -train_accuracies = [] -test_accuracies = [] - -# main training loop -for i in range(epochs): - cost = 0 - test_cost = 0 - for j in range(n_batches): - Xbatch = Xtrain[j*batch_size:(j+1)*batch_size] - Ybatch = Ytrain[j*batch_size:(j+1)*batch_size] - cost += train(model, loss, optimizer, Xbatch, Ybatch) - - - # we could have also calculated the train cost here - # but I wanted to show you that we could also return it - # from the train function itself - train_acc = score(model, Xtrain, Ytrain) - test_acc = score(model, Xtest, Ytest) - test_cost = get_cost(model, loss, Xtest, Ytest) - - print("Epoch: %d, cost: %f, acc: %.2f" % (i, test_cost, test_acc)) - - # for plotting - train_costs.append(cost / n_batches) - train_accuracies.append(train_acc) - test_costs.append(test_cost) - test_accuracies.append(test_acc) - - - -# plot the results -plt.plot(train_costs, label='Train cost') -plt.plot(test_costs, label='Test cost') -plt.title('Cost') -plt.legend() -plt.show() - -plt.plot(train_accuracies, label='Train accuracy') -plt.plot(test_accuracies, label='Test accuracy') -plt.title('Accuracy') -plt.legend() -plt.show() diff --git a/ann_class2/random_search.py b/ann_class2/random_search.py deleted file mode 100644 index f028615e..00000000 --- a/ann_class2/random_search.py +++ /dev/null @@ -1,76 +0,0 @@ -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -# import theano.tensor as T -from theano_ann import ANN -from util import get_spiral, get_clouds -from sklearn.utils import shuffle -import matplotlib.pyplot as plt -import numpy as np - - -def random_search(): - # get the data and split into train/test - X, Y = get_spiral() - # X, Y = get_clouds() - X, Y = shuffle(X, Y) - Ntrain = int(0.7*len(X)) - Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] - Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - - # starting hyperparameters - M = 20 - nHidden = 2 - log_lr = -4 - log_l2 = -2 # since we always want it to be positive - max_tries = 30 - - - # loop through all possible hyperparameter settings - best_validation_rate = 0 - best_hls = None - best_lr = None - best_l2 = None - for _ in range(max_tries): - model = ANN([M]*nHidden) - model.fit( - Xtrain, Ytrain, - learning_rate=10**log_lr, reg=10**log_l2, - mu=0.99, epochs=3000, show_fig=False - ) - validation_accuracy = model.score(Xtest, Ytest) - train_accuracy = model.score(Xtrain, Ytrain) - print( - "validation_accuracy: %.3f, train_accuracy: %.3f, settings: %s, %s, %s" % - (validation_accuracy, train_accuracy, [M]*nHidden, log_lr, log_l2) - ) - if validation_accuracy > best_validation_rate: - best_validation_rate = validation_accuracy - best_M = M - best_nHidden = nHidden - best_lr = log_lr - best_l2 = log_l2 - - # select new hyperparams - nHidden = best_nHidden + np.random.randint(-1, 2) # -1, 0, or 1 - nHidden = max(1, nHidden) - M = best_M + np.random.randint(-1, 2)*10 - M = max(10, M) - log_lr = best_lr + np.random.randint(-1, 2) - log_l2 = best_l2 + np.random.randint(-1, 2) - print("Best validation_accuracy:", best_validation_rate) - print("Best settings:") - print("best_M:", best_M) - print("best_nHidden:", best_nHidden) - print("learning_rate:", best_lr) - print("l2:", best_l2) - - -if __name__ == '__main__': - random_search() diff --git a/ann_class2/rmsprop.py b/ann_class2/rmsprop.py index f0bb093e..0fdfdf63 100644 --- a/ann_class2/rmsprop.py +++ b/ann_class2/rmsprop.py @@ -1,11 +1,6 @@ # Compare RMSprop vs. constant learning rate -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow +# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow # https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import numpy as np from sklearn.utils import shuffle @@ -19,20 +14,24 @@ def main(): max_iter = 20 # make it 30 for sigmoid print_period = 10 - Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() + X, Y = get_normalized_data() lr = 0.00004 reg = 0.01 + Xtrain = X[:-1000,] + Ytrain = Y[:-1000] + Xtest = X[-1000:,] + Ytest = Y[-1000:] Ytrain_ind = y2indicator(Ytrain) Ytest_ind = y2indicator(Ytest) N, D = Xtrain.shape batch_sz = 500 - n_batches = N // batch_sz + n_batches = N / batch_sz M = 300 K = 10 - W1 = np.random.randn(D, M) / np.sqrt(D) + W1 = np.random.randn(D, M) / 28 b1 = np.zeros(M) W2 = np.random.randn(M, K) / np.sqrt(M) b2 = np.zeros(K) @@ -41,24 +40,18 @@ def main(): # cost = -16 LL_batch = [] CR_batch = [] - for i in range(max_iter): - for j in range(n_batches): + for i in xrange(max_iter): + for j in xrange(n_batches): Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] pYbatch, Z = forward(Xbatch, W1, b1, W2, b2) # print "first batch cost:", cost(pYbatch, Ybatch) - # gradients - gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2 - gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2 - gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1 - gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1 - # updates - W2 -= lr*gW2 - b2 -= lr*gb2 - W1 -= lr*gW1 - b1 -= lr*gb1 + W2 -= lr*(derivative_w2(Z, Ybatch, pYbatch) + reg*W2) + b2 -= lr*(derivative_b2(Ybatch, pYbatch) + reg*b2) + W1 -= lr*(derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1) + b1 -= lr*(derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1) if j % print_period == 0: # calculate just for LL @@ -66,53 +59,52 @@ def main(): # print "pY:", pY ll = cost(pY, Ytest_ind) LL_batch.append(ll) - print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll)) + print "Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll) err = error_rate(pY, Ytest) CR_batch.append(err) - print("Error rate:", err) + print "Error rate:", err pY, _ = forward(Xtest, W1, b1, W2, b2) - print("Final error rate:", error_rate(pY, Ytest)) + print "Final error rate:", error_rate(pY, Ytest) # 2. RMSprop - W1 = np.random.randn(D, M) / np.sqrt(D) + W1 = np.random.randn(D, M) / 28 b1 = np.zeros(M) W2 = np.random.randn(M, K) / np.sqrt(M) b2 = np.zeros(K) LL_rms = [] CR_rms = [] lr0 = 0.001 # if you set this too high you'll get NaN! - cache_W2 = 1 - cache_b2 = 1 - cache_W1 = 1 - cache_b1 = 1 + cache_W2 = 0 + cache_b2 = 0 + cache_W1 = 0 + cache_b1 = 0 decay_rate = 0.999 - eps = 1e-10 - for i in range(max_iter): - for j in range(n_batches): + eps = 0.0000000001 + for i in xrange(max_iter): + for j in xrange(n_batches): Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] pYbatch, Z = forward(Xbatch, W1, b1, W2, b2) # print "first batch cost:", cost(pYbatch, Ybatch) - # gradients + # updates gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2 - gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2 - gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1 - gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1 - - # caches cache_W2 = decay_rate*cache_W2 + (1 - decay_rate)*gW2*gW2 - cache_b2 = decay_rate*cache_b2 + (1 - decay_rate)*gb2*gb2 - cache_W1 = decay_rate*cache_W1 + (1 - decay_rate)*gW1*gW1 - cache_b1 = decay_rate*cache_b1 + (1 - decay_rate)*gb1*gb1 - - # updates W2 -= lr0 * gW2 / (np.sqrt(cache_W2) + eps) + + gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2 + cache_b2 = decay_rate*cache_b2 + (1 - decay_rate)*gb2*gb2 b2 -= lr0 * gb2 / (np.sqrt(cache_b2) + eps) + + gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1 + cache_W1 = decay_rate*cache_W1 + (1 - decay_rate)*gW1*gW1 W1 -= lr0 * gW1 / (np.sqrt(cache_W1) + eps) + + gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1 + cache_b1 = decay_rate*cache_b1 + (1 - decay_rate)*gb1*gb1 b1 -= lr0 * gb1 / (np.sqrt(cache_b1) + eps) if j % print_period == 0: @@ -121,14 +113,14 @@ def main(): # print "pY:", pY ll = cost(pY, Ytest_ind) LL_rms.append(ll) - print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll)) + print "Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll) err = error_rate(pY, Ytest) CR_rms.append(err) - print("Error rate:", err) + print "Error rate:", err pY, _ = forward(Xtest, W1, b1, W2, b2) - print("Final error rate:", error_rate(pY, Ytest)) + print "Final error rate:", error_rate(pY, Ytest) plt.plot(LL_batch, label='const') plt.plot(LL_rms, label='rms') diff --git a/ann_class2/rmsprop_test.py b/ann_class2/rmsprop_test.py deleted file mode 100644 index 0d4bd481..00000000 --- a/ann_class2/rmsprop_test.py +++ /dev/null @@ -1,134 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.models import Sequential -from keras.layers import Dense, Activation -from util import get_normalized_data, y2indicator - -import matplotlib.pyplot as plt -import numpy as np -import tensorflow as tf - -import theano -import theano.tensor as T - - -# RMSprop experiment -# to compare TF / Keras / Theano - -N = 10 -D = 2 -X = np.random.randn(N, D).astype(np.float32) -w = np.array([0.5, -0.5], dtype=np.float32) -Y = X.dot(w) + 1 -Y = Y.reshape(-1, 1) - - - -# keras -# the model will be a sequence of layers -model = Sequential() -model.add(Dense(units=1, input_dim=D)) - - -# copy the weights for later -weights = model.layers[0].get_weights() -w0 = weights[0].copy() -b0 = weights[1].copy() - - -model.compile( - loss='mean_squared_error', - optimizer='rmsprop', -) - - -r = model.fit(X, Y, epochs=15, batch_size=10) - - -# print the available keys -print(r.history.keys()) - - - -# tf -inputs = tf.placeholder(tf.float32, shape=(None, 2)) -targets = tf.placeholder(tf.float32, shape=(None, 1)) -tfw = tf.Variable(w0) -tfb = tf.Variable(b0) -pred = tf.matmul(inputs, tfw) + tfb - -loss = tf.reduce_mean(tf.square(targets - pred)) -train_op = tf.train.RMSPropOptimizer(1e-3, epsilon=1e-8).minimize(loss) - -tflosses = [] -init = tf.global_variables_initializer() -with tf.Session() as sess: - sess.run(init) - for e in range(15): - _, l = sess.run([train_op, loss], feed_dict={inputs: X, targets: Y}) - tflosses.append(l) - - - -# theano -def rmsprop(cost, params, lr=1e-3, decay=0.9, eps=1e-8): - # return updates - lr = np.float32(lr) - decay = np.float32(decay) - eps = np.float32(eps) - - updates = [] - grads = T.grad(cost, params) - - # tf-like - # caches = [theano.shared(np.ones_like(p.get_value(), dtype=np.float32)) for p in params] - - # keras-like - caches = [theano.shared(np.zeros_like(p.get_value(), dtype=np.float32)) for p in params] - - new_caches = [] - for c, g in zip(caches, grads): - new_c = decay*c + (np.float32(1) - decay)*g*g - updates.append((c, new_c)) - new_caches.append(new_c) - - for p, new_c, g in zip(params, new_caches, grads): - new_p = p - lr*g / T.sqrt(new_c + eps) - updates.append((p, new_p)) - - return updates - -thX = T.matrix('X') -thY = T.matrix('Y') -thw = theano.shared(w0) -thb = theano.shared(b0) -thP = thX.dot(thw) + thb -cost = T.mean((thY - thP)**2) -params = [thw, thb] -updates = rmsprop(cost, params) - -train_op = theano.function( - inputs=[thX, thY], - outputs=cost, - updates=updates, -) - -thlosses = [] -for e in range(15): - c = train_op(X, Y) - thlosses.append(c) - - -# plot results -plt.plot(r.history['loss'], label='keras loss') -plt.plot(tflosses, label='tf loss') -plt.plot(thlosses, label='theano loss') -plt.legend() -plt.show() - - diff --git a/ann_class2/sgd.py b/ann_class2/sgd.py index 91ab78b1..5a17a374 100644 --- a/ann_class2/sgd.py +++ b/ann_class2/sgd.py @@ -1,16 +1,17 @@ # In this file we compare the progression of the cost function vs. iteration # for 3 cases: # 1) full gradient descent -# 2) mini-batch gradient descent +# 2) batch gradient descent # 3) stochastic gradient descent # -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow +# We use the PCA-transformed data to keep the dimensionality down (D=300) +# I've tailored this example so that the training time for each is feasible. +# So what we are really comparing is how quickly each type of GD can converge, +# (but not actually waiting for convergence) and what the cost looks like at +# each iteration. +# +# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow # https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import numpy as np import pandas as pd @@ -18,162 +19,124 @@ from sklearn.utils import shuffle from datetime import datetime -from util import get_normalized_data, forward, error_rate, cost, gradW, gradb, y2indicator +from util import get_transformed_data, forward, error_rate, cost, gradW, gradb, y2indicator def main(): - Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - print("Performing logistic regression...") + X, Y, _, _ = get_transformed_data() + X = X[:, :300] + + # normalize X first + mu = X.mean(axis=0) + std = X.std(axis=0) + X = (X - mu) / std + + print "Performing logistic regression..." + Xtrain = X[:-1000,] + Ytrain = Y[:-1000] + Xtest = X[-1000:,] + Ytest = Y[-1000:] N, D = Xtrain.shape Ytrain_ind = y2indicator(Ytrain) Ytest_ind = y2indicator(Ytest) # 1. full - W = np.random.randn(D, 10) / np.sqrt(D) - W0 = W.copy() # save for later + W = np.random.randn(D, 10) / 28 b = np.zeros(10) - test_losses_full = [] - lr = 0.9 - reg = 0. + LL = [] + lr = 0.0001 + reg = 0.01 t0 = datetime.now() - last_dt = 0 - intervals = [] - for i in range(50): + for i in xrange(200): p_y = forward(Xtrain, W, b) - gW = gradW(Ytrain_ind, p_y, Xtrain) / N - gb = gradb(Ytrain_ind, p_y) / N - - W += lr*(gW - reg*W) - b += lr*(gb - reg*b) + W += lr*(gradW(Ytrain_ind, p_y, Xtrain) - reg*W) + b += lr*(gradb(Ytrain_ind, p_y) - reg*b) + p_y_test = forward(Xtest, W, b) - test_loss = cost(p_y_test, Ytest_ind) - dt = (datetime.now() - t0).total_seconds() - - # save these - dt2 = dt - last_dt - last_dt = dt - intervals.append(dt2) - - test_losses_full.append([dt, test_loss]) - if (i + 1) % 10 == 0: - print("Cost at iteration %d: %.6f" % (i + 1, test_loss)) + ll = cost(p_y_test, Ytest_ind) + LL.append(ll) + if i % 10 == 0: + err = error_rate(p_y_test, Ytest) + print "Cost at iteration %d: %.6f" % (i, ll) + print "Error rate:", err p_y = forward(Xtest, W, b) - print("Final error rate:", error_rate(p_y, Ytest)) - print("Elapsted time for full GD:", datetime.now() - t0) - - # save the max time so we don't surpass it in subsequent iterations - max_dt = dt - avg_interval_dt = np.mean(intervals) + print "Final error rate:", error_rate(p_y, Ytest) + print "Elapsted time for full GD:", datetime.now() - t0 # 2. stochastic - W = W0.copy() + W = np.random.randn(D, 10) / 28 b = np.zeros(10) - test_losses_sgd = [] - lr = 0.001 - reg = 0. + LL_stochastic = [] + lr = 0.0001 + reg = 0.01 t0 = datetime.now() - last_dt_calculated_loss = 0 - done = False - for i in range(50): # takes very long since we're computing cost for 41k samples + for i in xrange(1): # takes very long since we're computing cost for 41k samples tmpX, tmpY = shuffle(Xtrain, Ytrain_ind) - for n in range(N): + for n in xrange(min(N, 500)): # shortcut so it won't take so long... x = tmpX[n,:].reshape(1,D) y = tmpY[n,:].reshape(1,10) p_y = forward(x, W, b) - gW = gradW(y, p_y, x) - gb = gradb(y, p_y) - - W += lr*(gW - reg*W) - b += lr*(gb - reg*b) + W += lr*(gradW(y, p_y, x) - reg*W) + b += lr*(gradb(y, p_y) - reg*b) - dt = (datetime.now() - t0).total_seconds() - dt2 = dt - last_dt_calculated_loss + p_y_test = forward(Xtest, W, b) + ll = cost(p_y_test, Ytest_ind) + LL_stochastic.append(ll) - if dt2 > avg_interval_dt: - last_dt_calculated_loss = dt - p_y_test = forward(Xtest, W, b) - test_loss = cost(p_y_test, Ytest_ind) - test_losses_sgd.append([dt, test_loss]) - - # time to quit - if dt > max_dt: - done = True - break - if done: - break - - if (i + 1) % 1 == 0: - print("Cost at iteration %d: %.6f" % (i + 1, test_loss)) + if n % (N/2) == 0: + err = error_rate(p_y_test, Ytest) + print "Cost at iteration %d: %.6f" % (i, ll) + print "Error rate:", err p_y = forward(Xtest, W, b) - print("Final error rate:", error_rate(p_y, Ytest)) - print("Elapsted time for SGD:", datetime.now() - t0) + print "Final error rate:", error_rate(p_y, Ytest) + print "Elapsted time for SGD:", datetime.now() - t0 - # 3. mini-batch - W = W0.copy() + # 3. batch + W = np.random.randn(D, 10) / 28 b = np.zeros(10) - test_losses_batch = [] + LL_batch = [] + lr = 0.0001 + reg = 0.01 batch_sz = 500 - lr = 0.08 - reg = 0. - n_batches = int(np.ceil(N / batch_sz)) - + n_batches = N / batch_sz t0 = datetime.now() - last_dt_calculated_loss = 0 - done = False - for i in range(50): + for i in xrange(50): tmpX, tmpY = shuffle(Xtrain, Ytrain_ind) - for j in range(n_batches): - x = tmpX[j*batch_sz:(j + 1)*batch_sz,:] - y = tmpY[j*batch_sz:(j + 1)*batch_sz,:] + for j in xrange(n_batches): + x = tmpX[j*batch_sz:(j*batch_sz + batch_sz),:] + y = tmpY[j*batch_sz:(j*batch_sz + batch_sz),:] p_y = forward(x, W, b) - current_batch_sz = len(x) - gW = gradW(y, p_y, x) / current_batch_sz - gb = gradb(y, p_y) / current_batch_sz - - W += lr*(gW - reg*W) - b += lr*(gb - reg*b) - - dt = (datetime.now() - t0).total_seconds() - dt2 = dt - last_dt_calculated_loss - - if dt2 > avg_interval_dt: - last_dt_calculated_loss = dt - p_y_test = forward(Xtest, W, b) - test_loss = cost(p_y_test, Ytest_ind) - test_losses_batch.append([dt, test_loss]) - - # time to quit - if dt > max_dt: - done = True - break - if done: - break + W += lr*(gradW(y, p_y, x) - reg*W) + b += lr*(gradb(y, p_y) - reg*b) - if (i + 1) % 10 == 0: - print("Cost at iteration %d: %.6f" % (i + 1, test_loss)) + p_y_test = forward(Xtest, W, b) + ll = cost(p_y_test, Ytest_ind) + LL_batch.append(ll) + if j % (n_batches/2) == 0: + err = error_rate(p_y_test, Ytest) + print "Cost at iteration %d: %.6f" % (i, ll) + print "Error rate:", err p_y = forward(Xtest, W, b) - print("Final error rate:", error_rate(p_y, Ytest)) - print("Elapsted time for mini-batch GD:", datetime.now() - t0) + print "Final error rate:", error_rate(p_y, Ytest) + print "Elapsted time for batch GD:", datetime.now() - t0 - # convert to numpy arrays - test_losses_full = np.array(test_losses_full) - test_losses_sgd = np.array(test_losses_sgd) - test_losses_batch = np.array(test_losses_batch) - - plt.plot(test_losses_full[:,0], test_losses_full[:,1], label="full") - plt.plot(test_losses_sgd[:,0], test_losses_sgd[:,1], label="sgd") - plt.plot(test_losses_batch[:,0], test_losses_batch[:,1], label="mini-batch") + x1 = np.linspace(0, 1, len(LL)) + plt.plot(x1, LL, label="full") + x2 = np.linspace(0, 1, len(LL_stochastic)) + plt.plot(x2, LL_stochastic, label="stochastic") + x3 = np.linspace(0, 1, len(LL_batch)) + plt.plot(x3, LL_batch, label="batch") plt.legend() plt.show() diff --git a/ann_class2/tensorflow1.py b/ann_class2/tensorflow1.py index 0b29c10d..b9455805 100644 --- a/ann_class2/tensorflow1.py +++ b/ann_class2/tensorflow1.py @@ -4,13 +4,8 @@ # I compare this to theano1.py multiple times. # So you might want to check that out first. -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow +# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow # https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import numpy as np import tensorflow as tf @@ -38,7 +33,7 @@ output = session.run(w, feed_dict={A: np.random.randn(5, 5), v: np.random.randn(5, 1)}) # what's this output that is returned by the session? let's print it - print(output, type(output)) + print output, type(output) # luckily, the output type is just a numpy array. back to safety! @@ -55,15 +50,15 @@ t = tf.Variable(0) # a scalar # you need to "initialize" the variables first -init = tf.global_variables_initializer() +init = tf.initialize_all_variables() with tf.Session() as session: out = session.run(init) # and then "run" the init operation - print(out) # it's just None + print out # it's just None # eval() in tf is like get_value() in Theano - print(x.eval()) # the initial value of x - print(t.eval()) + print x.eval() # the initial value of x + print t.eval() # let's now try to find the minimum of a simple cost function like we did in Theano @@ -76,14 +71,14 @@ train_op = tf.train.GradientDescentOptimizer(0.3).minimize(cost) # let's run a session again -init = tf.global_variables_initializer() +init = tf.initialize_all_variables() with tf.Session() as session: session.run(init) # Strangely, while the weight update is automated, the loop itself is not. # So we'll just call train_op until convergence. # This is useful for us anyway since we want to track the cost function. - for i in range(12): + for i in xrange(12): session.run(train_op) - print("i = %d, cost = %.3f, u = %.3f" % (i, cost.eval(), u.eval())) + print "i = %d, cost = %.3f, u = %.3f" % (i, cost.eval(), u.eval()) diff --git a/ann_class2/tensorflow2.py b/ann_class2/tensorflow2.py index a07f0104..d48478a4 100644 --- a/ann_class2/tensorflow2.py +++ b/ann_class2/tensorflow2.py @@ -2,13 +2,8 @@ # This code is not optimized for speed. # It's just to get something working, using the principles we know. -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow +# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow # https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import numpy as np import tensorflow as tf @@ -25,26 +20,30 @@ def error_rate(p, t): # copy this first part from theano2.py def main(): # step 1: get the data and define all the usual variables - Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() + X, Y = get_normalized_data() max_iter = 15 - print_period = 50 + print_period = 10 lr = 0.00004 reg = 0.01 + Xtrain = X[:-1000,] + Ytrain = Y[:-1000] + Xtest = X[-1000:,] + Ytest = Y[-1000:] Ytrain_ind = y2indicator(Ytrain) Ytest_ind = y2indicator(Ytest) N, D = Xtrain.shape batch_sz = 500 - n_batches = N // batch_sz + n_batches = N / batch_sz # add an extra layer just for fun M1 = 300 M2 = 100 K = 10 - W1_init = np.random.randn(D, M1) / np.sqrt(D) + W1_init = np.random.randn(D, M1) / 28 b1_init = np.zeros(M1) W2_init = np.random.randn(M1, M2) / np.sqrt(M1) b2_init = np.zeros(M2) @@ -70,7 +69,7 @@ def main(): # softmax_cross_entropy_with_logits take in the "logits" # if you wanted to know the actual output of the neural net, # you could pass "Yish" into tf.nn.softmax(logits) - cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits_v2(logits=Yish, labels=T)) + cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(Yish, T)) # we choose the optimizer but don't implement the algorithm ourselves # let's go with RMSprop, since we just learned about it. @@ -80,13 +79,13 @@ def main(): # we'll use this to calculate the error rate predict_op = tf.argmax(Yish, 1) - costs = [] - init = tf.global_variables_initializer() + LL = [] + init = tf.initialize_all_variables() with tf.Session() as session: session.run(init) - for i in range(max_iter): - for j in range(n_batches): + for i in xrange(max_iter): + for j in xrange(n_batches): Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] @@ -95,10 +94,10 @@ def main(): test_cost = session.run(cost, feed_dict={X: Xtest, T: Ytest_ind}) prediction = session.run(predict_op, feed_dict={X: Xtest}) err = error_rate(prediction, Ytest) - print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, test_cost, err)) - costs.append(test_cost) + print "Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, test_cost, err) + LL.append(test_cost) - plt.plot(costs) + plt.plot(LL) plt.show() # increase max_iter and notice how the test cost starts to increase. # are we overfitting by adding that extra layer? diff --git a/ann_class2/tf_with_save.py b/ann_class2/tf_with_save.py deleted file mode 100644 index 43e20503..00000000 --- a/ann_class2/tf_with_save.py +++ /dev/null @@ -1,147 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import os -import json -import numpy as np -import tensorflow as tf - -import matplotlib.pyplot as plt - -from util import get_normalized_data, y2indicator - - -def error_rate(p, t): - return np.mean(p != t) - - -class TFLogistic: - def __init__(self, savefile, D=None, K=None): - self.savefile = savefile - if D and K: - # we can define some parts in the model to be able to make predictions - self.build(D, K) - - def build(self, D, K): - W0 = np.random.randn(D, K) * np.sqrt(2.0 / D) - b0 = np.zeros(K) - - # define variables and expressions - self.inputs = tf.placeholder(tf.float32, shape=(None, D), name='inputs') - self.targets = tf.placeholder(tf.int64, shape=(None,), name='targets') - self.W = tf.Variable(W0.astype(np.float32), name='W') - self.b = tf.Variable(b0.astype(np.float32), name='b') - - # variables must exist when calling this - # try putting this line in the constructor and see what happens - self.saver = tf.train.Saver({'W': self.W, 'b': self.b}) - - logits = tf.matmul(self.inputs, self.W) + self.b - cost = tf.reduce_mean( - tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=logits, - labels=self.targets - ) - ) - self.predict_op = tf.argmax(logits, 1) - return cost - - - def fit(self, X, Y, Xtest, Ytest): - N, D = X.shape - K = len(set(Y)) - - # hyperparams - max_iter = 30 - lr = 1e-3 - mu = 0.9 - regularization = 1e-1 - batch_sz = 100 - n_batches = N // batch_sz - - cost = self.build(D, K) - l2_penalty = regularization*tf.reduce_mean(self.W**2) / 2 - cost += l2_penalty - train_op = tf.train.MomentumOptimizer(lr, momentum=mu).minimize(cost) - - costs = [] - init = tf.global_variables_initializer() - with tf.Session() as session: - session.run(init) - - for i in range(max_iter): - for j in range(n_batches): - Xbatch = X[j*batch_sz:(j*batch_sz + batch_sz),] - Ybatch = Y[j*batch_sz:(j*batch_sz + batch_sz),] - - session.run(train_op, feed_dict={self.inputs: Xbatch, self.targets: Ybatch}) - if j % 200 == 0: - test_cost = session.run(cost, feed_dict={self.inputs: Xtest, self.targets: Ytest}) - Ptest = session.run(self.predict_op, feed_dict={self.inputs: Xtest}) - err = error_rate(Ptest, Ytest) - print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, test_cost, err)) - costs.append(test_cost) - - # save the model - self.saver.save(session, self.savefile) - - # save dimensions for later - self.D = D - self.K = K - - plt.plot(costs) - plt.show() - - - def predict(self, X): - with tf.Session() as session: - # restore the model - self.saver.restore(session, self.savefile) - P = session.run(self.predict_op, feed_dict={self.inputs: X}) - return P - - - def score(self, X, Y): - return 1 - error_rate(self.predict(X), Y) - - def save(self, filename): - j = { - 'D': self.D, - 'K': self.K, - 'model': self.savefile - } - with open(filename, 'w') as f: - json.dump(j, f) - - @staticmethod - def load(filename): - with open(filename) as f: - j = json.load(f) - return TFLogistic(j['model'], j['D'], j['K']) - - -def main(): - Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() - - model = TFLogistic("./tf.model") - model.fit(Xtrain, Ytrain, Xtest, Ytest) - - # test out restoring the model via the predict function - print("final train accuracy:", model.score(Xtrain, Ytrain)) - print("final test accuracy:", model.score(Xtest, Ytest)) - - # save the model - model.save("my_trained_model.json") - - # load and score again - model = TFLogistic.load("my_trained_model.json") - print("final train accuracy (after reload):", model.score(Xtrain, Ytrain)) - print("final test accuracy (after reload):", model.score(Xtest, Ytest)) - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/ann_class2/theano1.py b/ann_class2/theano1.py index adec0125..b7d0ad5b 100644 --- a/ann_class2/theano1.py +++ b/ann_class2/theano1.py @@ -1,11 +1,6 @@ # Theano basics. -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow +# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow # https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import theano.tensor as T @@ -29,7 +24,7 @@ v_val = np.array([5,6]) w_val = matrix_times_vector(A_val, v_val) -print(w_val) +print w_val # let's create a shared variable to we can do gradient descent # this adds another layer of complexity to the theano function @@ -53,9 +48,9 @@ # write your own loop to call the training function. # it has no arguments! -for i in range(25): +for i in xrange(25): cost_val = train() - print(cost_val) + print cost_val # print the optimal value of x -print(x.get_value()) +print x.get_value() diff --git a/ann_class2/theano2.py b/ann_class2/theano2.py index 5dd86896..9ce492a4 100644 --- a/ann_class2/theano2.py +++ b/ann_class2/theano2.py @@ -2,13 +2,8 @@ # This code is not optimized for speed. # It's just to get something working, using the principles we know. -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow +# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow # https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import numpy as np import theano @@ -28,28 +23,28 @@ def relu(a): def main(): # step 1: get the data and define all the usual variables - Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() + X, Y = get_normalized_data() max_iter = 20 print_period = 10 - lr = 0.0004 + lr = 0.00004 reg = 0.01 - Xtrain = Xtrain.astype(np.float32) - Ytrain = Ytrain.astype(np.float32) - Xtest = Xtest.astype(np.float32) - Ytest = Ytest.astype(np.float32) - Ytrain_ind = y2indicator(Ytrain).astype(np.float32) - Ytest_ind = y2indicator(Ytest).astype(np.float32) + Xtrain = X[:-1000,] + Ytrain = Y[:-1000] + Xtest = X[-1000:,] + Ytest = Y[-1000:] + Ytrain_ind = y2indicator(Ytrain) + Ytest_ind = y2indicator(Ytest) N, D = Xtrain.shape batch_sz = 500 - n_batches = N // batch_sz + n_batches = N / batch_sz M = 300 K = 10 - W1_init = np.random.randn(D, M) / np.sqrt(D) + W1_init = np.random.randn(D, M) / 28 b1_init = np.zeros(M) W2_init = np.random.randn(M, K) / np.sqrt(M) b2_init = np.zeros(K) @@ -72,6 +67,10 @@ def main(): # step 3: training expressions and functions # we can just include regularization as part of the cost because it is also automatically differentiated! + # update_W1 = W1 - lr*(T.grad(cost, W1) + reg*W1) + # update_b1 = b1 - lr*(T.grad(cost, b1) + reg*b1) + # update_W2 = W2 - lr*(T.grad(cost, W2) + reg*W2) + # update_b2 = b2 - lr*(T.grad(cost, b2) + reg*b2) update_W1 = W1 - lr*T.grad(cost, W1) update_b1 = b1 - lr*T.grad(cost, b1) update_W2 = W2 - lr*T.grad(cost, W2) @@ -88,9 +87,9 @@ def main(): outputs=[cost, prediction], ) - costs = [] - for i in range(max_iter): - for j in range(n_batches): + LL = [] + for i in xrange(max_iter): + for j in xrange(n_batches): Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] @@ -98,10 +97,10 @@ def main(): if j % print_period == 0: cost_val, prediction_val = get_prediction(Xtest, Ytest_ind) err = error_rate(prediction_val, Ytest) - print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, cost_val, err)) - costs.append(cost_val) + print "Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, cost_val, err) + LL.append(cost_val) - plt.plot(costs) + plt.plot(LL) plt.show() # how would you incorporate momentum into the gradient descent procedure? diff --git a/ann_class2/theano_ann.py b/ann_class2/theano_ann.py deleted file mode 100644 index d1165f10..00000000 --- a/ann_class2/theano_ann.py +++ /dev/null @@ -1,129 +0,0 @@ -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import theano -import theano.tensor as T -from sklearn.utils import shuffle - - -def init_weight(M1, M2): - return np.random.randn(M1, M2) * np.sqrt(2.0 / M1) - - -class HiddenLayer(object): - def __init__(self, M1, M2, f): - self.M1 = M1 - self.M2 = M2 - self.f = f - W = init_weight(M1, M2) - b = np.zeros(M2) - self.W = theano.shared(W) - self.b = theano.shared(b) - self.params = [self.W, self.b] - - def forward(self, X): - if self.f == T.nnet.relu: - return self.f(X.dot(self.W) + self.b, alpha=0.1) - return self.f(X.dot(self.W) + self.b) - - -class ANN(object): - def __init__(self, hidden_layer_sizes): - self.hidden_layer_sizes = hidden_layer_sizes - - def fit(self, X, Y, activation=T.nnet.relu, learning_rate=1e-3, mu=0.0, reg=0, epochs=100, batch_sz=None, print_period=100, show_fig=True): - X = X.astype(np.float32) - Y = Y.astype(np.int32) - - # initialize hidden layers - N, D = X.shape - self.layers = [] - M1 = D - for M2 in self.hidden_layer_sizes: - h = HiddenLayer(M1, M2, activation) - self.layers.append(h) - M1 = M2 - - # final layer - K = len(set(Y)) - # print("K:", K) - h = HiddenLayer(M1, K, T.nnet.softmax) - self.layers.append(h) - - if batch_sz is None: - batch_sz = N - - # collect params for later use - self.params = [] - for h in self.layers: - self.params += h.params - - # for momentum - dparams = [theano.shared(np.zeros_like(p.get_value())) for p in self.params] - - # set up theano functions and variables - thX = T.matrix('X') - thY = T.ivector('Y') - p_y_given_x = self.forward(thX) - - rcost = reg*T.mean([(p*p).sum() for p in self.params]) - cost = -T.mean(T.log(p_y_given_x[T.arange(thY.shape[0]), thY])) #+ rcost - prediction = T.argmax(p_y_given_x, axis=1) - grads = T.grad(cost, self.params) - - # momentum only - updates = [ - (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads) - ] + [ - (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads) - ] - - train_op = theano.function( - inputs=[thX, thY], - outputs=[cost, prediction], - updates=updates, - ) - - self.predict_op = theano.function( - inputs=[thX], - outputs=prediction, - ) - - n_batches = N // batch_sz - costs = [] - for i in range(epochs): - if n_batches > 1: - X, Y = shuffle(X, Y) - for j in range(n_batches): - Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)] - Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)] - - c, p = train_op(Xbatch, Ybatch) - costs.append(c) - if (j+1) % print_period == 0: - print("i:", i, "j:", j, "nb:", n_batches, "cost:", c) - - if show_fig: - plt.plot(costs) - plt.show() - - def forward(self, X): - out = X - for h in self.layers: - out = h.forward(out) - return out - - def score(self, X, Y): - P = self.predict_op(X) - return np.mean(Y == P) - - def predict(self, X): - return self.predict_op(X) diff --git a/ann_class2/theano_gpu.py b/ann_class2/theano_gpu.py new file mode 100644 index 00000000..fda85af5 --- /dev/null +++ b/ann_class2/theano_gpu.py @@ -0,0 +1,109 @@ +# A 1-hidden-layer neural network in Theano. +# This code is not optimized for speed. +# It's just to get something working, using the principles we know. + +# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow +# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow + +import numpy as np +import theano +import theano.tensor as T +from datetime import datetime + +from util import get_normalized_data, y2indicator + + +def error_rate(p, t): + return np.mean(p != t) + + +def relu(a): + return a * (a > 0) + + +def main(): + # step 1: get the data and define all the usual variables + X, Y = get_normalized_data() + + max_iter = 20 + print_period = 10 + + lr = 0.00004 + reg = 0.01 + + X = X.astype(np.float32) + Y = Y.astype(np.float32) + Xtrain = X[:-1000,] + Ytrain = Y[:-1000] + Xtest = X[-1000:,] + Ytest = Y[-1000:] + Ytrain_ind = y2indicator(Ytrain).astype(np.float32) + Ytest_ind = y2indicator(Ytest).astype(np.float32) + + N, D = Xtrain.shape + batch_sz = 500 + n_batches = N / batch_sz + + M = 300 + K = 10 + W1_init = np.random.randn(D, M) / 28 + b1_init = np.zeros(M) + W2_init = np.random.randn(M, K) / np.sqrt(M) + b2_init = np.zeros(K) + + # step 2: define theano variables and expressions + thX = T.matrix('X') + thT = T.matrix('T') + W1 = theano.shared(W1_init.astype(np.float32), 'W1') + b1 = theano.shared(b1_init.astype(np.float32), 'b1') + W2 = theano.shared(W2_init.astype(np.float32), 'W2') + b2 = theano.shared(b2_init.astype(np.float32), 'b2') + + # we can use the built-in theano functions to do relu and softmax + thZ = relu( thX.dot(W1) + b1 ) # relu is new in version 0.7.1 but just in case you don't have it + thY = T.nnet.softmax( thZ.dot(W2) + b2 ) + + # define the cost function and prediction + cost = -(thT * T.log(thY)).sum() + reg*((W1*W1).sum() + (b1*b1).sum() + (W2*W2).sum() + (b2*b2).sum()) + prediction = T.argmax(thY, axis=1) + + # step 3: training expressions and functions + # we can just include regularization as part of the cost because it is also automatically differentiated! + # update_W1 = W1 - lr*(T.grad(cost, W1) + reg*W1) + # update_b1 = b1 - lr*(T.grad(cost, b1) + reg*b1) + # update_W2 = W2 - lr*(T.grad(cost, W2) + reg*W2) + # update_b2 = b2 - lr*(T.grad(cost, b2) + reg*b2) + update_W1 = W1 - lr*T.grad(cost, W1) + update_b1 = b1 - lr*T.grad(cost, b1) + update_W2 = W2 - lr*T.grad(cost, W2) + update_b2 = b2 - lr*T.grad(cost, b2) + + train = theano.function( + inputs=[thX, thT], + updates=[(W1, update_W1), (b1, update_b1), (W2, update_W2), (b2, update_b2)], + ) + + # create another function for this because we want it over the whole dataset + get_prediction = theano.function( + inputs=[thX, thT], + outputs=[cost, prediction], + ) + + t0 = datetime.now() + for i in xrange(max_iter): + for j in xrange(n_batches): + Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] + Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] + + train(Xbatch, Ybatch) + if j % print_period == 0: + cost_val, prediction_val = get_prediction(Xtest, Ytest_ind) + err = error_rate(prediction_val, Ytest) + print "Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, cost_val, err) + + print "Training time:", datetime.now() - t0 + # how would you incorporate momentum into the gradient descent procedure? + + +if __name__ == '__main__': + main() diff --git a/ann_class2/util.py b/ann_class2/util.py index 5c8ad934..50232f21 100644 --- a/ann_class2/util.py +++ b/ann_class2/util.py @@ -1,16 +1,9 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - # Some utility functions we need for the class. -# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFlow -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow +# For the class Data Science: Practical Deep Learning Concepts in Theano and TensorFLow # https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow # Note: run this from the current folder it is in. -import os import numpy as np import pandas as pd import matplotlib.pyplot as plt @@ -18,131 +11,34 @@ from sklearn.linear_model import LogisticRegression -def get_clouds(): - Nclass = 500 - D = 2 - - X1 = np.random.randn(Nclass, D) + np.array([0, -2]) - X2 = np.random.randn(Nclass, D) + np.array([2, 2]) - X3 = np.random.randn(Nclass, D) + np.array([-2, 2]) - X = np.vstack([X1, X2, X3]) - - Y = np.array([0]*Nclass + [1]*Nclass + [2]*Nclass) - return X, Y - - -def get_spiral(): - # Idea: radius -> low...high - # (don't start at 0, otherwise points will be "mushed" at origin) - # angle = low...high proportional to radius - # [0, 2pi/6, 4pi/6, ..., 10pi/6] --> [pi/2, pi/3 + pi/2, ..., ] - # x = rcos(theta), y = rsin(theta) as usual - - radius = np.linspace(1, 10, 100) - thetas = np.empty((6, 100)) - for i in range(6): - start_angle = np.pi*i / 3.0 - end_angle = start_angle + np.pi / 2 - points = np.linspace(start_angle, end_angle, 100) - thetas[i] = points - - # convert into cartesian coordinates - x1 = np.empty((6, 100)) - x2 = np.empty((6, 100)) - for i in range(6): - x1[i] = radius * np.cos(thetas[i]) - x2[i] = radius * np.sin(thetas[i]) - - # inputs - X = np.empty((600, 2)) - X[:,0] = x1.flatten() - X[:,1] = x2.flatten() - - # add noise - X += np.random.randn(600, 2)*0.5 - - # targets - Y = np.array([0]*100 + [1]*100 + [0]*100 + [1]*100 + [0]*100 + [1]*100) - return X, Y - - - def get_transformed_data(): - print("Reading in and transforming data...") - - if not os.path.exists('../large_files/train.csv'): - print('Looking for ../large_files/train.csv') - print('You have not downloaded the data and/or not placed the files in the correct location.') - print('Please get the data from: https://www.kaggle.com/c/digit-recognizer') - print('Place train.csv in the folder large_files adjacent to the class folder') - exit() - + print "Reading in and transforming data..." df = pd.read_csv('../large_files/train.csv') - data = df.values.astype(np.float32) + data = df.as_matrix().astype(np.float32) np.random.shuffle(data) X = data[:, 1:] - Y = data[:, 0].astype(np.int32) - - Xtrain = X[:-1000] - Ytrain = Y[:-1000] - Xtest = X[-1000:] - Ytest = Y[-1000:] - - # center the data - mu = Xtrain.mean(axis=0) - Xtrain = Xtrain - mu - Xtest = Xtest - mu - - # transform the data + mu = X.mean(axis=0) + X = X - mu # center the data pca = PCA() - Ztrain = pca.fit_transform(Xtrain) - Ztest = pca.transform(Xtest) - - plot_cumulative_variance(pca) - - # take first 300 cols of Z - Ztrain = Ztrain[:, :300] - Ztest = Ztest[:, :300] - - # normalize Z - mu = Ztrain.mean(axis=0) - std = Ztrain.std(axis=0) - Ztrain = (Ztrain - mu) / std - Ztest = (Ztest - mu) / std + Z = pca.fit_transform(X) + Y = data[:, 0] - return Ztrain, Ztest, Ytrain, Ytest + return Z, Y, pca, mu def get_normalized_data(): - print("Reading in and transforming data...") - - if not os.path.exists('../large_files/train.csv'): - print('Looking for ../large_files/train.csv') - print('You have not downloaded the data and/or not placed the files in the correct location.') - print('Please get the data from: https://www.kaggle.com/c/digit-recognizer') - print('Place train.csv in the folder large_files adjacent to the class folder') - exit() - + print "Reading in and transforming data..." df = pd.read_csv('../large_files/train.csv') - data = df.values.astype(np.float32) + data = df.as_matrix().astype(np.float32) np.random.shuffle(data) X = data[:, 1:] - Y = data[:, 0] - - Xtrain = X[:-1000] - Ytrain = Y[:-1000] - Xtest = X[-1000:] - Ytest = Y[-1000:] - - # normalize the data - mu = Xtrain.mean(axis=0) - std = Xtrain.std(axis=0) + mu = X.mean(axis=0) + std = X.std(axis=0) np.place(std, std == 0, 1) - Xtrain = (Xtrain - mu) / std - Xtest = (Xtest - mu) / std - - return Xtrain, Xtest, Ytrain, Ytest + X = (X - mu) / std # normalize the data + Y = data[:, 0] + return X, Y def plot_cumulative_variance(pca): @@ -160,8 +56,16 @@ def plot_cumulative_variance(pca): def forward(X, W, b): # softmax a = X.dot(W) + b + # print "any nan in X?:", np.any(np.isnan(X)) + # print "any nan in W?:", np.any(np.isnan(W)) + # print "W:", W + # print "X.dot(W):", X.dot(W) + # print "b:", b + # print "a:", a expa = np.exp(a) + # print "expa:", expa y = expa / expa.sum(axis=1, keepdims=True) + # exit() return y @@ -175,6 +79,9 @@ def error_rate(p_y, t): def cost(p_y, t): + # print "any nan in log p_y?:", np.any(np.isnan(np.log(p_y))) + # print "log(p_y):", np.log(p_y) + # exit() tot = t * np.log(p_y) return -tot.sum() @@ -189,26 +96,39 @@ def gradb(t, y): def y2indicator(y): N = len(y) - y = y.astype(np.int32) ind = np.zeros((N, 10)) - for i in range(N): + for i in xrange(N): ind[i, y[i]] = 1 return ind def benchmark_full(): - Xtrain, Xtest, Ytrain, Ytest = get_normalized_data() + X, Y = get_normalized_data() - print("Performing logistic regression...") + print "Performing logistic regression..." # lr = LogisticRegression(solver='lbfgs') + # # test on the last 1000 points + # lr.fit(X[:-1000, :200], Y[:-1000]) # use only first 200 dimensions + # print lr.score(X[-1000:, :200], Y[-1000:]) + # print "X:", X + + # normalize X first + # mu = X.mean(axis=0) + # std = X.std(axis=0) + # X = (X - mu) / std + + Xtrain = X[:-1000,] + Ytrain = Y[:-1000] + Xtest = X[-1000:,] + Ytest = Y[-1000:] # convert Ytrain and Ytest to (N x K) matrices of indicator variables N, D = Xtrain.shape Ytrain_ind = y2indicator(Ytrain) Ytest_ind = y2indicator(Ytest) - W = np.random.randn(D, 10) / np.sqrt(D) + W = np.random.randn(D, 10) / 28 b = np.zeros(10) LL = [] LLtest = [] @@ -224,7 +144,7 @@ def benchmark_full(): # reg = 0.01, still around 0.31 error lr = 0.00004 reg = 0.01 - for i in range(500): + for i in xrange(500): p_y = forward(Xtrain, W, b) # print "p_y:", p_y ll = cost(p_y, Ytrain_ind) @@ -240,11 +160,11 @@ def benchmark_full(): W += lr*(gradW(Ytrain_ind, p_y, Xtrain) - reg*W) b += lr*(gradb(Ytrain_ind, p_y) - reg*b) if i % 10 == 0: - print("Cost at iteration %d: %.6f" % (i, ll)) - print("Error rate:", err) + print "Cost at iteration %d: %.6f" % (i, ll) + print "Error rate:", err p_y = forward(Xtest, W, b) - print("Final error rate:", error_rate(p_y, Ytest)) + print "Final error rate:", error_rate(p_y, Ytest) iters = range(len(LL)) plt.plot(iters, LL, iters, LLtest) plt.show() @@ -253,20 +173,31 @@ def benchmark_full(): def benchmark_pca(): - Xtrain, Xtest, Ytrain, Ytest = get_transformed_data() - print("Performing logistic regression...") + X, Y, _, _ = get_transformed_data() + X = X[:, :300] + + # normalize X first + mu = X.mean(axis=0) + std = X.std(axis=0) + X = (X - mu) / std + + print "Performing logistic regression..." + Xtrain = X[:-1000,] + Ytrain = Y[:-1000] + Xtest = X[-1000:,] + Ytest = Y[-1000:] N, D = Xtrain.shape Ytrain_ind = np.zeros((N, 10)) - for i in range(N): + for i in xrange(N): Ytrain_ind[i, Ytrain[i]] = 1 Ntest = len(Ytest) Ytest_ind = np.zeros((Ntest, 10)) - for i in range(Ntest): + for i in xrange(Ntest): Ytest_ind[i, Ytest[i]] = 1 - W = np.random.randn(D, 10) / np.sqrt(D) + W = np.random.randn(D, 10) / 28 b = np.zeros(10) LL = [] LLtest = [] @@ -275,7 +206,7 @@ def benchmark_pca(): # D = 300 -> error = 0.07 lr = 0.0001 reg = 0.01 - for i in range(200): + for i in xrange(200): p_y = forward(Xtrain, W, b) # print "p_y:", p_y ll = cost(p_y, Ytrain_ind) @@ -291,11 +222,11 @@ def benchmark_pca(): W += lr*(gradW(Ytrain_ind, p_y, Xtrain) - reg*W) b += lr*(gradb(Ytrain_ind, p_y) - reg*b) if i % 10 == 0: - print("Cost at iteration %d: %.6f" % (i, ll)) - print("Error rate:", err) + print "Cost at iteration %d: %.6f" % (i, ll) + print "Error rate:", err p_y = forward(Xtest, W, b) - print("Final error rate:", error_rate(p_y, Ytest)) + print "Final error rate:", error_rate(p_y, Ytest) iters = range(len(LL)) plt.plot(iters, LL, iters, LLtest) plt.show() @@ -304,6 +235,6 @@ def benchmark_pca(): if __name__ == '__main__': - # benchmark_pca() - benchmark_full() + benchmark_pca() + # benchmark_full() diff --git a/ann_logistic_extra/__init__.py b/ann_logistic_extra/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/ann_logistic_extra/ann_predict.py b/ann_logistic_extra/ann_predict.py index 23b9deab..1386cc96 100644 --- a/ann_logistic_extra/ann_predict.py +++ b/ann_logistic_extra/ann_predict.py @@ -1,13 +1,7 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np from process import get_data -X, Y, _, _ = get_data() +X, Y = get_data() # randomly initialize weights M = 5 @@ -28,11 +22,10 @@ def forward(X, W1, b1, W2, b2): return softmax(Z.dot(W2) + b2) P_Y_given_X = forward(X, W1, b1, W2, b2) -print("P_Y_given_X.shape:", P_Y_given_X.shape) predictions = np.argmax(P_Y_given_X, axis=1) # calculate the accuracy def classification_rate(Y, P): return np.mean(Y == P) -print("Score:", classification_rate(Y, predictions)) \ No newline at end of file +print "Score:", classification_rate(Y, predictions) \ No newline at end of file diff --git a/ann_logistic_extra/ann_train.py b/ann_logistic_extra/ann_train.py index 15710e76..92dfa850 100644 --- a/ann_logistic_extra/ann_train.py +++ b/ann_logistic_extra/ann_train.py @@ -1,9 +1,3 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import matplotlib.pyplot as plt @@ -13,17 +7,23 @@ def y2indicator(y, K): N = len(y) ind = np.zeros((N, K)) - for i in range(N): + for i in xrange(N): ind[i, y[i]] = 1 return ind -Xtrain, Ytrain, Xtest, Ytest = get_data() -D = Xtrain.shape[1] -K = len(set(Ytrain) | set(Ytest)) -M = 5 # num hidden units +X, Y = get_data() +X, Y = shuffle(X, Y) +Y = Y.astype(np.int32) +M = 5 +D = X.shape[1] +K = len(set(Y)) -# convert to indicator +# create train and test sets +Xtrain = X[:-100] +Ytrain = Y[:-100] Ytrain_ind = y2indicator(Ytrain, K) +Xtest = X[-100:] +Ytest = Y[-100:] Ytest_ind = y2indicator(Ytest, K) # randomly initialize weights @@ -48,15 +48,15 @@ def predict(P_Y_given_X): def classification_rate(Y, P): return np.mean(Y == P) -def cross_entropy(Y, pY): - return -np.sum(Y * np.log(pY)) / len(T) +def cross_entropy(T, pY): + return -np.mean(T*np.log(pY)) # train loop train_costs = [] test_costs = [] learning_rate = 0.001 -for i in range(10000): +for i in xrange(10000): pYtrain, Ztrain = forward(Xtrain, W1, b1, W2, b2) pYtest, Ztest = forward(Xtest, W1, b1, W2, b2) @@ -66,22 +66,18 @@ def cross_entropy(Y, pY): test_costs.append(ctest) # gradient descent - gW2 = Ztrain.T.dot(pYtrain - Ytrain_ind) - gb2 = (pYtrain - Ytrain_ind).sum(axis=0) - dZ = (pYtrain - Ytrain_ind).dot(W2.T) * (1 - Ztrain * Ztrain) - gW1 = Xtrain.T.dot(dZ) - gb1 = dZ.sum(axis=0) - W2 -= learning_rate * gW2 - b2 -= learning_rate * gb2 - W1 -= learning_rate * gW1 - b1 -= learning_rate * gb1 + W2 -= learning_rate*Ztrain.T.dot(pYtrain - Ytrain_ind) + b2 -= learning_rate*(pYtrain - Ytrain_ind).sum() + dZ = (pYtrain - Ytrain_ind).dot(W2.T) * (1 - Ztrain*Ztrain) + W1 -= learning_rate*Xtrain.T.dot(dZ) + b1 -= learning_rate*dZ.sum(axis=0) if i % 1000 == 0: - print(i, ctrain, ctest) + print i, ctrain, ctest -print("Final train classification_rate:", classification_rate(Ytrain, predict(pYtrain))) -print("Final test classification_rate:", classification_rate(Ytest, predict(pYtest))) +print "Final train classification_rate:", classification_rate(Ytrain, predict(pYtrain)) +print "Final test classification_rate:", classification_rate(Ytest, predict(pYtest)) -plt.plot(train_costs, label='train cost') -plt.plot(test_costs, label='test cost') -plt.legend() +legend1, = plt.plot(train_costs, label='train cost') +legend2, = plt.plot(test_costs, label='test cost') +plt.legend([legend1, legend2]) plt.show() \ No newline at end of file diff --git a/ann_logistic_extra/logistic_predict.py b/ann_logistic_extra/logistic_predict.py index 576cc81b..dbb65811 100644 --- a/ann_logistic_extra/logistic_predict.py +++ b/ann_logistic_extra/logistic_predict.py @@ -1,13 +1,7 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np from process import get_binary_data -X, Y, _, _ = get_binary_data() +X, Y = get_binary_data() # randomly initialize weights D = X.shape[1] @@ -28,4 +22,4 @@ def forward(X, W, b): def classification_rate(Y, P): return np.mean(Y == P) -print("Score:", classification_rate(Y, predictions)) \ No newline at end of file +print "Score:", classification_rate(Y, predictions) \ No newline at end of file diff --git a/ann_logistic_extra/logistic_softmax_train.py b/ann_logistic_extra/logistic_softmax_train.py index 94874f14..761a2f8a 100644 --- a/ann_logistic_extra/logistic_softmax_train.py +++ b/ann_logistic_extra/logistic_softmax_train.py @@ -1,9 +1,3 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import matplotlib.pyplot as plt @@ -13,16 +7,22 @@ def y2indicator(y, K): N = len(y) ind = np.zeros((N, K)) - for i in range(N): + for i in xrange(N): ind[i, y[i]] = 1 return ind -Xtrain, Ytrain, Xtest, Ytest = get_data() -D = Xtrain.shape[1] -K = len(set(Ytrain) | set(Ytest)) +X, Y = get_data() +X, Y = shuffle(X, Y) +Y = Y.astype(np.int32) +D = X.shape[1] +K = len(set(Y)) -# convert to indicator +# create train and test sets +Xtrain = X[:-100] +Ytrain = Y[:-100] Ytrain_ind = y2indicator(Ytrain, K) +Xtest = X[-100:] +Ytest = Y[-100:] Ytest_ind = y2indicator(Ytest, K) # randomly initialize weights @@ -44,15 +44,15 @@ def predict(P_Y_given_X): def classification_rate(Y, P): return np.mean(Y == P) -def cross_entropy(Y, pY): - return -np.sum(Y * np.log(pY)) / len(Y) +def cross_entropy(T, pY): + return -np.mean(T*np.log(pY)) # train loop train_costs = [] test_costs = [] learning_rate = 0.001 -for i in range(10000): +for i in xrange(10000): pYtrain = forward(Xtrain, W, b) pYtest = forward(Xtest, W, b) @@ -65,12 +65,12 @@ def cross_entropy(Y, pY): W -= learning_rate*Xtrain.T.dot(pYtrain - Ytrain_ind) b -= learning_rate*(pYtrain - Ytrain_ind).sum(axis=0) if i % 1000 == 0: - print(i, ctrain, ctest) + print i, ctrain, ctest -print("Final train classification_rate:", classification_rate(Ytrain, predict(pYtrain))) -print("Final test classification_rate:", classification_rate(Ytest, predict(pYtest))) +print "Final train classification_rate:", classification_rate(Ytrain, predict(pYtrain)) +print "Final test classification_rate:", classification_rate(Ytest, predict(pYtest)) -plt.plot(train_costs, label='train cost') -plt.plot(test_costs, label='test cost') -plt.legend() +legend1, = plt.plot(train_costs, label='train cost') +legend2, = plt.plot(test_costs, label='test cost') +plt.legend([legend1, legend2]) plt.show() \ No newline at end of file diff --git a/ann_logistic_extra/logistic_train.py b/ann_logistic_extra/logistic_train.py index abedd5ba..a35b1c0c 100644 --- a/ann_logistic_extra/logistic_train.py +++ b/ann_logistic_extra/logistic_train.py @@ -1,20 +1,20 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import matplotlib.pyplot as plt from sklearn.utils import shuffle from process import get_binary_data -# get the data -Xtrain, Ytrain, Xtest, Ytest = get_binary_data() +X, Y = get_binary_data() +X, Y = shuffle(X, Y) + +# create train and test sets +Xtrain = X[:-100] +Ytrain = Y[:-100] +Xtest = X[-100:] +Ytest = Y[-100:] # randomly initialize weights -D = Xtrain.shape[1] +D = X.shape[1] W = np.random.randn(D) b = 0 # bias term @@ -37,7 +37,7 @@ def cross_entropy(T, pY): train_costs = [] test_costs = [] learning_rate = 0.001 -for i in range(10000): +for i in xrange(10000): pYtrain = forward(Xtrain, W, b) pYtest = forward(Xtest, W, b) @@ -50,14 +50,14 @@ def cross_entropy(T, pY): W -= learning_rate*Xtrain.T.dot(pYtrain - Ytrain) b -= learning_rate*(pYtrain - Ytrain).sum() if i % 1000 == 0: - print(i, ctrain, ctest) + print i, ctrain, ctest -print("Final train classification_rate:", classification_rate(Ytrain, np.round(pYtrain))) -print("Final test classification_rate:", classification_rate(Ytest, np.round(pYtest))) +print "Final train classification_rate:", classification_rate(Ytrain, np.round(pYtrain)) +print "Final test classification_rate:", classification_rate(Ytest, np.round(pYtest)) -plt.plot(train_costs, label='train cost') -plt.plot(test_costs, label='test cost') -plt.legend() +legend1, = plt.plot(train_costs, label='train cost') +legend2, = plt.plot(test_costs, label='test cost') +plt.legend([legend1, legend2]) plt.show() diff --git a/ann_logistic_extra/process.py b/ann_logistic_extra/process.py index 0048f9e0..07ca64dd 100644 --- a/ann_logistic_extra/process.py +++ b/ann_logistic_extra/process.py @@ -1,76 +1,47 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import pandas as pd -import os - -# so scripts from other folders can import this file -dir_path = os.path.abspath(os.path.dirname(os.path.realpath(__file__))) # normalize numerical columns # one-hot categorical columns def get_data(): - df = pd.read_csv(dir_path + '/ecommerce_data.csv') - - # just in case you're curious what's in it - # df.head() - - # easier to work with numpy array - data = df.to_numpy() - - # shuffle it - np.random.shuffle(data) + df = pd.read_csv('ecommerce_data.csv') - # split features and labels - X = data[:,:-1] - Y = data[:,-1].astype(np.int32) + # just in case you're curious what's in it + # df.head() - # one-hot encode the categorical data - # create a new matrix X2 with the correct number of columns - N, D = X.shape - X2 = np.zeros((N, D+3)) - X2[:,0:(D-1)] = X[:,0:(D-1)] # non-categorical + # easier to work with numpy array + data = df.as_matrix() - # one-hot - for n in range(N): - t = int(X[n,D-1]) - X2[n,t+D-1] = 1 + X = data[:,:-1] + Y = data[:,-1] - # method 2 - # Z = np.zeros((N, 4)) - # Z[np.arange(N), X[:,D-1].astype(np.int32)] = 1 - # # assign: X2[:,-4:] = Z - # assert(np.abs(X2[:,-4:] - Z).sum() < 1e-10) + # normalize columns 1 and 2 + X[:,1] = (X[:,1] - X[:,1].mean()) / X[:,1].std() + X[:,2] = (X[:,2] - X[:,2].mean()) / X[:,2].std() - # assign X2 back to X, since we don't need original anymore - X = X2 + # create a new matrix X2 with the correct number of columns + N, D = X.shape + X2 = np.zeros((N, D+3)) + X2[:,0:(D-1)] = X[:,0:(D-1)] # non-categorical - # split train and test - Xtrain = X[:-100] - Ytrain = Y[:-100] - Xtest = X[-100:] - Ytest = Y[-100:] + # one-hot + for n in xrange(N): + t = int(X[n,D-1]) + X2[n,t+D-1] = 1 - # normalize columns 1 and 2 - for i in (1, 2): - m = Xtrain[:,i].mean() - s = Xtrain[:,i].std() - Xtrain[:,i] = (Xtrain[:,i] - m) / s - Xtest[:,i] = (Xtest[:,i] - m) / s + # method 2 + # Z = np.zeros((N, 4)) + # Z[np.arange(N), X[:,D-1].astype(np.int32)] = 1 + # # assign: X2[:,-4:] = Z + # assert(np.abs(X2[:,-4:] - Z).sum() < 10e-10) - return Xtrain, Ytrain, Xtest, Ytest + return X2, Y def get_binary_data(): - # return only the data from the first 2 classes - Xtrain, Ytrain, Xtest, Ytest = get_data() - X2train = Xtrain[Ytrain <= 1] - Y2train = Ytrain[Ytrain <= 1] - X2test = Xtest[Ytest <= 1] - Y2test = Ytest[Ytest <= 1] - return X2train, Y2train, X2test, Y2test + # return only the data from the first 2 classes + X, Y = get_data() + X2 = X[Y <= 1] + Y2 = Y[Y <= 1] + return X2, Y2 diff --git a/bayes.py b/bayes.py new file mode 100644 index 00000000..4892c0d3 --- /dev/null +++ b/bayes.py @@ -0,0 +1,74 @@ +''' +This is an example of a Bayes classifier on MNIST data using +multivariate Gaussians to model the data. + +This is not production code! + +See the tutorial here: +http://lazyprogrammer.me/post/114077813814/bayes-classifier-and-naive-bayes-tutorial-using +''' + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import sys + +# easier to work with as pandas dataframes because we can filter classes +Xtest = pd.read_csv("mnist_csv/Xtest.txt", header=None) +Xtrain = pd.read_csv("mnist_csv/Xtrain.txt", header=None) +Ytest = pd.read_csv("mnist_csv/label_test.txt", header=None) +Ytrain = pd.read_csv("mnist_csv/label_train.txt", header=None) + +class Bayes(object): + def fit(self, X, y): + self.gaussians = dict() + labels = set(y.as_matrix().flatten()) + for c in labels: + current_x = Xtrain[Ytrain[0] == c] + self.gaussians[c] = { + 'mu': current_x.mean(), + 'sigma': np.cov(current_x.T), + } + # plt.imshow(self.gaussians[c]['sigma']) + # plt.show() + + def predict_one(self, x): + lls = self.distributions(x) + return np.argmax(lls) + + def predict(self, X): + Ypred = X.apply(lambda x: self.predict_one(x), axis=1) + return Ypred + + def distributions(self, x): + lls = np.zeros(len(self.gaussians)) + for c,g in self.gaussians.iteritems(): + x_minus_mu = x - g['mu'] + k1 = np.log(2*np.pi)*x.shape + np.log(np.linalg.det(g['sigma'])) + k2 = np.dot( np.dot(x_minus_mu, np.linalg.inv(g['sigma'])), x_minus_mu) + ll = -0.5*(k1 + k2) + lls[c] = ll + return lls + + +if __name__ == '__main__': + bayes = Bayes() + bayes.fit(Xtrain, Ytrain) + Ypred = bayes.predict(Xtest) + C = np.zeros((10,10), dtype=np.int) + # print len(Ypred), len(Ytest) + for p,t in zip(Ypred.as_matrix().flatten(), Ytest.as_matrix().flatten()): + C[t,p] += 1 + print "Confusion matrix:" + print C + print "Accuracy:", np.trace(C) / 500.0 + + if 'reconstruct' in sys.argv: + # show means as images + Q = pd.read_csv("mnist_csv/Q.txt", header=None).as_matrix() + for c,g in bayes.gaussians.iteritems(): + y = np.dot(Q, g['mu'].as_matrix()) + y = np.reshape(y, (28,28)) + plt.imshow(y) + plt.title(c) + plt.show() diff --git a/bayesian_bandit_tutorial.py b/bayesian_bandit_tutorial.py new file mode 100644 index 00000000..015abf12 --- /dev/null +++ b/bayesian_bandit_tutorial.py @@ -0,0 +1,70 @@ +""" +Code for Bayesian Bandit tutorial, which can be found here: +http://lazyprogrammer.tumblr.com/post/93446998489/bayesian-bandit-tutorial +""" + +import matplotlib.pyplot as plt +import numpy as np +from scipy.stats import beta + + +NUM_TRIALS = 500 +BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] + + +class Bandit(object): + def __init__(self, p): + self.p = p + self.a = 1 + self.b = 1 + + def pull(self): + return np.random.random() < self.p + + def sample(self): + return np.random.beta(self.a, self.b) + + def update(self, x): + self.a += x + self.b += 1 - x + + +def plot(bandits, trial): + x = np.linspace(0, 1, 200) + for b in bandits: + y = beta.pdf(x, b.a, b.b) + plt.plot(x, y, label="real p: %.4f" % b.p) + plt.title("Bandit distributions after %s trials" % trial) + plt.legend() + plt.show() + + +def experiment(): + bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] + + sample_points = [5,10,20,50,100,200,499] + for i in xrange(NUM_TRIALS): + + # take a sample from each bandit + bestb = None + maxsample = -1 + allsamples = [] # let's collect these just to print for debugging + for b in bandits: + sample = b.sample() + allsamples.append("%.4f" % sample) + if sample > maxsample: + maxsample = sample + bestb = b + if i in sample_points: + print "current samples: %s" % allsamples + plot(bandits, i) + + # pull the arm for the bandit with the largest sample + x = bestb.pull() + + # update the distribution for the bandit whose arm we just pulled + bestb.update(x) + + +if __name__ == "__main__": + experiment() diff --git a/bayesian_ml/1/Q.csv b/bayesian_ml/1/Q.csv deleted file mode 100644 index 3dbc34f1..00000000 --- a/bayesian_ml/1/Q.csv +++ /dev/null @@ -1,784 +0,0 @@ --7.4867e-19,5.6315e-19,1.973e-19,-6.7717e-19,2.373e-18,-1.4162e-19,-5.6552e-19,-1.2146e-19,7.3088e-19,5.5656e-19,1.2218e-18,1.0673e-18,3.0638e-18,1.7933e-18,-1.5816e-19 --6.011e-18,4.4018e-18,4.4044e-18,-6.3448e-18,1.94e-17,-1.7822e-18,-4.086e-18,1.1235e-18,5.5901e-18,4.6042e-18,8.9926e-18,8.3237e-18,2.6068e-17,1.5782e-17,-1.501e-18 --5.5492e-18,3.9852e-18,5.3138e-18,-5.9229e-18,1.9185e-17,-1.1093e-18,-3.3366e-18,-8.1893e-19,3.7768e-18,2.4595e-18,8.6731e-18,8.3354e-18,2.3839e-17,1.4832e-17,-6.7029e-20 --6.583e-18,3.9562e-18,4.6542e-18,-8.8559e-18,2.2705e-17,-2.3685e-18,-4.0349e-18,-2.6403e-18,6.2138e-18,5.6977e-18,1.0569e-17,9.7509e-18,2.853e-17,1.7109e-17,-1.7819e-18 --4.8539e-18,5.8247e-18,4.1718e-18,-6.5081e-18,1.8419e-17,-5.2626e-19,-4.8044e-18,-6.2812e-19,6.141e-18,3.5582e-18,8.9691e-18,9.1986e-18,2.4707e-17,1.563e-17,-1.5153e-18 --3.5854e-18,2.3606e-18,4.5114e-18,-4.9643e-18,1.3842e-17,-6.5907e-19,-2.8941e-18,-4.7605e-19,6.2247e-18,3.8328e-18,7.1909e-18,7.6724e-18,1.9494e-17,1.1422e-17,-1.2724e-18 --1.4039e-18,8.9214e-19,1.291e-18,-1.5682e-18,5.3071e-18,8.1127e-23,-1.161e-18,-4.9599e-19,1.7598e-18,1.2958e-18,2.5956e-18,2.3683e-18,7.2358e-18,4.2655e-18,-3.6548e-19 --8.5218e-18,8.5522e-18,4.3194e-18,-8.4601e-18,2.6117e-17,-6.0864e-19,-6.3716e-18,-2.0189e-18,8.9574e-18,7.1407e-18,1.2315e-17,8.913e-18,3.3396e-17,1.9457e-17,-1.142e-18 --9.7639e-18,8.021e-18,8.6407e-18,-9.2142e-18,3.1929e-17,-1.7551e-18,-5.5628e-18,-1.6311e-18,7.3616e-18,7.4003e-18,1.5293e-17,1.5608e-17,3.8518e-17,2.7512e-17,-2.8847e-18 --1.2744e-18,1.2661e-18,1.2556e-18,-1.4812e-18,4.9142e-18,-1.6944e-19,-7.32e-19,-3.3056e-19,1.5951e-18,1.0363e-18,2.4082e-18,2.0966e-18,6.1122e-18,4.1287e-18,-1.5852e-19 --6.0466e-18,5.6187e-18,8.087e-18,-7.2409e-18,2.62e-17,-2.5222e-18,-6.13e-18,-2.9832e-18,1.31e-17,7.866e-18,1.5546e-17,1.0797e-17,3.5016e-17,2.1604e-17,-1.9611e-18 --1.17e-17,6.4707e-18,5.5805e-18,-1.0522e-17,2.6971e-17,-1.7895e-19,-5.9102e-18,-6.0565e-19,9.2093e-18,4.9204e-18,1.4625e-17,1.4817e-17,3.5127e-17,2.5327e-17,-2.1979e-18 --6.9103e-19,4.3463e-19,5.4274e-19,-8.8561e-19,2.5707e-18,-1.9468e-19,-4.6275e-19,-1.576e-19,6.161e-19,4.676e-19,1.0759e-18,8.5213e-19,3.1821e-18,1.769e-18,-2.4132e-20 --4.0365e-18,3.2797e-18,2.5166e-18,-5.2998e-18,1.4414e-17,6.5557e-19,-4.0213e-18,-8.4823e-19,4.3092e-18,3.7896e-18,7.7954e-18,7.4451e-18,2.0082e-17,1.2891e-17,-1.3899e-18 --8.2524e-18,4.5777e-18,7.0009e-18,-9.3984e-18,2.5118e-17,-1.8716e-18,-4.3765e-18,-1.0959e-18,7.6141e-18,6.2938e-18,1.2644e-17,8.0634e-18,3.3784e-17,1.9519e-17,-9.6456e-19 --5.8104e-18,6.2973e-18,7.8161e-18,-8.5119e-18,2.5462e-17,-3.1688e-20,-5.6616e-18,-1.7517e-18,6.2827e-18,6.1381e-18,1.2008e-17,8.9887e-18,3.2988e-17,1.9922e-17,-2.5239e-18 --8.8931e-18,8.3657e-18,6.6394e-18,-8.7401e-18,2.6354e-17,-7.5187e-19,-5.6616e-18,-2.2361e-18,7.7238e-18,8.2954e-18,1.3295e-17,1.3675e-17,3.1326e-17,2.3226e-17,-2.6304e-18 --6.3602e-18,4.8369e-18,2.4537e-18,-2.2582e-18,1.6375e-17,1.2161e-19,-4.0497e-18,-2.5718e-18,8.129e-18,4.004e-18,7.6075e-18,6.2324e-18,2.49e-17,1.4367e-17,-5.7086e-19 --5.4728e-18,3.7045e-18,6.4009e-18,-6.9563e-18,1.9124e-17,-7.9748e-19,-3.7507e-18,-1.13e-18,5.5516e-18,3.3933e-18,8.2959e-18,8.7071e-18,2.4927e-17,1.4197e-17,-4.0817e-19 --3.1113e-18,2.6183e-18,3.2548e-18,-4.1522e-18,1.2249e-17,-5.3486e-19,-2.1024e-18,1.3337e-19,2.7854e-18,3.6371e-18,6.0933e-18,5.9073e-18,1.4433e-17,9.33e-18,-1.3596e-18 --5.5648e-18,2.4347e-18,4.7689e-18,-3.9755e-18,1.9524e-17,-2.0925e-18,-3.6363e-18,-4.0473e-19,6.2775e-18,3.9464e-18,8.6585e-18,8.1866e-18,2.4051e-17,1.4696e-17,-5.3165e-19 --1.6568e-19,1.6894e-19,2.3257e-19,-2.9092e-19,6.6811e-19,-9.2e-21,-1.19e-19,-1.0538e-20,1.5553e-19,2.2983e-19,3.4635e-19,3.013e-19,8.5785e-19,5.5171e-19,-2.9391e-20 --8.0709e-18,5.3261e-18,7.4744e-18,-6.7263e-18,2.0751e-17,2.1036e-19,-5.4563e-18,-3.6482e-19,1.0639e-17,6.109e-18,1.1437e-17,9.0511e-18,3.1213e-17,1.9498e-17,-1.9533e-18 --9.7557e-18,8.1887e-18,6.4029e-18,-7.5209e-18,2.6108e-17,-1.9721e-18,-6.5026e-18,-3.232e-18,1.1508e-17,6.8542e-18,1.3334e-17,1.0599e-17,3.6994e-17,2.1498e-17,-1.2942e-18 --9.4016e-18,9.503e-18,5.0208e-18,-8.5922e-18,2.5033e-17,-1.6837e-18,-5.8412e-18,2.7844e-19,1.2177e-17,5.7217e-18,1.4385e-17,9.9386e-18,3.4004e-17,2.155e-17,-1.8267e-18 --3.0226e-19,1.9717e-19,1.955e-19,-2.7872e-19,1.0685e-18,-3.0572e-20,-2.3457e-19,-1.031e-19,4.1522e-19,2.4631e-19,5.5285e-19,4.3941e-19,1.5418e-18,9.076e-19,-2.3226e-21 --7.9695e-18,6.0614e-18,1.0013e-17,-9.945e-18,3.0856e-17,-8.3311e-19,-5.6679e-18,-2.8781e-18,1.0762e-17,7.2886e-18,1.519e-17,1.4133e-17,3.8734e-17,2.3253e-17,-2.1544e-18 --7.5238e-19,6.6841e-19,7.9005e-19,-1.2413e-18,3.2491e-18,-9.0119e-20,-7.8506e-19,5.7969e-20,8.9031e-19,9.3871e-19,1.488e-18,1.1394e-18,3.8504e-18,2.5809e-18,-2.853e-19 --7.3665e-18,6.5073e-18,7.3888e-18,-8.8375e-18,2.7931e-17,-2.3988e-18,-6.2852e-18,-7.1268e-19,1.1131e-17,6.6605e-18,1.2079e-17,1.3042e-17,3.6386e-17,2.1699e-17,-7.6382e-19 --5.1186e-18,5.4054e-18,3.1762e-18,-3.3917e-18,1.6205e-17,8.8137e-19,-3.808e-18,-2.611e-18,7.856e-18,4.3272e-18,7.7098e-18,7.2169e-18,2.3605e-17,1.4044e-17,-9.7976e-19 --1.7855e-19,1.5614e-19,9.3929e-20,-1.5899e-19,5.5801e-19,-8.6565e-21,-7.2631e-20,-1.4526e-20,2.1996e-19,1.004e-19,2.7594e-19,2.2051e-19,7.342e-19,4.0715e-19,-2.1427e-20 --9.1687e-19,5.9531e-19,1.0908e-18,-1.671e-18,4.4768e-18,-3.7063e-19,-4.5806e-19,-7.6409e-20,6.859e-19,1.0078e-18,2.1513e-18,1.9582e-18,5.5499e-18,3.3266e-18,-1.4692e-19 --6.4241e-18,4.6888e-18,3.7933e-18,-5.0429e-18,1.8474e-17,-9.317e-20,-4.4275e-18,-1.1298e-18,5.9189e-18,4.8484e-18,9.2033e-18,8.4812e-18,2.5681e-17,1.7036e-17,-1.5144e-18 --7.6229e-18,5.6977e-18,5.3822e-18,-6.9519e-18,2.2989e-17,-1.5386e-18,-4.0534e-18,-7.4967e-19,1.0114e-17,7.9531e-18,1.1145e-17,8.6446e-18,3.2806e-17,2.021e-17,-1.2672e-18 --3.4401e-19,2.8835e-19,2.6754e-19,-3.4559e-19,1.2342e-18,-9.3413e-20,-2.8662e-19,-2.5366e-20,3.3675e-19,2.3142e-19,6.0428e-19,6.1936e-19,1.5875e-18,9.2196e-19,-1.3078e-19 --1.8568e-18,1.947e-18,7.4171e-19,-1.3479e-18,5.0503e-18,-1.2942e-19,-1.1902e-18,-2.9588e-19,2.2434e-18,1.7638e-18,2.7028e-18,2.4343e-18,7.1509e-18,4.4264e-18,-2.6336e-19 --2.628e-18,1.5108e-18,1.9317e-18,-3.2226e-18,8.1468e-18,-4.4557e-19,-1.819e-18,-4.941e-19,2.1306e-18,1.9153e-18,3.1653e-18,3.7328e-18,9.5465e-18,6.0664e-18,-6.0891e-19 --7.1712e-18,5.177e-18,5.4347e-18,-8.5082e-18,2.2077e-17,-1.1687e-18,-4.3743e-18,-1.4563e-20,9.265e-18,7.2652e-18,1.1607e-17,9.5899e-18,2.9107e-17,2.0585e-17,-1.8832e-18 --8.4372e-18,6.2943e-18,9.1348e-18,-9.626e-18,2.7854e-17,-3.9201e-19,-5.2248e-18,-2.3334e-18,6.7699e-18,5.3806e-18,1.1857e-17,1.1552e-17,3.2906e-17,1.9219e-17,-2.0727e-18 --5.6143e-18,4.9264e-18,4.0871e-18,-4.7651e-18,1.8423e-17,-3.6894e-19,-4.6373e-18,-9.9158e-19,8.0117e-18,4.6903e-18,9.5468e-18,7.3021e-18,2.6782e-17,1.5236e-17,-7.0794e-19 --8.9782e-18,4.9943e-18,6.8061e-18,-1.0214e-17,2.7158e-17,-1.0415e-18,-5.6317e-18,-8.8953e-19,8.0052e-18,6.3716e-18,1.4146e-17,1.4546e-17,3.3497e-17,2.5973e-17,-1.2204e-18 -2.6742e-06,-3.9108e-06,-8.3876e-06,1.041e-05,4.11e-06,1.1774e-05,-2.2292e-06,7.0048e-06,-1.0741e-05,7.4552e-06,1.7807e-05,1.5329e-06,1.2606e-05,1.596e-05,5.9898e-06 -1.6649e-05,-2.4348e-05,-5.2219e-05,6.4811e-05,2.5588e-05,7.33e-05,-1.3878e-05,4.3611e-05,-6.6873e-05,4.6415e-05,0.00011086,9.5438e-06,7.848e-05,9.9362e-05,3.7291e-05 --1.8994e-18,2.0585e-18,1.9547e-18,-2.2397e-18,9.1769e-18,-7.1572e-19,-1.6663e-18,-9.6119e-19,3.4165e-18,2.069e-18,4.2013e-18,4.1718e-18,1.0752e-17,7.3523e-18,-4.7849e-19 --6.7659e-18,6.0993e-18,4.8304e-18,-6.3006e-18,2.2213e-17,4.4344e-19,-4.3298e-18,-8.2245e-19,7.8443e-18,7.0471e-18,1.0383e-17,7.9354e-18,3.1485e-17,1.7833e-17,-1.9253e-18 --4.7512e-18,5.6858e-18,3.9184e-18,-5.427e-18,1.9438e-17,-1.653e-18,-3.4258e-18,-1.7144e-18,3.8818e-18,5.0842e-18,8.1706e-18,7.8706e-18,2.5202e-17,1.4741e-17,-1.2817e-18 --3.3212e-18,1.1474e-18,1.9358e-18,-3.0037e-18,9.6331e-18,-8.4001e-19,-2.1879e-18,-4.0259e-19,3.4389e-18,2.1535e-18,4.245e-18,4.2825e-18,1.228e-17,7.5649e-18,-6.4102e-19 --2.9699e-18,1.56e-18,2.827e-18,-2.8602e-18,9.3821e-18,4.7582e-19,-2.1342e-18,-7.8143e-21,2.715e-18,2.039e-18,4.1308e-18,3.9274e-18,1.1371e-17,7.477e-18,-3.7638e-19 --6.2843e-18,6.2969e-18,4.78e-18,-7.6064e-18,2.2227e-17,-3.0409e-19,-4.5653e-18,-1.429e-18,9.034e-18,6.748e-18,1.1373e-17,9.1603e-18,2.9611e-17,1.7497e-17,-1.3087e-18 --7.9674e-18,7.5603e-18,7.8196e-18,-9.6804e-18,2.7418e-17,-1.3495e-18,-6.8006e-18,-2.0382e-18,1.2934e-17,8.2375e-18,1.3833e-17,1.1695e-17,3.4047e-17,1.9915e-17,-6.0819e-19 --6.3578e-19,4.8709e-19,7.6807e-19,-7.3746e-19,2.5702e-18,-3.6972e-19,-4.5962e-19,9.4102e-20,8.9146e-19,6.7732e-19,1.1939e-18,8.9589e-19,3.3392e-18,2.1373e-18,-1.408e-19 --3.1544e-18,3.123e-18,2.6608e-18,-4.7286e-18,1.3265e-17,-6.8872e-20,-3.0776e-18,-2.6737e-19,3.9299e-18,2.9514e-18,6.1965e-18,5.3772e-18,1.5413e-17,9.9149e-18,-1.0193e-18 --8.6787e-18,6.4853e-18,7.7946e-18,-8.2065e-18,2.5559e-17,-1.4277e-18,-3.8277e-18,-3.1358e-19,8.8797e-18,8.1033e-18,1.3607e-17,1.2367e-17,3.1997e-17,2.1245e-17,-1.2535e-18 --7.6224e-18,1.0242e-17,7.6707e-18,-6.8145e-18,2.8599e-17,-1.6883e-18,-6.0616e-18,-3.9097e-18,9.7391e-18,6.3037e-18,1.2661e-17,1.4045e-17,3.9981e-17,2.6453e-17,-2.3571e-18 --7.2868e-18,6.5039e-18,6.3996e-18,-7.9398e-18,3.2759e-17,6.1023e-19,-7.2255e-18,-3.9272e-18,1.2008e-17,6.9312e-18,1.5032e-17,1.51e-17,4.0119e-17,2.6737e-17,-3.3262e-18 --4.0425e-18,4.3115e-18,2.6734e-18,-4.8384e-18,1.386e-17,-8.7521e-19,-2.456e-18,-1.3359e-18,5.4004e-18,2.9597e-18,6.6391e-18,5.92e-18,1.8596e-17,1.1114e-17,-4.4445e-19 --4.4216e-18,3.2291e-18,1.5271e-18,-3.5975e-18,1.3635e-17,-8.3325e-20,-2.7205e-18,-1.4131e-18,5.6898e-18,4.0095e-18,7.4963e-18,6.8818e-18,1.8148e-17,1.1686e-17,-5.023e-19 --3.1465e-18,2.7346e-18,3.4786e-18,-4.1496e-18,1.1374e-17,-1.5511e-19,-2.605e-18,-6.8765e-19,3.8746e-18,3.1941e-18,6.2502e-18,3.5983e-18,1.5197e-17,8.0763e-18,-4.539e-19 --4.6141e-18,1.4846e-18,4.9131e-18,-5.2146e-18,1.5235e-17,-3.2172e-19,-2.1165e-18,-1.37e-18,4.2322e-18,3.5789e-18,7.5732e-18,6.7182e-18,1.8684e-17,1.1998e-17,-1.0667e-18 --8.8585e-18,6.8642e-18,5.9722e-18,-9.5771e-18,2.5803e-17,-1.5152e-19,-6.0697e-18,-9.3551e-19,9.1064e-18,7.7482e-18,1.2058e-17,1.1341e-17,3.2675e-17,1.9182e-17,-1.5826e-18 --7.1299e-18,8.292e-18,5.1338e-18,-8.2061e-18,2.4985e-17,-1.567e-19,-4.3796e-18,-1.7807e-18,6.1613e-18,7.4217e-18,1.2353e-17,1.0756e-17,3.3648e-17,1.9956e-17,-1.2352e-18 --1.0828e-17,8.0402e-18,8.0811e-18,-1.3406e-17,3.4545e-17,-9.7561e-20,-6.1218e-18,-3.643e-18,1.0333e-17,7.1302e-18,1.5749e-17,1.3164e-17,4.3321e-17,2.6609e-17,-8.1487e-19 -6.4694e-06,-9.4603e-06,-1.0078e-05,7.7447e-06,-5.5471e-06,1.4203e-05,1.3504e-05,3.1463e-06,-7.1776e-06,-8.8839e-06,-9.1686e-06,-3.1624e-06,6.9852e-06,-3.5731e-06,2.2555e-05 -2.8609e-05,-4.1835e-05,-4.4569e-05,3.4249e-05,-2.4531e-05,6.2809e-05,5.9716e-05,1.3914e-05,-3.1741e-05,-3.9287e-05,-4.0545e-05,-1.3985e-05,3.089e-05,-1.5801e-05,9.9745e-05 -4.888e-06,-7.1478e-06,-7.6148e-06,5.8516e-06,-4.1912e-06,1.0731e-05,1.0203e-05,2.3772e-06,-5.4231e-06,-6.7123e-06,-6.9273e-06,-2.3893e-06,5.2777e-06,-2.6997e-06,1.7042e-05 --1.6092e-18,1.2223e-18,1.5229e-18,-1.3892e-18,5.4861e-18,1.6348e-19,-8.7242e-19,-3.5358e-19,1.9845e-18,1.0677e-18,2.4753e-18,1.7891e-18,7.5554e-18,3.8181e-18,-4.503e-19 --3.3041e-18,2.1427e-18,2.4793e-18,-2.4913e-18,1.0987e-17,1.0585e-19,-1.8949e-18,1.9877e-19,3.7746e-18,2.4294e-18,5.0455e-18,3.9981e-18,1.4454e-17,8.4402e-18,-7.775e-19 --7.0684e-18,4.3143e-18,8.6404e-18,-9.1849e-18,2.7604e-17,-2.5187e-18,-7.829e-18,-2.2718e-18,1.3329e-17,8.2034e-18,1.3999e-17,1.1174e-17,3.3428e-17,2.0334e-17,-3.2371e-18 --2.1551e-06,-8.378e-06,-4.5739e-05,1.9506e-05,4.3006e-06,5.3625e-05,-5.0767e-06,2.3797e-05,-2.3369e-05,1.1366e-07,8.4292e-05,-3.8914e-05,-3.005e-05,4.6631e-05,1.2869e-05 -1.9713e-05,-5.2561e-05,-0.0001356,0.00011115,4.566e-05,0.00019023,-1.7383e-05,7.7408e-05,-0.00014171,2.0583e-05,0.00031239,-2.9313e-05,9.2688e-05,0.00017932,8.866e-05 -5.9989e-05,-0.00010983,-0.00015249,0.00019054,9.635e-05,0.00029193,3.9397e-07,5.9761e-05,-0.00031059,2.7356e-06,0.00049016,0.00012992,0.00044133,0.00020208,0.00020924 -5.6053e-05,-8.3478e-05,-6.7279e-05,8.9638e-05,6.7749e-05,0.00012999,9.6288e-05,-3.3406e-05,-0.00020448,-0.00010109,0.00019692,5.4193e-05,0.00031791,6.6772e-05,0.00022575 -3.6807e-05,-7.8617e-05,-0.00010387,0.00012643,9.3715e-05,5.1959e-05,0.00017023,6.9351e-05,-6.1543e-05,-0.00021954,6.095e-05,-0.00021287,4.0036e-05,7.3258e-05,0.00019928 -6.8734e-06,-5.3957e-05,-9.4628e-05,0.00012633,5.4401e-05,0.00010279,7.664e-05,0.00010068,6.5391e-06,-0.0001141,5.4569e-05,-0.0002037,1.5638e-05,4.1766e-05,9.9944e-05 --8.5956e-07,-3.0861e-06,-6.9614e-06,1.1706e-05,3.7293e-06,1.1453e-05,3.0828e-07,1.5003e-05,5.6551e-06,-7.5721e-06,5.3779e-06,-1.5065e-05,-4.8293e-06,5.1222e-06,1.9137e-06 -3.5295e-05,-4.0209e-05,-2.9711e-05,0.00014926,-1.6098e-05,-2.3494e-05,4.1101e-05,-6.6314e-05,-5.1053e-05,-0.00018861,3.6144e-05,-9.1797e-05,1.1123e-06,5.6759e-05,8.7077e-05 -3.7641e-05,-3.8615e-05,-2.5589e-05,0.00011406,-1.4862e-05,-6.0409e-06,3.7926e-05,-6.6041e-05,-5.2377e-05,-0.00016497,1.522e-05,-7.9934e-05,1.9817e-05,2.9949e-05,7.9821e-05 -2.0833e-05,-1.7388e-05,-3.7037e-05,4.4289e-05,-2.7201e-05,4.6729e-05,4.8587e-05,-1.8031e-05,-4.0012e-05,-0.00013744,-2.3535e-05,-2.9109e-06,3.6024e-05,-3.9255e-05,-6.0973e-05 -2.1625e-05,-1.8027e-05,-3.9359e-05,4.6513e-05,-2.8922e-05,4.9671e-05,5.1499e-05,-1.8224e-05,-4.1951e-05,-0.00014513,-2.4885e-05,-2.1174e-06,3.7741e-05,-4.1805e-05,-6.6433e-05 --6.2415e-18,4.5474e-18,5.0909e-18,-7.5057e-18,2.298e-17,-4.0389e-19,-4.6354e-18,-1.2713e-18,8.3293e-18,4.0202e-18,1.0501e-17,7.6003e-18,2.8137e-17,1.831e-17,-1.9473e-18 --3.4319e-18,2.077e-18,2.8904e-18,-2.463e-18,9.9222e-18,-7.7195e-19,-2.083e-18,-9.8393e-19,3.1913e-18,2.4886e-18,4.9975e-18,4.3545e-18,1.2986e-17,7.8974e-18,-4.0729e-19 --2.9183e-18,2.4051e-18,2.8226e-18,-2.3766e-18,9.9352e-18,-1.0158e-19,-2.156e-18,4.3101e-19,3.2521e-18,2.2285e-18,4.2611e-18,4.1291e-18,1.209e-17,7.5939e-18,-5.6952e-19 --3.7494e-18,2.4643e-18,4.3757e-18,-5.0826e-18,1.4431e-17,-1.0053e-18,-2.7093e-18,-1.4038e-18,6.0738e-18,3.5464e-18,6.3066e-18,6.6156e-18,1.828e-17,1.118e-17,-6.1964e-19 --5.3215e-18,3.5663e-18,3.5087e-18,-4.2389e-18,1.7043e-17,-4.1235e-19,-2.3153e-18,-1.6167e-18,5.9134e-18,3.2008e-18,8.9973e-18,7.6483e-18,2.4141e-17,1.4903e-17,-1.2704e-18 --8.7151e-19,7.6823e-19,1.211e-18,-1.187e-18,4.1537e-18,6.6014e-20,-8.8294e-19,-6.4227e-19,9.6003e-19,8.7648e-19,1.7563e-18,1.8387e-18,4.903e-18,3.202e-18,-1.429e-19 --1.5918e-18,1.3528e-18,2.6789e-19,-1.4177e-18,4.356e-18,8.7318e-20,-6.8501e-19,-1.6034e-19,2.2792e-18,9.75e-19,1.9844e-18,1.5875e-18,5.9237e-18,3.2288e-18,6.6258e-20 --2.3099e-18,1.2181e-18,1.8501e-18,-2.3671e-18,7.5468e-18,-3.5792e-19,-1.5001e-18,-3.3773e-19,2.5236e-18,1.8185e-18,3.7081e-18,3.7768e-18,9.5708e-18,6.5458e-18,-4.149e-19 --1.2498e-18,8.243e-19,8.2959e-19,-1.1563e-18,3.653e-18,-1.0448e-19,-4.8433e-19,-1.4448e-19,1.3926e-18,1.0461e-18,1.6666e-18,1.8868e-18,4.3182e-18,2.7665e-18,-2.5084e-19 --8.1551e-19,8.3201e-19,8.3968e-19,-1.0152e-18,3.4141e-18,-2.3319e-19,-6.5658e-19,8.6422e-21,1.0061e-18,8.9211e-19,1.3886e-18,1.1027e-18,4.1348e-18,2.3607e-18,-1.9545e-19 --1.8176e-18,2.2603e-18,1.2185e-18,-2.1019e-18,6.428e-18,-3.1747e-19,-1.5121e-18,-2.5807e-19,1.648e-18,1.8698e-18,2.7467e-18,2.5902e-18,7.9883e-18,5.11e-18,-6.0254e-19 -3.6894e-05,-5.3099e-05,-5.807e-05,3.9376e-05,-3.5624e-05,8.5327e-05,7.5306e-05,2.3531e-05,-5.1719e-05,-4.4491e-05,-6.4021e-05,-2.2862e-05,5.0988e-05,-2.1447e-05,0.00011642 -6.3653e-05,-8.6375e-05,-0.00010548,3.6932e-05,-8.7901e-05,0.00017682,0.00012001,7.8439e-05,-0.00016028,-3.819e-05,-0.00018647,-6.7156e-05,0.00016199,-4.2224e-05,0.00011888 -5.2341e-05,-5.9961e-05,-8.1962e-05,5.4645e-06,-6.8719e-05,0.00010304,9.4559e-05,5.1249e-05,-0.00014234,-2.2382e-05,-0.00015889,-5.01e-05,0.00013684,-2.1249e-05,8.6391e-05 -0.00011963,-6.358e-05,-9.2838e-05,5.3614e-05,-0.00011075,1.5646e-05,0.00016273,-5.1569e-05,-0.00018009,-2.0207e-05,-5.5806e-05,-6.5297e-05,0.00023916,4.8959e-05,0.00032171 -0.00030281,-0.00016815,-0.00012329,0.00019493,-0.00022158,0.00018734,0.00019801,-0.0001992,-0.00052077,0.00015213,9.1006e-05,-0.00036796,0.00066034,0.00025523,0.00092973 -0.00029452,-0.00020923,-0.00012844,0.00031789,-0.00020667,0.00035123,-7.8173e-05,-0.0001872,-0.00062707,0.00021755,0.00049991,-0.0005273,0.00054541,0.00033615,0.00086209 -0.00022643,-0.00016856,-0.00020185,0.00032198,1.1193e-05,0.00044852,-0.00019369,2.1843e-05,-0.00056453,0.00025422,0.00087188,-0.00030941,0.00050832,0.00036622,0.00032497 -0.0001807,-0.00017614,-0.00029351,0.00043024,0.00016365,0.00046536,4.2203e-06,9.4897e-05,-0.00069159,0.00022598,0.0009191,-0.0001182,0.00040247,0.0003287,0.00027211 -0.00015643,-0.00018372,-0.00061893,0.00075562,0.00051469,0.00079526,-0.00017425,0.00039428,-0.00073848,-3.3423e-05,0.0015909,-0.00014926,0.00043693,0.00055696,0.00044277 -0.00019338,-0.00015797,-0.00073611,0.00094681,0.00065199,0.0005495,9.5064e-05,0.0004938,-0.00062624,-0.00042104,0.00087914,-0.00089808,-3.4636e-05,0.00076066,0.00049885 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--0.048788,-0.024612,0.019901,-0.05742,0.023944,0.018575,0.02144,-0.036034,0.048307,-0.0064616,-0.0076647,-0.069489,0.017531,0.015795,-0.035222 --0.049084,-0.007335,0.048885,-0.04382,0.012557,0.029533,0.066611,-0.013648,0.060379,0.0012486,0.027345,-0.062516,0.012127,-0.019225,-0.015022 --0.037868,0.016734,0.067666,-0.013634,-0.0010147,0.037291,0.079761,0.01986,0.051237,-0.0039605,0.030985,0.0026496,-0.010781,-0.04666,0.047377 --0.019301,0.039999,0.066731,0.012206,-0.0062411,0.033623,0.043992,0.038361,0.025966,-0.017999,-0.0043836,0.073702,-0.031481,-0.044137,0.081214 -0.0018102,0.055857,0.048004,0.020738,0.0027015,0.02146,-0.013359,0.027046,0.0046486,-0.025878,-0.048066,0.093072,-0.033388,-0.030952,0.036522 -0.020773,0.058545,0.023941,0.0098714,0.022617,0.010328,-0.056241,-0.0033019,0.0060605,-0.020495,-0.057702,0.050395,-0.017198,-0.028333,-0.041639 -0.031529,0.045358,0.00029413,-0.014175,0.037375,0.003919,-0.054442,-0.023592,0.014571,-0.0015735,-0.022899,-0.011026,-0.0082249,-0.04104,-0.071165 -0.031248,0.025498,-0.013234,-0.02964,0.037542,0.00072081,-0.022088,-0.023929,0.017311,0.017265,0.017156,-0.030682,-0.013563,-0.04531,-0.034632 -0.023698,0.0093475,-0.015337,-0.029958,0.028095,0.0022443,0.0073806,-0.010701,0.013745,0.021415,0.029165,-0.0084124,-0.01813,-0.035795,0.0057999 -0.014343,0.0011434,-0.011035,-0.020794,0.017117,0.0041645,0.016519,0.0005988,0.0081541,0.015134,0.020584,0.012184,-0.015773,-0.021726,0.017732 -0.0069124,-0.0012088,-0.0063177,-0.010456,0.0082728,0.0037438,0.012613,0.0032566,0.0029275,0.007279,0.010506,0.014688,-0.0097011,-0.0094678,0.012127 -0.0027931,-0.00095418,-0.0027189,-0.0042249,0.0031276,0.0019661,0.0064658,0.0022677,0.00010807,0.0026961,0.0031756,0.0079649,-0.004266,-0.0033714,0.0045409 -0.0010608,-0.00055435,-0.00085607,-0.0013614,0.0012286,0.00097188,0.0025499,0.00079173,-0.00050745,0.00082786,0.00048584,0.0034398,-0.0014504,-0.0012638,0.0014938 -0.00020405,-0.00014044,-0.00012319,-0.00014834,0.00020197,0.00030756,0.00050092,-6.6322e-06,-0.00025147,6.7242e-05,-1.7699e-05,0.00046788,-0.00028987,-0.00025976,0.00012542 -1.3372e-05,3.3648e-05,4.0861e-05,-4.8252e-06,-6.777e-06,1.3692e-06,-9.2513e-06,-1.0966e-05,-1.754e-06,-1.712e-05,-4.4532e-05,3.3684e-05,-0.00015123,3.3067e-05,3.284e-05 --1.2513e-18,7.8315e-19,7.8035e-19,-8.145e-19,3.6961e-18,-1.6204e-19,-4.1468e-19,-4.496e-19,1.5256e-18,6.3388e-19,1.7807e-18,1.6229e-18,4.7419e-18,2.8765e-18,-2.003e-19 --2.7217e-18,2.4793e-18,1.4197e-18,-2.3169e-18,8.8738e-18,-7.8863e-19,-1.5427e-18,-9.1127e-19,2.2999e-18,2.0101e-18,3.8057e-18,3.5919e-18,1.127e-17,6.4546e-18,-3.7567e-19 --1.4917e-18,1.1169e-18,1.209e-18,-1.5189e-18,4.876e-18,-4.6337e-19,-9.9906e-19,-4.1063e-19,2.0934e-18,1.2157e-18,2.516e-18,1.7153e-18,7.4353e-18,3.709e-18,-3.9409e-19 --8.9794e-18,7.2143e-18,5.6274e-18,-9.0055e-18,2.8513e-17,-6.7062e-19,-4.6562e-18,-8.6158e-19,1.1588e-17,8.5418e-18,1.3029e-17,1.1431e-17,3.2389e-17,2.2972e-17,-1.0473e-18 --7.1485e-18,3.3918e-18,4.4439e-18,-6.6609e-18,1.9683e-17,-4.4646e-19,-3.8152e-18,-1.4051e-18,4.8388e-18,5.7898e-18,9.3483e-18,8.6693e-18,2.4543e-17,1.7546e-17,-2.7329e-18 --3.648e-06,-2.5282e-06,-1.1417e-05,-3.0845e-06,2.8581e-06,5.7071e-06,-9.051e-06,9.4753e-06,6.3928e-06,-1.731e-05,-1.408e-05,1.1484e-05,-8.4639e-06,1.9742e-06,2.7681e-05 --0.00010227,-9.5275e-05,-0.00023664,-9.167e-05,7.1478e-05,9.9229e-05,-0.00030645,0.00015054,4.1469e-06,-0.00039585,-0.00028524,0.00031007,-9.9368e-05,-0.00010509,0.00087003 --0.000222,-0.00027955,-0.00044241,-0.00026843,0.00023459,0.00028691,-0.00066337,0.00021509,0.00014216,-0.00067013,-0.00082934,0.00073394,0.00015675,-0.00022952,0.0018642 --0.00058168,-0.00077404,-0.00082475,-0.0007839,0.00064033,0.00028413,-0.0016572,0.00039917,0.00021232,-0.0012281,-0.0022784,0.0011162,0.00024682,-0.00019338,0.0039884 --0.0012115,-0.0015148,-0.0012456,-0.0017454,0.0013588,-6.2457e-05,-0.0032027,0.00042666,0.00029212,-0.0017397,-0.0041663,0.0016862,-0.00019176,0.00032618,0.0066862 --0.0028513,-0.0030822,-0.0018105,-0.0044657,0.0026166,-0.00022862,-0.0053097,-0.0003249,0.0021016,-0.002269,-0.0081879,0.00091042,0.00060446,0.00061378,0.0095632 --0.0054241,-0.0045263,-0.0019345,-0.008644,0.003411,0.00038241,-0.005094,-0.0018303,0.0062893,-0.0034878,-0.010869,-0.0046409,0.0035455,-0.0011929,0.0086997 --0.0086938,-0.004898,0.00021502,-0.012843,0.0035512,0.0022635,-9.0637e-05,-0.0038384,0.011993,-0.0039345,-0.0081467,-0.015894,0.0083772,-0.0078343,0.0043804 --0.011775,-0.0028038,0.0061903,-0.014865,0.0026811,0.0052535,0.010577,-0.0025626,0.019944,-0.0035666,0.00030465,-0.025115,0.011062,-0.022244,0.0058374 --0.011895,0.0017884,0.013027,-0.011428,0.0010155,0.0072042,0.019135,0.0057189,0.024358,-0.0035902,0.0059213,-0.019104,0.0091108,-0.036353,0.019955 --0.0088858,0.0076483,0.016222,-0.004642,-0.00035076,0.0084278,0.01859,0.013928,0.02179,-0.0044371,0.0027452,0.0015121,0.0044439,-0.042926,0.037527 --0.0044623,0.012107,0.014567,0.0011921,0.00069264,0.0068857,0.0080767,0.016991,0.015017,-0.0065713,-0.0073812,0.019086,0.00046179,-0.039889,0.038044 -0.00081198,0.013889,0.0088165,0.0026246,0.004302,0.0036879,-0.006885,0.011577,0.0095872,-0.0061327,-0.017122,0.022262,0.00040551,-0.032768,0.013395 -0.0046609,0.014413,0.0041509,-4.433e-05,0.0079781,0.0027358,-0.0157,0.0039729,0.010559,-0.0044726,-0.019583,0.013566,0.0044083,-0.033289,-0.0076319 -0.0067403,0.011853,-0.00094585,-0.0057642,0.0098764,0.0017078,-0.015785,-0.00082708,0.010473,-6.5833e-05,-0.0090469,-0.0020177,0.0058743,-0.031422,-0.016101 -0.0066519,0.0072044,-0.0041795,-0.0091304,0.0089927,0.00078486,-0.0074949,-0.0011834,0.0083483,0.0051043,0.0025324,-0.0071402,0.0015666,-0.025219,-0.0086197 -0.0056236,0.0028332,-0.0048834,-0.0088525,0.0076719,0.00056646,0.0014715,0.00085891,0.006073,0.0068472,0.0069128,-0.00099608,-0.002476,-0.019491,0.00066936 -0.0039132,0.00043526,-0.0035894,-0.0066518,0.0059031,0.0011672,0.0046443,0.0024153,0.0039341,0.005245,0.0060166,0.0049257,-0.0017128,-0.01315,0.0059007 -0.0016437,-0.00011517,-0.001796,-0.0029732,0.0022762,0.00097651,0.0028808,0.0015896,0.0015578,0.0020519,0.0029496,0.0038546,-0.00056504,-0.00535,0.0041954 -0.00041219,-0.00010842,-0.00038464,-0.00081213,0.00057471,0.00023278,0.00079489,0.00056025,0.00029155,0.00049624,0.00061075,0.0013742,-8.8809e-05,-0.0014475,0.0012718 -7.9149e-05,-2.5634e-05,-2.2128e-05,-0.0002548,8.7142e-05,8.0726e-05,0.00017031,6.3288e-05,0.0001044,0.00020962,0.00013625,0.00013625,-9.7967e-05,-0.0004231,0.00037197 -7.0145e-06,-3.4583e-06,2.1637e-06,-1.8028e-05,-1.7113e-06,5.7134e-06,2.3255e-05,1.2368e-05,-2.677e-06,7.738e-06,2.0042e-06,1.6557e-05,-8.8915e-06,-2.3156e-05,3.0561e-06 --7.9702e-18,7.8171e-18,5.4509e-18,-9.2591e-18,2.5686e-17,-8.6231e-19,-6.7834e-18,-1.5894e-18,9.1237e-18,5.9336e-18,1.3275e-17,1.1263e-17,3.4739e-17,2.2616e-17,-2.0519e-18 --9.1249e-18,8.5799e-18,6.171e-18,-1.0619e-17,2.6404e-17,-7.9654e-19,-5.379e-18,-9.7775e-19,4.1576e-18,7.6104e-18,1.2284e-17,1.0285e-17,3.3577e-17,1.9424e-17,-1.766e-18 --4.8522e-18,2.8013e-18,4.2871e-18,-4.5582e-18,1.4995e-17,6.6027e-20,-2.7101e-18,-1.6668e-18,5.0375e-18,3.545e-18,7.0797e-18,6.9411e-18,1.9496e-17,1.2991e-17,-1.3443e-18 --6.8585e-18,4.4628e-18,1.0327e-17,-8.5455e-18,2.7143e-17,-1.8007e-18,-7.4696e-18,-1.1295e-18,1.0468e-17,5.6414e-18,1.2565e-17,1.2537e-17,3.6054e-17,2.2361e-17,-1.8541e-18 --8.4702e-18,5.9321e-18,7.2048e-18,-9.9953e-18,3.0992e-17,5.3531e-19,-4.7575e-18,-9.8686e-19,1.0505e-17,7.1051e-18,1.2485e-17,1.4747e-17,3.8284e-17,2.4689e-17,-1.1145e-18 --2.3521e-19,1.6648e-19,1.9839e-19,-3.5341e-19,8.1403e-19,-1.2761e-20,-1.5886e-19,-5.5747e-20,2.566e-19,2.6764e-19,4.3114e-19,3.2346e-19,9.9876e-19,6.5286e-19,-4.2664e-20 --5.5685e-18,7.0597e-18,4.4148e-18,-6.7863e-18,2.4657e-17,-1.224e-18,-5.8503e-18,-1.246e-18,7.8692e-18,6.3238e-18,1.1872e-17,8.0914e-18,3.163e-17,1.9333e-17,-1.47e-18 --5.5485e-18,3.4686e-18,2.5588e-18,-4.4786e-18,1.5385e-17,-4.6693e-20,-3.0593e-18,-8.2001e-19,5.7333e-18,3.9865e-18,7.0301e-18,6.4728e-18,1.9814e-17,1.1787e-17,-1.1913e-18 --6.0446e-18,5.4978e-18,3.7151e-18,-6.578e-18,1.86e-17,2.5527e-19,-3.685e-18,-1.3904e-18,7.1219e-18,4.1015e-18,8.385e-18,7.8945e-18,2.6086e-17,1.5427e-17,-1.0637e-18 -1.7558e-06,-5.473e-06,-7.7584e-06,1.0202e-05,3.6771e-07,-4.1952e-08,-2.2646e-06,1.1307e-05,8.3283e-06,5.8044e-06,-4.5859e-06,-1.329e-05,6.8732e-06,-3.2826e-06,1.0041e-05 -9.9782e-06,-3.1102e-05,-4.409e-05,5.7976e-05,2.0897e-06,-2.3841e-07,-1.287e-05,6.4259e-05,4.7329e-05,3.2986e-05,-2.6061e-05,-7.5527e-05,3.906e-05,-1.8655e-05,5.706e-05 -5.9094e-06,-2.9668e-05,-4.2827e-05,4.9497e-05,1.9104e-06,-4.0132e-06,-1.4015e-05,5.8001e-05,4.7019e-05,2.9339e-05,-2.5022e-05,-7.0216e-05,3.592e-05,-2.5129e-05,5.4955e-05 --4.9123e-05,-3.6012e-05,-4.4427e-05,-4.0232e-05,1.8939e-05,-7.574e-05,-4.209e-05,6.8796e-07,6.8304e-05,8.1707e-06,-5.1626e-05,-5.3922e-05,-4.4392e-06,-0.00015868,8.1288e-05 --0.00011665,-4.9814e-05,-9.7963e-05,-8.6564e-05,-3.8387e-05,-0.00011114,-2.8027e-05,-1.3315e-05,0.0001635,-3.6901e-05,-1.5857e-05,-0.0002678,6.0779e-05,-0.00040732,0.0001597 --0.00010753,-5.2404e-05,8.4834e-06,-0.00011324,-9.1562e-05,7.2322e-05,4.3192e-05,2.2807e-05,0.00020766,-2.5711e-05,4.8348e-05,-0.0004112,0.00015469,-0.00043859,0.00031232 --0.00012699,-2.9163e-05,0.00010284,-5.828e-05,-3.7666e-05,4.0591e-05,0.00012887,0.00030505,0.00013449,-3.3603e-05,3.0185e-05,-0.00055927,0.00013408,-0.00078662,0.00064205 --7.9653e-05,0.00014042,0.00020581,-5.212e-05,-0.00010755,-4.215e-05,-2.7696e-05,0.00054146,4.3422e-05,-0.00029837,2.1333e-05,-0.00010364,1.2323e-05,-0.0010792,0.00099845 --0.00010332,0.00025029,0.0001503,1.0185e-05,-0.00011858,-2.8729e-05,-3.6815e-05,0.00069394,0.00023861,-7.8483e-05,-0.00013288,-8.1734e-05,0.00043926,-0.0016867,0.0013 -3.1113e-05,0.00021198,8.8685e-05,-2.3685e-05,3.7238e-05,-0.00011411,-0.00029697,0.00040007,0.00011803,-0.00017292,-0.00029102,-4.0648e-05,0.00046595,-0.0011052,0.00035029 -9.8935e-05,0.00017121,6.3645e-05,-7.8558e-05,0.00017428,-0.00026321,-0.00040379,0.00043469,0.00022883,-0.00018688,-0.00026344,7.6597e-05,0.0005853,-0.0010928,-0.00012586 -0.0001003,7.7363e-05,-4.4523e-05,-0.0002141,0.00013217,-0.00020093,-0.00026494,0.0002164,0.00024936,5.6636e-05,3.7897e-05,2.8567e-05,0.00043999,-0.00067444,-0.00012791 -0.00011067,0.00010472,-0.00019867,-0.00025975,0.00014781,-0.00023355,-0.00024144,0.00024076,0.00023904,0.00025323,0.00031511,-8.3454e-05,0.00032121,-0.00075664,0.0001861 -0.00016434,5.2984e-05,-0.00014123,-0.00035764,0.00019302,-0.00018894,-4.2477e-05,0.00032808,0.00016022,0.00010135,0.00038461,-8.5255e-06,0.00019109,-0.00084698,-5.117e-05 -0.0001533,-2.5614e-05,-0.00021212,-0.00028137,0.00017782,6.1944e-05,0.00028183,0.00025963,0.00022119,6.273e-05,0.00026047,0.00047286,0.00011224,-0.00075678,0.00031987 -5.8973e-05,-7.8102e-06,-0.00011597,-0.00014815,6.2479e-05,6.9614e-05,0.00016885,0.000107,6.4561e-05,1.0248e-06,0.00014121,0.00027755,6.3216e-05,-0.00031414,0.00025603 -2.0599e-05,-9.3492e-06,-5.9032e-05,-2.0303e-05,1.8197e-05,6.9861e-05,6.8667e-05,3.2682e-05,2.6315e-05,-1.4557e-05,5.0224e-05,8.1297e-05,-1.2244e-06,-5.0326e-05,0.00014253 --4.9105e-18,3.5796e-18,2.1421e-18,-3.995e-18,1.3178e-17,-3.0618e-19,-3.5121e-18,-9.5113e-19,4.444e-18,3.9248e-18,5.844e-18,4.7113e-18,1.717e-17,9.8824e-18,-5.513e-19 --2.6452e-19,2.5503e-19,1.6538e-19,-2.2287e-19,7.8614e-19,-1.1368e-21,-1.8564e-19,-4.2237e-20,2.4702e-19,2.395e-19,3.9663e-19,2.9824e-19,1.0212e-18,6.0554e-19,-6.4166e-20 --1.9474e-18,1.4069e-18,1.0622e-18,-1.539e-18,5.291e-18,-1.2984e-19,-1.198e-18,-3.11e-19,1.3536e-18,1.6155e-18,2.3607e-18,2.8383e-18,6.3186e-18,4.0955e-18,-6.0926e-19 --3.4173e-18,4.6477e-18,4.5719e-18,-5.4046e-18,1.7839e-17,-1.4963e-18,-3.0443e-18,-1.7041e-18,4.2817e-18,3.6529e-18,6.9903e-18,7.0989e-18,2.0907e-17,1.4601e-17,-5.1371e-19 --8.9807e-18,7.395e-18,6.5898e-18,-8.9913e-18,2.3895e-17,1.1806e-18,-5.4747e-18,-1.8107e-19,1.1714e-17,8.4051e-18,1.3189e-17,9.8731e-18,3.3631e-17,1.9141e-17,-9.4678e-19 diff --git a/bayesian_ml/1/README b/bayesian_ml/1/README deleted file mode 100644 index d4a5711a..00000000 --- a/bayesian_ml/1/README +++ /dev/null @@ -1,14 +0,0 @@ -This data set contains the following: - -Xtrain: 11791 training examples of 4's and 9's, each 15 dimensions in length -ytrain: the corresponding labels of the training examples (0 = 4 digit, 1 = 9 digit) - -Xtest: 1991 testing examples of 4's and 9's, each 15 dimensions in length used for prediction -ytest: the corresponding ground truth labels used for evaluating performance (0 = 4 digit, 1 = 9 digit) - -Q: A matrix of eigenvectors that is used to map the 15-dimensional feature vectors back into image space. For a 15-dimensional feature vector x, this can be done as follows in pseudocode - - 1. Map x2 = Q*x - 2. Reshape x2 into a 28x28 matrix and show it as an image - -Because the data has had the mean subtracted off, this image will not look exacly like a handwritten digit, but the contours of the 4 or 9 should be clear. diff --git a/bayesian_ml/1/Xtest.csv b/bayesian_ml/1/Xtest.csv deleted file mode 100644 index 25038c6b..00000000 --- a/bayesian_ml/1/Xtest.csv +++ /dev/null @@ -1,1991 +0,0 @@ -3.7638,-0.92236,-0.53047,1.233,-0.71848,1.525,-1.3769,-1.551,-0.17975,-1.1499,-0.11756,-1.5398,0.34207,-0.60512,0.065985 --0.40127,0.26977,-0.20437,-1.0767,-3.4491,0.96986,0.62241,0.15118,-1.3787,-1.6964,-0.19497,-1.3029,-1.4716,-1.3327,0.47252 -0.54033,1.5158,0.0057301,1.2152,-2.1241,-1.1308,-0.91144,-0.32863,0.60841,-0.64354,-0.00087909,0.45768,1.3614,0.71921,0.20154 -1.7504,1.5461,-1.9151,1.3689,-1.044,0.36394,-0.20749,-0.97425,0.13825,0.94529,-0.76716,-1.0765,0.94106,-0.32831,-0.29621 -2.0957,-0.3642,1.0851,1.2778,-0.67157,-0.49899,-2.7037,-0.54487,0.84837,-0.086419,1.4492,0.3441,0.46331,0.51728,-0.51133 -1.2966,-1.6362,-2.1231,2.2668,0.021024,2.4636,0.064225,0.16694,-2.6767,1.1264,1.011,0.54725,2.7373,-0.096381,0.96821 -0.34242,1.4374,1.1921,0.60321,-2.1172,-2.108,1.3842,-0.99591,-0.44959,-0.24658,-1.6997,0.24477,-0.73409,-0.32675,1.1555 -2.241,1.0479,3.7299,-1.3708,-0.57897,-2.135,-1.0227,0.19116,-1.6525,-1.1759,0.86843,0.45257,-0.22053,-0.058321,-0.29706 -4.373,-1.6854,-1.1732,0.25269,-0.11805,-0.8278,0.49605,-1.0704,1.3663,-0.83527,0.16838,-1.3209,-0.18926,0.4433,0.72966 -4.9061,-1.5189,0.69141,-0.40059,-1.6001,-0.33886,-2.0876,0.63703,0.071592,0.42244,-0.32596,-1.2406,-0.11299,0.32148,0.14613 --1.1768,2.7211,-1.9466,0.58319,-0.91973,0.20794,-0.69717,1.9253,0.14132,-0.92793,-0.511,0.13961,0.12623,0.61935,-0.8245 -2.8328,-1.342,-0.60797,2.4566,-1.0702,0.081054,0.34898,-0.19849,0.16284,-2.2237,-1.2937,0.43102,-1.0052,-1.0385,-1.8365 -3.3843,0.10076,1.9631,-1.3811,-1.4517,-2.9031,-0.49936,0.63798,-1.4444,-2.2475,-0.39671,-0.010771,-0.11308,-0.31941,-0.4091 -0.91302,-1.1661,2.6844,1.0152,1.7795,-2.2122,1.3515,0.085464,-2.9011,0.45318,-1.0435,0.71729,1.4514,-0.015185,-1.3481 -4.2955,-1.6204,-0.58862,-0.16197,-1.1359,-1.3578,0.23008,-0.68053,1.5515,-1.1303,0.20523,-0.54476,0.53337,0.091987,-0.51841 --0.43154,-0.5402,0.21269,2.9266,-0.50234,0.42145,0.90034,-1.2784,-0.54621,0.15436,1.5119,0.56681,0.31507,-0.43955,0.4408 --2.3812,-0.056082,-0.89467,2.1133,-0.061934,0.45003,0.6943,-0.29364,0.67109,1.4262,1.0858,-0.25398,-0.91268,-0.17939,-0.37981 --0.95273,1.8538,-0.51732,1.07,-2.034,0.048668,-1.1583,0.23106,-0.102,-0.45604,0.8759,1.6835,-0.73838,0.10093,-0.78889 -3.7697,-2.694,-1.1779,-0.58756,-0.95878,-0.65286,-1.261,1.6118,1.2474,0.34145,0.43167,-0.49113,-0.74164,-0.23089,-0.49057 --1.1123,-2.4263,-1.0913,1.6137,0.5331,2.2532,-0.91974,0.04655,-0.28936,1.8647,0.79794,-1.0007,-0.29407,1.5734,-0.080667 --0.97907,-3.5842,0.83276,1.1758,0.33957,1.7708,-0.89337,-1.9453,-0.29453,1.6292,-0.23561,-0.52882,-0.69093,-0.96737,-0.33392 --0.92034,1.0165,-1.5838,2.6534,0.30206,0.51838,0.20312,2.3375,0.34387,0.53061,-0.75727,-0.65845,-0.66545,-0.14811,0.42866 --0.4128,1.6385,0.10098,0.73246,-1.3821,-1.4382,0.0056794,-1.0218,-1.9022,0.79076,0.6974,0.12319,-1.4008,0.1837,0.50581 -4.3155,-0.86847,0.13385,-0.38718,-1.0473,-2.2516,0.013736,0.90739,1.069,-2.0064,-0.559,0.57882,-1.2897,0.6017,-1.755 -0.14999,0.42963,-0.58948,3.25,0.50981,0.20315,-0.34092,0.13586,-1.1356,-1.5288,0.88303,0.75972,-0.63656,0.79188,-0.64232 -0.13371,2.9465,0.38271,0.30973,-1.6684,-0.57221,-0.4777,0.18647,-1.1962,-0.73395,-0.40575,-0.35481,1.027,0.72181,1.044 -0.39796,2.3227,-0.91017,0.75586,-0.79276,-1.022,-1.5964,-0.76549,0.44418,0.80759,0.68862,-0.092809,1.0605,1.3595,-0.31052 --0.31917,1.6648,0.64912,1.7022,-1.5824,-0.2252,-1.661,-0.33282,-0.11859,-0.95826,1.3468,1.529,-1.0806,0.64468,-0.35981 -3.5727,-0.30127,-0.58371,-0.099989,-2.5259,1.1521,-1.2565,1.3432,1.2963,-1.6346,-1.3604,-0.39803,-0.67149,0.12627,-0.80097 --1.0041,0.6032,-1.6764,2.4257,-1.107,0.63235,0.15688,0.80541,1.2245,-0.22032,0.78663,0.32348,-1.4664,-0.080609,-0.43975 -1.6308,0.20822,1.9008,1.0694,-1.6549,-2.084,1.1008,-0.40759,0.12833,-0.11555,-1.3258,0.55165,0.96202,0.17506,-0.0023905 --0.40222,-0.43273,-1.8198,0.91728,0.21727,-0.10447,0.86552,1.318,0.025507,0.046009,-0.1571,-1.0809,-0.51187,0.098009,-0.10941 -3.9565,-1.2563,2.506,1.9081,-0.60668,-1.0695,0.34605,-1.2199,-1.4162,-2.0128,-1.0664,-0.054556,-0.417,-0.98366,-0.30727 -0.81574,3.0566,2.289,-0.50296,1.639,-2.3539,-0.86279,0.13462,-1.6316,0.46145,0.5696,-0.76657,-0.1792,1.1106,0.17598 -2.7468,-0.041577,1.5932,1.7917,-0.037159,0.0097491,-2.843,-1.403,-0.13851,0.25666,1.2053,-0.25774,-0.84266,-0.079885,0.050416 -1.5408,-2.2518,-2.1077,-2.5579,-2.0117,-0.043408,0.92866,2.8049,-0.36265,-1.369,0.97054,2.6708,1.1902,-0.29285,0.21365 -1.9787,2.2517,-1.4535,-2.4867,0.068123,-1.7132,-0.035312,-0.10147,0.010606,-0.39286,0.68479,-1.6169,1.578,-0.05316,1.2401 --1.0301,-0.37195,-3.661,0.9618,1.3711,0.3213,-0.24447,1.6466,-0.39916,0.69686,-0.99213,-0.7632,-0.7145,0.32256,0.50338 --2.4193,1.572,0.35908,-1.4502,-1.1063,-0.0028337,1.4753,0.14714,-1.9692,1.6347,-0.55687,-1.1012,-0.12835,1.0784,0.96491 --1.1137,-0.90372,1.7438,0.085859,-2.3113,-2.1816,0.807,-1.7858,-1.0542,0.050647,-0.44683,-0.55498,-1.7587,-0.18655,0.30135 --2.1967,-2.0592,-1.6914,0.35186,0.54814,1.8469,-1.0361,0.80551,0.84533,1.0164,0.25508,-0.93665,-1.656,1.3473,-0.88658 -2.7053,-2.2029,-2.4455,-1.6052,-1.9665,0.58056,0.86087,2.7574,1.2817,1.4748,0.80605,1.6223,0.10048,1.1145,1.4053 --1.5346,3.1249,-0.4748,0.88492,0.0234,-0.63569,-0.92912,2.1023,-0.056685,0.10577,-0.28825,0.51454,-0.044401,0.81438,-0.362 -2.0875,-0.26424,0.378,0.19741,-0.9255,-0.68818,-2.7935,-0.2866,0.76996,-0.75514,1.8216,0.37945,1.5647,0.25816,-0.55408 -3.0603,-0.87985,-0.86525,-0.77944,-1.7417,-0.33689,-1.3959,-0.24315,0.94422,0.42438,1.0795,-0.82541,1.0619,0.078842,0.86652 -2.8919,-1.5595,-0.59407,2.2368,-0.10432,-0.69982,1.2093,-0.92448,0.095945,-3.1662,-0.86215,-0.0051159,0.64919,-0.38798,-1.5991 -3.7854,-0.44607,-1.7454,-3.1411,0.3424,-1.7297,2.8718,1.9969,1.0067,0.37295,0.16245,1.0341,-0.28486,0.12756,0.6074 --0.44981,-2.1734,-2.0654,0.91182,-0.58209,1.854,-0.81142,0.25185,1.083,0.90555,-1.238,-0.38249,0.82766,-0.73798,-0.14168 -2.4032,1.1523,-0.079068,1.3982,-0.18156,-1.7786,0.17533,-0.56812,-0.40898,0.35255,-1.8008,-0.95312,0.35507,1.1984,0.78389 --1.4607,-1.1005,-0.68788,1.6004,0.76708,-1.4474,0.80188,-1.8577,-0.26251,0.66604,1.0168,0.41301,0.36997,-0.16078,-0.90854 -0.81954,1.3353,-2.2173,0.58146,-0.48759,-0.13079,-1.383,0.97311,-1.1434,1.4961,1.9932,-0.61506,-0.038171,1.2265,-0.65666 --2.0452,-3.0451,-1.1892,-0.95887,1.3935,-2.9836,-0.080132,0.79598,-1.3505,-1.2132,-1.2478,-0.13659,-0.75352,-0.40583,-0.1965 -4.4884,-3.0626,-1.1004,-2.9842,-0.76224,-1.9503,2.4615,1.93,0.23667,-0.12883,0.87591,2.5484,-0.098641,0.074974,1.2203 -0.63381,1.978,0.6194,0.25913,-2.5354,-0.3736,0.02305,0.35139,-0.60295,1.5774,-0.9892,0.12374,-0.060908,0.53163,1.1194 --2.358,1.8266,-1.2238,0.70428,-1.0954,0.255,0.64953,0.29402,-1.8366,0.61871,0.81548,1.257,-1.286,0.18837,-1.2388 --2.2613,-0.61961,-3.5804,-0.23972,-0.24774,0.23334,-0.89487,-0.95756,-0.67418,-0.6264,-0.33634,1.9032,-0.72671,-0.71193,-0.78463 --1.8846,2.6068,-0.12463,0.59326,-1.5609,-1.1768,1.0718,0.40365,-1.3633,0.9183,-0.97397,0.79023,0.39526,0.84893,0.75749 --0.55534,0.8539,-0.54733,2.6591,1.046,0.42409,-0.644,-0.10672,-0.43075,-2.1469,1.0066,0.16846,-1.2704,0.54731,-0.077969 -4.0474,-0.4676,-0.77568,1.3887,0.8846,-1.815,1.0046,-1.822,-0.56842,-0.90365,-0.089729,-1.4371,1.165,0.40632,0.73696 -4.5168,-1.592,-0.62623,0.16543,-1.0957,1.5522,-1.1707,1.3664,-0.2239,1.174,-1.2326,-0.32009,0.90044,0.58932,-1.0276 -3.9218,-0.38356,0.9695,1.2891,-0.59278,-1.6457,0.1423,-1.8046,0.049312,-1.9831,-1.4045,-0.78403,-0.53162,-0.40722,-0.29982 -3.0769,0.52986,0.16044,-1.0867,-1.3272,-1.451,-1.1608,-0.33139,-0.51325,-2.1848,0.81618,-1.7039,-0.057088,0.1491,0.40838 --1.1562,-1.6269,-0.30911,2.3549,-1.2109,1.6556,0.012148,-0.76975,0.27307,-0.30364,1.5571,-0.62827,-0.83108,-0.93158,-0.12309 -5.2308,-3.5151,-0.60352,-0.59996,-0.36846,-1.1652,0.29643,1.7861,0.73144,-1.3648,0.34666,0.69157,-0.84562,-0.020109,-1.6488 --2.108,2.2727,0.61176,-0.74275,-0.70468,1.4316,1.5487,0.48497,-2.1253,0.94976,-0.28239,0.35185,0.26971,0.18572,1.1757 --0.35934,0.97506,0.65817,2.4938,1.1914,-1.5882,0.88811,1.5892,1.0424,-0.29683,-1.2559,0.032407,0.3631,-0.30626,0.44347 -0.54464,2.7811,-1.992,-0.35361,-1.8189,-0.37498,-0.4641,-0.64747,-0.1323,-0.60307,-0.63055,-0.90568,0.84154,0.81357,0.1086 -0.14617,-0.6869,0.18721,1.6617,0.64625,0.43758,-0.19493,2.4525,0.97561,-1.2853,-0.025493,-0.87577,-1.7902,0.80324,0.068641 -1.2524,-0.68466,1.2283,3.3157,-0.17111,-0.11085,-0.84733,-1.5101,-1.3879,-1.8233,1.1386,0.48944,-0.021459,0.49209,0.78038 -0.20586,0.16506,-0.31511,3.5071,-0.45864,-0.43469,0.3321,-0.37442,-0.24537,-0.87045,1.2402,0.86369,0.14008,0.20747,-0.068582 -1.1577,1.9164,-1.53,0.84862,0.45301,-0.41597,-0.46588,0.45156,-0.63333,2.064,-0.63312,-1.5777,-0.31247,1.3432,0.53745 --2.2822,-2.0643,-2.3015,-0.15817,-0.95628,-1.0987,-2.8685,0.077507,-0.83794,-0.60885,-0.028351,1.5561,-0.99211,-1.0076,1.2382 -1.2049,0.29418,0.50598,1.5095,-0.38319,-2.053,-1.7238,-0.20215,0.26209,-0.39916,1.4091,0.48051,0.78386,1.2746,-0.61396 -2.5865,0.52106,0.92594,-1.3622,-2.6689,1.4491,-1.0107,2.4534,-0.076083,-0.63016,-1.1825,0.38557,-0.49278,0.67755,-1.0037 --2.6447,1.6587,1.3069,-0.018988,0.83454,-0.14742,0.44842,1.4082,-0.87815,2.3075,0.00025322,0.32687,-0.4531,0.34292,0.27814 -1.892,2.519,0.041843,-0.92365,-2.0563,-0.95511,0.40588,-0.447,-1.3861,-1.9108,-1.0661,-1.5248,0.13987,0.62908,-0.45487 -0.59066,1.8528,-0.64092,2.1798,2.7102,-1.7747,-1.0101,1.7168,0.8331,-0.19192,-0.2558,-1.4812,-0.59796,0.11496,-1.0738 -4.8991,-2.3024,-1.3074,-3.5703,-1.4354,-0.71609,-0.55404,3.241,-0.23504,0.13868,0.7099,0.049474,-0.51817,0.13951,-0.43583 -4.3969,-0.74904,-0.42801,-0.58328,-0.53535,-2.1894,0.53714,0.11517,1.8126,-1.8343,-0.58371,-0.24888,0.18243,0.78879,-1.704 -5.093,-2.5583,1.1529,-1.0643,0.76018,-2.8453,0.78253,-0.48239,-2.0042,-1.0478,0.71103,-0.033028,0.3963,0.89118,1.0166 -0.13901,3.7996,-0.71364,-0.41747,1.3994,0.68817,-1.4873,0.018002,-0.96753,-0.03059,-0.4219,-1.1434,1.4715,-0.93822,-0.70939 -1.0729,1.3453,-1.0138,1.4238,2.5018,-1.7004,-0.62863,0.85582,1.5549,0.12785,0.14197,-1.2517,0.77403,0.36473,-1.0873 --1.0758,-0.37314,1.4116,2.7594,0.69036,-1.1032,-0.13392,0.93752,-0.47929,-1.8487,1.3163,0.84524,-2.0211,-0.2401,-0.3972 --0.51766,1.1565,1.1249,1.4802,-2.1051,0.011754,-1.2928,-0.23307,-0.042572,-0.94596,1.5556,1.3646,-1.0552,0.87095,-0.022778 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--0.86501,1.8269,1.8864,0.82353,0.16481,-0.96009,-1.3262,1.3451,1.9519,-0.76493,0.00082201,1.0462,-0.017008,-0.27395,1.2726 -2.8718,1.4545,0.28106,0.57411,1.8414,-0.15682,-0.89278,-2.1422,1.0469,0.89119,0.060694,-0.097027,0.36345,-0.71868,-1.2242 --3.3559,1.8717,0.61015,-0.33285,-0.039845,1.8987,1.1504,0.45523,-0.65273,-1.0452,1.118,-0.019043,0.42415,-0.044583,-0.56812 --2.996,2.3415,0.21198,-0.49739,0.1029,1.2252,1.2277,-0.71635,-1.5889,0.27889,-0.14811,1.5142,-0.094881,0.47981,0.012817 --4.0224,1.0896,0.12891,-0.93667,0.20576,0.69049,1.318,-0.90628,-0.44208,1.062,0.64491,0.58975,0.75091,0.69736,-0.35565 --3.5007,-1.5184,-0.43853,-1.0788,1.3891,-0.36047,-1.1897,-0.47265,1.1726,0.93503,0.032153,-0.34411,-0.74978,0.52841,0.33534 --3.8263,-1.2909,-2.2384,-1.5012,1.4405,0.084568,0.33689,-0.84492,0.18049,-1.4755,-2.0432,-0.12424,-0.79779,-0.044543,-0.19503 --3.4842,-0.75133,-2.4599,-1.3851,1.8223,-0.12463,-0.77209,-0.79595,-0.052669,-2.021,-0.73191,1.4756,-0.41105,-0.87432,0.31742 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+++ /dev/null @@ -1,11791 +0,0 @@ -2.5666,-1.3258,-2.3719,-0.81289,-1.7141,0.7571,-1.3487,1.482,1.7461,-0.623,1.1981,-0.059047,0.71099,-0.091248,-0.80487 --1.4274,-3.2869,-1.7332,0.44458,-0.75227,-0.5145,-2.0378,0.84484,0.050713,-0.57453,-0.079848,-0.7359,-1.4958,-0.74878,-0.64043 -3.9474,-1.4721,-1.0213,-1.7703,-0.25065,-2.9527,1.7425,0.74606,0.9182,-1.2281,1.0997,1.2411,0.59785,-0.0074702,0.094585 --1.4837,2.2081,-0.77793,0.50082,-2.4392,0.28392,1.1439,0.11527,-0.21909,0.023451,-0.15907,0.39537,0.59824,0.46849,0.88638 --2.1028,0.0073963,-3.9499,-0.90592,0.029504,-0.46591,-0.4158,1.562,-0.99494,0.11942,-1.2049,0.25491,0.14717,1.0847,0.36351 -3.2991,2.085,1.0111,-1.9042,-0.35306,-3.4281,0.38001,0.18513,-0.35228,-1.5426,-0.84675,-0.88732,0.35325,0.90059,-0.002708 -3.9044,-3.9422,-2.3386,0.81279,-1.009,1.4146,2.2834,1.6637,-1.0557,-0.053001,-1.1022,1.2403,-0.76104,0.95952,-0.77029 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-0.13954,1.5859,1.9573,-0.60427,-2.2064,2.0621,0.70664,-0.42521,0.56877,-0.26408,0.18389,-0.35845,-1.3181,1.2522,0.9065 --0.50503,0.13444,1.9475,0.88809,-0.69158,1.1039,1.3886,-1.9,1.0064,1.337,0.42843,-0.51994,-0.83409,0.51843,0.64189 -1.2003,0.12027,2.8145,1.0958,-1.0676,1.5475,0.75708,-1.1756,1.4236,-0.075795,-0.28824,0.36078,-0.7748,0.79759,0.60926 --1.0975,-0.63526,1.7708,1.1485,-0.13851,1.2654,1.5995,-1.9212,1.9441,0.5053,0.33788,-0.050851,-0.011933,-0.07779,0.32587 --2.4074,0.40932,1.0581,0.083151,-0.87563,0.21587,1.2185,-1.5935,1.3928,1.18,1.159,0.51043,0.25603,0.29616,-0.44371 --0.99513,-1.5752,0.2075,1.0424,0.29787,2.5733,0.21577,-0.57694,1.2696,0.144,0.16333,-1.5539,1.1258,0.3693,1.3714 -0.70384,-0.70877,1.6032,2.2865,-0.94945,1.7626,1.1941,-1.8413,-0.4262,-0.53409,0.25382,-0.1709,-0.17228,-0.01404,1.0004 --1.648,0.22327,1.6412,-0.52334,-1.146,0.5149,1.4125,-2.3362,1.4294,1.0253,0.37004,0.13459,-0.39543,0.12975,-0.01775 -0.028787,-1.7517,1.9139,0.058782,-0.51782,2.3901,1.3224,-1.5177,1.3938,0.74673,-0.83325,-1.679,-1.2501,1.0808,0.23999 --1.1576,0.86803,2.7128,0.51759,-0.59808,-0.10683,1.6587,-1.8075,1.4339,0.75073,0.31149,0.56679,-0.015906,0.40294,0.12174 --3.3557,0.25716,0.072183,-1.4746,-0.18102,0.51878,0.5539,-0.18569,1.2607,0.066842,0.8022,-0.043918,0.34464,-1.0587,0.20358 --0.24844,0.55793,2.8218,1.084,0.57273,1.3958,-0.24938,0.84315,0.57681,0.45005,1.796,0.41191,-0.70099,2.3779,0.37048 --1.6353,-0.28412,1.0791,-0.0053575,0.19456,0.5982,-0.16811,0.88048,2.5314,-0.24361,1.158,-0.6806,0.56981,1.23,-0.4213 --1.5482,0.72995,0.77668,0.62277,-1.1175,0.70789,0.14963,0.022555,1.6846,0.9218,1.0566,-0.13013,0.76615,0.40605,-0.25907 -0.95561,0.58846,1.0535,0.38081,-0.30675,0.90444,-0.58385,1.0578,2.5344,0.096769,-0.59706,0.85602,1.224,0.16505,1.0344 --1.7575,1.7019,1.6023,-1.704,-0.53182,2.0095,0.93342,-1.1873,-1.1285,-0.3885,1.2568,-1.3931,0.38235,-0.32176,-0.27483 -1.7821,-1.2689,3.909,-0.23324,2.3708,0.63382,1.5356,0.90857,1.025,-0.60872,-0.55817,0.52837,0.62441,0.42472,-0.28822 --0.50136,-1.6394,1.4154,-1.0351,-0.21638,0.93853,0.010422,-0.16923,1.8844,1.0124,-1.1504,-1.1136,1.6999,1.3248,-0.88751 -0.62915,1.2517,2.9099,-1.6836,-0.14533,0.48485,0.33117,0.7022,1.8623,-0.057267,0.29125,0.18375,0.28661,-0.78679,0.76897 --1.4683,-1.9497,-0.54703,1.8648,1.3782,1.4116,0.58023,0.76056,-0.81816,-1.335,-0.2391,-2.2389,2.503,-0.19565,0.21321 --1.9792,-1.2515,0.94148,0.53975,-0.21616,-0.78323,0.6725,1.5383,1.4933,0.20876,-0.19252,-2.0413,0.83638,-0.82798,0.060837 --0.51067,0.028018,3.1893,0.47417,1.0322,-0.39077,-0.078098,1.8968,2.0608,-0.24565,0.79953,0.14745,0.17607,-0.32799,1.6636 --3.9494,-0.65429,-0.84353,-1.8513,1.0095,0.64986,-0.56265,-1.1181,0.78401,-0.67825,0.34365,0.40068,0.3322,0.86838,-0.63091 --2.7601,0.65037,1.1647,-1.1299,0.088305,0.78145,0.16021,1.0871,1.1993,-1.3556,1.441,-1.2018,0.67257,-0.56771,0.58463 --2.0302,-0.42459,1.2436,-2.4052,-0.12528,-0.010035,0.42007,-1.116,1.4495,0.072658,-0.44407,-0.64332,0.80154,-1.3731,0.23347 -2.4741,2.2545,0.29933,-1.4894,0.27213,0.9274,-1.7194,-0.63558,0.78123,0.18668,0.1224,0.59813,1.051,-0.26173,-0.87989 -0.87224,1.4833,2.2474,-1.1949,-0.17681,0.89284,0.0038537,1.3304,1.059,0.14387,-0.020942,0.85052,1.489,-1.073,0.73611 --2.4822,0.22936,0.18743,-0.32501,-0.49603,-0.16499,0.37154,2.1742,1.7382,-0.96736,0.7363,-1.6878,-0.014577,0.43129,0.37208 --1.2545,-0.97717,1.6001,-0.72235,0.54767,0.69105,-0.22069,0.32841,1.8741,0.83213,0.017644,-0.94282,1.3775,1.4132,0.36698 --3.0983,-0.55126,-0.22251,0.094539,-0.73066,-0.56379,0.72553,1.2106,1.7061,0.066325,0.18447,-1.2879,-0.044157,0.053816,0.37502 --4.0725,1.2195,-0.39915,-1.6724,1.0472,0.62845,1.4239,-1.0101,-0.80734,0.4353,0.53477,-0.37295,0.6239,-1.188,-0.081675 --3.4676,-0.796,-2.8672,-1.8067,1.3801,-0.55145,-0.96443,0.058398,0.52063,-1.5413,-1.1735,0.70318,-0.67923,-0.041355,0.49925 -1.9334,-0.29478,2.3251,2.0716,2.7862,2.4223,0.4495,-0.029801,-0.029477,-1.3309,-0.7474,0.9125,1.193,0.20497,0.82723 --1.4874,-1.5139,2.1591,1.7292,1.1417,-0.17584,1.1095,1.4209,1.5597,-0.23111,-0.012189,-0.96294,-0.45018,-1.0046,1.7353 --0.15928,-0.18978,3.3728,1.8179,2.69,1.0752,1.7763,-0.60453,-0.084359,-1.1578,-0.9258,0.44,-0.2627,-0.92011,0.92918 --4.3082,-0.29095,-1.098,-1.5904,0.84163,-0.63402,0.71184,1.0418,0.10144,-0.081221,0.1629,-0.7167,0.22019,-0.28333,0.072374 --3.7304,0.98452,1.4461,-0.71581,0.099413,-0.52307,1.1656,0.36865,0.96444,-0.4254,0.91102,0.10761,0.68936,0.23966,0.30848 --3.9019,-0.96718,-0.95242,-1.0812,0.70043,-1.7655,-0.097167,-0.36234,0.64107,0.73424,-0.37472,0.02413,0.88537,0.19002,-0.32446 --3.6377,-0.11524,0.22216,-1.4145,1.4846,-1.4785,0.84057,-1.4927,1.0633,1.135,-0.10588,0.46041,0.77243,0.12377,-0.44409 --3.4264,1.5059,1.139,-0.6112,0.5221,-1.1104,1.5495,1.3499,0.72282,-0.22459,0.42378,-0.12763,-0.1292,-1.1343,0.98985 --1.695,-3.6906,0.58147,0.47004,2.454,-0.56253,1.1748,0.47573,0.44499,-0.49939,-0.57136,-1.8374,0.15731,-0.89349,0.20193 --3.0507,-1.2956,-1.6691,-1.4194,0.5129,0.36311,-1.4334,-0.60235,0.41229,-1.9675,-0.29582,0.30957,0.26316,-0.03379,0.78652 --3.1384,-1.9205,-1.8359,-1.5583,1.1638,1.0038,-1.4861,0.30843,0.47555,-1.4681,-1.4656,0.34139,-0.42305,0.44757,0.87923 --2.6533,-1.6315,-2.5949,-0.68357,0.76346,2.1294,-1.1561,-0.55532,0.25017,-1.2454,-1.6974,0.36847,-0.18314,0.68786,0.23761 --0.83746,-0.96006,-0.9514,-1.29,-0.64656,2.0835,-0.99411,-0.84631,0.022955,-0.55621,-0.86837,0.69627,1.367,1.5034,0.87398 --2.8452,-0.57388,-2.6382,-0.19688,-0.13348,0.93657,0.040835,-0.39971,0.46478,-1.675,-0.29827,-1.0589,0.43039,-0.71133,-0.15874 --2.3371,-0.58789,-2.3648,-0.026632,0.2275,2.34,-0.3747,-0.87236,0.71903,-1.3137,-0.1337,-0.19942,0.95104,0.80286,-0.32855 --2.35,-0.76406,-0.47972,-1.0338,0.085147,1.6397,-0.83987,0.1465,0.79905,-0.95084,0.16913,-0.83299,1.9207,1.796,0.54669 --2.7514,-1.028,0.21153,-1.3997,0.28374,1.6727,-0.25639,-0.44063,1.1628,-1.2411,0.42992,-1.607,1.5005,0.49365,0.73299 --2.3512,-1.0148,-1.8745,-0.96903,0.43199,1.8583,-1.3756,-0.43813,0.056795,-1.0012,-0.76345,0.024682,1.1742,0.91184,1.5851 --2.307,-0.97455,-2.5102,-0.35901,0.24727,1.7813,-0.99627,-1.243,0.34366,-1.7268,-0.61829,0.75832,0.54855,0.43315,0.77306 diff --git a/bayesian_ml/1/nb.py b/bayesian_ml/1/nb.py deleted file mode 100644 index 73634cf9..00000000 --- a/bayesian_ml/1/nb.py +++ /dev/null @@ -1,142 +0,0 @@ -# Naive Bayes with prior on mean and precision of Gaussian -# mean | precision ~ N(0, c / precision) -# precision ~ Gamma(a, b) -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from scipy.stats import t -from sortedcontainers import SortedList - - -class NB: - def fit(self, X, Y): - self.pyy = [] - self.tinfo = [] - N, D = X.shape - for c in (0, 1): - pyy_c = (1.0 + np.sum(Y == c)) / (N + 1.0 + 1.0) - self.pyy.append(pyy_c) - - # for each dimension, we need to store the data we need to calculate - # the posterior predictive distribution - # t-distribution with 3 params: df, center, scale - Xc = X[Y == c] - tinfo_c = [] - for d in xrange(D): - # first calculate the parameters of the normal gamma - xbar = Xc[:,d].mean() - mu = N*xbar / (1.0 + N) - precision = 1.0 + N - alpha = 1.0 + N/2.0 - beta = 1.0 + 0.5*Xc[:,d].var()*N + 0.5*N*(xbar*xbar)/precision - - tinfo_cd = { - 'df': 2*alpha, - 'center': mu, - 'scale': np.sqrt( beta*(precision + 1)/(alpha * precision) ), - } - tinfo_c.append(tinfo_cd) - self.tinfo.append(tinfo_c) - - def predict_proba(self, X): - N, D = X.shape - # P = np.zeros(N) - # for n in xrange(N): - # x = X[n] - - # pyx = [] - # for c in (0, 1): - # pycx = self.pyy[c] - # for d in xrange(D): - # tinfo_cd = self.tinfo[c][d] - # pdf_d = t.pdf(x[d], df=tinfo_cd['df'], loc=tinfo_cd['center'], scale=tinfo_cd['scale']) - # pycx *= pdf_d - # pyx.append(pycx) - - # py1x = pyx[1] / (pyx[0] + pyx[1]) - # # print "p(y=1|x):", py1x - # P[n] = py1x - - posteriors = np.zeros((N, 2)) - for c in (0, 1): - probability_matrix = np.zeros((N, D)) - for d in xrange(D): - tinfo_cd = self.tinfo[c][d] - pdf_d = t.pdf(X[:,d], df=tinfo_cd['df'], loc=tinfo_cd['center'], scale=tinfo_cd['scale']) - probability_matrix[:,d] = pdf_d - posteriors_c = np.prod(probability_matrix, axis=1)*self.pyy[c] - posteriors[:,c] = posteriors_c - P = posteriors[:,1] / np.sum(posteriors, axis=1) - return P - - def predict(self, X): - return np.round(self.predict_proba(X)) - - def score(self, X, Y): - return np.mean(self.predict(X) == Y) - - def confusion_matrix(self, X, Y): - P = self.predict(X) - M = np.zeros((2, 2)) - M[0,0] = np.sum(P[Y == 0] == Y[Y == 0]) - M[0,1] = np.sum(P[Y == 0] != Y[Y == 0]) - M[1,0] = np.sum(P[Y == 1] != Y[Y == 1]) - M[1,1] = np.sum(P[Y == 1] == Y[Y == 1]) - return M - - def get_3_misclassified(self, X, Y): - P = self.predict(X) - N = len(Y) - samples = np.random.choice(N, 3, replace=False, p=(P != Y)/float(np.sum(P != Y))) - return X[samples], Y[samples], P[samples] - - def get_3_most_ambiguous(self, X, Y): - P = self.predict_proba(X) - N = len(X) - sl = SortedList(load=3) # stores (distance, sample index) tuples - for n in xrange(N): - p = P[n] - dist = np.abs(p - 0.5) - if len(sl) < 3: - sl.add( (dist, n) ) - else: - if dist < sl[-1][0]: - del sl[-1] - sl.add( (dist, n) ) - indexes = [v for k, v in sl] - return X[indexes], Y[indexes] - -def plot_image(x, Q, title): - im = Q.dot(x) - plt.imshow(im.reshape(28,28), cmap='gray') - plt.title(title) - plt.show() - -if __name__ == '__main__': - Xtrain = pd.read_csv('Xtrain.csv', header=None).as_matrix() - Xtest = pd.read_csv('Xtest.csv', header=None).as_matrix() - Ytrain = pd.read_csv('ytrain.csv', header=None).as_matrix().flatten() - # print "Ytrain.shape:", Ytrain.shape - Ytest = pd.read_csv('ytest.csv', header=None).as_matrix().flatten() - model = NB() - model.fit(Xtrain, Ytrain) - print "train accuracy:", model.score(Xtrain, Ytrain) - print "test accuracy:", model.score(Xtest, Ytest) - - # confusion matrix - M = model.confusion_matrix(Xtest, Ytest) - print "confusion matrix:" - print M - print "N:", len(Ytest) - print "sum(M):", M.sum() - - # plot 3 misclassified - Q = pd.read_csv('Q.csv', header=None).as_matrix() - misclassified, targets, predictions = model.get_3_misclassified(Xtrain, Ytrain) - for x, y, p in zip(misclassified, targets, predictions): - plot_image(x, Q, 'misclassified target=%s prediction=%s' % (y, int(p))) - - # ambiguous - ambiguous, targets = model.get_3_most_ambiguous(Xtrain, Ytrain) - for x, y in zip(ambiguous, targets): - plot_image(x, Q, 'ambiguous target=%s' % y) diff --git a/bayesian_ml/1/ytest.csv b/bayesian_ml/1/ytest.csv deleted file mode 100644 index 6632f69f..00000000 --- a/bayesian_ml/1/ytest.csv +++ /dev/null @@ -1,1991 +0,0 @@ -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 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--5.5492e-18,3.9852e-18,5.3138e-18,-5.9229e-18,1.9185e-17,-1.1093e-18,-3.3366e-18,-8.1893e-19,3.7768e-18,2.4595e-18,8.6731e-18,8.3354e-18,2.3839e-17,1.4832e-17,-6.7029e-20 --6.583e-18,3.9562e-18,4.6542e-18,-8.8559e-18,2.2705e-17,-2.3685e-18,-4.0349e-18,-2.6403e-18,6.2138e-18,5.6977e-18,1.0569e-17,9.7509e-18,2.853e-17,1.7109e-17,-1.7819e-18 --4.8539e-18,5.8247e-18,4.1718e-18,-6.5081e-18,1.8419e-17,-5.2626e-19,-4.8044e-18,-6.2812e-19,6.141e-18,3.5582e-18,8.9691e-18,9.1986e-18,2.4707e-17,1.563e-17,-1.5153e-18 --3.5854e-18,2.3606e-18,4.5114e-18,-4.9643e-18,1.3842e-17,-6.5907e-19,-2.8941e-18,-4.7605e-19,6.2247e-18,3.8328e-18,7.1909e-18,7.6724e-18,1.9494e-17,1.1422e-17,-1.2724e-18 --1.4039e-18,8.9214e-19,1.291e-18,-1.5682e-18,5.3071e-18,8.1127e-23,-1.161e-18,-4.9599e-19,1.7598e-18,1.2958e-18,2.5956e-18,2.3683e-18,7.2358e-18,4.2655e-18,-3.6548e-19 --8.5218e-18,8.5522e-18,4.3194e-18,-8.4601e-18,2.6117e-17,-6.0864e-19,-6.3716e-18,-2.0189e-18,8.9574e-18,7.1407e-18,1.2315e-17,8.913e-18,3.3396e-17,1.9457e-17,-1.142e-18 --9.7639e-18,8.021e-18,8.6407e-18,-9.2142e-18,3.1929e-17,-1.7551e-18,-5.5628e-18,-1.6311e-18,7.3616e-18,7.4003e-18,1.5293e-17,1.5608e-17,3.8518e-17,2.7512e-17,-2.8847e-18 --1.2744e-18,1.2661e-18,1.2556e-18,-1.4812e-18,4.9142e-18,-1.6944e-19,-7.32e-19,-3.3056e-19,1.5951e-18,1.0363e-18,2.4082e-18,2.0966e-18,6.1122e-18,4.1287e-18,-1.5852e-19 --6.0466e-18,5.6187e-18,8.087e-18,-7.2409e-18,2.62e-17,-2.5222e-18,-6.13e-18,-2.9832e-18,1.31e-17,7.866e-18,1.5546e-17,1.0797e-17,3.5016e-17,2.1604e-17,-1.9611e-18 --1.17e-17,6.4707e-18,5.5805e-18,-1.0522e-17,2.6971e-17,-1.7895e-19,-5.9102e-18,-6.0565e-19,9.2093e-18,4.9204e-18,1.4625e-17,1.4817e-17,3.5127e-17,2.5327e-17,-2.1979e-18 --6.9103e-19,4.3463e-19,5.4274e-19,-8.8561e-19,2.5707e-18,-1.9468e-19,-4.6275e-19,-1.576e-19,6.161e-19,4.676e-19,1.0759e-18,8.5213e-19,3.1821e-18,1.769e-18,-2.4132e-20 --4.0365e-18,3.2797e-18,2.5166e-18,-5.2998e-18,1.4414e-17,6.5557e-19,-4.0213e-18,-8.4823e-19,4.3092e-18,3.7896e-18,7.7954e-18,7.4451e-18,2.0082e-17,1.2891e-17,-1.3899e-18 --8.2524e-18,4.5777e-18,7.0009e-18,-9.3984e-18,2.5118e-17,-1.8716e-18,-4.3765e-18,-1.0959e-18,7.6141e-18,6.2938e-18,1.2644e-17,8.0634e-18,3.3784e-17,1.9519e-17,-9.6456e-19 --5.8104e-18,6.2973e-18,7.8161e-18,-8.5119e-18,2.5462e-17,-3.1688e-20,-5.6616e-18,-1.7517e-18,6.2827e-18,6.1381e-18,1.2008e-17,8.9887e-18,3.2988e-17,1.9922e-17,-2.5239e-18 --8.8931e-18,8.3657e-18,6.6394e-18,-8.7401e-18,2.6354e-17,-7.5187e-19,-5.6616e-18,-2.2361e-18,7.7238e-18,8.2954e-18,1.3295e-17,1.3675e-17,3.1326e-17,2.3226e-17,-2.6304e-18 --6.3602e-18,4.8369e-18,2.4537e-18,-2.2582e-18,1.6375e-17,1.2161e-19,-4.0497e-18,-2.5718e-18,8.129e-18,4.004e-18,7.6075e-18,6.2324e-18,2.49e-17,1.4367e-17,-5.7086e-19 --5.4728e-18,3.7045e-18,6.4009e-18,-6.9563e-18,1.9124e-17,-7.9748e-19,-3.7507e-18,-1.13e-18,5.5516e-18,3.3933e-18,8.2959e-18,8.7071e-18,2.4927e-17,1.4197e-17,-4.0817e-19 --3.1113e-18,2.6183e-18,3.2548e-18,-4.1522e-18,1.2249e-17,-5.3486e-19,-2.1024e-18,1.3337e-19,2.7854e-18,3.6371e-18,6.0933e-18,5.9073e-18,1.4433e-17,9.33e-18,-1.3596e-18 --5.5648e-18,2.4347e-18,4.7689e-18,-3.9755e-18,1.9524e-17,-2.0925e-18,-3.6363e-18,-4.0473e-19,6.2775e-18,3.9464e-18,8.6585e-18,8.1866e-18,2.4051e-17,1.4696e-17,-5.3165e-19 --1.6568e-19,1.6894e-19,2.3257e-19,-2.9092e-19,6.6811e-19,-9.2e-21,-1.19e-19,-1.0538e-20,1.5553e-19,2.2983e-19,3.4635e-19,3.013e-19,8.5785e-19,5.5171e-19,-2.9391e-20 --8.0709e-18,5.3261e-18,7.4744e-18,-6.7263e-18,2.0751e-17,2.1036e-19,-5.4563e-18,-3.6482e-19,1.0639e-17,6.109e-18,1.1437e-17,9.0511e-18,3.1213e-17,1.9498e-17,-1.9533e-18 --9.7557e-18,8.1887e-18,6.4029e-18,-7.5209e-18,2.6108e-17,-1.9721e-18,-6.5026e-18,-3.232e-18,1.1508e-17,6.8542e-18,1.3334e-17,1.0599e-17,3.6994e-17,2.1498e-17,-1.2942e-18 --9.4016e-18,9.503e-18,5.0208e-18,-8.5922e-18,2.5033e-17,-1.6837e-18,-5.8412e-18,2.7844e-19,1.2177e-17,5.7217e-18,1.4385e-17,9.9386e-18,3.4004e-17,2.155e-17,-1.8267e-18 --3.0226e-19,1.9717e-19,1.955e-19,-2.7872e-19,1.0685e-18,-3.0572e-20,-2.3457e-19,-1.031e-19,4.1522e-19,2.4631e-19,5.5285e-19,4.3941e-19,1.5418e-18,9.076e-19,-2.3226e-21 --7.9695e-18,6.0614e-18,1.0013e-17,-9.945e-18,3.0856e-17,-8.3311e-19,-5.6679e-18,-2.8781e-18,1.0762e-17,7.2886e-18,1.519e-17,1.4133e-17,3.8734e-17,2.3253e-17,-2.1544e-18 --7.5238e-19,6.6841e-19,7.9005e-19,-1.2413e-18,3.2491e-18,-9.0119e-20,-7.8506e-19,5.7969e-20,8.9031e-19,9.3871e-19,1.488e-18,1.1394e-18,3.8504e-18,2.5809e-18,-2.853e-19 --7.3665e-18,6.5073e-18,7.3888e-18,-8.8375e-18,2.7931e-17,-2.3988e-18,-6.2852e-18,-7.1268e-19,1.1131e-17,6.6605e-18,1.2079e-17,1.3042e-17,3.6386e-17,2.1699e-17,-7.6382e-19 --5.1186e-18,5.4054e-18,3.1762e-18,-3.3917e-18,1.6205e-17,8.8137e-19,-3.808e-18,-2.611e-18,7.856e-18,4.3272e-18,7.7098e-18,7.2169e-18,2.3605e-17,1.4044e-17,-9.7976e-19 --1.7855e-19,1.5614e-19,9.3929e-20,-1.5899e-19,5.5801e-19,-8.6565e-21,-7.2631e-20,-1.4526e-20,2.1996e-19,1.004e-19,2.7594e-19,2.2051e-19,7.342e-19,4.0715e-19,-2.1427e-20 --9.1687e-19,5.9531e-19,1.0908e-18,-1.671e-18,4.4768e-18,-3.7063e-19,-4.5806e-19,-7.6409e-20,6.859e-19,1.0078e-18,2.1513e-18,1.9582e-18,5.5499e-18,3.3266e-18,-1.4692e-19 --6.4241e-18,4.6888e-18,3.7933e-18,-5.0429e-18,1.8474e-17,-9.317e-20,-4.4275e-18,-1.1298e-18,5.9189e-18,4.8484e-18,9.2033e-18,8.4812e-18,2.5681e-17,1.7036e-17,-1.5144e-18 --7.6229e-18,5.6977e-18,5.3822e-18,-6.9519e-18,2.2989e-17,-1.5386e-18,-4.0534e-18,-7.4967e-19,1.0114e-17,7.9531e-18,1.1145e-17,8.6446e-18,3.2806e-17,2.021e-17,-1.2672e-18 --3.4401e-19,2.8835e-19,2.6754e-19,-3.4559e-19,1.2342e-18,-9.3413e-20,-2.8662e-19,-2.5366e-20,3.3675e-19,2.3142e-19,6.0428e-19,6.1936e-19,1.5875e-18,9.2196e-19,-1.3078e-19 --1.8568e-18,1.947e-18,7.4171e-19,-1.3479e-18,5.0503e-18,-1.2942e-19,-1.1902e-18,-2.9588e-19,2.2434e-18,1.7638e-18,2.7028e-18,2.4343e-18,7.1509e-18,4.4264e-18,-2.6336e-19 --2.628e-18,1.5108e-18,1.9317e-18,-3.2226e-18,8.1468e-18,-4.4557e-19,-1.819e-18,-4.941e-19,2.1306e-18,1.9153e-18,3.1653e-18,3.7328e-18,9.5465e-18,6.0664e-18,-6.0891e-19 --7.1712e-18,5.177e-18,5.4347e-18,-8.5082e-18,2.2077e-17,-1.1687e-18,-4.3743e-18,-1.4563e-20,9.265e-18,7.2652e-18,1.1607e-17,9.5899e-18,2.9107e-17,2.0585e-17,-1.8832e-18 --8.4372e-18,6.2943e-18,9.1348e-18,-9.626e-18,2.7854e-17,-3.9201e-19,-5.2248e-18,-2.3334e-18,6.7699e-18,5.3806e-18,1.1857e-17,1.1552e-17,3.2906e-17,1.9219e-17,-2.0727e-18 --5.6143e-18,4.9264e-18,4.0871e-18,-4.7651e-18,1.8423e-17,-3.6894e-19,-4.6373e-18,-9.9158e-19,8.0117e-18,4.6903e-18,9.5468e-18,7.3021e-18,2.6782e-17,1.5236e-17,-7.0794e-19 --8.9782e-18,4.9943e-18,6.8061e-18,-1.0214e-17,2.7158e-17,-1.0415e-18,-5.6317e-18,-8.8953e-19,8.0052e-18,6.3716e-18,1.4146e-17,1.4546e-17,3.3497e-17,2.5973e-17,-1.2204e-18 -2.6742e-06,-3.9108e-06,-8.3876e-06,1.041e-05,4.11e-06,1.1774e-05,-2.2292e-06,7.0048e-06,-1.0741e-05,7.4552e-06,1.7807e-05,1.5329e-06,1.2606e-05,1.596e-05,5.9898e-06 -1.6649e-05,-2.4348e-05,-5.2219e-05,6.4811e-05,2.5588e-05,7.33e-05,-1.3878e-05,4.3611e-05,-6.6873e-05,4.6415e-05,0.00011086,9.5438e-06,7.848e-05,9.9362e-05,3.7291e-05 --1.8994e-18,2.0585e-18,1.9547e-18,-2.2397e-18,9.1769e-18,-7.1572e-19,-1.6663e-18,-9.6119e-19,3.4165e-18,2.069e-18,4.2013e-18,4.1718e-18,1.0752e-17,7.3523e-18,-4.7849e-19 --6.7659e-18,6.0993e-18,4.8304e-18,-6.3006e-18,2.2213e-17,4.4344e-19,-4.3298e-18,-8.2245e-19,7.8443e-18,7.0471e-18,1.0383e-17,7.9354e-18,3.1485e-17,1.7833e-17,-1.9253e-18 --4.7512e-18,5.6858e-18,3.9184e-18,-5.427e-18,1.9438e-17,-1.653e-18,-3.4258e-18,-1.7144e-18,3.8818e-18,5.0842e-18,8.1706e-18,7.8706e-18,2.5202e-17,1.4741e-17,-1.2817e-18 --3.3212e-18,1.1474e-18,1.9358e-18,-3.0037e-18,9.6331e-18,-8.4001e-19,-2.1879e-18,-4.0259e-19,3.4389e-18,2.1535e-18,4.245e-18,4.2825e-18,1.228e-17,7.5649e-18,-6.4102e-19 --2.9699e-18,1.56e-18,2.827e-18,-2.8602e-18,9.3821e-18,4.7582e-19,-2.1342e-18,-7.8143e-21,2.715e-18,2.039e-18,4.1308e-18,3.9274e-18,1.1371e-17,7.477e-18,-3.7638e-19 --6.2843e-18,6.2969e-18,4.78e-18,-7.6064e-18,2.2227e-17,-3.0409e-19,-4.5653e-18,-1.429e-18,9.034e-18,6.748e-18,1.1373e-17,9.1603e-18,2.9611e-17,1.7497e-17,-1.3087e-18 --7.9674e-18,7.5603e-18,7.8196e-18,-9.6804e-18,2.7418e-17,-1.3495e-18,-6.8006e-18,-2.0382e-18,1.2934e-17,8.2375e-18,1.3833e-17,1.1695e-17,3.4047e-17,1.9915e-17,-6.0819e-19 --6.3578e-19,4.8709e-19,7.6807e-19,-7.3746e-19,2.5702e-18,-3.6972e-19,-4.5962e-19,9.4102e-20,8.9146e-19,6.7732e-19,1.1939e-18,8.9589e-19,3.3392e-18,2.1373e-18,-1.408e-19 --3.1544e-18,3.123e-18,2.6608e-18,-4.7286e-18,1.3265e-17,-6.8872e-20,-3.0776e-18,-2.6737e-19,3.9299e-18,2.9514e-18,6.1965e-18,5.3772e-18,1.5413e-17,9.9149e-18,-1.0193e-18 --8.6787e-18,6.4853e-18,7.7946e-18,-8.2065e-18,2.5559e-17,-1.4277e-18,-3.8277e-18,-3.1358e-19,8.8797e-18,8.1033e-18,1.3607e-17,1.2367e-17,3.1997e-17,2.1245e-17,-1.2535e-18 --7.6224e-18,1.0242e-17,7.6707e-18,-6.8145e-18,2.8599e-17,-1.6883e-18,-6.0616e-18,-3.9097e-18,9.7391e-18,6.3037e-18,1.2661e-17,1.4045e-17,3.9981e-17,2.6453e-17,-2.3571e-18 --7.2868e-18,6.5039e-18,6.3996e-18,-7.9398e-18,3.2759e-17,6.1023e-19,-7.2255e-18,-3.9272e-18,1.2008e-17,6.9312e-18,1.5032e-17,1.51e-17,4.0119e-17,2.6737e-17,-3.3262e-18 --4.0425e-18,4.3115e-18,2.6734e-18,-4.8384e-18,1.386e-17,-8.7521e-19,-2.456e-18,-1.3359e-18,5.4004e-18,2.9597e-18,6.6391e-18,5.92e-18,1.8596e-17,1.1114e-17,-4.4445e-19 --4.4216e-18,3.2291e-18,1.5271e-18,-3.5975e-18,1.3635e-17,-8.3325e-20,-2.7205e-18,-1.4131e-18,5.6898e-18,4.0095e-18,7.4963e-18,6.8818e-18,1.8148e-17,1.1686e-17,-5.023e-19 --3.1465e-18,2.7346e-18,3.4786e-18,-4.1496e-18,1.1374e-17,-1.5511e-19,-2.605e-18,-6.8765e-19,3.8746e-18,3.1941e-18,6.2502e-18,3.5983e-18,1.5197e-17,8.0763e-18,-4.539e-19 --4.6141e-18,1.4846e-18,4.9131e-18,-5.2146e-18,1.5235e-17,-3.2172e-19,-2.1165e-18,-1.37e-18,4.2322e-18,3.5789e-18,7.5732e-18,6.7182e-18,1.8684e-17,1.1998e-17,-1.0667e-18 --8.8585e-18,6.8642e-18,5.9722e-18,-9.5771e-18,2.5803e-17,-1.5152e-19,-6.0697e-18,-9.3551e-19,9.1064e-18,7.7482e-18,1.2058e-17,1.1341e-17,3.2675e-17,1.9182e-17,-1.5826e-18 --7.1299e-18,8.292e-18,5.1338e-18,-8.2061e-18,2.4985e-17,-1.567e-19,-4.3796e-18,-1.7807e-18,6.1613e-18,7.4217e-18,1.2353e-17,1.0756e-17,3.3648e-17,1.9956e-17,-1.2352e-18 --1.0828e-17,8.0402e-18,8.0811e-18,-1.3406e-17,3.4545e-17,-9.7561e-20,-6.1218e-18,-3.643e-18,1.0333e-17,7.1302e-18,1.5749e-17,1.3164e-17,4.3321e-17,2.6609e-17,-8.1487e-19 -6.4694e-06,-9.4603e-06,-1.0078e-05,7.7447e-06,-5.5471e-06,1.4203e-05,1.3504e-05,3.1463e-06,-7.1776e-06,-8.8839e-06,-9.1686e-06,-3.1624e-06,6.9852e-06,-3.5731e-06,2.2555e-05 -2.8609e-05,-4.1835e-05,-4.4569e-05,3.4249e-05,-2.4531e-05,6.2809e-05,5.9716e-05,1.3914e-05,-3.1741e-05,-3.9287e-05,-4.0545e-05,-1.3985e-05,3.089e-05,-1.5801e-05,9.9745e-05 -4.888e-06,-7.1478e-06,-7.6148e-06,5.8516e-06,-4.1912e-06,1.0731e-05,1.0203e-05,2.3772e-06,-5.4231e-06,-6.7123e-06,-6.9273e-06,-2.3893e-06,5.2777e-06,-2.6997e-06,1.7042e-05 --1.6092e-18,1.2223e-18,1.5229e-18,-1.3892e-18,5.4861e-18,1.6348e-19,-8.7242e-19,-3.5358e-19,1.9845e-18,1.0677e-18,2.4753e-18,1.7891e-18,7.5554e-18,3.8181e-18,-4.503e-19 --3.3041e-18,2.1427e-18,2.4793e-18,-2.4913e-18,1.0987e-17,1.0585e-19,-1.8949e-18,1.9877e-19,3.7746e-18,2.4294e-18,5.0455e-18,3.9981e-18,1.4454e-17,8.4402e-18,-7.775e-19 --7.0684e-18,4.3143e-18,8.6404e-18,-9.1849e-18,2.7604e-17,-2.5187e-18,-7.829e-18,-2.2718e-18,1.3329e-17,8.2034e-18,1.3999e-17,1.1174e-17,3.3428e-17,2.0334e-17,-3.2371e-18 --2.1551e-06,-8.378e-06,-4.5739e-05,1.9506e-05,4.3006e-06,5.3625e-05,-5.0767e-06,2.3797e-05,-2.3369e-05,1.1366e-07,8.4292e-05,-3.8914e-05,-3.005e-05,4.6631e-05,1.2869e-05 -1.9713e-05,-5.2561e-05,-0.0001356,0.00011115,4.566e-05,0.00019023,-1.7383e-05,7.7408e-05,-0.00014171,2.0583e-05,0.00031239,-2.9313e-05,9.2688e-05,0.00017932,8.866e-05 -5.9989e-05,-0.00010983,-0.00015249,0.00019054,9.635e-05,0.00029193,3.9397e-07,5.9761e-05,-0.00031059,2.7356e-06,0.00049016,0.00012992,0.00044133,0.00020208,0.00020924 -5.6053e-05,-8.3478e-05,-6.7279e-05,8.9638e-05,6.7749e-05,0.00012999,9.6288e-05,-3.3406e-05,-0.00020448,-0.00010109,0.00019692,5.4193e-05,0.00031791,6.6772e-05,0.00022575 -3.6807e-05,-7.8617e-05,-0.00010387,0.00012643,9.3715e-05,5.1959e-05,0.00017023,6.9351e-05,-6.1543e-05,-0.00021954,6.095e-05,-0.00021287,4.0036e-05,7.3258e-05,0.00019928 -6.8734e-06,-5.3957e-05,-9.4628e-05,0.00012633,5.4401e-05,0.00010279,7.664e-05,0.00010068,6.5391e-06,-0.0001141,5.4569e-05,-0.0002037,1.5638e-05,4.1766e-05,9.9944e-05 --8.5956e-07,-3.0861e-06,-6.9614e-06,1.1706e-05,3.7293e-06,1.1453e-05,3.0828e-07,1.5003e-05,5.6551e-06,-7.5721e-06,5.3779e-06,-1.5065e-05,-4.8293e-06,5.1222e-06,1.9137e-06 -3.5295e-05,-4.0209e-05,-2.9711e-05,0.00014926,-1.6098e-05,-2.3494e-05,4.1101e-05,-6.6314e-05,-5.1053e-05,-0.00018861,3.6144e-05,-9.1797e-05,1.1123e-06,5.6759e-05,8.7077e-05 -3.7641e-05,-3.8615e-05,-2.5589e-05,0.00011406,-1.4862e-05,-6.0409e-06,3.7926e-05,-6.6041e-05,-5.2377e-05,-0.00016497,1.522e-05,-7.9934e-05,1.9817e-05,2.9949e-05,7.9821e-05 -2.0833e-05,-1.7388e-05,-3.7037e-05,4.4289e-05,-2.7201e-05,4.6729e-05,4.8587e-05,-1.8031e-05,-4.0012e-05,-0.00013744,-2.3535e-05,-2.9109e-06,3.6024e-05,-3.9255e-05,-6.0973e-05 -2.1625e-05,-1.8027e-05,-3.9359e-05,4.6513e-05,-2.8922e-05,4.9671e-05,5.1499e-05,-1.8224e-05,-4.1951e-05,-0.00014513,-2.4885e-05,-2.1174e-06,3.7741e-05,-4.1805e-05,-6.6433e-05 --6.2415e-18,4.5474e-18,5.0909e-18,-7.5057e-18,2.298e-17,-4.0389e-19,-4.6354e-18,-1.2713e-18,8.3293e-18,4.0202e-18,1.0501e-17,7.6003e-18,2.8137e-17,1.831e-17,-1.9473e-18 --3.4319e-18,2.077e-18,2.8904e-18,-2.463e-18,9.9222e-18,-7.7195e-19,-2.083e-18,-9.8393e-19,3.1913e-18,2.4886e-18,4.9975e-18,4.3545e-18,1.2986e-17,7.8974e-18,-4.0729e-19 --2.9183e-18,2.4051e-18,2.8226e-18,-2.3766e-18,9.9352e-18,-1.0158e-19,-2.156e-18,4.3101e-19,3.2521e-18,2.2285e-18,4.2611e-18,4.1291e-18,1.209e-17,7.5939e-18,-5.6952e-19 --3.7494e-18,2.4643e-18,4.3757e-18,-5.0826e-18,1.4431e-17,-1.0053e-18,-2.7093e-18,-1.4038e-18,6.0738e-18,3.5464e-18,6.3066e-18,6.6156e-18,1.828e-17,1.118e-17,-6.1964e-19 --5.3215e-18,3.5663e-18,3.5087e-18,-4.2389e-18,1.7043e-17,-4.1235e-19,-2.3153e-18,-1.6167e-18,5.9134e-18,3.2008e-18,8.9973e-18,7.6483e-18,2.4141e-17,1.4903e-17,-1.2704e-18 --8.7151e-19,7.6823e-19,1.211e-18,-1.187e-18,4.1537e-18,6.6014e-20,-8.8294e-19,-6.4227e-19,9.6003e-19,8.7648e-19,1.7563e-18,1.8387e-18,4.903e-18,3.202e-18,-1.429e-19 --1.5918e-18,1.3528e-18,2.6789e-19,-1.4177e-18,4.356e-18,8.7318e-20,-6.8501e-19,-1.6034e-19,2.2792e-18,9.75e-19,1.9844e-18,1.5875e-18,5.9237e-18,3.2288e-18,6.6258e-20 --2.3099e-18,1.2181e-18,1.8501e-18,-2.3671e-18,7.5468e-18,-3.5792e-19,-1.5001e-18,-3.3773e-19,2.5236e-18,1.8185e-18,3.7081e-18,3.7768e-18,9.5708e-18,6.5458e-18,-4.149e-19 --1.2498e-18,8.243e-19,8.2959e-19,-1.1563e-18,3.653e-18,-1.0448e-19,-4.8433e-19,-1.4448e-19,1.3926e-18,1.0461e-18,1.6666e-18,1.8868e-18,4.3182e-18,2.7665e-18,-2.5084e-19 --8.1551e-19,8.3201e-19,8.3968e-19,-1.0152e-18,3.4141e-18,-2.3319e-19,-6.5658e-19,8.6422e-21,1.0061e-18,8.9211e-19,1.3886e-18,1.1027e-18,4.1348e-18,2.3607e-18,-1.9545e-19 --1.8176e-18,2.2603e-18,1.2185e-18,-2.1019e-18,6.428e-18,-3.1747e-19,-1.5121e-18,-2.5807e-19,1.648e-18,1.8698e-18,2.7467e-18,2.5902e-18,7.9883e-18,5.11e-18,-6.0254e-19 -3.6894e-05,-5.3099e-05,-5.807e-05,3.9376e-05,-3.5624e-05,8.5327e-05,7.5306e-05,2.3531e-05,-5.1719e-05,-4.4491e-05,-6.4021e-05,-2.2862e-05,5.0988e-05,-2.1447e-05,0.00011642 -6.3653e-05,-8.6375e-05,-0.00010548,3.6932e-05,-8.7901e-05,0.00017682,0.00012001,7.8439e-05,-0.00016028,-3.819e-05,-0.00018647,-6.7156e-05,0.00016199,-4.2224e-05,0.00011888 -5.2341e-05,-5.9961e-05,-8.1962e-05,5.4645e-06,-6.8719e-05,0.00010304,9.4559e-05,5.1249e-05,-0.00014234,-2.2382e-05,-0.00015889,-5.01e-05,0.00013684,-2.1249e-05,8.6391e-05 -0.00011963,-6.358e-05,-9.2838e-05,5.3614e-05,-0.00011075,1.5646e-05,0.00016273,-5.1569e-05,-0.00018009,-2.0207e-05,-5.5806e-05,-6.5297e-05,0.00023916,4.8959e-05,0.00032171 -0.00030281,-0.00016815,-0.00012329,0.00019493,-0.00022158,0.00018734,0.00019801,-0.0001992,-0.00052077,0.00015213,9.1006e-05,-0.00036796,0.00066034,0.00025523,0.00092973 -0.00029452,-0.00020923,-0.00012844,0.00031789,-0.00020667,0.00035123,-7.8173e-05,-0.0001872,-0.00062707,0.00021755,0.00049991,-0.0005273,0.00054541,0.00033615,0.00086209 -0.00022643,-0.00016856,-0.00020185,0.00032198,1.1193e-05,0.00044852,-0.00019369,2.1843e-05,-0.00056453,0.00025422,0.00087188,-0.00030941,0.00050832,0.00036622,0.00032497 -0.0001807,-0.00017614,-0.00029351,0.00043024,0.00016365,0.00046536,4.2203e-06,9.4897e-05,-0.00069159,0.00022598,0.0009191,-0.0001182,0.00040247,0.0003287,0.00027211 -0.00015643,-0.00018372,-0.00061893,0.00075562,0.00051469,0.00079526,-0.00017425,0.00039428,-0.00073848,-3.3423e-05,0.0015909,-0.00014926,0.00043693,0.00055696,0.00044277 -0.00019338,-0.00015797,-0.00073611,0.00094681,0.00065199,0.0005495,9.5064e-05,0.0004938,-0.00062624,-0.00042104,0.00087914,-0.00089808,-3.4636e-05,0.00076066,0.00049885 -0.00025636,-9.2508e-05,-0.00084423,0.0010709,0.00064502,0.00016521,0.00083431,0.00066654,-0.00051644,-0.00021899,0.00029204,-0.0012965,0.00035466,0.001093,0.00096927 -0.00017942,-5.3088e-05,-0.00073935,0.0012022,0.00054855,-6.5127e-05,0.00096535,0.00028323,-0.00081561,-0.00023706,6.5678e-05,-0.0012941,0.0014011,0.0013944,0.0011095 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--0.048788,-0.024612,0.019901,-0.05742,0.023944,0.018575,0.02144,-0.036034,0.048307,-0.0064616,-0.0076647,-0.069489,0.017531,0.015795,-0.035222 --0.049084,-0.007335,0.048885,-0.04382,0.012557,0.029533,0.066611,-0.013648,0.060379,0.0012486,0.027345,-0.062516,0.012127,-0.019225,-0.015022 --0.037868,0.016734,0.067666,-0.013634,-0.0010147,0.037291,0.079761,0.01986,0.051237,-0.0039605,0.030985,0.0026496,-0.010781,-0.04666,0.047377 --0.019301,0.039999,0.066731,0.012206,-0.0062411,0.033623,0.043992,0.038361,0.025966,-0.017999,-0.0043836,0.073702,-0.031481,-0.044137,0.081214 -0.0018102,0.055857,0.048004,0.020738,0.0027015,0.02146,-0.013359,0.027046,0.0046486,-0.025878,-0.048066,0.093072,-0.033388,-0.030952,0.036522 -0.020773,0.058545,0.023941,0.0098714,0.022617,0.010328,-0.056241,-0.0033019,0.0060605,-0.020495,-0.057702,0.050395,-0.017198,-0.028333,-0.041639 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-0.0010608,-0.00055435,-0.00085607,-0.0013614,0.0012286,0.00097188,0.0025499,0.00079173,-0.00050745,0.00082786,0.00048584,0.0034398,-0.0014504,-0.0012638,0.0014938 -0.00020405,-0.00014044,-0.00012319,-0.00014834,0.00020197,0.00030756,0.00050092,-6.6322e-06,-0.00025147,6.7242e-05,-1.7699e-05,0.00046788,-0.00028987,-0.00025976,0.00012542 -1.3372e-05,3.3648e-05,4.0861e-05,-4.8252e-06,-6.777e-06,1.3692e-06,-9.2513e-06,-1.0966e-05,-1.754e-06,-1.712e-05,-4.4532e-05,3.3684e-05,-0.00015123,3.3067e-05,3.284e-05 --1.2513e-18,7.8315e-19,7.8035e-19,-8.145e-19,3.6961e-18,-1.6204e-19,-4.1468e-19,-4.496e-19,1.5256e-18,6.3388e-19,1.7807e-18,1.6229e-18,4.7419e-18,2.8765e-18,-2.003e-19 --2.7217e-18,2.4793e-18,1.4197e-18,-2.3169e-18,8.8738e-18,-7.8863e-19,-1.5427e-18,-9.1127e-19,2.2999e-18,2.0101e-18,3.8057e-18,3.5919e-18,1.127e-17,6.4546e-18,-3.7567e-19 --1.4917e-18,1.1169e-18,1.209e-18,-1.5189e-18,4.876e-18,-4.6337e-19,-9.9906e-19,-4.1063e-19,2.0934e-18,1.2157e-18,2.516e-18,1.7153e-18,7.4353e-18,3.709e-18,-3.9409e-19 --8.9794e-18,7.2143e-18,5.6274e-18,-9.0055e-18,2.8513e-17,-6.7062e-19,-4.6562e-18,-8.6158e-19,1.1588e-17,8.5418e-18,1.3029e-17,1.1431e-17,3.2389e-17,2.2972e-17,-1.0473e-18 --7.1485e-18,3.3918e-18,4.4439e-18,-6.6609e-18,1.9683e-17,-4.4646e-19,-3.8152e-18,-1.4051e-18,4.8388e-18,5.7898e-18,9.3483e-18,8.6693e-18,2.4543e-17,1.7546e-17,-2.7329e-18 --3.648e-06,-2.5282e-06,-1.1417e-05,-3.0845e-06,2.8581e-06,5.7071e-06,-9.051e-06,9.4753e-06,6.3928e-06,-1.731e-05,-1.408e-05,1.1484e-05,-8.4639e-06,1.9742e-06,2.7681e-05 --0.00010227,-9.5275e-05,-0.00023664,-9.167e-05,7.1478e-05,9.9229e-05,-0.00030645,0.00015054,4.1469e-06,-0.00039585,-0.00028524,0.00031007,-9.9368e-05,-0.00010509,0.00087003 --0.000222,-0.00027955,-0.00044241,-0.00026843,0.00023459,0.00028691,-0.00066337,0.00021509,0.00014216,-0.00067013,-0.00082934,0.00073394,0.00015675,-0.00022952,0.0018642 --0.00058168,-0.00077404,-0.00082475,-0.0007839,0.00064033,0.00028413,-0.0016572,0.00039917,0.00021232,-0.0012281,-0.0022784,0.0011162,0.00024682,-0.00019338,0.0039884 --0.0012115,-0.0015148,-0.0012456,-0.0017454,0.0013588,-6.2457e-05,-0.0032027,0.00042666,0.00029212,-0.0017397,-0.0041663,0.0016862,-0.00019176,0.00032618,0.0066862 --0.0028513,-0.0030822,-0.0018105,-0.0044657,0.0026166,-0.00022862,-0.0053097,-0.0003249,0.0021016,-0.002269,-0.0081879,0.00091042,0.00060446,0.00061378,0.0095632 --0.0054241,-0.0045263,-0.0019345,-0.008644,0.003411,0.00038241,-0.005094,-0.0018303,0.0062893,-0.0034878,-0.010869,-0.0046409,0.0035455,-0.0011929,0.0086997 --0.0086938,-0.004898,0.00021502,-0.012843,0.0035512,0.0022635,-9.0637e-05,-0.0038384,0.011993,-0.0039345,-0.0081467,-0.015894,0.0083772,-0.0078343,0.0043804 --0.011775,-0.0028038,0.0061903,-0.014865,0.0026811,0.0052535,0.010577,-0.0025626,0.019944,-0.0035666,0.00030465,-0.025115,0.011062,-0.022244,0.0058374 --0.011895,0.0017884,0.013027,-0.011428,0.0010155,0.0072042,0.019135,0.0057189,0.024358,-0.0035902,0.0059213,-0.019104,0.0091108,-0.036353,0.019955 --0.0088858,0.0076483,0.016222,-0.004642,-0.00035076,0.0084278,0.01859,0.013928,0.02179,-0.0044371,0.0027452,0.0015121,0.0044439,-0.042926,0.037527 --0.0044623,0.012107,0.014567,0.0011921,0.00069264,0.0068857,0.0080767,0.016991,0.015017,-0.0065713,-0.0073812,0.019086,0.00046179,-0.039889,0.038044 -0.00081198,0.013889,0.0088165,0.0026246,0.004302,0.0036879,-0.006885,0.011577,0.0095872,-0.0061327,-0.017122,0.022262,0.00040551,-0.032768,0.013395 -0.0046609,0.014413,0.0041509,-4.433e-05,0.0079781,0.0027358,-0.0157,0.0039729,0.010559,-0.0044726,-0.019583,0.013566,0.0044083,-0.033289,-0.0076319 -0.0067403,0.011853,-0.00094585,-0.0057642,0.0098764,0.0017078,-0.015785,-0.00082708,0.010473,-6.5833e-05,-0.0090469,-0.0020177,0.0058743,-0.031422,-0.016101 -0.0066519,0.0072044,-0.0041795,-0.0091304,0.0089927,0.00078486,-0.0074949,-0.0011834,0.0083483,0.0051043,0.0025324,-0.0071402,0.0015666,-0.025219,-0.0086197 -0.0056236,0.0028332,-0.0048834,-0.0088525,0.0076719,0.00056646,0.0014715,0.00085891,0.006073,0.0068472,0.0069128,-0.00099608,-0.002476,-0.019491,0.00066936 -0.0039132,0.00043526,-0.0035894,-0.0066518,0.0059031,0.0011672,0.0046443,0.0024153,0.0039341,0.005245,0.0060166,0.0049257,-0.0017128,-0.01315,0.0059007 -0.0016437,-0.00011517,-0.001796,-0.0029732,0.0022762,0.00097651,0.0028808,0.0015896,0.0015578,0.0020519,0.0029496,0.0038546,-0.00056504,-0.00535,0.0041954 -0.00041219,-0.00010842,-0.00038464,-0.00081213,0.00057471,0.00023278,0.00079489,0.00056025,0.00029155,0.00049624,0.00061075,0.0013742,-8.8809e-05,-0.0014475,0.0012718 -7.9149e-05,-2.5634e-05,-2.2128e-05,-0.0002548,8.7142e-05,8.0726e-05,0.00017031,6.3288e-05,0.0001044,0.00020962,0.00013625,0.00013625,-9.7967e-05,-0.0004231,0.00037197 -7.0145e-06,-3.4583e-06,2.1637e-06,-1.8028e-05,-1.7113e-06,5.7134e-06,2.3255e-05,1.2368e-05,-2.677e-06,7.738e-06,2.0042e-06,1.6557e-05,-8.8915e-06,-2.3156e-05,3.0561e-06 --7.9702e-18,7.8171e-18,5.4509e-18,-9.2591e-18,2.5686e-17,-8.6231e-19,-6.7834e-18,-1.5894e-18,9.1237e-18,5.9336e-18,1.3275e-17,1.1263e-17,3.4739e-17,2.2616e-17,-2.0519e-18 --9.1249e-18,8.5799e-18,6.171e-18,-1.0619e-17,2.6404e-17,-7.9654e-19,-5.379e-18,-9.7775e-19,4.1576e-18,7.6104e-18,1.2284e-17,1.0285e-17,3.3577e-17,1.9424e-17,-1.766e-18 --4.8522e-18,2.8013e-18,4.2871e-18,-4.5582e-18,1.4995e-17,6.6027e-20,-2.7101e-18,-1.6668e-18,5.0375e-18,3.545e-18,7.0797e-18,6.9411e-18,1.9496e-17,1.2991e-17,-1.3443e-18 --6.8585e-18,4.4628e-18,1.0327e-17,-8.5455e-18,2.7143e-17,-1.8007e-18,-7.4696e-18,-1.1295e-18,1.0468e-17,5.6414e-18,1.2565e-17,1.2537e-17,3.6054e-17,2.2361e-17,-1.8541e-18 --8.4702e-18,5.9321e-18,7.2048e-18,-9.9953e-18,3.0992e-17,5.3531e-19,-4.7575e-18,-9.8686e-19,1.0505e-17,7.1051e-18,1.2485e-17,1.4747e-17,3.8284e-17,2.4689e-17,-1.1145e-18 --2.3521e-19,1.6648e-19,1.9839e-19,-3.5341e-19,8.1403e-19,-1.2761e-20,-1.5886e-19,-5.5747e-20,2.566e-19,2.6764e-19,4.3114e-19,3.2346e-19,9.9876e-19,6.5286e-19,-4.2664e-20 --5.5685e-18,7.0597e-18,4.4148e-18,-6.7863e-18,2.4657e-17,-1.224e-18,-5.8503e-18,-1.246e-18,7.8692e-18,6.3238e-18,1.1872e-17,8.0914e-18,3.163e-17,1.9333e-17,-1.47e-18 --5.5485e-18,3.4686e-18,2.5588e-18,-4.4786e-18,1.5385e-17,-4.6693e-20,-3.0593e-18,-8.2001e-19,5.7333e-18,3.9865e-18,7.0301e-18,6.4728e-18,1.9814e-17,1.1787e-17,-1.1913e-18 --6.0446e-18,5.4978e-18,3.7151e-18,-6.578e-18,1.86e-17,2.5527e-19,-3.685e-18,-1.3904e-18,7.1219e-18,4.1015e-18,8.385e-18,7.8945e-18,2.6086e-17,1.5427e-17,-1.0637e-18 -1.7558e-06,-5.473e-06,-7.7584e-06,1.0202e-05,3.6771e-07,-4.1952e-08,-2.2646e-06,1.1307e-05,8.3283e-06,5.8044e-06,-4.5859e-06,-1.329e-05,6.8732e-06,-3.2826e-06,1.0041e-05 -9.9782e-06,-3.1102e-05,-4.409e-05,5.7976e-05,2.0897e-06,-2.3841e-07,-1.287e-05,6.4259e-05,4.7329e-05,3.2986e-05,-2.6061e-05,-7.5527e-05,3.906e-05,-1.8655e-05,5.706e-05 -5.9094e-06,-2.9668e-05,-4.2827e-05,4.9497e-05,1.9104e-06,-4.0132e-06,-1.4015e-05,5.8001e-05,4.7019e-05,2.9339e-05,-2.5022e-05,-7.0216e-05,3.592e-05,-2.5129e-05,5.4955e-05 --4.9123e-05,-3.6012e-05,-4.4427e-05,-4.0232e-05,1.8939e-05,-7.574e-05,-4.209e-05,6.8796e-07,6.8304e-05,8.1707e-06,-5.1626e-05,-5.3922e-05,-4.4392e-06,-0.00015868,8.1288e-05 --0.00011665,-4.9814e-05,-9.7963e-05,-8.6564e-05,-3.8387e-05,-0.00011114,-2.8027e-05,-1.3315e-05,0.0001635,-3.6901e-05,-1.5857e-05,-0.0002678,6.0779e-05,-0.00040732,0.0001597 --0.00010753,-5.2404e-05,8.4834e-06,-0.00011324,-9.1562e-05,7.2322e-05,4.3192e-05,2.2807e-05,0.00020766,-2.5711e-05,4.8348e-05,-0.0004112,0.00015469,-0.00043859,0.00031232 --0.00012699,-2.9163e-05,0.00010284,-5.828e-05,-3.7666e-05,4.0591e-05,0.00012887,0.00030505,0.00013449,-3.3603e-05,3.0185e-05,-0.00055927,0.00013408,-0.00078662,0.00064205 --7.9653e-05,0.00014042,0.00020581,-5.212e-05,-0.00010755,-4.215e-05,-2.7696e-05,0.00054146,4.3422e-05,-0.00029837,2.1333e-05,-0.00010364,1.2323e-05,-0.0010792,0.00099845 --0.00010332,0.00025029,0.0001503,1.0185e-05,-0.00011858,-2.8729e-05,-3.6815e-05,0.00069394,0.00023861,-7.8483e-05,-0.00013288,-8.1734e-05,0.00043926,-0.0016867,0.0013 -3.1113e-05,0.00021198,8.8685e-05,-2.3685e-05,3.7238e-05,-0.00011411,-0.00029697,0.00040007,0.00011803,-0.00017292,-0.00029102,-4.0648e-05,0.00046595,-0.0011052,0.00035029 -9.8935e-05,0.00017121,6.3645e-05,-7.8558e-05,0.00017428,-0.00026321,-0.00040379,0.00043469,0.00022883,-0.00018688,-0.00026344,7.6597e-05,0.0005853,-0.0010928,-0.00012586 -0.0001003,7.7363e-05,-4.4523e-05,-0.0002141,0.00013217,-0.00020093,-0.00026494,0.0002164,0.00024936,5.6636e-05,3.7897e-05,2.8567e-05,0.00043999,-0.00067444,-0.00012791 -0.00011067,0.00010472,-0.00019867,-0.00025975,0.00014781,-0.00023355,-0.00024144,0.00024076,0.00023904,0.00025323,0.00031511,-8.3454e-05,0.00032121,-0.00075664,0.0001861 -0.00016434,5.2984e-05,-0.00014123,-0.00035764,0.00019302,-0.00018894,-4.2477e-05,0.00032808,0.00016022,0.00010135,0.00038461,-8.5255e-06,0.00019109,-0.00084698,-5.117e-05 -0.0001533,-2.5614e-05,-0.00021212,-0.00028137,0.00017782,6.1944e-05,0.00028183,0.00025963,0.00022119,6.273e-05,0.00026047,0.00047286,0.00011224,-0.00075678,0.00031987 -5.8973e-05,-7.8102e-06,-0.00011597,-0.00014815,6.2479e-05,6.9614e-05,0.00016885,0.000107,6.4561e-05,1.0248e-06,0.00014121,0.00027755,6.3216e-05,-0.00031414,0.00025603 -2.0599e-05,-9.3492e-06,-5.9032e-05,-2.0303e-05,1.8197e-05,6.9861e-05,6.8667e-05,3.2682e-05,2.6315e-05,-1.4557e-05,5.0224e-05,8.1297e-05,-1.2244e-06,-5.0326e-05,0.00014253 --4.9105e-18,3.5796e-18,2.1421e-18,-3.995e-18,1.3178e-17,-3.0618e-19,-3.5121e-18,-9.5113e-19,4.444e-18,3.9248e-18,5.844e-18,4.7113e-18,1.717e-17,9.8824e-18,-5.513e-19 --2.6452e-19,2.5503e-19,1.6538e-19,-2.2287e-19,7.8614e-19,-1.1368e-21,-1.8564e-19,-4.2237e-20,2.4702e-19,2.395e-19,3.9663e-19,2.9824e-19,1.0212e-18,6.0554e-19,-6.4166e-20 --1.9474e-18,1.4069e-18,1.0622e-18,-1.539e-18,5.291e-18,-1.2984e-19,-1.198e-18,-3.11e-19,1.3536e-18,1.6155e-18,2.3607e-18,2.8383e-18,6.3186e-18,4.0955e-18,-6.0926e-19 --3.4173e-18,4.6477e-18,4.5719e-18,-5.4046e-18,1.7839e-17,-1.4963e-18,-3.0443e-18,-1.7041e-18,4.2817e-18,3.6529e-18,6.9903e-18,7.0989e-18,2.0907e-17,1.4601e-17,-5.1371e-19 --8.9807e-18,7.395e-18,6.5898e-18,-8.9913e-18,2.3895e-17,1.1806e-18,-5.4747e-18,-1.8107e-19,1.1714e-17,8.4051e-18,1.3189e-17,9.8731e-18,3.3631e-17,1.9141e-17,-9.4678e-19 diff --git a/bayesian_ml/2/README b/bayesian_ml/2/README deleted file mode 100644 index d4a5711a..00000000 --- a/bayesian_ml/2/README +++ /dev/null @@ -1,14 +0,0 @@ -This data set contains the following: - -Xtrain: 11791 training examples of 4's and 9's, each 15 dimensions in length -ytrain: the corresponding labels of the training examples (0 = 4 digit, 1 = 9 digit) - -Xtest: 1991 testing examples of 4's and 9's, each 15 dimensions in length used for prediction -ytest: the corresponding ground truth labels used for evaluating performance (0 = 4 digit, 1 = 9 digit) - -Q: A matrix of eigenvectors that is used to map the 15-dimensional feature vectors back into image space. For a 15-dimensional feature vector x, this can be done as follows in pseudocode - - 1. Map x2 = Q*x - 2. Reshape x2 into a 28x28 matrix and show it as an image - -Because the data has had the mean subtracted off, this image will not look exacly like a handwritten digit, but the contours of the 4 or 9 should be clear. diff --git a/bayesian_ml/2/Xtest.csv b/bayesian_ml/2/Xtest.csv deleted file mode 100644 index 25038c6b..00000000 --- a/bayesian_ml/2/Xtest.csv +++ /dev/null @@ -1,1991 +0,0 @@ -3.7638,-0.92236,-0.53047,1.233,-0.71848,1.525,-1.3769,-1.551,-0.17975,-1.1499,-0.11756,-1.5398,0.34207,-0.60512,0.065985 --0.40127,0.26977,-0.20437,-1.0767,-3.4491,0.96986,0.62241,0.15118,-1.3787,-1.6964,-0.19497,-1.3029,-1.4716,-1.3327,0.47252 -0.54033,1.5158,0.0057301,1.2152,-2.1241,-1.1308,-0.91144,-0.32863,0.60841,-0.64354,-0.00087909,0.45768,1.3614,0.71921,0.20154 -1.7504,1.5461,-1.9151,1.3689,-1.044,0.36394,-0.20749,-0.97425,0.13825,0.94529,-0.76716,-1.0765,0.94106,-0.32831,-0.29621 -2.0957,-0.3642,1.0851,1.2778,-0.67157,-0.49899,-2.7037,-0.54487,0.84837,-0.086419,1.4492,0.3441,0.46331,0.51728,-0.51133 -1.2966,-1.6362,-2.1231,2.2668,0.021024,2.4636,0.064225,0.16694,-2.6767,1.1264,1.011,0.54725,2.7373,-0.096381,0.96821 -0.34242,1.4374,1.1921,0.60321,-2.1172,-2.108,1.3842,-0.99591,-0.44959,-0.24658,-1.6997,0.24477,-0.73409,-0.32675,1.1555 -2.241,1.0479,3.7299,-1.3708,-0.57897,-2.135,-1.0227,0.19116,-1.6525,-1.1759,0.86843,0.45257,-0.22053,-0.058321,-0.29706 -4.373,-1.6854,-1.1732,0.25269,-0.11805,-0.8278,0.49605,-1.0704,1.3663,-0.83527,0.16838,-1.3209,-0.18926,0.4433,0.72966 -4.9061,-1.5189,0.69141,-0.40059,-1.6001,-0.33886,-2.0876,0.63703,0.071592,0.42244,-0.32596,-1.2406,-0.11299,0.32148,0.14613 --1.1768,2.7211,-1.9466,0.58319,-0.91973,0.20794,-0.69717,1.9253,0.14132,-0.92793,-0.511,0.13961,0.12623,0.61935,-0.8245 -2.8328,-1.342,-0.60797,2.4566,-1.0702,0.081054,0.34898,-0.19849,0.16284,-2.2237,-1.2937,0.43102,-1.0052,-1.0385,-1.8365 -3.3843,0.10076,1.9631,-1.3811,-1.4517,-2.9031,-0.49936,0.63798,-1.4444,-2.2475,-0.39671,-0.010771,-0.11308,-0.31941,-0.4091 -0.91302,-1.1661,2.6844,1.0152,1.7795,-2.2122,1.3515,0.085464,-2.9011,0.45318,-1.0435,0.71729,1.4514,-0.015185,-1.3481 -4.2955,-1.6204,-0.58862,-0.16197,-1.1359,-1.3578,0.23008,-0.68053,1.5515,-1.1303,0.20523,-0.54476,0.53337,0.091987,-0.51841 --0.43154,-0.5402,0.21269,2.9266,-0.50234,0.42145,0.90034,-1.2784,-0.54621,0.15436,1.5119,0.56681,0.31507,-0.43955,0.4408 --2.3812,-0.056082,-0.89467,2.1133,-0.061934,0.45003,0.6943,-0.29364,0.67109,1.4262,1.0858,-0.25398,-0.91268,-0.17939,-0.37981 --0.95273,1.8538,-0.51732,1.07,-2.034,0.048668,-1.1583,0.23106,-0.102,-0.45604,0.8759,1.6835,-0.73838,0.10093,-0.78889 -3.7697,-2.694,-1.1779,-0.58756,-0.95878,-0.65286,-1.261,1.6118,1.2474,0.34145,0.43167,-0.49113,-0.74164,-0.23089,-0.49057 --1.1123,-2.4263,-1.0913,1.6137,0.5331,2.2532,-0.91974,0.04655,-0.28936,1.8647,0.79794,-1.0007,-0.29407,1.5734,-0.080667 --0.97907,-3.5842,0.83276,1.1758,0.33957,1.7708,-0.89337,-1.9453,-0.29453,1.6292,-0.23561,-0.52882,-0.69093,-0.96737,-0.33392 --0.92034,1.0165,-1.5838,2.6534,0.30206,0.51838,0.20312,2.3375,0.34387,0.53061,-0.75727,-0.65845,-0.66545,-0.14811,0.42866 --0.4128,1.6385,0.10098,0.73246,-1.3821,-1.4382,0.0056794,-1.0218,-1.9022,0.79076,0.6974,0.12319,-1.4008,0.1837,0.50581 -4.3155,-0.86847,0.13385,-0.38718,-1.0473,-2.2516,0.013736,0.90739,1.069,-2.0064,-0.559,0.57882,-1.2897,0.6017,-1.755 -0.14999,0.42963,-0.58948,3.25,0.50981,0.20315,-0.34092,0.13586,-1.1356,-1.5288,0.88303,0.75972,-0.63656,0.79188,-0.64232 -0.13371,2.9465,0.38271,0.30973,-1.6684,-0.57221,-0.4777,0.18647,-1.1962,-0.73395,-0.40575,-0.35481,1.027,0.72181,1.044 -0.39796,2.3227,-0.91017,0.75586,-0.79276,-1.022,-1.5964,-0.76549,0.44418,0.80759,0.68862,-0.092809,1.0605,1.3595,-0.31052 --0.31917,1.6648,0.64912,1.7022,-1.5824,-0.2252,-1.661,-0.33282,-0.11859,-0.95826,1.3468,1.529,-1.0806,0.64468,-0.35981 -3.5727,-0.30127,-0.58371,-0.099989,-2.5259,1.1521,-1.2565,1.3432,1.2963,-1.6346,-1.3604,-0.39803,-0.67149,0.12627,-0.80097 --1.0041,0.6032,-1.6764,2.4257,-1.107,0.63235,0.15688,0.80541,1.2245,-0.22032,0.78663,0.32348,-1.4664,-0.080609,-0.43975 -1.6308,0.20822,1.9008,1.0694,-1.6549,-2.084,1.1008,-0.40759,0.12833,-0.11555,-1.3258,0.55165,0.96202,0.17506,-0.0023905 --0.40222,-0.43273,-1.8198,0.91728,0.21727,-0.10447,0.86552,1.318,0.025507,0.046009,-0.1571,-1.0809,-0.51187,0.098009,-0.10941 -3.9565,-1.2563,2.506,1.9081,-0.60668,-1.0695,0.34605,-1.2199,-1.4162,-2.0128,-1.0664,-0.054556,-0.417,-0.98366,-0.30727 -0.81574,3.0566,2.289,-0.50296,1.639,-2.3539,-0.86279,0.13462,-1.6316,0.46145,0.5696,-0.76657,-0.1792,1.1106,0.17598 -2.7468,-0.041577,1.5932,1.7917,-0.037159,0.0097491,-2.843,-1.403,-0.13851,0.25666,1.2053,-0.25774,-0.84266,-0.079885,0.050416 -1.5408,-2.2518,-2.1077,-2.5579,-2.0117,-0.043408,0.92866,2.8049,-0.36265,-1.369,0.97054,2.6708,1.1902,-0.29285,0.21365 -1.9787,2.2517,-1.4535,-2.4867,0.068123,-1.7132,-0.035312,-0.10147,0.010606,-0.39286,0.68479,-1.6169,1.578,-0.05316,1.2401 --1.0301,-0.37195,-3.661,0.9618,1.3711,0.3213,-0.24447,1.6466,-0.39916,0.69686,-0.99213,-0.7632,-0.7145,0.32256,0.50338 --2.4193,1.572,0.35908,-1.4502,-1.1063,-0.0028337,1.4753,0.14714,-1.9692,1.6347,-0.55687,-1.1012,-0.12835,1.0784,0.96491 --1.1137,-0.90372,1.7438,0.085859,-2.3113,-2.1816,0.807,-1.7858,-1.0542,0.050647,-0.44683,-0.55498,-1.7587,-0.18655,0.30135 --2.1967,-2.0592,-1.6914,0.35186,0.54814,1.8469,-1.0361,0.80551,0.84533,1.0164,0.25508,-0.93665,-1.656,1.3473,-0.88658 -2.7053,-2.2029,-2.4455,-1.6052,-1.9665,0.58056,0.86087,2.7574,1.2817,1.4748,0.80605,1.6223,0.10048,1.1145,1.4053 --1.5346,3.1249,-0.4748,0.88492,0.0234,-0.63569,-0.92912,2.1023,-0.056685,0.10577,-0.28825,0.51454,-0.044401,0.81438,-0.362 -2.0875,-0.26424,0.378,0.19741,-0.9255,-0.68818,-2.7935,-0.2866,0.76996,-0.75514,1.8216,0.37945,1.5647,0.25816,-0.55408 -3.0603,-0.87985,-0.86525,-0.77944,-1.7417,-0.33689,-1.3959,-0.24315,0.94422,0.42438,1.0795,-0.82541,1.0619,0.078842,0.86652 -2.8919,-1.5595,-0.59407,2.2368,-0.10432,-0.69982,1.2093,-0.92448,0.095945,-3.1662,-0.86215,-0.0051159,0.64919,-0.38798,-1.5991 -3.7854,-0.44607,-1.7454,-3.1411,0.3424,-1.7297,2.8718,1.9969,1.0067,0.37295,0.16245,1.0341,-0.28486,0.12756,0.6074 --0.44981,-2.1734,-2.0654,0.91182,-0.58209,1.854,-0.81142,0.25185,1.083,0.90555,-1.238,-0.38249,0.82766,-0.73798,-0.14168 -2.4032,1.1523,-0.079068,1.3982,-0.18156,-1.7786,0.17533,-0.56812,-0.40898,0.35255,-1.8008,-0.95312,0.35507,1.1984,0.78389 --1.4607,-1.1005,-0.68788,1.6004,0.76708,-1.4474,0.80188,-1.8577,-0.26251,0.66604,1.0168,0.41301,0.36997,-0.16078,-0.90854 -0.81954,1.3353,-2.2173,0.58146,-0.48759,-0.13079,-1.383,0.97311,-1.1434,1.4961,1.9932,-0.61506,-0.038171,1.2265,-0.65666 --2.0452,-3.0451,-1.1892,-0.95887,1.3935,-2.9836,-0.080132,0.79598,-1.3505,-1.2132,-1.2478,-0.13659,-0.75352,-0.40583,-0.1965 -4.4884,-3.0626,-1.1004,-2.9842,-0.76224,-1.9503,2.4615,1.93,0.23667,-0.12883,0.87591,2.5484,-0.098641,0.074974,1.2203 -0.63381,1.978,0.6194,0.25913,-2.5354,-0.3736,0.02305,0.35139,-0.60295,1.5774,-0.9892,0.12374,-0.060908,0.53163,1.1194 --2.358,1.8266,-1.2238,0.70428,-1.0954,0.255,0.64953,0.29402,-1.8366,0.61871,0.81548,1.257,-1.286,0.18837,-1.2388 --2.2613,-0.61961,-3.5804,-0.23972,-0.24774,0.23334,-0.89487,-0.95756,-0.67418,-0.6264,-0.33634,1.9032,-0.72671,-0.71193,-0.78463 --1.8846,2.6068,-0.12463,0.59326,-1.5609,-1.1768,1.0718,0.40365,-1.3633,0.9183,-0.97397,0.79023,0.39526,0.84893,0.75749 --0.55534,0.8539,-0.54733,2.6591,1.046,0.42409,-0.644,-0.10672,-0.43075,-2.1469,1.0066,0.16846,-1.2704,0.54731,-0.077969 -4.0474,-0.4676,-0.77568,1.3887,0.8846,-1.815,1.0046,-1.822,-0.56842,-0.90365,-0.089729,-1.4371,1.165,0.40632,0.73696 -4.5168,-1.592,-0.62623,0.16543,-1.0957,1.5522,-1.1707,1.3664,-0.2239,1.174,-1.2326,-0.32009,0.90044,0.58932,-1.0276 -3.9218,-0.38356,0.9695,1.2891,-0.59278,-1.6457,0.1423,-1.8046,0.049312,-1.9831,-1.4045,-0.78403,-0.53162,-0.40722,-0.29982 -3.0769,0.52986,0.16044,-1.0867,-1.3272,-1.451,-1.1608,-0.33139,-0.51325,-2.1848,0.81618,-1.7039,-0.057088,0.1491,0.40838 --1.1562,-1.6269,-0.30911,2.3549,-1.2109,1.6556,0.012148,-0.76975,0.27307,-0.30364,1.5571,-0.62827,-0.83108,-0.93158,-0.12309 -5.2308,-3.5151,-0.60352,-0.59996,-0.36846,-1.1652,0.29643,1.7861,0.73144,-1.3648,0.34666,0.69157,-0.84562,-0.020109,-1.6488 --2.108,2.2727,0.61176,-0.74275,-0.70468,1.4316,1.5487,0.48497,-2.1253,0.94976,-0.28239,0.35185,0.26971,0.18572,1.1757 --0.35934,0.97506,0.65817,2.4938,1.1914,-1.5882,0.88811,1.5892,1.0424,-0.29683,-1.2559,0.032407,0.3631,-0.30626,0.44347 -0.54464,2.7811,-1.992,-0.35361,-1.8189,-0.37498,-0.4641,-0.64747,-0.1323,-0.60307,-0.63055,-0.90568,0.84154,0.81357,0.1086 -0.14617,-0.6869,0.18721,1.6617,0.64625,0.43758,-0.19493,2.4525,0.97561,-1.2853,-0.025493,-0.87577,-1.7902,0.80324,0.068641 -1.2524,-0.68466,1.2283,3.3157,-0.17111,-0.11085,-0.84733,-1.5101,-1.3879,-1.8233,1.1386,0.48944,-0.021459,0.49209,0.78038 -0.20586,0.16506,-0.31511,3.5071,-0.45864,-0.43469,0.3321,-0.37442,-0.24537,-0.87045,1.2402,0.86369,0.14008,0.20747,-0.068582 -1.1577,1.9164,-1.53,0.84862,0.45301,-0.41597,-0.46588,0.45156,-0.63333,2.064,-0.63312,-1.5777,-0.31247,1.3432,0.53745 --2.2822,-2.0643,-2.3015,-0.15817,-0.95628,-1.0987,-2.8685,0.077507,-0.83794,-0.60885,-0.028351,1.5561,-0.99211,-1.0076,1.2382 -1.2049,0.29418,0.50598,1.5095,-0.38319,-2.053,-1.7238,-0.20215,0.26209,-0.39916,1.4091,0.48051,0.78386,1.2746,-0.61396 -2.5865,0.52106,0.92594,-1.3622,-2.6689,1.4491,-1.0107,2.4534,-0.076083,-0.63016,-1.1825,0.38557,-0.49278,0.67755,-1.0037 --2.6447,1.6587,1.3069,-0.018988,0.83454,-0.14742,0.44842,1.4082,-0.87815,2.3075,0.00025322,0.32687,-0.4531,0.34292,0.27814 -1.892,2.519,0.041843,-0.92365,-2.0563,-0.95511,0.40588,-0.447,-1.3861,-1.9108,-1.0661,-1.5248,0.13987,0.62908,-0.45487 -0.59066,1.8528,-0.64092,2.1798,2.7102,-1.7747,-1.0101,1.7168,0.8331,-0.19192,-0.2558,-1.4812,-0.59796,0.11496,-1.0738 -4.8991,-2.3024,-1.3074,-3.5703,-1.4354,-0.71609,-0.55404,3.241,-0.23504,0.13868,0.7099,0.049474,-0.51817,0.13951,-0.43583 -4.3969,-0.74904,-0.42801,-0.58328,-0.53535,-2.1894,0.53714,0.11517,1.8126,-1.8343,-0.58371,-0.24888,0.18243,0.78879,-1.704 -5.093,-2.5583,1.1529,-1.0643,0.76018,-2.8453,0.78253,-0.48239,-2.0042,-1.0478,0.71103,-0.033028,0.3963,0.89118,1.0166 -0.13901,3.7996,-0.71364,-0.41747,1.3994,0.68817,-1.4873,0.018002,-0.96753,-0.03059,-0.4219,-1.1434,1.4715,-0.93822,-0.70939 -1.0729,1.3453,-1.0138,1.4238,2.5018,-1.7004,-0.62863,0.85582,1.5549,0.12785,0.14197,-1.2517,0.77403,0.36473,-1.0873 --1.0758,-0.37314,1.4116,2.7594,0.69036,-1.1032,-0.13392,0.93752,-0.47929,-1.8487,1.3163,0.84524,-2.0211,-0.2401,-0.3972 --0.51766,1.1565,1.1249,1.4802,-2.1051,0.011754,-1.2928,-0.23307,-0.042572,-0.94596,1.5556,1.3646,-1.0552,0.87095,-0.022778 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+++ /dev/null @@ -1,11791 +0,0 @@ -2.5666,-1.3258,-2.3719,-0.81289,-1.7141,0.7571,-1.3487,1.482,1.7461,-0.623,1.1981,-0.059047,0.71099,-0.091248,-0.80487 --1.4274,-3.2869,-1.7332,0.44458,-0.75227,-0.5145,-2.0378,0.84484,0.050713,-0.57453,-0.079848,-0.7359,-1.4958,-0.74878,-0.64043 -3.9474,-1.4721,-1.0213,-1.7703,-0.25065,-2.9527,1.7425,0.74606,0.9182,-1.2281,1.0997,1.2411,0.59785,-0.0074702,0.094585 --1.4837,2.2081,-0.77793,0.50082,-2.4392,0.28392,1.1439,0.11527,-0.21909,0.023451,-0.15907,0.39537,0.59824,0.46849,0.88638 --2.1028,0.0073963,-3.9499,-0.90592,0.029504,-0.46591,-0.4158,1.562,-0.99494,0.11942,-1.2049,0.25491,0.14717,1.0847,0.36351 -3.2991,2.085,1.0111,-1.9042,-0.35306,-3.4281,0.38001,0.18513,-0.35228,-1.5426,-0.84675,-0.88732,0.35325,0.90059,-0.002708 -3.9044,-3.9422,-2.3386,0.81279,-1.009,1.4146,2.2834,1.6637,-1.0557,-0.053001,-1.1022,1.2403,-0.76104,0.95952,-0.77029 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-0.13954,1.5859,1.9573,-0.60427,-2.2064,2.0621,0.70664,-0.42521,0.56877,-0.26408,0.18389,-0.35845,-1.3181,1.2522,0.9065 --0.50503,0.13444,1.9475,0.88809,-0.69158,1.1039,1.3886,-1.9,1.0064,1.337,0.42843,-0.51994,-0.83409,0.51843,0.64189 -1.2003,0.12027,2.8145,1.0958,-1.0676,1.5475,0.75708,-1.1756,1.4236,-0.075795,-0.28824,0.36078,-0.7748,0.79759,0.60926 --1.0975,-0.63526,1.7708,1.1485,-0.13851,1.2654,1.5995,-1.9212,1.9441,0.5053,0.33788,-0.050851,-0.011933,-0.07779,0.32587 --2.4074,0.40932,1.0581,0.083151,-0.87563,0.21587,1.2185,-1.5935,1.3928,1.18,1.159,0.51043,0.25603,0.29616,-0.44371 --0.99513,-1.5752,0.2075,1.0424,0.29787,2.5733,0.21577,-0.57694,1.2696,0.144,0.16333,-1.5539,1.1258,0.3693,1.3714 -0.70384,-0.70877,1.6032,2.2865,-0.94945,1.7626,1.1941,-1.8413,-0.4262,-0.53409,0.25382,-0.1709,-0.17228,-0.01404,1.0004 --1.648,0.22327,1.6412,-0.52334,-1.146,0.5149,1.4125,-2.3362,1.4294,1.0253,0.37004,0.13459,-0.39543,0.12975,-0.01775 -0.028787,-1.7517,1.9139,0.058782,-0.51782,2.3901,1.3224,-1.5177,1.3938,0.74673,-0.83325,-1.679,-1.2501,1.0808,0.23999 --1.1576,0.86803,2.7128,0.51759,-0.59808,-0.10683,1.6587,-1.8075,1.4339,0.75073,0.31149,0.56679,-0.015906,0.40294,0.12174 --3.3557,0.25716,0.072183,-1.4746,-0.18102,0.51878,0.5539,-0.18569,1.2607,0.066842,0.8022,-0.043918,0.34464,-1.0587,0.20358 --0.24844,0.55793,2.8218,1.084,0.57273,1.3958,-0.24938,0.84315,0.57681,0.45005,1.796,0.41191,-0.70099,2.3779,0.37048 --1.6353,-0.28412,1.0791,-0.0053575,0.19456,0.5982,-0.16811,0.88048,2.5314,-0.24361,1.158,-0.6806,0.56981,1.23,-0.4213 --1.5482,0.72995,0.77668,0.62277,-1.1175,0.70789,0.14963,0.022555,1.6846,0.9218,1.0566,-0.13013,0.76615,0.40605,-0.25907 -0.95561,0.58846,1.0535,0.38081,-0.30675,0.90444,-0.58385,1.0578,2.5344,0.096769,-0.59706,0.85602,1.224,0.16505,1.0344 --1.7575,1.7019,1.6023,-1.704,-0.53182,2.0095,0.93342,-1.1873,-1.1285,-0.3885,1.2568,-1.3931,0.38235,-0.32176,-0.27483 -1.7821,-1.2689,3.909,-0.23324,2.3708,0.63382,1.5356,0.90857,1.025,-0.60872,-0.55817,0.52837,0.62441,0.42472,-0.28822 --0.50136,-1.6394,1.4154,-1.0351,-0.21638,0.93853,0.010422,-0.16923,1.8844,1.0124,-1.1504,-1.1136,1.6999,1.3248,-0.88751 -0.62915,1.2517,2.9099,-1.6836,-0.14533,0.48485,0.33117,0.7022,1.8623,-0.057267,0.29125,0.18375,0.28661,-0.78679,0.76897 --1.4683,-1.9497,-0.54703,1.8648,1.3782,1.4116,0.58023,0.76056,-0.81816,-1.335,-0.2391,-2.2389,2.503,-0.19565,0.21321 --1.9792,-1.2515,0.94148,0.53975,-0.21616,-0.78323,0.6725,1.5383,1.4933,0.20876,-0.19252,-2.0413,0.83638,-0.82798,0.060837 --0.51067,0.028018,3.1893,0.47417,1.0322,-0.39077,-0.078098,1.8968,2.0608,-0.24565,0.79953,0.14745,0.17607,-0.32799,1.6636 --3.9494,-0.65429,-0.84353,-1.8513,1.0095,0.64986,-0.56265,-1.1181,0.78401,-0.67825,0.34365,0.40068,0.3322,0.86838,-0.63091 --2.7601,0.65037,1.1647,-1.1299,0.088305,0.78145,0.16021,1.0871,1.1993,-1.3556,1.441,-1.2018,0.67257,-0.56771,0.58463 --2.0302,-0.42459,1.2436,-2.4052,-0.12528,-0.010035,0.42007,-1.116,1.4495,0.072658,-0.44407,-0.64332,0.80154,-1.3731,0.23347 -2.4741,2.2545,0.29933,-1.4894,0.27213,0.9274,-1.7194,-0.63558,0.78123,0.18668,0.1224,0.59813,1.051,-0.26173,-0.87989 -0.87224,1.4833,2.2474,-1.1949,-0.17681,0.89284,0.0038537,1.3304,1.059,0.14387,-0.020942,0.85052,1.489,-1.073,0.73611 --2.4822,0.22936,0.18743,-0.32501,-0.49603,-0.16499,0.37154,2.1742,1.7382,-0.96736,0.7363,-1.6878,-0.014577,0.43129,0.37208 --1.2545,-0.97717,1.6001,-0.72235,0.54767,0.69105,-0.22069,0.32841,1.8741,0.83213,0.017644,-0.94282,1.3775,1.4132,0.36698 --3.0983,-0.55126,-0.22251,0.094539,-0.73066,-0.56379,0.72553,1.2106,1.7061,0.066325,0.18447,-1.2879,-0.044157,0.053816,0.37502 --4.0725,1.2195,-0.39915,-1.6724,1.0472,0.62845,1.4239,-1.0101,-0.80734,0.4353,0.53477,-0.37295,0.6239,-1.188,-0.081675 --3.4676,-0.796,-2.8672,-1.8067,1.3801,-0.55145,-0.96443,0.058398,0.52063,-1.5413,-1.1735,0.70318,-0.67923,-0.041355,0.49925 -1.9334,-0.29478,2.3251,2.0716,2.7862,2.4223,0.4495,-0.029801,-0.029477,-1.3309,-0.7474,0.9125,1.193,0.20497,0.82723 --1.4874,-1.5139,2.1591,1.7292,1.1417,-0.17584,1.1095,1.4209,1.5597,-0.23111,-0.012189,-0.96294,-0.45018,-1.0046,1.7353 --0.15928,-0.18978,3.3728,1.8179,2.69,1.0752,1.7763,-0.60453,-0.084359,-1.1578,-0.9258,0.44,-0.2627,-0.92011,0.92918 --4.3082,-0.29095,-1.098,-1.5904,0.84163,-0.63402,0.71184,1.0418,0.10144,-0.081221,0.1629,-0.7167,0.22019,-0.28333,0.072374 --3.7304,0.98452,1.4461,-0.71581,0.099413,-0.52307,1.1656,0.36865,0.96444,-0.4254,0.91102,0.10761,0.68936,0.23966,0.30848 --3.9019,-0.96718,-0.95242,-1.0812,0.70043,-1.7655,-0.097167,-0.36234,0.64107,0.73424,-0.37472,0.02413,0.88537,0.19002,-0.32446 --3.6377,-0.11524,0.22216,-1.4145,1.4846,-1.4785,0.84057,-1.4927,1.0633,1.135,-0.10588,0.46041,0.77243,0.12377,-0.44409 --3.4264,1.5059,1.139,-0.6112,0.5221,-1.1104,1.5495,1.3499,0.72282,-0.22459,0.42378,-0.12763,-0.1292,-1.1343,0.98985 --1.695,-3.6906,0.58147,0.47004,2.454,-0.56253,1.1748,0.47573,0.44499,-0.49939,-0.57136,-1.8374,0.15731,-0.89349,0.20193 --3.0507,-1.2956,-1.6691,-1.4194,0.5129,0.36311,-1.4334,-0.60235,0.41229,-1.9675,-0.29582,0.30957,0.26316,-0.03379,0.78652 --3.1384,-1.9205,-1.8359,-1.5583,1.1638,1.0038,-1.4861,0.30843,0.47555,-1.4681,-1.4656,0.34139,-0.42305,0.44757,0.87923 --2.6533,-1.6315,-2.5949,-0.68357,0.76346,2.1294,-1.1561,-0.55532,0.25017,-1.2454,-1.6974,0.36847,-0.18314,0.68786,0.23761 --0.83746,-0.96006,-0.9514,-1.29,-0.64656,2.0835,-0.99411,-0.84631,0.022955,-0.55621,-0.86837,0.69627,1.367,1.5034,0.87398 --2.8452,-0.57388,-2.6382,-0.19688,-0.13348,0.93657,0.040835,-0.39971,0.46478,-1.675,-0.29827,-1.0589,0.43039,-0.71133,-0.15874 --2.3371,-0.58789,-2.3648,-0.026632,0.2275,2.34,-0.3747,-0.87236,0.71903,-1.3137,-0.1337,-0.19942,0.95104,0.80286,-0.32855 --2.35,-0.76406,-0.47972,-1.0338,0.085147,1.6397,-0.83987,0.1465,0.79905,-0.95084,0.16913,-0.83299,1.9207,1.796,0.54669 --2.7514,-1.028,0.21153,-1.3997,0.28374,1.6727,-0.25639,-0.44063,1.1628,-1.2411,0.42992,-1.607,1.5005,0.49365,0.73299 --2.3512,-1.0148,-1.8745,-0.96903,0.43199,1.8583,-1.3756,-0.43813,0.056795,-1.0012,-0.76345,0.024682,1.1742,0.91184,1.5851 --2.307,-0.97455,-2.5102,-0.35901,0.24727,1.7813,-0.99627,-1.243,0.34366,-1.7268,-0.61829,0.75832,0.54855,0.43315,0.77306 diff --git a/bayesian_ml/2/em.py b/bayesian_ml/2/em.py deleted file mode 100644 index ad958963..00000000 --- a/bayesian_ml/2/em.py +++ /dev/null @@ -1,69 +0,0 @@ -# expectation-maximization for the model: -# x(n) ~ N(Wz(n), sigma**2 I) (observed variables) -# z(n) ~ N(0, I) (latent variables) -# W ~ N(0, 1/lambda) - -import numpy as np -import matplotlib.pyplot as plt -from scipy.stats import multivariate_normal as mvn -from scipy.stats import norm - -N = 1000 -lam = 1.0 -sigma = 1.0 -D = 2 -K = 3 -sigmaI = sigma * sigma * np.eye(D) - -# generate the data -Z = np.random.randn(N, K) -W0 = np.random.randn(D, K)*lam -X = np.zeros((N, D)) -for i in xrange(N): - X[i] = np.random.multivariate_normal(mean=W0.dot(Z[i]), cov=sigmaI) - -def loglikelihood(X, Z, W): - ZW = Z.dot(W.T) - LL = 0 - for i in xrange(N): - ll = mvn.logpdf(X[i], mean=ZW[i], cov=sigmaI) - LL += ll - LL += norm.logpdf(W.flatten(), scale=1/lam).sum() - return LL - -# do EM -W = np.random.randn(D, K) / np.sqrt(D + K) -costs = [] -for t in xrange(50): - # E-step - R = np.linalg.solve(W.dot(W.T) + sigmaI, W).T - # test = W.T.dot( np.linalg.inv(W.dot(W.T) + sigmaI) ) - # print "R:", R - # print "test:", test - # diff = np.abs(R - test).sum() - # print "diff:", diff - Ez = X.dot(R.T) - - # M-step - xzT = X.T.dot(Ez) - toinvert = N*(np.eye(K) - R.dot(W)) + Ez.T.dot(Ez) + sigma*sigma*lam*np.eye(K) - W = np.linalg.solve(toinvert, xzT.T).T - # test = xzT.dot( np.linalg.inv(toinvert) ) - # print "W:", W - # print "test:", test - # diff = np.abs(W - test).sum() - # print "diff:", diff - - # likelihood - cost = loglikelihood(X, Ez, W) - costs.append(cost) - -plt.plot(costs) -plt.show() - -print "actual W:", W0 -print "predicted W:", W - -print "log-likelihood given real W:", loglikelihood(X, Z, W0) - -print "log-likelihood found:", costs[-1] diff --git a/bayesian_ml/2/probit.py b/bayesian_ml/2/probit.py deleted file mode 100644 index a1cfc379..00000000 --- a/bayesian_ml/2/probit.py +++ /dev/null @@ -1,128 +0,0 @@ -# probit regression - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from scipy.stats import norm -# from scipy.stats import t -from sortedcontainers import SortedList - - -class ProbitRegression: - def fit(self, X, Y, sigma=1.5, lam=1, show_w=set(), Q=None): - - # setup - N, D = X.shape - self.w = np.random.randn(D) / np.sqrt(D) # does not work if you don't scale first! - Eq = np.zeros(N) - idx1 = (Y == 1) - idx0 = (Y == 0) - A = lam*np.eye(D) + X.T.dot(X) / sigma**2 - costs = [] - - for t in xrange(100): - # calculate ln(p(Y, w | X)) - cdf = norm.cdf(X.dot(self.w) / sigma) - cost = -lam/2 * self.w.dot(self.w) + Y.dot(np.log(cdf)) + (1 - Y).dot(np.log(1 - cdf)) - costs.append(cost) - - # E step - Xw = X.dot(self.w) - pdf = norm.pdf(-Xw / sigma) - cdf = norm.cdf(-Xw / sigma) - Eq[idx1] = Xw[idx1] + sigma*(pdf[idx1] / (1 - cdf[idx1])) - Eq[idx0] = Xw[idx0] + sigma*(-pdf[idx0] / cdf[idx0]) - - # M step - b = X.T.dot(Eq) / sigma**2 - self.w = np.linalg.solve(A, b) - - if show_w and t in show_w: - plot_image(self.w, Q, "iteration: %s" % (t+1)) - - - plt.plot(costs) - plt.show() - - self.sigma = sigma - self.lam = lam - - - def predict_proba(self, X): - N, D = X.shape - return norm.cdf(X.dot(self.w) / self.sigma) - - def predict(self, X): - return np.round(self.predict_proba(X)) - - def score(self, X, Y): - return np.mean(self.predict(X) == Y) - - def confusion_matrix(self, X, Y): - P = self.predict(X) - M = np.zeros((2, 2)) - M[0,0] = np.sum(P[Y == 0] == Y[Y == 0]) - M[0,1] = np.sum(P[Y == 0] != Y[Y == 0]) - M[1,0] = np.sum(P[Y == 1] != Y[Y == 1]) - M[1,1] = np.sum(P[Y == 1] == Y[Y == 1]) - return M - - def get_3_misclassified(self, X, Y): - P = self.predict(X) - N = len(Y) - samples = np.random.choice(N, 3, replace=False, p=(P != Y)/float(np.sum(P != Y))) - return X[samples], Y[samples], P[samples] - - def get_3_most_ambiguous(self, X, Y): - P = self.predict_proba(X) - N = len(X) - sl = SortedList(load=3) # stores (distance, sample index) tuples - for n in xrange(N): - p = P[n] - dist = np.abs(p - 0.5) - if len(sl) < 3: - sl.add( (dist, n) ) - else: - if dist < sl[-1][0]: - del sl[-1] - sl.add( (dist, n) ) - indexes = [v for k, v in sl] - return X[indexes], Y[indexes] - -def plot_image(x, Q, title): - im = Q.dot(x) - plt.imshow(im.reshape(28,28), cmap='gray') - plt.title(title) - plt.show() - -if __name__ == '__main__': - Xtrain = pd.read_csv('Xtrain.csv', header=None).as_matrix() - Xtest = pd.read_csv('Xtest.csv', header=None).as_matrix() - Ytrain = pd.read_csv('ytrain.csv', header=None).as_matrix().flatten() - # print "Ytrain.shape:", Ytrain.shape - Ytest = pd.read_csv('ytest.csv', header=None).as_matrix().flatten() - model = ProbitRegression() - model.fit(Xtrain, Ytrain) - print "train accuracy:", model.score(Xtrain, Ytrain) - print "test accuracy:", model.score(Xtest, Ytest) - - # confusion matrix - M = model.confusion_matrix(Xtest, Ytest) - print "confusion matrix:" - print M - print "N:", len(Ytest) - print "sum(M):", M.sum() - - # plot 3 misclassified - Q = pd.read_csv('Q.csv', header=None).as_matrix() - misclassified, targets, predictions = model.get_3_misclassified(Xtrain, Ytrain) - for x, y, p in zip(misclassified, targets, predictions): - plot_image(x, Q, 'misclassified target=%s prediction=%s' % (y, int(p))) - - # ambiguous - ambiguous, targets = model.get_3_most_ambiguous(Xtrain, Ytrain) - for x, y in zip(ambiguous, targets): - plot_image(x, Q, 'ambiguous target=%s' % y) - - # show w - model.fit(Xtrain, Ytrain, show_w=set([0, 4, 9, 24, 49, 99]), Q=Q) diff --git a/bayesian_ml/2/ytest.csv 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diff --git a/bayesian_ml/3/X_set3.csv b/bayesian_ml/3/X_set3.csv deleted file mode 100644 index def7ea2c..00000000 --- a/bayesian_ml/3/X_set3.csv +++ /dev/null @@ -1,500 +0,0 @@ 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diff --git a/bayesian_ml/3/run.py b/bayesian_ml/3/run.py deleted file mode 100644 index 72d9d8ee..00000000 --- a/bayesian_ml/3/run.py +++ /dev/null @@ -1,156 +0,0 @@ -# variational-inference for linear regression -# y(i) ~ N( x(i).dot(w), 1/lambda ) -# w ~ N( 0, diag(alpha_1, alpha_2, ..., alpha_D)^-1 ) -# alpha_i ~ Gamma(a, b) -# lambda ~ Gamma(e, f) - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from scipy.stats import multivariate_normal as mvn -from scipy.stats import gamma as gamma_dist -from scipy.special import gamma, digamma - -def e_ln_q_gamma(a, b): - return np.log(b) - a - np.log(np.abs(gamma(a))) + (a - 1)*digamma(a) - -def objective(X, Y, C, mu, a, b, e, f, a0, b0, e0, f0): - log2pi = np.log(2*np.pi) - N, D = X.shape - - # E(lnX) = digamma(a) - ln(b) for X ~ Gamma(a,b) - E_ln_lambda = digamma(e) - np.log(f) - E_ln_alpha = digamma(a) - np.log(b) - - # model likelihood - total = (N/2.0)*(E_ln_lambda - log2pi) - data_total = 0 - for i in xrange(N): - delta = Y[i] - X[i].dot(mu) - data_total += delta*delta + X[i].dot(C).dot(X[i]) - total -= (float(e)/f)/2.0 * data_total - - # print "total after model likelihood:", total - - # w likelihood - total -= (D/2.0)*log2pi - for k in xrange(D): - total += 0.5*(E_ln_alpha[k] - (float(a[k])/b[k])*(C[k,k] + mu[k]*mu[k])) - - # print "total after w likelihood:", total - - # lambda likelihood - total += e0*np.log(f0) - np.log(gamma(e0)) + (e0 - 1)*E_ln_lambda - f0*(float(e)/f) - - # print "total after lambda likelihood:", total - - # alpha likelihood - for k in xrange(D): - total += a0*np.log(b0) - np.log(gamma(a0)) + (a0 - 1)*E_ln_alpha[k] - b0*(float(a[k])/b[k]) - - # print "total after alpha likelihood:", total - - # entropy - # TODO: calculate this manually - # total -= mvn.entropy(mean=mu, cov=C) - # e1 = mvn.entropy(cov=C) - # e2 = 0.5*np.log( np.linalg.det(2*np.pi*np.e*C) ) - # print "e1:", e1, "e2:", e2 - # total += 0.5*np.log( np.linalg.det(2*np.pi*np.e*C) ) - - total += mvn.entropy(cov=C) - # print "det(C):", np.linalg.det(C) - # print "total after lnq(w):", total - - # total -= gamma_dist.entropy(e, scale=1.0/f) - # e3 = gamma_dist.entropy(e, scale=1.0/f) - # e4 = -e_ln_q_gamma(e, f) - # print "e3:", e3, "e4:", e4 - # assert(np.abs(e3 - e4) < 1e-8) - total += gamma_dist.entropy(e, scale=1.0/f) - # total -= e_ln_q_gamma(e, f) - # print "total after lnq(lambda):", total - for k in xrange(D): - # total -= e_ln_q_gamma(a[k], b[k]) - total += gamma_dist.entropy(a[k], scale=1.0/b[k]) - return total - - -def run(num=1, T=500): - X = pd.read_csv('X_set%s.csv' % num, header=None).as_matrix() - Y = pd.read_csv('y_set%s.csv' % num, header=None).as_matrix().flatten() - Z = pd.read_csv('z_set%s.csv' % num, header=None).as_matrix().flatten() - N, D = X.shape - print X.shape, Y.shape, Z.shape - - a0 = 1e-16 - b0 = 1e-16 - e0 = 1 - f0 = 1 - - # params for q(w) - doesn't matter what we set it to, we'll update this first - C = np.eye(D) - mu = np.zeros(D) - - # params for q(lambda) - e = e0 - f = f0 - - # params for q(alpha) - a = np.ones(D)*a0 - b = np.ones(D)*b0 - a0ones = np.ones(D)*a0 - - # objective - L = np.empty(T) - - for t in xrange(T): - # update q(w) - C = np.linalg.inv(np.diag(1.0*a/b) + (1.0*e/f)*X.T.dot(X)) - mu = C.dot((1.0*e/f)*X.T.dot(Y)) - - # update q(alpha) - a = a0ones + 0.5 - b = b0 + 0.5*(np.diag(C) + mu*mu) - # for k in xrange(D): - # a[k] = a0 + 0.5 - # b[k] = b0 + 0.5*(C[k,k] + mu[k]*mu[k]) - - # update q(lambda) - e = e0 + N/2.0 - sum_for_f = 0 - # for i in xrange(N): - # delta = Y[i] - X[i].dot(mu) - # sum_for_f += delta*delta + X[i].dot(C).dot(X[i]) - delta = Y - X.dot(mu) - sum_for_f = delta.dot(delta) + np.trace(X.dot(C).dot(X.T)) - f = f0 + 0.5*sum_for_f - - # update L - L[t] = objective(X, Y, C, mu, a, b, e, f, a0, b0, e0, f0) - if t % 20 == 0: - print "t:", t - if num == 3: - print "L:", L[t] - - # plot 1/E[alpha] - plt.plot(b/a) - plt.show() - - # 1/E[lambda] - print "1/E[lambda]:", f/e - - # plot L - plt.plot(L) - plt.show() - - Yhat = X.dot(mu) - plt.plot(Z, Yhat) - plt.scatter(Z, Y) - plt.plot(Z, 10*np.sinc(Z)) - plt.show() - - -run(1) -run(2) -run(3) \ No newline at end of file diff --git a/bayesian_ml/3/y_set1.csv b/bayesian_ml/3/y_set1.csv deleted file mode 100644 index 29b30f32..00000000 --- a/bayesian_ml/3/y_set1.csv +++ /dev/null @@ -1,100 +0,0 @@ --1.9807 -1.3977 -0.39627 -0.11422 -0.59669 -1.6194 -1.9997 -0.27002 --1.2381 -0.71259 -1.1677 --0.56528 --0.16157 -1.4075 -1.3544 --0.0078934 -1.1667 -0.83691 --0.75035 --0.76558 -0.26084 --1.4914 --0.11861 --0.77599 -0.65591 -1.753 -0.53411 -0.2034 -3.2292 --1.9822 --1.7943 --1.167 -0.49746 --1.9873 --2.7712 --2.2993 --3.1986 --1.6758 --1.3253 --2.7287 -0.26081 --2.1141 --1.7219 --1.0972 -0.39853 -1.278 -5.6629 -4.4801 -7.797 -8.4042 -6.9353 -8.2126 -5.4825 -9.6335 -11.187 -11.906 -9.3574 -7.6739 -8.2142 -6.3477 -5.4952 -2.8962 -0.48351 --0.30745 --1.6697 --1.567 --3.1646 --1.103 --1.578 --2.5523 --2.1199 --2.1978 --1.8076 --2.9934 --1.1843 --0.94178 --0.97797 --1.0436 --2.7953 --0.74118 -0.62131 -0.41625 -1.3788 -1.4919 -0.11158 -0.51488 --0.95601 --1.0738 -0.069906 --2.1404 --0.90422 --0.31789 --0.34168 -1.0552 --1.2299 -0.55841 --1.0027 --0.038141 --1.3129 --0.47181 diff --git a/bayesian_ml/3/y_set2.csv b/bayesian_ml/3/y_set2.csv deleted file mode 100644 index 572324fe..00000000 --- a/bayesian_ml/3/y_set2.csv +++ /dev/null @@ -1,250 +0,0 @@ -1.0719 -0.11583 -0.93244 -0.57572 -0.31694 -0.83109 --1.0948 --0.96628 --0.70567 --0.42234 -0.50279 --0.62063 -1.4233 -0.15329 -0.29718 -1.9027 --0.29558 -0.70807 --1.7488 --0.055268 --0.11002 -0.29835 --1.2306 -0.12409 -0.87917 -0.11703 -2.1908 -1.9126 -1.3058 -1.2737 -1.3056 -0.041982 --0.11861 --0.0028592 -0.91428 --0.88784 --0.14079 --0.47711 --1.017 --0.47484 --2.1037 --1.8068 --1.1703 --0.66441 -0.27772 --1.43 --0.38931 --0.21783 --0.23204 --1.7272 --1.5208 -1.7792 --1.4753 --1.1052 -0.11862 -1.7058 -2.9352 -1.5628 -1.3946 -0.11424 -0.4934 -1.2871 -2.5238 -2.0262 -1.0801 --0.9816 --0.36913 -2.1336 -0.61729 -0.2564 -0.84102 -0.23374 --0.26162 --2.0896 --1.1948 --1.937 --1.435 --0.81724 --1.2405 --2.9074 --4.0468 --2.4879 --2.7022 -0.64254 --1.4789 -0.081845 --1.3779 --0.23709 -0.28329 -0.17127 --0.091373 -0.21315 --0.054294 -0.47168 -1.0184 -0.50646 -0.34773 -0.63896 -1.9126 -1.882 -3.6891 -2.5319 -1.5174 -2.2353 -3.4245 -5.2983 -3.8079 -5.8691 -4.2966 -6.1311 -6.8197 -10.243 -8.5904 -8.5367 -10.189 -10.478 -10.912 -9.3415 -9.3864 -11.178 -8.9284 -10.957 -9.5685 -9.5365 -10.038 -8.5009 -9.8221 -10.111 -8.9823 -7.4138 -6.0708 -5.0509 -6.1225 -3.1199 -3.2787 -4.1355 -2.0782 -2.6844 -0.59051 -0.67348 -0.9691 --0.35254 --0.82645 --0.22224 --0.65941 --0.89189 --2.1968 --1.6648 --2.2095 --2.7901 --0.91662 --2.7168 --2.1209 --2.3851 --0.82882 --1.7077 --2.1833 --2.2892 --0.88709 --0.97589 --2.6419 -0.95409 --0.46452 --1.5276 --0.84018 --1.9559 --0.81199 -0.48569 --1.2974 --1.3495 -1.2276 --1.3513 --0.69919 --0.57888 --0.02456 -1.7478 -0.1268 -0.74479 -1.5368 -0.28655 -1.1954 --1.1805 --0.15926 -1.946 -2.1353 -1.6037 -1.8437 -1.1915 -0.19297 -2.1556 -0.26826 --0.65444 --0.7975 -1.0501 -0.0019153 -0.47655 --0.50907 -0.96367 --0.329 -0.48126 -2.3427 -0.14366 --0.34113 -0.023812 -0.85333 -0.81691 --2.7054 --1.062 --1.2612 --1.1259 --2.5775 --1.2307 --0.9116 --2.7058 --1.7947 -0.032767 -1.361 --1.4488 -0.78974 -1.0157 --0.79251 --0.46814 -0.52801 -1.801 --0.39036 -1.387 --0.51833 -1.3714 --0.031317 -0.35643 --0.36886 -1.4649 -0.63804 -0.27513 --1.7868 -0.15545 -1.8684 -0.32499 -2.4063 -1.9406 -0.75147 -0.65898 -0.72895 -0.28186 -2.2324 --0.53944 -0.54644 -0.117 -0.41722 -1.3173 diff --git a/bayesian_ml/3/y_set3.csv b/bayesian_ml/3/y_set3.csv deleted file mode 100644 index 11ff70f8..00000000 --- a/bayesian_ml/3/y_set3.csv +++ /dev/null @@ -1,500 +0,0 @@ -1.2493 -0.34608 --0.65115 --0.65096 -0.43414 -1.8492 --0.4061 --0.38145 --1.2443 -1.1232 --0.20684 -0.39811 -2.4196 -0.030414 -0.81584 -1.1718 -1.5879 -1.4314 -1.4397 --1.1339 -0.69808 --0.3128 -1.1161 -1.2201 -1.6538 --0.35806 -0.72805 -1.7007 -1.2565 --1.1105 -1.3889 -0.55208 -0.22673 --0.21678 -0.44269 --0.33804 -0.39935 -1.2985 -2.2668 -1.4106 -0.11432 -0.4936 --1.3862 -2.6217 --0.31597 -0.82576 -2.5351 --0.10319 --0.40329 --0.40897 --0.24345 -1.4331 -0.16827 -0.36301 -0.16318 -2.1634 --0.68517 -0.78599 --0.64304 -0.1045 --0.86334 --0.65277 -0.25854 --0.089523 --0.11344 --0.017831 --0.34328 -0.53786 --0.58527 --2.1837 --0.60024 --1.2742 --1.0972 --1.5656 --1.1829 --1.219 --0.29995 --1.3236 --1.5228 --2.175 --2.7898 --1.1056 --1.327 --1.4542 -0.10067 -0.23424 --1.4687 --0.72758 --0.45606 --2.2871 -0.97331 --1.1038 -0.9757 --1.244 -1.222 --0.031121 -0.50533 --0.013989 -0.64676 --1.0884 -0.90698 -2.0415 --0.38962 -1.7227 -0.83094 --0.74901 --0.96453 -2.1476 -0.46637 -0.76593 -0.51508 -1.0626 -2.1497 --0.30601 -0.16019 -0.31719 --0.76152 -2.0413 -1.0623 -0.19511 -0.46962 -1.2896 -0.47874 -1.5794 -2.1143 -0.9692 -2.5658 --0.038618 -2.6827 -1.4048 -0.62164 -0.031624 -1.2829 -0.86453 -1.4091 -2.088 -0.27013 -1.2093 --0.092303 -1.3038 -1.7231 -0.26136 -0.52718 -1.0105 --0.1066 --0.3479 -0.03181 -0.73896 --1.1217 -0.40028 --0.9078 --1.4733 -0.82008 --1.6905 -0.0035708 --2.9985 --0.2411 --0.2193 --2.0608 -0.01023 --1.8287 --0.99157 --1.8855 --2.6693 --1.5502 --1.3649 --2.8221 --1.0708 --3.424 --1.2715 --1.6072 --1.9263 --2.5412 --1.4759 --3.3407 --1.3589 --3.2652 --2.7701 --1.3492 --1.714 --3.1749 --3.09 --2.4606 --1.5065 --3.14 --2.6178 --3.5084 --1.124 --1.7163 --0.68931 --2.8995 --2.5311 --1.4396 --2.2828 --2.8695 --2.1709 --1.2409 --2.82 --1.2289 -0.42502 --1.8517 --0.80668 --0.37975 --0.33459 --0.96075 --0.6551 -2.0577 -1.0784 --0.60036 -1.4198 -0.96653 --0.22334 -2.0814 -2.1007 -1.8345 -2.3843 -2.2154 -2.3614 -3.0337 -2.4449 -3.4899 -4.7464 -3.6022 -4.5685 -4.9579 -6.5489 -6.1826 -8.9815 -6.6195 -4.8026 -6.3658 -5.2625 -6.9559 -5.7759 -8.0737 -7.6743 -5.7853 -9.151 -5.7291 -7.5521 -6.4452 -8.7975 -7.5657 -10.933 -8.1625 -11.465 -10.255 -8.5447 -10.529 -8.2317 -10.387 -9.6344 -10.132 -8.8802 -12.059 -8.9192 -10.167 -8.8654 -11.663 -8.9277 -7.5368 -7.809 -10.241 -10.957 -8.7962 -8.1113 -9.8184 -8.1635 -8.8731 -8.6392 -8.5438 -7.2449 -6.9828 -6.7644 -8.412 -6.7862 -7.1257 -7.0037 -5.1117 -7.1309 -3.25 -5.9281 -5.4059 -6.7685 -5.7655 -4.0587 -3.9134 -5.2006 -5.8704 -4.6025 -3.8403 -2.8321 -1.6716 -5.3248 -1.8202 -5.583 -2.0066 -3.45 -0.71284 -0.17722 --0.14384 --0.58807 --0.14126 -1.3068 --0.29165 -0.74503 -1.1632 -0.10811 --1.8745 --1.2791 --2.0427 --1.0949 -0.5153 --2.7345 --1.1477 --2.9508 --0.82559 --1.9091 --1.2254 --1.1877 --2.783 --3.1284 --0.31351 --0.53083 --1.9842 --1.8107 --2.072 --1.4959 --0.40421 --1.5028 --2.3395 --2.5132 --0.63996 --2.5281 --3.5389 --1.2183 --2.7964 --2.7305 --1.7721 --2.1682 --4.5821 --0.99998 --3.6361 --0.62262 --1.7563 --1.8093 --1.5712 --0.58419 --0.88992 --1.1049 --1.0056 --0.95563 --1.724 --0.25034 --0.53804 --0.57163 --0.58468 -0.19074 --1.6817 -0.65123 -0.33642 -0.60973 -1.4934 -0.98693 -1.0309 --0.74636 -0.092361 --0.64439 -2.0457 -0.73481 -1.5588 -0.23443 -0.27491 -1.669 -0.17705 -0.73712 -2.8887 -1.0358 -1.3774 -0.79075 -0.81847 -0.66837 -1.3098 -0.46651 -1.2713 -1.1663 -0.97329 --0.49777 -3.224 -1.1858 -1.1946 -1.1575 -0.98207 -0.052651 --1.1588 -1.3643 -0.52026 -1.4307 --0.37155 -0.57522 -3.3092 -1.3529 --0.15252 -0.57262 -1.5748 -1.0346 -1.9275 --0.71601 -0.32853 --0.74274 --3.8771 -0.95606 --1.6782 --0.53801 -0.19508 -0.32788 --0.18941 --0.80008 --1.9245 --0.27624 --0.77962 -1.1747 --1.8651 -0.046986 --0.52209 --0.91784 --0.97409 --0.56569 --0.7398 --0.90443 --1.0496 --0.5113 --0.71248 --0.5631 --0.53569 --1.5918 --0.47134 --2.0132 --1.1266 --0.5549 --0.2684 --1.5061 --0.027946 --1.2328 -0.24876 --0.65791 --0.81527 --0.35707 --0.81745 --1.5423 --1.1902 --0.23834 --0.88487 -1.4281 -0.1909 --0.22065 -1.088 -0.043623 -1.244 -0.72797 --0.25048 --0.097561 --0.19166 -1.4331 -1.2264 -0.13112 -0.29906 -2.4459 -1.0515 -1.4143 --0.099597 --0.8996 --0.26254 -2.5685 -1.0277 -1.5229 -0.81648 -1.2022 -1.5323 --0.37481 -0.69914 -1.5554 -0.11952 -1.0232 -0.37087 -0.92954 -0.15541 -0.79317 -1.8288 -1.2101 -0.082028 -1.7327 --1.3822 --0.5564 -2.1984 --1.0316 -0.52191 -0.3748 --0.62216 -0.70765 diff --git a/bayesian_ml/3/z_set1.csv b/bayesian_ml/3/z_set1.csv deleted file mode 100644 index f08fc98d..00000000 --- a/bayesian_ml/3/z_set1.csv +++ /dev/null @@ -1,100 +0,0 @@ --4.9721 --4.9644 --4.8984 --4.8012 --4.7609 --4.7172 --4.5434 --4.5347 --4.4954 --4.4841 --4.4806 --4.4494 --4.3964 --4.3493 --4.3348 --4.1115 --4.1092 --4.064 --3.9887 --3.8479 --3.6875 --3.5039 --3.1283 --3.0908 --3.0679 --2.8088 --2.7464 --2.6872 --2.4361 --2.0802 --1.7881 --1.6403 --1.5986 --1.5728 --1.5019 --1.4374 --1.3605 --1.3552 --1.2699 --1.2649 --1.2424 --1.1851 --1.1478 --1.0591 --0.89585 --0.8062 --0.64684 --0.60975 --0.46749 --0.45814 --0.43733 --0.36286 --0.32266 --0.13261 --0.027699 --0.018297 -0.098857 -0.34143 -0.34161 -0.48671 -0.72093 -0.90776 -1.0157 -1.0277 -1.183 -1.1881 -1.227 -1.2457 -1.2758 -1.3855 -1.4545 -1.4829 -1.4978 -1.5107 -1.5649 -1.6524 -1.6583 -1.7102 -1.7265 -1.7456 -2.0177 -2.0619 -2.5383 -2.657 -2.6603 -2.6838 -2.9611 -3.1443 -3.2889 -3.3979 -3.4234 -3.6852 -3.7243 -4.0176 -4.0548 -4.2511 -4.4312 -4.7979 -4.8796 -4.9119 diff --git a/bayesian_ml/3/z_set2.csv b/bayesian_ml/3/z_set2.csv deleted file mode 100644 index a25d4b58..00000000 --- a/bayesian_ml/3/z_set2.csv +++ /dev/null @@ -1,250 +0,0 @@ --4.9765 --4.9613 --4.9528 --4.9236 --4.8866 --4.8857 --4.8759 --4.8363 --4.7532 --4.7377 --4.7316 --4.7314 --4.7074 --4.6649 --4.6594 --4.6303 --4.5411 --4.4546 --4.4431 --4.3895 --4.3588 --4.3341 --4.3252 --4.2924 --4.2905 --4.2591 --4.2126 --4.1717 --4.1544 --4.076 --4.0729 --4.0091 --3.9728 --3.9633 --3.9224 --3.8991 --3.8987 --3.8864 --3.7039 --3.5211 --3.4777 --3.4763 --3.4762 --3.4289 --3.3513 --3.3399 --3.3322 --3.209 --3.1948 --3.1872 --3.0625 --2.9618 --2.8997 --2.8583 --2.8083 --2.624 --2.5591 --2.5514 --2.4628 --2.3633 --2.3183 --2.3181 --2.254 --2.218 --2.1932 --2.1615 --2.1348 --2.0976 --2.0817 --2.0356 --1.9548 --1.9188 --1.8617 --1.7471 --1.7356 --1.6913 --1.6651 --1.5879 --1.4535 --1.4169 --1.3156 --1.2344 --1.1901 --1.1122 --1.1017 --1.092 --1.043 --1.0154 --1.0148 --0.99199 --0.9747 --0.94525 --0.94279 --0.94171 --0.93568 --0.9191 --0.8916 --0.87727 --0.81128 --0.81116 --0.73165 --0.7306 --0.71359 --0.70257 --0.69548 --0.67237 --0.64835 --0.57223 --0.56135 --0.52631 --0.37393 --0.32748 --0.31186 --0.21541 --0.20386 --0.19103 --0.09329 --0.079393 --0.03808 --0.02132 --0.015546 --0.0044207 -0.067874 -0.14238 -0.15005 -0.16991 -0.20516 -0.3118 -0.31648 -0.32398 -0.50465 -0.62527 -0.67597 -0.69745 -0.75686 -0.78574 -0.79969 -0.852 -0.95128 -1.0048 -1.0158 -1.027 -1.0287 -1.0535 -1.2647 -1.3018 -1.31 -1.3295 -1.3842 -1.4111 -1.493 -1.5035 -1.5178 -1.5246 -1.5462 -1.5795 -1.595 -1.6073 -1.6493 -1.6646 -1.7112 -1.7234 -1.7612 -1.7706 -1.7846 -1.7853 -1.7957 -1.8252 -1.8866 -1.8879 -1.939 -1.9662 -1.9899 -2.1183 -2.1591 -2.1631 -2.2643 -2.2876 -2.2902 -2.3418 -2.3431 -2.3861 -2.4438 -2.5224 -2.5352 -2.5658 -2.6266 -2.6528 -2.6764 -2.7016 -2.7756 -2.8139 -2.8143 -2.8341 -2.8639 -2.8686 -2.9098 -2.9258 -2.9401 -2.9788 -2.9892 -3.0096 -3.0318 -3.0738 -3.1109 -3.2397 -3.2542 -3.2701 -3.2979 -3.3347 -3.3391 -3.3935 -3.3966 -3.4124 -3.4318 -3.4588 -3.7823 -3.8058 -3.8168 -3.8369 -3.8445 -3.8677 -3.9517 -3.9631 -3.983 -4.0057 -4.0207 -4.0905 -4.0963 -4.1593 -4.232 -4.2943 -4.2997 -4.3686 -4.4294 -4.433 -4.4595 -4.5141 -4.5154 -4.5181 -4.5848 -4.5911 -4.6296 -4.6485 -4.6909 -4.7025 -4.758 -4.7679 -4.8249 -4.8402 diff --git a/bayesian_ml/3/z_set3.csv b/bayesian_ml/3/z_set3.csv deleted file mode 100644 index f1651195..00000000 --- a/bayesian_ml/3/z_set3.csv +++ /dev/null @@ -1,500 +0,0 @@ --4.9868 --4.9519 --4.9502 --4.9396 --4.9315 --4.9209 --4.9002 --4.8511 --4.8198 --4.815 --4.7901 --4.786 --4.7699 --4.7306 --4.7182 --4.7096 --4.6725 --4.6661 --4.659 --4.6353 --4.6327 --4.6312 --4.6131 --4.6024 --4.602 --4.5815 --4.5796 --4.5703 --4.5691 --4.5679 --4.5048 --4.4445 --4.439 --4.4213 --4.4054 --4.3931 --4.3785 --4.3615 --4.3316 --4.3151 --4.2883 --4.2547 --4.2516 --4.2449 --4.2388 --4.207 --4.207 --4.131 --4.1254 --4.1223 --4.0995 --4.0978 --4.0531 --4.0356 --4.0282 --4.0122 --4.002 --3.9845 --3.9791 --3.9373 --3.9115 --3.8305 --3.8083 --3.8009 --3.7876 --3.751 --3.7435 --3.7369 --3.7056 --3.6763 --3.6655 --3.6566 --3.6244 --3.5779 --3.5289 --3.4959 --3.4889 --3.4271 --3.4058 --3.3958 --3.3914 --3.3734 --3.2996 --3.2972 --3.2843 --3.2638 --3.2598 --3.2306 --3.15 --3.1489 --3.1262 --3.118 --3.0249 --3.0015 --2.9998 --2.9788 --2.9505 --2.9499 --2.9476 --2.9305 --2.9304 --2.9149 --2.9089 --2.8231 --2.7855 --2.7842 --2.7415 --2.7301 --2.7124 --2.7037 --2.6987 --2.693 --2.6912 --2.6863 --2.6666 --2.6644 --2.6596 --2.6558 --2.6266 --2.5936 --2.5512 --2.5352 --2.5015 --2.4638 --2.448 --2.4398 --2.3495 --2.339 --2.3314 --2.3161 --2.2968 --2.2801 --2.2665 --2.264 --2.2625 --2.2454 --2.2004 --2.1971 --2.1794 --2.152 --2.1368 --2.1344 --2.0479 --2.047 --2.0299 --1.9965 --1.9904 --1.9678 --1.9624 --1.925 --1.9007 --1.8453 --1.8439 --1.7807 --1.7735 --1.7712 --1.7302 --1.7275 --1.7128 --1.6933 --1.6872 --1.6826 --1.6645 --1.6615 --1.6596 --1.6452 --1.6402 --1.6389 --1.627 --1.6227 --1.599 --1.5597 --1.5527 --1.5509 --1.5333 --1.526 --1.4965 --1.4448 --1.4288 --1.4035 --1.3942 --1.3684 --1.3427 --1.3406 --1.339 --1.3388 --1.3312 --1.3306 --1.3069 --1.2961 --1.2756 --1.2747 --1.2692 --1.2339 --1.2329 --1.216 --1.1969 --1.1628 --1.1584 --1.1456 --1.1427 --1.1355 --1.1234 --1.1048 --1.1043 --1.0623 --1.0145 --1.0094 --1.0038 --1.0021 --0.97338 --0.95574 --0.89607 --0.89295 --0.88666 --0.87605 --0.83668 --0.79571 --0.75097 --0.73753 --0.72952 --0.70918 --0.66817 --0.65016 --0.62682 --0.59099 --0.56011 --0.55454 --0.49508 --0.48726 --0.4844 --0.47054 --0.46502 --0.42104 --0.40219 --0.39675 --0.39619 --0.38477 --0.36937 --0.36224 --0.35906 --0.3035 --0.30343 --0.25345 --0.19644 --0.14387 --0.095543 --0.074672 --0.069207 --0.056429 --0.045271 --0.019002 --0.01611 -0.034415 -0.04716 -0.072667 -0.10431 -0.12347 -0.12563 -0.1412 -0.15163 -0.17724 -0.21526 -0.21882 -0.23551 -0.26181 -0.28315 -0.28414 -0.35762 -0.3696 -0.37536 -0.3901 -0.39776 -0.40981 -0.42899 -0.43673 -0.44751 -0.46484 -0.48725 -0.50859 -0.51076 -0.53679 -0.5539 -0.56897 -0.60319 -0.6188 -0.62447 -0.64458 -0.6458 -0.65461 -0.6585 -0.70988 -0.72304 -0.72816 -0.75148 -0.765 -0.76705 -0.79859 -0.85904 -0.877 -0.88625 -0.90779 -0.90964 -0.93382 -0.93647 -0.96761 -0.98591 -1.0226 -1.0259 -1.029 -1.0307 -1.0364 -1.0411 -1.0439 -1.0736 -1.0799 -1.1388 -1.1585 -1.1715 -1.1767 -1.2195 -1.2203 -1.2344 -1.241 -1.2741 -1.2752 -1.2776 -1.3022 -1.309 -1.3115 -1.3188 -1.3283 -1.3373 -1.342 -1.3878 -1.3894 -1.4134 -1.4494 -1.4832 -1.5302 -1.5309 -1.5475 -1.5685 -1.5962 -1.5965 -1.6061 -1.6511 -1.6615 -1.6627 -1.7156 -1.7273 -1.734 -1.7546 -1.775 -1.8126 -1.8184 -1.8468 -1.9102 -1.911 -1.9162 -1.9268 -1.9351 -1.9454 -1.9574 -2.0696 -2.099 -2.144 -2.1774 -2.179 -2.1828 -2.2325 -2.2441 -2.245 -2.2924 -2.3723 -2.3748 -2.3983 -2.4032 -2.4571 -2.4722 -2.5051 -2.5222 -2.5411 -2.5448 -2.5673 -2.5712 -2.5731 -2.624 -2.6393 -2.6457 -2.6665 -2.673 -2.703 -2.7098 -2.7236 -2.7579 -2.7582 -2.7746 -2.7802 -2.7802 -2.8164 -2.8176 -2.8325 -2.8482 -2.8825 -2.9134 -2.9356 -2.9709 -3.0034 -3.0457 -3.0474 -3.0799 -3.1111 -3.1294 -3.1786 -3.1931 -3.2223 -3.2396 -3.2757 -3.2796 -3.2874 -3.3056 -3.3436 -3.3827 -3.4034 -3.424 -3.4348 -3.4363 -3.4558 -3.4784 -3.5473 -3.5656 -3.5757 -3.5979 -3.5981 -3.62 -3.6227 -3.6376 -3.6495 -3.6897 -3.6955 -3.7268 -3.756 -3.8512 -3.869 -3.8777 -3.9003 -3.9215 -3.9295 -3.9456 -3.9572 -3.9715 -3.9952 -3.9953 -4.0379 -4.0492 -4.0516 -4.0528 -4.096 -4.097 -4.1014 -4.1046 -4.1131 -4.1272 -4.1398 -4.1958 -4.3148 -4.3501 -4.3532 -4.3558 -4.3642 -4.3688 -4.413 -4.4421 -4.4633 -4.4751 -4.4999 -4.5209 -4.5248 -4.5544 -4.5711 -4.5915 -4.6243 -4.6676 -4.6703 -4.6849 -4.6951 -4.6997 -4.7483 -4.7616 -4.8221 -4.8389 -4.8685 -4.8843 -4.8894 -4.9174 -4.921 -4.9289 -4.9461 -4.979 diff --git a/bayesian_ml/4/data.txt b/bayesian_ml/4/data.txt deleted file mode 100644 index 6367a588..00000000 --- a/bayesian_ml/4/data.txt +++ /dev/null @@ -1,250 +0,0 @@ -4.1018,2.7755 --2.7843,-2.5328 -4.9085,1.7415 --0.98785,-0.096165 -5.0936,1.4871 --3.2698,-2.0138 -4.322,1.7522 -1.1916,1.0932 -2.8191,2.5074 -4.6657,1.7354 --2.5031,3.9679 -2.4564,2.9697 --6.737,-8.7944 --3.3788,-4.7085 -2.9108,2.7162 --3.4432,-1.5452 -2.2044,2.2752 -3.1178,2.0867 -2.0801,4.5122 --0.4534,-0.40019 --3.9462,-3.4262 --2.7851,-2.8649 --6.1236,-6.1929 -5.7535,0.34864 --3.3615,3.2837 --4.2186,5.1414 -2.9101,4.2141 -0.84418,-2.1573 -1.578,0.24561 --3.4763,4.7377 -3.0744,2.1029 -3.4406,1.6932 -2.9462,3.1246 -5.3141,1.5204 -3.2627,1.7641 -1.3692,0.019343 -0.93969,4.4613 -0.84051,3.5441 --4.4498,4.2623 -0.24152,-0.96248 -2.1622,3.4838 --3.4546,4.983 -0.16953,0.58335 --0.027432,-1.4383 -3.6449,3.4297 --0.079127,-1.7929 -2.046,3.7616 --5.4619,-4.3469 -0.66641,-0.53428 --3.7058,-6.0215 --3.8574,-5.3334 -2.2173,-0.91211 --4.588,4.9909 --0.29331,-0.6333 --0.79856,-1.3326 --2.1379,-3.148 --3.3361,4.1204 -3.3964,2.8128 --6.1827,-9.194 --1.9853,-3.7974 --0.12541,-0.92221 -4.3319,2.9815 --2.9299,-5.7147 --6.2677,-6.8165 -3.3636,1.8345 --2.246,-2.6986 -2.4218,3.3688 -1.2219,-0.50176 --3.9583,2.6322 --5.1256,-6.3729 -0.54738,4.0857 -1.7943,-1.1886 --0.69529,-0.70864 -3.4995,2.9259 --3.7161,5.5869 --5.675,-4.773 --3.1841,-1.647 -1.3342,-1.3613 -2.0226,3.3061 -1.5212,3.6252 --2.9016,-4.4155 -1.3775,-0.95756 --2.9503,-3.7117 -5.0034,1.932 -0.76731,-0.74763 -0.032336,-2.7383 --6.3487,-6.5736 --0.2545,-0.75344 --4.169,-4.1116 -2.3763,3.0075 -2.7965,3.1516 --5.6324,-8.3578 --1.5831,-2.5032 -3.8007,2.7588 -6.4521,1.3254 --1.6098,-3.3926 -4.0821,2.7777 -1.845,-1.9623 --1.3677,-0.55576 -2.9786,2.0606 -1.5702,3.6537 --0.11889,-0.39907 -2.2032,4.5764 -3.0725,2.9404 -4.8477,2.3742 -3.051,3.4929 -2.0157,3.0993 -1.6125,5.1709 -4.9322,2.9016 -3.2214,1.1894 -3.9556,2.2825 --0.57541,0.88392 -3.0282,2.5043 -3.3044,2.5408 -6.084,0.41893 -5.2028,1.8809 -1.1464,-1.4103 --0.99623,-1.873 -3.1967,3.157 --0.81208,0.49219 -5.6915,1.7463 -1.3163,-1.5315 --4.9834,4.4425 --5.0667,-5.3556 -2.7229,1.9391 -0.94494,-1.2182 -3.8887,1.3523 -2.3424,4.1084 -4.3535,2.5696 -0.079367,0.25433 -4.4885,2.1952 --3.3392,4.5667 --0.83181,-2.5322 -1.0295,-1.5603 --2.6943,-3.7788 -1.1898,-1.0653 --0.25662,-0.43771 --3.8077,-5.9955 -0.45538,3.307 -2.7107,4.3586 --4.4701,-6.4745 -2.4627,3.404 -2.9282,2.7614 -3.3275,2.9878 -4.4574,1.255 --2.769,-3.073 --3.1109,-3.9208 -4.0042,2.8929 --8.463,-8.9882 --3.1489,-2.696 --3.9129,-3.7642 -4.243,2.8379 --5.6065,-6.5221 --1.189,-1.1011 --1.5842,-1.6023 --0.41358,-0.63273 -2.4407,3.3573 -3.9408,1.7018 -5.9457,1.1287 -1.0341,-0.52669 --2.8595,4.4397 -0.63557,-0.30127 --3.7764,-6.9249 -0.91297,-1.954 -5.0938,3.5936 -3.9196,2.5586 --0.46777,-1.0298 -3.6313,1.6239 --0.031369,-0.10111 --4.3877,-4.8732 -1.2262,0.65494 --2.6655,-2.2333 -3.9422,2.4165 -1.2014,-0.098558 -4.501,2.944 --5.6844,-5.7906 --4.0166,2.947 -0.28418,-1.8048 -4.6023,0.8213 -0.24267,-3.878 --2.2338,-5.1638 -2.9347,1.7524 --1.3805,-2.5762 --0.90795,1.2333 -4.2075,3.1063 --2.1278,4.3067 --4.0478,-5.1684 -1.9329,2.1893 --7.6256,-8.1264 --4.7174,-7.1461 --0.13106,0.014465 --5.8557,-5.1366 --5.9314,-4.6967 -4.2642,2.0679 --6.727,-6.2622 -3.2245,3.4363 --5.1829,5.1906 --5.5965,-6.3422 -2.9379,3.3463 -5.2847,1.5374 -2.8052,2.5514 --4.7574,5.5592 --2.9844,-5.2619 --3.31,4.0064 -2.8413,2.407 -3.8481,2.4584 -0.65418,-1.8281 -3.6013,2.1847 -2.4326,0.29105 --4.5797,-3.4686 --3.3286,3.1836 --0.34821,-1.0012 --5.3663,-9.3532 --2.3033,-4.401 -5.0091,1.5007 --3.7659,4.7209 --4.4302,4.4777 --4.3974,3.0623 --6.6131,-5.6686 -0.25319,-1.2064 -3.6856,1.0241 --5.6857,-6.4256 -3.0639,2.8238 -3.7332,1.5943 --0.14387,-2.4385 -0.88598,-0.56042 -5.3827,1.2769 --3.0166,-4.7408 --2.4587,4.1896 --0.62788,-1.3862 -2.4177,3.9261 --0.85756,1.1946 --6.3911,-5.8408 -4.5023,3.372 --4.4713,-4.049 -2.1595,2.8096 -1.2035,4.5017 -3.5571,3.5699 --3.4531,4.6434 --3.3611,-5.4515 -4.5575,1.9208 --3.9329,-5.3362 -2.9503,2.3361 -1.4231,-0.14189 -3.2051,2.5119 --4.5733,4.2999 --2.5123,-3.5152 --4.4529,-5.1957 --3.235,-3.6716 -0.57698,-2.2389 diff --git a/bayesian_ml/4/emgmm.py b/bayesian_ml/4/emgmm.py deleted file mode 100644 index 42d1f1f4..00000000 --- a/bayesian_ml/4/emgmm.py +++ /dev/null @@ -1,76 +0,0 @@ -# GMM using Expectation-Maximization - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from scipy.stats import multivariate_normal - - -def gmm(X, K, max_iter=20, smoothing=1e-2): - N, D = X.shape - M = np.zeros((K, D)) - R = np.zeros((N, K)) - C = np.zeros((K, D, D)) - pi = np.ones(K) / K # uniform - - # initialize M to random, initialize C to spherical with variance 1 - for k in xrange(K): - M[k] = X[np.random.choice(N)] - C[k] = np.eye(D) - - costs = np.zeros(max_iter) - weighted_pdfs = np.zeros((N, K)) # we'll use these to store the PDF value of sample n and Gaussian k - for i in xrange(max_iter): - # step 1: determine assignments / resposibilities - for k in xrange(K): - weighted_pdfs[:,k] = pi[k]*multivariate_normal.pdf(X, M[k], C[k]) - - R = weighted_pdfs / weighted_pdfs.sum(axis=1, keepdims=True) - # for k in xrange(K): - # for n in xrange(N): - # R[n,k] = weighted_pdfs[n,k] / weighted_pdfs[n,:].sum() - - # step 2: recalculate params - for k in xrange(K): - Nk = R[:,k].sum() - pi[k] = Nk / N - M[k] = R[:,k].dot(X) / Nk - C[k] = np.sum(R[n,k]*np.outer(X[n] - M[k], X[n] - M[k]) for n in xrange(N)) / Nk + np.eye(D)*smoothing - - - costs[i] = np.log(weighted_pdfs.sum(axis=1)).sum() - if i > 0: - if np.abs(costs[i] - costs[i-1]) < 0.1: - break - - plt.plot(costs) - plt.title("Costs") - plt.show() - - random_colors = np.random.random((K, 3)) - # colors = R.dot(random_colors) - # plt.scatter(X[:,0], X[:,1], c=colors) - plt.scatter(X[:,0], X[:,1], c=R.argmax(axis=1)) - plt.show() - - print "pi:", pi - print "means:", M - print "covariances:", C - return R - - -def main(): - X = pd.read_csv('data.txt', header=None).as_matrix() - - # what does it look like without clustering? - plt.scatter(X[:,0], X[:,1]) - plt.show() - - for K in (2,4,8,10): - gmm(X, K, max_iter=100, smoothing=0) - - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/bayesian_ml/4/npbgmm.py b/bayesian_ml/4/npbgmm.py deleted file mode 100644 index ceb977af..00000000 --- a/bayesian_ml/4/npbgmm.py +++ /dev/null @@ -1,201 +0,0 @@ -# GMM using Bayesian Nonparametric Clustering -# Gaussian Mixture Model -# Dirichlet Process -# Gibbs Sampling - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from scipy import stats -from scipy.stats import multivariate_normal as mvn, dirichlet, wishart -from scipy.special import digamma, gamma - -# scipy wishart!!! -# parameters are df, sigma=scale -# for us, df = a, inv(sigma) = B - - -def marginal(x, c, m, a, B): - D = len(x) - k0 = ( c / (np.pi * (1 + c)) )**(D/2.0) - k1top = np.linalg.det(B + (c/(1+c)*np.outer(x - m, x - m)))**(-(a + 1.0)/2.0) - k1bot = np.linalg.det(B)**(-a/2.0) - k1 = k1top/k1bot - k2log = 0 - for d in xrange(D): - k2log += np.log(gamma( (a+1.0)/2.0 + (1.0-d)/2.0 )) - np.log(gamma( a/2.0 + (1.0-d)/2.0 )) - k2 = np.exp(k2log) - return k0*k1*k2 - - -def normalize_phi_hat(phi_hat): - # phi_hat is a dictionary: cluster index -> non-normalized probability of that cluster - # normalization done in place so no need to return anything - total = np.sum(phi_hat.values()) - - for j, p_hat in phi_hat.iteritems(): - phi_hat[j] = p_hat / total - - -def sample_cluster_identity(phi): - # phi is a dictionary: cluster index -> probability of that cluster - # print "dictionary sample from:", phi - p = np.random.random() - cumulative = 0 - for j, q in phi.iteritems(): - cumulative += q - if p < cumulative: - return j - # print "cumulative:", cumulative - assert(False) # should never get here because cumulative = 1 by now - - -def sample_from_prior(c0, m0, a0, B0): - precision0 = wishart.rvs(df=a0, scale=np.linalg.inv(B0)) - cov = np.linalg.inv(precision0) - mean = mvn.rvs(mean=m0, cov=cov/c0) - return mean, cov - - -# samples mu, sigma from P(mu, sigma | X) -def sample_from_X(X, m0, c0, a0, B0): - N = len(X) - s = float(N) - m = (c0 / (s + c0))*m0 + (1 / (s + c0))*X.sum(axis=0) - c = s + c0 - a = s + a0 - meanX = X.mean(axis=0) - B = (s / (a0*s + 1)) * np.outer(meanX - m0, meanX - m0) + B0 - for i in xrange(N): - B += np.outer(X[i] - meanX, X[i] - meanX) - return sample_from_prior(c, m, a, B) - - -def gmm(X, T=500): - N, D = X.shape - - m0 = X.mean(axis=0) - c0 = 0.1 - a0 = float(D) - B0 = c0*D*np.cov(X.T) - alpha0 = 1.0 - - # cluster assignments - originally everything is assigned to cluster 0 - C = np.zeros(N) - - # keep as many as we need for each gaussian - # originally we sample from the prior - # TODO: just use the function above - precision0 = wishart.rvs(df=a0, scale=np.linalg.inv(B0)) - covariances = [np.linalg.inv(precision0)] - means = [mvn.rvs(mean=m0, cov=covariances[0]/c0)] - - cluster_counts = [1] - K = 1 - observations_per_cluster = np.zeros((T, 6)) - for t in xrange(T): - if t % 20 == 0: - print t - # 1) calculate phi[i,j] - # Notes: - # MANY new clusters can be made each iteration - # A cluster can be DESTROYED if a x[i] is the only pt in cluster j and gets assigned to a new cluster - # phi = np.empty((N, K)) - list_of_cluster_indices = range(K) - next_cluster_index = K - # phi = [] # TODO: do we need this at all? - for i in xrange(N): - phi_i = {} - for j in list_of_cluster_indices: - # don't loop through xrange(K) because clusters can be created or destroyed as we loop through i - nj_noti = np.sum(C[:i] == j) + np.sum(C[i+1:] == j) - if nj_noti > 0: - # existing cluster - # phi[i,j] = N(x[i] | mu[j], cov[j]) * nj_noti / (alpha0 + N - 1) - # using the sampled mu / covs - phi_i[j] = mvn.pdf(X[i], mean=means[j], cov=covariances[j]) * nj_noti / (alpha0 + N - 1.0) - - # new cluster - # create a possible new cluster for every sample i - # but only keep it if sample i occupies this new cluster j' - # i.e. if C[i] = j' when we sample C[i] - # phi[i,j'] = alpha0 / (alpha0 + N - 1) * p(x[i]) - # p(x[i]) is a marginal integrated over mu and precision - phi_i[next_cluster_index] = alpha0 / (alpha0 + N - 1.0) * marginal(X[i], c0, m0, a0, B0) - - # normalize phi[i] and assign C[i] to its new cluster by sampling from phi[i] - normalize_phi_hat(phi_i) - - # if C[i] = j' (new cluster), generate mu[j'] and cov[j'] - C[i] = sample_cluster_identity(phi_i) - if C[i] == next_cluster_index: - list_of_cluster_indices.append(next_cluster_index) - next_cluster_index += 1 - new_mean, new_cov = sample_from_prior(c0, m0, a0, B0) - means.append(new_mean) - covariances.append(new_cov) - - # destroy any cluster with no points in it - clusters_to_remove = [] - tot = 0 - for j in list_of_cluster_indices: - nj = np.sum(C == j) - # print "number of pts in cluster %d:" % j, nj - tot += nj - if nj == 0: - clusters_to_remove.append(j) - # print "tot:", tot - assert(tot == N) - for j in clusters_to_remove: - list_of_cluster_indices.remove(j) - - # DEBUG - make sure no clusters are empty - # counts = [np.sum(C == j) for j in list_of_cluster_indices] - # for c in counts: - # assert(c > 0) - - # re-order the cluster indexes so they range from 0..new K - 1 - new_C = np.zeros(N) - for new_j in xrange(len(list_of_cluster_indices)): - old_j = list_of_cluster_indices[new_j] - new_C[C == old_j] = new_j - C = new_C - K = len(list_of_cluster_indices) - list_of_cluster_indices = range(K) # redundant but if removed will break counts - - cluster_counts.append(K) - # 2) calculate the new mu, covariance for every currently non-empty cluster - # i.e. SAMPLE mu, cov from the new cluster assignments - means = [] - covariances = [] - for j in xrange(K): - # first calculate m', c', a', B' - # then call the function that samples a mean and covariance using these - mean, cov = sample_from_X(X[C == j], m0, c0, a0, B0) - means.append(mean) - covariances.append(cov) - - # plot number of observations per cluster for 6 most probable clusters per iteration - counts = sorted([np.sum(C == j) for j in list_of_cluster_indices], reverse=True) - # print "counts:", counts - if len(counts) < 6: - observations_per_cluster[t,:len(counts)] = counts - else: - observations_per_cluster[t] = counts[:6] - - # plot number of clusters per iteration - plt.plot(cluster_counts) - plt.show() - - # plot number of observations per cluster for 6 most probable clusters per iteration - plt.plot(observations_per_cluster) - plt.show() - - -def main(): - X = pd.read_csv('data.txt', header=None).as_matrix() - gmm(X) - -if __name__ == '__main__': - main() diff --git a/bayesian_ml/4/vigmm.py b/bayesian_ml/4/vigmm.py deleted file mode 100644 index 95887b62..00000000 --- a/bayesian_ml/4/vigmm.py +++ /dev/null @@ -1,215 +0,0 @@ -# GMM using Variational Inference - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from scipy import stats -from scipy.stats import multivariate_normal as mvn, dirichlet, wishart -from scipy.special import digamma, gamma - -def get_cost(X, K, cluster_assignments, phi, alphas, mu_means, mu_covs, a, B, orig_alphas, orig_c, orig_a, orig_B): - N, D = X.shape - total = 0 - ln2pi = np.log(2*np.pi) - - # calculate B inverse since we will need it - Binv = np.empty((K, D, D)) - for j in xrange(K): - Binv[j] = np.linalg.inv(B[j]) - - # calculate expectations first - Elnpi = digamma(alphas) - digamma(alphas.sum()) # E[ln(pi)] - Elambda = np.empty((K, D, D)) - Elnlambda = np.empty(K) - for j in xrange(K): - Elambda[j] = a[j]*Binv[j] - Elnlambda[j] = D*np.log(2) - np.log(np.linalg.det(B[j])) - for d in xrange(D): - Elnlambda[j] += digamma(a[j]/2.0 + (1 - d)/2.0) - - # now calculate the log joint likelihood - # Gaussian part - # total -= N*D*ln2pi - # total += 0.5*Elnlambda.sum() - # for j in xrange(K): - # # total += 0.5*Elnlambda[j] # vectorized - # for i in xrange(N): - # if cluster_assignments[i] == j: - # diff_ij = X[i] - mu_means[j] - # total -= 0.5*( diff_ij.dot(Elambda[j]).dot(diff_ij) + np.trace(Elambda[j].dot(mu_covs[j])) ) - - # mixture coefficient part - # total += Elnpi.sum() - - # use phi instead - for j in xrange(K): - for i in xrange(N): - diff_ij = X[i] - mu_means[j] - inside = Elnlambda[j] - D*ln2pi - inside += -diff_ij.dot(Elambda[j]).dot(diff_ij) - np.trace(Elambda[j].dot(mu_covs[j])) - # inside += Elnpi[j] - total += phi[i,j]*(0.5*inside + Elnpi[j]) - - - # E{lnp(mu)} - based on original prior - for j in xrange(K): - E_mu_dot_mu = np.trace(mu_covs[j]) + mu_means[j].dot(mu_means[j]) - total += -0.5*D*np.log(2*np.pi*orig_c) - 0.5*E_mu_dot_mu/orig_c - - # print "total:", total - - # E{lnp(lambda)} - based on original prior - for j in xrange(K): - total += (orig_a[j] - D - 1)/2.0*Elnlambda[j] - 0.5*np.trace(orig_B[j].dot(Elambda[j])) - # print "total 1:", total - total += -orig_a[j]*D/2.0*np.log(2) + 0.5*orig_a[j]*np.log(np.linalg.det(orig_B[j])) - # print "total 2:", total - total -= D*(D-1)/4.0*np.log(np.pi) - # print "total 3:", total - for d in xrange(D): - total -= np.log(gamma(orig_a[j]/2.0 + (1 - d)/2.0)) - - # E{lnp(pi)} - based on original prior - # - lnB(orig_alpha) + sum[j]{ orig_alpha[j] - 1}*E[lnpi_j] - total += np.log(gamma(orig_alphas.sum())) - np.log(gamma(orig_alphas)).sum() - total += ((orig_alphas - 1)*Elnpi).sum() # should be 0 since orig_alpha = 1 - - # calculate entropies of the q distributions - # q(c) - for i in xrange(N): - total += stats.entropy(phi[i]) # categorical entropy - - # q(pi) - total += dirichlet.entropy(alphas) - - # q(mu) - for j in xrange(K): - total += mvn.entropy(cov=mu_covs[j]) - - # q(lambda) - for j in xrange(K): - total += wishart.entropy(df=a[j], scale=Binv[j]) - - return total - - - -def gmm(X, K, max_iter=100): - N, D = X.shape - - # parameters for pi, mu, and precision - alphas = np.ones(K, dtype=np.float32) # prior parameter for pi (dirichlet) - orig_alphas = np.ones(K, dtype=np.float32) # prior parameter for pi (dirichlet) - # mu_means = np.zeros((K, D), dtype=np.float32) # prior mean for mu (normal) ### No! - # mu_covs = np.empty((K, D, D), dtype=np.float32) # prior covariance for mu (normal) - - orig_c = 10.0 - # for k in xrange(K): - # mu_covs[k] = np.eye(D)*orig_c - - - orig_a = np.ones(K, dtype=np.float32)*D - a = np.ones(K, dtype=np.float32)*D # prior for precision (wishart) - orig_B = np.empty((K, D, D)) - B = np.empty((K, D, D)) # precision (wishart) - empirical_cov = np.cov(X.T) - for k in xrange(K): - B[k] = (D/10.0)*empirical_cov - orig_B[k] = (D/10.0)*empirical_cov - - - # try random init instead - # mu_means = np.random.randn(K, D)*orig_c - mu_means = np.empty((K, D)) - for j in xrange(K): - mu_means[j] = X[np.random.choice(N)] - mu_covs = wishart.rvs(df=orig_a[0], scale=np.linalg.inv(B[0]), size=K) - - costs = np.zeros(max_iter) - for iter_idx in xrange(max_iter): - # calculate q(c[i]) - # phi = np.empty((N,K)) # index i = sample, index j = cluster - t1 = np.empty(K) - t2 = np.empty((N,K)) - t3 = np.empty(K) - t4 = np.empty(K) - - # calculate this first because we will use it multiple times - Binv = np.empty((K, D, D)) - for j in range(K): - Binv[j] = np.linalg.inv(B[j]) - - for j in xrange(K): - # calculate t1 - t1[j] = -np.log(np.linalg.det(B[j])) - for d in xrange(D): - t1[j] += digamma( (1 - d + a[j])/2.0 ) - - # calculate t2 - for i in xrange(N): - diff_ij = X[i] - mu_means[j] - t2[i,j] = diff_ij.dot( (a[j]*Binv[j] ).dot(diff_ij) ) - - # calculate t3 - t3[j] = np.trace( a[j]*Binv[j].dot(mu_covs[j]) ) - - # calculate t4 - t4[j] = digamma(alphas[j]) - digamma(alphas.sum()) - - # calculate phi from t's - # MAKE SURE 1-d array gets added to 2-d array correctly - phi = np.exp(0.5*t1 - 0.5*t2 - 0.5*t3 + t4) - # print "phi before normalize:", phi - phi = phi / phi.sum(axis=1, keepdims=True) - - # print "phi:", phi - - cluster_assignments = phi.argmax(axis=1) - - n = phi.sum(axis=0) # there should be K of these - # print "n[j]:", n - - # update q(pi) - alphas = orig_alphas + n - # print "alphas:", alphas - - # update q(mu) - for j in xrange(K): - mu_covs[j] = np.linalg.inv( (1.0/orig_c)*np.eye(D) + n[j]*a[j]*Binv[j] ) - mu_means[j] = mu_covs[j].dot( a[j]*Binv[j] ).dot(phi[:,j].dot(X)) - - # print "means:", mu_means - # print "mu_covs:", mu_covs - - # update q(lambda) - a = orig_a + n - for j in xrange(K): - B[j] = orig_B[j].copy() - for i in xrange(N): - diff_ij = X[i] - mu_means[j] - B[j] += phi[i,j]*(np.outer(diff_ij, diff_ij) + mu_covs[j]) - - # print "a[j]:", a - # print "B[j]:", B - - costs[iter_idx] = get_cost(X, K, cluster_assignments, phi, alphas, mu_means, mu_covs, a, B, orig_alphas, orig_c, orig_a, orig_B) - - plt.plot(costs) - plt.title("Costs") - plt.show() - - print "cluster assignments:\n", cluster_assignments - plt.scatter(X[:,0], X[:,1], c=cluster_assignments, s=100, alpha=0.7) - plt.show() - - -def main(): - X = pd.read_csv('data.txt', header=None).as_matrix() - - # for K in (2,4): - for K in (2,4,10,25): - gmm(X, K) - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/best_fit_line.py b/best_fit_line.py index 5f71dbc4..f3ab1aa1 100644 --- a/best_fit_line.py +++ b/best_fit_line.py @@ -45,14 +45,3 @@ # print "\tb:", value(b) print "\tc:", value(c) print "\tz:", value(z) - - -# extra part to plot everything -import numpy as np -import matplotlib.pyplot as plt -data = np.array(points) -plt.scatter(data[:,0], data[:,1]) -x = np.linspace(0, 11, 100) -y = value(a)*x - value(c) -plt.plot(x, y) -plt.show() diff --git a/calculus/WHERE ARE THE NOTEBOOKS.txt b/calculus/WHERE ARE THE NOTEBOOKS.txt deleted file mode 100644 index 5446ce25..00000000 --- a/calculus/WHERE ARE THE NOTEBOOKS.txt +++ /dev/null @@ -1,5 +0,0 @@ -As stated in the "where to get the code" / "where to get the notebooks" lecture, the notebooks are NOT on Github. - -If you missed this, please review the lecture for the actual location of the notebooks. - -If, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com. \ No newline at end of file diff --git a/calculus/extra_reading.txt b/calculus/extra_reading.txt deleted file mode 100644 index 404cc6d0..00000000 --- a/calculus/extra_reading.txt +++ /dev/null @@ -1,2 +0,0 @@ -Calculus: Early Transcendentals -https://amzn.to/3Kwmabe \ No newline at end of file diff --git a/chatgpt_trading/WHERE ARE THE NOTEBOOKS.txt b/chatgpt_trading/WHERE ARE THE NOTEBOOKS.txt deleted file mode 100644 index 5446ce25..00000000 --- a/chatgpt_trading/WHERE ARE THE NOTEBOOKS.txt +++ /dev/null @@ -1,5 +0,0 @@ -As stated in the "where to get the code" / "where to get the notebooks" lecture, the notebooks are NOT on Github. - -If you missed this, please review the lecture for the actual location of the notebooks. - -If, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com. \ No newline at end of file diff --git a/chatgpt_trading/extra_reading.txt b/chatgpt_trading/extra_reading.txt deleted file mode 100644 index 59a5da5d..00000000 --- a/chatgpt_trading/extra_reading.txt +++ /dev/null @@ -1,5 +0,0 @@ -ARIMA (for mean reversion) -https://deeplearningcourses.com/c/time-series-analysis - -Financial Engineering -https://deeplearningcourses.com/c/ai-finance \ No newline at end of file diff --git a/cnn_class/WHERE ARE THE NOTEBOOKS.txt b/cnn_class/WHERE ARE THE NOTEBOOKS.txt deleted file mode 100644 index 5446ce25..00000000 --- a/cnn_class/WHERE ARE THE NOTEBOOKS.txt +++ /dev/null @@ -1,5 +0,0 @@ -As stated in the "where to get the code" / "where to get the notebooks" lecture, the notebooks are NOT on Github. - -If you missed this, please review the lecture for the actual location of the notebooks. - -If, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com. \ No newline at end of file diff --git a/cnn_class/benchmark.py b/cnn_class/benchmark.py index 75c526dd..804d44a4 100644 --- a/cnn_class/benchmark.py +++ b/cnn_class/benchmark.py @@ -1,13 +1,7 @@ # Vanilla deep network -# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow + # https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow -# get the data: http://ufldl.stanford.edu/housenumbers/ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future -import os import numpy as np import tensorflow as tf import matplotlib.pyplot as plt @@ -17,6 +11,14 @@ from datetime import datetime +def y2indicator(y): + N = len(y) + ind = np.zeros((N, 10)) + for i in xrange(N): + ind[i, y[i]] = 1 + return ind + + def error_rate(p, t): return np.mean(p != t) @@ -26,7 +28,7 @@ def flatten(X): # output will be (N, 3072) N = X.shape[-1] flat = np.zeros((N, 3072)) - for i in range(N): + for i in xrange(N): flat[i] = X[:,:,:,i].reshape(3072) return flat @@ -46,41 +48,29 @@ def flatten(X): # In [13]: test['y'].shape # Out[13]: (26032, 1) - -def get_data(): - if not os.path.exists('../large_files/train_32x32.mat'): - print('Looking for ../large_files/train_32x32.mat') - print('You have not downloaded the data and/or not placed the files in the correct location.') - print('Please get the data from: http://ufldl.stanford.edu/housenumbers') - print('Place train_32x32.mat and test_32x32.mat in the folder large_files adjacent to the class folder') - exit() - +def main(): train = loadmat('../large_files/train_32x32.mat') test = loadmat('../large_files/test_32x32.mat') - return train, test - - -def main(): - train, test = get_data() - # Need to scale! don't leave as 0..255 # Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1) # So flatten it and make it 0..9 # Also need indicator matrix for cost calculation - Xtrain = flatten(train['X'].astype(np.float32) / 255.) + Xtrain = flatten(train['X'].astype(np.float32) / 255) Ytrain = train['y'].flatten() - 1 Xtrain, Ytrain = shuffle(Xtrain, Ytrain) + Ytrain_ind = y2indicator(Ytrain) - Xtest = flatten(test['X'].astype(np.float32) / 255.) + Xtest = flatten(test['X'].astype(np.float32) / 255) Ytest = test['y'].flatten() - 1 + Ytest_ind = y2indicator(Ytest) # gradient descent params max_iter = 20 print_period = 10 N, D = Xtrain.shape batch_sz = 500 - n_batches = N // batch_sz + n_batches = N / batch_sz # initial weights M1 = 1000 # hidden layer size @@ -95,7 +85,7 @@ def main(): # define variables and expressions X = tf.placeholder(tf.float32, shape=(None, D), name='X') - T = tf.placeholder(tf.int32, shape=(None,), name='T') + T = tf.placeholder(tf.float32, shape=(None, K), name='T') W1 = tf.Variable(W1_init.astype(np.float32)) b1 = tf.Variable(b1_init.astype(np.float32)) W2 = tf.Variable(W2_init.astype(np.float32)) @@ -105,39 +95,34 @@ def main(): Z1 = tf.nn.relu( tf.matmul(X, W1) + b1 ) Z2 = tf.nn.relu( tf.matmul(Z1, W2) + b2 ) - logits = tf.matmul(Z2, W3) + b3 + Yish = tf.matmul(Z2, W3) + b3 - cost = tf.reduce_sum( - tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=logits, - labels=T - ) - ) + cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(Yish, T)) train_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99, momentum=0.9).minimize(cost) # we'll use this to calculate the error rate - predict_op = tf.argmax(logits, 1) + predict_op = tf.argmax(Yish, 1) t0 = datetime.now() LL = [] - init = tf.global_variables_initializer() + init = tf.initialize_all_variables() with tf.Session() as session: session.run(init) - for i in range(max_iter): - for j in range(n_batches): + for i in xrange(max_iter): + for j in xrange(n_batches): Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] - Ybatch = Ytrain[j*batch_sz:(j*batch_sz + batch_sz),] + Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] session.run(train_op, feed_dict={X: Xbatch, T: Ybatch}) if j % print_period == 0: - test_cost = session.run(cost, feed_dict={X: Xtest, T: Ytest}) + test_cost = session.run(cost, feed_dict={X: Xtest, T: Ytest_ind}) prediction = session.run(predict_op, feed_dict={X: Xtest}) err = error_rate(prediction, Ytest) - print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, test_cost, err)) + print "Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, test_cost, err) LL.append(test_cost) - print("Elapsed time:", (datetime.now() - t0)) + print "Elapsed time:", (datetime.now() - t0) plt.plot(LL) plt.show() diff --git a/cnn_class/blur.py b/cnn_class/blur.py index 59cb9335..fe046d1f 100644 --- a/cnn_class/blur.py +++ b/cnn_class/blur.py @@ -1,9 +1,4 @@ -# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow # https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import numpy as np from scipy.signal import convolve2d @@ -24,11 +19,10 @@ # create a Gaussian filter W = np.zeros((20, 20)) -for i in range(20): - for j in range(20): +for i in xrange(20): + for j in xrange(20): dist = (i - 9.5)**2 + (j - 9.5)**2 W[i, j] = np.exp(-dist / 50.) -W /= W.sum() # normalize the kernel # let's see what the filter looks like plt.imshow(W, cmap='gray') @@ -40,7 +34,7 @@ plt.show() # what's that weird black stuff on the edges? let's check the size of output -print(out.shape) +print out.shape # after convolution, the output signal is N1 + N2 - 1 @@ -48,15 +42,13 @@ out = convolve2d(bw, W, mode='same') plt.imshow(out, cmap='gray') plt.show() -print(out.shape) +print out.shape # in color out3 = np.zeros(img.shape) -print(out3.shape) -for i in range(3): +for i in xrange(3): out3[:,:,i] = convolve2d(img[:,:,i], W, mode='same') -# out3 /= out3.max() # can also do this if you didn't normalize the kernel plt.imshow(out3) plt.show() # does not look like anything diff --git a/cnn_class/cifar.py b/cnn_class/cifar.py deleted file mode 100644 index cf7d4025..00000000 --- a/cnn_class/cifar.py +++ /dev/null @@ -1,234 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow -import os -import numpy as np -import pandas as pd -import theano -import theano.tensor as T -import matplotlib.pyplot as plt - -from PIL import Image -from sklearn.utils import shuffle -from theano.tensor.nnet import conv2d -from theano.tensor.signal import downsample - -# from util import getImageData, error_rate, init_weight_and_bias, init_filter -# from ann_theano import HiddenLayer - -def init_weight_and_bias(M1, M2): - W = np.random.randn(M1, M2) / np.sqrt(M1 + M2) - b = np.zeros(M2) - return W.astype(np.float32), b.astype(np.float32) - - -def init_filter(shape, poolsz): - w = np.random.randn(*shape) / np.sqrt(np.prod(shape[1:]) + shape[0]*np.prod(shape[2:] / np.prod(poolsz))) - return w.astype(np.float32) - - -def error_rate(targets, predictions): - return np.mean(targets != predictions) - - -def image2array(im): - arr = np.array(im) # will be (H, W, 3) - return arr.transpose((2, 1, 0)) - - -def getImageData(): - N = 50000 - savedXpath = '../large_files/cifar10/train_all.npy' - if not os.path.exists(savedXpath): - X = np.zeros((N, 3, 32, 32)) - for i in xrange(N): - im = Image.open("../large_files/cifar10/train/%s.png" % (i + 1)) - X[i] = image2array(im) - if i % 1000 == 0: - print i - np.save(savedXpath, X.astype(np.uint8)) - else: - X = np.load(savedXpath) - X = X.astype(np.float32) / 255.0 - - # load labels - Y = np.zeros(N) - df = pd.read_csv('../large_files/cifar10/trainLabels.csv') - S = df['label'].tolist() - idx = 0 - label2idx = {} - i = 0 - for s in S: - if s not in label2idx: - label2idx[s] = idx - idx += 1 - Y[i] = label2idx[s] - i += 1 - print "done loading data" - X, Y = shuffle(X, Y) - return X[:30000], Y[:30000] - - -class HiddenLayer(object): - def __init__(self, M1, M2, an_id): - self.id = an_id - self.M1 = M1 - self.M2 = M2 - W, b = init_weight_and_bias(M1, M2) - self.W = theano.shared(W, 'W_%s' % self.id) - self.b = theano.shared(b, 'b_%s' % self.id) - self.params = [self.W, self.b] - - def forward(self, X): - return T.nnet.relu(X.dot(self.W) + self.b) - - -class ConvPoolLayer(object): - def __init__(self, mi, mo, fw=5, fh=5, poolsz=(2, 2)): - # mi = input feature map size - # mo = output feature map size - sz = (mo, mi, fw, fh) - W0 = init_filter(sz, poolsz) - self.W = theano.shared(W0) - b0 = np.zeros(mo, dtype=np.float32) - self.b = theano.shared(b0) - self.poolsz = poolsz - self.params = [self.W, self.b] - - def forward(self, X): - conv_out = conv2d(input=X, filters=self.W) - pooled_out = downsample.max_pool_2d( - input=conv_out, - ds=self.poolsz, - ignore_border=True - ) - return T.nnet.relu(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x')) - - -class CNN(object): - def __init__(self, convpool_layer_sizes, hidden_layer_sizes): - self.convpool_layer_sizes = convpool_layer_sizes - self.hidden_layer_sizes = hidden_layer_sizes - - def fit(self, X, Y, lr=1e-4, mu=0.99, reg=1e-6, decay=0.99999, eps=1e-2, batch_sz=30, epochs=100, show_fig=True): - lr = np.float32(lr) - mu = np.float32(mu) - reg = np.float32(reg) - decay = np.float32(decay) - eps = np.float32(eps) - - # make a validation set - X, Y = shuffle(X, Y) - X = X.astype(np.float32) - Y = Y.astype(np.int32) - Xvalid, Yvalid = X[-1000:], Y[-1000:] - X, Y = X[:-1000], Y[:-1000] - - # initialize convpool layers - N, c, width, height = X.shape - mi = c - outw = width - outh = height - self.convpool_layers = [] - for mo, fw, fh in self.convpool_layer_sizes: - layer = ConvPoolLayer(mi, mo, fw, fh) - self.convpool_layers.append(layer) - outw = (outw - fw + 1) / 2 - outh = (outh - fh + 1) / 2 - mi = mo - - # initialize mlp layers - K = len(set(Y)) - self.hidden_layers = [] - M1 = self.convpool_layer_sizes[-1][0]*outw*outh # size must be same as output of last convpool layer - count = 0 - for M2 in self.hidden_layer_sizes: - h = HiddenLayer(M1, M2, count) - self.hidden_layers.append(h) - M1 = M2 - count += 1 - - # logistic regression layer - W, b = init_weight_and_bias(M1, K) - self.W = theano.shared(W, 'W_logreg') - self.b = theano.shared(b, 'b_logreg') - - # collect params for later use - self.params = [self.W, self.b] - for c in self.convpool_layers: - self.params += c.params - for h in self.hidden_layers: - self.params += h.params - - # for momentum - dparams = [theano.shared(np.zeros(p.get_value().shape, dtype=np.float32)) for p in self.params] - - # for rmsprop - cache = [theano.shared(np.zeros(p.get_value().shape, dtype=np.float32)) for p in self.params] - - # set up theano functions and variables - thX = T.tensor4('X', dtype='float32') - thY = T.ivector('Y') - pY = self.forward(thX) - - rcost = reg*T.sum([(p*p).sum() for p in self.params]) - cost = -T.mean(T.log(pY[T.arange(thY.shape[0]), thY])) + rcost - prediction = self.predict(thX) - - cost_predict_op = theano.function(inputs=[thX, thY], outputs=[cost, prediction]) - - - # momentum only - updates = [ - (p, p + mu*dp - lr*T.grad(cost, p)) for p, dp in zip(self.params, dparams) - ] + [ - (dp, mu*dp - lr*T.grad(cost, p)) for p, dp in zip(self.params, dparams) - ] - - train_op = theano.function( - inputs=[thX, thY], - updates=updates - ) - - n_batches = N / batch_sz - costs = [] - for i in xrange(epochs): - X, Y = shuffle(X, Y) - for j in xrange(n_batches): - Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)] - Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)] - - train_op(Xbatch, Ybatch) - - if j % 20 == 0: - c, p = cost_predict_op(Xvalid, Yvalid) - costs.append(c) - e = error_rate(Yvalid, p) - print "i:", i, "j:", j, "nb:", n_batches, "cost:", c, "error rate:", e - - if show_fig: - plt.plot(costs) - plt.show() - - def forward(self, X): - Z = X - for c in self.convpool_layers: - Z = c.forward(Z) - Z = Z.flatten(ndim=2) - for h in self.hidden_layers: - Z = h.forward(Z) - return T.nnet.softmax(Z.dot(self.W) + self.b) - - def predict(self, X): - pY = self.forward(X) - return T.argmax(pY, axis=1) - - -def main(): - X, Y = getImageData() - model = CNN( - convpool_layer_sizes=[(20, 5, 5), (20, 5, 5)], - hidden_layer_sizes=[500, 300], - ) - model.fit(X, Y) - -if __name__ == '__main__': - main() diff --git a/cnn_class/cnn_tf.py b/cnn_class/cnn_tf.py index 284910e8..f2757f55 100644 --- a/cnn_class/cnn_tf.py +++ b/cnn_class/cnn_tf.py @@ -4,12 +4,7 @@ # since not doing that caused us to start swapping # - the output after convpool is a different size (8,8) here, (5,5) in Theano -# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow # https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import numpy as np import tensorflow as tf @@ -20,7 +15,17 @@ from scipy.io import loadmat from sklearn.utils import shuffle -from benchmark import get_data, error_rate + +def y2indicator(y): + N = len(y) + ind = np.zeros((N, 10)) + for i in xrange(N): + ind[i, y[i]] = 1 + return ind + + +def error_rate(p, t): + return np.mean(p != t) def convpool(X, W, b): @@ -28,28 +33,28 @@ def convpool(X, W, b): conv_out = tf.nn.conv2d(X, W, strides=[1, 1, 1, 1], padding='SAME') conv_out = tf.nn.bias_add(conv_out, b) pool_out = tf.nn.max_pool(conv_out, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') - return tf.nn.relu(pool_out) + return pool_out def init_filter(shape, poolsz): - w = np.random.randn(*shape) * np.sqrt(2.0 / np.prod(shape[:-1])) + w = np.random.randn(*shape) / np.sqrt(np.prod(shape[:-1]) + shape[-1]*np.prod(shape[:-2] / np.prod(poolsz))) return w.astype(np.float32) def rearrange(X): # input is (32, 32, 3, N) # output is (N, 32, 32, 3) - # N = X.shape[-1] - # out = np.zeros((N, 32, 32, 3), dtype=np.float32) - # for i in xrange(N): - # for j in xrange(3): - # out[i, :, :, j] = X[:, :, j, i] - # return out / 255 - return (X.transpose(3, 0, 1, 2) / 255).astype(np.float32) + N = X.shape[-1] + out = np.zeros((N, 32, 32, 3), dtype=np.float32) + for i in xrange(N): + for j in xrange(3): + out[i, :, :, j] = X[:, :, j, i] + return out / 255 def main(): - train, test = get_data() + train = loadmat('../large_files/train_32x32.mat') # N = 73257 + test = loadmat('../large_files/test_32x32.mat') # N = 26032 # Need to scale! don't leave as 0..255 # Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1) @@ -57,20 +62,22 @@ def main(): # Also need indicator matrix for cost calculation Xtrain = rearrange(train['X']) Ytrain = train['y'].flatten() - 1 - # print len(Ytrain) + print len(Ytrain) del train Xtrain, Ytrain = shuffle(Xtrain, Ytrain) + Ytrain_ind = y2indicator(Ytrain) Xtest = rearrange(test['X']) Ytest = test['y'].flatten() - 1 del test + Ytest_ind = y2indicator(Ytest) # gradient descent params - max_iter = 6 + max_iter = 20 print_period = 10 N = Xtrain.shape[0] batch_sz = 500 - n_batches = N // batch_sz + n_batches = N / batch_sz # limit samples since input will always have to be same size # you could also just do N = N / batch_sz * batch_sz @@ -78,6 +85,7 @@ def main(): Ytrain = Ytrain[:73000] Xtest = Xtest[:26000,] Ytest = Ytest[:26000] + Ytest_ind = Ytest_ind[:26000,] # print "Xtest.shape:", Xtest.shape # print "Ytest.shape:", Ytest.shape @@ -104,7 +112,7 @@ def main(): # define variables and expressions # using None as the first shape element takes up too much RAM unfortunately X = tf.placeholder(tf.float32, shape=(batch_sz, 32, 32, 3), name='X') - T = tf.placeholder(tf.int32, shape=(batch_sz,), name='T') + T = tf.placeholder(tf.float32, shape=(batch_sz, K), name='T') W1 = tf.Variable(W1_init.astype(np.float32)) b1 = tf.Variable(b1_init.astype(np.float32)) W2 = tf.Variable(W2_init.astype(np.float32)) @@ -121,12 +129,7 @@ def main(): Z3 = tf.nn.relu( tf.matmul(Z2r, W3) + b3 ) Yish = tf.matmul(Z3, W4) + b4 - cost = tf.reduce_sum( - tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=Yish, - labels=T - ) - ) + cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(Yish, T)) train_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99, momentum=0.9).minimize(cost) @@ -135,16 +138,14 @@ def main(): t0 = datetime.now() LL = [] - W1_val = None - W2_val = None - init = tf.global_variables_initializer() + init = tf.initialize_all_variables() with tf.Session() as session: session.run(init) - for i in range(max_iter): - for j in range(n_batches): + for i in xrange(max_iter): + for j in xrange(n_batches): Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] - Ybatch = Ytrain[j*batch_sz:(j*batch_sz + batch_sz),] + Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] if len(Xbatch) == batch_sz: session.run(train_op, feed_dict={X: Xbatch, T: Ybatch}) @@ -153,61 +154,19 @@ def main(): # so as a result, we have this ugly total cost and prediction computation test_cost = 0 prediction = np.zeros(len(Xtest)) - for k in range(len(Xtest) // batch_sz): + for k in xrange(len(Xtest) / batch_sz): Xtestbatch = Xtest[k*batch_sz:(k*batch_sz + batch_sz),] - Ytestbatch = Ytest[k*batch_sz:(k*batch_sz + batch_sz),] + Ytestbatch = Ytest_ind[k*batch_sz:(k*batch_sz + batch_sz),] test_cost += session.run(cost, feed_dict={X: Xtestbatch, T: Ytestbatch}) prediction[k*batch_sz:(k*batch_sz + batch_sz)] = session.run( predict_op, feed_dict={X: Xtestbatch}) err = error_rate(prediction, Ytest) - print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, test_cost, err)) + print "Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, test_cost, err) LL.append(test_cost) - - W1_val = W1.eval() - W2_val = W2.eval() - print("Elapsed time:", (datetime.now() - t0)) + print "Elapsed time:", (datetime.now() - t0) plt.plot(LL) plt.show() - W1_val = W1_val.transpose(3, 2, 0, 1) - W2_val = W2_val.transpose(3, 2, 0, 1) - - - # visualize W1 (20, 3, 5, 5) - # W1_val = W1.get_value() - grid = np.zeros((8*5, 8*5)) - m = 0 - n = 0 - for i in range(20): - for j in range(3): - filt = W1_val[i,j] - grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt - m += 1 - if m >= 8: - m = 0 - n += 1 - plt.imshow(grid, cmap='gray') - plt.title("W1") - plt.show() - - # visualize W2 (50, 20, 5, 5) - # W2_val = W2.get_value() - grid = np.zeros((32*5, 32*5)) - m = 0 - n = 0 - for i in range(50): - for j in range(20): - filt = W2_val[i,j] - grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt - m += 1 - if m >= 32: - m = 0 - n += 1 - plt.imshow(grid, cmap='gray') - plt.title("W2") - plt.show() - - if __name__ == '__main__': main() diff --git a/cnn_class/cnn_tf_plot_filters.py b/cnn_class/cnn_tf_plot_filters.py deleted file mode 100644 index 8ab88671..00000000 --- a/cnn_class/cnn_tf_plot_filters.py +++ /dev/null @@ -1,213 +0,0 @@ -# New concepts and differences from Theano: -# - stride is the interval at which to apply the convolution -# - unlike previous course, we use constant-size input to the network -# since not doing that caused us to start swapping -# - the output after convpool is a different size (8,8) here, (5,5) in Theano - -# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow -# https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt - -from datetime import datetime -from scipy.signal import convolve2d -from scipy.io import loadmat -from sklearn.utils import shuffle - -from benchmark import get_data, error_rate - - -def convpool(X, W, b): - # just assume pool size is (2,2) because we need to augment it with 1s - conv_out = tf.nn.conv2d(X, W, strides=[1, 1, 1, 1], padding='SAME') - conv_out = tf.nn.bias_add(conv_out, b) - pool_out = tf.nn.max_pool(conv_out, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') - return tf.nn.relu(pool_out) - - -def init_filter(shape, poolsz): - w = np.random.randn(*shape) * np.sqrt(2.0 / np.prod(shape[:-1])) - return w.astype(np.float32) - - -def rearrange(X): - # input is (32, 32, 3, N) - # output is (N, 32, 32, 3) - # N = X.shape[-1] - # out = np.zeros((N, 32, 32, 3), dtype=np.float32) - # for i in xrange(N): - # for j in xrange(3): - # out[i, :, :, j] = X[:, :, j, i] - # return out / 255 - return (X.transpose(3, 0, 1, 2) / 255).astype(np.float32) - - - -train, test = get_data() - -# Need to scale! don't leave as 0..255 -# Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1) -# So flatten it and make it 0..9 -# Also need indicator matrix for cost calculation -Xtrain = rearrange(train['X']) -Ytrain = train['y'].flatten() - 1 -# print len(Ytrain) -del train -Xtrain, Ytrain = shuffle(Xtrain, Ytrain) - -Xtest = rearrange(test['X']) -Ytest = test['y'].flatten() - 1 -del test - -# gradient descent params -max_iter = 6 -print_period = 10 -N = Xtrain.shape[0] -batch_sz = 500 -n_batches = N // batch_sz - -# limit samples since input will always have to be same size -# you could also just do N = N / batch_sz * batch_sz -Xtrain = Xtrain[:73000,] -Ytrain = Ytrain[:73000] -Xtest = Xtest[:26000,] -Ytest = Ytest[:26000] -# print "Xtest.shape:", Xtest.shape -# print "Ytest.shape:", Ytest.shape - -# initial weights -M = 500 -K = 10 -poolsz = (2, 2) - -W1_shape = (5, 5, 3, 20) # (filter_width, filter_height, num_color_channels, num_feature_maps) -W1_init = init_filter(W1_shape, poolsz) -b1_init = np.zeros(W1_shape[-1], dtype=np.float32) # one bias per output feature map - -W2_shape = (5, 5, 20, 50) # (filter_width, filter_height, old_num_feature_maps, num_feature_maps) -W2_init = init_filter(W2_shape, poolsz) -b2_init = np.zeros(W2_shape[-1], dtype=np.float32) - -# vanilla ANN weights -W3_init = np.random.randn(W2_shape[-1]*8*8, M) / np.sqrt(W2_shape[-1]*8*8 + M) -b3_init = np.zeros(M, dtype=np.float32) -W4_init = np.random.randn(M, K) / np.sqrt(M + K) -b4_init = np.zeros(K, dtype=np.float32) - - -# define variables and expressions -# using None as the first shape element takes up too much RAM unfortunately -X = tf.placeholder(tf.float32, shape=(batch_sz, 32, 32, 3), name='X') -T = tf.placeholder(tf.int32, shape=(batch_sz,), name='T') -W1 = tf.Variable(W1_init.astype(np.float32)) -b1 = tf.Variable(b1_init.astype(np.float32)) -W2 = tf.Variable(W2_init.astype(np.float32)) -b2 = tf.Variable(b2_init.astype(np.float32)) -W3 = tf.Variable(W3_init.astype(np.float32)) -b3 = tf.Variable(b3_init.astype(np.float32)) -W4 = tf.Variable(W4_init.astype(np.float32)) -b4 = tf.Variable(b4_init.astype(np.float32)) -params = [W1, b1, W2, b2, W3, b3, W4, b4] - -Z1 = convpool(X, W1, b1) -Z2 = convpool(Z1, W2, b2) -Z2_shape = Z2.get_shape().as_list() -Z2r = tf.reshape(Z2, [Z2_shape[0], np.prod(Z2_shape[1:])]) -Z3 = tf.nn.relu( tf.matmul(Z2r, W3) + b3 ) -Yish = tf.matmul(Z3, W4) + b4 - -cost = tf.reduce_sum( - tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=Yish, - labels=T - ) -) + 0.01*sum(tf.reduce_sum(p*p) for p in params) - -train_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99, momentum=0.9).minimize(cost) -# train_op = tf.train.MomentumOptimizer(0.00001, momentum=0.99).minimize(cost) - -# we'll use this to calculate the error rate -predict_op = tf.argmax(Yish, 1) - -t0 = datetime.now() -LL = [] -W1_val = None -W2_val = None -init = tf.global_variables_initializer() -with tf.Session() as session: - session.run(init) - - for i in range(max_iter): - for j in range(n_batches): - Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] - Ybatch = Ytrain[j*batch_sz:(j*batch_sz + batch_sz),] - - if len(Xbatch) == batch_sz: - session.run(train_op, feed_dict={X: Xbatch, T: Ybatch}) - if j % print_period == 0: - # due to RAM limitations we need to have a fixed size input - # so as a result, we have this ugly total cost and prediction computation - test_cost = 0 - prediction = np.zeros(len(Xtest)) - for k in range(len(Xtest) // batch_sz): - Xtestbatch = Xtest[k*batch_sz:(k*batch_sz + batch_sz),] - Ytestbatch = Ytest[k*batch_sz:(k*batch_sz + batch_sz),] - test_cost += session.run(cost, feed_dict={X: Xtestbatch, T: Ytestbatch}) - prediction[k*batch_sz:(k*batch_sz + batch_sz)] = session.run( - predict_op, feed_dict={X: Xtestbatch}) - err = error_rate(prediction, Ytest) - print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, test_cost, err)) - LL.append(test_cost) - - W1_val = W1.eval() - W2_val = W2.eval() -print("Elapsed time:", (datetime.now() - t0)) -plt.plot(LL) -plt.show() - - -W1_val = W1_val.transpose(3, 2, 0, 1) -W2_val = W2_val.transpose(3, 2, 0, 1) - - -# visualize W1 (20, 3, 5, 5) -# W1_val = W1.get_value() -grid = np.zeros((8*5, 8*5)) -m = 0 -n = 0 -for i in range(20): - for j in range(3): - filt = W1_val[i,j] - grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt - m += 1 - if m >= 8: - m = 0 - n += 1 -plt.imshow(grid, cmap='gray') -plt.title("W1") -plt.show() - -# visualize W2 (50, 20, 5, 5) -# W2_val = W2.get_value() -grid = np.zeros((32*5, 32*5)) -m = 0 -n = 0 -for i in range(50): - for j in range(20): - filt = W2_val[i,j] - grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt - m += 1 - if m >= 32: - m = 0 - n += 1 -plt.imshow(grid, cmap='gray') -plt.title("W2") -plt.show() - - diff --git a/cnn_class/cnn_theano.py b/cnn_class/cnn_theano.py index 41a297b9..9cdfeb01 100644 --- a/cnn_class/cnn_theano.py +++ b/cnn_class/cnn_theano.py @@ -1,9 +1,4 @@ -# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow # https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import numpy as np import theano @@ -11,27 +6,37 @@ import matplotlib.pyplot as plt from theano.tensor.nnet import conv2d -from theano.tensor.signal import pool +from theano.tensor.signal import downsample from scipy.io import loadmat from sklearn.utils import shuffle from datetime import datetime -from benchmark import get_data, error_rate + +def error_rate(p, t): + return np.mean(p != t) def relu(a): return a * (a > 0) +def y2indicator(y): + N = len(y) + ind = np.zeros((N, 10)) + for i in xrange(N): + ind[i, y[i]] = 1 + return ind + + def convpool(X, W, b, poolsize=(2, 2)): conv_out = conv2d(input=X, filters=W) # downsample each feature map individually, using maxpooling - pooled_out = pool.pool_2d( + pooled_out = downsample.max_pool_2d( input=conv_out, - ws=poolsize, + ds=poolsize, ignore_border=True ) @@ -44,25 +49,25 @@ def convpool(X, W, b, poolsize=(2, 2)): def init_filter(shape, poolsz): - w = np.random.randn(*shape) * np.sqrt(2.0 / np.prod(shape[1:])) + w = np.random.randn(*shape) / np.sqrt(np.prod(shape[1:]) + shape[0]*np.prod(shape[2:] / np.prod(poolsz))) return w.astype(np.float32) def rearrange(X): # input is (32, 32, 3, N) # output is (N, 3, 32, 32) - # N = X.shape[-1] - # out = np.zeros((N, 3, 32, 32), dtype=np.float32) - # for i in range(N): - # for j in range(3): - # out[i, j, :, :] = X[:, :, j, i] - # return out / 255 - return (X.transpose(3, 2, 0, 1) / 255).astype(np.float32) + N = X.shape[-1] + out = np.zeros((N, 3, 32, 32), dtype=np.float32) + for i in xrange(N): + for j in xrange(3): + out[i, j, :, :] = X[:, :, j, i] + return out / 255 def main(): # step 1: load the data, transform as needed - train, test = get_data() + train = loadmat('../large_files/train_32x32.mat') + test = loadmat('../large_files/test_32x32.mat') # Need to scale! don't leave as 0..255 # Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1) @@ -72,21 +77,24 @@ def main(): Ytrain = train['y'].flatten() - 1 del train Xtrain, Ytrain = shuffle(Xtrain, Ytrain) + Ytrain_ind = y2indicator(Ytrain) Xtest = rearrange(test['X']) Ytest = test['y'].flatten() - 1 del test + Ytest_ind = y2indicator(Ytest) - max_iter = 6 + max_iter = 8 print_period = 10 - lr = np.float32(1e-3) - mu = np.float32(0.9) + lr = np.float32(0.00001) + reg = np.float32(0.01) + mu = np.float32(0.99) N = Xtrain.shape[0] batch_sz = 500 - n_batches = N // batch_sz + n_batches = N / batch_sz M = 500 K = 10 @@ -113,7 +121,7 @@ def main(): # step 2: define theano variables and expressions X = T.tensor4('X', dtype='float32') - Y = T.ivector('T') + Y = T.matrix('T') W1 = theano.shared(W1_init, 'W1') b1 = theano.shared(b1_init, 'b1') W2 = theano.shared(W2_init, 'W2') @@ -123,6 +131,16 @@ def main(): W4 = theano.shared(W4_init.astype(np.float32), 'W4') b4 = theano.shared(b4_init, 'b4') + # momentum changes + dW1 = theano.shared(np.zeros(W1_init.shape, dtype=np.float32), 'dW1') + db1 = theano.shared(np.zeros(b1_init.shape, dtype=np.float32), 'db1') + dW2 = theano.shared(np.zeros(W2_init.shape, dtype=np.float32), 'dW2') + db2 = theano.shared(np.zeros(b2_init.shape, dtype=np.float32), 'db2') + dW3 = theano.shared(np.zeros(W3_init.shape, dtype=np.float32), 'dW3') + db3 = theano.shared(np.zeros(b3_init.shape, dtype=np.float32), 'db3') + dW4 = theano.shared(np.zeros(W4_init.shape, dtype=np.float32), 'dW4') + db4 = theano.shared(np.zeros(b4_init.shape, dtype=np.float32), 'db4') + # forward pass Z1 = convpool(X, W1, b1) Z2 = convpool(Z1, W2, b2) @@ -130,34 +148,51 @@ def main(): pY = T.nnet.softmax( Z3.dot(W4) + b4) # define the cost function and prediction - cost = -(T.log(pY[T.arange(Y.shape[0]), Y])).mean() + params = (W1, b1, W2, b2, W3, b3, W4, b4) + reg_cost = reg*np.sum((param*param).sum() for param in params) + cost = -(Y * T.log(pY)).sum() + reg_cost prediction = T.argmax(pY, axis=1) # step 3: training expressions and functions - params = [W1, b1, W2, b2, W3, b3, W4, b4] - - # momentum changes - dparams = [ - theano.shared( - np.zeros_like( - p.get_value(), - dtype=np.float32 - ) - ) for p in params - ] - - updates = [] - grads = T.grad(cost, params) - for p, dp, g in zip(params, dparams, grads): - dp_update = mu*dp - lr*g - p_update = p + dp_update - - updates.append((dp, dp_update)) - updates.append((p, p_update)) + update_W1 = W1 + mu*dW1 - lr*T.grad(cost, W1) + update_b1 = b1 + mu*db1 - lr*T.grad(cost, b1) + update_W2 = W2 + mu*dW2 - lr*T.grad(cost, W2) + update_b2 = b2 + mu*db2 - lr*T.grad(cost, b2) + update_W3 = W3 + mu*dW3 - lr*T.grad(cost, W3) + update_b3 = b3 + mu*db3 - lr*T.grad(cost, b3) + update_W4 = W4 + mu*dW4 - lr*T.grad(cost, W4) + update_b4 = b4 + mu*db4 - lr*T.grad(cost, b4) + + # update weight changes + update_dW1 = mu*dW1 - lr*T.grad(cost, W1) + update_db1 = mu*db1 - lr*T.grad(cost, b1) + update_dW2 = mu*dW2 - lr*T.grad(cost, W2) + update_db2 = mu*db2 - lr*T.grad(cost, b2) + update_dW3 = mu*dW3 - lr*T.grad(cost, W3) + update_db3 = mu*db3 - lr*T.grad(cost, b3) + update_dW4 = mu*dW4 - lr*T.grad(cost, W4) + update_db4 = mu*db4 - lr*T.grad(cost, b4) train = theano.function( inputs=[X, Y], - updates=updates, + updates=[ + (W1, update_W1), + (b1, update_b1), + (W2, update_W2), + (b2, update_b2), + (W3, update_W3), + (b3, update_b3), + (W4, update_W4), + (b4, update_b4), + (dW1, update_dW1), + (db1, update_db1), + (dW2, update_dW2), + (db2, update_db2), + (dW3, update_dW3), + (db3, update_db3), + (dW4, update_dW4), + (db4, update_db4), + ], ) # create another function for this because we want it over the whole dataset @@ -167,21 +202,20 @@ def main(): ) t0 = datetime.now() - costs = [] - for i in range(max_iter): - Xtrain, Ytrain = shuffle(Xtrain, Ytrain) - for j in range(n_batches): + LL = [] + for i in xrange(max_iter): + for j in xrange(n_batches): Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] - Ybatch = Ytrain[j*batch_sz:(j*batch_sz + batch_sz),] + Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] train(Xbatch, Ybatch) if j % print_period == 0: - cost_val, prediction_val = get_prediction(Xtest, Ytest) + cost_val, prediction_val = get_prediction(Xtest, Ytest_ind) err = error_rate(prediction_val, Ytest) - print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, cost_val, err)) - costs.append(cost_val) - print("Elapsed time:", (datetime.now() - t0)) - plt.plot(costs) + print "Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, cost_val, err) + LL.append(cost_val) + print "Elapsed time:", (datetime.now() - t0) + plt.plot(LL) plt.show() diff --git a/cnn_class/cnn_theano_plot_filters.py b/cnn_class/cnn_theano_plot_filters.py deleted file mode 100644 index 931a4754..00000000 --- a/cnn_class/cnn_theano_plot_filters.py +++ /dev/null @@ -1,211 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow -# https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import theano -import theano.tensor as T -import matplotlib.pyplot as plt - -from scipy.io import loadmat -from sklearn.utils import shuffle - -from datetime import datetime - -from benchmark import get_data, error_rate, y2indicator -from cnn_theano import convpool, relu, init_filter, rearrange - - -def main(): - # step 1: load the data, transform as needed - train, test = get_data() - - # Need to scale! don't leave as 0..255 - # Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1) - # So flatten it and make it 0..9 - # Also need indicator matrix for cost calculation - Xtrain = rearrange(train['X']) - Ytrain = train['y'].flatten() - 1 - del train - Xtrain, Ytrain = shuffle(Xtrain, Ytrain) - Ytrain_ind = y2indicator(Ytrain) - - Xtest = rearrange(test['X']) - Ytest = test['y'].flatten() - 1 - del test - Ytest_ind = y2indicator(Ytest) - - - max_iter = 8 - print_period = 10 - - lr = np.float32(0.00001) - reg = np.float32(0.01) - mu = np.float32(0.99) - - N = Xtrain.shape[0] - batch_sz = 500 - n_batches = N // batch_sz - - M = 500 - K = 10 - poolsz = (2, 2) - - # after conv will be of dimension 32 - 5 + 1 = 28 - # after downsample 28 / 2 = 14 - W1_shape = (20, 3, 5, 5) # (num_feature_maps, num_color_channels, filter_width, filter_height) - W1_init = init_filter(W1_shape, poolsz) - b1_init = np.zeros(W1_shape[0], dtype=np.float32) # one bias per output feature map - - # after conv will be of dimension 14 - 5 + 1 = 10 - # after downsample 10 / 2 = 5 - W2_shape = (50, 20, 5, 5) # (num_feature_maps, old_num_feature_maps, filter_width, filter_height) - W2_init = init_filter(W2_shape, poolsz) - b2_init = np.zeros(W2_shape[0], dtype=np.float32) - - # vanilla ANN weights - W3_init = np.random.randn(W2_shape[0]*5*5, M) / np.sqrt(W2_shape[0]*5*5 + M) - b3_init = np.zeros(M, dtype=np.float32) - W4_init = np.random.randn(M, K) / np.sqrt(M + K) - b4_init = np.zeros(K, dtype=np.float32) - - - # step 2: define theano variables and expressions - X = T.tensor4('X', dtype='float32') - Y = T.matrix('T') - W1 = theano.shared(W1_init, 'W1') - b1 = theano.shared(b1_init, 'b1') - W2 = theano.shared(W2_init, 'W2') - b2 = theano.shared(b2_init, 'b2') - W3 = theano.shared(W3_init.astype(np.float32), 'W3') - b3 = theano.shared(b3_init, 'b3') - W4 = theano.shared(W4_init.astype(np.float32), 'W4') - b4 = theano.shared(b4_init, 'b4') - - # momentum changes - dW1 = theano.shared(np.zeros(W1_init.shape, dtype=np.float32), 'dW1') - db1 = theano.shared(np.zeros(b1_init.shape, dtype=np.float32), 'db1') - dW2 = theano.shared(np.zeros(W2_init.shape, dtype=np.float32), 'dW2') - db2 = theano.shared(np.zeros(b2_init.shape, dtype=np.float32), 'db2') - dW3 = theano.shared(np.zeros(W3_init.shape, dtype=np.float32), 'dW3') - db3 = theano.shared(np.zeros(b3_init.shape, dtype=np.float32), 'db3') - dW4 = theano.shared(np.zeros(W4_init.shape, dtype=np.float32), 'dW4') - db4 = theano.shared(np.zeros(b4_init.shape, dtype=np.float32), 'db4') - - # forward pass - Z1 = convpool(X, W1, b1) - Z2 = convpool(Z1, W2, b2) - Z3 = relu(Z2.flatten(ndim=2).dot(W3) + b3) - pY = T.nnet.softmax( Z3.dot(W4) + b4) - - # define the cost function and prediction - params = (W1, b1, W2, b2, W3, b3, W4, b4) - reg_cost = reg*sum((param*param).sum() for param in params) - cost = -(Y * T.log(pY)).sum() + reg_cost - prediction = T.argmax(pY, axis=1) - - # step 3: training expressions and functions - update_W1 = W1 + mu*dW1 - lr*T.grad(cost, W1) - update_b1 = b1 + mu*db1 - lr*T.grad(cost, b1) - update_W2 = W2 + mu*dW2 - lr*T.grad(cost, W2) - update_b2 = b2 + mu*db2 - lr*T.grad(cost, b2) - update_W3 = W3 + mu*dW3 - lr*T.grad(cost, W3) - update_b3 = b3 + mu*db3 - lr*T.grad(cost, b3) - update_W4 = W4 + mu*dW4 - lr*T.grad(cost, W4) - update_b4 = b4 + mu*db4 - lr*T.grad(cost, b4) - - # update weight changes - update_dW1 = mu*dW1 - lr*T.grad(cost, W1) - update_db1 = mu*db1 - lr*T.grad(cost, b1) - update_dW2 = mu*dW2 - lr*T.grad(cost, W2) - update_db2 = mu*db2 - lr*T.grad(cost, b2) - update_dW3 = mu*dW3 - lr*T.grad(cost, W3) - update_db3 = mu*db3 - lr*T.grad(cost, b3) - update_dW4 = mu*dW4 - lr*T.grad(cost, W4) - update_db4 = mu*db4 - lr*T.grad(cost, b4) - - train = theano.function( - inputs=[X, Y], - updates=[ - (W1, update_W1), - (b1, update_b1), - (W2, update_W2), - (b2, update_b2), - (W3, update_W3), - (b3, update_b3), - (W4, update_W4), - (b4, update_b4), - (dW1, update_dW1), - (db1, update_db1), - (dW2, update_dW2), - (db2, update_db2), - (dW3, update_dW3), - (db3, update_db3), - (dW4, update_dW4), - (db4, update_db4), - ], - ) - - # create another function for this because we want it over the whole dataset - get_prediction = theano.function( - inputs=[X, Y], - outputs=[cost, prediction], - ) - - t0 = datetime.now() - LL = [] - for i in range(max_iter): - for j in range(n_batches): - Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] - Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] - - train(Xbatch, Ybatch) - if j % print_period == 0: - cost_val, prediction_val = get_prediction(Xtest, Ytest_ind) - err = error_rate(prediction_val, Ytest) - print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, cost_val, err)) - LL.append(cost_val) - print("Elapsed time:", (datetime.now() - t0)) - plt.plot(LL) - plt.show() - - # visualize W1 (20, 3, 5, 5) - W1_val = W1.get_value() - grid = np.zeros((8*5, 8*5)) - m = 0 - n = 0 - for i in range(20): - for j in range(3): - filt = W1_val[i,j] - grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt - m += 1 - if m >= 8: - m = 0 - n += 1 - plt.imshow(grid, cmap='gray') - plt.title("W1") - plt.show() - - # visualize W2 (50, 20, 5, 5) - W2_val = W2.get_value() - grid = np.zeros((32*5, 32*5)) - m = 0 - n = 0 - for i in range(50): - for j in range(20): - filt = W2_val[i,j] - grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt - m += 1 - if m >= 32: - m = 0 - n += 1 - plt.imshow(grid, cmap='gray') - plt.title("W2") - plt.show() - - -if __name__ == '__main__': - main() diff --git a/cnn_class/custom_blur.py b/cnn_class/custom_blur.py deleted file mode 100644 index e7c3749d..00000000 --- a/cnn_class/custom_blur.py +++ /dev/null @@ -1,103 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow -# https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -# from scipy.signal import convolve2d -import matplotlib.pyplot as plt -import matplotlib.image as mpimg -from datetime import datetime - -# def convolve2d(X, W): -# t0 = datetime.now() -# n1, n2 = X.shape -# m1, m2 = W.shape -# Y = np.zeros((n1 + m1 - 1, n2 + m2 - 1)) -# for i in xrange(n1 + m1 - 1): -# for ii in xrange(m1): -# for j in xrange(n2 + m2 - 1): -# for jj in xrange(m2): -# if i >= ii and j >= jj and i - ii < n1 and j - jj < n2: -# Y[i,j] += W[ii,jj]*X[i - ii,j - jj] -# print "elapsed time:", (datetime.now() - t0) -# return Y - -# def convolve2d(X, W): -# t0 = datetime.now() -# n1, n2 = X.shape -# m1, m2 = W.shape -# Y = np.zeros((n1 + m1 - 1, n2 + m2 - 1)) -# for i in xrange(n1): -# for j in xrange(n2): -# Y[i:i+m1,j:j+m2] += X[i,j]*W -# print "elapsed time:", (datetime.now() - t0) -# return Y - -# same size as input -def convolve2d(X, W): - n1, n2 = X.shape - m1, m2 = W.shape - Y = np.zeros((n1 + m1 - 1, n2 + m2 - 1)) - for i in range(n1): - for j in range(n2): - Y[i:i+m1,j:j+m2] += X[i,j]*W - ret = Y[m1//2:-m1//2+1,m2//2:-m2//2+1] - assert(ret.shape == X.shape) - return ret - -# smaller than input -# def convolve2d(X, W): -# n1, n2 = X.shape -# m1, m2 = W.shape -# Y = np.zeros((n1 + m1 - 1, n2 + m2 - 1)) -# for i in xrange(n1): -# for j in xrange(n2): -# Y[i:i+m1,j:j+m2] += X[i,j]*W -# ret = Y[m1-1:-m1+1,m2-1:-m2+1] -# return ret - -# load the famous Lena image -img = mpimg.imread('lena.png') - -# what does it look like? -plt.imshow(img) -plt.show() - -# make it B&W -bw = img.mean(axis=2) -plt.imshow(bw, cmap='gray') -plt.show() - -# create a Gaussian filter -W = np.zeros((20, 20)) -for i in range(20): - for j in range(20): - dist = (i - 9.5)**2 + (j - 9.5)**2 - W[i, j] = np.exp(-dist / 50.) - -# let's see what the filter looks like -plt.imshow(W, cmap='gray') -plt.show() - -# now the convolution -out = convolve2d(bw, W) -plt.imshow(out, cmap='gray') -plt.show() - -# what's that weird black stuff on the edges? let's check the size of output -print(out.shape) -# after convolution, the output signal is N1 + N2 - 1 - -# try it in color -out = np.zeros(img.shape) -W /= W.sum() -for i in range(3): - out[:,:,i] = convolve2d(img[:,:,i], W) -plt.imshow(out) -plt.show() - - - diff --git a/cnn_class/echo.py b/cnn_class/echo.py index 37d68f52..6b9efb90 100644 --- a/cnn_class/echo.py +++ b/cnn_class/echo.py @@ -1,9 +1,4 @@ -# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow # https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import matplotlib.pyplot as plt import numpy as np @@ -26,7 +21,7 @@ #Extract Raw Audio from Wav File signal = spf.readframes(-1) signal = np.fromstring(signal, 'Int16') -print("numpy signal:", signal.shape) +print "numpy signal:", signal.shape plt.plot(signal) plt.title("Hello world without echo") @@ -34,7 +29,7 @@ delta = np.array([1., 0., 0.]) noecho = np.convolve(signal, delta) -print("noecho signal:", noecho.shape) +print "noecho signal:", noecho.shape assert(np.abs(noecho[:len(signal)] - signal).sum() < 0.000001) noecho = noecho.astype(np.int16) # make sure you do this, otherwise, you will get VERY LOUD NOISE diff --git a/cnn_class/edge.py b/cnn_class/edge.py index 9cfecfc3..4baf022d 100644 --- a/cnn_class/edge.py +++ b/cnn_class/edge.py @@ -1,9 +1,4 @@ -# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow # https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import numpy as np from scipy.signal import convolve2d diff --git a/cnn_class/edge_benchmark.py b/cnn_class/edge_benchmark.py index 5e68d774..801557ef 100644 --- a/cnn_class/edge_benchmark.py +++ b/cnn_class/edge_benchmark.py @@ -1,10 +1,6 @@ # Vanilla deep network -# https://deeplearningcourses.com/c/deep-learning-convolutional-neural-networks-theano-tensorflow + # https://udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future import numpy as np import tensorflow as tf @@ -14,8 +10,6 @@ from scipy.io import loadmat from sklearn.utils import shuffle -from benchmark import error_rate - Hx = np.array([ [-1, 0, 1], @@ -31,12 +25,24 @@ ], dtype=np.float32) +def y2indicator(y): + N = len(y) + ind = np.zeros((N, 10)) + for i in xrange(N): + ind[i, y[i]] = 1 + return ind + + +def error_rate(p, t): + return np.mean(p != t) + + def convolve_flatten(X): # input will be (32, 32, 3, N) # output will be (N, 32*32) N = X.shape[-1] flat = np.zeros((N, 32*32)) - for i in range(N): + for i in xrange(N): #flat[i] = X[:,:,:,i].reshape(3072) bw = X[:,:,:,i].mean(axis=2) # make it grayscale Gx = convolve2d(bw, Hx, mode='same') @@ -58,16 +64,18 @@ def main(): Xtrain = convolve_flatten(train['X'].astype(np.float32)) Ytrain = train['y'].flatten() - 1 Xtrain, Ytrain = shuffle(Xtrain, Ytrain) + Ytrain_ind = y2indicator(Ytrain) Xtest = convolve_flatten(test['X'].astype(np.float32)) Ytest = test['y'].flatten() - 1 + Ytest_ind = y2indicator(Ytest) # gradient descent params - max_iter = 15 + max_iter = 20 print_period = 10 N, D = Xtrain.shape batch_sz = 500 - n_batches = N // batch_sz + n_batches = N / batch_sz # initial weights M1 = 1000 # hidden layer size @@ -82,7 +90,7 @@ def main(): # define variables and expressions X = tf.placeholder(tf.float32, shape=(None, D), name='X') - T = tf.placeholder(tf.int32, shape=(None,), name='T') + T = tf.placeholder(tf.float32, shape=(None, K), name='T') W1 = tf.Variable(W1_init.astype(np.float32)) b1 = tf.Variable(b1_init.astype(np.float32)) W2 = tf.Variable(W2_init.astype(np.float32)) @@ -94,12 +102,7 @@ def main(): Z2 = tf.nn.relu( tf.matmul(Z1, W2) + b2 ) Yish = tf.matmul(Z2, W3) + b3 - cost = tf.reduce_sum( - tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=Yish, - labels=T - ) - ) + cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(Yish, T)) train_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99, momentum=0.9).minimize(cost) @@ -107,21 +110,21 @@ def main(): predict_op = tf.argmax(Yish, 1) LL = [] - init = tf.global_variables_initializer() + init = tf.initialize_all_variables() with tf.Session() as session: session.run(init) - for i in range(max_iter): - for j in range(n_batches): + for i in xrange(max_iter): + for j in xrange(n_batches): Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),] - Ybatch = Ytrain[j*batch_sz:(j*batch_sz + batch_sz),] + Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),] session.run(train_op, feed_dict={X: Xbatch, T: Ybatch}) if j % print_period == 0: - test_cost = session.run(cost, feed_dict={X: Xtest, T: Ytest}) + test_cost = session.run(cost, feed_dict={X: Xtest, T: Ytest_ind}) prediction = session.run(predict_op, feed_dict={X: Xtest}) err = error_rate(prediction, Ytest) - print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, test_cost, err)) + print "Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, test_cost, err) LL.append(test_cost) plt.plot(LL) diff --git a/cnn_class/exercises.txt b/cnn_class/exercises.txt deleted file mode 100644 index 81a2e5a4..00000000 --- a/cnn_class/exercises.txt +++ /dev/null @@ -1,20 +0,0 @@ -Logistic Regression -https://www.kaggle.com/uciml/pima-indians-diabetes-database -https://lazyprogrammer.me/course_files/exercises/diabetes.csv - -Linear Regression -https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html -https://lazyprogrammer.me/course_files/exercises/boston.txt - -ANN -https://archive.ics.uci.edu/ml/datasets/ecoli (orig) -https://www.kaggle.com/elikplim/ecoli-data-set (alt) -https://lazyprogrammer.me/course_files/exercises/ecoli.csv - -CNN -https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge -https://archive.org/download/fer2013_202311/fer2013.csv - -NLP -https://www.kaggle.com/crowdflower/twitter-airline-sentiment -https://lazyprogrammer.me/course_files/exercises/AirlineSentimentTweets.csv \ No newline at end of file diff --git a/cnn_class/extra_reading.txt b/cnn_class/extra_reading.txt deleted file mode 100644 index ed16dc09..00000000 --- a/cnn_class/extra_reading.txt +++ /dev/null @@ -1,17 +0,0 @@ -Gradient-Based Learning Applied to Document Recognition -http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf - -ImageNet Classification with Deep Convolutional Neural Networks -https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf - -Convolution arithmetic tutorial -https://theano-pymc.readthedocs.io/en/latest/tutorial/conv_arithmetic.html - -Very Deep Convolutional Networks for Large-Scale Visual Recognition -http://www.robots.ox.ac.uk/~vgg/research/very_deep/ - -ImageNet Classification with Deep Convolutional Neural Networks -http://image-net.org/challenges/LSVRC/2012/supervision.pdf - -Going deeper with convolutions -https://arxiv.org/pdf/1409.4842.pdf \ No newline at end of file diff --git a/cnn_class/keras_example.py b/cnn_class/keras_example.py deleted file mode 100644 index d0463588..00000000 --- a/cnn_class/keras_example.py +++ /dev/null @@ -1,121 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.models import Sequential, Model -from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization, Input - -import matplotlib.pyplot as plt -import pandas as pd -import numpy as np - -from datetime import datetime -from scipy.io import loadmat -from sklearn.utils import shuffle - -from benchmark import get_data, error_rate - - -# helper -# def y2indicator(Y): -# N = len(Y) -# K = len(set(Y)) -# I = np.zeros((N, K)) -# I[np.arange(N), Y] = 1 -# return I - -def rearrange(X): - # input is (32, 32, 3, N) - # output is (N, 32, 32, 3) - # N = X.shape[-1] - # out = np.zeros((N, 32, 32, 3), dtype=np.float32) - # for i in xrange(N): - # for j in xrange(3): - # out[i, :, :, j] = X[:, :, j, i] - # return out / 255 - return (X.transpose(3, 0, 1, 2) / 255.).astype(np.float32) - - -# get the data -train, test = get_data() - -# Need to scale! don't leave as 0..255 -# Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1) -# So flatten it and make it 0..9 -# Also need indicator matrix for cost calculation -Xtrain = rearrange(train['X']) -Ytrain = train['y'].flatten() - 1 -del train - -Xtest = rearrange(test['X']) -Ytest = test['y'].flatten() - 1 -del test - - - -# get shapes -K = len(set(Ytrain)) - - - -# make the CNN -i = Input(shape=Xtrain.shape[1:]) -x = Conv2D(filters=20, kernel_size=(5, 5))(i) -x = BatchNormalization()(x) -x = Activation('relu')(x) -x = MaxPooling2D()(x) - -x = Conv2D(filters=50, kernel_size=(5, 5))(x) -x = BatchNormalization()(x) -x = Activation('relu')(x) -x = MaxPooling2D()(x) - -x = Flatten()(x) -x = Dense(units=500)(x) -x = Activation('relu')(x) -x = Dropout(0.3)(x) -x = Dense(units=K)(x) -x = Activation('softmax')(x) - -model = Model(inputs=i, outputs=x) - - -# list of losses: https://keras.io/losses/ -# list of optimizers: https://keras.io/optimizers/ -# list of metrics: https://keras.io/metrics/ -model.compile( - loss='sparse_categorical_crossentropy', - optimizer='adam', - metrics=['accuracy'] -) - -# note: multiple ways to choose a backend -# either theano, tensorflow, or cntk -# https://keras.io/backend/ - - -# gives us back a -r = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=10, batch_size=32) -print("Returned:", r) - -# print the available keys -# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc']) -print(r.history.keys()) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='acc') -plt.plot(r.history['val_accuracy'], label='val_acc') -plt.legend() -plt.show() - - diff --git a/cnn_class2/WHERE ARE THE NOTEBOOKS.txt b/cnn_class2/WHERE ARE THE NOTEBOOKS.txt deleted file mode 100644 index 5446ce25..00000000 --- a/cnn_class2/WHERE ARE THE NOTEBOOKS.txt +++ /dev/null @@ -1,5 +0,0 @@ -As stated in the "where to get the code" / "where to get the notebooks" lecture, the notebooks are NOT on Github. - -If you missed this, please review the lecture for the actual location of the notebooks. - -If, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com. \ No newline at end of file diff --git a/cnn_class2/class_activation_maps.py b/cnn_class2/class_activation_maps.py deleted file mode 100644 index 19033ff5..00000000 --- a/cnn_class2/class_activation_maps.py +++ /dev/null @@ -1,94 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision - -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.models import Model -from keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions -from keras.preprocessing import image - -import numpy as np -import scipy as sp -import matplotlib.pyplot as plt - -from glob import glob - - - -# get the image files -# http://www.vision.caltech.edu/Image_Datasets/Caltech101/ -# http://www.vision.caltech.edu/Image_Datasets/Caltech256/ -image_files = glob('../large_files/256_ObjectCategories/*/*.jp*g') -image_files += glob('../large_files/101_ObjectCategories/*/*.jp*g') - - - -# look at an image for fun -plt.imshow(image.load_img(np.random.choice(image_files))) -plt.show() - - -# add preprocessing layer to the front of VGG -resnet = ResNet50(input_shape=(224, 224, 3), weights='imagenet', include_top=True) - -# view the structure of the model -# if you want to confirm we need activation_49 -resnet.summary() - -# make a model to get output before flatten -activation_layer = resnet.get_layer('activation_49') - -# create a model object -model = Model(inputs=resnet.input, outputs=activation_layer.output) - -# get the feature map weights -final_dense = resnet.get_layer('fc1000') -W = final_dense.get_weights()[0] - - -while True: - img = image.load_img(np.random.choice(image_files), target_size=(224, 224)) - x = preprocess_input(np.expand_dims(img, 0)) - fmaps = model.predict(x)[0] # 7 x 7 x 2048 - - # get predicted class - probs = resnet.predict(x) - classnames = decode_predictions(probs)[0] - print(classnames) - classname = classnames[0][1] - pred = np.argmax(probs[0]) - - # get the 2048 weights for the relevant class - w = W[:, pred] - - # "dot" w with fmaps - cam = fmaps.dot(w) - - # upsample to 224 x 224 - # 7 x 32 = 224 - cam = sp.ndimage.zoom(cam, (32, 32), order=1) - - plt.subplot(1,2,1) - plt.imshow(img, alpha=0.8) - plt.imshow(cam, cmap='jet', alpha=0.5) - plt.subplot(1,2,2) - plt.imshow(img) - plt.title(classname) - plt.show() - - ans = input("Continue? (Y/n)") - if ans and ans[0].lower() == 'n': - break - - - -# def slowversion(A, w): -# N = len(w) -# result = np.zeros(A.shape[:-1]) -# for i in range(N): -# result += A[:,:,i]*w[i] -# return result - diff --git a/cnn_class2/content/elephant.jpg b/cnn_class2/content/elephant.jpg deleted file mode 100644 index 9df55b30..00000000 Binary files a/cnn_class2/content/elephant.jpg and /dev/null differ diff --git a/cnn_class2/content/sydney.jpg b/cnn_class2/content/sydney.jpg deleted file mode 100644 index bf1f6162..00000000 Binary files a/cnn_class2/content/sydney.jpg and /dev/null differ diff --git a/cnn_class2/extra_reading.txt b/cnn_class2/extra_reading.txt deleted file mode 100644 index 593bc0cc..00000000 --- a/cnn_class2/extra_reading.txt +++ /dev/null @@ -1,20 +0,0 @@ -A Neural Algorithm of Artistic Style -https://arxiv.org/abs/1508.06576 - -SSD: Single Shot MultiBox Detector -https://arxiv.org/abs/1512.02325 - -Very Deep Convolutional Networks for Large-Scale Image Recognition (VGG) -https://arxiv.org/abs/1409.1556 - -Deep Residual Learning for Image Recognition -https://arxiv.org/abs/1512.03385 - -Going Deeper with Convolutions (Inception) -https://arxiv.org/abs/1409.4842 - -Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift -https://arxiv.org/abs/1502.03167 - -Deep learning improved by biological activation functions -https://arxiv.org/pdf/1804.11237.pdf \ No newline at end of file diff --git a/cnn_class2/fashion.py b/cnn_class2/fashion.py deleted file mode 100644 index f1be1654..00000000 --- a/cnn_class2/fashion.py +++ /dev/null @@ -1,109 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.models import Sequential -from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization - -import matplotlib.pyplot as plt -import pandas as pd -import numpy as np - - -# helper -def y2indicator(Y): - N = len(Y) - K = len(set(Y)) - I = np.zeros((N, K)) - I[np.arange(N), Y] = 1 - return I - - -# get the data -# https://www.kaggle.com/zalando-research/fashionmnist -data = pd.read_csv('../large_files/fashionmnist/fashion-mnist_train.csv') -data = data.values -np.random.shuffle(data) - -X = data[:, 1:].reshape(-1, 28, 28, 1) / 255.0 -Y = data[:, 0].astype(np.int32) - -# get shapes -# N = len(Y) -K = len(set(Y)) - -# by default Keras wants one-hot encoded labels -# there's another cost function we can use -# where we can just pass in the integer labels directly -# just like Tensorflow / Theano -Y = y2indicator(Y) - - -# the model will be a sequence of layers -model = Sequential() - - -# make the CNN -# model.add(Input(shape=(28, 28, 1))) -model.add(Conv2D(input_shape=(28, 28, 1), filters=32, kernel_size=(3, 3))) -model.add(BatchNormalization()) -model.add(Activation('relu')) -model.add(MaxPooling2D()) - -model.add(Conv2D(filters=64, kernel_size=(3, 3))) -model.add(BatchNormalization()) -model.add(Activation('relu')) -model.add(MaxPooling2D()) - -model.add(Conv2D(filters=128, kernel_size=(3, 3))) -model.add(BatchNormalization()) -model.add(Activation('relu')) -model.add(MaxPooling2D()) - -model.add(Flatten()) -model.add(Dense(units=300)) -model.add(Activation('relu')) -model.add(Dropout(0.2)) -model.add(Dense(units=K)) -model.add(Activation('softmax')) - - -# list of losses: https://keras.io/losses/ -# list of optimizers: https://keras.io/optimizers/ -# list of metrics: https://keras.io/metrics/ -model.compile( - loss='categorical_crossentropy', - optimizer='adam', - metrics=['accuracy'] -) - -# note: multiple ways to choose a backend -# either theano, tensorflow, or cntk -# https://keras.io/backend/ - - -# gives us back a -r = model.fit(X, Y, validation_split=0.33, epochs=15, batch_size=32) -print("Returned:", r) - -# print the available keys -# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc']) -print(r.history.keys()) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='acc') -plt.plot(r.history['val_accuracy'], label='val_acc') -plt.legend() -plt.show() - - diff --git a/cnn_class2/fashion2.py b/cnn_class2/fashion2.py deleted file mode 100644 index d035e974..00000000 --- a/cnn_class2/fashion2.py +++ /dev/null @@ -1,104 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.models import Sequential, Model -from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization, Input - -import matplotlib.pyplot as plt -import pandas as pd -import numpy as np - - -# helper -def y2indicator(Y): - N = len(Y) - K = len(set(Y)) - I = np.zeros((N, K)) - I[np.arange(N), Y] = 1 - return I - - -# get the data -# https://www.kaggle.com/zalando-research/fashionmnist -data = pd.read_csv('../large_files/fashionmnist/fashion-mnist_train.csv') -data = data.values -np.random.shuffle(data) - -X = data[:, 1:].reshape(-1, 28, 28, 1) / 255.0 -Y = data[:, 0].astype(np.int32) - -# get shapes -# N = len(Y) -K = len(set(Y)) - -# by default Keras wants one-hot encoded labels -# there's another cost function we can use -# where we can just pass in the integer labels directly -# just like Tensorflow / Theano -Y = y2indicator(Y) - - - - -# make the CNN -i = Input(shape=(28, 28, 1)) -x = Conv2D(filters=32, kernel_size=(3, 3))(i) -x = BatchNormalization()(x) -x = Activation('relu')(x) -x = MaxPooling2D()(x) - -x = Conv2D(filters=64, kernel_size=(3, 3))(x) -x = BatchNormalization()(x) -x = Activation('relu')(x) -x = MaxPooling2D()(x) - -x = Flatten()(x) -x = Dense(units=100)(x) -x = Activation('relu')(x) -x = Dropout(0.3)(x) -x = Dense(units=K)(x) -x = Activation('softmax')(x) - -model = Model(inputs=i, outputs=x) - - -# list of losses: https://keras.io/losses/ -# list of optimizers: https://keras.io/optimizers/ -# list of metrics: https://keras.io/metrics/ -model.compile( - loss='categorical_crossentropy', - optimizer='adam', - metrics=['accuracy'] -) - -# note: multiple ways to choose a backend -# either theano, tensorflow, or cntk -# https://keras.io/backend/ - - -# gives us back a -r = model.fit(X, Y, validation_split=0.33, epochs=15, batch_size=32) -print("Returned:", r) - -# print the available keys -# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc']) -print(r.history.keys()) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='acc') -plt.plot(r.history['val_accuracy'], label='val_acc') -plt.legend() -plt.show() - - diff --git a/cnn_class2/make_limited_datasets.py b/cnn_class2/make_limited_datasets.py deleted file mode 100644 index 911c4563..00000000 --- a/cnn_class2/make_limited_datasets.py +++ /dev/null @@ -1,39 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision -import os - -def mkdir(p): - if not os.path.exists(p): - os.mkdir(p) - -def link(src, dst): - if not os.path.exists(dst): - os.symlink(src, dst, target_is_directory=True) - -mkdir('../large_files/fruits-360-small') - - -classes = [ - 'Apple Golden 1', - 'Avocado', - 'Lemon', - 'Mango', - 'Kiwi', - 'Banana', - 'Strawberry', - 'Raspberry' -] - -train_path_from = os.path.abspath('../large_files/fruits-360/Training') -valid_path_from = os.path.abspath('../large_files/fruits-360/Validation') - -train_path_to = os.path.abspath('../large_files/fruits-360-small/Training') -valid_path_to = os.path.abspath('../large_files/fruits-360-small/Validation') - -mkdir(train_path_to) -mkdir(valid_path_to) - - -for c in classes: - link(train_path_from + '/' + c, train_path_to + '/' + c) - link(valid_path_from + '/' + c, valid_path_to + '/' + c) \ No newline at end of file diff --git a/cnn_class2/siamese.py b/cnn_class2/siamese.py deleted file mode 100644 index 4c43f163..00000000 --- a/cnn_class2/siamese.py +++ /dev/null @@ -1,443 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.layers import Input, Lambda, Dense, Flatten, Conv2D, BatchNormalization, Activation, MaxPooling2D -from keras.models import Model -from keras.preprocessing import image - -import keras.backend as K - -import numpy as np -import matplotlib.pyplot as plt - -from glob import glob -from collections import Counter - - -# get the data from: http://vision.ucsd.edu/content/yale-face-database -files = glob('../large_files/yalefaces/subject*') - -# easier to randomize later -np.random.shuffle(files) - -# number of samples -N = len(files) - - -def load_img(filepath): - # load image and downsample - img = image.img_to_array(image.load_img(filepath, target_size=[60, 80])).astype('uint8') - return img - - - -# look at an image for fun -img = load_img(np.random.choice(files)) -plt.imshow(img) -plt.show() - - -# try load images as arrays -# yes, I cheated and checked beforehand that all the images were the same shape! -shape = [N] + list(img.shape) -images = np.zeros(shape) -for i, f in enumerate(files): - # img = image.img_to_array(image.load_img(f)).astype('uint8') - img = load_img(f) - images[i] = img - - -# make the labels -# all the filenames are something like 'subject13.happy' -labels = np.zeros(N) -for i, f in enumerate(files): - filename = f.rsplit('/', 1)[-1] - subject_num = filename.split('.', 1)[0] - - # subtract 1 since the filenames start from 1 - idx = int(subject_num.replace('subject', '')) - 1 - labels[i] = idx - - -# how many of each subject do we have? -label_count = Counter(labels) - -# set of unique labels -unique_labels = set(label_count.keys()) - -# get the number of subjects -n_subjects = len(label_count) - -# let's make it so 3 images for each subject are test data -# number of test points is then -n_test = 3 * n_subjects -n_train = N - n_test - - -# initialize arrays to hold train and test images -train_images = np.zeros([n_train] + list(img.shape)) -train_labels = np.zeros(n_train) -test_images = np.zeros([n_test] + list(img.shape)) -test_labels = np.zeros(n_test) - - -count_so_far = {} -train_idx = 0 -test_idx = 0 -for img, label in zip(images, labels): - # increment the count - count_so_far[label] = count_so_far.get(label, 0) + 1 - - if count_so_far[label] > 3: - # we have already added 3 test images for this subject - # so add the rest to train - train_images[train_idx] = img - train_labels[train_idx] = label - train_idx += 1 - - else: - # add the first 3 images to test - test_images[test_idx] = img - test_labels[test_idx] = label - test_idx += 1 - - -# create label2idx mapping for easy access -train_label2idx = {} -test_label2idx = {} - -for i, label in enumerate(train_labels): - if label not in train_label2idx: - train_label2idx[label] = [i] - else: - train_label2idx[label].append(i) - -for i, label in enumerate(test_labels): - if label not in test_label2idx: - test_label2idx[label] = [i] - else: - test_label2idx[label].append(i) - - -# come up with all possible training sample indices -train_positives = [] -train_negatives = [] -test_positives = [] -test_negatives = [] - -for label, indices in train_label2idx.items(): - # all indices that do NOT belong to this subject - other_indices = set(range(n_train)) - set(indices) - - for i, idx1 in enumerate(indices): - for idx2 in indices[i+1:]: - train_positives.append((idx1, idx2)) - - for idx2 in other_indices: - train_negatives.append((idx1, idx2)) - -for label, indices in test_label2idx.items(): - # all indices that do NOT belong to this subject - other_indices = set(range(n_test)) - set(indices) - - for i, idx1 in enumerate(indices): - for idx2 in indices[i+1:]: - test_positives.append((idx1, idx2)) - - for idx2 in other_indices: - test_negatives.append((idx1, idx2)) - - -batch_size = 64 -def train_generator(): - # for each batch, we will send 1 pair of each subject - # and the same number of non-matching pairs - n_batches = int(np.ceil(len(train_positives) / batch_size)) - - while True: - np.random.shuffle(train_positives) - - n_samples = batch_size * 2 - shape = [n_samples] + list(img.shape) - x_batch_1 = np.zeros(shape) - x_batch_2 = np.zeros(shape) - y_batch = np.zeros(n_samples) - - for i in range(n_batches): - pos_batch_indices = train_positives[i * batch_size: (i + 1) * batch_size] - - # fill up x_batch and y_batch - j = 0 - for idx1, idx2 in pos_batch_indices: - x_batch_1[j] = train_images[idx1] - x_batch_2[j] = train_images[idx2] - y_batch[j] = 1 # match - j += 1 - - # get negative samples - neg_indices = np.random.choice(len(train_negatives), size=len(pos_batch_indices), replace=False) - for neg in neg_indices: - idx1, idx2 = train_negatives[neg] - x_batch_1[j] = train_images[idx1] - x_batch_2[j] = train_images[idx2] - y_batch[j] = 0 # non-match - j += 1 - - x1 = x_batch_1[:j] - x2 = x_batch_2[:j] - y = y_batch[:j] - yield [x1, x2], y - - -# same thing as the train generator except no shuffling and it uses the test set -def test_generator(): - n_batches = int(np.ceil(len(test_positives) / batch_size)) - - while True: - n_samples = batch_size * 2 - shape = [n_samples] + list(img.shape) - x_batch_1 = np.zeros(shape) - x_batch_2 = np.zeros(shape) - y_batch = np.zeros(n_samples) - - for i in range(n_batches): - pos_batch_indices = test_positives[i * batch_size: (i + 1) * batch_size] - - # fill up x_batch and y_batch - j = 0 - for idx1, idx2 in pos_batch_indices: - x_batch_1[j] = test_images[idx1] - x_batch_2[j] = test_images[idx2] - y_batch[j] = 1 # match - j += 1 - - # get negative samples - neg_indices = np.random.choice(len(test_negatives), size=len(pos_batch_indices), replace=False) - for neg in neg_indices: - idx1, idx2 = test_negatives[neg] - x_batch_1[j] = test_images[idx1] - x_batch_2[j] = test_images[idx2] - y_batch[j] = 0 # non-match - j += 1 - - x1 = x_batch_1[:j] - x2 = x_batch_2[:j] - y = y_batch[:j] - yield [x1, x2], y - - - - -# build the base neural network -i = Input(shape=img.shape) -x = Conv2D(filters=32, kernel_size=(3, 3))(i) -x = BatchNormalization()(x) -x = Activation('relu')(x) -x = MaxPooling2D()(x) - -x = Conv2D(filters=64, kernel_size=(3, 3))(x) -x = BatchNormalization()(x) -x = Activation('relu')(x) -x = MaxPooling2D()(x) - -x = Flatten()(x) -x = Dense(units=128, activation='relu')(x) -x = Dense(units=50)(x) # feature vector - -cnn = Model(inputs=i, outputs=x) - - -# feed both images into the same CNN -img_placeholder1 = Input(shape=img.shape) -img_placeholder2 = Input(shape=img.shape) - -# get image features -feat1 = cnn(img_placeholder1) -feat2 = cnn(img_placeholder2) - - -# calculate the Euclidean distance between feature 1 and feature 2 -def euclidean_distance(features): - x, y = features - return K.sqrt(K.sum(K.square(x - y), axis=1, keepdims=True)) - - -# lambda layer to output distance between feat1 and feat2 -dist_layer = Lambda(euclidean_distance)([feat1, feat2]) - - -# the model we will actually train -model = Model(inputs=[img_placeholder1, img_placeholder2], outputs=dist_layer) - - -# loss function for siamese network -def contrastive_loss(y_true, y_pred): - margin = 1 - return K.mean(y_true * K.square(y_pred) + (1 - y_true) * K.square(K.maximum(margin - y_pred, 0))) - - -# compile the model -model.compile( - loss=contrastive_loss, - optimizer='adam', -) - - -# calculate accuracy before training -# since the dataset is imbalanced, we'll report tp, tn, fp, fn -def get_train_accuracy(threshold=0.85): - positive_distances = [] - negative_distances = [] - - tp = 0 - tn = 0 - fp = 0 - fn = 0 - - batch_size = 64 - x_batch_1 = np.zeros([batch_size] + list(img.shape)) - x_batch_2 = np.zeros([batch_size] + list(img.shape)) - n_batches = int(np.ceil(len(train_positives) / batch_size)) - for i in range(n_batches): - print(f"pos batch: {i+1}/{n_batches}") - pos_batch_indices = train_positives[i * batch_size: (i + 1) * batch_size] - - # fill up x_batch and y_batch - j = 0 - for idx1, idx2 in pos_batch_indices: - x_batch_1[j] = train_images[idx1] - x_batch_2[j] = train_images[idx2] - j += 1 - - x1 = x_batch_1[:j] - x2 = x_batch_2[:j] - distances = model.predict([x1, x2]).flatten() - positive_distances += distances.tolist() - - # update tp, tn, fp, fn - tp += (distances < threshold).sum() - fn += (distances > threshold).sum() - - n_batches = int(np.ceil(len(train_negatives) / batch_size)) - for i in range(n_batches): - print(f"neg batch: {i+1}/{n_batches}") - neg_batch_indices = train_negatives[i * batch_size: (i + 1) * batch_size] - - # fill up x_batch and y_batch - j = 0 - for idx1, idx2 in neg_batch_indices: - x_batch_1[j] = train_images[idx1] - x_batch_2[j] = train_images[idx2] - j += 1 - - x1 = x_batch_1[:j] - x2 = x_batch_2[:j] - distances = model.predict([x1, x2]).flatten() - negative_distances += distances.tolist() - - # update tp, tn, fp, fn - fp += (distances < threshold).sum() - tn += (distances > threshold).sum() - - tpr = tp / (tp + fn) - tnr = tn / (tn + fp) - print(f"sensitivity (tpr): {tpr}, specificity (tnr): {tnr}") - - plt.hist(negative_distances, bins=20, density=True, label='negative_distances') - plt.hist(positive_distances, bins=20, density=True, label='positive_distances') - plt.legend() - plt.show() - - - -def get_test_accuracy(threshold=0.85): - positive_distances = [] - negative_distances = [] - - tp = 0 - tn = 0 - fp = 0 - fn = 0 - - batch_size = 64 - x_batch_1 = np.zeros([batch_size] + list(img.shape)) - x_batch_2 = np.zeros([batch_size] + list(img.shape)) - n_batches = int(np.ceil(len(test_positives) / batch_size)) - for i in range(n_batches): - print(f"pos batch: {i+1}/{n_batches}") - pos_batch_indices = test_positives[i * batch_size: (i + 1) * batch_size] - - # fill up x_batch and y_batch - j = 0 - for idx1, idx2 in pos_batch_indices: - x_batch_1[j] = test_images[idx1] - x_batch_2[j] = test_images[idx2] - j += 1 - - x1 = x_batch_1[:j] - x2 = x_batch_2[:j] - distances = model.predict([x1, x2]).flatten() - positive_distances += distances.tolist() - - # update tp, tn, fp, fn - tp += (distances < threshold).sum() - fn += (distances > threshold).sum() - - n_batches = int(np.ceil(len(test_negatives) / batch_size)) - for i in range(n_batches): - print(f"neg batch: {i+1}/{n_batches}") - neg_batch_indices = test_negatives[i * batch_size: (i + 1) * batch_size] - - # fill up x_batch and y_batch - j = 0 - for idx1, idx2 in neg_batch_indices: - x_batch_1[j] = test_images[idx1] - x_batch_2[j] = test_images[idx2] - j += 1 - - x1 = x_batch_1[:j] - x2 = x_batch_2[:j] - distances = model.predict([x1, x2]).flatten() - negative_distances += distances.tolist() - - # update tp, tn, fp, fn - fp += (distances < threshold).sum() - tn += (distances > threshold).sum() - - - tpr = tp / (tp + fn) - tnr = tn / (tn + fp) - print(f"sensitivity (tpr): {tpr}, specificity (tnr): {tnr}") - - plt.hist(negative_distances, bins=20, density=True, label='negative_distances') - plt.hist(positive_distances, bins=20, density=True, label='positive_distances') - plt.legend() - plt.show() - - - - -# params for training -train_steps = int(np.ceil(len(train_positives) * 2 / batch_size)) -valid_steps = int(np.ceil(len(test_positives) * 2 / batch_size)) - -# fit the model -r = model.fit( - train_generator(), - steps_per_epoch=train_steps, - epochs=20, - validation_data=test_generator(), - validation_steps=valid_steps, -) - -# plot the loss -plt.plot(r.history['loss'], label='train loss') -plt.plot(r.history['val_loss'], label='val loss') -plt.legend() -plt.show() - -get_train_accuracy() -get_test_accuracy() diff --git a/cnn_class2/ssd.py b/cnn_class2/ssd.py deleted file mode 100644 index 192b8e54..00000000 --- a/cnn_class2/ssd.py +++ /dev/null @@ -1,133 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision - -# simple script to adapt object detection notebook from -# https://github.com/tensorflow/models -# to work on videos -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import os, sys -from datetime import datetime - -import numpy as np -import tensorflow as tf -from matplotlib import pyplot as plt -from PIL import Image -import imageio - -if tf.__version__ < '1.4.0': - raise ImportError( - 'Please upgrade your tensorflow installation to v1.4.* or later!' - ) - - -# change this to wherever you cloned the tensorflow models repo -# which I assume you've already downloaded from: -# https://github.com/tensorflow/models -RESEARCH_PATH = '../../tf-models/research' -MODELS_PATH = '../../tf-models/research/object_detection' -sys.path.append(RESEARCH_PATH) -sys.path.append(MODELS_PATH) - -# import local modules -import object_detection -from utils import label_map_util -from utils import visualization_utils as vis_util - - -# I've assumed you already ran the notebook and downloaded the model -MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17' -PATH_TO_CKPT = '%s/%s/frozen_inference_graph.pb' % (MODELS_PATH, MODEL_NAME) -PATH_TO_LABELS = '%s/data/mscoco_label_map.pbtxt' % MODELS_PATH -NUM_CLASSES = 90 - - -# load the model into memory -detection_graph = tf.Graph() -with detection_graph.as_default(): - od_graph_def = tf.GraphDef() - with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: - serialized_graph = fid.read() - od_graph_def.ParseFromString(serialized_graph) - tf.import_graph_def(od_graph_def, name='') - - -# load label map -label_map = label_map_util.load_labelmap(PATH_TO_LABELS) -categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) -category_index = label_map_util.create_category_index(categories) -print("categories:") -print(categories) - - -# convert image -> numpy array -def load_image_into_numpy_array(image): - (im_width, im_height) = image.size - return np.array(image.getdata()).reshape( - (im_height, im_width, 3)).astype(np.uint8) - - -# do some object detection -with detection_graph.as_default(): - with tf.Session(graph=detection_graph) as sess: - # Definite input and output Tensors for detection_graph - image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') - # Each box represents a part of the image where a particular object was detected. - detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0') - # Each score represent how level of confidence for each of the objects. - # Score is shown on the result image, together with the class label. - detection_scores = detection_graph.get_tensor_by_name('detection_scores:0') - detection_classes = detection_graph.get_tensor_by_name('detection_classes:0') - num_detections = detection_graph.get_tensor_by_name('num_detections:0') - - # instead of looping through test images, we'll now loop - # through our video! - - # get the videos from: - # https://lazyprogrammer.me/cnn_class2_videos.zip - # and put them into the same folder as this file - - # open the video - # input_video = 'catdog' - # input_video = 'safari' - input_video = 'traffic' - video_reader = imageio.get_reader('%s.mp4' % input_video) - video_writer = imageio.get_writer('%s_annotated.mp4' % input_video, fps=10) - - # loop through and process each frame - t0 = datetime.now() - n_frames = 0 - for frame in video_reader: - # rename for convenience - image_np = frame - n_frames += 1 - - # Expand dimensions since the model expects images to have shape: [1, None, None, 3] - image_np_expanded = np.expand_dims(image_np, axis=0) - - # Actual detection. - (boxes, scores, classes, num) = sess.run( - [detection_boxes, detection_scores, detection_classes, num_detections], - feed_dict={image_tensor: image_np_expanded}) - - # Visualization of the results of a detection. - vis_util.visualize_boxes_and_labels_on_image_array( - image_np, - np.squeeze(boxes), - np.squeeze(classes).astype(np.int32), - np.squeeze(scores), - category_index, - use_normalized_coordinates=True, - line_thickness=8) - - # instead of plotting image, we write the frame to video - video_writer.append_data(image_np) - - fps = n_frames / (datetime.now() - t0).total_seconds() - print("Frames processed: %s, Speed: %s fps" % (n_frames, fps)) - - # clean up - video_writer.close() diff --git a/cnn_class2/style_transfer1.py b/cnn_class2/style_transfer1.py deleted file mode 100644 index 421a0a19..00000000 --- a/cnn_class2/style_transfer1.py +++ /dev/null @@ -1,189 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision - -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -# In this script, we will focus on generating the content -# E.g. given an image, can we recreate the same image - -from keras.layers import Input, Lambda, Dense, Flatten -from keras.layers import AveragePooling2D, MaxPooling2D -from keras.layers.convolutional import Conv2D -from keras.models import Model, Sequential -from keras.applications.vgg16 import VGG16 -from keras.applications.vgg16 import preprocess_input -from keras.preprocessing import image - -import keras.backend as K -import numpy as np -import matplotlib.pyplot as plt - -from scipy.optimize import fmin_l_bfgs_b - - -import tensorflow as tf -if tf.__version__.startswith('2'): - tf.compat.v1.disable_eager_execution() - - -def VGG16_AvgPool(shape): - # we want to account for features across the entire image - # so get rid of the maxpool which throws away information - vgg = VGG16(input_shape=shape, weights='imagenet', include_top=False) - - # new_model = Sequential() - # for layer in vgg.layers: - # if layer.__class__ == MaxPooling2D: - # # replace it with average pooling - # new_model.add(AveragePooling2D()) - # else: - # new_model.add(layer) - - i = vgg.input - x = i - for layer in vgg.layers: - if layer.__class__ == MaxPooling2D: - # replace it with average pooling - x = AveragePooling2D()(x) - else: - x = layer(x) - - return Model(i, x) - -def VGG16_AvgPool_CutOff(shape, num_convs): - # there are 13 convolutions in total - # we can pick any of them as the "output" - # of our content model - - if num_convs < 1 or num_convs > 13: - print("num_convs must be in the range [1, 13]") - return None - - model = VGG16_AvgPool(shape) - # new_model = Sequential() - # n = 0 - # for layer in model.layers: - # if layer.__class__ == Conv2D: - # n += 1 - # new_model.add(layer) - # if n >= num_convs: - # break - - n = 0 - output = None - for layer in model.layers: - if layer.__class__ == Conv2D: - n += 1 - if n >= num_convs: - output = layer.output - break - - return Model(model.input, output) - - -def unpreprocess(img): - img[..., 0] += 103.939 - img[..., 1] += 116.779 - img[..., 2] += 126.68 - img = img[..., ::-1] - return img - - -def scale_img(x): - x = x - x.min() - x = x / x.max() - return x - - -if __name__ == '__main__': - - # open an image - # feel free to try your own - # path = '../large_files/caltech101/101_ObjectCategories/elephant/image_0002.jpg' - path = 'content/elephant.jpg' - img = image.load_img(path) - - # convert image to array and preprocess for vgg - x = image.img_to_array(img) - x = np.expand_dims(x, axis=0) - x = preprocess_input(x) - - # we'll use this throughout the rest of the script - batch_shape = x.shape - shape = x.shape[1:] - - # see the image - # plt.imshow(img) - # plt.show() - - - # make a content model - # try different cutoffs to see the images that result - content_model = VGG16_AvgPool_CutOff(shape, 11) - - # make the target - target = K.variable(content_model.predict(x)) - - - # try to match the image - - # define our loss in keras - loss = K.mean(K.square(target - content_model.output)) - - # gradients which are needed by the optimizer - grads = K.gradients(loss, content_model.input) - - # just like theano.function - get_loss_and_grads = K.function( - inputs=[content_model.input], - outputs=[loss] + grads - ) - - - def get_loss_and_grads_wrapper(x_vec): - # scipy's minimizer allows us to pass back - # function value f(x) and its gradient f'(x) - # simultaneously, rather than using the fprime arg - # - # we cannot use get_loss_and_grads() directly - # input to minimizer func must be a 1-D array - # input to get_loss_and_grads must be [batch_of_images] - # - # gradient must also be a 1-D array - # and both loss and gradient must be np.float64 - # will get an error otherwise - - l, g = get_loss_and_grads([x_vec.reshape(*batch_shape)]) - return l.astype(np.float64), g.flatten().astype(np.float64) - - - - from datetime import datetime - t0 = datetime.now() - losses = [] - x = np.random.randn(np.prod(batch_shape)) - for i in range(10): - x, l, _ = fmin_l_bfgs_b( - func=get_loss_and_grads_wrapper, - x0=x, - # bounds=[[-127, 127]]*len(x.flatten()), - maxfun=20 - ) - x = np.clip(x, -127, 127) - # print("min:", x.min(), "max:", x.max()) - print("iter=%s, loss=%s" % (i, l)) - losses.append(l) - - print("duration:", datetime.now() - t0) - plt.plot(losses) - plt.show() - - newimg = x.reshape(*batch_shape) - final_img = unpreprocess(newimg) - - - plt.imshow(scale_img(final_img[0])) - plt.show() diff --git a/cnn_class2/style_transfer2.py b/cnn_class2/style_transfer2.py deleted file mode 100644 index f385ad53..00000000 --- a/cnn_class2/style_transfer2.py +++ /dev/null @@ -1,144 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision - -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -# In this script, we will focus on generating an image -# with the same style as the input image. -# But NOT the same content. -# It should capture only the essence of the style. - -from keras.models import Model, Sequential -from keras.applications.vgg16 import preprocess_input -from keras.preprocessing import image -from keras.applications.vgg16 import VGG16 - -from style_transfer1 import VGG16_AvgPool, unpreprocess, scale_img -# from skimage.transform import resize -from scipy.optimize import fmin_l_bfgs_b -from datetime import datetime - -import numpy as np -import matplotlib.pyplot as plt -import keras.backend as K - - - -def gram_matrix(img): - # input is (H, W, C) (C = # feature maps) - # we first need to convert it to (C, H*W) - X = K.batch_flatten(K.permute_dimensions(img, (2, 0, 1))) - - # now, calculate the gram matrix - # gram = XX^T / N - # the constant is not important since we'll be weighting these - G = K.dot(X, K.transpose(X)) / img.get_shape().num_elements() - return G - - -def style_loss(y, t): - return K.mean(K.square(gram_matrix(y) - gram_matrix(t))) - - -# let's generalize this and put it into a function -def minimize(fn, epochs, batch_shape): - t0 = datetime.now() - losses = [] - x = np.random.randn(np.prod(batch_shape)) - for i in range(epochs): - x, l, _ = fmin_l_bfgs_b( - func=fn, - x0=x, - maxfun=20 - ) - x = np.clip(x, -127, 127) - print("iter=%s, loss=%s" % (i, l)) - losses.append(l) - - print("duration:", datetime.now() - t0) - plt.plot(losses) - plt.show() - - newimg = x.reshape(*batch_shape) - final_img = unpreprocess(newimg) - return final_img[0] - - -if __name__ == '__main__': - # try these, or pick your own! - path = 'styles/starrynight.jpg' - # path = 'styles/flowercarrier.jpg' - # path = 'styles/monalisa.jpg' - # path = 'styles/lesdemoisellesdavignon.jpg' - - - # load the data - img = image.load_img(path) - - # convert image to array and preprocess for vgg - x = image.img_to_array(img) - - # look at the image - # plt.imshow(x) - # plt.show() - - # make it (1, H, W, C) - x = np.expand_dims(x, axis=0) - - # preprocess into VGG expected format - x = preprocess_input(x) - - # we'll use this throughout the rest of the script - batch_shape = x.shape - shape = x.shape[1:] - - # let's take the first convolution at each block of convolutions - # to be our target outputs - # remember that you can print out the model summary if you want - vgg = VGG16_AvgPool(shape) - - # Note: need to select output at index 1, since outputs at - # index 0 correspond to the original vgg with maxpool - symbolic_conv_outputs = [ - layer.get_output_at(1) for layer in vgg.layers \ - if layer.name.endswith('conv1') - ] - - # pick the earlier layers for - # a more "localized" representation - # this is opposed to the content model - # where the later layers represent a more "global" structure - # symbolic_conv_outputs = symbolic_conv_outputs[:2] - - # make a big model that outputs multiple layers' outputs - multi_output_model = Model(vgg.input, symbolic_conv_outputs) - - # calculate the targets that are output at each layer - style_layers_outputs = [K.variable(y) for y in multi_output_model.predict(x)] - - # calculate the total style loss - loss = 0 - for symbolic, actual in zip(symbolic_conv_outputs, style_layers_outputs): - # gram_matrix() expects a (H, W, C) as input - loss += style_loss(symbolic[0], actual[0]) - - grads = K.gradients(loss, multi_output_model.input) - - # just like theano.function - get_loss_and_grads = K.function( - inputs=[multi_output_model.input], - outputs=[loss] + grads - ) - - - def get_loss_and_grads_wrapper(x_vec): - l, g = get_loss_and_grads([x_vec.reshape(*batch_shape)]) - return l.astype(np.float64), g.flatten().astype(np.float64) - - - final_img = minimize(get_loss_and_grads_wrapper, 10, batch_shape) - plt.imshow(scale_img(final_img)) - plt.show() diff --git a/cnn_class2/style_transfer3.py b/cnn_class2/style_transfer3.py deleted file mode 100644 index 8f383d1d..00000000 --- a/cnn_class2/style_transfer3.py +++ /dev/null @@ -1,132 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision - -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -# In this script, we will focus on generating an image -# that attempts to match the content of one input image -# and the style of another input image. -# -# We accomplish this by balancing the content loss -# and style loss simultaneously. - -from keras.layers import Input, Lambda, Dense, Flatten -from keras.layers import AveragePooling2D, MaxPooling2D -from keras.layers.convolutional import Conv2D -from keras.models import Model, Sequential -from keras.applications.vgg16 import VGG16 -from keras.applications.vgg16 import preprocess_input -from keras.preprocessing import image -from skimage.transform import resize - -import keras.backend as K -import numpy as np -import matplotlib.pyplot as plt - -from style_transfer1 import VGG16_AvgPool, VGG16_AvgPool_CutOff, unpreprocess, scale_img -from style_transfer2 import gram_matrix, style_loss, minimize -from scipy.optimize import fmin_l_bfgs_b - - -# load the content image -def load_img_and_preprocess(path, shape=None): - img = image.load_img(path, target_size=shape) - - # convert image to array and preprocess for vgg - x = image.img_to_array(img) - x = np.expand_dims(x, axis=0) - x = preprocess_input(x) - - return x - - - -content_img = load_img_and_preprocess( - # '../large_files/caltech101/101_ObjectCategories/elephant/image_0002.jpg', - # 'batman.jpg', - 'content/sydney.jpg', - # (225, 300), -) - -# resize the style image -# since we don't care too much about warping it -h, w = content_img.shape[1:3] -style_img = load_img_and_preprocess( - # 'styles/starrynight.jpg', - # 'styles/flowercarrier.jpg', - # 'styles/monalisa.jpg', - 'styles/lesdemoisellesdavignon.jpg', - (h, w) -) - - -# we'll use this throughout the rest of the script -batch_shape = content_img.shape -shape = content_img.shape[1:] - - -# we want to make only 1 VGG here -# as you'll see later, the final model needs -# to have a common input -vgg = VGG16_AvgPool(shape) - - -# create the content model -# we only want 1 output -# remember you can call vgg.summary() to see a list of layers -# 1,2,4,5,7-9,11-13,15-17 -content_model = Model(vgg.input, vgg.layers[13].get_output_at(0)) -content_target = K.variable(content_model.predict(content_img)) - - -# create the style model -# we want multiple outputs -# we will take the same approach as in style_transfer2.py -symbolic_conv_outputs = [ - layer.get_output_at(1) for layer in vgg.layers \ - if layer.name.endswith('conv1') -] - -# make a big model that outputs multiple layers' outputs -style_model = Model(vgg.input, symbolic_conv_outputs) - -# calculate the targets that are output at each layer -style_layers_outputs = [K.variable(y) for y in style_model.predict(style_img)] - -# we will assume the weight of the content loss is 1 -# and only weight the style losses -style_weights = [0.2,0.4,0.3,0.5,0.2] - - - -# create the total loss which is the sum of content + style loss -loss = K.mean(K.square(content_model.output - content_target)) - -for w, symbolic, actual in zip(style_weights, symbolic_conv_outputs, style_layers_outputs): - # gram_matrix() expects a (H, W, C) as input - loss += w * style_loss(symbolic[0], actual[0]) - - -# once again, create the gradients and loss + grads function -# note: it doesn't matter which model's input you use -# they are both pointing to the same keras Input layer in memory -grads = K.gradients(loss, vgg.input) - -# just like theano.function -get_loss_and_grads = K.function( - inputs=[vgg.input], - outputs=[loss] + grads -) - - -def get_loss_and_grads_wrapper(x_vec): - l, g = get_loss_and_grads([x_vec.reshape(*batch_shape)]) - return l.astype(np.float64), g.flatten().astype(np.float64) - - -final_img = minimize(get_loss_and_grads_wrapper, 10, batch_shape) -plt.imshow(scale_img(final_img)) -plt.show() diff --git a/cnn_class2/styles/flowercarrier.jpg b/cnn_class2/styles/flowercarrier.jpg deleted file mode 100644 index 64f4937c..00000000 Binary files a/cnn_class2/styles/flowercarrier.jpg and /dev/null differ diff --git a/cnn_class2/styles/lesdemoisellesdavignon.jpg b/cnn_class2/styles/lesdemoisellesdavignon.jpg deleted file mode 100644 index 57ca08ed..00000000 Binary files a/cnn_class2/styles/lesdemoisellesdavignon.jpg and /dev/null differ diff --git a/cnn_class2/styles/monalisa.jpg b/cnn_class2/styles/monalisa.jpg deleted file mode 100644 index ce542ef0..00000000 Binary files a/cnn_class2/styles/monalisa.jpg and /dev/null differ diff --git a/cnn_class2/styles/starrynight.jpg b/cnn_class2/styles/starrynight.jpg deleted file mode 100644 index a3928aa0..00000000 Binary files a/cnn_class2/styles/starrynight.jpg and /dev/null differ diff --git a/cnn_class2/test_softmax.py b/cnn_class2/test_softmax.py deleted file mode 100644 index ed33fdab..00000000 --- a/cnn_class2/test_softmax.py +++ /dev/null @@ -1,34 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import tensorflow as tf -import numpy as np -import keras -import keras.backend as K - -def custom_softmax(x): - m = tf.reduce_max(x, 1) - x = x - m - e = tf.exp(x) - return e / tf.reduce_sum(e, -1) - - -a = np.random.randn(1, 1000) - -tfy = tf.nn.softmax(a) -ky = keras.activations.softmax(K.variable(a)) -tfc = custom_softmax(a) - -session = K.get_session() - -tfy_ = session.run(tfy) -ky_ = session.run(ky) -tfc_ = session.run(tfc) - -print("tf vs k", np.abs(tfy_ - ky_).sum()) -print("tf vs custom", np.abs(tfy_ - tfc_).sum()) -print("custom vs k", np.abs(tfc_ - ky_).sum()) \ No newline at end of file diff --git a/cnn_class2/tf_resnet.py b/cnn_class2/tf_resnet.py deleted file mode 100644 index 4a3c5fa2..00000000 --- a/cnn_class2/tf_resnet.py +++ /dev/null @@ -1,260 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -# Let's go up to the end of the first conv block -# to make sure everything has been loaded correctly -# compared to keras -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt -import keras - -from keras.applications.resnet50 import ResNet50 -from keras.models import Model -from keras.preprocessing import image -from keras.layers import Dense -from keras.applications.resnet50 import preprocess_input, decode_predictions - -from tf_resnet_convblock import ConvLayer, BatchNormLayer, ConvBlock -from tf_resnet_identity_block import IdentityBlock -from tf_resnet_first_layers import ReLULayer, MaxPoolLayer - - -# NOTE: dependent on your Keras version -# this script used 2.1.1 -# [, -# , -# , -# , -# , -# -# ConvBlock -# IdentityBlock x 2 -# -# ConvBlock -# IdentityBlock x 3 -# -# ConvBlock -# IdentityBlock x 5 -# -# ConvBlock -# IdentityBlock x 2 -# -# AveragePooling2D -# Flatten -# Dense (Softmax) -# ] - - -# define some additional layers so they have a forward function -class AvgPool: - def __init__(self, ksize): - self.ksize = ksize - - def forward(self, X): - return tf.nn.avg_pool( - X, - ksize=[1, self.ksize, self.ksize, 1], - strides=[1, 1, 1, 1], - padding='VALID' - ) - - def get_params(self): - return [] - -class Flatten: - def forward(self, X): - return tf.contrib.layers.flatten(X) - - def get_params(self): - return [] - - -def custom_softmax(x): - m = tf.reduce_max(x, 1) - x = x - m - e = tf.exp(x) - return e / tf.reduce_sum(e, -1) - - -class DenseLayer: - def __init__(self, mi, mo): - self.W = tf.Variable((np.random.randn(mi, mo) * np.sqrt(2.0 / mi)).astype(np.float32)) - self.b = tf.Variable(np.zeros(mo, dtype=np.float32)) - - def forward(self, X): - # unfortunately these all yield slightly different answers - # return tf.nn.softmax(tf.matmul(X, self.W) + self.b) - # return custom_softmax(tf.matmul(X, self.W) + self.b) - # return keras.activations.softmax(tf.matmul(X, self.W) + self.b) - return tf.matmul(X, self.W) + self.b - - def copyFromKerasLayers(self, layer): - W, b = layer.get_weights() - op1 = self.W.assign(W) - op2 = self.b.assign(b) - self.session.run((op1, op2)) - - def get_params(self): - return [self.W, self.b] - - -class TFResNet: - def __init__(self): - self.layers = [ - # before conv block - ConvLayer(d=7, mi=3, mo=64, stride=2, padding='SAME'), - BatchNormLayer(64), - ReLULayer(), - MaxPoolLayer(dim=3), - # conv block - ConvBlock(mi=64, fm_sizes=[64, 64, 256], stride=1), - # identity block x 2 - IdentityBlock(mi=256, fm_sizes=[64, 64, 256]), - IdentityBlock(mi=256, fm_sizes=[64, 64, 256]), - # conv block - ConvBlock(mi=256, fm_sizes=[128, 128, 512], stride=2), - # identity block x 3 - IdentityBlock(mi=512, fm_sizes=[128, 128, 512]), - IdentityBlock(mi=512, fm_sizes=[128, 128, 512]), - IdentityBlock(mi=512, fm_sizes=[128, 128, 512]), - # conv block - ConvBlock(mi=512, fm_sizes=[256, 256, 1024], stride=2), - # identity block x 5 - IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]), - IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]), - IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]), - IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]), - IdentityBlock(mi=1024, fm_sizes=[256, 256, 1024]), - # conv block - ConvBlock(mi=1024, fm_sizes=[512, 512, 2048], stride=2), - # identity block x 2 - IdentityBlock(mi=2048, fm_sizes=[512, 512, 2048]), - IdentityBlock(mi=2048, fm_sizes=[512, 512, 2048]), - # pool / flatten / dense - AvgPool(ksize=7), - Flatten(), - DenseLayer(mi=2048, mo=1000) - ] - self.input_ = tf.placeholder(tf.float32, shape=(None, 224, 224, 3)) - self.output = self.forward(self.input_) - - def copyFromKerasLayers(self, layers): - # conv - self.layers[0].copyFromKerasLayers(layers[1]) - # bn - self.layers[1].copyFromKerasLayers(layers[2]) - # cb - self.layers[4].copyFromKerasLayers(layers[5:17]) # size=12 - # ib x 2 - self.layers[5].copyFromKerasLayers(layers[17:27]) # size=10 - self.layers[6].copyFromKerasLayers(layers[27:37]) - # cb - self.layers[7].copyFromKerasLayers(layers[37:49]) - # ib x 3 - self.layers[8].copyFromKerasLayers(layers[49:59]) - self.layers[9].copyFromKerasLayers(layers[59:69]) - self.layers[10].copyFromKerasLayers(layers[69:79]) - # cb - self.layers[11].copyFromKerasLayers(layers[79:91]) - # ib x 5 - self.layers[12].copyFromKerasLayers(layers[91:101]) - self.layers[13].copyFromKerasLayers(layers[101:111]) - self.layers[14].copyFromKerasLayers(layers[111:121]) - self.layers[15].copyFromKerasLayers(layers[121:131]) - self.layers[16].copyFromKerasLayers(layers[131:141]) - # cb - self.layers[17].copyFromKerasLayers(layers[141:153]) - # ib x 2 - self.layers[18].copyFromKerasLayers(layers[153:163]) - self.layers[19].copyFromKerasLayers(layers[163:173]) - # dense - self.layers[22].copyFromKerasLayers(layers[175]) - - - def forward(self, X): - for layer in self.layers: - X = layer.forward(X) - return X - - def predict(self, X): - assert(self.session is not None) - return self.session.run( - self.output, - feed_dict={self.input_: X} - ) - - def set_session(self, session): - self.session = session - for layer in self.layers: - if isinstance(layer, ConvBlock) or isinstance(layer, IdentityBlock): - layer.set_session(session) - else: - layer.session = session - - def get_params(self): - params = [] - for layer in self.layers: - params += layer.get_params() - - -if __name__ == '__main__': - # you can also set weights to None, it doesn't matter - resnet_ = ResNet50(weights='imagenet') - - # make a new resnet without the softmax - x = resnet_.layers[-2].output - W, b = resnet_.layers[-1].get_weights() - y = Dense(1000)(x) - resnet = Model(resnet_.input, y) - resnet.layers[-1].set_weights([W, b]) - - # you can determine the correct layer - # by looking at resnet.layers in the console - partial_model = Model( - inputs=resnet.input, - outputs=resnet.layers[175].output - ) - - # maybe useful when building your model - # to look at the layers you're trying to copy - print(partial_model.summary()) - - # create an instance of our own model - my_partial_resnet = TFResNet() - - # make a fake image - X = np.random.random((1, 224, 224, 3)) - - # get keras output - keras_output = partial_model.predict(X) - - ### get my model output ### - - # init only the variables in our net - init = tf.variables_initializer(my_partial_resnet.get_params()) - - # note: starting a new session messes up the Keras model - session = keras.backend.get_session() - my_partial_resnet.set_session(session) - session.run(init) - - # first, just make sure we can get any output - first_output = my_partial_resnet.predict(X) - print("first_output.shape:", first_output.shape) - - # copy params from Keras model - my_partial_resnet.copyFromKerasLayers(partial_model.layers) - - # compare the 2 models - output = my_partial_resnet.predict(X) - diff = np.abs(output - keras_output).sum() - if diff < 1e-10: - print("Everything's great!") - else: - print("diff = %s" % diff) diff --git a/cnn_class2/tf_resnet_convblock.py b/cnn_class2/tf_resnet_convblock.py deleted file mode 100644 index 397f160b..00000000 --- a/cnn_class2/tf_resnet_convblock.py +++ /dev/null @@ -1,203 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt - - -def init_filter(d, mi, mo, stride): - return (np.random.randn(d, d, mi, mo) * np.sqrt(2.0 / (d * d * mi))).astype(np.float32) - - -class ConvLayer: - def __init__(self, d, mi, mo, stride=2, padding='VALID'): - self.W = tf.Variable(init_filter(d, mi, mo, stride)) - self.b = tf.Variable(np.zeros(mo, dtype=np.float32)) - self.stride = stride - self.padding = padding - - def forward(self, X): - X = tf.nn.conv2d( - X, - self.W, - strides=[1, self.stride, self.stride, 1], - padding=self.padding - ) - X = X + self.b - return X - - def copyFromKerasLayers(self, layer): - # only 1 layer to copy from - W, b = layer.get_weights() - op1 = self.W.assign(W) - op2 = self.b.assign(b) - self.session.run((op1, op2)) - - # def copyFromWeights(self, W, b): - # op1 = self.W.assign(W) - # op2 = self.b.assign(b) - # self.session.run((op1, op2)) - - def get_params(self): - return [self.W, self.b] - - -class BatchNormLayer: - def __init__(self, D): - self.running_mean = tf.Variable(np.zeros(D, dtype=np.float32), trainable=False) - self.running_var = tf.Variable(np.ones(D, dtype=np.float32), trainable=False) - self.gamma = tf.Variable(np.ones(D, dtype=np.float32)) - self.beta = tf.Variable(np.zeros(D, dtype=np.float32)) - - def forward(self, X): - return tf.nn.batch_normalization( - X, - self.running_mean, - self.running_var, - self.beta, - self.gamma, - 1e-3 - ) - - def copyFromKerasLayers(self, layer): - # only 1 layer to copy from - # order: - # gamma, beta, moving mean, moving variance - gamma, beta, running_mean, running_var = layer.get_weights() - op1 = self.running_mean.assign(running_mean) - op2 = self.running_var.assign(running_var) - op3 = self.gamma.assign(gamma) - op4 = self.beta.assign(beta) - self.session.run((op1, op2, op3, op4)) - - def get_params(self): - return [self.running_mean, self.running_var, self.gamma, self.beta] - - -class ConvBlock: - def __init__(self, mi, fm_sizes, stride=2, activation=tf.nn.relu): - # conv1, conv2, conv3 - # note: # feature maps shortcut = # feauture maps conv 3 - assert(len(fm_sizes) == 3) - - # note: kernel size in 2nd conv is always 3 - # so we won't bother including it as an arg - - # note: stride only applies to conv 1 in main branch - # and conv in shortcut, otherwise stride is 1 - - self.session = None - self.f = tf.nn.relu - - # init main branch - # Conv -> BN -> F() ---> Conv -> BN -> F() ---> Conv -> BN - self.conv1 = ConvLayer(1, mi, fm_sizes[0], stride) - self.bn1 = BatchNormLayer(fm_sizes[0]) - self.conv2 = ConvLayer(3, fm_sizes[0], fm_sizes[1], 1, 'SAME') - self.bn2 = BatchNormLayer(fm_sizes[1]) - self.conv3 = ConvLayer(1, fm_sizes[1], fm_sizes[2], 1) - self.bn3 = BatchNormLayer(fm_sizes[2]) - - # init shortcut branch - # Conv -> BN - self.convs = ConvLayer(1, mi, fm_sizes[2], stride) - self.bns = BatchNormLayer(fm_sizes[2]) - - # in case needed later - self.layers = [ - self.conv1, self.bn1, - self.conv2, self.bn2, - self.conv3, self.bn3, - self.convs, self.bns - ] - - # this will not be used when input passed in from - # a previous layer - self.input_ = tf.placeholder(tf.float32, shape=(1, 224, 224, mi)) - self.output = self.forward(self.input_) - - def forward(self, X): - # main branch - FX = self.conv1.forward(X) - FX = self.bn1.forward(FX) - FX = self.f(FX) - FX = self.conv2.forward(FX) - FX = self.bn2.forward(FX) - FX = self.f(FX) - FX = self.conv3.forward(FX) - FX = self.bn3.forward(FX) - - # shortcut branch - SX = self.convs.forward(X) - SX = self.bns.forward(SX) - - return self.f(FX + SX) - - def predict(self, X): - assert(self.session is not None) - return self.session.run( - self.output, - feed_dict={self.input_: X} - ) - - def set_session(self, session): - # need to make this a session - # so assignment happens on sublayers too - self.session = session - self.conv1.session = session - self.bn1.session = session - self.conv2.session = session - self.bn2.session = session - self.conv3.session = session - self.bn3.session = session - self.convs.session = session - self.bns.session = session - - def copyFromKerasLayers(self, layers): - # [, - # , - # , - # , - # , - # , - # , - # , - # , - # , - # , - # ] - self.conv1.copyFromKerasLayers(layers[0]) - self.bn1.copyFromKerasLayers(layers[1]) - self.conv2.copyFromKerasLayers(layers[3]) - self.bn2.copyFromKerasLayers(layers[4]) - self.conv3.copyFromKerasLayers(layers[6]) - self.bn3.copyFromKerasLayers(layers[8]) - self.convs.copyFromKerasLayers(layers[7]) - self.bns.copyFromKerasLayers(layers[9]) - - def get_params(self): - params = [] - for layer in self.layers: - params += layer.get_params() - return params - - -if __name__ == '__main__': - conv_block = ConvBlock(mi=3, fm_sizes=[64, 64, 256], stride=1) - - # make a fake image - X = np.random.random((1, 224, 224, 3)) - - init = tf.global_variables_initializer() - with tf.Session() as session: - conv_block.set_session(session) - session.run(init) - - output = conv_block.predict(X) - print("output.shape:", output.shape) diff --git a/cnn_class2/tf_resnet_convblock_starter.py b/cnn_class2/tf_resnet_convblock_starter.py deleted file mode 100644 index 4c61019b..00000000 --- a/cnn_class2/tf_resnet_convblock_starter.py +++ /dev/null @@ -1,35 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt - - -class ConvBlock: - def __init__(self): - pass - - def predict(self, X): - pass - - -if __name__ == '__main__': - conv_block = ConvBlock() - - - # make a fake image - X = np.random.random((1, 224, 224, 3)) - - init = tf.global_variables_initializer() - with tf.Session() as session: - conv_block.session = session - session.run(init) - - output = conv_block.predict(X): - print("output.shape:", output.shape) \ No newline at end of file diff --git a/cnn_class2/tf_resnet_first_layers.py b/cnn_class2/tf_resnet_first_layers.py deleted file mode 100644 index 9157b65c..00000000 --- a/cnn_class2/tf_resnet_first_layers.py +++ /dev/null @@ -1,155 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -# Let's go up to the end of the first conv block -# to make sure everything has been loaded correctly -# compared to keras -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt -import keras - -from keras.applications.resnet50 import ResNet50 -from keras.models import Model -from keras.preprocessing import image -from keras.applications.resnet50 import preprocess_input, decode_predictions - -from tf_resnet_convblock import ConvLayer, BatchNormLayer, ConvBlock - - -# NOTE: dependent on your Keras version -# this script used 2.1.1 -# [, -# , -# , -# , -# , -# , -# , -# , -# , -# , -# , -# , -# , -# , -# , -# , -# ] - - -# define some additional layers so they have a forward function -class ReLULayer: - def forward(self, X): - return tf.nn.relu(X) - - def get_params(self): - return [] - -class MaxPoolLayer: - def __init__(self, dim): - self.dim = dim - - def forward(self, X): - return tf.nn.max_pool( - X, - ksize=[1, self.dim, self.dim, 1], - strides=[1, 2, 2, 1], - padding='VALID' - ) - - def get_params(self): - return [] - -class PartialResNet: - def __init__(self): - self.layers = [ - # before conv block - ConvLayer(d=7, mi=3, mo=64, stride=2, padding='SAME'), - BatchNormLayer(64), - ReLULayer(), - MaxPoolLayer(dim=3), - # conv block - ConvBlock(mi=64, fm_sizes=[64, 64, 256], stride=1), - ] - self.input_ = tf.placeholder(tf.float32, shape=(None, 224, 224, 3)) - self.output = self.forward(self.input_) - - def copyFromKerasLayers(self, layers): - self.layers[0].copyFromKerasLayers(layers[1]) - self.layers[1].copyFromKerasLayers(layers[2]) - self.layers[4].copyFromKerasLayers(layers[5:]) - - def forward(self, X): - for layer in self.layers: - X = layer.forward(X) - return X - - def predict(self, X): - assert(self.session is not None) - return self.session.run( - self.output, - feed_dict={self.input_: X} - ) - - def set_session(self, session): - self.session = session - self.layers[0].session = session - self.layers[1].session = session - self.layers[4].set_session(session) - - def get_params(self): - params = [] - for layer in self.layers: - params += layer.get_params() - - -if __name__ == '__main__': - # you can also set weights to None, it doesn't matter - resnet = ResNet50(weights='imagenet') - - # you can determine the correct layer - # by looking at resnet.layers in the console - partial_model = Model( - inputs=resnet.input, - outputs=resnet.layers[16].output - ) - print(partial_model.summary()) - # for layer in partial_model.layers: - # layer.trainable = False - - my_partial_resnet = PartialResNet() - - # make a fake image - X = np.random.random((1, 224, 224, 3)) - - # get keras output - keras_output = partial_model.predict(X) - - # get my model output - init = tf.variables_initializer(my_partial_resnet.get_params()) - - # note: starting a new session messes up the Keras model - session = keras.backend.get_session() - my_partial_resnet.set_session(session) - session.run(init) - - # first, just make sure we can get any output - first_output = my_partial_resnet.predict(X) - print("first_output.shape:", first_output.shape) - - # copy params from Keras model - my_partial_resnet.copyFromKerasLayers(partial_model.layers) - - # compare the 2 models - output = my_partial_resnet.predict(X) - diff = np.abs(output - keras_output).sum() - if diff < 1e-10: - print("Everything's great!") - else: - print("diff = %s" % diff) diff --git a/cnn_class2/tf_resnet_first_layers_starter.py b/cnn_class2/tf_resnet_first_layers_starter.py deleted file mode 100644 index 6d207358..00000000 --- a/cnn_class2/tf_resnet_first_layers_starter.py +++ /dev/null @@ -1,91 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -# Let's go up to the end of the first conv block -# to make sure everything has been loaded correctly -# compared to keras -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt -import keras - -from keras.applications.resnet50 import ResNet50 -from keras.models import Model -from keras.preprocessing import image -from keras.applications.resnet50 import preprocess_input, decode_predictions - -from tf_resnet_convblock import ConvLayer, BatchNormLayer, ConvBlock - - - -class PartialResNet: - def __init__(self): - # TODO - pass - - def copyFromKerasLayers(self, layers): - # TODO - pass - - def predict(self, X): - # TODO - pass - - def set_session(self, session): - self.session = session - # TODO: finish this - - def get_params(self): - params = [] - # TODO: finish this - - -if __name__ == '__main__': - # you can also set weights to None, it doesn't matter - resnet = ResNet50(weights='imagenet') - - # you can determine the correct layer - # by looking at resnet.layers in the console - partial_model = Model( - inputs=resnet.input, - outputs=resnet.layers[16].output - ) - print(partial_model.summary()) - # for layer in partial_model.layers: - # layer.trainable = False - - my_partial_resnet = PartialResNet() - - # make a fake image - X = np.random.random((1, 224, 224, 3)) - - # get keras output - keras_output = partial_model.predict(X) - - # get my model output - init = tf.variables_initializer(my_partial_resnet.get_params()) - - # note: starting a new session messes up the Keras model - session = keras.backend.get_session() - my_partial_resnet.set_session(session) - session.run(init) - - # first, just make sure we can get any output - first_output = my_partial_resnet.predict(X) - print("first_output.shape:", first_output.shape) - - # copy params from Keras model - my_partial_resnet.copyFromKerasLayers(partial_model.layers) - - # compare the 2 models - output = my_partial_resnet.predict(X) - diff = np.abs(output - keras_output).sum() - if diff < 1e-10: - print("Everything's great!") - else: - print("diff = %s" % diff) diff --git a/cnn_class2/tf_resnet_identity_block.py b/cnn_class2/tf_resnet_identity_block.py deleted file mode 100644 index 3e30d30c..00000000 --- a/cnn_class2/tf_resnet_identity_block.py +++ /dev/null @@ -1,118 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt - -from tf_resnet_convblock import ConvLayer, BatchNormLayer - - -class IdentityBlock: - def __init__(self, mi, fm_sizes, activation=tf.nn.relu): - # conv1, conv2, conv3 - # note: # feature maps shortcut = # feauture maps conv 3 - assert(len(fm_sizes) == 3) - - # note: kernel size in 2nd conv is always 3 - # so we won't bother including it as an arg - - self.session = None - self.f = tf.nn.relu - - # init main branch - # Conv -> BN -> F() ---> Conv -> BN -> F() ---> Conv -> BN - self.conv1 = ConvLayer(1, mi, fm_sizes[0], 1) - self.bn1 = BatchNormLayer(fm_sizes[0]) - self.conv2 = ConvLayer(3, fm_sizes[0], fm_sizes[1], 1, 'SAME') - self.bn2 = BatchNormLayer(fm_sizes[1]) - self.conv3 = ConvLayer(1, fm_sizes[1], fm_sizes[2], 1) - self.bn3 = BatchNormLayer(fm_sizes[2]) - - # in case needed later - self.layers = [ - self.conv1, self.bn1, - self.conv2, self.bn2, - self.conv3, self.bn3, - ] - - # this will not be used when input passed in from - # a previous layer - self.input_ = tf.placeholder(tf.float32, shape=(1, 224, 224, mi)) - self.output = self.forward(self.input_) - - def forward(self, X): - # main branch - FX = self.conv1.forward(X) - FX = self.bn1.forward(FX) - FX = self.f(FX) - FX = self.conv2.forward(FX) - FX = self.bn2.forward(FX) - FX = self.f(FX) - FX = self.conv3.forward(FX) - FX = self.bn3.forward(FX) - - return self.f(FX + X) - - def predict(self, X): - assert(self.session is not None) - return self.session.run( - self.output, - feed_dict={self.input_: X} - ) - - def set_session(self, session): - # need to make this a session - # so assignment happens on sublayers too - self.session = session - self.conv1.session = session - self.bn1.session = session - self.conv2.session = session - self.bn2.session = session - self.conv3.session = session - self.bn3.session = session - - def copyFromKerasLayers(self, layers): - assert(len(layers) == 10) - # , - # , - # , - # , - # , - # , - # , - # , - # , - # - self.conv1.copyFromKerasLayers(layers[0]) - self.bn1.copyFromKerasLayers(layers[1]) - self.conv2.copyFromKerasLayers(layers[3]) - self.bn2.copyFromKerasLayers(layers[4]) - self.conv3.copyFromKerasLayers(layers[6]) - self.bn3.copyFromKerasLayers(layers[7]) - - def get_params(self): - params = [] - for layer in self.layers: - params += layer.get_params() - return params - - -if __name__ == '__main__': - identity_block = IdentityBlock(mi=256, fm_sizes=[64, 64, 256]) - - # make a fake image - X = np.random.random((1, 224, 224, 256)) - - init = tf.global_variables_initializer() - with tf.Session() as session: - identity_block.set_session(session) - session.run(init) - - output = identity_block.predict(X) - print("output.shape:", output.shape) diff --git a/cnn_class2/tf_resnet_identity_block_starter.py b/cnn_class2/tf_resnet_identity_block_starter.py deleted file mode 100644 index 87f7fff7..00000000 --- a/cnn_class2/tf_resnet_identity_block_starter.py +++ /dev/null @@ -1,40 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt - -from tf_resnet_convblock import ConvLayer, BatchNormLayer - - -class IdentityBlock: - def __init__(self): - # TODO - pass - - - def predict(self, X): - # TODO - pass - - - -if __name__ == '__main__': - identity_block = IdentityBlock() - - # make a fake image - X = np.random.random((1, 224, 224, 256)) - - init = tf.global_variables_initializer() - with tf.Session() as session: - identity_block.set_session(session) - session.run(init) - - output = identity_block.predict(X) - print("output.shape:", output.shape) diff --git a/cnn_class2/use_pretrained_weights_resnet.py b/cnn_class2/use_pretrained_weights_resnet.py deleted file mode 100644 index 8f3aae71..00000000 --- a/cnn_class2/use_pretrained_weights_resnet.py +++ /dev/null @@ -1,183 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision - -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.layers import Input, Lambda, Dense, Flatten -from keras.models import Model -from keras.applications.resnet import ResNet50, preprocess_input -# from keras.applications.inception_v3 import InceptionV3, preprocess_input -from keras.preprocessing import image -from keras.preprocessing.image import ImageDataGenerator - -from sklearn.metrics import confusion_matrix -import numpy as np -import matplotlib.pyplot as plt - -from glob import glob - - -# re-size all the images to this -IMAGE_SIZE = [100, 100] # feel free to change depending on dataset - -# training config: -epochs = 16 -batch_size = 32 - -# https://www.kaggle.com/paultimothymooney/blood-cells -# train_path = '../large_files/blood_cell_images/TRAIN' -# valid_path = '../large_files/blood_cell_images/TEST' - -# https://www.kaggle.com/moltean/fruits -# train_path = '../large_files/fruits-360/Training' -# valid_path = '../large_files/fruits-360/Validation' -train_path = '../large_files/fruits-360-small/Training' -valid_path = '../large_files/fruits-360-small/Validation' - -# useful for getting number of files -image_files = glob(train_path + '/*/*.jp*g') -valid_image_files = glob(valid_path + '/*/*.jp*g') - -# useful for getting number of classes -folders = glob(train_path + '/*') - - -# look at an image for fun -plt.imshow(image.load_img(np.random.choice(image_files))) -plt.show() - - -# add preprocessing layer to the front of VGG -res = ResNet50(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False) - -# don't train existing weights -for layer in res.layers: - layer.trainable = False - -# our layers - you can add more if you want -x = Flatten()(res.output) -# x = Dense(1000, activation='relu')(x) -prediction = Dense(len(folders), activation='softmax')(x) - - -# create a model object -model = Model(inputs=res.input, outputs=prediction) - -# view the structure of the model -model.summary() - -# tell the model what cost and optimization method to use -model.compile( - loss='categorical_crossentropy', - optimizer='rmsprop', - metrics=['accuracy'] -) - - - -# create an instance of ImageDataGenerator -gen = ImageDataGenerator( - rotation_range=20, - width_shift_range=0.1, - height_shift_range=0.1, - shear_range=0.1, - zoom_range=0.2, - horizontal_flip=True, - vertical_flip=True, - preprocessing_function=preprocess_input -) - - -# test generator to see how it works and some other useful things - -# get label mapping for confusion matrix plot later -test_gen = gen.flow_from_directory(valid_path, target_size=IMAGE_SIZE) -print(test_gen.class_indices) -labels = [None] * len(test_gen.class_indices) -for k, v in test_gen.class_indices.items(): - labels[v] = k - -# should be a strangely colored image (due to VGG weights being BGR) -for x, y in test_gen: - print("min:", x[0].min(), "max:", x[0].max()) - plt.title(labels[np.argmax(y[0])]) - plt.imshow(x[0]) - plt.show() - break - - -# create generators -train_generator = gen.flow_from_directory( - train_path, - target_size=IMAGE_SIZE, - shuffle=True, - batch_size=batch_size, -) -valid_generator = gen.flow_from_directory( - valid_path, - target_size=IMAGE_SIZE, - shuffle=True, - batch_size=batch_size, -) - - -# fit the model -r = model.fit( - train_generator, - validation_data=valid_generator, - epochs=epochs, - steps_per_epoch=len(image_files) // batch_size, - validation_steps=len(valid_image_files) // batch_size, -) - - - -def get_confusion_matrix(data_path, N): - # we need to see the data in the same order - # for both predictions and targets - print("Generating confusion matrix", N) - predictions = [] - targets = [] - i = 0 - for x, y in gen.flow_from_directory(data_path, target_size=IMAGE_SIZE, shuffle=False, batch_size=batch_size * 2): - i += 1 - if i % 50 == 0: - print(i) - p = model.predict(x) - p = np.argmax(p, axis=1) - y = np.argmax(y, axis=1) - predictions = np.concatenate((predictions, p)) - targets = np.concatenate((targets, y)) - if len(targets) >= N: - break - - cm = confusion_matrix(targets, predictions) - return cm - - -cm = get_confusion_matrix(train_path, len(image_files)) -print(cm) -valid_cm = get_confusion_matrix(valid_path, len(valid_image_files)) -print(valid_cm) - - -# plot some data - -# loss -plt.plot(r.history['loss'], label='train loss') -plt.plot(r.history['val_loss'], label='val loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='train acc') -plt.plot(r.history['val_accuracy'], label='val acc') -plt.legend() -plt.show() - -from util import plot_confusion_matrix -plot_confusion_matrix(cm, labels, title='Train confusion matrix') -plot_confusion_matrix(valid_cm, labels, title='Validation confusion matrix') \ No newline at end of file diff --git a/cnn_class2/use_pretrained_weights_vgg.py b/cnn_class2/use_pretrained_weights_vgg.py deleted file mode 100644 index 849dd9f6..00000000 --- a/cnn_class2/use_pretrained_weights_vgg.py +++ /dev/null @@ -1,181 +0,0 @@ -# https://deeplearningcourses.com/c/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.layers import Input, Lambda, Dense, Flatten -from keras.models import Model -from keras.applications.vgg16 import VGG16 -from keras.applications.vgg16 import preprocess_input -from keras.preprocessing import image -from keras.preprocessing.image import ImageDataGenerator - -from sklearn.metrics import confusion_matrix -import numpy as np -import matplotlib.pyplot as plt - -from glob import glob - - -# re-size all the images to this -IMAGE_SIZE = [100, 100] # feel free to change depending on dataset - -# training config: -epochs = 5 -batch_size = 32 - -# https://www.kaggle.com/paultimothymooney/blood-cells -# train_path = '../large_files/blood_cell_images/TRAIN' -# valid_path = '../large_files/blood_cell_images/TEST' - -# https://www.kaggle.com/moltean/fruits -train_path = '../large_files/fruits-360/Training' -valid_path = '../large_files/fruits-360/Validation' -# train_path = '../large_files/fruits-360-small/Training' -# valid_path = '../large_files/fruits-360-small/Validation' - -# useful for getting number of files -image_files = glob(train_path + '/*/*.jp*g') -valid_image_files = glob(valid_path + '/*/*.jp*g') - -# useful for getting number of classes -folders = glob(train_path + '/*') - - -# look at an image for fun -plt.imshow(image.img_to_array(image.load_img(np.random.choice(image_files))).astype('uint8')) -plt.show() - - -# add preprocessing layer to the front of VGG -vgg = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False) - -# don't train existing weights -for layer in vgg.layers: - layer.trainable = False - -# our layers - you can add more if you want -x = Flatten()(vgg.output) -# x = Dense(1000, activation='relu')(x) -prediction = Dense(len(folders), activation='softmax')(x) - - -# create a model object -model = Model(inputs=vgg.input, outputs=prediction) - -# view the structure of the model -model.summary() - -# tell the model what cost and optimization method to use -model.compile( - loss='categorical_crossentropy', - optimizer='rmsprop', - metrics=['accuracy'] -) - - -# create an instance of ImageDataGenerator -gen = ImageDataGenerator( - rotation_range=20, - width_shift_range=0.1, - height_shift_range=0.1, - shear_range=0.1, - zoom_range=0.2, - horizontal_flip=True, - vertical_flip=True, - preprocessing_function=preprocess_input -) - - -# test generator to see how it works and some other useful things - -# get label mapping for confusion matrix plot later -test_gen = gen.flow_from_directory(valid_path, target_size=IMAGE_SIZE) -print(test_gen.class_indices) -labels = [None] * len(test_gen.class_indices) -for k, v in test_gen.class_indices.items(): - labels[v] = k - -# should be a strangely colored image (due to VGG weights being BGR) -for x, y in test_gen: - print("min:", x[0].min(), "max:", x[0].max()) - plt.title(labels[np.argmax(y[0])]) - plt.imshow(x[0]) - plt.show() - break - - -# create generators -train_generator = gen.flow_from_directory( - train_path, - target_size=IMAGE_SIZE, - shuffle=True, - batch_size=batch_size, -) -valid_generator = gen.flow_from_directory( - valid_path, - target_size=IMAGE_SIZE, - shuffle=True, - batch_size=batch_size, -) - - -# fit the model -r = model.fit( - train_generator, - validation_data=valid_generator, - epochs=epochs, - steps_per_epoch=len(image_files) // batch_size, - validation_steps=len(valid_image_files) // batch_size, -) - - - -def get_confusion_matrix(data_path, N): - # we need to see the data in the same order - # for both predictions and targets - print("Generating confusion matrix", N) - predictions = [] - targets = [] - i = 0 - for x, y in gen.flow_from_directory(data_path, target_size=IMAGE_SIZE, shuffle=False, batch_size=batch_size * 2): - i += 1 - if i % 50 == 0: - print(i) - p = model.predict(x) - p = np.argmax(p, axis=1) - y = np.argmax(y, axis=1) - predictions = np.concatenate((predictions, p)) - targets = np.concatenate((targets, y)) - if len(targets) >= N: - break - - cm = confusion_matrix(targets, predictions) - return cm - - -cm = get_confusion_matrix(train_path, len(image_files)) -print(cm) -valid_cm = get_confusion_matrix(valid_path, len(valid_image_files)) -print(valid_cm) - - -# plot some data - -# loss -plt.plot(r.history['loss'], label='train loss') -plt.plot(r.history['val_loss'], label='val loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='train acc') -plt.plot(r.history['val_accuracy'], label='val acc') -plt.legend() -plt.show() - -from util import plot_confusion_matrix -plot_confusion_matrix(cm, labels, title='Train confusion matrix') -plot_confusion_matrix(valid_cm, labels, title='Validation confusion matrix') \ No newline at end of file diff --git a/cnn_class2/util.py b/cnn_class2/util.py deleted file mode 100644 index 333e3548..00000000 --- a/cnn_class2/util.py +++ /dev/null @@ -1,55 +0,0 @@ -# https://deeplearningcourses.com/advanced-computer-vision -# https://www.udemy.com/advanced-computer-vision - -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import itertools -import numpy as np -import matplotlib.pyplot as plt - - -def plot_confusion_matrix(cm, classes, - normalize=False, - title='Confusion matrix', - cmap=plt.cm.Blues): - """ - This function prints and plots the confusion matrix. - Normalization can be applied by setting `normalize=True`. - """ - if normalize: - cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] - print("Normalized confusion matrix") - else: - print('Confusion matrix, without normalization') - - print(cm) - - plt.imshow(cm, interpolation='nearest', cmap=cmap) - plt.title(title) - plt.colorbar() - tick_marks = np.arange(len(classes)) - plt.xticks(tick_marks, classes, rotation=45) - plt.yticks(tick_marks, classes) - - fmt = '.2f' if normalize else 'd' - thresh = cm.max() / 2. - for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): - plt.text(j, i, format(cm[i, j], fmt), - horizontalalignment="center", - color="white" if cm[i, j] > thresh else "black") - - plt.tight_layout() - plt.ylabel('True label') - plt.xlabel('Predicted label') - plt.show() - - -def y2indicator(Y): - K = len(set(Y)) - N = len(Y) - I = np.empty((N, K)) - I[np.arange(N), Y] = 1 - return I \ No newline at end of file diff --git a/financial_engineering/go_here_instead.txt b/financial_engineering/go_here_instead.txt deleted file mode 100644 index 614b0afd..00000000 --- a/financial_engineering/go_here_instead.txt +++ /dev/null @@ -1 +0,0 @@ -https://github.com/lazyprogrammer/financial_engineering diff --git a/gmm.py b/gmm.py new file mode 100644 index 00000000..fb4a674b --- /dev/null +++ b/gmm.py @@ -0,0 +1,129 @@ +import numpy as np +import matplotlib.pyplot as plt +from sklearn.mixture import GMM + + +def generate_data(pi, mu, sigma, N): + # pi should be K x 1 + # mu should be K x D + # sigma should be K x D x D + K,D = mu.shape + X = np.zeros((N,D)) + C = np.random.random(N) + colors = np.zeros(N) + + pi_cdf = np.zeros(len(pi)) + pi_cdf[0] = pi[0] + for i,p in enumerate(pi[1:]): + pi_cdf[i+1] = pi_cdf[i] + p + + for i in xrange(N): + k = -1 + for j,P in enumerate(pi_cdf): + if C[i] < P: + k = j + colors[i] = j + break + X[i] = np.random.multivariate_normal(mu[k], sigma[k]) + return X, colors + + +def pdf(x,m,S): + D = len(x) + diff = x - m + c = ((2*np.pi)**(-D/2.0)) * np.sqrt(np.linalg.det(S)) + inv = np.linalg.inv(S) + e = np.exp(-0.5*np.dot( np.dot(diff, inv), diff )) + if np.any(np.isnan(S)): + print "S:", S + raise Exception("NAN in S") + if np.any(np.isnan(c)): + print "S:", S + raise Exception("NAN in c") + if np.any(np.isnan(e)): + raise Exception("NAN in e") + return c*e + + +def expectation_maximization(X, K, max_iter=100): + # return guess for pi, mu, sigma + N,D = X.shape + + # initialize pi, mu, sigma + pi = np.ones(K)/K + #mu = np.random.randn(K,D) + mu = np.array([[1,1], [-1,-1], [-1,1]])*3.0 + I = np.eye(D) + sigma = np.zeros((K,D,D)) + for k in xrange(K): + sigma[k] = I + + phi = np.zeros((N,K)) + piP = np.zeros((N,K)) + L = np.zeros(max_iter) + for t in xrange(max_iter): + print "t:", t + + # E step: populate phi + for i in xrange(N): + for k in xrange(K): + piP[i,k] = pi[k]*pdf(X[i], mu[k], sigma[k]) + for i in xrange(N): + phi[i] = piP[i] / piP[i].sum() + + if np.any(np.isnan(phi)): + raise Exception("NAN in phi") + + # log the objective function + L[t] = np.log(piP[i].sum()).sum() + + # M step + for k in xrange(K): + nk = phi[:,k].sum() + pi[k] = nk / N + mu[k] = np.dot(phi[:,k], X) / nk + for i in xrange(N): + diff = X[i] - mu[k] + sigma[k] += phi[i,k]*np.outer(diff, diff) + sigma[k] /= nk + + if np.any(np.isnan(pi)): + raise Exception("NAN in pi") + if np.any(np.isnan(mu)): + raise Exception("NAN in mu") + if np.any(np.isnan(sigma)): + raise Exception("NAN in sigma") + + return pi, mu, sigma, L + + +def main(): + pi = np.array([0.3, 0.5, 0.2]) + mu = np.array([[1,1], [-1,-1], [-1,1]])*3 + sigma = np.array([ + [[1,0], [0,1]], + [[2,0], [0,2]], + [[0.5,0], [0, 0.5]], + ]) + X, C = generate_data(pi, mu, sigma, 1000) + plt.scatter(X[:,0], X[:,1], c=C, s=100, alpha=0.5) + plt.show() + + + # sklearn + gmm = GMM(n_components=3, covariance_type='full') + gmm.fit(X) + print "pi:", gmm.weights_ + print "mu:", gmm.means_ + print "sigma:", gmm.covars_ + + pi2, mu2, sigma2, L = expectation_maximization(X, len(pi)) + print "pi:", pi2 + print "mu:", mu2 + print "sigma:", sigma2 + plt.plot(L) + plt.show() + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/hmm_class/__init__.py b/hmm_class/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/hmm_class/extra_reading.txt b/hmm_class/extra_reading.txt deleted file mode 100644 index c0bb93d1..00000000 --- a/hmm_class/extra_reading.txt +++ /dev/null @@ -1,8 +0,0 @@ -A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition -https://www.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/tutorial%20on%20hmm%20and%20applications.pdf - -Some Mathematics for HMM by Dawei Shen -https://pdfs.semanticscholar.org/4ce1/9ab0e07da9aa10be1c336400c8e4d8fc36c5.pdf - -A Revealing Introduction to Hidden Markov Models -https://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf \ No newline at end of file diff --git a/hmm_class/frost.py b/hmm_class/frost.py index 36ae900f..77132c58 100644 --- a/hmm_class/frost.py +++ b/hmm_class/frost.py @@ -1,37 +1,17 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me # Model and generate Robert Frost poems. -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import string -import sys - initial = {} # start of a phrase second_word = {} transitions = {} -# unfortunately these work different ways -def remove_punctuation_2(s): +def remove_punctuation(s): return s.translate(None, string.punctuation) -def remove_punctuation_3(s): - return s.translate(str.maketrans('','',string.punctuation)) - -if sys.version.startswith('2'): - remove_punctuation = remove_punctuation_2 -else: - remove_punctuation = remove_punctuation_3 - - def add2dict(d, k, v): if k not in d: d[k] = [] @@ -41,7 +21,7 @@ def add2dict(d, k, v): tokens = remove_punctuation(line.rstrip().lower()).split() T = len(tokens) - for i in range(T): + for i in xrange(T): t = tokens[i] if i == 0: # measure the distribution of the first word @@ -62,7 +42,7 @@ def add2dict(d, k, v): # normalize the distributions initial_total = sum(initial.values()) -for t, c in iteritems(initial): +for t, c in initial.iteritems(): initial[t] = c / initial_total def list2pdict(ts): @@ -71,15 +51,15 @@ def list2pdict(ts): n = len(ts) for t in ts: d[t] = d.get(t, 0.) + 1 - for t, c in iteritems(d): + for t, c in d.iteritems(): d[t] = c / n return d -for t_1, ts in iteritems(second_word): +for t_1, ts in second_word.iteritems(): # replace list with dictionary of probabilities second_word[t_1] = list2pdict(ts) -for k, ts in iteritems(transitions): +for k, ts in transitions.iteritems(): transitions[k] = list2pdict(ts) # generate 4 lines @@ -88,14 +68,14 @@ def sample_word(d): p0 = np.random.random() # print "p0:", p0 cumulative = 0 - for t, p in iteritems(d): + for t, p in d.iteritems(): cumulative += p if p0 < cumulative: return t assert(False) # should never get here def generate(): - for i in range(4): + for i in xrange(4): sentence =[] # initial word @@ -114,7 +94,7 @@ def generate(): sentence.append(w2) w0 = w1 w1 = w2 - print(' '.join(sentence)) + print ' '.join(sentence) generate() diff --git a/hmm_class/generate_c.py b/hmm_class/generate_c.py index 37c06025..b26a9af9 100644 --- a/hmm_class/generate_c.py +++ b/hmm_class/generate_c.py @@ -1,13 +1,6 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me # Generate continuous data from an HMM. -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import matplotlib.pyplot as plt @@ -49,20 +42,20 @@ def big_init(): ]) # M x K x D sigma = np.zeros((M, K, D, D)) - for m in range(M): - for k in range(K): + for m in xrange(M): + for k in xrange(K): sigma[m,k] = np.eye(D) return M, K, D, pi, A, R, mu, sigma def get_signals(N=20, T=100, init=big_init): M, K, D, pi, A, R, mu, sigma = init() X = [] - for n in range(N): + for n in xrange(N): x = np.zeros((T, D)) s = 0 # initial state is 0 since pi[0] = 1 r = np.random.choice(K, p=R[s]) # choose mixture x[0] = np.random.multivariate_normal(mu[s][r], sigma[s][r]) - for t in range(1, T): + for t in xrange(1, T): s = np.random.choice(M, p=A[s]) # choose state r = np.random.choice(K, p=R[s]) # choose mixture x[t] = np.random.multivariate_normal(mu[s][r], sigma[s][r]) diff --git a/hmm_class/generate_ht.py b/hmm_class/generate_ht.py index 62a8742a..924ffed4 100644 --- a/hmm_class/generate_ht.py +++ b/hmm_class/generate_ht.py @@ -1,14 +1,6 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me # Generate discrete data from an HMM. -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np @@ -23,7 +15,7 @@ def generate_sequence(N): s = np.random.choice(xrange(M), p=pi) # initial state x = np.random.choice(xrange(V), p=B[s]) # initial observation sequence = [x] - for n in range(N-1): + for n in xrange(N-1): s = np.random.choice(xrange(M), p=A[s]) # next state x = np.random.choice(xrange(V), p=B[s]) # next observation sequence.append(x) @@ -32,7 +24,7 @@ def generate_sequence(N): def main(): with open('coin_data.txt', 'w') as f: - for n in range(50): + for n in xrange(50): sequence = generate_sequence(30) sequence = ''.join(symbol_map[s] for s in sequence) print sequence diff --git a/hmm_class/hmm_classifier.py b/hmm_class/hmm_classifier.py index 8fa62939..ef78103f 100644 --- a/hmm_class/hmm_classifier.py +++ b/hmm_class/hmm_classifier.py @@ -1,20 +1,11 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me # Demonstrate how HMMs can be used for classification. -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - - import string import numpy as np import matplotlib.pyplot as plt -from hmmd_theano2 import HMM +from hmmd_theano import HMM from sklearn.utils import shuffle from nltk import pos_tag, word_tokenize @@ -24,17 +15,16 @@ def __init__(self): def fit(self, X, Y, V): K = len(set(Y)) # number of classes - assume 0..K-1 - N = len(Y) self.models = [] self.priors = [] - for k in range(K): + for k in xrange(K): # gather all the training data for this class thisX = [x for x, y in zip(X, Y) if y == k] C = len(thisX) - self.priors.append(np.log(C) - np.log(N)) + self.priors.append(np.log(C)) hmm = HMM(5) - hmm.fit(thisX, V=V, print_period=1, learning_rate=1e-2, max_iter=80) + hmm.fit(thisX, V=V, p_cost=0.1, print_period=1, learning_rate=10e-5, max_iter=100) self.models.append(hmm) def score(self, X, Y): @@ -65,7 +55,7 @@ def get_data(): for line in open(fn): line = line.rstrip() if line: - print(line) + print line # tokens = remove_punctuation(line.lower()).split() tokens = get_tags(line) if len(tokens) > 1: @@ -78,17 +68,18 @@ def get_data(): X.append(sequence) Y.append(label) count += 1 - print(count) + print count if count >= 50: break - print("Vocabulary:", word2idx.keys()) + print "Vocabulary:", word2idx.keys() return X, Y, current_idx def main(): X, Y, V = get_data() - print("len(X):", len(X)) - print("Vocabulary size:", V) + # print "Finished loading data" + print "len(X):", len(X) + print "Vocabulary size:", V X, Y = shuffle(X, Y) N = 20 # number to test Xtrain, Ytrain = X[:-N], Y[:-N] @@ -96,7 +87,7 @@ def main(): model = HMMClassifier() model.fit(Xtrain, Ytrain, V) - print("Score:", model.score(Xtest, Ytest)) + print "Score:", model.score(Xtest, Ytest) if __name__ == '__main__': diff --git a/hmm_class/hmmc.py b/hmm_class/hmmc.py index c66f4476..cb19d0c3 100644 --- a/hmm_class/hmmc.py +++ b/hmm_class/hmmc.py @@ -1,13 +1,6 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me # Continuous-observation HMM with no scaling, but allowing multiple observations -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import wave import numpy as np import matplotlib.pyplot as plt @@ -25,7 +18,7 @@ def __init__(self, M, K): self.M = M # number of hidden states self.K = K # number of Gaussians - def fit(self, X, max_iter=30, eps=1e0): + def fit(self, X, max_iter=30, eps=10e-1): # train the HMM model using the Baum-Welch algorithm # a specific instance of the expectation-maximization algorithm @@ -35,24 +28,24 @@ def fit(self, X, max_iter=30, eps=1e0): self.pi = np.ones(self.M) / self.M # initial state distribution self.A = random_normalized(self.M, self.M) # state transition matrix self.R = np.ones((self.M, self.K)) / self.K # mixture proportions - print("initial A:", self.A) - print("initial R:", self.R) + print "initial A:", self.A + print "initial R:", self.R self.mu = np.zeros((self.M, self.K, D)) - for i in range(self.M): - for k in range(self.K): + for i in xrange(self.M): + for k in xrange(self.K): random_idx = np.random.choice(N) x = X[random_idx] random_time_idx = np.random.choice(len(x)) self.mu[i,k] = x[random_time_idx] self.sigma = np.zeros((self.M, self.K, D, D)) - for j in range(self.M): - for k in range(self.K): + for j in xrange(self.M): + for k in xrange(self.K): self.sigma[j,k] = np.eye(D) costs = [] - for it in range(max_iter): + for it in xrange(max_iter): if it % 1 == 0: - print("it:", it) + print "it:", it alphas = [] betas = [] gammas = [] @@ -60,16 +53,17 @@ def fit(self, X, max_iter=30, eps=1e0): # components = [] P = np.zeros(N) - for n in range(N): + for n in xrange(N): x = X[n] T = len(x) # calculate B so we can lookup when updating alpha and beta B = np.zeros((self.M, T)) component = np.zeros((self.M, self.K, T)) # we'll need these later - for j in range(self.M): - for t in range(T): - for k in range(self.K): + for j in xrange(self.M): + for t in xrange(T): + for k in xrange(self.K): + # print "sigma:", self.sigma[j,k] p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k]) component[j,k,t] = p B[j,t] += p @@ -77,7 +71,7 @@ def fit(self, X, max_iter=30, eps=1e0): alpha = np.zeros((T, self.M)) alpha[0] = self.pi*B[:,0] - for t in range(1, T): + for t in xrange(1, T): alpha[t] = alpha[t-1].dot(self.A) * B[:,t] P[n] = alpha[-1].sum() assert(P[n] <= 1) @@ -85,26 +79,28 @@ def fit(self, X, max_iter=30, eps=1e0): beta = np.zeros((T, self.M)) beta[-1] = 1 - for t in range(T - 2, -1, -1): + for t in xrange(T - 2, -1, -1): beta[t] = self.A.dot(B[:,t+1] * beta[t+1]) betas.append(beta) # update for Gaussians gamma = np.zeros((T, self.M, self.K)) - for t in range(T): + for t in xrange(T): alphabeta = (alphas[n][t,:] * betas[n][t,:]).sum() - for j in range(self.M): + # print "alpabeta:", alphabeta + for j in xrange(self.M): factor = alphas[n][t,j] * betas[n][t,j] / alphabeta # mixture_j = component[j,:,t].sum() - for k in range(self.K): + for k in xrange(self.K): gamma[t,j,k] = factor * component[j,k,t] / B[j,t] + # assert(np.abs(mixture_j - B[j,t]).sum() < 10e-10) gammas.append(gamma) cost = np.log(P).sum() costs.append(cost) # now re-estimate pi, A, R, mu, sigma - self.pi = np.sum((alphas[n][0] * betas[n][0])/P[n] for n in range(N)) / N + self.pi = np.sum((alphas[n][0] * betas[n][0])/P[n] for n in xrange(N)) / N a_den = np.zeros((self.M, 1)) a_num = 0 @@ -112,7 +108,7 @@ def fit(self, X, max_iter=30, eps=1e0): r_den = np.zeros(self.M) mu_num = np.zeros((self.M, self.K, D)) sigma_num = np.zeros((self.M, self.K, D, D)) - for n in range(N): + for n in xrange(N): x = X[n] T = len(x) B = Bs[n] @@ -123,9 +119,9 @@ def fit(self, X, max_iter=30, eps=1e0): # numerator for A a_num_n = np.zeros((self.M, self.M)) - for i in range(self.M): - for j in range(self.M): - for t in range(T-1): + for i in xrange(self.M): + for j in xrange(self.M): + for t in xrange(T-1): a_num_n[i,j] += alphas[n][t,i] * self.A[i,j] * B[j,t+1] * betas[n][t+1,j] a_num += a_num_n / P[n] @@ -133,9 +129,9 @@ def fit(self, X, max_iter=30, eps=1e0): # update mixture components r_num_n = np.zeros((self.M, self.K)) r_den_n = np.zeros(self.M) - for j in range(self.M): - for k in range(self.K): - for t in range(T): + for j in xrange(self.M): + for k in xrange(self.K): + for t in xrange(T): r_num_n[j,k] += gamma[t,j,k] r_den_n[j] += gamma[t,j,k] r_num += r_num_n / P[n] @@ -143,9 +139,9 @@ def fit(self, X, max_iter=30, eps=1e0): mu_num_n = np.zeros((self.M, self.K, D)) sigma_num_n = np.zeros((self.M, self.K, D, D)) - for j in range(self.M): - for k in range(self.K): - for t in range(T): + for j in xrange(self.M): + for k in xrange(self.K): + for t in xrange(T): # update means mu_num_n[j,k] += gamma[t,j,k] * x[t] @@ -155,20 +151,21 @@ def fit(self, X, max_iter=30, eps=1e0): sigma_num += sigma_num_n / P[n] self.A = a_num / a_den + # print self.A assert(np.all(self.A <= 1)) # update R, mu, sigma - for j in range(self.M): - for k in range(self.K): + for j in xrange(self.M): + for k in xrange(self.K): self.R[j,k] = r_num[j,k] / r_den[j] self.mu[j,k] = mu_num[j,k] / r_num[j,k] self.sigma[j,k] = sigma_num[j,k] / r_num[j,k] - print("A:", self.A) - print("mu:", self.mu) - print("sigma:", self.sigma) - print("R:", self.R) - print("pi:", self.pi) + print "A:", self.A + print "mu:", self.mu + print "sigma:", self.sigma + print "R:", self.R + print "pi:", self.pi plt.plot(costs) plt.show() @@ -180,46 +177,17 @@ def likelihood(self, x): alpha = np.zeros((T, self.M)) B = np.zeros((self.M, T)) - for j in range(self.M): - for t in range(T): - for k in range(self.K): + for j in xrange(self.M): + for t in xrange(T): + for k in xrange(self.K): p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k]) B[j,t] += p alpha[0] = self.pi*B[:,0] - for t in range(1, T): + for t in xrange(1, T): alpha[t] = alpha[t-1].dot(self.A) * B[:,t] return alpha[-1].sum() - def get_state_sequence(self, x): - # returns the most likely state sequence given observed sequence x - # using the Viterbi algorithm - T = len(x) - - # make the emission matrix B - B = np.zeros((self.M, T)) - for j in range(self.M): - for t in range(T): - for k in range(self.K): - p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k]) - B[j,t] += p - - # perform Viterbi as usual - delta = np.zeros((T, self.M)) - psi = np.zeros((T, self.M)) - delta[0] = self.pi*B[:,0] - for t in range(1, T): - for j in range(self.M): - delta[t,j] = np.max(delta[t-1]*self.A[:,j]) * B[j,t] - psi[t,j] = np.argmax(delta[t-1]*self.A[:,j]) - - # backtrack - states = np.zeros(T, dtype=np.int32) - states[T-1] = np.argmax(delta[T-1]) - for t in range(T-2, -1, -1): - states[t] = psi[t+1, states[t+1]] - return states - def likelihood_multi(self, X): return np.array([self.likelihood(x) for x in X]) @@ -266,17 +234,13 @@ def fake_signal(init=simple_init): hmm = HMM(2, 2) hmm.fit(signals) L = hmm.log_likelihood_multi(signals).sum() - print("LL for fitted params:", L) + print "LL for fitted params:", L # test in actual params _, _, _, pi, A, R, mu, sigma = init() hmm.set(pi, A, R, mu, sigma) L = hmm.log_likelihood_multi(signals).sum() - print("LL for actual params:", L) - - # print most likely state sequence - print("Most likely state sequence for initial observation:") - print(hmm.get_state_sequence(signals[0])) + print "LL for actual params:", L if __name__ == '__main__': # real_signal() # will break diff --git a/hmm_class/hmmc_concat.py b/hmm_class/hmmc_scaled.py similarity index 53% rename from hmm_class/hmmc_concat.py rename to hmm_class/hmmc_scaled.py index 1055609e..067f78ee 100644 --- a/hmm_class/hmmc_concat.py +++ b/hmm_class/hmmc_scaled.py @@ -1,14 +1,6 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me -# Continuous-observation HMM with no scaling, but treats multiple observations -# as a concatenated single observation -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - +# Continuous-observation HMM with scaling and multiple observations import wave import numpy as np import matplotlib.pyplot as plt @@ -20,13 +12,18 @@ def random_normalized(d1, d2): x = np.random.random((d1, d2)) return x / x.sum(axis=1, keepdims=True) +def mvn_pdf(x, mu, sigma): + D = len(x) + c = 1 / np.sqrt(((2*np.pi)**D) * np.linalg.det(sigma)) + e = np.exp( -0.5*(x - mu).T.dot(np.linalg.solve(sigma, x - mu)) ) + return c * e class HMM: def __init__(self, M, K): self.M = M # number of hidden states self.K = K # number of Gaussians - def fit(self, X, max_iter=30, eps=1e0): + def fit(self, X, max_iter=25, eps=10e-2): # train the HMM model using the Baum-Welch algorithm # a specific instance of the expectation-maximization algorithm @@ -36,167 +33,188 @@ def fit(self, X, max_iter=30, eps=1e0): self.pi = np.ones(self.M) / self.M # initial state distribution self.A = random_normalized(self.M, self.M) # state transition matrix self.R = np.ones((self.M, self.K)) / self.K # mixture proportions - print("initial A:", self.A) - print("initial R:", self.R) self.mu = np.zeros((self.M, self.K, D)) - for i in range(self.M): - for k in range(self.K): + for i in xrange(self.M): + for k in xrange(self.K): random_idx = np.random.choice(N) x = X[random_idx] random_time_idx = np.random.choice(len(x)) self.mu[i,k] = x[random_time_idx] self.sigma = np.zeros((self.M, self.K, D, D)) - for j in range(self.M): - for k in range(self.K): + for j in xrange(self.M): + for k in xrange(self.K): self.sigma[j,k] = np.eye(D) costs = [] - for it in range(max_iter): + for it in xrange(max_iter): if it % 1 == 0: - print("it:", it) + print "it:", it alphas = [] betas = [] + scales = [] gammas = [] Bs = [] # components = [] - P = np.zeros(N) + logP = np.zeros(N) - for n in range(N): + for n in xrange(N): x = X[n] T = len(x) + scale = np.zeros(T) # calculate B so we can lookup when updating alpha and beta B = np.zeros((self.M, T)) + component = np.zeros((self.M, self.K, T)) # we'll need these later - for j in range(self.M): - for t in range(T): - for k in range(self.K): + C = np.zeros(T) + for j in xrange(self.M): + for t in xrange(T): + for k in xrange(self.K): + # print "sigma:", self.sigma[j,k] p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k]) + # tmp = self.R[j,k] * mvn_pdf(x[t], self.mu[j,k], self.sigma[j,k]) + # print "p - tmp:", (p - tmp) + # assert(np.abs(tmp - p).sum() < 10e-10) + # p = tmp component[j,k,t] = p B[j,t] += p + # components.append(component) Bs.append(B) + # assert(np.all(B <= 1)) alpha = np.zeros((T, self.M)) alpha[0] = self.pi*B[:,0] - for t in range(1, T): - alpha[t] = alpha[t-1].dot(self.A) * B[:,t] - P[n] = alpha[-1].sum() - assert(P[n] <= 1) + scale[0] = alpha[0].sum() + alpha[0] /= scale[0] + for t in xrange(1, T): + alpha_t_prime = alpha[t-1].dot(self.A) * B[:,t] + scale[t] = alpha_t_prime.sum() + alpha[t] = alpha_t_prime / scale[t] + logP[n] = np.log(scale).sum() alphas.append(alpha) + scales.append(scale) beta = np.zeros((T, self.M)) beta[-1] = 1 - for t in range(T - 2, -1, -1): - beta[t] = self.A.dot(B[:,t+1] * beta[t+1]) + for t in xrange(T - 2, -1, -1): + beta[t] = self.A.dot(B[:,t+1] * beta[t+1]) / scale[t+1] betas.append(beta) # update for Gaussians gamma = np.zeros((T, self.M, self.K)) - for t in range(T): - alphabeta = (alphas[n][t,:] * betas[n][t,:]).sum() - for j in range(self.M): + for t in xrange(T): + alphabeta = alphas[n][t,:].dot(betas[n][t,:]) + # print "alpabeta:", alphabeta + for j in xrange(self.M): factor = alphas[n][t,j] * betas[n][t,j] / alphabeta # mixture_j = component[j,:,t].sum() - for k in range(self.K): + for k in xrange(self.K): gamma[t,j,k] = factor * component[j,k,t] / B[j,t] gammas.append(gamma) - cost = np.log(P).sum() + cost = logP.sum() costs.append(cost) # now re-estimate pi, A, R, mu, sigma - self.pi = np.sum((alphas[n][0] * betas[n][0])/P[n] for n in range(N)) / N + self.pi = np.sum((alphas[n][0] * betas[n][0]) for n in xrange(N)) / N a_den = np.zeros((self.M, 1)) - a_num = 0 + a_num = np.zeros((self.M, self.M)) r_num = np.zeros((self.M, self.K)) r_den = np.zeros(self.M) mu_num = np.zeros((self.M, self.K, D)) sigma_num = np.zeros((self.M, self.K, D, D)) - for n in range(N): + for n in xrange(N): x = X[n] T = len(x) B = Bs[n] gamma = gammas[n] - # denominator for A - a_den += (alphas[n][:-1] * betas[n][:-1]).sum(axis=0, keepdims=True).T / P[n] + a_den += (alphas[n][:-1] * betas[n][:-1]).sum(axis=0, keepdims=True).T # numerator for A - a_num_n = np.zeros((self.M, self.M)) - for i in range(self.M): - for j in range(self.M): - for t in range(T-1): - a_num_n[i,j] += alphas[n][t,i] * self.A[i,j] * B[j,t+1] * betas[n][t+1,j] - a_num += a_num_n / P[n] + for i in xrange(self.M): + for j in xrange(self.M): + for t in xrange(T-1): + a_num[i,j] += alphas[n][t,i] * betas[n][t+1,j] * self.A[i,j] * B[j,t+1] / scales[n][t+1] # update mixture components r_num_n = np.zeros((self.M, self.K)) r_den_n = np.zeros(self.M) - for j in range(self.M): - for k in range(self.K): - for t in range(T): + for j in xrange(self.M): + for k in xrange(self.K): + for t in xrange(T): r_num_n[j,k] += gamma[t,j,k] r_den_n[j] += gamma[t,j,k] - r_num += r_num_n / P[n] - r_den += r_den_n / P[n] + r_num += r_num_n * (-logP[n]) + r_den += r_den_n * (-logP[n]) mu_num_n = np.zeros((self.M, self.K, D)) sigma_num_n = np.zeros((self.M, self.K, D, D)) - for j in range(self.M): - for k in range(self.K): - for t in range(T): + for j in xrange(self.M): + for k in xrange(self.K): + for t in xrange(T): # update means mu_num_n[j,k] += gamma[t,j,k] * x[t] # update covariances sigma_num_n[j,k] += gamma[t,j,k] * np.outer(x[t] - self.mu[j,k], x[t] - self.mu[j,k]) - mu_num += mu_num_n / P[n] - sigma_num += sigma_num_n / P[n] - + mu_num += mu_num_n * (-logP[n]) + sigma_num += sigma_num_n * (-logP[n]) + self.A = a_num / a_den - assert(np.all(self.A <= 1)) + # tmp2 = np.zeros(a_num.shape) + # for i in xrange(self.M): + # for j in xrange(self.M): + # tmp2[i,j] = a_num[i,j] / a_den[i] + # if tmp2[i,j] > 1: + # print "A(%s,%s) = %s" % (i, j, tmp2[i,j]) # update R, mu, sigma - for j in range(self.M): - for k in range(self.K): + for j in xrange(self.M): + for k in xrange(self.K): self.R[j,k] = r_num[j,k] / r_den[j] self.mu[j,k] = mu_num[j,k] / r_num[j,k] - self.sigma[j,k] = sigma_num[j,k] / r_num[j,k] - - print("A:", self.A) - print("mu:", self.mu) - print("sigma:", self.sigma) - print("R:", self.R) - print("pi:", self.pi) + self.sigma[j,k] = sigma_num[j,k] / r_num[j,k] + np.eye(D)*eps + print "sigma:", self.sigma + # assert(np.abs(self.A - tmp2).sum() < 10e-10) + assert(np.all(self.R <= 1)) + assert(np.all(self.A <= 1)) + print "A:", self.A + print "mu:", self.mu + print "sigma:", self.sigma + print "R:", self.R + print "pi:", self.pi plt.plot(costs) plt.show() - def likelihood(self, x): + def log_likelihood(self, x): # returns log P(x | model) # using the forward part of the forward-backward algorithm T = len(x) - alpha = np.zeros((T, self.M)) - + scale = np.zeros(T) B = np.zeros((self.M, T)) - for j in range(self.M): - for t in range(T): - for k in range(self.K): - p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k]) + for j in xrange(self.M): + for t in xrange(T): + for k in xrange(self.K): + # p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k]) + p = self.R[j,k] * mvn_pdf(x[t], self.mu[j,k], self.sigma[j,k]) B[j,t] += p + alpha = np.zeros((T, self.M)) alpha[0] = self.pi*B[:,0] - for t in range(1, T): - alpha[t] = alpha[t-1].dot(self.A) * B[:,t] - return alpha[-1].sum() - - def likelihood_multi(self, X): - return np.array([self.likelihood(x) for x in X]) + scale[0] = alpha[0].sum() + alpha[0] /= scale[0] + for t in xrange(1, T): + alpha_t_prime = alpha[t-1].dot(self.A) * B[:,t] + scale[t] = alpha_t_prime.sum() + alpha[t] = alpha_t_prime / scale[t] + return np.log(scale).sum() def log_likelihood_multi(self, X): - return np.log(self.likelihood_multi(X)) + return np.array([self.log_likelihood(x) for x in X]) def set(self, pi, A, R, mu, sigma): self.pi = pi @@ -223,31 +241,30 @@ def real_signal(): signal = spf.readframes(-1) signal = np.fromstring(signal, 'Int16') T = len(signal) - - hmm = HMM(10) - hmm.fit(signal.reshape(1, T)) + signal = (signal - signal.mean()) / signal.std() + hmm = HMM(5, 3) + hmm.fit(signal.reshape(1, T, 1)) -def fake_signal(init=simple_init): - signals = get_signals(N=10, T=10, init=init) - # for signal in signals: - # for d in xrange(signal.shape[1]): - # plt.plot(signal[:,d]) - # plt.show() +def fake_signal(init=big_init): + signals = get_signals(init=init) + for signal in signals: + for d in xrange(signal.shape[1]): + plt.plot(signal[:,d]) + plt.show() - hmm = HMM(2, 2) + hmm = HMM(5, 3) hmm.fit(signals) L = hmm.log_likelihood_multi(signals).sum() - print("LL for fitted params:", L) + print "LL for fitted params:", L # test in actual params _, _, _, pi, A, R, mu, sigma = init() hmm.set(pi, A, R, mu, sigma) L = hmm.log_likelihood_multi(signals).sum() - print("LL for actual params:", L) + print "LL for actual params:", L if __name__ == '__main__': - # real_signal() # will break - fake_signal(init=simple_init) - # fake_signal(init=big_init) # will break + real_signal() + # fake_signal() diff --git a/hmm_class/hmmc_scaled_concat.py b/hmm_class/hmmc_scaled_concat.py deleted file mode 100644 index ff07bf54..00000000 --- a/hmm_class/hmmc_scaled_concat.py +++ /dev/null @@ -1,255 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python -# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python -# https://lazyprogrammer.me -# Continuous-observation HMM with scaling and multiple observations (treated as concatenated sequence) -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import wave -import numpy as np -import matplotlib.pyplot as plt - -from generate_c import get_signals, big_init, simple_init -from scipy.stats import multivariate_normal as mvn - -def random_normalized(d1, d2): - x = np.random.random((d1, d2)) - return x / x.sum(axis=1, keepdims=True) - -class HMM: - def __init__(self, M, K): - self.M = M # number of hidden states - self.K = K # number of Gaussians - - def fit(self, X, max_iter=25, eps=1e-1): - # train the HMM model using the Baum-Welch algorithm - # a specific instance of the expectation-maximization algorithm - - # concatenate sequences in X and determine start/end positions - sequenceLengths = [] - for x in X: - sequenceLengths.append(len(x)) - Xc = np.concatenate(X) - T = len(Xc) - startPositions = np.zeros(len(Xc), dtype=np.bool) - endPositions = np.zeros(len(Xc), dtype=np.bool) - startPositionValues = [] - last = 0 - for length in sequenceLengths: - startPositionValues.append(last) - startPositions[last] = 1 - if last > 0: - endPositions[last - 1] = 1 - last += length - - D = X[0].shape[1] # assume each x is organized (T, D) - - # randomly initialize all parameters - self.pi = np.ones(self.M) / self.M # initial state distribution - self.A = random_normalized(self.M, self.M) # state transition matrix - self.R = np.ones((self.M, self.K)) / self.K # mixture proportions - self.mu = np.zeros((self.M, self.K, D)) - for i in range(self.M): - for k in range(self.K): - random_idx = np.random.choice(T) - self.mu[i,k] = Xc[random_idx] - self.sigma = np.zeros((self.M, self.K, D, D)) - for j in range(self.M): - for k in range(self.K): - self.sigma[j,k] = np.eye(D) - - # main EM loop - costs = [] - for it in range(max_iter): - if it % 1 == 0: - print("it:", it) - - scale = np.zeros(T) - - # calculate B so we can lookup when updating alpha and beta - B = np.zeros((self.M, T)) - component = np.zeros((self.M, self.K, T)) # we'll need these later - for j in range(self.M): - for k in range(self.K): - p = self.R[j,k] * mvn.pdf(Xc, self.mu[j,k], self.sigma[j,k]) - component[j,k,:] = p - B[j,:] += p - - - alpha = np.zeros((T, self.M)) - alpha[0] = self.pi*B[:,0] - scale[0] = alpha[0].sum() - alpha[0] /= scale[0] - for t in range(1, T): - if startPositions[t] == 0: - alpha_t_prime = alpha[t-1].dot(self.A) * B[:,t] - else: - alpha_t_prime = self.pi * B[:,t] - scale[t] = alpha_t_prime.sum() - alpha[t] = alpha_t_prime / scale[t] - logP = np.log(scale).sum() - - beta = np.zeros((T, self.M)) - beta[-1] = 1 - for t in range(T - 2, -1, -1): - if startPositions[t + 1] == 1: - beta[t] = 1 - else: - beta[t] = self.A.dot(B[:,t+1] * beta[t+1]) / scale[t+1] - - # update for Gaussians - gamma = np.zeros((T, self.M, self.K)) - for t in range(T): - alphabeta = alpha[t,:].dot(beta[t,:]) - for j in range(self.M): - factor = alpha[t,j] * beta[t,j] / alphabeta - for k in range(self.K): - gamma[t,j,k] = factor * component[j,k,t] / B[j,t] - - costs.append(logP) - - # now re-estimate pi, A, R, mu, sigma - self.pi = np.sum((alpha[t] * beta[t]) for t in startPositionValues) / len(startPositionValues) - - a_den = np.zeros((self.M, 1)) # prob don't need this - a_num = np.zeros((self.M, self.M)) - r_num = np.zeros((self.M, self.K)) - r_den = np.zeros(self.M) - mu_num = np.zeros((self.M, self.K, D)) - sigma_num = np.zeros((self.M, self.K, D, D)) - - - - nonEndPositions = (1 - endPositions).astype(np.bool) - a_den += (alpha[nonEndPositions] * beta[nonEndPositions]).sum(axis=0, keepdims=True).T - - # numerator for A - for i in range(self.M): - for j in range(self.M): - for t in range(T-1): - if endPositions[t] != 1: - a_num[i,j] += alpha[t,i] * beta[t+1,j] * self.A[i,j] * B[j,t+1] / scale[t+1] - self.A = a_num / a_den - - - # update mixture components - r_num_n = np.zeros((self.M, self.K)) - r_den_n = np.zeros(self.M) - for j in range(self.M): - for k in range(self.K): - for t in range(T): - r_num_n[j,k] += gamma[t,j,k] - r_den_n[j] += gamma[t,j,k] - r_num = r_num_n - r_den = r_den_n - - mu_num_n = np.zeros((self.M, self.K, D)) - sigma_num_n = np.zeros((self.M, self.K, D, D)) - for j in range(self.M): - for k in range(self.K): - for t in range(T): - # update means - mu_num_n[j,k] += gamma[t,j,k] * Xc[t] - - # update covariances - sigma_num_n[j,k] += gamma[t,j,k] * np.outer(Xc[t] - self.mu[j,k], Xc[t] - self.mu[j,k]) - mu_num = mu_num_n - sigma_num = sigma_num_n - - - # update R, mu, sigma - for j in range(self.M): - for k in range(self.K): - self.R[j,k] = r_num[j,k] / r_den[j] - self.mu[j,k] = mu_num[j,k] / r_num[j,k] - self.sigma[j,k] = sigma_num[j,k] / r_num[j,k] + np.eye(D)*eps - assert(np.all(self.R <= 1)) - assert(np.all(self.A <= 1)) - print("A:", self.A) - print("mu:", self.mu) - print("sigma:", self.sigma) - print("R:", self.R) - print("pi:", self.pi) - - plt.plot(costs) - plt.show() - - def log_likelihood(self, x): - # returns log P(x | model) - # using the forward part of the forward-backward algorithm - T = len(x) - scale = np.zeros(T) - B = np.zeros((self.M, T)) - for j in range(self.M): - for k in range(self.K): - p = self.R[j,k] * mvn.pdf(x, self.mu[j,k], self.sigma[j,k]) - B[j,:] += p - - alpha = np.zeros((T, self.M)) - alpha[0] = self.pi*B[:,0] - scale[0] = alpha[0].sum() - alpha[0] /= scale[0] - for t in range(1, T): - alpha_t_prime = alpha[t-1].dot(self.A) * B[:,t] - scale[t] = alpha_t_prime.sum() - alpha[t] = alpha_t_prime / scale[t] - return np.log(scale).sum() - - def log_likelihood_multi(self, X): - return np.array([self.log_likelihood(x) for x in X]) - - def set(self, pi, A, R, mu, sigma): - self.pi = pi - self.A = A - self.R = R - self.mu = mu - self.sigma = sigma - M, K = R.shape - self.M = M - self.K = K - - -def real_signal(): - spf = wave.open('helloworld.wav', 'r') - - #Extract Raw Audio from Wav File - # If you right-click on the file and go to "Get Info", you can see: - # sampling rate = 16000 Hz - # bits per sample = 16 - # The first is quantization in time - # The second is quantization in amplitude - # We also do this for images! - # 2^16 = 65536 is how many different sound levels we have - signal = spf.readframes(-1) - signal = np.fromstring(signal, 'Int16') - T = len(signal) - signal = (signal - signal.mean()) / signal.std() - hmm = HMM(5, 3) - hmm.fit(signal.reshape(1, T, 1)) - print("LL for fitted params:", hmm.log_likelihood(signal.reshape(T, 1))) - - -def fake_signal(init=big_init): - signals = get_signals(init=init) - # for signal in signals: - # for d in xrange(signal.shape[1]): - # plt.plot(signal[:,d]) - # plt.show() - - hmm = HMM(5, 3) - hmm.fit(signals) - L = hmm.log_likelihood_multi(signals).sum() - print("LL for fitted params:", L) - - # test in actual params - _, _, _, pi, A, R, mu, sigma = init() - hmm.set(pi, A, R, mu, sigma) - L = hmm.log_likelihood_multi(signals).sum() - print("LL for actual params:", L) - -if __name__ == '__main__': - # real_signal() - fake_signal() - diff --git a/hmm_class/hmmc_scaled_concat_diag.py b/hmm_class/hmmc_scaled_concat_diag.py deleted file mode 100644 index 692c1782..00000000 --- a/hmm_class/hmmc_scaled_concat_diag.py +++ /dev/null @@ -1,293 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python -# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python -# https://lazyprogrammer.me -# Continuous-observation HMM with scaling and multiple observations (treated as concatenated sequence) -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import wave -import numpy as np -import matplotlib.pyplot as plt - -from generate_c import get_signals, big_init, simple_init -from scipy.stats import multivariate_normal as mvn - -def random_normalized(d1, d2): - x = np.random.random((d1, d2)) - return x / x.sum(axis=1, keepdims=True) - -class HMM: - def __init__(self, M, K): - self.M = M # number of hidden states - self.K = K # number of Gaussians - - def fit(self, X, max_iter=25, eps=1e-1): - # train the HMM model using the Baum-Welch algorithm - # a specific instance of the expectation-maximization algorithm - - # concatenate sequences in X and determine start/end positions - sequenceLengths = [] - for x in X: - sequenceLengths.append(len(x)) - Xc = np.concatenate(X) - T = len(Xc) - startPositions = np.zeros(len(Xc), dtype=np.bool) - endPositions = np.zeros(len(Xc), dtype=np.bool) - startPositionValues = [] - last = 0 - for length in sequenceLengths: - startPositionValues.append(last) - startPositions[last] = 1 - if last > 0: - endPositions[last - 1] = 1 - last += length - - D = X[0].shape[1] # assume each x is organized (T, D) - - # randomly initialize all parameters - self.pi = np.ones(self.M) / self.M # initial state distribution - self.A = random_normalized(self.M, self.M) # state transition matrix - self.R = np.ones((self.M, self.K)) / self.K # mixture proportions - self.mu = np.zeros((self.M, self.K, D)) - for i in range(self.M): - for k in range(self.K): - random_idx = np.random.choice(T) - self.mu[i,k] = Xc[random_idx] - self.sigma = np.ones((self.M, self.K, D)) - - # main EM loop - costs = [] - for it in range(max_iter): - if it % 1 == 0: - print("it:", it) - - scale = np.zeros(T) - - # calculate B so we can lookup when updating alpha and beta - B = np.zeros((self.M, T)) - component = np.zeros((self.M, self.K, T)) # we'll need these later - for j in range(self.M): - for k in range(self.K): - p = self.R[j,k] * mvn.pdf(Xc, self.mu[j,k], self.sigma[j,k]) - component[j,k,:] = p - B[j,:] += p - - - alpha = np.zeros((T, self.M)) - alpha[0] = self.pi*B[:,0] - scale[0] = alpha[0].sum() - alpha[0] /= scale[0] - for t in range(1, T): - if startPositions[t] == 0: - alpha_t_prime = alpha[t-1].dot(self.A) * B[:,t] - else: - alpha_t_prime = self.pi * B[:,t] - scale[t] = alpha_t_prime.sum() - alpha[t] = alpha_t_prime / scale[t] - logP = np.log(scale).sum() - - beta = np.zeros((T, self.M)) - beta[-1] = 1 - for t in range(T - 2, -1, -1): - if startPositions[t + 1] == 1: - beta[t] = 1 - else: - beta[t] = self.A.dot(B[:,t+1] * beta[t+1]) / scale[t+1] - - # update for Gaussians - gamma = np.zeros((T, self.M, self.K)) - for t in range(T): - alphabeta = alpha[t,:].dot(beta[t,:]) - for j in range(self.M): - factor = alpha[t,j] * beta[t,j] / alphabeta - for k in range(self.K): - gamma[t,j,k] = factor * component[j,k,t] / B[j,t] - - costs.append(logP) - - # now re-estimate pi, A, R, mu, sigma - self.pi = np.sum((alpha[t] * beta[t]) for t in startPositionValues) / len(startPositionValues) - - a_den = np.zeros((self.M, 1)) # prob don't need this - a_num = np.zeros((self.M, self.M)) - r_num = np.zeros((self.M, self.K)) - r_den = np.zeros(self.M) - mu_num = np.zeros((self.M, self.K, D)) - sigma_num = np.zeros((self.M, self.K, D)) - - - - nonEndPositions = (1 - endPositions).astype(np.bool) - a_den += (alpha[nonEndPositions] * beta[nonEndPositions]).sum(axis=0, keepdims=True).T - - # numerator for A - for i in range(self.M): - for j in range(self.M): - for t in range(T-1): - if endPositions[t] != 1: - a_num[i,j] += alpha[t,i] * beta[t+1,j] * self.A[i,j] * B[j,t+1] / scale[t+1] - self.A = a_num / a_den - - - # update mixture components - r_num_n = np.zeros((self.M, self.K)) - r_den_n = np.zeros(self.M) - for j in range(self.M): - for k in range(self.K): - for t in range(T): - r_num_n[j,k] += gamma[t,j,k] - r_den_n[j] += gamma[t,j,k] - r_num = r_num_n - r_den = r_den_n - - mu_num_n = np.zeros((self.M, self.K, D)) - sigma_num_n = np.zeros((self.M, self.K, D)) - for j in range(self.M): - for k in range(self.K): - for t in range(T): - # update means - mu_num_n[j,k] += gamma[t,j,k] * Xc[t] - - # update covariances - sigma_num_n[j,k] += gamma[t,j,k] * (Xc[t] - self.mu[j,k])**2 - mu_num = mu_num_n - sigma_num = sigma_num_n - - - # update R, mu, sigma - for j in range(self.M): - for k in range(self.K): - self.R[j,k] = r_num[j,k] / r_den[j] - self.mu[j,k] = mu_num[j,k] / r_num[j,k] - self.sigma[j,k] = sigma_num[j,k] / r_num[j,k] + np.ones(D)*eps - assert(np.all(self.R <= 1)) - assert(np.all(self.A <= 1)) - print("A:", self.A) - print("mu:", self.mu) - print("sigma:", self.sigma) - print("R:", self.R) - print("pi:", self.pi) - - plt.plot(costs) - plt.show() - - def log_likelihood(self, x): - # returns log P(x | model) - # using the forward part of the forward-backward algorithm - T = len(x) - scale = np.zeros(T) - B = np.zeros((self.M, T)) - for j in range(self.M): - for k in range(self.K): - p = self.R[j,k] * mvn.pdf(x, self.mu[j,k], self.sigma[j,k]) - B[j,:] += p - - alpha = np.zeros((T, self.M)) - alpha[0] = self.pi*B[:,0] - scale[0] = alpha[0].sum() - alpha[0] /= scale[0] - for t in range(1, T): - alpha_t_prime = alpha[t-1].dot(self.A) * B[:,t] - scale[t] = alpha_t_prime.sum() - alpha[t] = alpha_t_prime / scale[t] - return np.log(scale).sum() - - def get_state_sequence(self, x): - # returns the most likely state sequence given observed sequence x - # using the Viterbi algorithm - T = len(x) - - # make the emission matrix B - logB = np.zeros((self.M, T)) - for j in range(self.M): - for t in range(T): - for k in range(self.K): - p = np.log(self.R[j,k]) + mvn.logpdf(x[t], self.mu[j,k], self.sigma[j,k]) - logB[j,t] += p - print("logB:", logB) - - # perform Viterbi as usual - delta = np.zeros((T, self.M)) - psi = np.zeros((T, self.M)) - - # smooth pi in case it is 0 - pi = self.pi + 1e-10 - pi /= pi.sum() - - delta[0] = np.log(pi) + logB[:,0] - for t in range(1, T): - for j in range(self.M): - next_delta = delta[t-1] + np.log(self.A[:,j]) - delta[t,j] = np.max(next_delta) + logB[j,t] - psi[t,j] = np.argmax(next_delta) - - # backtrack - states = np.zeros(T, dtype=np.int32) - states[T-1] = np.argmax(delta[T-1]) - for t in range(T-2, -1, -1): - states[t] = psi[t+1, states[t+1]] - return states - - def log_likelihood_multi(self, X): - return np.array([self.log_likelihood(x) for x in X]) - - def set(self, pi, A, R, mu, sigma): - self.pi = pi - self.A = A - self.R = R - self.mu = mu - self.sigma = sigma - M, K = R.shape - self.M = M - self.K = K - - -def real_signal(): - spf = wave.open('helloworld.wav', 'r') - - #Extract Raw Audio from Wav File - # If you right-click on the file and go to "Get Info", you can see: - # sampling rate = 16000 Hz - # bits per sample = 16 - # The first is quantization in time - # The second is quantization in amplitude - # We also do this for images! - # 2^16 = 65536 is how many different sound levels we have - signal = spf.readframes(-1) - signal = np.fromstring(signal, 'Int16') - T = len(signal) - signal = (signal - signal.mean()) / signal.std() - hmm = HMM(5, 3) - hmm.fit(signal.reshape(1, T, 1), max_iter=35) - print("LL for fitted params:", hmm.log_likelihood(signal.reshape(T, 1))) - - -def fake_signal(init=big_init): - signals = get_signals(init=init) - # for signal in signals: - # for d in xrange(signal.shape[1]): - # plt.plot(signal[:,d]) - # plt.show() - - hmm = HMM(5, 3) - hmm.fit(signals) - L = hmm.log_likelihood_multi(signals).sum() - print("LL for fitted params:", L) - - # test in actual params - _, _, _, pi, A, R, mu, sigma = init() - hmm.set(pi, A, R, mu, sigma) - L = hmm.log_likelihood_multi(signals).sum() - print("LL for actual params:", L) - - # print most likely state sequence - print("Most likely state sequence for initial observation:") - print(hmm.get_state_sequence(signals[0])) - -if __name__ == '__main__': - # real_signal() - fake_signal() - diff --git a/hmm_class/hmmc_scaled_diag.py b/hmm_class/hmmc_scaled_diag.py new file mode 100644 index 00000000..ec5a39d8 --- /dev/null +++ b/hmm_class/hmmc_scaled_diag.py @@ -0,0 +1,265 @@ +# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python +# http://lazyprogrammer.me +# Continuous-observation HMM with scaling and diagonal covariance. +import wave +import numpy as np +import matplotlib.pyplot as plt + +from generate_c import get_signals, big_init, simple_init + +def random_normalized(d1, d2): + x = np.random.random((d1, d2)) + return x / x.sum(axis=1, keepdims=True) + +def mvn_pdf(x, mu, sigma): + D = len(x) + c = 1 / np.sqrt(((2*np.pi)**D) * sigma.prod()) + e = np.exp( -0.5*((x - mu) * (x - mu) / sigma).sum() ) + return c * e + +class HMM: + def __init__(self, M, K): + self.M = M # number of hidden states + self.K = K # number of Gaussians + + def fit(self, X, max_iter=30, eps=10e-2): + # train the HMM model using the Baum-Welch algorithm + # a specific instance of the expectation-maximization algorithm + + N = len(X) + D = X[0].shape[1] # assume each x is organized (T, D) + + self.pi = np.ones(self.M) / self.M # initial state distribution + self.A = random_normalized(self.M, self.M) # state transition matrix + self.R = np.ones((self.M, self.K)) / self.K # mixture proportions + self.mu = np.zeros((self.M, self.K, D)) + for i in xrange(self.M): + for k in xrange(self.K): + random_idx = np.random.choice(N) + x = X[random_idx] + random_time_idx = np.random.choice(len(x)) + self.mu[i,k] = x[random_time_idx] + self.sigma = np.ones((self.M, self.K, D)) + + costs = [] + for it in xrange(max_iter): + if it % 1 == 0: + print "it:", it + alphas = [] + betas = [] + scales = [] + gammas = [] + Bs = [] + # components = [] + logP = np.zeros(N) + + for n in xrange(N): + x = X[n] + T = len(x) + scale = np.zeros(T) + + # calculate B so we can lookup when updating alpha and beta + B = np.zeros((self.M, T)) + + component = np.zeros((self.M, self.K, T)) # we'll need these later + C = np.zeros(T) + for j in xrange(self.M): + for t in xrange(T): + for k in xrange(self.K): + # print "sigma:", self.sigma[j,k] + # p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k]) + tmp = self.R[j,k] * mvn_pdf(x[t], self.mu[j,k], self.sigma[j,k]) + # print "p - tmp:", (p - tmp) + # assert(np.abs(tmp - p).sum() < 10e-10) + p = tmp + component[j,k,t] = p + B[j,t] += p + # components.append(component) + Bs.append(B) + # assert(np.all(B <= 1)) + + alpha = np.zeros((T, self.M)) + alpha[0] = self.pi*B[:,0] + scale[0] = alpha[0].sum() + alpha[0] /= scale[0] + for t in xrange(1, T): + alpha_t_prime = alpha[t-1].dot(self.A) * B[:,t] + scale[t] = alpha_t_prime.sum() + alpha[t] = alpha_t_prime / scale[t] + logP[n] = np.log(scale).sum() + alphas.append(alpha) + scales.append(scale) + + beta = np.zeros((T, self.M)) + beta[-1] = 1 + for t in xrange(T - 2, -1, -1): + beta[t] = self.A.dot(B[:,t+1] * beta[t+1]) / scale[t+1] + betas.append(beta) + + # update for Gaussians + gamma = np.zeros((T, self.M, self.K)) + for t in xrange(T): + alphabeta = alphas[n][t,:].dot(betas[n][t,:]) + # print "alpabeta:", alphabeta + for j in xrange(self.M): + factor = alphas[n][t,j] * betas[n][t,j] / alphabeta + # mixture_j = component[j,:,t].sum() + for k in xrange(self.K): + gamma[t,j,k] = factor * component[j,k,t] / B[j,t] + gammas.append(gamma) + + cost = logP.sum() + costs.append(cost) + + # now re-estimate pi, A, R, mu, sigma + self.pi = np.sum((alphas[n][0] * betas[n][0]) for n in xrange(N)) / N + + a_den = np.zeros((self.M, 1)) + a_num = np.zeros((self.M, self.M)) + r_num = np.zeros((self.M, self.K)) + r_den = np.zeros(self.M) + mu_num = np.zeros((self.M, self.K, D)) + sigma_num = np.zeros((self.M, self.K, D)) + for n in xrange(N): + x = X[n] + T = len(x) + B = Bs[n] + gamma = gammas[n] + + a_den += (alphas[n][:-1] * betas[n][:-1]).sum(axis=0, keepdims=True).T + + # numerator for A + for i in xrange(self.M): + for j in xrange(self.M): + for t in xrange(T-1): + a_num[i,j] += alphas[n][t,i] * betas[n][t+1,j] * self.A[i,j] * B[j,t+1] / scales[n][t+1] + + # update mixture components + r_num_n = np.zeros((self.M, self.K)) + r_den_n = np.zeros(self.M) + for j in xrange(self.M): + for k in xrange(self.K): + for t in xrange(T): + r_num_n[j,k] += gamma[t,j,k] + r_den_n[j] += gamma[t,j,k] + r_num += r_num_n * (-logP[n]) + r_den += r_den_n * (-logP[n]) + # r_num += r_num_n + # r_den += r_den_n + + mu_num_n = np.zeros((self.M, self.K, D)) + sigma_num_n = np.zeros((self.M, self.K, D)) + for j in xrange(self.M): + for k in xrange(self.K): + for t in xrange(T): + # update means + mu_num_n[j,k] += gamma[t,j,k] * x[t] + + # update covariances + sigma_num_n[j,k] += gamma[t,j,k] * (x[t] - self.mu[j,k]) * (x[t] - self.mu[j,k]) + mu_num += mu_num_n * (-logP[n]) + sigma_num += sigma_num_n * (-logP[n]) + # mu_num += mu_num_n + # sigma_num += sigma_num_n + self.A = a_num / a_den + + # update R, mu, sigma + for j in xrange(self.M): + for k in xrange(self.K): + self.R[j,k] = r_num[j,k] / r_den[j] + self.mu[j,k] = mu_num[j,k] / r_num[j,k] + self.sigma[j,k] = sigma_num[j,k] / r_num[j,k] + self.sigma += eps + + assert(np.all(self.R <= 1)) + assert(np.all(self.A <= 1)) + print "A:", self.A + print "mu:", self.mu + print "sigma:", self.sigma + print "R:", self.R + print "pi:", self.pi + + plt.plot(costs) + plt.show() + + def log_likelihood(self, x): + # returns log P(x | model) + # using the forward part of the forward-backward algorithm + T = len(x) + scale = np.zeros(T) + B = np.zeros((self.M, T)) + for j in xrange(self.M): + for t in xrange(T): + for k in xrange(self.K): + # p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k]) + p = self.R[j,k] * mvn_pdf(x[t], self.mu[j,k], self.sigma[j,k]) + B[j,t] += p + + alpha = np.zeros((T, self.M)) + alpha[0] = self.pi*B[:,0] + scale[0] = alpha[0].sum() + alpha[0] /= scale[0] + for t in xrange(1, T): + alpha_t_prime = alpha[t-1].dot(self.A) * B[:,t] + scale[t] = alpha_t_prime.sum() + alpha[t] = alpha_t_prime / scale[t] + return np.log(scale).sum() + + def log_likelihood_multi(self, X): + return np.array([self.log_likelihood(x) for x in X]) + + def set(self, pi, A, R, mu, sigma): + self.pi = pi + self.A = A + self.R = R + self.mu = mu + self.sigma = sigma + M, K = R.shape + self.M = M + self.K = K + + +def real_signal(): + spf = wave.open('helloworld.wav', 'r') + + #Extract Raw Audio from Wav File + # If you right-click on the file and go to "Get Info", you can see: + # sampling rate = 16000 Hz + # bits per sample = 16 + # The first is quantization in time + # The second is quantization in amplitude + # We also do this for images! + # 2^16 = 65536 is how many different sound levels we have + signal = spf.readframes(-1) + signal = np.fromstring(signal, 'Int16') + T = len(signal) + signal = (signal - signal.mean()) / signal.std() + hmm = HMM(5, 3) + hmm.fit(signal.reshape(1, T, 1)) + + +def fake_signal(init=big_init): + signals = get_signals(init=init) + # for signal in signals: + # print "mean:", signal.mean(axis=0), "var:", signal.var(axis=0) + + hmm = HMM(5, 3) + hmm.fit(signals) + L = hmm.log_likelihood_multi(signals).sum() + print "LL for fitted params:", L + + # test in actual params + _, _, _, pi, A, R, mu, sigma = init() + M, K, D = mu.shape + diag_sigma = np.zeros((M, K, D)) + for j in xrange(M): + for k in xrange(K): + diag_sigma[j,k] = np.diag(sigma[j,k]) + hmm.set(pi, A, R, mu, diag_sigma) + L = hmm.log_likelihood_multi(signals).sum() + print "LL for actual params:", L + +if __name__ == '__main__': + real_signal() + # fake_signal() + diff --git a/hmm_class/hmmc_tf.py b/hmm_class/hmmc_tf.py deleted file mode 100644 index b39e68fb..00000000 --- a/hmm_class/hmmc_tf.py +++ /dev/null @@ -1,237 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python -# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python -# http://lazyprogrammer.me -# Continuous-observation HMM in Theano using gradient descent. -# TODO: seems to be broken on TF v1.2, but fine with TF v1.0 -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import wave -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt -MVN = tf.contrib.distributions.MultivariateNormalDiag -from generate_c import get_signals, big_init - - -class HMM: - def __init__(self, M, K, D): - self.M = M # number of hidden states - self.K = K # number of Gaussians - self.D = D # Gaussian dimensionality - - def set_session(self, session): - self.session = session - - def init_random(self, X): - pi0 = np.ones(self.M).astype(np.float32) # initial state distribution - A0 = np.random.randn(self.M, self.M).astype(np.float32) # state transition matrix - R0 = np.ones((self.M, self.K)).astype(np.float32) # mixture proportions - # mu0 = np.random.randn(self.M, self.K, self.D).astype(np.float32) - - mu0 = np.zeros((self.M, self.K, self.D)) - for j in range(self.M): - for k in range(self.K): - n = np.random.randint(X.shape[0]) - t = np.random.randint(X.shape[1]) - mu0[j,k] = X[n,t] - mu0 = mu0.astype(np.float32) - - sigma0 = np.random.randn(self.M, self.K, self.D).astype(np.float32) - self.build(pi0, A0, R0, mu0, sigma0) - - def build(self, preSoftmaxPi, preSoftmaxA, preSoftmaxR, mu, logSigma): - self.preSoftmaxPi = tf.Variable(preSoftmaxPi) - self.preSoftmaxA = tf.Variable(preSoftmaxA) - self.preSoftmaxR = tf.Variable(preSoftmaxR) - self.mu = tf.Variable(mu) - self.logSigma = tf.Variable(logSigma) - - - pi = tf.nn.softmax(self.preSoftmaxPi) - A = tf.nn.softmax(self.preSoftmaxA) - R = tf.nn.softmax(self.preSoftmaxR) - sigma = tf.exp(self.logSigma) - - - # X will be TxD - self.tfx = tf.placeholder(tf.float32, shape=(None, self.D), name='X') - - - # first we need to calculate B - # B[j,t] = probability of X being in state j at time t - # = Gaussian mixture P( x(t) | mu(j), sigma(j) ) - # idea: first calculate components and sum - # note: we can use a for loop because M and K are not TF variables - self.mvns = [] - for j in range(self.M): - self.mvns.append([]) - for k in range(self.K): - self.mvns[j].append( - MVN(self.mu[j,k], sigma[j,k]) - ) - - # note: we can use a for loop because M and K are not TF variables - B = [] - for j in range(self.M): - components = [] - for k in range(self.K): - components.append( - self.mvns[j][k].prob(self.tfx) - ) - - # why? - # because we can stack a list of tensors - # but not a list of lists of tensors - - # components[j] will be K x T - # we now want to multiply by the mixture probability (R) - # result is M x T - # which gives us P( X(t) | state j ) - components = tf.stack(components) - R_j = tf.reshape(R[j], [1, self.K]) - p_x_t_j = tf.matmul(R_j, components) - - # now components[j] is just 1 x T --> T - components = tf.reshape(p_x_t_j, [-1]) - - # now append it to B - B.append(components) - - - # should now be M x T - B = tf.stack(B) - - # we should make it T x M since scan will loop through first index - B = tf.transpose(B, [1, 0]) - - - - # now perform the forward algorithm - def recurrence(old_a_old_s, B_t): - old_a = tf.reshape(old_a_old_s[0], (1, self.M)) - a = tf.matmul(old_a, A) * B_t - a = tf.reshape(a, (self.M,)) - s = tf.reduce_sum(a) - return (a / s), s - - alpha, scale = tf.scan( - fn=recurrence, - elems=B[1:], - initializer=(pi*B[0], np.float32(1.0)), - ) - # note: tensorflow is very strict about what types you pass in to initializer! - # - cannot be list, must be tuple - # - cannot be 1 (int), must be float32 - - self.cost_op = -tf.reduce_sum(tf.log(scale)) - self.train_op = tf.train.AdamOptimizer(1e-2).minimize(self.cost_op) - - def set(self, preSoftmaxPi, preSoftmaxA, preSoftmaxR, mu, logSigma): - # assume build has already been called - # we just assign these new variables - op1 = self.preSoftmaxPi.assign(preSoftmaxPi) - op2 = self.preSoftmaxA.assign(preSoftmaxA) - op3 = self.preSoftmaxR.assign(preSoftmaxR) - op4 = self.mu.assign(mu) - op5 = self.logSigma.assign(logSigma) - self.session.run([op1, op2, op3, op4, op5]) - - def fit(self, X, max_iter=10): - # train the HMM model using stochastic gradient descent - - N = len(X) - print("number of train samples:", N) - - costs = [] - for it in range(max_iter): - if it % 1 == 0: - print("it:", it) - - for n in range(N): - # this would of course be much faster if we didn't do this on - # every iteration of the loop - c = self.get_cost_multi(X).sum() - costs.append(c) - self.session.run(self.train_op, feed_dict={self.tfx: X[n]}) - - plt.plot(costs) - plt.show() - - def get_cost(self, x): - return self.session.run(self.cost_op, feed_dict={self.tfx: x}) - - def get_cost_multi(self, X): - return np.array([self.get_cost(x) for x in X]) - - -def real_signal(): - spf = wave.open('helloworld.wav', 'r') - - #Extract Raw Audio from Wav File - # If you right-click on the file and go to "Get Info", you can see: - # sampling rate = 16000 Hz - # bits per sample = 16 - # The first is quantization in time - # The second is quantization in amplitude - # We also do this for images! - # 2^16 = 65536 is how many different sound levels we have - signal = spf.readframes(-1) - signal = np.fromstring(signal, 'Int16') - T = len(signal) - signal = (signal - signal.mean()) / signal.std() - - # signal needs to be of shape N x T(n) x D - signals = signal.reshape(1, T, 1) - - hmm = HMM(3, 3, 1) - hmm.init_random(signals) - - init = tf.global_variables_initializer() - session = tf.InteractiveSession() - session.run(init) - hmm.set_session(session) - - hmm.fit(signals, max_iter=30) - - -def fake_signal(): - signals = get_signals() - # assume all signals are same length - signals = np.array(signals) - hmm = HMM(5, 3, signals[0].shape[1]) # M, K, D - hmm.init_random(signals) - - init = tf.global_variables_initializer() - session = tf.InteractiveSession() - session.run(init) - hmm.set_session(session) - - hmm.fit(signals, max_iter=30) - L = hmm.get_cost_multi(signals).sum() - print("LL for fitted params:", L) - - # test in actual params - _, _, _, pi, A, R, mu, sigma = big_init() - - # turn these into their "pre-softmax" forms - pi = np.log(pi) - A = np.log(A) - R = np.log(R) - M, K, D, _ = sigma.shape # need to convert full cov into diag cov - logSigma = np.zeros((M, K, D)) - for j in range(M): - for k in range(D): - logSigma[j,k] = np.log(np.diag(sigma[j,k])) - - hmm.set(pi, A, R, mu, logSigma) - L = hmm.get_cost_multi(signals).sum() - print("LL for actual params:", L) - -if __name__ == '__main__': - # real_signal() - fake_signal() - diff --git a/hmm_class/hmmc_theano.py b/hmm_class/hmmc_theano.py index 57d96ff1..02b4c9a2 100644 --- a/hmm_class/hmmc_theano.py +++ b/hmm_class/hmmc_theano.py @@ -1,13 +1,6 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me # Continuous-observation HMM in Theano using gradient descent. -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import wave import theano import theano.tensor as T @@ -27,8 +20,9 @@ def __init__(self, M, K): self.M = M # number of hidden states self.K = K # number of Gaussians - def fit(self, X, learning_rate=1e-2, max_iter=10): - # train the HMM model using gradient descent + def fit(self, X, learning_rate=10e-3, max_iter=10): + # train the HMM model using the Baum-Welch algorithm + # a specific instance of the expectation-maximization algorithm N = len(X) D = X[0].shape[1] # assume each x is organized (T, D) @@ -37,15 +31,15 @@ def fit(self, X, learning_rate=1e-2, max_iter=10): A0 = random_normalized(self.M, self.M) # state transition matrix R0 = np.ones((self.M, self.K)) / self.K # mixture proportions mu0 = np.zeros((self.M, self.K, D)) - for i in range(self.M): - for k in range(self.K): + for i in xrange(self.M): + for k in xrange(self.K): random_idx = np.random.choice(N) x = X[random_idx] random_time_idx = np.random.choice(len(x)) mu0[i,k] = x[random_time_idx] sigma0 = np.zeros((self.M, self.K, D, D)) - for j in range(self.M): - for k in range(self.K): + for j in xrange(self.M): + for k in xrange(self.K): sigma0[j,k] = np.eye(D) thx, cost = self.set(pi0, A0, R0, mu0, sigma0) @@ -73,20 +67,20 @@ def fit(self, X, learning_rate=1e-2, max_iter=10): ) costs = [] - for it in range(max_iter): - print("it:", it) + for it in xrange(max_iter): + print "it:", it - for n in range(N): + for n in xrange(N): c = self.log_likelihood_multi(X).sum() - print("c:", c) + print "c:", c costs.append(c) train_op(X[n]) - print("A:", self.A.get_value()) - print("mu:", self.mu.get_value()) - print("sigma:", self.sigma.get_value()) - print("R:", self.R.get_value()) - print("pi:", self.pi.get_value()) + print "A:", self.A.get_value() + print "mu:", self.mu.get_value() + print "sigma:", self.sigma.get_value() + print "R:", self.R.get_value() + print "pi:", self.pi.get_value() plt.plot(costs) plt.show() @@ -115,7 +109,7 @@ def gmm_pdf(x): def state_pdfs(xt): def component_pdf(j, xt): Bj_t = 0 - for k in range(self.K): + for k in xrange(self.K): Bj_t += self.R[j,k] * mvn_pdf(xt, self.mu[j,k], self.sigma[j,k]) return Bj_t @@ -182,7 +176,7 @@ def real_signal(): hmm = HMM(5, 3) # signal needs to be of shape N x T(n) x D - hmm.fit(signal.reshape(1, T, 1), learning_rate=1e-5, max_iter=20) + hmm.fit(signal.reshape(1, T, 1), learning_rate=10e-6, max_iter=20) def fake_signal(): @@ -190,13 +184,13 @@ def fake_signal(): hmm = HMM(5, 3) hmm.fit(signals) L = hmm.log_likelihood_multi(signals).sum() - print("LL for fitted params:", L) + print "LL for fitted params:", L # test in actual params _, _, _, pi, A, R, mu, sigma = big_init() hmm.set(pi, A, R, mu, sigma) L = hmm.log_likelihood_multi(signals).sum() - print("LL for actual params:", L) + print "LL for actual params:", L if __name__ == '__main__': # real_signal() diff --git a/hmm_class/hmmc_theano2.py b/hmm_class/hmmc_theano2.py deleted file mode 100644 index 647c8173..00000000 --- a/hmm_class/hmmc_theano2.py +++ /dev/null @@ -1,215 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python -# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python -# http://lazyprogrammer.me -# Continuous-observation HMM in Theano using gradient descent. - -# This script differs from hmmc_theano.py in the following way: -# Instead of re-normalizing the parameters at each iteration, -# we instead make the parameters free to vary between -inf to +inf. -# We then use softmax to ensure the probabilities are positive and sum to 1. - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import wave -import theano -import theano.tensor as T -import numpy as np -import matplotlib.pyplot as plt - -# from theano.sandbox import solve # does not have gradient functionality -from generate_c import get_signals, big_init - - -class HMM: - def __init__(self, M, K): - self.M = M # number of hidden states - self.K = K # number of Gaussians - - def fit(self, X, learning_rate=1e-2, max_iter=10): - # train the HMM model using gradient descent - - N = len(X) - D = X[0].shape[1] # assume each x is organized (T, D) - - pi0 = np.ones(self.M) # initial state distribution - A0 = np.random.randn(self.M, self.M) # state transition matrix - R0 = np.ones((self.M, self.K)) # mixture proportions - mu0 = np.zeros((self.M, self.K, D)) - for i in range(self.M): - for k in range(self.K): - random_idx = np.random.choice(N) - x = X[random_idx] - random_time_idx = np.random.choice(len(x)) - mu0[i,k] = x[random_time_idx] - sigma0 = np.random.randn(self.M, self.K, D, D) - - thx, cost = self.set(pi0, A0, R0, mu0, sigma0) - - pi_update = self.preSoftmaxPi - learning_rate*T.grad(cost, self.preSoftmaxPi) - A_update = self.preSoftmaxA - learning_rate*T.grad(cost, self.preSoftmaxA) - R_update = self.preSoftmaxR - learning_rate*T.grad(cost, self.preSoftmaxR) - mu_update = self.mu - learning_rate*T.grad(cost, self.mu) - sigma_update = self.sigmaFactor - learning_rate*T.grad(cost, self.sigmaFactor) - - updates = [ - (self.preSoftmaxPi, pi_update), - (self.preSoftmaxA, A_update), - (self.preSoftmaxR, R_update), - (self.mu, mu_update), - (self.sigmaFactor, sigma_update), - ] - - train_op = theano.function( - inputs=[thx], - updates=updates, - ) - - costs = [] - for it in range(max_iter): - print("it:", it) - - for n in range(N): - c = self.log_likelihood_multi(X).sum() - print("c:", c) - costs.append(c) - train_op(X[n]) - - plt.plot(costs) - plt.show() - - def set(self, preSoftmaxPi, preSoftmaxA, preSoftmaxR, mu, sigmaFactor): - self.preSoftmaxPi = theano.shared(preSoftmaxPi) - self.preSoftmaxA = theano.shared(preSoftmaxA) - self.preSoftmaxR = theano.shared(preSoftmaxR) - self.mu = theano.shared(mu) - self.sigmaFactor = theano.shared(sigmaFactor) - M, K = preSoftmaxR.shape - self.M = M - self.K = K - - pi = T.nnet.softmax(self.preSoftmaxPi).flatten() - A = T.nnet.softmax(self.preSoftmaxA) - R = T.nnet.softmax(self.preSoftmaxR) - - - D = self.mu.shape[2] - twopiD = (2*np.pi)**D - - # set up theano variables and functions - thx = T.matrix('X') # represents a TxD matrix of sequential observations - def mvn_pdf(x, m, S): - k = 1 / T.sqrt(twopiD * T.nlinalg.det(S)) - e = T.exp(-0.5*(x - m).T.dot(T.nlinalg.matrix_inverse(S).dot(x - m))) - return k*e - - def gmm_pdf(x): - def state_pdfs(xt): - def component_pdf(j, xt): - Bj_t = 0 - # j = T.cast(j, 'int32') - for k in range(self.K): - # k = int(k) - # a = R[j,k] - # b = mu[j,k] - # c = sigma[j,k] - L = self.sigmaFactor[j,k] - S = L.dot(L.T) - Bj_t += R[j,k] * mvn_pdf(xt, self.mu[j,k], S) - return Bj_t - - Bt, _ = theano.scan( - fn=component_pdf, - sequences=T.arange(self.M), - n_steps=self.M, - outputs_info=None, - non_sequences=[xt], - ) - return Bt - - B, _ = theano.scan( - fn=state_pdfs, - sequences=x, - n_steps=x.shape[0], - outputs_info=None, - ) - return B.T - - B = gmm_pdf(thx) - # scale = T.zeros((thx.shape[0], 1), dtype=theano.config.floatX) - # scale[0] = (self.pi*B[:,0]).sum() - - def recurrence(t, old_a, B): - a = old_a.dot(A) * B[:, t] - s = a.sum() - return (a / s), s - - [alpha, scale], _ = theano.scan( - fn=recurrence, - sequences=T.arange(1, thx.shape[0]), - outputs_info=[pi*B[:,0], None], - n_steps=thx.shape[0]-1, - non_sequences=[B], - ) - - cost = -T.log(scale).sum() - self.cost_op = theano.function( - inputs=[thx], - outputs=cost, - ) - return thx, cost - - def log_likelihood_multi(self, X): - return np.array([self.cost_op(x) for x in X]) - - -def real_signal(): - spf = wave.open('helloworld.wav', 'r') - - #Extract Raw Audio from Wav File - # If you right-click on the file and go to "Get Info", you can see: - # sampling rate = 16000 Hz - # bits per sample = 16 - # The first is quantization in time - # The second is quantization in amplitude - # We also do this for images! - # 2^16 = 65536 is how many different sound levels we have - signal = spf.readframes(-1) - signal = np.fromstring(signal, 'Int16') - T = len(signal) - signal = (signal - signal.mean()) / signal.std() - - hmm = HMM(3, 3) - # signal needs to be of shape N x T(n) x D - hmm.fit(signal.reshape(1, T, 1), learning_rate=2e-7, max_iter=20) - - -def fake_signal(): - signals = get_signals() - hmm = HMM(5, 3) - hmm.fit(signals, max_iter=3) - L = hmm.log_likelihood_multi(signals).sum() - print("LL for fitted params:", L) - - # test in actual params - _, _, _, pi, A, R, mu, sigma = big_init() - - # turn these into their "pre-softmax" forms - pi = np.log(pi) - A = np.log(A) - R = np.log(R) - - # decompose sigma using cholesky factorization - sigma = np.linalg.cholesky(sigma) - - hmm.set(pi, A, R, mu, sigma) - L = hmm.log_likelihood_multi(signals).sum() - print("LL for actual params:", L) - -if __name__ == '__main__': - # real_signal() - fake_signal() - diff --git a/hmm_class/hmmd.py b/hmm_class/hmmd.py index d50d5832..29d1b98e 100644 --- a/hmm_class/hmmd.py +++ b/hmm_class/hmmd.py @@ -1,16 +1,8 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me # Discrete Hidden Markov Model (HMM) -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import matplotlib.pyplot as plt -from datetime import datetime def random_normalized(d1, d2): @@ -23,7 +15,6 @@ def __init__(self, M): self.M = M # number of hidden states def fit(self, X, max_iter=30): - t0 = datetime.now() np.random.seed(123) # train the HMM model using the Baum-Welch algorithm # a specific instance of the expectation-maximization algorithm @@ -38,35 +29,36 @@ def fit(self, X, max_iter=30): self.A = random_normalized(self.M, self.M) # state transition matrix self.B = random_normalized(self.M, V) # output distribution - print("initial A:", self.A) - print("initial B:", self.B) + print "initial A:", self.A + print "initial B:", self.B costs = [] - for it in range(max_iter): + for it in xrange(max_iter): if it % 10 == 0: - print("it:", it) + print "it:", it alphas = [] betas = [] P = np.zeros(N) - for n in range(N): + for n in xrange(N): x = X[n] T = len(x) alpha = np.zeros((T, self.M)) alpha[0] = self.pi*self.B[:,x[0]] - for t in range(1, T): + for t in xrange(1, T): tmp1 = alpha[t-1].dot(self.A) * self.B[:, x[t]] # tmp2 = np.zeros(self.M) - # for i in range(self.M): - # for j in range(self.M): + # for i in xrange(self.M): + # for j in xrange(self.M): # tmp2[j] += alpha[t-1,i] * self.A[i,j] * self.B[j, x[t]] # print "diff:", np.abs(tmp1 - tmp2).sum() + # assert(np.abs(tmp1 - tmp2).sum() < 10e-10) alpha[t] = tmp1 P[n] = alpha[-1].sum() alphas.append(alpha) beta = np.zeros((T, self.M)) beta[-1] = 1 - for t in range(T - 2, -1, -1): + for t in xrange(T - 2, -1, -1): beta[t] = self.A.dot(self.B[:, x[t+1]] * beta[t+1]) betas.append(beta) @@ -77,7 +69,7 @@ def fit(self, X, max_iter=30): costs.append(cost) # now re-estimate pi, A, B - self.pi = np.sum((alphas[n][0] * betas[n][0])/P[n] for n in range(N)) / N + self.pi = np.sum((alphas[n][0] * betas[n][0])/P[n] for n in xrange(N)) / N # print "self.pi:", self.pi # break @@ -85,7 +77,7 @@ def fit(self, X, max_iter=30): den2 = np.zeros((self.M, 1)) a_num = 0 b_num = 0 - for n in range(N): + for n in xrange(N): x = X[n] T = len(x) # print "den shape:", den.shape @@ -95,50 +87,51 @@ def fit(self, X, max_iter=30): den2 += (alphas[n] * betas[n]).sum(axis=0, keepdims=True).T / P[n] # tmp2 = np.zeros((self.M, 1)) - # for i in range(self.M): - # for t in range(T-1): + # for i in xrange(self.M): + # for t in xrange(T-1): # tmp2[i] += alphas[n][t,i] * betas[n][t,i] # tmp2 /= P[n] # # print "diff:", np.abs(tmp1 - tmp2).sum() + # assert(np.abs(tmp1 - tmp2).sum() < 10e-10) # den += tmp1 # numerator for A a_num_n = np.zeros((self.M, self.M)) - for i in range(self.M): - for j in range(self.M): - for t in range(T-1): + for i in xrange(self.M): + for j in xrange(self.M): + for t in xrange(T-1): a_num_n[i,j] += alphas[n][t,i] * self.A[i,j] * self.B[j, x[t+1]] * betas[n][t+1,j] a_num += a_num_n / P[n] # numerator for B # b_num_n = np.zeros((self.M, V)) - # for i in range(self.M): - # for j in range(V): - # for t in range(T): + # for i in xrange(self.M): + # for j in xrange(V): + # for t in xrange(T): # if x[t] == j: # b_num_n[i,j] += alphas[n][t][i] * betas[n][t][i] b_num_n2 = np.zeros((self.M, V)) - for i in range(self.M): - for t in range(T): + for i in xrange(self.M): + for t in xrange(T): b_num_n2[i,x[t]] += alphas[n][t,i] * betas[n][t,i] + # assert(np.abs(b_num_n - b_num_n2).sum() < 10e-10) b_num += b_num_n2 / P[n] # tmp1 = a_num / den1 # tmp2 = np.zeros(a_num.shape) - # for i in range(self.M): - # for j in range(self.M): + # for i in xrange(self.M): + # for j in xrange(self.M): # tmp2[i,j] = a_num[i,j] / den1[i] # print "diff:", np.abs(tmp1 - tmp2).sum() # print "tmp1:", tmp1 # print "tmp2:", tmp2 + # assert(np.abs(tmp1 - tmp2).sum() < 10e-10) self.A = a_num / den1 self.B = b_num / den2 # print "P:", P # break - print("A:", self.A) - print("B:", self.B) - print("pi:", self.pi) - - print("Fit duration:", (datetime.now() - t0)) + print "A:", self.A + print "B:", self.B + print "pi:", self.pi plt.plot(costs) plt.show() @@ -149,7 +142,7 @@ def likelihood(self, x): T = len(x) alpha = np.zeros((T, self.M)) alpha[0] = self.pi*self.B[:,x[0]] - for t in range(1, T): + for t in xrange(1, T): alpha[t] = alpha[t-1].dot(self.A) * self.B[:, x[t]] return alpha[-1].sum() @@ -166,15 +159,15 @@ def get_state_sequence(self, x): delta = np.zeros((T, self.M)) psi = np.zeros((T, self.M)) delta[0] = self.pi*self.B[:,x[0]] - for t in range(1, T): - for j in range(self.M): + for t in xrange(1, T): + for j in xrange(self.M): delta[t,j] = np.max(delta[t-1]*self.A[:,j]) * self.B[j, x[t]] psi[t,j] = np.argmax(delta[t-1]*self.A[:,j]) # backtrack states = np.zeros(T, dtype=np.int32) states[T-1] = np.argmax(delta[T-1]) - for t in range(T-2, -1, -1): + for t in xrange(T-2, -1, -1): states[t] = psi[t+1, states[t+1]] return states @@ -188,18 +181,18 @@ def fit_coin(): hmm = HMM(2) hmm.fit(X) L = hmm.log_likelihood_multi(X).sum() - print("LL with fitted params:", L) + print "LL with fitted params:", L # try true values hmm.pi = np.array([0.5, 0.5]) hmm.A = np.array([[0.1, 0.9], [0.8, 0.2]]) hmm.B = np.array([[0.6, 0.4], [0.3, 0.7]]) L = hmm.log_likelihood_multi(X).sum() - print("LL with true params:", L) + print "LL with true params:", L # try viterbi - print("Best state sequence for:", X[0]) - print(hmm.get_state_sequence(X[0])) + print "Best state sequence for:", X[0] + print hmm.get_state_sequence(X[0]) if __name__ == '__main__': diff --git a/hmm_class/hmmd_scaled.py b/hmm_class/hmmd_scaled.py index d6bffb04..d6432413 100644 --- a/hmm_class/hmmd_scaled.py +++ b/hmm_class/hmmd_scaled.py @@ -1,13 +1,6 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me # Discrete Hidden Markov Model (HMM) with scaling -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import matplotlib.pyplot as plt @@ -36,19 +29,19 @@ def fit(self, X, max_iter=30): self.A = random_normalized(self.M, self.M) # state transition matrix self.B = random_normalized(self.M, V) # output distribution - print("initial A:", self.A) - print("initial B:", self.B) + print "initial A:", self.A + print "initial B:", self.B costs = [] - for it in range(max_iter): + for it in xrange(max_iter): if it % 10 == 0: - print("it:", it) + print "it:", it # alpha1 = np.zeros((N, self.M)) alphas = [] betas = [] scales = [] logP = np.zeros(N) - for n in range(N): + for n in xrange(N): x = X[n] T = len(x) scale = np.zeros(T) @@ -57,17 +50,19 @@ def fit(self, X, max_iter=30): alpha[0] = self.pi*self.B[:,x[0]] scale[0] = alpha[0].sum() alpha[0] /= scale[0] - for t in range(1, T): + for t in xrange(1, T): alpha_t_prime = alpha[t-1].dot(self.A) * self.B[:, x[t]] scale[t] = alpha_t_prime.sum() alpha[t] = alpha_t_prime / scale[t] + # P[n] = alpha[-1].sum() + # print "alpha[-1].sum():", alpha[-1].sum() logP[n] = np.log(scale).sum() alphas.append(alpha) scales.append(scale) beta = np.zeros((T, self.M)) beta[-1] = 1 - for t in range(T - 2, -1, -1): + for t in xrange(T - 2, -1, -1): beta[t] = self.A.dot(self.B[:, x[t+1]] * beta[t+1]) / scale[t+1] betas.append(beta) @@ -76,40 +71,48 @@ def fit(self, X, max_iter=30): costs.append(cost) # now re-estimate pi, A, B - self.pi = np.sum((alphas[n][0] * betas[n][0]) for n in range(N)) / N + self.pi = np.sum((alphas[n][0] * betas[n][0]) for n in xrange(N)) / N + # print "self.pi:", self.pi + # break den1 = np.zeros((self.M, 1)) den2 = np.zeros((self.M, 1)) a_num = np.zeros((self.M, self.M)) b_num = np.zeros((self.M, V)) - for n in range(N): + for n in xrange(N): x = X[n] T = len(x) + # print "den shape:", den.shape + # test = (alphas[n][:-1] * betas[n][:-1]).sum(axis=0, keepdims=True).T + # print "shape (alphas[n][:-1] * betas[n][:-1]).sum(axis=0): ", test.shape den1 += (alphas[n][:-1] * betas[n][:-1]).sum(axis=0, keepdims=True).T den2 += (alphas[n] * betas[n]).sum(axis=0, keepdims=True).T # numerator for A # a_num_n = np.zeros((self.M, self.M)) - for i in range(self.M): - for j in range(self.M): - for t in range(T-1): + for i in xrange(self.M): + for j in xrange(self.M): + for t in xrange(T-1): a_num[i,j] += alphas[n][t,i] * betas[n][t+1,j] * self.A[i,j] * self.B[j, x[t+1]] / scales[n][t+1] # a_num += a_num_n # numerator for B - # for i in range(self.M): - # for j in range(V): - # for t in range(T): + # for i in xrange(self.M): + # for j in xrange(V): + # for t in xrange(T): # if x[t] == j: # b_num[i,j] += alphas[n][t][i] * betas[n][t][i] - for i in range(self.M): - for t in range(T): + for i in xrange(self.M): + for t in xrange(T): b_num[i,x[t]] += alphas[n][t,i] * betas[n][t,i] self.A = a_num / den1 self.B = b_num / den2 - print("A:", self.A) - print("B:", self.B) - print("pi:", self.pi) + # print "new A:", self.A + # break + # print "P:", P + print "A:", self.A + print "B:", self.B + print "pi:", self.pi plt.plot(costs) plt.show() @@ -123,7 +126,7 @@ def log_likelihood(self, x): alpha[0] = self.pi*self.B[:,x[0]] scale[0] = alpha[0].sum() alpha[0] /= scale[0] - for t in range(1, T): + for t in xrange(1, T): alpha_t_prime = alpha[t-1].dot(self.A) * self.B[:, x[t]] scale[t] = alpha_t_prime.sum() alpha[t] = alpha_t_prime / scale[t] @@ -139,15 +142,15 @@ def get_state_sequence(self, x): delta = np.zeros((T, self.M)) psi = np.zeros((T, self.M)) delta[0] = np.log(self.pi) + np.log(self.B[:,x[0]]) - for t in range(1, T): - for j in range(self.M): + for t in xrange(1, T): + for j in xrange(self.M): delta[t,j] = np.max(delta[t-1] + np.log(self.A[:,j])) + np.log(self.B[j, x[t]]) psi[t,j] = np.argmax(delta[t-1] + np.log(self.A[:,j])) # backtrack states = np.zeros(T, dtype=np.int32) states[T-1] = np.argmax(delta[T-1]) - for t in range(T-2, -1, -1): + for t in xrange(T-2, -1, -1): states[t] = psi[t+1, states[t+1]] return states @@ -162,18 +165,18 @@ def fit_coin(): hmm = HMM(2) hmm.fit(X) L = hmm.log_likelihood_multi(X).sum() - print("LL with fitted params:", L) + print "LL with fitted params:", L # try true values hmm.pi = np.array([0.5, 0.5]) hmm.A = np.array([[0.1, 0.9], [0.8, 0.2]]) hmm.B = np.array([[0.6, 0.4], [0.3, 0.7]]) L = hmm.log_likelihood_multi(X).sum() - print("LL with true params:", L) + print "LL with true params:", L # try viterbi - print("Best state sequence for:", X[0]) - print(hmm.get_state_sequence(X[0])) + print "Best state sequence for:", X[0] + print hmm.get_state_sequence(X[0]) if __name__ == '__main__': diff --git a/hmm_class/hmmd_tf.py b/hmm_class/hmmd_tf.py deleted file mode 100644 index d3ecfc2b..00000000 --- a/hmm_class/hmmd_tf.py +++ /dev/null @@ -1,137 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python -# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python -# http://lazyprogrammer.me -# Discrete Hidden Markov Model (HMM) in Tensorflow using gradient descent. -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt - - -class HMM: - def __init__(self, M): - self.M = M # number of hidden states - - def set_session(self, session): - self.session = session - - def fit(self, X, max_iter=10, print_period=1): - # train the HMM model using stochastic gradient descent - - N = len(X) - print("number of train samples:", N) - - costs = [] - for it in range(max_iter): - if it % print_period == 0: - print("it:", it) - - for n in range(N): - # this would of course be much faster if we didn't do this on - # every iteration of the loop - c = self.get_cost_multi(X).sum() - costs.append(c) - self.session.run(self.train_op, feed_dict={self.tfx: X[n]}) - - plt.plot(costs) - plt.show() - - def get_cost(self, x): - # returns log P(x | model) - # using the forward part of the forward-backward algorithm - # print "getting cost for:", x - return self.session.run(self.cost, feed_dict={self.tfx: x}) - - def log_likelihood(self, x): - return -self.session.run(self.cost, feed_dict={self.tfx: x}) - - def get_cost_multi(self, X): - return np.array([self.get_cost(x) for x in X]) - - def build(self, preSoftmaxPi, preSoftmaxA, preSoftmaxB): - M, V = preSoftmaxB.shape - - self.preSoftmaxPi = tf.Variable(preSoftmaxPi) - self.preSoftmaxA = tf.Variable(preSoftmaxA) - self.preSoftmaxB = tf.Variable(preSoftmaxB) - - pi = tf.nn.softmax(self.preSoftmaxPi) - A = tf.nn.softmax(self.preSoftmaxA) - B = tf.nn.softmax(self.preSoftmaxB) - - # define cost - self.tfx = tf.placeholder(tf.int32, shape=(None,), name='x') - def recurrence(old_a_old_s, x_t): - old_a = tf.reshape(old_a_old_s[0], (1, M)) - a = tf.matmul(old_a, A) * B[:, x_t] - a = tf.reshape(a, (M,)) - s = tf.reduce_sum(a) - return (a / s), s - - # remember, tensorflow scan is going to loop through - # all the values! - # we treat the first value differently than the rest - # so we only want to loop through tfx[1:] - # the first scale being 1 doesn't affect the log-likelihood - # because log(1) = 0 - alpha, scale = tf.scan( - fn=recurrence, - elems=self.tfx[1:], - initializer=(pi*B[:,self.tfx[0]], np.float32(1.0)), - ) - - self.cost = -tf.reduce_sum(tf.log(scale)) - self.train_op = tf.train.AdamOptimizer(1e-2).minimize(self.cost) - - def init_random(self, V): - preSoftmaxPi0 = np.zeros(self.M).astype(np.float32) # initial state distribution - preSoftmaxA0 = np.random.randn(self.M, self.M).astype(np.float32) # state transition matrix - preSoftmaxB0 = np.random.randn(self.M, V).astype(np.float32) # output distribution - - self.build(preSoftmaxPi0, preSoftmaxA0, preSoftmaxB0) - - def set(self, preSoftmaxPi, preSoftmaxA, preSoftmaxB): - op1 = self.preSoftmaxPi.assign(preSoftmaxPi) - op2 = self.preSoftmaxA.assign(preSoftmaxA) - op3 = self.preSoftmaxB.assign(preSoftmaxB) - self.session.run([op1, op2, op3]) - - -def fit_coin(): - X = [] - for line in open('coin_data.txt'): - # 1 for H, 0 for T - x = [1 if e == 'H' else 0 for e in line.rstrip()] - X.append(x) - - # X = np.array(X).astype(np.int32) - - hmm = HMM(2) - # the entire graph (including optimizer's variables) must be built - # before calling global variables initializer! - hmm.init_random(2) - init = tf.global_variables_initializer() - with tf.Session() as session: - session.run(init) - hmm.set_session(session) - hmm.fit(X, max_iter=5) - L = hmm.get_cost_multi(X).sum() - print("LL with fitted params:", L) - - # try true values - # remember these must be in their "pre-softmax" forms - pi = np.log( np.array([0.5, 0.5]) ).astype(np.float32) - A = np.log( np.array([[0.1, 0.9], [0.8, 0.2]]) ).astype(np.float32) - B = np.log( np.array([[0.6, 0.4], [0.3, 0.7]]) ).astype(np.float32) - hmm.set(pi, A, B) - L = hmm.get_cost_multi(X).sum() - print("LL with true params:", L) - - -if __name__ == '__main__': - fit_coin() diff --git a/hmm_class/hmmd_theano.py b/hmm_class/hmmd_theano.py index 7017c993..b5701e92 100644 --- a/hmm_class/hmmd_theano.py +++ b/hmm_class/hmmd_theano.py @@ -1,13 +1,6 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me # Discrete Hidden Markov Model (HMM) in Theano using gradient descent. -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import theano import theano.tensor as T @@ -25,6 +18,7 @@ def __init__(self, M): def fit(self, X, learning_rate=0.001, max_iter=10, V=None, p_cost=1.0, print_period=10): # train the HMM model using stochastic gradient descent + # print "X to train:", X # determine V, the vocabulary size # assume observables are already integers from 0..V-1 @@ -32,7 +26,7 @@ def fit(self, X, learning_rate=0.001, max_iter=10, V=None, p_cost=1.0, print_per if V is None: V = max(max(x) for x in X) + 1 N = len(X) - print("number of train samples:", N) + print "number of train samples:", N pi0 = np.ones(self.M) / self.M # initial state distribution A0 = random_normalized(self.M, self.M) # state transition matrix @@ -68,28 +62,30 @@ def fit(self, X, learning_rate=0.001, max_iter=10, V=None, p_cost=1.0, print_per # ) costs = [] - for it in range(max_iter): + for it in xrange(max_iter): if it % print_period == 0: - print("it:", it) + print "it:", it - for n in range(N): + for n in xrange(N): # print "about to get the cost" # this would of course be much faster if we didn't do this on # every iteration of the loop c = self.get_cost_multi(X, p_cost).sum() costs.append(c) + # print "training on:", X[n] train_op(X[n]) - print("A:", self.A.get_value()) - print("B:", self.B.get_value()) - print("pi:", self.pi.get_value()) - print("len(costs):", len(costs)) + print "A:", self.A.get_value() + print "B:", self.B.get_value() + print "pi:", self.pi.get_value() + print "len(costs):", len(costs) plt.plot(costs) plt.show() def get_cost(self, x): # returns log P(x | model) # using the forward part of the forward-backward algorithm + # print "getting cost for:", x return self.cost_op(x) def log_likelihood(self, x): @@ -138,7 +134,7 @@ def fit_coin(): hmm = HMM(2) hmm.fit(X) L = hmm.get_cost_multi(X).sum() - print("LL with fitted params:", L) + print "LL with fitted params:", L # try true values pi = np.array([0.5, 0.5]) @@ -146,7 +142,7 @@ def fit_coin(): B = np.array([[0.6, 0.4], [0.3, 0.7]]) hmm.set(pi, A, B) L = hmm.get_cost_multi(X).sum() - print("LL with true params:", L) + print "LL with true params:", L if __name__ == '__main__': diff --git a/hmm_class/hmmd_theano2.py b/hmm_class/hmmd_theano2.py deleted file mode 100644 index f652526c..00000000 --- a/hmm_class/hmmd_theano2.py +++ /dev/null @@ -1,149 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python -# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python -# http://lazyprogrammer.me -# Discrete Hidden Markov Model (HMM) in Theano using gradient descent. - -# This script differs from hmmd_theano.py in the following way: -# Instead of re-normalizing the parameters at each iteration, -# we instead make the parameters free to vary between -inf to +inf. -# We then use softmax to ensure the probabilities are positive and sum to 1. - - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import theano -import theano.tensor as T -import matplotlib.pyplot as plt - - -class HMM: - def __init__(self, M): - self.M = M # number of hidden states - - def fit(self, X, learning_rate=0.001, max_iter=10, V=None, print_period=1): - # train the HMM model using stochastic gradient descent - # print "X to train:", X - - # determine V, the vocabulary size - # assume observables are already integers from 0..V-1 - # X is a jagged array of observed sequences - if V is None: - V = max(max(x) for x in X) + 1 - N = len(X) - print("number of train samples:", N) - - preSoftmaxPi0 = np.zeros(self.M) # initial state distribution - preSoftmaxA0 = np.random.randn(self.M, self.M) # state transition matrix - preSoftmaxB0 = np.random.randn(self.M, V) # output distribution - - thx, cost = self.set(preSoftmaxPi0, preSoftmaxA0, preSoftmaxB0) - - pi_update = self.preSoftmaxPi - learning_rate*T.grad(cost, self.preSoftmaxPi) - A_update = self.preSoftmaxA - learning_rate*T.grad(cost, self.preSoftmaxA) - B_update = self.preSoftmaxB - learning_rate*T.grad(cost, self.preSoftmaxB) - - updates = [ - (self.preSoftmaxPi, pi_update), - (self.preSoftmaxA, A_update), - (self.preSoftmaxB, B_update), - ] - - train_op = theano.function( - inputs=[thx], - updates=updates, - allow_input_downcast=True, - ) - - costs = [] - for it in range(max_iter): - if it % print_period == 0: - print("it:", it) - - for n in range(N): - # this would of course be much faster if we didn't do this on - # every iteration of the loop - c = self.get_cost_multi(X).sum() - costs.append(c) - train_op(X[n]) - - # print "A:", self.A.get_value() - # print "B:", self.B.get_value() - # print "pi:", self.pi.get_value() - plt.plot(costs) - plt.show() - - def get_cost(self, x): - # returns log P(x | model) - # using the forward part of the forward-backward algorithm - # print "getting cost for:", x - return self.cost_op(x) - - def log_likelihood(self, x): - return -self.cost_op(x) - - def get_cost_multi(self, X): - return np.array([self.get_cost(x) for x in X]) - - def set(self, preSoftmaxPi, preSoftmaxA, preSoftmaxB): - self.preSoftmaxPi = theano.shared(preSoftmaxPi) - self.preSoftmaxA = theano.shared(preSoftmaxA) - self.preSoftmaxB = theano.shared(preSoftmaxB) - - pi = T.nnet.softmax(self.preSoftmaxPi).flatten() - # softmax returns 1xD if input is a 1-D array of size D - A = T.nnet.softmax(self.preSoftmaxA) - B = T.nnet.softmax(self.preSoftmaxB) - - # define cost - thx = T.ivector('thx') - def recurrence(t, old_a, x): - a = old_a.dot(A) * B[:, x[t]] - s = a.sum() - return (a / s), s - - [alpha, scale], _ = theano.scan( - fn=recurrence, - sequences=T.arange(1, thx.shape[0]), - outputs_info=[pi*B[:,thx[0]], None], - n_steps=thx.shape[0]-1, - non_sequences=thx - ) - - cost = -T.log(scale).sum() - self.cost_op = theano.function( - inputs=[thx], - outputs=cost, - allow_input_downcast=True, - ) - return thx, cost - - -def fit_coin(): - X = [] - for line in open('coin_data.txt'): - # 1 for H, 0 for T - x = [1 if e == 'H' else 0 for e in line.rstrip()] - X.append(x) - - hmm = HMM(2) - hmm.fit(X) - L = hmm.get_cost_multi(X).sum() - print("LL with fitted params:", L) - - # try true values - # remember these must be in their "pre-softmax" forms - pi = np.log( np.array([0.5, 0.5]) ) - A = np.log( np.array([[0.1, 0.9], [0.8, 0.2]]) ) - B = np.log( np.array([[0.6, 0.4], [0.3, 0.7]]) ) - hmm.set(pi, A, B) - L = hmm.get_cost_multi(X).sum() - print("LL with true params:", L) - - -if __name__ == '__main__': - fit_coin() diff --git a/hmm_class/scan1.py b/hmm_class/scan1.py index cbddecc2..0a322afc 100644 --- a/hmm_class/scan1.py +++ b/hmm_class/scan1.py @@ -1,12 +1,6 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me # theano scan example: calculate x^2 -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import theano @@ -16,19 +10,19 @@ x = T.vector('x') def square(x): - return x*x + return x*x outputs, updates = theano.scan( - fn=square, - sequences=x, - n_steps=x.shape[0], + fn=square, + sequences=x, + n_steps=x.shape[0], ) square_op = theano.function( - inputs=[x], - outputs=[outputs], + inputs=[x], + outputs=[outputs], ) o_val = square_op(np.array([1, 2, 3, 4, 5])) -print("output:", o_val) \ No newline at end of file +print "output:", o_val \ No newline at end of file diff --git a/hmm_class/scan2.py b/hmm_class/scan2.py index cbd431b5..25db5bde 100644 --- a/hmm_class/scan2.py +++ b/hmm_class/scan2.py @@ -1,12 +1,6 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me # theano scan example: calculate fibonacci -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import theano @@ -16,20 +10,20 @@ N = T.iscalar('N') def recurrence(n, fn_1, fn_2): - return fn_1 + fn_2, fn_1 + return fn_1 + fn_2, fn_1 outputs, updates = theano.scan( - fn=recurrence, - sequences=T.arange(N), - n_steps=N, - outputs_info=[1., 1.] + fn=recurrence, + sequences=T.arange(N), + n_steps=N, + outputs_info=[1., 1.] ) fibonacci = theano.function( - inputs=[N], - outputs=outputs, + inputs=[N], + outputs=outputs, ) o_val = fibonacci(8) -print("output:", o_val) +print "output:", o_val diff --git a/hmm_class/scan3.py b/hmm_class/scan3.py index 3116d12f..50224d63 100644 --- a/hmm_class/scan3.py +++ b/hmm_class/scan3.py @@ -1,12 +1,6 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me # theano scan example - low pass filter -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import matplotlib.pyplot as plt @@ -23,19 +17,19 @@ sequence = T.vector('sequence') def recurrence(x, last, decay): - return (1-decay)*x + decay*last + return (1-decay)*x + decay*last outputs, _ = theano.scan( - fn=recurrence, - sequences=sequence, - n_steps=sequence.shape[0], - outputs_info=[np.float64(0)], - non_sequences=[decay] + fn=recurrence, + sequences=sequence, + n_steps=sequence.shape[0], + outputs_info=[np.float64(0)], + non_sequences=[decay] ) lpf = theano.function( - inputs=[sequence, decay], - outputs=outputs, + inputs=[sequence, decay], + outputs=outputs, ) Y = lpf(X, 0.99) diff --git a/hmm_class/sites.py b/hmm_class/sites.py index 0187e03a..c26e244d 100644 --- a/hmm_class/sites.py +++ b/hmm_class/sites.py @@ -1,9 +1,6 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python # https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python # http://lazyprogrammer.me # Create a Markov model for site data. -from __future__ import print_function, division -from future.utils import iteritems import numpy as np transitions = {} @@ -16,19 +13,19 @@ row_sums[s] = row_sums.get(s, 0.) + 1 # normalize -for k, v in iteritems(transitions): +for k, v in transitions.iteritems(): s, e = k transitions[k] = v / row_sums[s] # initial state distribution -print("initial state distribution:") -for k, v in iteritems(transitions): +print "initial state distribution:" +for k, v in transitions.iteritems(): s, e = k if s == '-1': - print(e, v) + print e, v # which page has the highest bounce? -for k, v in iteritems(transitions): +for k, v in transitions.iteritems(): s, e = k if e == 'B': - print("bounce rate for %s: %s" % (s, v)) + print "bounce rate for %s: %s" % (s, v) diff --git a/hmm_class/tf_scan1.py b/hmm_class/tf_scan1.py deleted file mode 100644 index af477866..00000000 --- a/hmm_class/tf_scan1.py +++ /dev/null @@ -1,32 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python -# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python -# https://lazyprogrammer.me -# tensorflow scan example: calculate x^2 - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import tensorflow as tf - -# sequence of elements we want to square -x = tf.placeholder(tf.int32, shape=(None,), name='x') - -# thing to do to every element of the sequence -# notice how it always ignores the last output -def square(last, current): - return current*current - -# this is a "fancy for loop" -# it says: apply square to every element of x -square_op = tf.scan( - fn=square, - elems=x, -) - -# run it! -with tf.Session() as session: - o_val = session.run(square_op, feed_dict={x: [1, 2, 3, 4, 5]}) - print("output:", o_val) diff --git a/hmm_class/tf_scan2.py b/hmm_class/tf_scan2.py deleted file mode 100644 index 42163e6a..00000000 --- a/hmm_class/tf_scan2.py +++ /dev/null @@ -1,33 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python -# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python -# https://lazyprogrammer.me -# tensorflow scan example: calculate fibonacci - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import tensorflow as tf - - -# N = number of fibonacci numbers we want -# shape=() means scalar -N = tf.placeholder(tf.int32, shape=(), name='N') - -# recurrence and loop -# notice how we don't use current_input at all! -def recurrence(last_output, current_input): - return (last_output[1], last_output[0] + last_output[1]) - -fibonacci = tf.scan( - fn=recurrence, - elems=tf.range(N), - initializer=(0, 1), -) - -# run it! -with tf.Session() as session: - o_val = session.run(fibonacci, feed_dict={N: 8}) - print("output:", o_val) diff --git a/hmm_class/tf_scan3.py b/hmm_class/tf_scan3.py deleted file mode 100644 index 61626826..00000000 --- a/hmm_class/tf_scan3.py +++ /dev/null @@ -1,43 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python -# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python -# https://lazyprogrammer.me -# tensorflow scan example - low pass filter - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt -import tensorflow as tf - -# original sequence is a noisy sine wave -original = np.sin(np.linspace(0, 3*np.pi, 300)) -X = 2*np.random.randn(300) + original -plt.plot(X) -plt.title("original") -plt.show() - -# set up placeholders -decay = tf.placeholder(tf.float32, shape=(), name='decay') -sequence = tf.placeholder(tf.float32, shape=(None,), name='sequence') - -# the recurrence function and loop -def recurrence(last, x): - return (1.0-decay)*x + decay*last - -lpf = tf.scan( - fn=recurrence, - elems=sequence, - initializer=0.0, # sequence[0] to use the first value of the sequence -) - -# run it! -with tf.Session() as session: - Y = session.run(lpf, feed_dict={sequence: X, decay: 0.97}) - - plt.plot(Y) - plt.plot(original) - plt.title("filtered") - plt.show() diff --git a/keras_examples/ann.py b/keras_examples/ann.py deleted file mode 100644 index 08636b15..00000000 --- a/keras_examples/ann.py +++ /dev/null @@ -1,71 +0,0 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - -from util import getKaggleMNIST -from keras.models import Model -from keras.layers import Dense, Activation, Input - - -# get the data -Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - -# get shapes -N, D = Xtrain.shape -K = len(set(Ytrain)) - - -# ANN with layers [784] -> [500] -> [300] -> [10] -i = Input(shape=(D,)) -x = Dense(500, activation='relu')(i) -x = Dense(300, activation='relu')(x) -x = Dense(K, activation='softmax')(x) - -# instantiate the model object -model = Model(inputs=i, outputs=x) - - -# list of losses: https://keras.io/losses/ -# list of optimizers: https://keras.io/optimizers/ -# list of metrics: https://keras.io/metrics/ -model.compile( - loss='sparse_categorical_crossentropy', - optimizer='adam', - metrics=['accuracy'] -) - -# note: multiple ways to choose a backend -# either theano, tensorflow, or cntk -# https://keras.io/backend/ - - -# gives us back a -r = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32) -print("Returned:", r) - -# print the available keys -# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc']) -print(r.history.keys()) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['acc'], label='acc') -plt.plot(r.history['val_acc'], label='val_acc') -plt.legend() -plt.show() - - -# make predictions and evaluate -probs = model.predict(Xtest) # N x K matrix of probabilities -Ptest = np.argmax(probs, axis=1) -print("Validation acc:", np.mean(Ptest == Ytest)) - diff --git a/keras_examples/basic_mlp.py b/keras_examples/basic_mlp.py deleted file mode 100644 index 8c79533f..00000000 --- a/keras_examples/basic_mlp.py +++ /dev/null @@ -1,44 +0,0 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - -from util import getKaggleMNIST, getKaggleFashionMNIST -from sklearn.neural_network import MLPClassifier - - -# get the data -Xtrain, Ytrain, Xtest, Ytest = getKaggleFashionMNIST() - -# inspect your data -print(Xtrain.shape) -print(Ytrain.shape) - -# look at an example -i = np.random.choice(Xtrain.shape[0]) -plt.imshow(Xtrain[i].reshape(28, 28)) -plt.title(Ytrain[i]) -plt.show() - -# instantiate the model -model = MLPClassifier() - -# train the model -model.fit(Xtrain, Ytrain) - -# evaluate the model -print(model.score(Xtrain, Ytrain)) -print(model.score(Xtest, Ytest)) - -# for completion's sake, this is how you make predictions -Ptest = model.predict(Xtest) - -# an alternate way to calculate accuracy -print(np.mean(Ptest == Ytest)) - -# get output probabilities -probs = model.predict_proba(Xtest) -print("np.argmax(probs, axis=1) == Ptest?", np.all(np.argmax(probs, axis=1) == Ptest)) \ No newline at end of file diff --git a/keras_examples/batchnorm.py b/keras_examples/batchnorm.py deleted file mode 100644 index 8b041729..00000000 --- a/keras_examples/batchnorm.py +++ /dev/null @@ -1,75 +0,0 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - -from util import getKaggleMNIST -from keras.models import Model -from keras.layers import Dense, Activation, Input, BatchNormalization - - -# get the data -Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - -# get shapes -N, D = Xtrain.shape -K = len(set(Ytrain)) - - -# ANN with layers [784] -> [500] -> [300] -> [10] -i = Input(shape=(D,)) -x = Dense(500)(i) -x = BatchNormalization()(x) -x = Activation('relu')(x) -x = Dense(300)(x) -x = BatchNormalization()(x) -x = Activation('relu')(x) -x = Dense(K, activation='softmax')(x) - -# instantiate the model object -model = Model(inputs=i, outputs=x) - - -# list of losses: https://keras.io/losses/ -# list of optimizers: https://keras.io/optimizers/ -# list of metrics: https://keras.io/metrics/ -model.compile( - loss='sparse_categorical_crossentropy', - optimizer='adam', - metrics=['accuracy'] -) - -# note: multiple ways to choose a backend -# either theano, tensorflow, or cntk -# https://keras.io/backend/ - - -# gives us back a -r = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32) -print("Returned:", r) - -# print the available keys -# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc']) -print(r.history.keys()) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['acc'], label='acc') -plt.plot(r.history['val_acc'], label='val_acc') -plt.legend() -plt.show() - - -# make predictions and evaluate -probs = model.predict(Xtest) # N x K matrix of probabilities -Ptest = np.argmax(probs, axis=1) -print("Validation acc:", np.mean(Ptest == Ytest)) - diff --git a/keras_examples/cnn.py b/keras_examples/cnn.py deleted file mode 100644 index 088cc5b2..00000000 --- a/keras_examples/cnn.py +++ /dev/null @@ -1,81 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.models import Model -from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Input - -import matplotlib.pyplot as plt -import pandas as pd -import numpy as np - -from util import getKaggleMNIST3D, getKaggleFashionMNIST3D, getCIFAR10 - - -# get the data -Xtrain, Ytrain, Xtest, Ytest = getKaggleFashionMNIST3D() - -# get shapes -N, H, W, C = Xtrain.shape -K = len(set(Ytrain)) - - - - -# make the CNN -i = Input(shape=(H, W, C)) -x = Conv2D(filters=32, kernel_size=(3, 3))(i) -x = Activation('relu')(x) -x = MaxPooling2D()(x) - -x = Conv2D(filters=64, kernel_size=(3, 3))(x) -x = Activation('relu')(x) -x = MaxPooling2D()(x) - -x = Flatten()(x) -x = Dense(units=100)(x) -x = Activation('relu')(x) -x = Dense(units=K)(x) -x = Activation('softmax')(x) - -model = Model(inputs=i, outputs=x) - - -# list of losses: https://keras.io/losses/ -# list of optimizers: https://keras.io/optimizers/ -# list of metrics: https://keras.io/metrics/ -model.compile( - loss='sparse_categorical_crossentropy', - optimizer='adam', - metrics=['accuracy'] -) - -# note: multiple ways to choose a backend -# either theano, tensorflow, or cntk -# https://keras.io/backend/ - - -# gives us back a -r = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32) -print("Returned:", r) - -# print the available keys -# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc']) -print(r.history.keys()) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['acc'], label='acc') -plt.plot(r.history['val_acc'], label='val_acc') -plt.legend() -plt.show() - - diff --git a/keras_examples/cnn_cifar.py b/keras_examples/cnn_cifar.py deleted file mode 100644 index 4bf16cda..00000000 --- a/keras_examples/cnn_cifar.py +++ /dev/null @@ -1,91 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.models import Sequential, Model -from keras.layers import Dense, Activation, Lambda, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization, Input - -import matplotlib.pyplot as plt -import pandas as pd -import numpy as np - -from util import getCIFAR10 - - -# get the data -Xtrain, Ytrain, Xtest, Ytest = getCIFAR10() - -# get shapes -N, H, W, C = Xtrain.shape -K = len(set(Ytrain)) - - - - -# make the CNN -i = Input(shape=(H, W, C)) -x = Lambda(lambda x: (x - 127.5) / 127.5)(i) -x = Conv2D(filters=32, kernel_size=(3, 3))(x) -x = BatchNormalization()(x) -x = Activation('relu')(x) -x = MaxPooling2D()(x) - -x = Conv2D(filters=64, kernel_size=(3, 3))(x) -x = BatchNormalization()(x) -x = Activation('relu')(x) -x = MaxPooling2D()(x) - -x = Conv2D(filters=128, kernel_size=(3, 3))(x) -x = BatchNormalization()(x) -x = Activation('relu')(x) -x = MaxPooling2D()(x) - -x = Flatten()(x) -x = Dropout(0.5)(x) -x = Dense(units=300)(x) -x = Activation('relu')(x) -x = Dropout(0.2)(x) -x = Dense(units=K)(x) -x = Activation('softmax')(x) - -model = Model(inputs=i, outputs=x) - - -# list of losses: https://keras.io/losses/ -# list of optimizers: https://keras.io/optimizers/ -# list of metrics: https://keras.io/metrics/ -model.compile( - loss='sparse_categorical_crossentropy', - optimizer='adam', - metrics=['accuracy'] -) - -# note: multiple ways to choose a backend -# either theano, tensorflow, or cntk -# https://keras.io/backend/ - - -# gives us back a -r = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=80, batch_size=32) -print("Returned:", r) - -# print the available keys -# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc']) -print(r.history.keys()) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['acc'], label='acc') -plt.plot(r.history['val_acc'], label='val_acc') -plt.legend() -plt.show() - - diff --git a/keras_examples/cnn_dropout_batchnorm.py b/keras_examples/cnn_dropout_batchnorm.py deleted file mode 100644 index f89cd37d..00000000 --- a/keras_examples/cnn_dropout_batchnorm.py +++ /dev/null @@ -1,84 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.models import Sequential, Model -from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization, Input - -import matplotlib.pyplot as plt -import pandas as pd -import numpy as np - -from util import getKaggleMNIST3D, getKaggleFashionMNIST3D, getCIFAR10 - - -# get the data -Xtrain, Ytrain, Xtest, Ytest = getKaggleFashionMNIST3D() - -# get shapes -N, H, W, C = Xtrain.shape -K = len(set(Ytrain)) - - - - -# make the CNN -i = Input(shape=(H, W, C)) -x = Conv2D(filters=32, kernel_size=(3, 3))(i) -x = BatchNormalization()(x) -x = Activation('relu')(x) -x = MaxPooling2D()(x) - -x = Conv2D(filters=64, kernel_size=(3, 3))(x) -x = BatchNormalization()(x) -x = Activation('relu')(x) -x = MaxPooling2D()(x) - -x = Flatten()(x) -x = Dense(units=100)(x) -x = Activation('relu')(x) -x = Dropout(0.2)(x) -x = Dense(units=K)(x) -x = Activation('softmax')(x) - -model = Model(inputs=i, outputs=x) - - -# list of losses: https://keras.io/losses/ -# list of optimizers: https://keras.io/optimizers/ -# list of metrics: https://keras.io/metrics/ -model.compile( - loss='sparse_categorical_crossentropy', - optimizer='adam', - metrics=['accuracy'] -) - -# note: multiple ways to choose a backend -# either theano, tensorflow, or cntk -# https://keras.io/backend/ - - -# gives us back a -r = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32) -print("Returned:", r) - -# print the available keys -# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc']) -print(r.history.keys()) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['acc'], label='acc') -plt.plot(r.history['val_acc'], label='val_acc') -plt.legend() -plt.show() - - diff --git a/keras_examples/dropout.py b/keras_examples/dropout.py deleted file mode 100644 index 0b027f0c..00000000 --- a/keras_examples/dropout.py +++ /dev/null @@ -1,74 +0,0 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - -from util import getKaggleMNIST -from keras.models import Model -from keras.layers import Dense, Activation, Input, Dropout - - -# get the data -Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - -# get shapes -N, D = Xtrain.shape -K = len(set(Ytrain)) - - -# ANN with layers [784] -> [500] -> [300] -> [10] -i = Input(shape=(D,)) -x = Dropout(0.2)(i) -x = Dense(500, activation='relu')(x) -x = Dropout(0.5)(x) -x = Dense(300, activation='relu')(x) -x = Dropout(0.5)(x) -x = Dense(K, activation='softmax')(x) - -# instantiate the model object -model = Model(inputs=i, outputs=x) - - -# list of losses: https://keras.io/losses/ -# list of optimizers: https://keras.io/optimizers/ -# list of metrics: https://keras.io/metrics/ -model.compile( - loss='sparse_categorical_crossentropy', - optimizer='adam', - metrics=['accuracy'] -) - -# note: multiple ways to choose a backend -# either theano, tensorflow, or cntk -# https://keras.io/backend/ - - -# gives us back a -r = model.fit(Xtrain, Ytrain, validation_data=(Xtest, Ytest), epochs=15, batch_size=32) -print("Returned:", r) - -# print the available keys -# should see: dict_keys(['val_loss', 'acc', 'loss', 'val_acc']) -print(r.history.keys()) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['acc'], label='acc') -plt.plot(r.history['val_acc'], label='val_acc') -plt.legend() -plt.show() - - -# make predictions and evaluate -probs = model.predict(Xtest) # N x K matrix of probabilities -Ptest = np.argmax(probs, axis=1) -print("Validation acc:", np.mean(Ptest == Ytest)) - diff --git a/keras_examples/sentiment_analysis.py b/keras_examples/sentiment_analysis.py deleted file mode 100644 index 96885004..00000000 --- a/keras_examples/sentiment_analysis.py +++ /dev/null @@ -1,107 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-advanced-nlp -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import os -import sys -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from keras.models import Model -from keras.layers import Dense, Embedding, Input -from keras.layers import LSTM, GRU -from keras.preprocessing.text import Tokenizer -from keras.preprocessing.sequence import pad_sequences - -import keras.backend as K -if len(K.tensorflow_backend._get_available_gpus()) > 0: - from keras.layers import CuDNNLSTM as LSTM - from keras.layers import CuDNNGRU as GRU - - - -# some configuration -MAX_SEQUENCE_LENGTH = 50 -MAX_VOCAB_SIZE = 20000 -EMBEDDING_DIM = 10 -VALIDATION_SPLIT = 0.2 -BATCH_SIZE = 128 -EPOCHS = 5 - - - -# get the data at: https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews -# prepare text samples and their labels -print('Loading in data...') -train = pd.read_csv("../large_files/kaggle-sentiment-analysis/train.tsv", sep='\t') -sentences = train["Phrase"].values -targets = (train['Sentiment'].values > 3) -K = len(set(targets)) - - - - -# convert the sentences (strings) into integers -tokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE) -tokenizer.fit_on_texts(sentences) -sequences = tokenizer.texts_to_sequences(sentences) - -print("max sequence length:", max(len(s) for s in sequences)) -print("min sequence length:", min(len(s) for s in sequences)) -s = sorted(len(s) for s in sequences) -print("median sequence length:", s[len(s) // 2]) - -maxlen = min(max(len(s) for s in sequences), MAX_SEQUENCE_LENGTH) - - - -# get word -> integer mapping -word2idx = tokenizer.word_index -print('Found %s unique tokens.' % len(word2idx)) - - -# pad sequences so that we get a N x T matrix -data = pad_sequences(sequences, maxlen=maxlen) -print('Shape of data tensor:', data.shape) - - -print('Building model...') - -# create an LSTM network with a single LSTM -input_ = Input(shape=(maxlen,)) -x = Embedding(len(word2idx) + 1, EMBEDDING_DIM)(input_) -x = LSTM(5)(x) -output = Dense(K, activation='softmax')(x) - -model = Model(input_, output) -model.compile( - loss='sparse_categorical_crossentropy', - optimizer='adam', - metrics=['accuracy'] -) - - -print('Training model...') -r = model.fit( - data, - targets, - batch_size=BATCH_SIZE, - epochs=EPOCHS, - validation_split=VALIDATION_SPLIT -) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['acc'], label='acc') -plt.plot(r.history['val_acc'], label='val_acc') -plt.legend() -plt.show() diff --git a/keras_examples/sine.py b/keras_examples/sine.py deleted file mode 100644 index 87580986..00000000 --- a/keras_examples/sine.py +++ /dev/null @@ -1,79 +0,0 @@ -# https://lazyprogrammer.me -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from keras.models import Model -from keras.layers import Input, LSTM, GRU, SimpleRNN, Dense -import keras.backend as K - -from keras.optimizers import SGD, Adam - - -# make the original data -series = np.sin(0.1*np.arange(200)) + np.random.randn(200)*0.1 - -# plot it -plt.plot(series) -plt.show() - - -### build the dataset -# let's see if we can use T past values to predict the next value -T = 10 -D = 1 -X = [] -Y = [] -for t in range(len(series) - T - 1): - x = series[t:t+T] - # print("x[-1]:", x[-1]) - X.append(x) - y = series[t+T] - # print("y:", y) - Y.append(y) - -X = np.array(X) -Y = np.array(Y) -N = len(X) - - - -### many-to-one RNN -inputs = np.expand_dims(X, -1) - -# make the RNN -i = Input(shape=(T, D)) -x = SimpleRNN(5)(i) -x = Dense(1)(x) -model = Model(i, x) -model.compile( - loss='mse', - optimizer=Adam(lr=0.1), -) - -# train the RNN -r = model.fit( - inputs[:-N//2], Y[:-N//2], - batch_size=32, - epochs=80, - validation_data=(inputs[-N//2:], Y[-N//2:]), -) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - - -# plot predictions vs targets -outputs = model.predict(inputs) -print(outputs.shape) -predictions = outputs[:,0] - -plt.plot(Y, label='targets') -plt.plot(predictions, label='predictions') -plt.title("many-to-one RNN") -plt.legend() -plt.show() - diff --git a/keras_examples/sine2.py b/keras_examples/sine2.py deleted file mode 100644 index cb6cc1a7..00000000 --- a/keras_examples/sine2.py +++ /dev/null @@ -1,80 +0,0 @@ -# https://lazyprogrammer.me -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from keras.models import Model -from keras.layers import Input, SimpleRNN, Dense -from keras.optimizers import SGD, Adam - - -# make the original data -series1 = np.sin(0.1*np.arange(200)) -series2 = np.sin(0.2*np.arange(200)) - -# plot it -plt.plot(series1) -plt.plot(series2) -plt.show() - - -### build the dataset -# let's see if we can use T past values to predict the next value -T = 10 -D = 2 -X = [] -Y = [] -for t in range(len(series1) - T - 1): - x = [series1[t:t+T], series2[t:t+T]] - # print("x[-1]:", x[-1]) - X.append(x) - y = series1[t+T] + series2[t+T] - # print("y:", y) - Y.append(y) - -X = np.array(X) -print("X.shape:", X.shape) -X = np.transpose(X, (0, 2, 1)) -Y = np.array(Y) -N = len(X) - - - -### many-to-one RNN - -# make the RNN -i = Input(shape=(T, D)) -x = SimpleRNN(5)(i) -x = Dense(1)(x) -model = Model(i, x) -model.compile( - loss='mse', - optimizer=Adam(lr=0.1), -) - -# train the RNN -r = model.fit( - X[:-N//2], Y[:-N//2], - batch_size=32, - epochs=80, - validation_data=(X[-N//2:], Y[-N//2:]), -) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - - -# plot predictions vs targets -outputs = model.predict(X) -print(outputs.shape) -predictions = outputs[:,0] - -plt.plot(Y, label='targets') -plt.plot(predictions, label='predictions') -plt.title("many-to-one RNN") -plt.legend() -plt.show() - diff --git a/keras_examples/translation.py b/keras_examples/translation.py deleted file mode 100644 index 2449f2f7..00000000 --- a/keras_examples/translation.py +++ /dev/null @@ -1,162 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-advanced-nlp -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import os -import sys -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from keras.models import Model -from keras.layers import Dense, Embedding, Input -from keras.layers import LSTM, Bidirectional -from keras.preprocessing.text import Tokenizer -from keras.preprocessing.sequence import pad_sequences -from keras.optimizers import Adam - -import keras.backend as K -if len(K.tensorflow_backend._get_available_gpus()) > 0: - from keras.layers import CuDNNLSTM as LSTM - from keras.layers import CuDNNGRU as GRU - - - - -# some configuration -MAX_VOCAB_SIZE = 20000 -EMBEDDING_DIM = 20 -VALIDATION_SPLIT = 0.2 -BATCH_SIZE = 128 -EPOCHS = 30 -NUM_SAMPLES = 10000 - - - -# Where we will store the data -input_texts = [] # sentence in original language -target_texts = [] # sentence in target language - - -# load in the data -# download the data at: http://www.manythings.org/anki/ -t = 0 -for line in open('../large_files/translation/spa.txt'): - # only keep a limited number of samples - t += 1 - if t > NUM_SAMPLES: - break - - # input and target are separated by tab - if '\t' not in line: - continue - - # split up the input and translation - input_text, translation = line.rstrip().split('\t') - - input_texts.append(input_text) - target_texts.append(translation) -print("num samples:", len(input_texts)) - - - -# tokenize the inputs -tokenizer_inputs = Tokenizer(num_words=MAX_VOCAB_SIZE) -tokenizer_inputs.fit_on_texts(input_texts) -input_sequences = tokenizer_inputs.texts_to_sequences(input_texts) - -# get the word to index mapping for input language -word2idx_inputs = tokenizer_inputs.word_index -print('Found %s unique input tokens.' % len(word2idx_inputs)) -num_words_input = len(word2idx_inputs) + 1 - -# determine maximum length input sequence -max_len_input = max(len(s) for s in input_sequences) - -# tokenize the outputs -# don't filter out special characters -# otherwise and won't appear -tokenizer_outputs = Tokenizer(num_words=MAX_VOCAB_SIZE, filters='') -tokenizer_outputs.fit_on_texts(target_texts) -target_sequences = tokenizer_outputs.texts_to_sequences(target_texts) - -# get the word to index mapping for output language -word2idx_outputs = tokenizer_outputs.word_index -print('Found %s unique output tokens.' % len(word2idx_outputs)) - -# store number of output words for later -# remember to add 1 since indexing starts at 1 -num_words_output = len(word2idx_outputs) + 1 - -# determine maximum length output sequence -max_len_target = max(len(s) for s in target_sequences) - - -max_len_both = max(max_len_input, max_len_target) - - - -# pad the sequences -inputs_padded = pad_sequences(input_sequences, maxlen=max_len_both) -targets_padded = pad_sequences(target_sequences, maxlen=max_len_both) - - - -# create targets, since we cannot use sparse -# categorical cross entropy when we have sequences -targets_padded_one_hot = np.zeros( - ( - len(targets_padded), - max_len_both, - num_words_output - ), - dtype='float32' -) - -# assign the values -for i, d in enumerate(targets_padded): - for t, word in enumerate(d): - targets_padded_one_hot[i, t, word] = 1 - - - - -print('Building model...') - -# create an LSTM network with a single LSTM -input_ = Input(shape=(max_len_both,)) -x = Embedding(num_words_input, EMBEDDING_DIM)(input_) -x = Bidirectional(LSTM(15, return_sequences=True))(x) -output = Dense(num_words_output, activation='softmax')(x) - -model = Model(input_, output) -model.compile( - loss='categorical_crossentropy', - optimizer=Adam(lr=0.1), - metrics=['accuracy'] -) - - -print('Training model...') -r = model.fit( - inputs_padded, - targets_padded_one_hot, - batch_size=BATCH_SIZE, - epochs=EPOCHS, - validation_split=VALIDATION_SPLIT -) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['acc'], label='acc') -plt.plot(r.history['val_acc'], label='val_acc') -plt.legend() -plt.show() diff --git a/keras_examples/util.py b/keras_examples/util.py deleted file mode 100644 index 2e3af106..00000000 --- a/keras_examples/util.py +++ /dev/null @@ -1,74 +0,0 @@ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import pandas as pd - -from sklearn.utils import shuffle -from scipy.io import loadmat - - -def getKaggleMNIST(): - # https://www.kaggle.com/c/digit-recognizer - return getMNISTFormat('../large_files/train.csv') - - -def getKaggleFashionMNIST(): - # https://www.kaggle.com/zalando-research/fashionmnist - return getMNISTFormat('../large_files/fashionmnist/fashion-mnist_train.csv') - -def getMNISTFormat(path): - # MNIST data: - # column 0 is labels - # column 1-785 is data, with values 0 .. 255 - # total size of CSV: (42000, 1, 28, 28) - train = pd.read_csv(path).values.astype(np.float32) - train = shuffle(train) - - Xtrain = train[:-1000,1:] / 255.0 - Ytrain = train[:-1000,0].astype(np.int32) - - Xtest = train[-1000:,1:] / 255.0 - Ytest = train[-1000:,0].astype(np.int32) - return Xtrain, Ytrain, Xtest, Ytest - -def getKaggleMNIST3D(): - Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - Xtrain = Xtrain.reshape(-1, 28, 28, 1) - Xtest = Xtest.reshape(-1, 28, 28, 1) - return Xtrain, Ytrain, Xtest, Ytest - -def getKaggleFashionMNIST3D(): - Xtrain, Ytrain, Xtest, Ytest = getKaggleFashionMNIST() - Xtrain = Xtrain.reshape(-1, 28, 28, 1) - Xtest = Xtest.reshape(-1, 28, 28, 1) - return Xtrain, Ytrain, Xtest, Ytest - -def getCIFAR10(): - Xtrain = np.zeros((50000, 32, 32, 3), dtype=np.uint8) - Ytrain = np.zeros(50000, dtype=np.uint8) - - # train data - for i in range(5): - fn = 'data_batch_%s.mat' % (i+1) - d = loadmat('../large_files/cifar-10-batches-mat/' + fn) - x = d['data'] - y = d['labels'].flatten() - x = x.reshape(10000, 3, 32, 32) - x = np.transpose(x, (0, 2, 3, 1)) - Xtrain[i*10000:(i+1)*10000] = x - Ytrain[i*10000:(i+1)*10000] = y - - # test data - d = loadmat('../large_files/cifar-10-batches-mat/test_batch.mat') - x = d['data'] - y = d['labels'].flatten() - x = x.reshape(10000, 3, 32, 32) - x = np.transpose(x, (0, 2, 3, 1)) - Xtest = x - Ytest = y - - return Xtrain, Ytrain, Xtest, Ytest - diff --git a/kerascv/extra_reading.txt b/kerascv/extra_reading.txt deleted file mode 100644 index 57ebec98..00000000 --- a/kerascv/extra_reading.txt +++ /dev/null @@ -1,8 +0,0 @@ -KerasCV List of Models -https://keras.io/api/keras_cv/models/ - -Fast R-CNN (Ross Girshick) -https://arxiv.org/pdf/1504.08083.pdf - -Focal Loss for Dense Object Detection (Lin et al.) -https://arxiv.org/abs/1708.02002 \ No newline at end of file diff --git a/kerascv/imagenet_label_names.json b/kerascv/imagenet_label_names.json deleted file mode 100644 index 37eeb166..00000000 --- a/kerascv/imagenet_label_names.json +++ /dev/null @@ -1,1000 +0,0 @@ -["tench", -"goldfish", -"great white shark", -"tiger shark", -"hammerhead shark", -"electric ray", -"stingray", -"cock", -"hen", -"ostrich", -"brambling", -"goldfinch", -"house finch", -"junco", -"indigo bunting", -"American robin", -"bulbul", -"jay", -"magpie", -"chickadee", -"American dipper", -"kite", -"bald eagle", -"vulture", -"great grey owl", -"fire salamander", -"smooth newt", -"newt", -"spotted salamander", -"axolotl", -"American bullfrog", -"tree frog", -"tailed frog", -"loggerhead sea turtle", -"leatherback sea turtle", -"mud turtle", -"terrapin", -"box turtle", -"banded gecko", -"green iguana", -"Carolina anole", -"desert grassland whiptail lizard", -"agama", -"frilled-necked lizard", -"alligator lizard", -"Gila monster", -"European green lizard", -"chameleon", -"Komodo dragon", -"Nile crocodile", -"American alligator", -"triceratops", -"worm snake", -"ring-necked snake", -"eastern hog-nosed snake", -"smooth green snake", -"kingsnake", -"garter snake", -"water snake", -"vine snake", -"night snake", -"boa constrictor", -"African rock python", -"Indian cobra", -"green mamba", -"sea snake", -"Saharan horned viper", -"eastern diamondback rattlesnake", -"sidewinder", -"trilobite", -"harvestman", -"scorpion", -"yellow garden spider", -"barn spider", -"European garden spider", -"southern black widow", -"tarantula", -"wolf spider", -"tick", -"centipede", -"black grouse", -"ptarmigan", -"ruffed grouse", -"prairie grouse", -"peacock", -"quail", -"partridge", -"grey parrot", -"macaw", -"sulphur-crested cockatoo", -"lorikeet", -"coucal", -"bee eater", -"hornbill", -"hummingbird", -"jacamar", -"toucan", -"duck", -"red-breasted merganser", -"goose", -"black swan", -"tusker", -"echidna", -"platypus", -"wallaby", -"koala", -"wombat", -"jellyfish", -"sea anemone", -"brain coral", -"flatworm", -"nematode", -"conch", -"snail", -"slug", -"sea slug", -"chiton", -"chambered nautilus", -"Dungeness crab", -"rock crab", -"fiddler crab", -"red king crab", -"American lobster", -"spiny lobster", -"crayfish", -"hermit crab", -"isopod", -"white stork", -"black stork", -"spoonbill", -"flamingo", -"little blue heron", -"great egret", -"bittern", -"crane (bird)", -"limpkin", -"common gallinule", -"American coot", -"bustard", -"ruddy turnstone", -"dunlin", -"common redshank", -"dowitcher", -"oystercatcher", -"pelican", -"king penguin", -"albatross", -"grey whale", -"killer whale", -"dugong", -"sea lion", -"Chihuahua", -"Japanese Chin", -"Maltese", -"Pekingese", -"Shih Tzu", -"King Charles Spaniel", -"Papillon", -"toy terrier", -"Rhodesian Ridgeback", -"Afghan Hound", -"Basset Hound", -"Beagle", -"Bloodhound", -"Bluetick Coonhound", -"Black and Tan Coonhound", -"Treeing Walker Coonhound", -"English foxhound", -"Redbone Coonhound", -"borzoi", -"Irish Wolfhound", -"Italian Greyhound", -"Whippet", -"Ibizan Hound", -"Norwegian Elkhound", -"Otterhound", -"Saluki", -"Scottish Deerhound", -"Weimaraner", -"Staffordshire Bull Terrier", -"American Staffordshire Terrier", -"Bedlington Terrier", -"Border Terrier", -"Kerry Blue Terrier", -"Irish Terrier", -"Norfolk Terrier", -"Norwich Terrier", -"Yorkshire Terrier", -"Wire Fox Terrier", -"Lakeland Terrier", -"Sealyham Terrier", -"Airedale Terrier", -"Cairn Terrier", -"Australian Terrier", -"Dandie Dinmont Terrier", -"Boston Terrier", -"Miniature Schnauzer", -"Giant Schnauzer", -"Standard Schnauzer", -"Scottish Terrier", -"Tibetan Terrier", -"Australian Silky Terrier", -"Soft-coated Wheaten Terrier", -"West Highland White Terrier", -"Lhasa Apso", -"Flat-Coated Retriever", -"Curly-coated Retriever", -"Golden Retriever", -"Labrador Retriever", -"Chesapeake Bay Retriever", -"German Shorthaired Pointer", -"Vizsla", -"English Setter", -"Irish Setter", -"Gordon Setter", -"Brittany", -"Clumber Spaniel", -"English Springer Spaniel", -"Welsh Springer Spaniel", -"Cocker Spaniels", -"Sussex Spaniel", -"Irish Water Spaniel", -"Kuvasz", -"Schipperke", -"Groenendael", -"Malinois", -"Briard", -"Australian Kelpie", -"Komondor", -"Old English Sheepdog", -"Shetland Sheepdog", -"collie", -"Border Collie", -"Bouvier des Flandres", -"Rottweiler", -"German Shepherd Dog", -"Dobermann", -"Miniature Pinscher", -"Greater Swiss Mountain Dog", -"Bernese Mountain Dog", -"Appenzeller Sennenhund", -"Entlebucher Sennenhund", -"Boxer", -"Bullmastiff", -"Tibetan Mastiff", -"French Bulldog", -"Great Dane", -"St. Bernard", -"husky", -"Alaskan Malamute", -"Siberian Husky", -"Dalmatian", -"Affenpinscher", -"Basenji", -"pug", -"Leonberger", -"Newfoundland", -"Pyrenean Mountain Dog", -"Samoyed", -"Pomeranian", -"Chow Chow", -"Keeshond", -"Griffon Bruxellois", -"Pembroke Welsh Corgi", -"Cardigan Welsh Corgi", -"Toy Poodle", -"Miniature Poodle", -"Standard Poodle", -"Mexican hairless dog", -"grey wolf", -"Alaskan tundra wolf", -"red wolf", -"coyote", -"dingo", -"dhole", -"African wild dog", -"hyena", -"red fox", -"kit fox", -"Arctic fox", -"grey fox", -"tabby cat", -"tiger cat", -"Persian cat", -"Siamese cat", -"Egyptian Mau", -"cougar", -"lynx", -"leopard", -"snow leopard", -"jaguar", -"lion", -"tiger", -"cheetah", -"brown bear", -"American black bear", -"polar bear", -"sloth bear", -"mongoose", -"meerkat", -"tiger beetle", -"ladybug", -"ground beetle", -"longhorn beetle", -"leaf beetle", -"dung beetle", -"rhinoceros beetle", -"weevil", -"fly", -"bee", -"ant", -"grasshopper", -"cricket", -"stick insect", -"cockroach", -"mantis", -"cicada", -"leafhopper", -"lacewing", -"dragonfly", -"damselfly", -"red admiral", -"ringlet", -"monarch butterfly", -"small white", -"sulphur butterfly", -"gossamer-winged butterfly", -"starfish", -"sea urchin", -"sea cucumber", -"cottontail rabbit", -"hare", -"Angora rabbit", -"hamster", -"porcupine", -"fox squirrel", -"marmot", -"beaver", -"guinea pig", -"common sorrel", -"zebra", -"pig", -"wild boar", -"warthog", -"hippopotamus", -"ox", -"water buffalo", -"bison", -"ram", -"bighorn sheep", -"Alpine ibex", -"hartebeest", -"impala", -"gazelle", -"dromedary", -"llama", -"weasel", -"mink", -"European polecat", -"black-footed ferret", -"otter", -"skunk", -"badger", -"armadillo", -"three-toed sloth", -"orangutan", -"gorilla", -"chimpanzee", -"gibbon", -"siamang", -"guenon", -"patas monkey", -"baboon", -"macaque", -"langur", -"black-and-white colobus", -"proboscis monkey", -"marmoset", -"white-headed capuchin", -"howler monkey", -"titi", -"Geoffroy's spider monkey", -"common squirrel monkey", -"ring-tailed lemur", -"indri", -"Asian elephant", -"African bush elephant", -"red panda", -"giant panda", -"snoek", -"eel", -"coho salmon", -"rock beauty", -"clownfish", -"sturgeon", -"garfish", -"lionfish", -"pufferfish", -"abacus", -"abaya", -"academic gown", -"accordion", -"acoustic guitar", -"aircraft carrier", -"airliner", -"airship", -"altar", -"ambulance", -"amphibious vehicle", -"analog clock", -"apiary", -"apron", -"waste container", -"assault rifle", -"backpack", -"bakery", -"balance beam", -"balloon", -"ballpoint pen", -"Band-Aid", -"banjo", -"baluster", -"barbell", -"barber chair", -"barbershop", -"barn", -"barometer", -"barrel", -"wheelbarrow", -"baseball", -"basketball", -"bassinet", -"bassoon", -"swimming cap", -"bath towel", -"bathtub", -"station wagon", -"lighthouse", -"beaker", -"military cap", -"beer bottle", -"beer glass", -"bell-cot", -"bib", -"tandem bicycle", -"bikini", -"ring binder", -"binoculars", -"birdhouse", -"boathouse", -"bobsleigh", -"bolo tie", -"poke bonnet", -"bookcase", -"bookstore", -"bottle cap", -"bow", -"bow tie", -"brass", -"bra", -"breakwater", -"breastplate", -"broom", -"bucket", -"buckle", -"bulletproof vest", -"high-speed train", -"butcher shop", -"taxicab", -"cauldron", -"candle", -"cannon", -"canoe", -"can opener", -"cardigan", -"car mirror", -"carousel", -"tool kit", -"carton", -"car wheel", -"automated teller machine", -"cassette", -"cassette player", -"castle", -"catamaran", -"CD player", -"cello", -"mobile phone", -"chain", -"chain-link fence", -"chain mail", -"chainsaw", -"chest", -"chiffonier", -"chime", -"china cabinet", -"Christmas stocking", -"church", -"movie theater", -"cleaver", -"cliff dwelling", -"cloak", -"clogs", -"cocktail shaker", -"coffee mug", -"coffeemaker", -"coil", -"combination lock", -"computer keyboard", -"confectionery store", -"container ship", -"convertible", -"corkscrew", -"cornet", -"cowboy boot", -"cowboy hat", -"cradle", -"crane (machine)", -"crash helmet", -"crate", -"infant bed", -"Crock Pot", -"croquet ball", -"crutch", -"cuirass", -"dam", -"desk", -"desktop computer", -"rotary dial telephone", -"diaper", -"digital clock", -"digital watch", -"dining table", -"dishcloth", -"dishwasher", -"disc brake", -"dock", -"dog sled", -"dome", -"doormat", -"drilling rig", -"drum", -"drumstick", -"dumbbell", -"Dutch oven", -"electric fan", -"electric guitar", -"electric locomotive", -"entertainment center", -"envelope", -"espresso machine", -"face powder", -"feather boa", -"filing cabinet", -"fireboat", -"fire engine", -"fire screen sheet", -"flagpole", -"flute", -"folding chair", -"football helmet", -"forklift", -"fountain", -"fountain pen", -"four-poster bed", -"freight car", -"French horn", -"frying pan", -"fur coat", -"garbage truck", -"gas mask", -"gas pump", -"goblet", -"go-kart", -"golf ball", -"golf cart", -"gondola", -"gong", -"gown", -"grand piano", -"greenhouse", -"grille", -"grocery store", -"guillotine", -"barrette", -"hair spray", -"half-track", -"hammer", -"hamper", -"hair dryer", -"hand-held computer", -"handkerchief", -"hard disk drive", -"harmonica", -"harp", -"harvester", -"hatchet", -"holster", -"home theater", -"honeycomb", -"hook", -"hoop skirt", -"horizontal bar", -"horse-drawn vehicle", -"hourglass", -"iPod", -"clothes iron", -"jack-o'-lantern", -"jeans", -"jeep", -"T-shirt", -"jigsaw puzzle", -"pulled rickshaw", -"joystick", -"kimono", -"knee pad", -"knot", -"lab coat", -"ladle", -"lampshade", -"laptop computer", -"lawn mower", -"lens cap", -"paper knife", -"library", -"lifeboat", -"lighter", -"limousine", -"ocean liner", -"lipstick", -"slip-on shoe", -"lotion", -"speaker", -"loupe", -"sawmill", -"magnetic compass", -"mail bag", -"mailbox", -"tights", -"tank suit", -"manhole cover", -"maraca", -"marimba", -"mask", -"match", -"maypole", -"maze", -"measuring cup", -"medicine chest", -"megalith", -"microphone", -"microwave oven", -"military uniform", -"milk can", -"minibus", -"miniskirt", -"minivan", -"missile", -"mitten", -"mixing bowl", -"mobile home", -"Model T", -"modem", -"monastery", -"monitor", -"moped", -"mortar", -"square academic cap", -"mosque", -"mosquito net", -"scooter", -"mountain bike", -"tent", -"computer mouse", -"mousetrap", -"moving van", -"muzzle", -"nail", -"neck brace", -"necklace", -"nipple", -"notebook computer", -"obelisk", -"oboe", -"ocarina", -"odometer", -"oil filter", -"organ", -"oscilloscope", -"overskirt", -"bullock cart", -"oxygen mask", -"packet", -"paddle", -"paddle wheel", -"padlock", -"paintbrush", -"pajamas", -"palace", -"pan flute", -"paper towel", -"parachute", -"parallel bars", -"park bench", -"parking meter", -"passenger car", -"patio", -"payphone", -"pedestal", -"pencil case", -"pencil sharpener", -"perfume", -"Petri dish", -"photocopier", -"plectrum", -"Pickelhaube", -"picket fence", -"pickup truck", -"pier", -"piggy bank", -"pill bottle", -"pillow", -"ping-pong ball", -"pinwheel", -"pirate ship", -"pitcher", -"hand plane", -"planetarium", -"plastic bag", -"plate rack", -"plow", -"plunger", -"Polaroid camera", -"pole", -"police van", -"poncho", -"billiard table", -"soda bottle", -"pot", -"potter's wheel", -"power drill", -"prayer rug", -"printer", -"prison", -"projectile", -"projector", -"hockey puck", -"punching bag", -"purse", -"quill", -"quilt", -"race car", -"racket", -"radiator", -"radio", -"radio telescope", -"rain barrel", -"recreational vehicle", -"reel", -"reflex camera", -"refrigerator", -"remote control", -"restaurant", -"revolver", -"rifle", -"rocking chair", -"rotisserie", -"eraser", -"rugby ball", -"ruler", -"running shoe", -"safe", -"safety pin", -"salt shaker", -"sandal", -"sarong", -"saxophone", -"scabbard", -"weighing scale", -"school bus", -"schooner", -"scoreboard", -"CRT screen", -"screw", -"screwdriver", -"seat belt", -"sewing machine", -"shield", -"shoe store", -"shoji", -"shopping basket", -"shopping cart", -"shovel", -"shower cap", -"shower curtain", -"ski", -"ski mask", -"sleeping bag", -"slide rule", -"sliding door", -"slot machine", -"snorkel", -"snowmobile", -"snowplow", -"soap dispenser", -"soccer ball", -"sock", -"solar thermal collector", -"sombrero", -"soup bowl", -"space bar", -"space heater", -"space shuttle", -"spatula", -"motorboat", -"spider web", -"spindle", -"sports car", -"spotlight", -"stage", -"steam locomotive", -"through arch bridge", -"steel drum", -"stethoscope", -"scarf", -"stone wall", -"stopwatch", -"stove", -"strainer", -"tram", -"stretcher", -"couch", -"stupa", -"submarine", -"suit", -"sundial", -"sunglass", -"sunglasses", -"sunscreen", -"suspension bridge", -"mop", -"sweatshirt", -"swimsuit", -"swing", -"switch", -"syringe", -"table lamp", -"tank", -"tape player", -"teapot", -"teddy bear", -"television", -"tennis ball", -"thatched roof", -"front curtain", -"thimble", -"threshing machine", -"throne", -"tile roof", -"toaster", -"tobacco shop", -"toilet seat", -"torch", -"totem pole", -"tow truck", -"toy store", -"tractor", -"semi-trailer truck", -"tray", -"trench coat", -"tricycle", -"trimaran", -"tripod", -"triumphal arch", -"trolleybus", -"trombone", -"tub", -"turnstile", -"typewriter keyboard", -"umbrella", -"unicycle", -"upright piano", -"vacuum cleaner", -"vase", -"vault", -"velvet", -"vending machine", -"vestment", -"viaduct", -"violin", -"volleyball", -"waffle iron", -"wall clock", -"wallet", -"wardrobe", -"military aircraft", -"sink", -"washing machine", -"water bottle", -"water jug", -"water tower", -"whiskey jug", -"whistle", -"wig", -"window screen", -"window shade", -"Windsor tie", -"wine bottle", -"wing", -"wok", -"wooden spoon", -"wool", -"split-rail fence", -"shipwreck", -"yawl", -"yurt", -"website", -"comic book", -"crossword", -"traffic sign", -"traffic light", -"dust jacket", -"menu", -"plate", -"guacamole", -"consomme", -"hot pot", -"trifle", -"ice cream", -"ice pop", -"baguette", -"bagel", -"pretzel", -"cheeseburger", -"hot dog", -"mashed potato", -"cabbage", -"broccoli", -"cauliflower", -"zucchini", -"spaghetti squash", -"acorn squash", -"butternut squash", -"cucumber", -"artichoke", -"bell pepper", -"cardoon", -"mushroom", -"Granny Smith", -"strawberry", -"orange", -"lemon", -"fig", -"pineapple", -"banana", -"jackfruit", -"custard apple", -"pomegranate", -"hay", -"carbonara", -"chocolate syrup", -"dough", -"meatloaf", -"pizza", -"pot pie", -"burrito", -"red wine", -"espresso", -"cup", -"eggnog", -"alp", -"bubble", -"cliff", -"coral reef", -"geyser", -"lakeshore", -"promontory", -"shoal", -"seashore", -"valley", -"volcano", -"baseball player", -"bridegroom", -"scuba diver", -"rapeseed", -"daisy", -"yellow lady's slipper", -"corn", -"acorn", -"rose hip", -"horse chestnut seed", -"coral fungus", -"agaric", -"gyromitra", -"stinkhorn mushroom", -"earth star", -"hen-of-the-woods", -"bolete", -"ear", -"toilet paper"] diff --git a/kerascv/makelist.py b/kerascv/makelist.py deleted file mode 100644 index 8498fa24..00000000 --- a/kerascv/makelist.py +++ /dev/null @@ -1,10 +0,0 @@ -''' -Use this script to generate a list of all XML files in a folder. -''' - -from glob import glob - -files = glob('*.xml') -with open('xml_list.txt', 'w') as f: - for fn in files: - f.write("%s\n" % fn) \ No newline at end of file diff --git a/kerascv/pascal2coco.py b/kerascv/pascal2coco.py deleted file mode 100644 index 3ffbd3b8..00000000 --- a/kerascv/pascal2coco.py +++ /dev/null @@ -1,152 +0,0 @@ -# adapted from https://blog.roboflow.com/how-to-convert-annotations-from-voc-xml-to-coco-json/ - -import os -import argparse -import json -import xml.etree.ElementTree as ET -from typing import Dict, List -from tqdm import tqdm -import re - - -def get_label2id(labels_path: str) -> Dict[str, int]: - """id is 1 start""" - with open(labels_path, 'r') as f: - labels_str = f.read().split() - labels_ids = list(range(0, len(labels_str))) - return dict(zip(labels_str, labels_ids)) - - -def get_annpaths(ann_dir_path: str = None, - ann_ids_path: str = None, - ext: str = '', - annpaths_list_path: str = None) -> List[str]: - # If use annotation paths list - if annpaths_list_path is not None: - with open(annpaths_list_path, 'r') as f: - ann_paths = f.read().split() - return ann_paths - - # If use annotaion ids list - ext_with_dot = '.' + ext if ext != '' else '' - with open(ann_ids_path, 'r') as f: - ann_ids = f.read().split() - ann_paths = [os.path.join(ann_dir_path, aid+ext_with_dot) for aid in ann_ids] - return ann_paths - - -def get_image_info(annotation_root, extract_num_from_imgid=True): - path = annotation_root.findtext('path') - if path is None: - filename = annotation_root.findtext('filename') - else: - filename = os.path.basename(path) - img_name = os.path.basename(filename) - img_id = os.path.splitext(img_name)[0] - if extract_num_from_imgid and isinstance(img_id, str): - img_id = int(re.findall(r'\d+', img_id)[0]) - - size = annotation_root.find('size') - width = int(size.findtext('width')) - height = int(size.findtext('height')) - - image_info = { - 'file_name': filename, - 'height': height, - 'width': width, - 'id': img_id - } - return image_info - - -def get_coco_annotation_from_obj(obj, label2id): - label = obj.findtext('name') - assert label in label2id, f"Error: {label} is not in label2id !" - category_id = label2id[label] - bndbox = obj.find('bndbox') - xmin = int(bndbox.findtext('xmin')) - 1 - ymin = int(bndbox.findtext('ymin')) - 1 - xmax = int(bndbox.findtext('xmax')) - ymax = int(bndbox.findtext('ymax')) - assert xmax > xmin and ymax > ymin, f"Box size error !: (xmin, ymin, xmax, ymax): {xmin, ymin, xmax, ymax}" - o_width = xmax - xmin - o_height = ymax - ymin - ann = { - 'area': o_width * o_height, - 'iscrowd': 0, - 'bbox': [xmin, ymin, o_width, o_height], - 'category_id': category_id, - 'ignore': 0, - 'segmentation': [] # This script is not for segmentation - } - return ann - - -def convert_xmls_to_cocojson(annotation_paths: List[str], - label2id: Dict[str, int], - output_jsonpath: str, - extract_num_from_imgid: bool = True): - output_json_dict = { - "images": [], - "type": "instances", - "annotations": [], - "categories": [] - } - bnd_id = 1 # START_BOUNDING_BOX_ID, TODO input as args ? - print('Start converting !') - for a_path in tqdm(annotation_paths): - # Read annotation xml - ann_tree = ET.parse(a_path) - ann_root = ann_tree.getroot() - - img_info = get_image_info(annotation_root=ann_root, - extract_num_from_imgid=extract_num_from_imgid) - img_id = img_info['id'] - output_json_dict['images'].append(img_info) - - for obj in ann_root.findall('object'): - ann = get_coco_annotation_from_obj(obj=obj, label2id=label2id) - ann.update({'image_id': img_id, 'id': bnd_id}) - output_json_dict['annotations'].append(ann) - bnd_id = bnd_id + 1 - - for label, label_id in label2id.items(): - category_info = {'supercategory': 'none', 'id': label_id, 'name': label} - output_json_dict['categories'].append(category_info) - - with open(output_jsonpath, 'w') as f: - output_json = json.dumps(output_json_dict) - f.write(output_json) - - -def main(): - parser = argparse.ArgumentParser( - description='This script support converting voc format xmls to coco format json') - parser.add_argument('--ann_dir', type=str, default=None, - help='path to annotation files directory. It is not need when use --ann_paths_list') - parser.add_argument('--ann_ids', type=str, default=None, - help='path to annotation files ids list. It is not need when use --ann_paths_list') - parser.add_argument('--ann_paths_list', type=str, default=None, - help='path of annotation paths list. It is not need when use --ann_dir and --ann_ids') - parser.add_argument('--labels', type=str, default=None, - help='path to label list.') - parser.add_argument('--output', type=str, default='output.json', help='path to output json file') - parser.add_argument('--ext', type=str, default='', help='additional extension of annotation file') - args = parser.parse_args() - label2id = get_label2id(labels_path=args.labels) - ann_paths = get_annpaths( - ann_dir_path=args.ann_dir, - ann_ids_path=args.ann_ids, - ext=args.ext, - annpaths_list_path=args.ann_paths_list - ) - convert_xmls_to_cocojson( - annotation_paths=ann_paths, - label2id=label2id, - output_jsonpath=args.output, - extract_num_from_imgid=True - ) - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/kmeans_clustering.py b/kmeans_clustering.py new file mode 100644 index 00000000..d96fda66 --- /dev/null +++ b/kmeans_clustering.py @@ -0,0 +1,143 @@ +# A pedagogical example of k-means clustering. +# Creates a set of means (cluster centers) and +# generates random points around each center +# using spherical Gaussian noise. Then applies +# k-means clustering on the random points to +# allow us to compare the true clusters vs. +# predicted clusters. + +# A link to the tutorial is here: +# http://lazyprogrammer.me/post/106693297569/k-means-clustering + +import numpy as np +import matplotlib.pyplot as plt + +class KMeans(object): + def __init__(self, num_clusters, max_iter=1000, epsilon=0.0001): + self.K = num_clusters + self.max_iter = max_iter + self.epsilon = epsilon + + + def fit(self, X): + N = len(X) + initial_indices = np.random.choice(np.arange(N), self.K) + self.centers = np.array([X[i] for i in initial_indices]) + + intermediate_predictions = [] + for i in xrange(self.max_iter): + # there are only 2 steps for vanilla k-means clustering + # 1. determing current cluster assignments + # 2. find new cluster centers based on these cluster assignments + + # 1. determing current cluster assignments + Y = self.predict(X) + intermediate_predictions.append(Y) + + # 2. find new cluster centers + self.centers, max_change = self.get_centers(X,Y) + if max_change < self.epsilon: + break + return intermediate_predictions + + + def predict(self, X): + if len(X.shape) > 1: + Y = np.zeros(len(X), dtype=int) + for i,x in enumerate(X): + min_c = -1 + min_dist = float("inf") + for c,m in enumerate(self.centers): + d = np.linalg.norm(x - m) + if d < min_dist: + min_c = c + min_dist = d + Y[i] = min_c + else: + Y = np.zeros(1, dtype=int) + min_c = -1 + min_dist = float("inf") + for c,m in enumerate(self.centers): + d = np.linalg.norm(X - m) + if d < min_dist: + min_c = c + min_dist = d + Y[0] = min_c + return Y + + + # returns new centers calculated from labeled inputs + # also returns 'max_change' between current centers + # and new centers + def get_centers(self, X, Y): + X_partitioned = [[] for i in xrange(self.K)] + + for x,y in zip(X,Y): + X_partitioned[y].append(x) + + new_centers = np.array([np.mean(x_part, 0) for x_part in X_partitioned]) + max_change = max(np.absolute(new_centers - self.centers).flatten()) + return new_centers, max_change + + +def test(): + # 5 cluster centers, 1 at origin, 4 at [+/-d, +/-d] from origin + d = 3 + means = np.array([ + [0,0], + [d,d], + [-d,d], + [-d,-d], + [d,-d], + ]) + cov = np.array([[1,0], [0,1]]) # circular + + print "actual centers:\n", means + + # generate random values + samples_per_cluster = 50 + X = None + color = 0 + colors = [] + for m in means: + if X is not None: + X = np.concatenate((X, np.random.multivariate_normal(m, cov, samples_per_cluster))) + else: + X = np.random.multivariate_normal(m, cov, samples_per_cluster) + colors += [color]*samples_per_cluster + color += 1 + + plt.scatter(X[:,0], X[:,1], s=100, c=colors, alpha=0.5) + plt.title("Target clusters") + plt.show() + + print "colors.shape:", np.array(colors).shape + print "X.shape:", X.shape + + plt.plot(colors) + plt.title("Colors") + plt.show() + + kmeans = KMeans(len(means)) + intermediate_predictions = kmeans.fit(X) + + print "predicted centers:\n", kmeans.centers + + c = kmeans.predict(np.array([[0.5, 0.5], [d, d]])) + print "[0.5, 0.5] is predicted to be part of cluster %s" % c[0] + print "[%d, %d] is predicted to be part of cluster %s" % (d, d, c[1]) + + C = kmeans.predict(X) + plt.scatter(X[:,0], X[:,1], c=C, s=100, alpha=0.5) + plt.title("Predicted clusters") + plt.show() + + for i, yhat in enumerate(intermediate_predictions): + plt.figure() + plt.scatter(X[:,0], X[:,1], c=yhat, s=100, alpha=0.5) + plt.title("Predictions at iteration %d" % (i+1)) + plt.show() + + +if __name__ == "__main__": + test() diff --git a/knn.py b/knn.py new file mode 100644 index 00000000..2367a47a --- /dev/null +++ b/knn.py @@ -0,0 +1,72 @@ +''' +This is an example of a K-Nearest Neighbors classifier on MNIST data. + +We try k=1...5 to show how we might choose the best k. + +This is not production code! + +See the tutorial here: +http://lazyprogrammer.me/post/114114892404/tutorial-k-nearest-neighbor-classifier-for-mnist +''' + +import pandas as pd +import numpy as np +from sortedcontainers import SortedDict + +Xtest = pd.read_csv("mnist_csv/Xtest.txt", header=None).as_matrix() +Xtrain = pd.read_csv("mnist_csv/Xtrain.txt", header=None).as_matrix() +Ytest = pd.read_csv("mnist_csv/label_test.txt", header=None).as_matrix().flatten() +Ytrain = pd.read_csv("mnist_csv/label_train.txt", header=None).as_matrix().flatten() + +class KNN(object): + def __init__(self, k): + self.k = k + + def fit(self, X, y): + self.X = X + self.y = y + + def predict(self, X): + y = np.zeros(len(X)) + for i,x in enumerate(X): # test points + sd = SortedDict() # distance -> class + for j,xt in enumerate(self.X): # training points + d = np.linalg.norm(x - xt) + # print d, sd + if len(sd) < self.k: + sd[d] = self.y[j] + else: + last = sd.viewkeys()[-1] + if d < last: + del sd[last] + sd[d] = self.y[j] + # print "sd:", sd + + # vote + votes = {} + # print "viewvalues:", sd.viewvalues() + for v in sd.viewvalues(): + # print "v:", v + votes[v] = votes.get(v,0) + 1 + # print "votes:", votes, "true:", Ytest[i] + max_votes = 0 + max_votes_class = -1 + for v,count in votes.iteritems(): + if count > max_votes: + max_votes = count + max_votes_class = v + y[i] = max_votes_class + return y + + +for k in (1,2,3,4,5): + C = np.zeros((10,10), dtype=np.int) + knn = KNN(k) + knn.fit(Xtrain, Ytrain) + Ypred = knn.predict(Xtest) + for p,t in zip(Ypred, Ytest): + C[t,p] += 1 + + print "Confusion matrix for k = %d:" % k + print C + print "Accuracy:", np.trace(C) / 500.0 diff --git a/linear_algebra/WHERE ARE THE NOTEBOOKS.txt b/linear_algebra/WHERE ARE THE NOTEBOOKS.txt deleted file mode 100644 index 5446ce25..00000000 --- a/linear_algebra/WHERE ARE THE NOTEBOOKS.txt +++ /dev/null @@ -1,5 +0,0 @@ -As stated in the "where to get the code" / "where to get the notebooks" lecture, the notebooks are NOT on Github. - -If you missed this, please review the lecture for the actual location of the notebooks. - -If, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com. \ No newline at end of file diff --git a/linear_algebra/extra_reading.txt b/linear_algebra/extra_reading.txt deleted file mode 100644 index 865e98be..00000000 --- a/linear_algebra/extra_reading.txt +++ /dev/null @@ -1,6 +0,0 @@ -Introduction to Linear Algebra by Gilbert Strang -https://amzn.to/2G3bvW1 - -Still Don't Understand Gravity? This Will Help -- this is included not because it's about calculus, but because it's yet another educator explaining why practice is important -https://www.youtube.com/watch?v=cP2uVarXi1A \ No newline at end of file diff --git a/linear_regression.py b/linear_regression.py new file mode 100644 index 00000000..eb799e1d --- /dev/null +++ b/linear_regression.py @@ -0,0 +1,49 @@ +import numpy as np +from numpy import linalg +from regressor import Regressor + +NUM_SAMPLES = 1000 + + +class LinearRegression(Regressor): + def fit(self, X, y): + N = len(X) + X = np.concatenate((np.array([[1]*N]).T, X), axis=1) + self.beta = np.dot( np.dot( np.linalg.inv(np.dot(X.T,X)), X.T ), y ) + + + def predict(self, X): + # prepend 1, dot product with beta + N = len(X) + X = np.concatenate((np.array([[1]*N]).T, X), axis=1) + return np.inner(self.beta, X) + + +def test(): + # create a bunch of random data for X-axis + # uniformly generate 2-D vectors in [-50, 50] + X = 100*np.random.random([NUM_SAMPLES, 2]) - 50 + + # create a bunch of random data for Y-axis + # let's say y = 5x1 - 2x2 + 3 + noise + # true beta is then: [3, 5, -2] + Y = np.fromiter((5*x1 - 2*x2 + 3 for x1, x2 in X), np.float, count=NUM_SAMPLES) + Y += np.random.standard_normal(NUM_SAMPLES) + + # fit + lr = LinearRegression() + lr.fit(X,Y) + print "beta estimated: %s" % lr.beta + + r2 = lr.score(X,Y) + print "R-square is: %s" % r2 + + # predict + x = (100, 100) + h = lr.predict(np.array([x])) + y = 5*x[0] - 2*x[1] + 3 + print "Extrapolated prediction: %.2f\nActual: %.2f" % (h, y) + + +if __name__ == "__main__": + test() diff --git a/linear_regression_class/gd.py b/linear_regression_class/gd.py deleted file mode 100644 index 04b36c1c..00000000 --- a/linear_regression_class/gd.py +++ /dev/null @@ -1,25 +0,0 @@ -import matplotlib.pyplot as plt - -lr = 1e-2 -x1 = 5 -x2 = -5 - -def J(x1, x2): - return x1**2 + x2**4 - -def g1(x1): - return 2*x1 - -def g2(x2): - return 4*x2**3 - -values = [] -for i in range(1000): - values.append(J(x1, x2)) - x1 -= lr * g1(x1) - x2 -= lr * g2(x2) -values.append(J(x1, x2)) - -print(x1, x2) -plt.plot(values) -plt.show() \ No newline at end of file diff --git a/linear_regression_class/generate_1d.py b/linear_regression_class/generate_1d.py index a1120372..417a67c5 100644 --- a/linear_regression_class/generate_1d.py +++ b/linear_regression_class/generate_1d.py @@ -1,15 +1,8 @@ # generates 1-dimensional data for linear regression analysis # -# notes for this course can be found at: -# https://deeplearningcourses.com/c/data-science-linear-regression-in-python +# notes for this course can be founda at: # https://www.udemy.com/data-science-linear-regression-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np @@ -17,6 +10,6 @@ with open('data_1d.csv', 'w') as f: X = np.random.uniform(low=0, high=100, size=N) Y = 2*X + 1 + np.random.normal(scale=5, size=N) - for i in range(N): + for i in xrange(N): f.write("%s,%s\n" % (X[i], Y[i])) diff --git a/linear_regression_class/generate_2d.py b/linear_regression_class/generate_2d.py index 0366c999..b1991132 100644 --- a/linear_regression_class/generate_2d.py +++ b/linear_regression_class/generate_2d.py @@ -1,15 +1,8 @@ # generates 2-dimensional data for linear regression analysis # -# notes for this course can be found at: -# https://deeplearningcourses.com/c/data-science-linear-regression-in-python +# notes for this course can be founda at: # https://www.udemy.com/data-science-linear-regression-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np @@ -18,5 +11,5 @@ with open('data_2d.csv', 'w') as f: X = np.random.uniform(low=0, high=100, size=(N,2)) Y = np.dot(X, w) + 1 + np.random.normal(scale=5, size=N) - for i in range(N): + for i in xrange(N): f.write("%s,%s,%s\n" % (X[i,0], X[i,1], Y[i])) diff --git a/linear_regression_class/generate_poly.py b/linear_regression_class/generate_poly.py index be15c71a..9ffb4d9a 100644 --- a/linear_regression_class/generate_poly.py +++ b/linear_regression_class/generate_poly.py @@ -1,14 +1,8 @@ # generates 1-dimensional polynomial data for linear regression analysis # -# notes for this course can be found at: -# https://deeplearningcourses.com/c/data-science-linear-regression-in-python +# notes for this course can be founda at: # https://www.udemy.com/data-science-linear-regression-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np @@ -17,6 +11,6 @@ X = np.random.uniform(low=0, high=100, size=N) X2 = X*X Y = 0.1*X2 + X + 3 + np.random.normal(scale=10, size=N) - for i in range(N): + for i in xrange(N): f.write("%s,%s\n" % (X[i], Y[i])) diff --git a/linear_regression_class/gradient_descent.py b/linear_regression_class/gradient_descent.py deleted file mode 100644 index 1b8fec83..00000000 --- a/linear_regression_class/gradient_descent.py +++ /dev/null @@ -1,52 +0,0 @@ -# notes for this course can be found at: -# https://deeplearningcourses.com/c/data-science-linear-regression-in-python -# https://www.udemy.com/data-science-linear-regression-in-python - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt - -N = 10 -D = 3 -X = np.zeros((N, D)) -X[:,0] = 1 # bias term -X[:5,1] = 1 -X[5:,2] = 1 -Y = np.array([0]*5 + [1]*5) - -# print X so you know what it looks like -print("X:", X) - -# won't work! -# w = np.linalg.solve(X.T.dot(X), X.T.dot(Y)) - -# let's try gradient descent -costs = [] # keep track of squared error cost -w = np.random.randn(D) / np.sqrt(D) # randomly initialize w -learning_rate = 0.001 -for t in range(1000): - # update w - Yhat = X.dot(w) - delta = Yhat - Y - w = w - learning_rate*X.T.dot(delta) - - # find and store the cost - mse = delta.dot(delta) / N - costs.append(mse) - -# plot the costs -plt.plot(costs) -plt.show() - -print("final w:", w) - -# plot prediction vs target -plt.plot(Yhat, label='prediction') -plt.plot(Y, label='target') -plt.legend() -plt.show() \ No newline at end of file diff --git a/linear_regression_class/l1_regularization.py b/linear_regression_class/l1_regularization.py deleted file mode 100644 index afadce8c..00000000 --- a/linear_regression_class/l1_regularization.py +++ /dev/null @@ -1,51 +0,0 @@ -# notes for this course can be found at: -# https://deeplearningcourses.com/c/data-science-linear-regression-in-python -# https://www.udemy.com/data-science-linear-regression-in-python - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt - -N = 50 -D = 50 - -# uniformly distributed numbers between -5, +5 -X = (np.random.random((N, D)) - 0.5)*10 - -# true weights - only the first 3 dimensions of X affect Y -true_w = np.array([1, 0.5, -0.5] + [0]*(D - 3)) - -# generate Y - add noise -Y = X.dot(true_w) + np.random.randn(N)*0.5 - -# perform gradient descent to find w -costs = [] # keep track of squared error cost -w = np.random.randn(D) / np.sqrt(D) # randomly initialize w -learning_rate = 0.001 -l1 = 10.0 # Also try 5.0, 2.0, 1.0, 0.1 - what effect does it have on w? -for t in range(500): - # update w - Yhat = X.dot(w) - delta = Yhat - Y - w = w - learning_rate*(X.T.dot(delta) + l1*np.sign(w)) - - # find and store the cost - mse = delta.dot(delta) / N - costs.append(mse) - -# plot the costs -plt.plot(costs) -plt.show() - -print("final w:", w) - -# plot our w vs true w -plt.plot(true_w, label='true w') -plt.plot(w, label='w_map') -plt.legend() -plt.show() \ No newline at end of file diff --git a/linear_regression_class/l2_regularization.py b/linear_regression_class/l2_regularization.py deleted file mode 100644 index e4041cde..00000000 --- a/linear_regression_class/l2_regularization.py +++ /dev/null @@ -1,51 +0,0 @@ -# demonstration of L2 regularization -# -# notes for this course can be found at: -# https://deeplearningcourses.com/c/data-science-linear-regression-in-python -# https://www.udemy.com/data-science-linear-regression-in-python - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - - -import numpy as np -import matplotlib.pyplot as plt - -N = 50 - -# generate the data -X = np.linspace(0,10,N) -Y = 0.5*X + np.random.randn(N) - -# make outliers -Y[-1] += 30 -Y[-2] += 30 - -# plot the data -plt.scatter(X, Y) -plt.show() - -# add bias term -X = np.vstack([np.ones(N), X]).T - -# plot the maximum likelihood solution -w_ml = np.linalg.solve(X.T.dot(X), X.T.dot(Y)) -Yhat_ml = X.dot(w_ml) -plt.scatter(X[:,1], Y) -plt.plot(X[:,1], Yhat_ml) -plt.show() - -# plot the regularized solution -# probably don't need an L2 regularization this high in many problems -# everything in this example is exaggerated for visualization purposes -l2 = 1000.0 -w_map = np.linalg.solve(l2*np.eye(2) + X.T.dot(X), X.T.dot(Y)) -Yhat_map = X.dot(w_map) -plt.scatter(X[:,1], Y) -plt.plot(X[:,1], Yhat_ml, label='maximum likelihood') -plt.plot(X[:,1], Yhat_map, label='map') -plt.legend() -plt.show() \ No newline at end of file diff --git a/linear_regression_class/lr_1d.py b/linear_regression_class/lr_1d.py index 499b123e..53ea2b16 100644 --- a/linear_regression_class/lr_1d.py +++ b/linear_regression_class/lr_1d.py @@ -1,15 +1,8 @@ # shows how linear regression analysis can be applied to 1-dimensional data # # notes for this course can be found at: -# https://deeplearningcourses.com/c/data-science-linear-regression-in-python # https://www.udemy.com/data-science-linear-regression-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import matplotlib.pyplot as plt diff --git a/linear_regression_class/lr_2d.py b/linear_regression_class/lr_2d.py index 8b8112c5..08db2044 100644 --- a/linear_regression_class/lr_2d.py +++ b/linear_regression_class/lr_2d.py @@ -1,15 +1,8 @@ # shows how linear regression analysis can be applied to 2-dimensional data # -# notes for this course can be found at: -# https://deeplearningcourses.com/c/data-science-linear-regression-in-python +# notes for this course can be founda at: # https://www.udemy.com/data-science-linear-regression-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt @@ -20,7 +13,7 @@ Y = [] for line in open('data_2d.csv'): x1, x2, y = line.split(',') - X.append([float(x1), float(x2), 1]) # add the bias term + X.append([1, float(x1), float(x2)]) # add the bias term x0 = 1 Y.append(float(y)) # let's turn X and Y into numpy arrays since that will be useful later @@ -48,4 +41,4 @@ d1 = Y - Yhat d2 = Y - Y.mean() r2 = 1 - d1.dot(d1) / d2.dot(d2) -print("the r-squared is:", r2) +print "the r-squared is:", r2 diff --git a/linear_regression_class/lr_poly.py b/linear_regression_class/lr_poly.py index 90fe3247..ab72bb76 100644 --- a/linear_regression_class/lr_poly.py +++ b/linear_regression_class/lr_poly.py @@ -1,12 +1,8 @@ # shows how linear regression analysis can be applied to polynomial data # -# notes for this course can be found at: -# https://deeplearningcourses.com/c/data-science-linear-regression-in-python +# notes for this course can be founda at: # https://www.udemy.com/data-science-linear-regression-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future + import numpy as np import matplotlib.pyplot as plt @@ -19,16 +15,15 @@ x, y = line.split(',') x = float(x) X.append([1, x, x*x]) # add the bias term x0 = 1 - # our model is therefore y_hat = w0 + w1 * x + w2 * x**2 Y.append(float(y)) # let's turn X and Y into numpy arrays since that will be useful later X = np.array(X) Y = np.array(Y) + # let's plot the data to see what it looks like plt.scatter(X[:,1], Y) -plt.title("The data we're trying to fit") plt.show() @@ -38,22 +33,19 @@ # note: the * operator does element-by-element multiplication in numpy # np.dot() does what we expect for matrix multiplication w = np.linalg.solve(np.dot(X.T, X), np.dot(X.T, Y)) +Yhat = np.dot(X, w) # let's plot everything together to make sure it worked plt.scatter(X[:,1], Y) - -# to plot our quadratic model predictions, let's -# create a line of x's and calculate the predicted y's -x_line = np.linspace(X[:,1].min(), X[:,1].max()) -y_line = w[0] + w[1] * x_line + w[2] * x_line * x_line -plt.plot(x_line, y_line) -plt.title("Our fitted quadratic") +plt.plot(sorted(X[:,1]), sorted(Yhat)) +# note: shortcut since monotonically increasing +# x-axis values have to be in order since the points +# are joined from one element to the next plt.show() # determine how good the model is by computing the r-squared -Yhat = X.dot(w) d1 = Y - Yhat d2 = Y - Y.mean() r2 = 1 - d1.dot(d1) / d2.dot(d2) diff --git a/linear_regression_class/moore.py b/linear_regression_class/moore.py index 3138abde..599cc56a 100644 --- a/linear_regression_class/moore.py +++ b/linear_regression_class/moore.py @@ -1,16 +1,8 @@ # shows how linear regression analysis can be applied to moore's law # # notes for this course can be found at: -# https://deeplearningcourses.com/c/data-science-linear-regression-in-python # https://www.udemy.com/data-science-linear-regression-in-python # transistor count from: https://en.wikipedia.org/wiki/Transistor_count - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import re import numpy as np import matplotlib.pyplot as plt @@ -23,12 +15,12 @@ non_decimal = re.compile(r'[^\d]+') for line in open('moore.csv'): - r = line.split('\t') + r = line.split('\t') - x = int(non_decimal.sub('', r[2].split('[')[0])) - y = int(non_decimal.sub('', r[1].split('[')[0])) - X.append(x) - Y.append(y) + x = int(non_decimal.sub('', r[2].split('[')[0])) + y = int(non_decimal.sub('', r[1].split('[')[0])) + X.append(x) + Y.append(y) X = np.array(X) diff --git a/linear_regression_class/overfitting.py b/linear_regression_class/overfitting.py deleted file mode 100644 index 3454f9b6..00000000 --- a/linear_regression_class/overfitting.py +++ /dev/null @@ -1,101 +0,0 @@ -# notes for this course can be found at: -# https://deeplearningcourses.com/c/data-science-linear-regression-in-python -# https://www.udemy.com/data-science-linear-regression-in-python - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt - - -def make_poly(X, deg): - n = len(X) - data = [np.ones(n)] - for d in range(deg): - data.append(X**(d+1)) - return np.vstack(data).T - - -def fit(X, Y): - return np.linalg.solve(X.T.dot(X), X.T.dot(Y)) - - -def fit_and_display(X, Y, sample, deg): - N = len(X) - train_idx = np.random.choice(N, sample) - Xtrain = X[train_idx] - Ytrain = Y[train_idx] - - plt.scatter(Xtrain, Ytrain) - plt.show() - - # fit polynomial - Xtrain_poly = make_poly(Xtrain, deg) - w = fit(Xtrain_poly, Ytrain) - - # display the polynomial - X_poly = make_poly(X, deg) - Y_hat = X_poly.dot(w) - plt.plot(X, Y) - plt.plot(X, Y_hat) - plt.scatter(Xtrain, Ytrain) - plt.title("deg = %d" % deg) - plt.show() - - -def get_mse(Y, Yhat): - d = Y - Yhat - return d.dot(d) / len(d) - - -def plot_train_vs_test_curves(X, Y, sample=20, max_deg=20): - N = len(X) - train_idx = np.random.choice(N, sample) - Xtrain = X[train_idx] - Ytrain = Y[train_idx] - - test_idx = [idx for idx in range(N) if idx not in train_idx] - # test_idx = np.random.choice(N, sample) - Xtest = X[test_idx] - Ytest = Y[test_idx] - - mse_trains = [] - mse_tests = [] - for deg in range(max_deg+1): - Xtrain_poly = make_poly(Xtrain, deg) - w = fit(Xtrain_poly, Ytrain) - Yhat_train = Xtrain_poly.dot(w) - mse_train = get_mse(Ytrain, Yhat_train) - - Xtest_poly = make_poly(Xtest, deg) - Yhat_test = Xtest_poly.dot(w) - mse_test = get_mse(Ytest, Yhat_test) - - mse_trains.append(mse_train) - mse_tests.append(mse_test) - - plt.plot(mse_trains, label="train mse") - plt.plot(mse_tests, label="test mse") - plt.legend() - plt.show() - - plt.plot(mse_trains, label="train mse") - plt.legend() - plt.show() - -if __name__ == "__main__": - # make up some data and plot it - N = 100 - X = np.linspace(0, 6*np.pi, N) - Y = np.sin(X) - - plt.plot(X, Y) - plt.show() - - for deg in (5, 6, 7, 8, 9): - fit_and_display(X, Y, 10, deg) - plot_train_vs_test_curves(X, Y) diff --git a/linear_regression_class/systolic.py b/linear_regression_class/systolic.py index 7d594670..1b268217 100644 --- a/linear_regression_class/systolic.py +++ b/linear_regression_class/systolic.py @@ -1,4 +1,3 @@ -# https://deeplearningcourses.com/c/data-science-linear-regression-in-python # need to sudo pip install xlrd to use pd.read_excel # data is from: # http://college.cengage.com/mathematics/brase/understandable_statistics/7e/students/datasets/mlr/frames/mlr02.html @@ -8,19 +7,12 @@ # X2 = age in years # X3 = weight in pounds -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - - import matplotlib.pyplot as plt import numpy as np import pandas as pd -df = pd.read_excel('mlr02.xls', engine='xlrd') -X = df.values +df = pd.read_excel('mlr02.xls') +X = df.as_matrix() # using age to predict systolic blood pressure plt.scatter(X[:,1], X[:,0]) @@ -48,9 +40,9 @@ def get_r2(X, Y): r2 = 1 - d1.dot(d1) / d2.dot(d2) return r2 -print("r2 for x2 only:", get_r2(X2only, Y)) -print("r2 for x3 only:", get_r2(X3only, Y)) -print("r2 for both:", get_r2(X, Y)) +print "r2 for x2 only:", get_r2(X2only, Y) +print "r2 for x3 only:", get_r2(X3only, Y) +print "r2 for both:", get_r2(X, Y) diff --git a/logistic_regression.py b/logistic_regression.py new file mode 100644 index 00000000..4bf76d76 --- /dev/null +++ b/logistic_regression.py @@ -0,0 +1,67 @@ +# The associated tutorial for this code is here: +# http://lazyprogrammer.me/post/117112987199/deep-learning-tutorial-part-1-3-logistic + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +Xtest = pd.read_csv("mnist_csv/Xtest.txt", header=None) +Xtest['ones'] = 1 # add bias term +Xtest = Xtest.as_matrix() +Xtrain = pd.read_csv("mnist_csv/Xtrain.txt", header=None) +Xtrain['ones'] = 1 # add bias term +Xtrain = Xtrain.as_matrix() +Ytest = pd.read_csv("mnist_csv/label_test.txt", header=None).as_matrix().flatten() +Ytrain = pd.read_csv("mnist_csv/label_train.txt", header=None).as_matrix().flatten() + +class Logistic(object): + def __init__(self, learningrate=0.00002, regularization=1, epochs=1000): + self.learningrate = learningrate + self.epochs = epochs + self.regularization = regularization + + def fit(self, X, y): + N, D = X.shape + self.C = set(y) + self.W = np.zeros((len(self.C),D)) + likelihoods = np.zeros(self.epochs) + T = np.zeros((N, len(self.C))) # matrix of targets + for n in xrange(N): + T[n,y[n]] = 1 + for epoch in xrange(self.epochs): + Y = self.forward(X) + gradW = np.dot((T - Y).T, X) - self.regularization*self.W + self.W += self.learningrate*gradW + for n in xrange(N): + likelihoods[epoch] += np.log(Y[n, y[n]]) + if epoch % 50 == 0: + print epoch, likelihoods[epoch] + return likelihoods + + def forward(self, X): + N = len(X) + Y = np.zeros((N, len(self.C))) + # this assumes the class labels are 0,1,2,...|C|-1 + for n in xrange(N): + z = np.exp(np.dot(self.W, X[n])) + Y[n] = z / sum(z) + return Y + + def predict(self, X): + f = self.forward(X) + return np.argmax(f, axis=1) + +lr = Logistic(epochs=1000) +likelihoods = lr.fit(Xtrain, Ytrain) +plt.plot(likelihoods) +plt.show() + +C = np.zeros((10,10), dtype=np.int) +Ypred = lr.predict(Xtest) +for p,t in zip(Ypred, Ytest): + C[t,p] += 1 + +print "Confusion matrix:" +print C +print "Accuracy:", np.trace(C) / 500.0 + diff --git a/logistic_regression_class/bad_xor.py b/logistic_regression_class/bad_xor.py deleted file mode 100644 index ca39f0b5..00000000 --- a/logistic_regression_class/bad_xor.py +++ /dev/null @@ -1,78 +0,0 @@ -# logisitc regression classifier for the XOR problem. -# -# the notes for this class can be found at: -# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python -# https://www.udemy.com/data-science-logistic-regression-in-python - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt - -N = 4 -D = 2 - -# XOR -X = np.array([ - [0, 0], - [0, 1], - [1, 0], - [1, 1], -]) -T = np.array([0, 1, 1, 0]) - -# add a column of ones -ones = np.ones((N, 1)) - -# add a column of xy = x*y -Xb = np.concatenate((ones, X), axis=1) - -# randomly initialize the weights -w = np.random.randn(D + 1) - -# calculate the model output -z = Xb.dot(w) - -def sigmoid(z): - return 1/(1 + np.exp(-z)) - - -Y = sigmoid(z) - -# calculate the cross-entropy error -def cross_entropy(T, Y): - return -(T*np.log(Y) + (1-T)*np.log(1-Y)).sum() - - -# let's do gradient descent 100 times -learning_rate = 0.001 -error = [] -w_mags = [] -for i in range(100000): - e = cross_entropy(T, Y) - error.append(e) - if i % 1000 == 0: - print(e) - - # gradient descent weight udpate with regularization - w += learning_rate * Xb.T.dot(T - Y) - - w_mags.append(w.dot(w)) - - # recalculate Y - Y = sigmoid(Xb.dot(w)) - -plt.plot(error) -plt.title("Cross-entropy per iteration") -plt.show() - -plt.plot(w_mags) -plt.title("w^2 magnitudes") -plt.show() - -print("Final w:", w) -print("Final classification rate:", 1 - np.abs(T - np.round(Y)).sum() / N) diff --git a/logistic_regression_class/l1_regularization.py b/logistic_regression_class/l1_regularization.py deleted file mode 100644 index 5bcbd809..00000000 --- a/logistic_regression_class/l1_regularization.py +++ /dev/null @@ -1,65 +0,0 @@ -# notes for this course can be found at: -# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python -# https://www.udemy.com/data-science-logistic-regression-in-python - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from mpl_toolkits.mplot3d import Axes3D -import matplotlib.pyplot as plt - -def sigmoid(z): - return 1/(1 + np.exp(-z)) - -N = 50 -D = 50 - -# uniformly distributed numbers between -5, +5 -X = (np.random.random((N, D)) - 0.5)*10 -# X = (np.random.randn(N, D) - 0.5)*10 - -# true weights - only the first 3 dimensions of X affect Y -true_w = np.array([1, 0.5, -0.5] + [0]*(D - 3)) - -# generate Y - add noise with variance 0.5 -Y = np.round(sigmoid(X.dot(true_w) + np.random.randn(N)*0.5)) - - - - -# let's plot the data to see what it looks like -fig = plt.figure() -ax = fig.add_subplot(111, projection='3d') -ax.scatter(X[:,0], X[:,1], X[:,2], c=Y) -plt.show() - -# perform gradient descent to find w -costs = [] # keep track of squared error cost -w = np.random.randn(D) / np.sqrt(D) # randomly initialize w -learning_rate = 0.001 -l1 = 3.0 # try different values - what effect does it have on w? -for t in range(5000): - # update w - Yhat = sigmoid(X.dot(w)) - delta = Yhat - Y - w = w - learning_rate*(X.T.dot(delta) + l1*np.sign(w)) - - # find and store the cost - cost = -(Y*np.log(Yhat) + (1-Y)*np.log(1 - Yhat)).mean() + l1*np.abs(w).mean() - costs.append(cost) - -# plot the costs -plt.plot(costs) -plt.show() - -print("final w:", w) - -# plot our w vs true w -plt.plot(true_w, label='true w') -plt.plot(w, label='w_map') -plt.legend() -plt.show() \ No newline at end of file diff --git a/logistic_regression_class/logistic1.py b/logistic_regression_class/logistic1.py index f2dc47d1..8d0f9c9e 100644 --- a/logistic_regression_class/logistic1.py +++ b/logistic_regression_class/logistic1.py @@ -3,15 +3,8 @@ # standard normal distribution. # # the notes for this class can be found at: -# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python # https://www.udemy.com/data-science-logistic-regression-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np N = 100 @@ -19,8 +12,7 @@ X = np.random.randn(N,D) -# ones = np.array([[1]*N]).T # old -ones = np.ones((N, 1)) +ones = np.array([[1]*N]).T Xb = np.concatenate((ones, X), axis=1) w = np.random.randn(D + 1) @@ -30,4 +22,4 @@ def sigmoid(z): return 1/(1 + np.exp(-z)) -print(sigmoid(z)) +print sigmoid(z) diff --git a/logistic_regression_class/logistic2.py b/logistic_regression_class/logistic2.py index f6d8614f..42c9b025 100644 --- a/logistic_regression_class/logistic2.py +++ b/logistic_regression_class/logistic2.py @@ -2,16 +2,8 @@ # in numpy. # # the notes for this class can be found at: -# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python # https://www.udemy.com/data-science-logistic-regression-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - - import numpy as np N = 100 @@ -30,8 +22,7 @@ T = np.array([0]*50 + [1]*50) # add a column of ones -# ones = np.array([[1]*N]).T # old -ones = np.ones((N, 1)) +ones = np.array([[1]*N]).T Xb = np.concatenate((ones, X), axis=1) # randomly initialize the weights @@ -48,14 +39,14 @@ def sigmoid(z): # calculate the cross-entropy error def cross_entropy(T, Y): E = 0 - for i in range(len(T)): + for i in xrange(N): if T[i] == 1: E -= np.log(Y[i]) else: E -= np.log(1 - Y[i]) return E -print(cross_entropy(T, Y)) +print cross_entropy(T, Y) # try it with our closed-form solution w = np.array([0, 4, 4]) @@ -65,5 +56,5 @@ def cross_entropy(T, Y): Y = sigmoid(z) # calculate the cross-entropy error -print(cross_entropy(T, Y)) +print cross_entropy(T, Y) diff --git a/logistic_regression_class/logistic3.py b/logistic_regression_class/logistic3.py index 67fd027a..f6dea683 100644 --- a/logistic_regression_class/logistic3.py +++ b/logistic_regression_class/logistic3.py @@ -1,38 +1,27 @@ # demonstrates how to do gradient descent with numpy matrices. # # the notes for this class can be found at: -# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python # https://www.udemy.com/data-science-logistic-regression-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np -import matplotlib.pyplot as plt N = 100 D = 2 -N_per_class = N//2 - X = np.random.randn(N,D) # center the first 50 points at (-2,-2) -X[:N_per_class,:] = X[:N_per_class,:] - 2*np.ones((N_per_class,D)) +X[:50,:] = X[:50,:] - 2*np.ones((50,D)) # center the last 50 points at (2, 2) -X[N_per_class:,:] = X[N_per_class:,:] + 2*np.ones((N_per_class,D)) +X[50:,:] = X[50:,:] + 2*np.ones((50,D)) -# labels: first N_per_class are 0, last N_per_class are 1 -T = np.array([0]*N_per_class + [1]*N_per_class) +# labels: first 50 are 0, last 50 are 1 +T = np.array([0]*50 + [1]*50) # add a column of ones -# ones = np.array([[1]*N]).T # old -ones = np.ones((N, 1)) +ones = np.array([[1]*N]).T Xb = np.concatenate((ones, X), axis=1) # randomly initialize the weights @@ -50,7 +39,7 @@ def sigmoid(z): # calculate the cross-entropy error def cross_entropy(T, Y): E = 0 - for i in range(len(T)): + for i in xrange(N): if T[i] == 1: E -= np.log(Y[i]) else: @@ -60,23 +49,17 @@ def cross_entropy(T, Y): # let's do gradient descent 100 times learning_rate = 0.1 -for i in range(100): +for i in xrange(100): if i % 10 == 0: - print(cross_entropy(T, Y)) + print cross_entropy(T, Y) # gradient descent weight udpate - w += learning_rate * Xb.T.dot(T - Y) + w += learning_rate * np.dot((T - Y).T, Xb) # recalculate Y Y = sigmoid(Xb.dot(w)) -print("Final w:", w) +print "Final w:", w -# plot the data and separating line -plt.scatter(X[:,0], X[:,1], c=T, s=100, alpha=0.5) -x_axis = np.linspace(-6, 6, 100) -y_axis = -(w[0] + x_axis*w[1]) / w[2] -plt.plot(x_axis, y_axis) -plt.show() diff --git a/logistic_regression_class/logistic4.py b/logistic_regression_class/logistic4.py index 6fa77c36..32470da7 100644 --- a/logistic_regression_class/logistic4.py +++ b/logistic_regression_class/logistic4.py @@ -2,16 +2,8 @@ # with regularization. # # the notes for this class can be found at: -# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python # https://www.udemy.com/data-science-logistic-regression-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - - import numpy as np N = 100 @@ -30,8 +22,7 @@ T = np.array([0]*50 + [1]*50) # add a column of ones -# ones = np.array([[1]*N]).T -ones = np.ones((N, 1)) +ones = np.array([[1]*N]).T Xb = np.concatenate((ones, X), axis=1) # randomly initialize the weights @@ -49,7 +40,7 @@ def sigmoid(z): # calculate the cross-entropy error def cross_entropy(T, Y): E = 0 - for i in range(len(T)): + for i in xrange(N): if T[i] == 1: E -= np.log(Y[i]) else: @@ -59,17 +50,17 @@ def cross_entropy(T, Y): # let's do gradient descent 100 times learning_rate = 0.1 -for i in range(100): +for i in xrange(100): if i % 10 == 0: - print(cross_entropy(T, Y)) + print cross_entropy(T, Y) # gradient descent weight udpate with regularization - w += learning_rate * ( Xb.T.dot(T - Y) - 0.1*w ) + w += learning_rate * ( np.dot((T - Y).T, Xb) - 0.1*w ) # recalculate Y Y = sigmoid(Xb.dot(w)) -print("Final w:", w) +print "Final w:", w diff --git a/logistic_regression_class/logistic_donut.py b/logistic_regression_class/logistic_donut.py index db8cc50a..ca98ee6a 100644 --- a/logistic_regression_class/logistic_donut.py +++ b/logistic_regression_class/logistic_donut.py @@ -1,16 +1,8 @@ # logisitc regression classifier for the donut problem. # # the notes for this class can be found at: -# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python # https://www.udemy.com/data-science-logistic-regression-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - - import numpy as np import matplotlib.pyplot as plt @@ -22,16 +14,16 @@ # distance from origin is radius + random normal # angle theta is uniformly distributed between (0, 2pi) -R1 = np.random.randn(N//2) + R_inner -theta = 2*np.pi*np.random.random(N//2) +R1 = np.random.randn(N/2) + R_inner +theta = 2*np.pi*np.random.random(N/2) X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T -R2 = np.random.randn(N//2) + R_outer -theta = 2*np.pi*np.random.random(N//2) +R2 = np.random.randn(N/2) + R_outer +theta = 2*np.pi*np.random.random(N/2) X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T X = np.concatenate([ X_inner, X_outer ]) -T = np.array([0]*(N//2) + [1]*(N//2)) # labels: first 50 are 0, last 50 are 1 +T = np.array([0]*(N/2) + [1]*(N/2)) # labels: first 50 are 0, last 50 are 1 plt.scatter(X[:,0], X[:,1], c=T) plt.show() @@ -39,11 +31,12 @@ # add a column of ones -# ones = np.array([[1]*N]).T # old -ones = np.ones((N, 1)) +ones = np.array([[1]*N]).T # add a column of r = sqrt(x^2 + y^2) -r = np.sqrt( (X * X).sum(axis=1) ).reshape(-1, 1) +r = np.zeros((N,1)) +for i in xrange(N): + r[i] = np.sqrt(X[i,:].dot(X[i,])) Xb = np.concatenate((ones, r, X), axis=1) # randomly initialize the weights @@ -60,20 +53,27 @@ def sigmoid(z): # calculate the cross-entropy error def cross_entropy(T, Y): - return -(T*np.log(Y) + (1-T)*np.log(1-Y)).sum() + # E = 0 + # for i in xrange(N): + # if T[i] == 1: + # E -= np.log(Y[i]) + # else: + # E -= np.log(1 - Y[i]) + # return E + return (T*np.log(Y) + (1-T)*np.log(1-Y)).sum() # let's do gradient descent 100 times learning_rate = 0.0001 error = [] -for i in range(5000): +for i in xrange(5000): e = cross_entropy(T, Y) error.append(e) - if i % 500 == 0: - print(e) + if i % 100 == 0: + print e # gradient descent weight udpate with regularization - w += learning_rate * ( Xb.T.dot(T - Y) - 0.1*w ) + w += learning_rate * ( np.dot((T - Y).T, Xb) - 0.01*w ) # recalculate Y Y = sigmoid(Xb.dot(w)) @@ -82,5 +82,5 @@ def cross_entropy(T, Y): plt.title("Cross-entropy per iteration") plt.show() -print("Final w:", w) -print("Final classification rate:", 1 - np.abs(T - np.round(Y)).sum() / N) +print "Final w:", w +print "Final classification rate:", 1 - np.abs(T - np.round(Y)).sum() / N diff --git a/logistic_regression_class/logistic_visualize.py b/logistic_regression_class/logistic_visualize.py index 65289535..50a25c1c 100644 --- a/logistic_regression_class/logistic_visualize.py +++ b/logistic_regression_class/logistic_visualize.py @@ -1,16 +1,8 @@ # visualizes the Bayes solution # # the notes for this class can be found at: -# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python # https://www.udemy.com/data-science-logistic-regression-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - - import numpy as np import matplotlib.pyplot as plt @@ -30,8 +22,7 @@ T = np.array([0]*50 + [1]*50) # add a column of ones -# ones = np.array([[1]*N]).T -ones = np.ones((N, 1)) +ones = np.array([[1]*N]).T Xb = np.concatenate((ones, X), axis=1) def sigmoid(z): diff --git a/logistic_regression_class/logistic_xor.py b/logistic_regression_class/logistic_xor.py index dccc0d97..5788437e 100644 --- a/logistic_regression_class/logistic_xor.py +++ b/logistic_regression_class/logistic_xor.py @@ -1,15 +1,8 @@ # logisitc regression classifier for the XOR problem. # # the notes for this class can be found at: -# https://deeplearningcourses.com/c/data-science-logistic-regression-in-python # https://www.udemy.com/data-science-logistic-regression-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import matplotlib.pyplot as plt @@ -26,12 +19,11 @@ T = np.array([0, 1, 1, 0]) # add a column of ones -# ones = np.array([[1]*N]).T -ones = np.ones((N, 1)) +ones = np.array([[1]*N]).T # add a column of xy = x*y -xy = (X[:,0] * X[:,1]).reshape(N, 1) -Xb = np.concatenate((ones, xy, X), axis=1) +xy = np.matrix(X[:,0] * X[:,1]).T +Xb = np.array(np.concatenate((ones, xy, X), axis=1)) # randomly initialize the weights w = np.random.randn(D + 2) @@ -48,7 +40,7 @@ def sigmoid(z): # calculate the cross-entropy error def cross_entropy(T, Y): E = 0 - for i in range(len(T)): + for i in xrange(N): if T[i] == 1: E -= np.log(Y[i]) else: @@ -57,16 +49,16 @@ def cross_entropy(T, Y): # let's do gradient descent 100 times -learning_rate = 0.01 +learning_rate = 0.001 error = [] -for i in range(10000): +for i in xrange(5000): e = cross_entropy(T, Y) error.append(e) - if i % 1000 == 0: - print(e) + if i % 100 == 0: + print e # gradient descent weight udpate with regularization - w += learning_rate * ( Xb.T.dot(T - Y) - 0.01*w ) + w += learning_rate * ( np.dot((T - Y).T, Xb) - 0.01*w ) # recalculate Y Y = sigmoid(Xb.dot(w)) @@ -75,5 +67,5 @@ def cross_entropy(T, Y): plt.title("Cross-entropy per iteration") plt.show() -print("Final w:", w) -print("Final classification rate:", 1 - np.abs(T - np.round(Y)).sum() / N) +print "Final w:", w +print "Final classification rate:", 1 - np.abs(T - np.round(Y)).sum() / N diff --git a/matrix_calculus/extra_reading.txt b/matrix_calculus/extra_reading.txt deleted file mode 100644 index a19af06d..00000000 --- a/matrix_calculus/extra_reading.txt +++ /dev/null @@ -1,2 +0,0 @@ -The Matrix Cookbook -https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf \ No newline at end of file diff --git a/naive_bayes/WHERE ARE THE NOTEBOOKS.txt b/naive_bayes/WHERE ARE THE NOTEBOOKS.txt deleted file mode 100644 index 5446ce25..00000000 --- a/naive_bayes/WHERE ARE THE NOTEBOOKS.txt +++ /dev/null @@ -1,5 +0,0 @@ -As stated in the "where to get the code" / "where to get the notebooks" lecture, the notebooks are NOT on Github. - -If you missed this, please review the lecture for the actual location of the notebooks. - -If, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com. \ No newline at end of file diff --git a/naive_bayes/extra_reading.txt b/naive_bayes/extra_reading.txt deleted file mode 100644 index 52e5228b..00000000 --- a/naive_bayes/extra_reading.txt +++ /dev/null @@ -1,8 +0,0 @@ -Complement Naive Bayes -https://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf - -Semi-Supervised Learning with Naive Bayes -http://www.kamalnigam.com/papers/emcat-aaai98.pdf - -An empirical study of the naive Bayes classifier -https://faculty.cc.gatech.edu/~isbell/reading/papers/Rish.pdf \ No newline at end of file diff --git a/nlp_class/article_spinner.py b/nlp_class/article_spinner.py index 35b40484..b15470d9 100644 --- a/nlp_class/article_spinner.py +++ b/nlp_class/article_spinner.py @@ -1,17 +1,9 @@ # Very basic article spinner for NLP class, which can be found at: -# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python # https://www.udemy.com/data-science-natural-language-processing-in-python # Author: http://lazyprogrammer.me # A very bad article spinner using trigrams. -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import nltk import random import numpy as np @@ -31,15 +23,16 @@ for review in positive_reviews: s = review.text.lower() tokens = nltk.tokenize.word_tokenize(s) - for i in range(len(tokens) - 2): + for i in xrange(len(tokens) - 2): k = (tokens[i], tokens[i+2]) if k not in trigrams: trigrams[k] = [] trigrams[k].append(tokens[i+1]) + # turn each array of middle-words into a probability vector trigram_probabilities = {} -for k, words in iteritems(trigrams): +for k, words in trigrams.iteritems(): # create a dictionary of word -> count if len(set(words)) > 1: # only do this when there are different possibilities for a middle word @@ -50,7 +43,7 @@ d[w] = 0 d[w] += 1 n += 1 - for w, c in iteritems(d): + for w, c in d.iteritems(): d[w] = float(c) / n trigram_probabilities[k] = d @@ -59,7 +52,7 @@ def random_sample(d): # choose a random sample from dictionary where values are the probabilities r = random.random() cumulative = 0 - for w, p in iteritems(d): + for w, p in d.iteritems(): cumulative += p if r < cumulative: return w @@ -68,17 +61,14 @@ def random_sample(d): def test_spinner(): review = random.choice(positive_reviews) s = review.text.lower() - print("Original:", s) + print "Original:", s tokens = nltk.tokenize.word_tokenize(s) - for i in range(len(tokens) - 2): + for i in xrange(len(tokens) - 2): if random.random() < 0.2: # 20% chance of replacement k = (tokens[i], tokens[i+2]) if k in trigram_probabilities: w = random_sample(trigram_probabilities[k]) tokens[i+1] = w - print("Spun:") - print(" ".join(tokens).replace(" .", ".").replace(" '", "'").replace(" ,", ",").replace("$ ", "$").replace(" !", "!")) - + print "Spun:" + print " ".join(tokens).replace(" .", ".").replace(" '", "'").replace(" ,", ",").replace("$ ", "$").replace(" !", "!") -if __name__ == '__main__': - test_spinner() diff --git a/nlp_class/cipher_placeholder.py b/nlp_class/cipher_placeholder.py deleted file mode 100644 index 0c08b818..00000000 --- a/nlp_class/cipher_placeholder.py +++ /dev/null @@ -1,23 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python -# https://www.udemy.com/data-science-natural-language-processing-in-python - -# Author: http://lazyprogrammer.me - -# Get the data from here: -# https://lazyprogrammer.me/course_files/moby_dick.txt - -### encode a message - -# this is a random excerpt from Project Gutenberg's -# The Adventures of Sherlock Holmes, by Arthur Conan Doyle -# https://www.gutenberg.org/ebooks/1661 - -original_message = '''I then lounged down the street and found, -as I expected, that there was a mews in a lane which runs down -by one wall of the garden. I lent the ostlers a hand in rubbing -down their horses, and received in exchange twopence, a glass of -half-and-half, two fills of shag tobacco, and as much information -as I could desire about Miss Adler, to say nothing of half a dozen -other people in the neighbourhood in whom I was not in the least -interested, but whose biographies I was compelled to listen to. -''' \ No newline at end of file diff --git a/nlp_class/extra_reading.txt b/nlp_class/extra_reading.txt deleted file mode 100644 index 3d5dcb21..00000000 --- a/nlp_class/extra_reading.txt +++ /dev/null @@ -1,23 +0,0 @@ -LEARNING THE NAIVE BAYES CLASSIFIER WITH OPTIMIZATION MODELS -https://pdfs.semanticscholar.org/059c/36439a84c8d51443022352a94e2751c60d1c.pdf - -RANDOM FORESTS -https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf - -Explaining AdaBoost -http://rob.schapire.net/papers/explaining-adaboost.pdf - -Understanding logistic regression analysis -https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3936971/ - -Indexing by Latent Semantic Analysis -http://lsa.colorado.edu/papers/JASIS.lsi.90.pdf - -Language Modeling -http://www.cs.columbia.edu/~mcollins/lm-spring2013.pdf - -Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit -https://www.nltk.org/book/ - -NLTK Documentation -https://media.readthedocs.org/pdf/nltk/latest/nltk.pdf \ No newline at end of file diff --git a/nlp_class/lsa.py b/nlp_class/lsa.py index b513b106..ae8bcb29 100644 --- a/nlp_class/lsa.py +++ b/nlp_class/lsa.py @@ -1,13 +1,7 @@ # Latent semantic analysis visualization for NLP class, which can be found at: -# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python # https://www.udemy.com/data-science-natural-language-processing-in-python # Author: http://lazyprogrammer.me -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import nltk import numpy as np @@ -22,11 +16,6 @@ # copy tokenizer from sentiment example stopwords = set(w.rstrip() for w in open('stopwords.txt')) - -# note: an alternative source of stopwords -# from nltk.corpus import stopwords -# stopwords.words('english') - # add more stopwords specific to this problem stopwords = stopwords.union({ 'introduction', 'edition', 'series', 'application', @@ -50,10 +39,9 @@ def my_tokenizer(s): all_tokens = [] all_titles = [] index_word_map = [] -error_count = 0 for title in titles: try: - title = title.encode('ascii', 'ignore').decode('utf-8') # this will throw exception if bad characters + title = title.encode('ascii', 'ignore') # this will throw exception if bad characters all_titles.append(title) tokens = my_tokenizer(title) all_tokens.append(tokens) @@ -62,16 +50,9 @@ def my_tokenizer(s): word_index_map[token] = current_index current_index += 1 index_word_map.append(token) - except Exception as e: - print(e) - print(title) - error_count += 1 - + except: + pass -print("Number of errors parsing file:", error_count, "number of lines in file:", len(titles)) -if error_count == len(titles): - print("There is no data to do anything with! Quitting...") - exit() # now let's create our input matrices - just indicator variables for this example - works better than proportions @@ -94,7 +75,7 @@ def main(): svd = TruncatedSVD() Z = svd.fit_transform(X) plt.scatter(Z[:,0], Z[:,1]) - for i in range(D): + for i in xrange(D): plt.annotate(s=index_word_map[i], xy=(Z[i,0], Z[i,1])) plt.show() diff --git a/nlp_class/nb.py b/nlp_class/nb.py index 5d575915..44f4c408 100644 --- a/nlp_class/nb.py +++ b/nlp_class/nb.py @@ -1,14 +1,7 @@ # Naive Bayes spam detection for NLP class, which can be found at: -# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python # https://www.udemy.com/data-science-natural-language-processing-in-python -# dataset: https://archive.ics.uci.edu/ml/datasets/Spambase # Author: http://lazyprogrammer.me -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - from sklearn.naive_bayes import MultinomialNB import pandas as pd @@ -18,7 +11,7 @@ # it will work for other types of "counts", like tf-idf, so it should # also work for our "word proportions" -data = pd.read_csv('spambase.data').values # use pandas for convenience +data = pd.read_csv('spambase.data').as_matrix() # use pandas for convenience np.random.shuffle(data) # shuffle each row in-place, but preserve the row X = data[:,:48] @@ -32,7 +25,7 @@ model = MultinomialNB() model.fit(Xtrain, Ytrain) -print("Classification rate for NB:", model.score(Xtest, Ytest)) +print "Classification rate for NB:", model.score(Xtest, Ytest) @@ -41,4 +34,4 @@ model = AdaBoostClassifier() model.fit(Xtrain, Ytrain) -print("Classification rate for AdaBoost:", model.score(Xtest, Ytest)) \ No newline at end of file +print "Classification rate for AdaBoost:", model.score(Xtest, Ytest) \ No newline at end of file diff --git a/nlp_class/sentiment.py b/nlp_class/sentiment.py index e5f58477..e57219d0 100644 --- a/nlp_class/sentiment.py +++ b/nlp_class/sentiment.py @@ -1,5 +1,4 @@ # This code is for my NLP Udemy class, which can be found at: -# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python # https://www.udemy.com/data-science-natural-language-processing-in-python # It is written in such a way that tells a story. # i.e. So you can follow a thought process of starting from a @@ -7,16 +6,9 @@ # i.e. It is not optimized for anything. # Author: http://lazyprogrammer.me -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import nltk import numpy as np -from sklearn.utils import shuffle from nltk.stem import WordNetLemmatizer from sklearn.linear_model import LogisticRegression @@ -28,19 +20,18 @@ # from http://www.lextek.com/manuals/onix/stopwords1.html stopwords = set(w.rstrip() for w in open('stopwords.txt')) -# note: an alternative source of stopwords -# from nltk.corpus import stopwords -# stopwords.words('english') - # load the reviews # data courtesy of http://www.cs.jhu.edu/~mdredze/datasets/sentiment/index2.html -positive_reviews = BeautifulSoup(open('electronics/positive.review').read(), features="html5lib") +positive_reviews = BeautifulSoup(open('electronics/positive.review').read()) positive_reviews = positive_reviews.findAll('review_text') -negative_reviews = BeautifulSoup(open('electronics/negative.review').read(), features="html5lib") +negative_reviews = BeautifulSoup(open('electronics/negative.review').read()) negative_reviews = negative_reviews.findAll('review_text') - +# there are more positive reviews than negative reviews +# so let's take a random sample so we have balanced classes +np.random.shuffle(positive_reviews) +positive_reviews = positive_reviews[:len(negative_reviews)] # first let's just try to tokenize the text using nltk's tokenizer # let's take the first review for example: @@ -68,10 +59,8 @@ def my_tokenizer(s): current_index = 0 positive_tokenized = [] negative_tokenized = [] -orig_reviews = [] for review in positive_reviews: - orig_reviews.append(review.text) tokens = my_tokenizer(review.text) positive_tokenized.append(tokens) for token in tokens: @@ -80,7 +69,6 @@ def my_tokenizer(s): current_index += 1 for review in negative_reviews: - orig_reviews.append(review.text) tokens = my_tokenizer(review.text) negative_tokenized.append(tokens) for token in tokens: @@ -88,7 +76,6 @@ def my_tokenizer(s): word_index_map[token] = current_index current_index += 1 -print("len(word_index_map):", len(word_index_map)) # now let's create our input matrices def tokens_to_vector(tokens, label): @@ -116,7 +103,7 @@ def tokens_to_vector(tokens, label): # shuffle the data and create train/test splits # try it multiple times! -orig_reviews, data = shuffle(orig_reviews, data) +np.random.shuffle(data) X = data[:,:-1] Y = data[:,-1] @@ -129,46 +116,13 @@ def tokens_to_vector(tokens, label): model = LogisticRegression() model.fit(Xtrain, Ytrain) -print("Train accuracy:", model.score(Xtrain, Ytrain)) -print("Test accuracy:", model.score(Xtest, Ytest)) +print "Classification rate:", model.score(Xtest, Ytest) # let's look at the weights for each word # try it with different threshold values! threshold = 0.5 -for word, index in iteritems(word_index_map): +for word, index in word_index_map.iteritems(): weight = model.coef_[0][index] if weight > threshold or weight < -threshold: - print(word, weight) - - -# check misclassified examples -preds = model.predict(X) -P = model.predict_proba(X)[:,1] # p(y = 1 | x) - -# since there are many, just print the "most" wrong samples -minP_whenYis1 = 1 -maxP_whenYis0 = 0 -wrong_positive_review = None -wrong_negative_review = None -wrong_positive_prediction = None -wrong_negative_prediction = None -for i in range(N): - p = P[i] - y = Y[i] - if y == 1 and p < 0.5: - if p < minP_whenYis1: - wrong_positive_review = orig_reviews[i] - wrong_positive_prediction = preds[i] - minP_whenYis1 = p - elif y == 0 and p > 0.5: - if p > maxP_whenYis0: - wrong_negative_review = orig_reviews[i] - wrong_negative_prediction = preds[i] - maxP_whenYis0 = p - -print("Most wrong positive review (prob = %s, pred = %s):" % (minP_whenYis1, wrong_positive_prediction)) -print(wrong_positive_review) -print("Most wrong negative review (prob = %s, pred = %s):" % (maxP_whenYis0, wrong_negative_prediction)) -print(wrong_negative_review) - + print word, weight diff --git a/nlp_class/spam2.py b/nlp_class/spam2.py deleted file mode 100644 index c8ae5414..00000000 --- a/nlp_class/spam2.py +++ /dev/null @@ -1,89 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python -# https://www.udemy.com/data-science-natural-language-processing-in-python - -# Author: http://lazyprogrammer.me -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer -from sklearn.model_selection import train_test_split -from sklearn.naive_bayes import MultinomialNB -from sklearn.svm import SVC -from wordcloud import WordCloud - - -# data from: -# https://www.kaggle.com/uciml/sms-spam-collection-dataset -# file contains some invalid chars -# depending on which version of pandas you have -# an error may be thrown -df = pd.read_csv('../large_files/spam.csv', encoding='ISO-8859-1') - -# drop unnecessary columns -df = df.drop(["Unnamed: 2", "Unnamed: 3", "Unnamed: 4"], axis=1) - -# rename columns to something better -df.columns = ['labels', 'data'] - -# create binary labels -df['b_labels'] = df['labels'].map({'ham': 0, 'spam': 1}) -Y = df['b_labels'].values - -# split up the data -df_train, df_test, Ytrain, Ytest = train_test_split(df['data'], Y, test_size=0.33) - -# try multiple ways of calculating features -tfidf = TfidfVectorizer(decode_error='ignore') -Xtrain = tfidf.fit_transform(df_train) -Xtest = tfidf.transform(df_test) - -# count_vectorizer = CountVectorizer(decode_error='ignore') -# Xtrain = count_vectorizer.fit_transform(df_train) -# Xtest = count_vectorizer.transform(df_test) - - - -# create the model, train it, print scores -model = MultinomialNB() -model.fit(Xtrain, Ytrain) -print("train score:", model.score(Xtrain, Ytrain)) -print("test score:", model.score(Xtest, Ytest)) -# exit() - - -# visualize the data -def visualize(label): - words = '' - for msg in df[df['labels'] == label]['data']: - msg = msg.lower() - words += msg + ' ' - wordcloud = WordCloud(width=600, height=400).generate(words) - plt.imshow(wordcloud) - plt.axis('off') - plt.show() - -visualize('spam') -visualize('ham') - - -# see what we're getting wrong -X = tfidf.transform(df['data']) -df['predictions'] = model.predict(X) - -# things that should be spam -sneaky_spam = df[(df['predictions'] == 0) & (df['b_labels'] == 1)]['data'] -for msg in sneaky_spam: - print(msg) - -# things that should not be spam -not_actually_spam = df[(df['predictions'] == 1) & (df['b_labels'] == 0)]['data'] -for msg in not_actually_spam: - print(msg) - - diff --git a/nlp_class2/bow_classifier.py b/nlp_class2/bow_classifier.py deleted file mode 100644 index 25588e3b..00000000 --- a/nlp_class2/bow_classifier.py +++ /dev/null @@ -1,147 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import sys -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier -from gensim.models import KeyedVectors - - -# data from https://www.cs.umb.edu/~smimarog/textmining/datasets/ -# alternate source: https://lazyprogrammer.me/course_files/deepnlp_classification_data.zip -train = pd.read_csv('../large_files/r8-train-all-terms.txt', header=None, sep='\t') -test = pd.read_csv('../large_files/r8-test-all-terms.txt', header=None, sep='\t') -train.columns = ['label', 'content'] -test.columns = ['label', 'content'] - - - -class GloveVectorizer: - def __init__(self): - # load in pre-trained word vectors - print('Loading word vectors...') - word2vec = {} - embedding = [] - idx2word = [] - with open('../large_files/glove.6B/glove.6B.50d.txt') as f: - # is just a space-separated text file in the format: - # word vec[0] vec[1] vec[2] ... - for line in f: - values = line.split() - word = values[0] - vec = np.asarray(values[1:], dtype='float32') - word2vec[word] = vec - embedding.append(vec) - idx2word.append(word) - print('Found %s word vectors.' % len(word2vec)) - - # save for later - self.word2vec = word2vec - self.embedding = np.array(embedding) - self.word2idx = {v:k for k,v in enumerate(idx2word)} - self.V, self.D = self.embedding.shape - - def fit(self, data): - pass - - def transform(self, data): - X = np.zeros((len(data), self.D)) - n = 0 - emptycount = 0 - for sentence in data: - tokens = sentence.lower().split() - vecs = [] - for word in tokens: - if word in self.word2vec: - vec = self.word2vec[word] - vecs.append(vec) - if len(vecs) > 0: - vecs = np.array(vecs) - X[n] = vecs.mean(axis=0) - else: - emptycount += 1 - n += 1 - print("Numer of samples with no words found: %s / %s" % (emptycount, len(data))) - return X - - def fit_transform(self, data): - self.fit(data) - return self.transform(data) - - - - -class Word2VecVectorizer: - def __init__(self): - print("Loading in word vectors...") - self.word_vectors = KeyedVectors.load_word2vec_format( - '../large_files/GoogleNews-vectors-negative300.bin', - binary=True - ) - print("Finished loading in word vectors") - - def fit(self, data): - pass - - def transform(self, data): - # determine the dimensionality of vectors - v = self.word_vectors.get_vector('king') - self.D = v.shape[0] - - X = np.zeros((len(data), self.D)) - n = 0 - emptycount = 0 - for sentence in data: - tokens = sentence.split() - vecs = [] - m = 0 - for word in tokens: - try: - # throws KeyError if word not found - vec = self.word_vectors.get_vector(word) - vecs.append(vec) - m += 1 - except KeyError: - pass - if len(vecs) > 0: - vecs = np.array(vecs) - X[n] = vecs.mean(axis=0) - else: - emptycount += 1 - n += 1 - print("Numer of samples with no words found: %s / %s" % (emptycount, len(data))) - return X - - - def fit_transform(self, data): - self.fit(data) - return self.transform(data) - - - -vectorizer = GloveVectorizer() -# vectorizer = Word2VecVectorizer() -Xtrain = vectorizer.fit_transform(train.content) -Ytrain = train.label - -Xtest = vectorizer.transform(test.content) -Ytest = test.label - - - -# create the model, train it, print scores -model = RandomForestClassifier(n_estimators=200) -model.fit(Xtrain, Ytrain) -print("train score:", model.score(Xtrain, Ytrain)) -print("test score:", model.score(Xtest, Ytest)) - - diff --git a/nlp_class2/extra_reading.txt b/nlp_class2/extra_reading.txt deleted file mode 100644 index 4f769441..00000000 --- a/nlp_class2/extra_reading.txt +++ /dev/null @@ -1,14 +0,0 @@ -Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation -https://nlp.stanford.edu/pubs/glove.pdf - -Neural Word Embedding as Implicit Matrix Factorization -http://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf - -Hierarchical Softmax -http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf - -More about Hierarchical Softmax -http://papers.nips.cc/paper/3583-a-scalable-hierarchical-distributed-language-model.pdf - -Distributed Representations of Words and Phrases and their Compositionality -https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf \ No newline at end of file diff --git a/nlp_class2/glove.py b/nlp_class2/glove.py deleted file mode 100644 index b46c13f2..00000000 --- a/nlp_class2/glove.py +++ /dev/null @@ -1,320 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import os -import json -import numpy as np -import matplotlib.pyplot as plt - -from datetime import datetime -from sklearn.utils import shuffle -from util import find_analogies - - -import sys -sys.path.append(os.path.abspath('..')) -from rnn_class.util import get_wikipedia_data -from rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx - -# using ALS, what's the least # files to get correct analogies? -# use this for word2vec training to make it faster -# first tried 20 files --> not enough -# how about 30 files --> some correct but still not enough -# 40 files --> half right but 50 is better - -class Glove: - def __init__(self, D, V, context_sz): - self.D = D - self.V = V - self.context_sz = context_sz - - def fit(self, sentences, cc_matrix=None, learning_rate=1e-4, reg=0.1, xmax=100, alpha=0.75, epochs=10, gd=False): - # build co-occurrence matrix - # paper calls it X, so we will call it X, instead of calling - # the training data X - # TODO: would it be better to use a sparse matrix? - t0 = datetime.now() - V = self.V - D = self.D - - if not os.path.exists(cc_matrix): - X = np.zeros((V, V)) - N = len(sentences) - print("number of sentences to process:", N) - it = 0 - for sentence in sentences: - it += 1 - if it % 10000 == 0: - print("processed", it, "/", N) - n = len(sentence) - for i in range(n): - # i is not the word index!!! - # j is not the word index!!! - # i just points to which element of the sequence (sentence) we're looking at - wi = sentence[i] - - start = max(0, i - self.context_sz) - end = min(n, i + self.context_sz) - - # we can either choose only one side as context, or both - # here we are doing both - - # make sure "start" and "end" tokens are part of some context - # otherwise their f(X) will be 0 (denominator in bias update) - if i - self.context_sz < 0: - points = 1.0 / (i + 1) - X[wi,0] += points - X[0,wi] += points - if i + self.context_sz > n: - points = 1.0 / (n - i) - X[wi,1] += points - X[1,wi] += points - - # left side - for j in range(start, i): - wj = sentence[j] - points = 1.0 / (i - j) # this is +ve - X[wi,wj] += points - X[wj,wi] += points - - # right side - for j in range(i + 1, end): - wj = sentence[j] - points = 1.0 / (j - i) # this is +ve - X[wi,wj] += points - X[wj,wi] += points - - # save the cc matrix because it takes forever to create - np.save(cc_matrix, X) - else: - X = np.load(cc_matrix) - - print("max in X:", X.max()) - - # weighting - fX = np.zeros((V, V)) - fX[X < xmax] = (X[X < xmax] / float(xmax)) ** alpha - fX[X >= xmax] = 1 - - print("max in f(X):", fX.max()) - - # target - logX = np.log(X + 1) - - print("max in log(X):", logX.max()) - - print("time to build co-occurrence matrix:", (datetime.now() - t0)) - - # initialize weights - W = np.random.randn(V, D) / np.sqrt(V + D) - b = np.zeros(V) - U = np.random.randn(V, D) / np.sqrt(V + D) - c = np.zeros(V) - mu = logX.mean() - - - costs = [] - sentence_indexes = range(len(sentences)) - for epoch in range(epochs): - delta = W.dot(U.T) + b.reshape(V, 1) + c.reshape(1, V) + mu - logX - cost = ( fX * delta * delta ).sum() - costs.append(cost) - print("epoch:", epoch, "cost:", cost) - - if gd: - # gradient descent method - # update W - # oldW = W.copy() - for i in range(V): - # for j in range(V): - # W[i] -= learning_rate*fX[i,j]*(W[i].dot(U[j]) + b[i] + c[j] + mu - logX[i,j])*U[j] - W[i] -= learning_rate*(fX[i,:]*delta[i,:]).dot(U) - W -= learning_rate*reg*W - # print "updated W" - - # update b - for i in range(V): - # for j in range(V): - # b[i] -= learning_rate*fX[i,j]*(W[i].dot(U[j]) + b[i] + c[j] + mu - logX[i,j]) - b[i] -= learning_rate*fX[i,:].dot(delta[i,:]) - # b -= learning_rate*reg*b - # print "updated b" - - # update U - for j in range(V): - # for i in range(V): - # U[j] -= learning_rate*fX[i,j]*(W[i].dot(U[j]) + b[i] + c[j] + mu - logX[i,j])*W[i] - U[j] -= learning_rate*(fX[:,j]*delta[:,j]).dot(W) - U -= learning_rate*reg*U - # print "updated U" - - # update c - for j in range(V): - # for i in range(V): - # c[j] -= learning_rate*fX[i,j]*(W[i].dot(U[j]) + b[i] + c[j] + mu - logX[i,j]) - c[j] -= learning_rate*fX[:,j].dot(delta[:,j]) - # c -= learning_rate*reg*c - # print "updated c" - - else: - # ALS method - - # update W - # fast way - # t0 = datetime.now() - for i in range(V): - # matrix = reg*np.eye(D) + np.sum((fX[i,j]*np.outer(U[j], U[j]) for j in range(V)), axis=0) - matrix = reg*np.eye(D) + (fX[i,:]*U.T).dot(U) - # assert(np.abs(matrix - matrix2).sum() < 1e-5) - vector = (fX[i,:]*(logX[i,:] - b[i] - c - mu)).dot(U) - W[i] = np.linalg.solve(matrix, vector) - # print "fast way took:", (datetime.now() - t0) - - # slow way - # t0 = datetime.now() - # for i in range(V): - # matrix2 = reg*np.eye(D) - # vector2 = 0 - # for j in range(V): - # matrix2 += fX[i,j]*np.outer(U[j], U[j]) - # vector2 += fX[i,j]*(logX[i,j] - b[i] - c[j])*U[j] - # print "slow way took:", (datetime.now() - t0) - - # assert(np.abs(matrix - matrix2).sum() < 1e-5) - # assert(np.abs(vector - vector2).sum() < 1e-5) - # W[i] = np.linalg.solve(matrix, vector) - # print "updated W" - - # update b - for i in range(V): - denominator = fX[i,:].sum() + reg - # assert(denominator > 0) - numerator = fX[i,:].dot(logX[i,:] - W[i].dot(U.T) - c - mu) - # for j in range(V): - # numerator += fX[i,j]*(logX[i,j] - W[i].dot(U[j]) - c[j]) - b[i] = numerator / denominator - # print "updated b" - - # update U - for j in range(V): - # matrix = reg*np.eye(D) + np.sum((fX[i,j]*np.outer(W[i], W[i]) for i in range(V)), axis=0) - matrix = reg*np.eye(D) + (fX[:,j]*W.T).dot(W) - # assert(np.abs(matrix - matrix2).sum() < 1e-8) - vector = (fX[:,j]*(logX[:,j] - b - c[j] - mu)).dot(W) - # matrix = reg*np.eye(D) - # vector = 0 - # for i in range(V): - # matrix += fX[i,j]*np.outer(W[i], W[i]) - # vector += fX[i,j]*(logX[i,j] - b[i] - c[j])*W[i] - U[j] = np.linalg.solve(matrix, vector) - # print "updated U" - - # update c - for j in range(V): - denominator = fX[:,j].sum() + reg - numerator = fX[:,j].dot(logX[:,j] - W.dot(U[j]) - b - mu) - # for i in range(V): - # numerator += fX[i,j]*(logX[i,j] - W[i].dot(U[j]) - b[i]) - c[j] = numerator / denominator - # print "updated c" - - self.W = W - self.U = U - - plt.plot(costs) - plt.show() - - def save(self, fn): - # function word_analogies expects a (V,D) matrx and a (D,V) matrix - arrays = [self.W, self.U.T] - np.savez(fn, *arrays) - - -def main(we_file, w2i_file, use_brown=True, n_files=100): - if use_brown: - cc_matrix = "cc_matrix_brown.npy" - else: - cc_matrix = "cc_matrix_%s.npy" % n_files - - # hacky way of checking if we need to re-load the raw data or not - # remember, only the co-occurrence matrix is needed for training - if os.path.exists(cc_matrix): - with open(w2i_file) as f: - word2idx = json.load(f) - sentences = [] # dummy - we won't actually use it - else: - if use_brown: - keep_words = set([ - 'king', 'man', 'woman', - 'france', 'paris', 'london', 'rome', 'italy', 'britain', 'england', - 'french', 'english', 'japan', 'japanese', 'chinese', 'italian', - 'australia', 'australian', 'december', 'november', 'june', - 'january', 'february', 'march', 'april', 'may', 'july', 'august', - 'september', 'october', - ]) - sentences, word2idx = get_sentences_with_word2idx_limit_vocab(n_vocab=5000, keep_words=keep_words) - else: - sentences, word2idx = get_wikipedia_data(n_files=n_files, n_vocab=2000) - - with open(w2i_file, 'w') as f: - json.dump(word2idx, f) - - V = len(word2idx) - model = Glove(100, V, 10) - - # alternating least squares method - model.fit(sentences, cc_matrix=cc_matrix, epochs=20) - - # gradient descent method - # model.fit( - # sentences, - # cc_matrix=cc_matrix, - # learning_rate=5e-4, - # reg=0.1, - # epochs=500, - # gd=True, - # ) - model.save(we_file) - - -if __name__ == '__main__': - we = 'glove_model_50.npz' - w2i = 'glove_word2idx_50.json' - # we = 'glove_model_brown.npz' - # w2i = 'glove_word2idx_brown.json' - main(we, w2i, use_brown=False) - - # load back embeddings - npz = np.load(we) - W1 = npz['arr_0'] - W2 = npz['arr_1'] - - with open(w2i) as f: - word2idx = json.load(f) - idx2word = {i:w for w,i in word2idx.items()} - - for concat in (True, False): - print("** concat:", concat) - - if concat: - We = np.hstack([W1, W2.T]) - else: - We = (W1 + W2.T) / 2 - - - find_analogies('king', 'man', 'woman', We, word2idx, idx2word) - find_analogies('france', 'paris', 'london', We, word2idx, idx2word) - find_analogies('france', 'paris', 'rome', We, word2idx, idx2word) - find_analogies('paris', 'france', 'italy', We, word2idx, idx2word) - find_analogies('france', 'french', 'english', We, word2idx, idx2word) - find_analogies('japan', 'japanese', 'chinese', We, word2idx, idx2word) - find_analogies('japan', 'japanese', 'italian', We, word2idx, idx2word) - find_analogies('japan', 'japanese', 'australian', We, word2idx, idx2word) - find_analogies('december', 'november', 'june', We, word2idx, idx2word) - diff --git a/nlp_class2/glove_svd.py b/nlp_class2/glove_svd.py deleted file mode 100644 index a0fd3c0a..00000000 --- a/nlp_class2/glove_svd.py +++ /dev/null @@ -1,195 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import os -import json -import numpy as np -import matplotlib.pyplot as plt -from sklearn.decomposition import TruncatedSVD - -from datetime import datetime -from sklearn.utils import shuffle -from util import find_analogies - - -import sys -sys.path.append(os.path.abspath('..')) -from rnn_class.util import get_wikipedia_data -from rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx - - -class Glove: - def __init__(self, D, V, context_sz): - self.D = D - self.V = V - self.context_sz = context_sz - - def fit(self, sentences, cc_matrix=None): - # build co-occurrence matrix - # paper calls it X, so we will call it X, instead of calling - # the training data X - # TODO: would it be better to use a sparse matrix? - t0 = datetime.now() - V = self.V - D = self.D - - if not os.path.exists(cc_matrix): - X = np.zeros((V, V)) - N = len(sentences) - print("number of sentences to process:", N) - it = 0 - for sentence in sentences: - it += 1 - if it % 10000 == 0: - print("processed", it, "/", N) - n = len(sentence) - for i in range(n): - # i is not the word index!!! - # j is not the word index!!! - # i just points to which element of the sequence (sentence) we're looking at - wi = sentence[i] - - start = max(0, i - self.context_sz) - end = min(n, i + self.context_sz) - - # we can either choose only one side as context, or both - # here we are doing both - - # make sure "start" and "end" tokens are part of some context - # otherwise their f(X) will be 0 (denominator in bias update) - if i - self.context_sz < 0: - points = 1.0 / (i + 1) - X[wi,0] += points - X[0,wi] += points - if i + self.context_sz > n: - points = 1.0 / (n - i) - X[wi,1] += points - X[1,wi] += points - - # left side - for j in range(start, i): - wj = sentence[j] - points = 1.0 / (i - j) # this is +ve - X[wi,wj] += points - X[wj,wi] += points - - # right side - for j in range(i + 1, end): - wj = sentence[j] - points = 1.0 / (j - i) # this is +ve - X[wi,wj] += points - X[wj,wi] += points - - # save the cc matrix because it takes forever to create - np.save(cc_matrix, X) - else: - X = np.load(cc_matrix) - - print("max in X:", X.max()) - - # target - logX = np.log(X + 1) - - print("max in log(X):", logX.max()) - - print("time to build co-occurrence matrix:", (datetime.now() - t0)) - - # subtract global mean - mu = logX.mean() - - model = TruncatedSVD(n_components=D) - Z = model.fit_transform(logX - mu) - S = np.diag(model.explained_variance_) - Sinv = np.linalg.inv(S) - self.W = Z.dot(Sinv) - self.U = model.components_.T - - # calculate cost once - delta = self.W.dot(S).dot(self.U.T) + mu - logX - cost = (delta * delta).sum() - print("svd cost:", cost) - - def save(self, fn): - # function word_analogies expects a (V,D) matrx and a (D,V) matrix - arrays = [self.W, self.U.T] - np.savez(fn, *arrays) - - -def main(we_file, w2i_file, use_brown=True, n_files=100): - if use_brown: - cc_matrix = "cc_matrix_brown.npy" - else: - cc_matrix = "cc_matrix_%s.npy" % n_files - - # hacky way of checking if we need to re-load the raw data or not - # remember, only the co-occurrence matrix is needed for training - if os.path.exists(cc_matrix): - with open(w2i_file) as f: - word2idx = json.load(f) - sentences = [] # dummy - we won't actually use it - else: - if use_brown: - keep_words = set([ - 'king', 'man', 'woman', - 'france', 'paris', 'london', 'rome', 'italy', 'britain', 'england', - 'french', 'english', 'japan', 'japanese', 'chinese', 'italian', - 'australia', 'australian', 'december', 'november', 'june', - 'january', 'february', 'march', 'april', 'may', 'july', 'august', - 'september', 'october', - ]) - sentences, word2idx = get_sentences_with_word2idx_limit_vocab(n_vocab=5000, keep_words=keep_words) - else: - sentences, word2idx = get_wikipedia_data(n_files=n_files, n_vocab=2000) - - with open(w2i_file, 'w') as f: - json.dump(word2idx, f) - - V = len(word2idx) - model = Glove(100, V, 10) - - # alternating least squares method - model.fit(sentences, cc_matrix=cc_matrix) - model.save(we_file) - - -if __name__ == '__main__': - we = 'glove_svd_50.npz' - w2i = 'glove_word2idx_50.json' - # we = 'glove_svd_brown.npz' - # w2i = 'glove_word2idx_brown.json' - main(we, w2i, use_brown=False) - - # load back embeddings - npz = np.load(we) - W1 = npz['arr_0'] - W2 = npz['arr_1'] - - with open(w2i) as f: - word2idx = json.load(f) - idx2word = {i:w for w,i in word2idx.items()} - - for concat in (True, False): - print("** concat:", concat) - - if concat: - We = np.hstack([W1, W2.T]) - else: - We = (W1 + W2.T) / 2 - - - find_analogies('king', 'man', 'woman', We, word2idx, idx2word) - find_analogies('france', 'paris', 'london', We, word2idx, idx2word) - find_analogies('france', 'paris', 'rome', We, word2idx, idx2word) - find_analogies('paris', 'france', 'italy', We, word2idx, idx2word) - find_analogies('france', 'french', 'english', We, word2idx, idx2word) - find_analogies('japan', 'japanese', 'chinese', We, word2idx, idx2word) - find_analogies('japan', 'japanese', 'italian', We, word2idx, idx2word) - find_analogies('japan', 'japanese', 'australian', We, word2idx, idx2word) - find_analogies('december', 'november', 'june', We, word2idx, idx2word) - diff --git a/nlp_class2/glove_tf.py b/nlp_class2/glove_tf.py deleted file mode 100644 index 9db18bb4..00000000 --- a/nlp_class2/glove_tf.py +++ /dev/null @@ -1,228 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import os -import json -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt - -from datetime import datetime -from sklearn.utils import shuffle -from util import find_analogies - -import sys -sys.path.append(os.path.abspath('..')) -from rnn_class.util import get_wikipedia_data -from rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx - -if tf.__version__.startswith('2'): - tf.compat.v1.disable_eager_execution() - - - -class Glove: - def __init__(self, D, V, context_sz): - self.D = D - self.V = V - self.context_sz = context_sz - - def fit(self, sentences, cc_matrix=None, learning_rate=1e-4, reg=0.1, xmax=100, alpha=0.75, epochs=10): - # build co-occurrence matrix - # paper calls it X, so we will call it X, instead of calling - # the training data X - # TODO: would it be better to use a sparse matrix? - t0 = datetime.now() - V = self.V - D = self.D - - if not os.path.exists(cc_matrix): - X = np.zeros((V, V)) - N = len(sentences) - print("number of sentences to process:", N) - it = 0 - for sentence in sentences: - it += 1 - if it % 10000 == 0: - print("processed", it, "/", N) - n = len(sentence) - for i in range(n): - # i is not the word index!!! - # j is not the word index!!! - # i just points to which element of the sequence (sentence) we're looking at - wi = sentence[i] - - start = max(0, i - self.context_sz) - end = min(n, i + self.context_sz) - - # we can either choose only one side as context, or both - # here we are doing both - - # make sure "start" and "end" tokens are part of some context - # otherwise their f(X) will be 0 (denominator in bias update) - if i - self.context_sz < 0: - points = 1.0 / (i + 1) - X[wi,0] += points - X[0,wi] += points - if i + self.context_sz > n: - points = 1.0 / (n - i) - X[wi,1] += points - X[1,wi] += points - - # left side - for j in range(start, i): - wj = sentence[j] - points = 1.0 / (i - j) # this is +ve - X[wi,wj] += points - X[wj,wi] += points - - # right side - for j in range(i + 1, end): - wj = sentence[j] - points = 1.0 / (j - i) # this is +ve - X[wi,wj] += points - X[wj,wi] += points - - # save the cc matrix because it takes forever to create - np.save(cc_matrix, X) - else: - X = np.load(cc_matrix) - - print("max in X:", X.max()) - - # weighting - fX = np.zeros((V, V)) - fX[X < xmax] = (X[X < xmax] / float(xmax)) ** alpha - fX[X >= xmax] = 1 - - print("max in f(X):", fX.max()) - - # target - logX = np.log(X + 1) - - print("max in log(X):", logX.max()) - - print("time to build co-occurrence matrix:", (datetime.now() - t0)) - - # initialize weights - W = np.random.randn(V, D) / np.sqrt(V + D) - b = np.zeros(V) - U = np.random.randn(V, D) / np.sqrt(V + D) - c = np.zeros(V) - mu = logX.mean() - - # initialize weights, inputs, targets placeholders - tfW = tf.Variable(W.astype(np.float32)) - tfb = tf.Variable(b.reshape(V, 1).astype(np.float32)) - tfU = tf.Variable(U.astype(np.float32)) - tfc = tf.Variable(c.reshape(1, V).astype(np.float32)) - tfLogX = tf.compat.v1.placeholder(tf.float32, shape=(V, V)) - tffX = tf.compat.v1.placeholder(tf.float32, shape=(V, V)) - - delta = tf.matmul(tfW, tf.transpose(a=tfU)) + tfb + tfc + mu - tfLogX - cost = tf.reduce_sum(input_tensor=tffX * delta * delta) - regularized_cost = cost - for param in (tfW, tfU): - regularized_cost += reg*tf.reduce_sum(input_tensor=param * param) - - train_op = tf.compat.v1.train.MomentumOptimizer( - learning_rate, - momentum=0.9 - ).minimize(regularized_cost) - # train_op = tf.train.AdamOptimizer(1e-3).minimize(regularized_cost) - init = tf.compat.v1.global_variables_initializer() - session = tf.compat.v1.InteractiveSession() - session.run(init) - - costs = [] - sentence_indexes = range(len(sentences)) - for epoch in range(epochs): - c, _ = session.run((cost, train_op), feed_dict={tfLogX: logX, tffX: fX}) - print("epoch:", epoch, "cost:", c) - costs.append(c) - - # save for future calculations - self.W, self.U = session.run([tfW, tfU]) - - plt.plot(costs) - plt.show() - - def save(self, fn): - # function word_analogies expects a (V,D) matrx and a (D,V) matrix - arrays = [self.W, self.U.T] - np.savez(fn, *arrays) - - -def main(we_file, w2i_file, use_brown=True, n_files=50): - if use_brown: - cc_matrix = "cc_matrix_brown.npy" - else: - cc_matrix = "cc_matrix_%s.npy" % n_files - - # hacky way of checking if we need to re-load the raw data or not - # remember, only the co-occurrence matrix is needed for training - if os.path.exists(cc_matrix): - with open(w2i_file) as f: - word2idx = json.load(f) - sentences = [] # dummy - we won't actually use it - else: - if use_brown: - keep_words = set([ - 'king', 'man', 'woman', - 'france', 'paris', 'london', 'rome', 'italy', 'britain', 'england', - 'french', 'english', 'japan', 'japanese', 'chinese', 'italian', - 'australia', 'australian', 'december', 'november', 'june', - 'january', 'february', 'march', 'april', 'may', 'july', 'august', - 'september', 'october', - ]) - sentences, word2idx = get_sentences_with_word2idx_limit_vocab(n_vocab=5000, keep_words=keep_words) - else: - sentences, word2idx = get_wikipedia_data(n_files=n_files, n_vocab=2000) - - with open(w2i_file, 'w') as f: - json.dump(word2idx, f) - - V = len(word2idx) - model = Glove(100, V, 10) - model.fit(sentences, cc_matrix=cc_matrix, epochs=200) - model.save(we_file) - - -if __name__ == '__main__': - we = 'glove_model_50.npz' - w2i = 'glove_word2idx_50.json' - main(we, w2i, use_brown=False) - - # load back embeddings - npz = np.load(we) - W1 = npz['arr_0'] - W2 = npz['arr_1'] - - with open(w2i) as f: - word2idx = json.load(f) - idx2word = {i:w for w,i in word2idx.items()} - - for concat in (True, False): - print("** concat:", concat) - - if concat: - We = np.hstack([W1, W2.T]) - else: - We = (W1 + W2.T) / 2 - - - find_analogies('king', 'man', 'woman', We, word2idx, idx2word) - find_analogies('france', 'paris', 'london', We, word2idx, idx2word) - find_analogies('france', 'paris', 'rome', We, word2idx, idx2word) - find_analogies('paris', 'france', 'italy', We, word2idx, idx2word) - find_analogies('france', 'french', 'english', We, word2idx, idx2word) - find_analogies('japan', 'japanese', 'chinese', We, word2idx, idx2word) - find_analogies('japan', 'japanese', 'italian', We, word2idx, idx2word) - find_analogies('japan', 'japanese', 'australian', We, word2idx, idx2word) - find_analogies('december', 'november', 'june', We, word2idx, idx2word) diff --git a/nlp_class2/glove_theano.py b/nlp_class2/glove_theano.py deleted file mode 100644 index c979e76f..00000000 --- a/nlp_class2/glove_theano.py +++ /dev/null @@ -1,255 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import os -import json -import numpy as np -import theano -import theano.tensor as T -import matplotlib.pyplot as plt - -from datetime import datetime -from sklearn.utils import shuffle -from util import find_analogies - -import sys -sys.path.append(os.path.abspath('..')) -from rnn_class.util import get_wikipedia_data -from rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx - - -def momentum_updates(cost, params, lr=1e-4, mu=0.9): - grads = T.grad(cost, params) - velocities = [theano.shared( - np.zeros_like(p.get_value()).astype(np.float32) - ) for p in params] - updates = [] - for p, v, g in zip(params, velocities, grads): - newv = mu*v - lr*g - newp = p + newv - updates.append((p, newp)) - updates.append((v, newv)) - return updates - - -class Glove: - def __init__(self, D, V, context_sz): - self.D = D - self.V = V - self.context_sz = context_sz - - def fit(self, sentences, cc_matrix=None, learning_rate=1e-4, reg=0.1, xmax=100, alpha=0.75, epochs=10, gd=False, use_theano=False, use_tensorflow=False): - # build co-occurrence matrix - # paper calls it X, so we will call it X, instead of calling - # the training data X - # TODO: would it be better to use a sparse matrix? - t0 = datetime.now() - V = self.V - D = self.D - - if not os.path.exists(cc_matrix): - X = np.zeros((V, V)) - N = len(sentences) - print("number of sentences to process:", N) - it = 0 - for sentence in sentences: - it += 1 - if it % 10000 == 0: - print("processed", it, "/", N) - n = len(sentence) - for i in range(n): - # i is not the word index!!! - # j is not the word index!!! - # i just points to which element of the sequence (sentence) we're looking at - wi = sentence[i] - - start = max(0, i - self.context_sz) - end = min(n, i + self.context_sz) - - # we can either choose only one side as context, or both - # here we are doing both - - # make sure "start" and "end" tokens are part of some context - # otherwise their f(X) will be 0 (denominator in bias update) - if i - self.context_sz < 0: - points = 1.0 / (i + 1) - X[wi,0] += points - X[0,wi] += points - if i + self.context_sz > n: - points = 1.0 / (n - i) - X[wi,1] += points - X[1,wi] += points - - # left side - for j in range(start, i): - wj = sentence[j] - points = 1.0 / (i - j) # this is +ve - X[wi,wj] += points - X[wj,wi] += points - - # right side - for j in range(i + 1, end): - wj = sentence[j] - points = 1.0 / (j - i) # this is +ve - X[wi,wj] += points - X[wj,wi] += points - - # save the cc matrix because it takes forever to create - np.save(cc_matrix, X) - else: - X = np.load(cc_matrix) - - print("max in X:", X.max()) - - # weighting - fX = np.zeros((V, V)) - fX[X < xmax] = (X[X < xmax] / float(xmax)) ** alpha - fX[X >= xmax] = 1 - - print("max in f(X):", fX.max()) - - # target - logX = np.log(X + 1) - - # cast - fX = fX.astype(np.float32) - logX = logX.astype(np.float32) - - print("max in log(X):", logX.max()) - - print("time to build co-occurrence matrix:", (datetime.now() - t0)) - - # initialize weights - W = np.random.randn(V, D) / np.sqrt(V + D) - b = np.zeros(V) - U = np.random.randn(V, D) / np.sqrt(V + D) - c = np.zeros(V) - mu = logX.mean() - - # initialize weights, inputs, targets placeholders - thW = theano.shared(W.astype(np.float32)) - thb = theano.shared(b.astype(np.float32)) - thU = theano.shared(U.astype(np.float32)) - thc = theano.shared(c.astype(np.float32)) - thLogX = T.matrix('logX') - thfX = T.matrix('fX') - - params = [thW, thb, thU, thc] - - thDelta = thW.dot(thU.T) + T.reshape(thb, (V, 1)) + T.reshape(thc, (1, V)) + mu - thLogX - thCost = ( thfX * thDelta * thDelta ).sum() - - # regularization - regularized_cost = thCost + reg*((thW * thW).sum() + (thU * thU).sum()) - - updates = momentum_updates(regularized_cost, params, learning_rate) - - train_op = theano.function( - inputs=[thfX, thLogX], - updates=updates, - ) - - cost_op = theano.function(inputs=[thfX, thLogX], outputs=thCost) - - costs = [] - sentence_indexes = range(len(sentences)) - for epoch in range(epochs): - train_op(fX, logX) - cost = cost_op(fX, logX) - costs.append(cost) - print("epoch:", epoch, "cost:", cost) - - - self.W = thW.get_value() - self.U = thU.get_value() - - plt.plot(costs) - plt.show() - - def save(self, fn): - # function word_analogies expects a (V,D) matrx and a (D,V) matrix - arrays = [self.W, self.U.T] - np.savez(fn, *arrays) - - -def main(we_file, w2i_file, use_brown=True, n_files=50): - if use_brown: - cc_matrix = "cc_matrix_brown.npy" - else: - cc_matrix = "cc_matrix_%s.npy" % n_files - - # hacky way of checking if we need to re-load the raw data or not - # remember, only the co-occurrence matrix is needed for training - if os.path.exists(cc_matrix): - with open(w2i_file) as f: - word2idx = json.load(f) - sentences = [] # dummy - we won't actually use it - else: - if use_brown: - keep_words = set([ - 'king', 'man', 'woman', - 'france', 'paris', 'london', 'rome', 'italy', 'britain', 'england', - 'french', 'english', 'japan', 'japanese', 'chinese', 'italian', - 'australia', 'australian', 'december', 'november', 'june', - 'january', 'february', 'march', 'april', 'may', 'july', 'august', - 'september', 'october', - ]) - sentences, word2idx = get_sentences_with_word2idx_limit_vocab(n_vocab=5000, keep_words=keep_words) - else: - sentences, word2idx = get_wikipedia_data(n_files=n_files, n_vocab=2000) - - with open(w2i_file, 'w') as f: - json.dump(word2idx, f) - - V = len(word2idx) - model = Glove(100, V, 10) - model.fit( - sentences, - cc_matrix=cc_matrix, - learning_rate=1e-4, - reg=0.1, - epochs=200, - ) - model.save(we_file) - - -if __name__ == '__main__': - we = 'glove_model_50.npz' - w2i = 'glove_word2idx_50.json' - # we = 'glove_model_brown.npz' - # w2i = 'glove_word2idx_brown.json' - main(we, w2i, use_brown=False) - - # load back embeddings - npz = np.load(we) - W1 = npz['arr_0'] - W2 = npz['arr_1'] - - with open(w2i) as f: - word2idx = json.load(f) - idx2word = {i:w for w,i in word2idx.items()} - - for concat in (True, False): - print("** concat:", concat) - - if concat: - We = np.hstack([W1, W2.T]) - else: - We = (W1 + W2.T) / 2 - - - find_analogies('king', 'man', 'woman', We, word2idx, idx2word) - find_analogies('france', 'paris', 'london', We, word2idx, idx2word) - find_analogies('france', 'paris', 'rome', We, word2idx, idx2word) - find_analogies('paris', 'france', 'italy', We, word2idx, idx2word) - find_analogies('france', 'french', 'english', We, word2idx, idx2word) - find_analogies('japan', 'japanese', 'chinese', We, word2idx, idx2word) - find_analogies('japan', 'japanese', 'italian', We, word2idx, idx2word) - find_analogies('japan', 'japanese', 'australian', We, word2idx, idx2word) - find_analogies('december', 'november', 'june', We, word2idx, idx2word) diff --git a/nlp_class2/logistic.py b/nlp_class2/logistic.py deleted file mode 100644 index 352c2f57..00000000 --- a/nlp_class2/logistic.py +++ /dev/null @@ -1,137 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt -import random -from datetime import datetime - -import os -import sys -sys.path.append(os.path.abspath('..')) -from rnn_class.util import get_wikipedia_data -from rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx - -from markov import get_bigram_probs - - -if __name__ == '__main__': - # load in the data - # note: sentences are already converted to sequences of word indexes - # note: you can limit the vocab size if you run out of memory - sentences, word2idx = get_sentences_with_word2idx_limit_vocab(2000) - # sentences, word2idx = get_sentences_with_word2idx() - - # vocab size - V = len(word2idx) - print("Vocab size:", V) - - # we will also treat beginning of sentence and end of sentence as bigrams - # START -> first word - # last word -> END - start_idx = word2idx['START'] - end_idx = word2idx['END'] - - - # a matrix where: - # row = last word - # col = current word - # value at [row, col] = p(current word | last word) - bigram_probs = get_bigram_probs(sentences, V, start_idx, end_idx, smoothing=0.1) - - - # train a logistic model - W = np.random.randn(V, V) / np.sqrt(V) - - losses = [] - epochs = 1 - lr = 1e-1 - - def softmax(a): - a = a - a.max() - exp_a = np.exp(a) - return exp_a / exp_a.sum(axis=1, keepdims=True) - - # what is the loss if we set W = log(bigram_probs)? - W_bigram = np.log(bigram_probs) - bigram_losses = [] - - - t0 = datetime.now() - for epoch in range(epochs): - # shuffle sentences at each epoch - random.shuffle(sentences) - - j = 0 # keep track of iterations - for sentence in sentences: - # convert sentence into one-hot encoded inputs and targets - sentence = [start_idx] + sentence + [end_idx] - n = len(sentence) - inputs = np.zeros((n - 1, V)) - targets = np.zeros((n - 1, V)) - inputs[np.arange(n - 1), sentence[:n-1]] = 1 - targets[np.arange(n - 1), sentence[1:]] = 1 - - # get output predictions - predictions = softmax(inputs.dot(W)) - - # do a gradient descent step - W = W - lr * inputs.T.dot(predictions - targets) - - # keep track of the loss - loss = -np.sum(targets * np.log(predictions)) / (n - 1) - losses.append(loss) - - # keep track of the bigram loss - # only do it for the first epoch to avoid redundancy - if epoch == 0: - bigram_predictions = softmax(inputs.dot(W_bigram)) - bigram_loss = -np.sum(targets * np.log(bigram_predictions)) / (n - 1) - bigram_losses.append(bigram_loss) - - - if j % 10 == 0: - print("epoch:", epoch, "sentence: %s/%s" % (j, len(sentences)), "loss:", loss) - j += 1 - - print("Elapsed time training:", datetime.now() - t0) - plt.plot(losses) - - # plot a horizontal line for the bigram loss - avg_bigram_loss = np.mean(bigram_losses) - print("avg_bigram_loss:", avg_bigram_loss) - plt.axhline(y=avg_bigram_loss, color='r', linestyle='-') - - - # plot smoothed losses to reduce variability - def smoothed_loss(x, decay=0.99): - y = np.zeros(len(x)) - last = 0 - for t in range(len(x)): - z = decay * last + (1 - decay) * x[t] - y[t] = z / (1 - decay ** (t + 1)) - last = z - return y - - plt.plot(smoothed_loss(losses)) - plt.show() - - # plot W and bigram probs side-by-side - # for the most common 200 words - plt.subplot(1,2,1) - plt.title("Logistic Model") - plt.imshow(softmax(W)) - plt.subplot(1,2,2) - plt.title("Bigram Probs") - plt.imshow(bigram_probs) - plt.show() - - - - diff --git a/nlp_class2/markov.py b/nlp_class2/markov.py deleted file mode 100644 index 4e639e98..00000000 --- a/nlp_class2/markov.py +++ /dev/null @@ -1,137 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np - -import os -import sys -sys.path.append(os.path.abspath('..')) -from rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx - - - -def get_bigram_probs(sentences, V, start_idx, end_idx, smoothing=1): - # structure of bigram probability matrix will be: - # (last word, current word) --> probability - # we will use add-1 smoothing - # note: we'll always ignore this from the END token - bigram_probs = np.ones((V, V)) * smoothing - for sentence in sentences: - for i in range(len(sentence)): - - if i == 0: - # beginning word - bigram_probs[start_idx, sentence[i]] += 1 - else: - # middle word - bigram_probs[sentence[i-1], sentence[i]] += 1 - - # if we're at the final word - # we update the bigram for last -> current - # AND current -> END token - if i == len(sentence) - 1: - # final word - bigram_probs[sentence[i], end_idx] += 1 - - # normalize the counts along the rows to get probabilities - bigram_probs /= bigram_probs.sum(axis=1, keepdims=True) - return bigram_probs - - - -if __name__ == '__main__': - # load in the data - # note: sentences are already converted to sequences of word indexes - # note: you can limit the vocab size if you run out of memory - sentences, word2idx = get_sentences_with_word2idx_limit_vocab(10000) - # sentences, word2idx = get_sentences_with_word2idx() - - # vocab size - V = len(word2idx) - print("Vocab size:", V) - - # we will also treat beginning of sentence and end of sentence as bigrams - # START -> first word - # last word -> END - start_idx = word2idx['START'] - end_idx = word2idx['END'] - - - # a matrix where: - # row = last word - # col = current word - # value at [row, col] = p(current word | last word) - bigram_probs = get_bigram_probs(sentences, V, start_idx, end_idx, smoothing=0.1) - - - # a function to calculate normalized log prob score - # for a sentence - def get_score(sentence): - score = 0 - for i in range(len(sentence)): - if i == 0: - # beginning word - score += np.log(bigram_probs[start_idx, sentence[i]]) - else: - # middle word - score += np.log(bigram_probs[sentence[i-1], sentence[i]]) - # final word - score += np.log(bigram_probs[sentence[-1], end_idx]) - - # normalize the score - return score / (len(sentence) + 1) - - - # a function to map word indexes back to real words - idx2word = dict((v, k) for k, v in iteritems(word2idx)) - def get_words(sentence): - return ' '.join(idx2word[i] for i in sentence) - - - # when we sample a fake sentence, we want to ensure not to sample - # start token or end token - sample_probs = np.ones(V) - sample_probs[start_idx] = 0 - sample_probs[end_idx] = 0 - sample_probs /= sample_probs.sum() - - # test our model on real and fake sentences - while True: - # real sentence - real_idx = np.random.choice(len(sentences)) - real = sentences[real_idx] - - # fake sentence - fake = np.random.choice(V, size=len(real), p=sample_probs) - - print("REAL:", get_words(real), "SCORE:", get_score(real)) - print("FAKE:", get_words(fake), "SCORE:", get_score(fake)) - - # input your own sentence - custom = input("Enter your own sentence:\n") - custom = custom.lower().split() - - # check that all tokens exist in word2idx (otherwise, we can't get score) - bad_sentence = False - for token in custom: - if token not in word2idx: - bad_sentence = True - - if bad_sentence: - print("Sorry, you entered words that are not in the vocabulary") - else: - # convert sentence into list of indexes - custom = [word2idx[token] for token in custom] - print("SCORE:", get_score(custom)) - - - cont = input("Continue? [Y/n]") - if cont and cont.lower() in ('N', 'n'): - break - diff --git a/nlp_class2/ner.txt b/nlp_class2/ner.txt deleted file mode 100644 index 1a95d53e..00000000 --- a/nlp_class2/ner.txt +++ /dev/null @@ -1,48863 +0,0 @@ -@paulwalk O -It O -'s O -the O -view O -from O -where O -I O -'m O -living O -for O -two O -weeks O -. O -Empire B-facility -State I-facility -Building I-facility -= O -ESB B-facility -. O -Pretty O -bad O -storm O -here O -last O -evening O -. O - -From O -Green O -Newsfeed O -: O -AHFA B-other -extends O -deadline O -for O -Sage B-other -Award I-other -to O -Nov O -. O -5 O -http://tinyurl.com/24agj38 O - -Pxleyes B-company -Top O -50 O -Photography O -Contest O -Pictures O -of O -August O -2010 O -... O -http://bit.ly/bgCyZ0 O -#photography O - -today O -is O -my O -last O -day O -at O -the O -office O -. O - -4Dbling B-person -'s O -place O -til O -monday O -, O -party O -party O -party O -. O -< O -3 O - -watching O -the O -VMA B-tvshow -pre-show O -again O -lol O -it O -was O -n't O -even O -a O -good O -show O -the O -first O -time O -... O -so O -bored O -! O - -27 O -followers O -! O -30 O -followers O -is O -my O -goal O -for O -today O -! O - -This O -is O -the O -2nd O -hospital O -ive O -been O -in O -today O -, O -but O -ive O -just O -seen O -a O -doctor O -who O -was O -an O -older O -version O -of O -justin O -:' O -) O - -Friday O -Night O -Eats O -http://twitpic.com/2pdvtr O - -Gotta O -dress O -up O -for O -london B-other -fashion I-other -week I-other -and O -party O -in O -style O -! O - -@Suzie55 O -whispering O -cause O -I O -may O -have O -had O -1 O -too O -many O -vodka O -'s O -last O -night O -and O -am O -a O -lil O -fragile O -, O -hold O -me O -? O - -#NFL O -Week O -2 O -picks O -blog O -is O -up O -on O -@The_Score O -website O -... O -http://blogs.thescore.com/nfl/2010/09/17/pizzolaw2/ O - -I O -like O -@SouljaBoy O -songs O -after O -a O -few O -drinks O - -... O -asked O -me O -a O -question O -for O -once O -, O -but O -no O -! O -You O -ask O -me O -about O -fucking O -donuts O -you O -donut O -... O -have O -a O -nice O -day O -:P O - -every O -year O -your O -" O -bestfriend O -" O -get O -yo O -ass O -in O -trouble O -smdh O -#doinme O - -RT O -@midgetmegs O -: O -hate O -people O -who O -write O -drunk O -status'. O -must O -be O -having O -a O -great O -time O -if O -your O -sat O -writing O -it O -on O -facebook B-company -..... O - -@MrzEndy O -tru O -tru O -I O -'m O -leavin O -again O -on O -Tuesday O -yo O - -The O -guys O -are O -cooking O -about O -a O -dozen O -pizzas O -at O -our O -house O -for O -the O -tailgate O -tonight O -. O -Nonetheless O -it O -smells O -fresh O -in O -this O -place O -. O - -I O -heart O -Park(ing B-other -) I-other -Day I-other -! O -Photo O -of O -meter O -shark O -: O -http://bit.ly/aQun1b O - -hows O -everyones O -day O -goin O -? O - -@Phoebe1_ O -and O -i O -also O -loved O -the O -last O -years O -eurovision B-tvshow -entry O -!! O -hoppaa O -! O -they O -were O -cool O -too O -!! O - -MetroPCS B-company -has O -the O -worst O -customer O -service O -I O -'ve O -experienced O -in O -months O -( O -trying O -to O -help O -someone O -order O -a O -phone O -) O -#metropcs O -#fail O - -@xSophieBx O -He O -has O -a O -habit O -of O -tweeting O -at O -the O -same O -time O -as O -you O -lol O - -A O -TRUE O -friend O -walks O -in O -when O -everybody O -else O -walks O -out O -! O - -Yay O -. O -@penguin O -is O -coming O -to O -see O -me O -next O -week O -... O -plotting O -my O -cooking O -now O -! O - -@_mistercory O -*Smiles O -up O -at O -him* O -Can O -we O -just O -... O -sit O -together O -for O -a O -while O -before O -round O -two O -? O - -the O -tears O -may O -have O -been O -and O -gone O -, O -but O -the O -feeling O -still O -stay O -the O -same O -. O - -@BieberEgypt O -I O -'ve O -got O -some O -bad O -news O -, O -Yeah O -I O -cant O -come O -to O -the O -meeting O -tomorrow O -. O -: O -S O - -@abzmedic O -I O -'m O -missing O -autumn O -because O -of O -work O -. O -:( O -I O -must O -get O -into O -the O -forests O -tomorrow O -! O - -Fellas O -Do O -n't O -just O -lick O -the O -PussyY O -, O -LOVE O -IT O -! O -enjoy O -it O -. O -like O -u O -just O -got O -married O -< O -the O -preacher O -said O -u O -may O -kiss O -the O -bride O - -RT O -@Sexstrology O -: O -Pisces B-other -tend O -to O -escape O -into O -fantasy O -and O -day O -dreams O -. O -There O -they O -are O -free O -. O - -Cant O -wait O -for O -the O -ravens B-sportsteam -game O -tomorrow O -.... O -go O -ray B-person -rice I-person -!!!!!!! O - -I O -just O -took O -" O -When O -your O -mom O -makes O -you O -go O -live O -with O -your O -dad O -Scooter B-person -Braun I-person -, O -your O -life O -..." O -and O -got O -: O -Part O -7 O -! O -Try O -it O -: O -http://tinyurl.com/27r2pdz O - -I O -miss O -being O -able O -to O -run O -and O -dance O -around O -all O -day O -instead O -of O -having O -to O -catch O -my O -breath O -every O -five O -minutes O -of O -my O -life O -. O - -@SnoopDogg O -hey O -snoop B-person -my O -wife O -Cath O -is O -30 O -today O -, O -any O -chance O -of O -a O -shout O -out O -to O -her O -, O - -Today O -I O -got O -a O -promotion O -at O -work O -, O -and O -tomorrow O -I O -'m O -going O -home O -to O -Wisconsin B-geo-loc -for O -a O -few O -days O -. O -So O -content O -with O -life O -right O -now O -. O -:) O - -TWEET O -! O -@stjosephs O -Empls O -of O -the O -Month O -: O -Deborah B-person -L I-person -#Speech O -#Pathologist-Childrens O -Rehab O -and O -Patricia B-person -M I-person -#Referral/#Auth O -#Spec-#Womens O -#Care O -! O - -RT O -@LilTwist O -: O -RT O -this O -if O -you O -want O -me O -to O -go O -back O -live O -on O -Ustream B-company -later O -tonight O - -Really O -hope O -I O -can O -get O -to O -@glasgowfilm O -for O -Winter B-movie -'s I-movie -Bone I-movie -- O -need O -to O -get O -on O -with O -job O -applications O -tonight O -then O -! O -Trailer O -: O -http://bit.ly/bhUlum O - -RT O -@WeSpazForJB O -: O -http://twitpic.com/2nn4ee O -< O ----- O -still O -the O -funniest O -photo O -. O -of O -all O -time O -. O -ALL O -TIME O -. O - -I O -have O -eaten O -a O -large O -quantity O -of O -oranges O -this O -week O -... O - -Bonfire O -tonite O -. O -All O -are O -welcome O -, O -joe B-person -included O - -@KFSH O -totally O -! O -I O -can O -go O -a O -week O -w/o O -tv O -, O -phones O -or O -a O -computer O -. O -I O -get O -some O -time O -to O -myself O -and O -God B-person -. O -Brings O -peace O -into O -my O -life O -. O - -i O -want O -a O -bath O -but O -do O -n't O -have O -a O -bath O -, O -shut O -up O -, O -sam B-person -'s O -coming O -tomorrow O -and O -steve B-person -and O -tanya B-person -will O -be O -round O -at O -10am O -so O -go O -away O -you O -mean O -people O - -What O -Do O -S.F. B-geo-loc -Rabbis O -Eat O -Before O -and O -After O -Yom B-other -Kippur I-other -? O -We O -Asked O -Five O -to O -Enlighten O -Us O -! O -via O -@SFoodie O -http://bit.ly/aQrFUz O - -Not O -only O -is O -it O -#BattlestarGalactica B-tvshow -'s O -32nd O -Anniversary O -, O -tonite O -is O -the O -3rd O -season O -premiere O -of O -#StarWars B-tvshow -#TheCloneWars I-tvshow -!! O - -just O -about O -off O -work O -! O -going O -to O -alderwood B-geo-loc -again O -:) O -yay O -. O -hopefully O -we O -got O -cody B-person -'s O -ipod B-product -in O -the O -mail O -today O -. O - -RT O -@Slijterijmeisje O -: O -Kreeg O -net O -een O -bruikbare O -tip O -van O -iemand O -die O -vorige O -week O -was O -begonnen O -met O -een O -whiskydieet O -, O -hij O -was O -nu O -al O -3 O -dagen O -kwijt O - -Its O -stupid O -I O -hate O -getn O -a O -attitude O -from O -ppl O -when O -I O -'m O -jus O -tryna O -be O -nice O -n O -shit O -. O -WTF O -now O -I O -'m O -mad O -so O -dnt O -talk O -to O -me O -right O -now O - -RT O -@DamnTeenQuotes O -: O -I O -remember O -when O -i O -was O -your O -age O -, O -spencer B-person -from O -iCarly B-tvshow -was O -Crazy B-person -Steve I-person -, O -Carly B-person -was O -Megan B-person -and O -Josh B-person -was O -fat O -. O -#damnteenquotes O - -Last O -stop O -of O -the O -day O -thank O -goddddd O -(@ O -H-E-B O -Plus O -) O -http://4sq.com/7RDhgd O - -@itzkeving O -im O -getting O -mine O -off O -this O -monday O -:' O -) O - -#Fancyflow O -.. O -im O -in O -the O -hair O -salon O -right O -now O -.. O -smh O -.. O -Friday O -, O -its O -packed O -in O -here O -RT O -@neimanMarcus203 O -: O -Where O -is O -@ihavesuperpower O -??? O - -@vogueglamGIRL O -Ah O -I O -know O -! O -She O -is O -simply O -the O -best O -in O -The O -Sept O -Issue O -. O -My O -boyfriend O -'s O -aunt O -worked O -for O -Anna B-person -Wintor I-person -in O -NY O -. O - -You O -gotta O -let O -em O -lick O -it O -before O -they O -stick O -it O - -There O -'s O -a O -lot O -of O -people O -showing O -off O -their O -iPhones B-product -on O -facebook B-company -today O -, O -so O -< O -so O -is O -at O -such O -a O -place O -, O -it O -'s O -really O -not O -that O -interesting O -;o O -) O - -@Infamous__Kid O -lol O -you O -always O -got O -problems O -with O -ur O -hair O -you O -said O -somethin O -last O -time O -like O -they O -didnt O -cut O -it O -right O -ha O - -For O -code O -I O -'ve O -written O -since O -Sept O -' O -09 O -, O -the O -delay O -between O -commit O -to O -SCM O -and O -running O -on O -customer O -hardware O -has O -been O -about O -10 O -days O -. O - -AND O -EVERYTHING O -YOU O -STAND O -FOR O -, O -TURNS O -ON O -YOU O -TO O -SPITE O -YOU O -? O -WHAT O -HAPPENS O -WHEN O -YOU O -BECOME O -THE O -MAIN O -SOURCE O -OF O -HER O -PAIN O -? O - -Yo O -kids O -! O -Today O -'s O -ACF B-other -Friday I-other -Large I-other -Group I-other -is O -at O -PITT B-facility -University I-facility -! O -It O -'s O -at O -Cathedral B-facility -of I-facility -Learning I-facility -G24 I-facility -at O -7pm O -! O -Be O -sure O -to O -check O -it O -out O -! O - -RT O -@WALuvsKatStacks O -: O -Ah O -, O -i O -asked O -for O -a O -follow O -back O -- O -when O -i O -already O -hadd O -one O -so O -they O -call O -me O -thirsty O -lmao O -? O -Fck O -you O -#teamkatstacksbitch O -... O - -:( O -RT O -@themaine O -Who O -is O -coming O -to O -the O -show O -tomorrow O -in O -Hawaii B-geo-loc -? O - -chinese O -food O -.. O -stayin O -in O -for O -tonight O -but O -goin O -to O -the O -archery O -range O -either O -tomorrow O -or O -sunday O - -@Strigy O -got O -mine O -in O -bbt O -aintree B-geo-loc -today O -. O -Played O -table O -tennis O -on O -it O -in O -store O -! O -V O -impressed O -. O -Did O -you O -get O -analogue O -controller O -2 O -? O - -RT O -@Luq_Combs O -: O -Funniest O -thing O -I O -heard O -this O -week O -. O -Wingo B-person -tellin O -me O -and O -wood O -. O -" O -I O -'m O -scared O -" O -#pow#pow#pow@Cowboy_Wingo O - -Today O -'s O -cheer O -went O -from O -awful O -to O -awesome O -from O -the O -moment O -I O -realized O -I O -did O -not O -have O -class O -today O -. O - -Time B-company -Warner I-company -Cable I-company -Boycotting O -Epix B-other -Movie I-other -Channel I-other -Because O -It O -Did O -A O -Deal O -With O -Netflix B-company -http://dlvr.it/5RgcL O - -Fresh O -out O -the O -psych O -ward O -today O - -Today O -was O -awesome O -, O -I O -finally O -get O -to O -relax O -:) O - -My O -#twitter O -age O -is O -458 O -days O -0 O -hours O -3 O -minutes O -49 O -seconds O -. O -Find O -out O -yours O -at O -http://twitter.seocoder.org/ O -#twittertime O - -What O -is O -it O -with O -people O -and O -punctuality O -? O -I O -used O -to O -be O -that O -guy O -and O -honestly O -, O -it O -'s O -a O -haze O -now O -. O -Just O -be O -on O -time O -. O -It O -'s O -not O -like O -its O -a O -surprise O -. O - -After O -I O -pet O -a O -kitten O -on O -JFK B-person -, O -the O -owners O -informed O -me O -it O -'s O -' O -double-pawed O -' O -( O -polydactyl) O -, O -which O -is O -" O -a O -form O -of O -retardation O -in O -the O -animal O -kingdom O -" O - -Soulja B-person -Boy I-person -| O -TheDeAndreWay O -. O -com O -- O -The O -DeAndre O -Way O -in O -stores O -Nov O -. O -2 O -, O -2010 O -http://t.co/woAiLlZ O -via O -@SouljaBoy O - -About O -online O -dietician O -: O -A O -thinner O -stool O -than O -usual O -can O -be O -identified O -and O -removed O -before O -they O -get O -are O -2 O -weeks O -ago O -J O -... O -http://bit.ly/cYGBdz O - -RT O -@RMBWilliams O -: O -Here O -in O -DC B-geo-loc -! O -About O -to O -get O -some O -rest O -! O -Looking O -forward O -to O -tonight O -! O -Yes-sir O -! O - -RT O -@twitter O -: O -#AskEv O -your O -big O -questions O -about O -the O -new O -Twitter B-company -. O -com O -today O -at O -1pm O -PST O -. O -'ll O -be O -responding O -from O -his O -own O -account O -(@ev) O -. O -Make O -... O - -I O -need O -a O -syringe O -of O -anything O -you O -want O -but O -which O -will O -switch O -me O -off O -till O -tomorrow O -noon O -, O -anyone O -?? O - -RT O -@high_n_fly O -: O -#FACT O -when O -you O -are O -comfortable O -with O -yourself O -nothing O -else O -matters O -nobody O -can O -tell O -you O -about O -yourself O -#HnF O - -Lindsay B-person -Lohan I-person -allegedly O -failed O -her O -drug O -test O -. O -So O -not O -fetch O -. O -Quick O -, O -let O -'s O -all O -put O -on O -our O -shocked O -faces O -that O -23 O -days O -of O -rehab O -did O -n't O -work O -! O - -I O -remember O -when O -you O -all O -were O -thiiis O -big O -, O -you O -know O -? O - -I O -have O -a O -strong O -hunch O -Adam B-person -will O -be O -a O -story O -on O -TMZ B-tvshow -tonight O -. O -Gulp O -. O - -WEDNESDAY O -NITES O -WE O -CRANK O -IT O -UP O -AT O -VISIONS B-facility -LOUNGE I-facility -!!!!! O -18+ O -NO O -COLLEGE O -ID O -NEEDED O -! O -LADIES O -FREE O -B4 O -11PM O -! O -VISIONS B-facility -LOUNGE I-facility -( O -247 O -1ST O -AVE O -HKY O -) O - -@angelportugues O -LMAO O -! O -When O -is O -tht O -one O -day O -?: O -P O - -RT O -@PLLGirls O -: O -Episodes O -1-10 O -of O -Pretty B-tvshow -Little I-tvshow -Liars I-tvshow -will O -air O -in O -the O -UK B-geo-loc -on O -October O -18 O -! O - -@Loserface_Laura O -when O -mike B-person -lets O -me O -know O -, O -I O -will O -let O -you O -know O -. O -I O -mean O -everyone O -might O -just O -switch O -out O -a O -lot O -. O - -Such O -a O -shiny O -morning O -!!! O -Love O -It O -. O -Anyways O -, O -today O -will O -be O -very O -busy O -! O - -@BornThisWayBaby O -thankk O -yhuu O -#monsterlove O -u O -just O -made O -my O -day O -:D O - -Why O -hate O -on O -somebody O -for O -doing O -well O -when O -you O -can O -simply O -say O -great O -job O -and O -move O -on O -.. O -[Haters O -u O -can O -kill O -yourself O -] O -cuz O -ur O -losers O - -If O -you O -are O -staying O -in O -Gainesville B-geo-loc -, O -come O -check O -out O -Costa B-facility -Lounge I-facility -TONIGHT O -! O -Say O -you O -are O -with O -salsa B-person -caliente I-person -and O -get O -in O -... O -http://fb.me/FZVzm8H9 O - -@jackjohnson O -where O -can O -I O -get O -the O -official O -set O -list O -to O -the O -show O -at O -Berkeley B-geo-loc -in O -Oct O -6th O -?! O - -Check O -this O -video O -out O --- O -Three B-musicartist -Days I-musicartist -Grace I-musicartist -- O -Break O -( O -Official O -Music O -Video O -) O -[ O -HQ O -] O -http://t.co/GOwCLQJ O -via O -@youtube O - -Haha O -I O -seen O -a O -ped O -egg O -called O -a O -foot O -cheese O -grater O -today O -. O -I O -found O -it O -proper O -hilarious O -xD O - -friday O -night O -missions O -iguess O -! O - -Just O -got O -home O -from O -school O -. O -:D O -it O -'s O -friday O -guys O -!, O - -@ohy22SD O -Hopefully O -the O -Force O -will O -be O -VERY O -strong O -with O -the O -#Padres B-sportsteam -tonight O -. O -We O -NEED O -a O -win O -. O -#ScoreSomeRuns O - -" O -before O -we O -start O -, O -how O -old O -are O -you O -? O -HER O -: O -I O -'m O -15 O -.. O -old O -enough O -!........ O -#FindTheNearestExit O - -RT O -@ericjohnsalut O -" O -Good O -Morning O -! O -Have O -a O -pleasant O -and O -happy O -day O -!" O -http://dlvr.it/5Rh4w O - -when O -did O -@thecodysimpson O -last O -tweet O -,? O -;) O - -@JoshRamsayArmy O -Kay O -sorry O -... O -Well O -my O -dad O -lest O -when O -i O -was O -3 O -. O -He O -has O -issues O -. O -He O -held O -a O -knife O -at O -mom O -. O -I O -can O -STILL O -remember O -that O -... O -=/ O - -OH O -: O -Power O -nap O -. O -I O -need O -it O -. O -Its O -been O -a O -stressful O -week O -. O -I'm O -excited O -for O -xmas B-other -, O -haha O -. O -Goodnight O -. O -:) O -http://bit.ly/9fQGkC O - -Wow O -.. O -saw O -pictures O -of O -Robert B-person -.... O -spent O -2 O -1/2 O -years O -with O -him O -. O -Broke O -up O -4 O -months O -ago O -& O -I O -miss O -when O -we O -were O -perfect O -.. O - -RT O -@rockerfuckerak O -: O -What O -'s O -done O -is O -done O -, O -Just O -leave O -it O -alone O -, O -and O -don't O -regret O -. O -Sometimes O -somethings O -turn O -in-to O -dumb O -things O -. O -And O -that O -'s O -when O -... O - -@N4T4YLOR O -he'll O -probaly O -have O -something O -completely O -different O -now O -! O -Hope O -your O -havin O -a O -good O -time O - -We O -are O -one O -step O -closer O -to O -our O -new O -kitchens O -. O -We O -chose O -a O -maker O -and O -had O -official O -measurements O -taken O -today O -! O - -@lofolulu O -RT O -@couponprincess O -: O -Please O -vote O -for O -me O -to O -be O -a O -Deal O -Pro O -in O -Chicago B-geo-loc -in O -November O -! O -TY O -http://bit.ly/cgmnoX O -- O -DO O -WHAT O -@LOFOLULU O -SAYS O -! O - -@zeeDOTi O -i O -might O -join O -in O -if O -I O -make O -it O -home O -in O -time O -. O -:) O - -@DanaTennille O -that O -'s O -wazzup O -. O -congrats O -on O -your O -win O -in O -week O -1 O -. O - -@Terrysagirl O -love O -to O -, O -it O -'s O -iffy O -though O -, O -i O -don't O -wanna O -give O -up O -privacy O -for O -the O -guys O -. O -But O -after O -episode O -one O -, O -they O -should O -be O -on O -imdb B-other -;) O - -One O -day O -the O -things O -i O -say O -and O -do O -is O -gonna O -get O -me O -a O -nice O -slap O -.. O - -Shows O -Tonight-Local O -Natives B-musicartist -@TheMusicBoxLA O -, O -Primus B-musicartist -@ClubNokia O -Marina&Diamonds O -@ElReyTheatre O -, O -Mystery B-musicartist -Jets I-musicartist -@TheTroubadour O -( O -via O -@TheScenestar O -) O - -@geek O -@borkware O -Somehow O -my O -iPad B-product -in O -Airplane O -mode O -with O -WiFi O -off O -has O -no O -problem O -connecting O -to O -internet O -. O -Time O -for O -a O -wipe O -or O -downgrade O -. O - -Just O -listened O -to O -a O -24 O -y/o O -kid O -from O -japan B-geo-loc -who O -won O -the O -national B-other -flat I-other -picking I-other -championship I-other -flipping O -amazing O -r/l O -august O -rush O - -Pxleyes O -Top O -50 O -Photography O -Contest O -Pictures O -of O -August O -2010 O -... O -http://bit.ly/bgCyZ0 O -#photography O - -RT O -@ravenalexis O -Wanna O -meet O -me O -, O -@jessejane O -, O -@rileysteele O -, O -and O -@kayden_kross O -? O -Come O -to O -the O -Body B-other -Heat I-other -signing O -at O -Hustler B-facility -Hollywood I-facility -Sep O -23rd O -8-10pm O - -Aggressive O -Kids O -With O -ADHD O -May O -Not O -Need O -Antipsychotic O -Meds O -http://t.co/JfGm0uH O - -@KisssMyASS_ O -when O -ya O -birthday O - -RT O -@WakaFlocka1017 O -: O -SO O -WHEN O -I O -GET O -ON O -106 O -N O -JOKE O -AROUND O -YALL O -TALK O -ABOUT O -ME O -LIKE O -A O -DOG O -SHAWTY O -BUT O -WHEN O -I O -BUY O -30,000 O -WORTH O -SCHOOL O -SUPPLY O -N O -... O - -I O -won O -an O -election O -today O -. O -It O -means O -I O -get O -to O -go O -to O -head O -office O -and O -have O -lots O -of O -meetings O -. O -It O -shall O -be O -tough O -but O -I'm O -looking O -forward O -to O -it O -. O - -@TobyHymer O -you O -whittier O -kids O -have O -been O -on O -a O -public O -bus O -before O -:o O -? O -Haha O -jk O -! O - -Ciroc O -it O -is O -dnt O -have O -a O -move O -tonite O -but O -imma O -make O -1 O - -Ima O -start O -going O -to O -the O -dr O -once O -a O -month O -so O -I O -get O -my O -moneys O -worth O - -Toy B-movie -story I-movie -3 I-movie -tonight O -on O -the O -lawn O -! O - -The O -EYE O -of O -the O -Storm O -.... O -The O -Calm O -Before O -It O -... O - -waiting O -one O -extra O -day O -was O -a O -STUPID O -IDEA O -because O -now O -everything O -is O -super O -expensive O -AAAAAHHH O -. O -x_x O -*freakingoutkhsf* O - -My O -dad O -is O -laughing O -because O -in O -some O -translations O -Mark B-other -14:30 I-other -says O -: O -"... O -Before O -the O -COCK O -crows O -...". O -I've O -never O -been O -so O -proud O -LOL O - -@DrHubaEvaluator O -Not O -long O -ago O -I O -responded O -to O -a O -angry O -cardiologist O -whom O -I O -have O -known O -yrs O -. O -when O -he O -spit O -venom O -to O -the O -newspaper O -but O -I O -did O -it O -+ O - -Soo O -Glad O -its O -Finally O -Friday O -! O -Thiss O -week O -went O -to O -slow O -fo'me:/ O - -Movies O -Tonight O -? O -( O -: O - -Is O -it O -cricket O -season O -? O -I've O -killed O -about O -20 O -in O -the O -laundry O -room O -in O -the O -past O -week O -. O - -Come O -to O -" O -6th B-other -Biannual I-other -24 I-other -Hour I-other -Prayer I-other -Focus I-other -" O -Saturday O -, O -November O -13 O -from O -10:00 O -am O -to O -1:00 O -pm O -. O -Mark O -the O -Date O -!!! O -http://fb.me/JyYXPmql O - -A O -nigga O -not O -even O -startin O -tonight O -, O -they O -must O -be O -tryna O -get O -our O -bakc O -up O -QB O -hurt O -. O -smdh O - -Apparently O -40 O -days O -, O -1 O -hour O -, O -49 O -mins O -and O -36 O -seconds O -to O -Doha B-geo-loc -.... O - -Having O -some O -coffee O -, O -the O -only O -way O -I'll O -make O -it O -through O -tonight O -. O - -i O -wonder O -if O -our O -football O -team O -is O -gonna O -win O -their O -game O -today O -... O - -I O -hate O -it O -when O -i O -forget O -that O -i O -wear O -glasses&i O -go O -to O -itch O -my O -eye&i O -hit O -my O -glasses O --_- O -. O -not O -cool O - -@SQicedragon O -How O -can O -I O -get O -the O -updates O -? O -I O -created O -a O -login O -for O -it O -today O -, O -will O -it O -let O -me O -know O -automatically O -next O -time O -? O - -RT O -@GdnPolitics O -: O -RT O -@AlJahom O -: O -Blair B-person -: O -" O -I'm O -gonna O -be O -the O -next O -Pope B-person -" O -Brown B-person -: O -" O -I O -won't O -stop O -you O -as O -long O -as O -you O -make O -sure O -it O -'s O -my O -turn O -next O -" O - -I'll O -go O -get O -some O -#sleep O -before O -some O -#TGS O -#news O -, O -later O -everyone O -x O -) O -! O - -An O -entire O -stack O -of O -papers O -has O -disappeared O -. O -I O -think O -that O -is O -a O -sign O -to O -wrap O -up O -for O -the O -week O -. O - -Best O -day O -of O -school O -:) O -haha O -i O -want O -a O -popsycle O -: O -x O - -@VicapherIsDaddy O -ilike O -to O -call O -it O -that O -too O -. O -I O -dnt O -knw O -why O -people O -talk O -hella O -sht O -over O -tha O -internet O -' O -imean O -iadmit O -ibe O -qassin O -hoes O -when O -.. O - -Ball O -Gowns O -: O -Becoming O -the O -Belle O -of O -the O -Ball O -. O -It O -'s O -easy O -to O -shine O -like O -a O -star O -when O -you O -know O -some O -basic O -tricks O -. O -http://bit.ly/d4s9RR O - -dnce O -2 O -day O -at O -skool O -2 O -dau O -wuz O -kinda O -corny O -evn O -doe O -i O -dnce O -a O -lil O -bit O -ugh O -bored O -lke O -always O -whn O -is O -ya O -gurl O -neva O -nt O -trin O -2 O -fin O -sunthn O -2 O - -Lmao O -. O -I O -have O -a O -feeling O -Imma O -get O -yelled O -at O -tomorrow O -. O -Big O -time O -. O -XD O -Ehh O -oh O -well O - -n O -dude O -.... O -one O -of O -the O -new O -choir O -teacher O -that O -they O -interviewed O -( O -we O -had O -him O -all O -week O -) O -gave O -us O -DONUTS O -!!!! O -haha O -hes O -nice O - -RT O -@diggy_simmons O -: O -Last O -day O -..... O -http://twitpic.com/2g9c1e O - -RT O -@Quotealicious O -: O -Today O -, O -I O -saw O -a O -guy O -driving O -a O -Pepsi B-company -truck O -, O -drinking O -a O -Coke B-product -. O -MLIA O -#Quotealicious O - -After O -I O -pet O -a O -kitten O -on O -JFK B-person -, O -the O -owners O -informed O -me O -it O -'s O -' O -double-pawed O -' O -( O -polydactyl) O -, O -which O -is O -" O -a O -form O -of O -retardation O -in O -the O -animal O -kingdom O -" O - -Oct O -9 O -( O -happy O -dance O -) O -RT O -@MiChY_pEaChY O -@LiifeSizeDoll O -wens O -ur O -bdayy O -boo O -??!! O - -@joshHnumber1fan O -its O -okay O -then O -.. O -make O -it O -when O -it O -works O -:D O - -@iSUPPORTBIEBER O -IM O -SORRRRRRRRRRRRRRRRRRRRY O -< O -/3 O -i O -have O -to O -go O -, O -her O -partys O -in O -a O -week O -and O -im O -on O -the O -dais O -! O - -@xXLauraJXx O -ino O -i O -did O -tweet O -him O -b4 O -n O -justthen B-person -about O -kenny B-person -signing O -his O -book O -but O -it O -was O -today O -ive O -already O -missed O -it O -:( O - -800 O -miles O -to O -see O -clients O -, O -3 O -ACC O -candidate/commissioner O -meetings O -, O -big O -press O -release O -, O -making O -it O -to O -Friday O -.. O -PRICELESS O -! O - -This O -is O -a O -battle O -song O -.. O -Brothers O -and O -sisters O -.. O -Time O -to O -go O -to O -war O -! O - -HAPPY O -B O -. O -DAY O -TORA O -*OOOOOOOOO* O -~ O - -@shoeboxL O -Happy O -Birthday O -! O -s/n O -what O -did O -I O -miss O -in O -Doc B-person -Lewis I-person -' O -class O -today O -? O -lol O - -- O -FantasyPro O -. O -net O -offers O -a O -100% O -Deposit O -Match O -and O -$5 O -in O -credits O -for O -each O -referral O -this O -week O -only O -! O -Must O -deposit O -by O -1pm O -ET O -Sunday O -Sept O -19th O -. O - -neck O -hurts O -and O -no O -time O -to O -schedule O -acupuncture O -and O -massage O -. O - -RT O -@justinbieber O -: O -RT O -@StephLeveridge O -@justinbieber O -i'm O -having O -a O -bad O -day O -:( O -Just O -a O -hi O -from O -you O -would O -turn O -it O -around( O -: O -Love O -you O -xxxxxxx O -= O -HI O -! O - -RT O -@mindykaling O -: O -When O -you O -wear O -your O -heart O -on O -your O -sleeve O -, O -make O -sure O -your O -shirt O -is O -f'ing O -dope O -. O - -We're O -on O -the B-facility -tall I-facility -ship I-facility -Silva I-facility -tonight O -. O -Still O -a O -few O -tickets O -available O -. O -Rain O -is O -gone O -and O -we're O -getting O -ready O -to O -set O -sail O -. O -1030 O -PM O -we O -depart O - -Hoyy O -One O -Day O -Lindooo O -Pero O -Un O -Touch O -De O -Aburrimiento O -: O -S O - -Playboy B-company -- O -10 O -October O -2010 O -Slovenia B-geo-loc -: O -Playboy B-company -- O -10 O -October O -2010 O -SloveniaSlovene O -| O -138 O -pages O -| O -PDF O -| O -39.45 O -Mb O -http://bit.ly/aJILb4 O - -RT O -@fresh_av8ted O -: O -Tonight O -! O -" O -Sex B-movie -In I-movie -The I-movie -City I-movie -" O -Pt O -. O -2 O -at O -#ZULA O -! O -Hosted O -By O -: O -30+ B-other -Beautiful I-other -Ladies I-other -! O -[ O -PLz O -RT O -] O - -RT O -@Slijterijmeisje O -: O -Kreeg O -net O -een O -bruikbare O -tip O -van O -iemand O -die O -vorige O -week O -was O -begonnen O -met O -een O -whiskydieet O -, O -hij O -was O -nu O -al O -3 O -dagen O -kwijt O - -Accounts O -Payable O -Coordinator O -North B-geo-loc -York I-geo-loc -- O -$37K O -( O -1 O -year O -Contract)Location O -: O -Toronto B-geo-loc -, O -Ontario B-geo-loc -, O -CANADA B-geo-loc -... O -http://bit.ly/aOe78A O -#jobs O - -Spent O -all O -of O -last O -night O -puking O -, O -and O -all O -of O -today O -fretting O -. O -Some O -people O -totally O -fail O -to O -pick O -up O -the O -slack O -when O -you O -are O -away O -. O - -If O -you O -are O -in O -Belfast B-geo-loc -this O -weekend O -go O -to O -Botanic B-facility -Gdns I-facility -, O -for O -Taste O -N O -I O -, O -free O -food O -drink O -face O -painting O -etc O -. O -Should O -be O -good O -day O -out O -. O -Sat O -& O -Sun O - -overheard O -on O -subway O -..." O -so O -when O -you O -run O -, O -do O -your O -elbows O -sweat O -?" O - -But O -what O -happens O -when O -karam O -turns O -right O -around O -and O -bites O -you O -? O -And O -everything O -you O -stand O -for O -turns O -on O -you O -despite O -you O -? O - -Getting O -this O -head O -right O -for O -whateva O -I O -get O -into O -tonite O -#sittinupunderthedryertweet O - -Owner O -says O -its O -cool O -. O -Trying O -to O -get O -info O -on O -when O -they'll O -be O -back O -in O -town O -now O -@AandLClothingCo O - -Easy O -A O -tonight O -. O -WOOOO O - -Whats O -goin O -on O -( O -in O -tally O -) O -tonight O -twitter B-company -!! O - -going O -now O -guiseeeee O -... O -if O -i O -dont O -see O -you O -through O -the O -week O -ill O -see O -you O -through O -the O -window O -:) O -:) O - -jus O -about O -to O -get O -dressed O -and O -leave O -again O -.... O -smh O -a O -very O -long O -day O - -@klutzy_girl O -@ratherastory O -I O -wrote O -74,000 O -words O -in O -February O -. O - -RT O -@davehodg O -: O -Pope B-person -says O -atheists O -pick O -& O -choose O -their O -morals O -. O -Correct O -. O -Today O -I O -will O -be O -frowning O -on O -child O -abuse O -& O -not O -having O -a O -problem O -wit O -... O - -Sunday O -can O -you O -please O -hurry O -up O -and O -get O -here O -already O -?! O -I O -want O -to O -see O -@nickjonas O -@kevinjonas O -@joejonas O -already O -! O -Concert O -'s O -going O -to O -be O -rad O -! O -:P O - -VMAs B-other -. O -( O -: O -I O -control O -my O -own O -life O -, O -Charles B-person -was O -never O -in O -charge O -!! O -It O -'s O -def O -one O -of O -THOSE O -days O -. O -Haha O - -@samanthaaai O -Up O -super O -duper O -early O -next O -week O -: O -| O - -FRIDAY O -Sept O -. O -17 O -http://goo.gl/fb/83ib3 O - -#blackholidays O -" O -National O -forget O -the O -he O -say O -she O -say O -... O -what O -do O -God B-person -say O -day O -" O - -@speedy1013 O -I O -ain't O -seen O -them O -!! O -Hmm O -maybe O -have O -to O -download O -next O -time O -we O -have O -a O -movie O -night O -here O -. O -Zombies O -are O -badass O -! O - -Beautiful O -day O -in O -Chicago B-geo-loc -! O -Nice O -to O -get O -away O -from O -the O -Florida B-geo-loc -heat O -. O - -Oscar B-person -'s O -new O -favorite O -pass O -time O -is O -running O -as O -fast O -as O -he O -can O -from O -one O -end O -of O -the O -house O -to O -another O -yelling O -BuhBYYYYYE O - -RT O -@geriatricus O -: O -May O -we O -all O -reflect O -on O -the O -workings O -of O -our O -hearts O -, O -and O -in O -our O -acts O -, O -with O -our O -Jewish O -friends O -and O -loved O -ones O -at O -the O -cusp O -o O -... O - -@iDame2Please O -yea O -u O -c O -da O -pic O -1st B-other -Presbyterian I-other -all O -day O -lol O - -This O -next O -question O -, O -may O -take O -a O -few O -moments O -to O -research O -. O -BUT O -the O -answer O -IS O -on O -JavaMonkeys O -. O -com O -. O -So O -, O -we O -will O -pick O -a O -... O -http://fb.me/GTRhPujh O - -Jan B-person -Brewer I-person -: O -Beheadings O -? O -http://bit.ly/a2LBMP O - -@EnticeMeBaby O -Well O -that O -makes O -sense O -. O -When O -you O -have O -real O -life O -trauma O -, O -it O -can O -often O -drain O -your O -energy O -& O -you O -just O -withdraw O -into O -yourself O -. O -I O -do O -. O - -@mekamoe927 O -I've O -had O -one O -before O -, O -but O -it O -was O -a O -short O -experience O -( O -double O -entendre O -intended) O -. O - -Tonight O -, O -dinner O -with O -friends O -< O -3 O - -Man O -I O -swear O -I O -bought O -2 O -new O -outfits O -but O -it O -'s O -cold O -them O -dresses O -not O -gettin O -worn O -today O -.... O - -A O -Hughie B-person -O'Donoghue I-person -painting O -, O -some O -great O -curvy O -pots O -and O -intricately O -illuminated O -Persian O -books O -.. O -time O -well O -spent O -in O -the B-facility -Fitzwilliam I-facility -. O - -Greek B-other -Festival I-other -at O -St B-facility -Johns I-facility -before O -ASPEN B-geo-loc - -@BeliebinMinajj O -OMG O -MILLIONS O -it O -was O -on O -repeat O -yesterday O -all O -day O -till O -my O -last O -lesson O -and O -then O -around O -9pm O -i O -put O -it O -back O -and O -then O -its O -been O -on O - -If O -you O -are O -staying O -in O -Gainesville B-geo-loc -, O -come O -check O -out O -Costa B-facility -Lounge I-facility -TONIGHT O -! O -Say O -you O -are O -with O -salsa B-person -caliente I-person -and O -get O -in O -... O -http://fb.me/FZVzm8H9 O - -I O -think O -one O -of O -the O -coolest O -things O -to O -do O -would O -be O -to O -hang O -out O -with O -the B-musicartist -Rza I-musicartist -for O -a O -day O -. O - -Going O -on O -a O -&#39 O -;d O -ate&#39 O -; O -with O -Whitney B-person -tonight O -, O -:P O -. O -Haha O -. O - -Day O -three O -of O -St B-other -John I-other -'s I-other -wort I-other -tea O -for O -anxiety O -. O -So O -far O -today O -I O -havent O -had O -a O -panic O -attack...we'll O -see O -if O -this O -works O -! O - -is O -at O -Home-Going O -Well O -- O -I O -take O -a O -Break-Online O -Saturday O -4:30 O -pm- O -6:00 O -pm O -ET- O -Good O -Day- O -Good O -Night-Good O -Week-End O -!!! O - -Ugh O -I O -DON'T O -HAVE O -MY O -EARPHONES O -I O -GUESS O -I O -WON'T O -B O -TALKING O -AND O -DRIVING O -TODAY O -LOL O -.... O -Excuses O -Excuses O -Excuses O -SMH O - -Now O -off O -for O -Sushi O -before O -Sundown O - -@GhostAdventures O -Did O -you O -know O -ur O -new O -season O -starts O -tonight O -? O -jk O -The O -best O -paranormal O -show O -#Ghostadventures O -! O - -There O -was O -just O -a O -fire O -at O -work O -. O -Today O -is O -looking O -up O -. O - -man O -, O -why O -you O -have O -to O -grill O -under O -my O -AC O -unit O -. O -*turns O -it O -off* O -On O -a O -happier O -note O -, O -IT'S O -BBQ O -TIME O -. O - -Katie B-person -up O -6-0 O -after O -1st O - -Morning O -. O -Work O -today O -. O -Hope O -I O -get O -lots O -of O -money O -. O -They're O -going O -to O -be O -in O -a O -bad O -mood O -due O -to O -#geelong O -... O -#MelbourneTomorrow O - -@jonasbrothers O -concert O -tomorrow O -with O -my O -crazy O -chicaaa O - -Man O -: O -Hey O -there O -, O -havent O -I O -seen O -you O -some O -place O -before O -?? O -Woman O -: O -Yes O -, O -and O -thats O -why O -I O -dont O -go O -there O -anymore O - -@Genuine O -Will O -I O -be O -seeing O -you O -at O -#typeamom O -next O -week O -? O - -RT O -@aBaddBeautyy O -: O -What O -did O -you O -say O -is O -happening O -at O -your O -house O -tonite O -@FlyHighBird O -? O -*cough* O --- O -> O -> O -*cough* O - -@tjmota O -I O -am O -wishing O -you O -happy O -birthday O -now O -, O -because O -I O -can't O -remember O -when O -your O -birthday O -really O -is O -except O -that O -it O -'s O -around O -now O -. O - -RT O -@bieberswag O -: O -you're O -a O -belieber O -when O -you O -know O -the O -word O -believer O -doesn't O -look O -right O -anymore O -. O - -Wtf O -? O -The O -library O -closes O -on O -Friday O -? O - -@skaffbm O -love O -that O -sonngg O -!! O -are O -u O -going O -to O -his O -concert O -when O -he O -goes O -to O -richmond B-geo-loc -?? O - -@jonronson O -But O -a O -mess O -on O -one O -day O -may O -not O -a O -mess O -eternally O -be O -. O - -Great O -#fail O -photo O -from O -@joshbuisch O -, O -who O -gets O -my O -#FF O -vote O -for O -the O -day O -for O -being O -hilarious O -! O -http://bit.ly/bA3lUl O - -Awesome O -day O - -3 O -an O -a O -half O -days O -then O -I'm O -on O -HOLIDAY O -oh O -yes O -can't O -wait O -;-) O -;-) O -;-) O - -RT O -@CocaineBesos O -: O -Time O -Is O -Money O -, O -Don't O -Have O -Time O -For O -That O -Other O -Shit O -* O - -@MiSS_SOTO O -I O -think O -that O -'s O -when O -I'm O -gonna O -be O -there O - -Winner O -will O -be O -chosen O -Monday O -morning O -. O -Get O -your O -caption O -in O -the O -funny O -photo O -caption O -contest- O -Nadal B-person -and O -Novak B-person -in O -the O -tub O -http://ow.ly/2G3Jh O - -On O -Thanksgiving B-other -after O -you O -done O -eating O -its O -#TimeToGetOut O -unless O -you O -wanna O -help O -with O -the O -dishes O - -Let O -'s O -get O -this O -show O -on O -the O -road O -! O -May O -you O -have O -an O -easy O -fast O -and O -be O -inscribed O -for O -a O -good O -year O -. O -Gmar O -Chatimah O -Tova O -! O -#YomKippur O -#5771 O - -Called O -my O -homie O -at O -the O -door O -so O -she O -could O -get O -in O -Secrets B-facility -free O -on O -Thursday O -..... O -#HoodRatsNeedLoveToo O - -What O -is O -everyone O -watching O -this O -weekend O -? O -Twins B-sportsteam -? O -Vikings B-sportsteam -? O -anyone O -going O -to O -see O -Friday B-tvshow -Night I-tvshow -Lights I-tvshow -? O - -RT O -@JoeCienkowski O -: O -When O -people O -try O -to O -say O -how O -mean O -and O -harsh O -God B-person -was O -, O -remind O -them O -the O -true O -nature O -of O -God B-person -came O -in O -the O -person O -of O -Jesus B-person -Christ I-person - -Over O -My O -Boyfriend O -House O -Tonite O -We O -Going O -Do O -The O -Damn O -Thing O -. O - -heheRT O -@yuuuiee O -: O -@nissawaww O -LOL O -RT O -MirLes O -: O -jamanSD O -kalo O -nyabutin O -uban O -bokap O -dpt O -100 O -perak O -satu O -uban O -... O -eh O -, O -i O -tweeted O -this O -before O -! O -Dejavu O -:-p O - -RT O -@TeenThings O -: O -I O -love O -waking O -up O -in O -the O -middle O -of O -the O -night O -knowing O -i O -have O -more O -time O -to O -sleep O -. O -#teenthings O -#comedownwithlove O - -ThAnK O -gOd O -ItS O -fRiDaY O -!! O - -Need O -to O -put O -summer O -to O -bed O -by O -Monday O --- O -just O -in O -time O -for O -the O -equinox O -. O - -http://images2.fanpop.com/images/photos/3900000/Edward-100-year-virgin-twilight-series-3937647-510-755.jpg O -ADOGUEI O -ESSA O -IMAGEM O - -Looking O -for O -something O -to O -do O -tonight O -? O -Samuel B-person -Savoirfaire I-person -Williams I-person -at O -Potbelly B-facility -Lincoln I-facility -Park I-facility -in O -Chicago B-geo-loc -, O -IL B-geo-loc -http://bit.ly/bKMFgi O - -Gotta O -dress O -up O -for O -london B-other -fashion I-other -week I-other -and O -party O -in O -style O -! O - -Sooo O -I O -jus O -found O -out O -that O -the O -rising O -senior O -class O -of O -The O -@Oprah O -Leadership O -Academy O -is O -comin O -to O -my O -skool O -nxt O -Saturday O -yeeee O -!!! O -:) O - -RT O -@broniaisdeadly O -: O -http://www.youtube.com/watch?v=yRyK9doEmHE O -< O -A O -DAY O -IN O -THE O -LIFE O -OF O -ME O -. O -Go O -comment O -and O -subscribe O -and O -I'll O -love O -you O -... O - -I O -just O -took O -" O -When O -your O -mom O -makes O -you O -go O -live O -with O -your O -dad O -Scooter B-person -Braun I-person -, O -your O -life O -..." O -and O -got O -: O -Part O -5 O -! O -Try O -it O -: O -http://tinyurl.com/3a5qeu6 O - -.. O -it O -'s O -that O -time O -of O -year O -when O -dreams O -of O -glassy O -waves O -turn O -to O -fluffy O -powder O -.. O -neither O -of O -which O -I O -will O -get O -to O -enjoy O -because O -I O -have O -a O -real O -job O -. O - -New O -Post O -: O -Beyonce B-person -: O -I O -Am O -Yours O -Playlist O -Featured O -on O -VEVO B-company -Live O -Week O -[ O -Videos O -] O -http://bit.ly/b1mqom O - -patate O -mai O -c O -long O -kan O -m O -pr O -just O -ki O -se O -dise O -oui O -#ss4 O - -@camamay O -i O -thought O -u O -were O -there O -when O -i O -was O -coming O -! O - -Pedersen B-person -: O -Hangeland B-other -won't O -be O -at O -Fulham B-geo-loc -for O -long O -http://bit.ly/aCXCNE O -#blackburnrovers O - -Vintage O -sports O -cars O -capture O -the O -feeling O -of O -an O -earlier O -time O -http://bit.ly/aZCBOm O -#classic O -#cars O - -The O -day O -you O -left O -us O -was O -a O -day O -we O -lost O -a O -loved O -one O -. O -But O -we O -will O -never O -leave O -you O -, O -Michael B-person -. O -All O -our O -love O -forever O -. O - -RT O -@VanessaSCassie O -: O -Ok O -, O -one O -more O -tweet O -before O -I O -go O -: O -Fresh O -Look O -Fridays O -post O -was O -on O -an O -math O -games O -site O -for O -#smartboard O -http://bit.ly/b4MXHG O - -RT O -@shanejoneill O -: O -Another O -day O -of O -allday O -and O -night O -skating O -starting O -it O -out O -with O -This O -vanilla O -iced O -latte O -http://plixi.com/p/45648946 O - -@MrOH1O O -I O -been O -waiting O -on O -that O -ALL O -DAY O - -Still O -dislike O -Early O -Decision O -for O -most O -kids O -, O -but O -Early O -Action O -can O -be O -great O -when O -used O -thoughtfully O -. O -http://ow.ly/2G1Cn O -#admissions O -#colleges O - -3 O -weeks O -of O -school O -and O -i'm O -already O -fed O -up O -with O -it O -. O -come O -back O -, O -summer O -. O - -Lohan B-person -faces O -more O -jail O -after O -failed O -drug O -test O -: O -Per O -the O -terms O -of O -her O -probation O -in O -her O -DUI O -case O -as O -laid O -out O -last O -mont O -... O -http://bit.ly/cXoiO2 O - -Twenty O -five O -after O -Seven O - -@pdparticle O -How O -do O -you O -think O -your O -parents O -are O -able O -to O -help O -you O -with O -your O -homework O -? O -It O -'s O -because O -they O -learned O -the O -same O -thing O -years O -ago O -. O - -overnight O -tonight O -... O -gheyyyy O - -Why O -is O -it O -that O -when O -we O -KNOW O -the O -batteries O -in O -a O -remote O -are O -dead O -, O -we O -push O -the O -buttons O -harder O -? O - -RT O -@Silje954 O -: O -AW O -C'MON O -VOTE O -FOR O -@justinbieber O -as O -BEST O -NEW O -ARTIST O -- O -> O -http://on.mtv.com/9w2tWQ O -< O --- O -GO O -AND O -VOTE O -, O -TIME O -IS O -RUNNING O -OUT O -! O -... O - -had O -a O -great O -day O -courtesy O -of O -my O -morning O -that O -I O -had O -talking O -to O -Rashad B-person -... O -:) O - -No O -one O -cares O -.... O -RT O -@RapperBigPooh O -so O -Cudi B-person -got O -a O -problem O -with O -Wale B-person -now O -? O -why O -? O -when O -did O -this O -happen O -? O -is O -this O -musical O -or O -personal O -? O - -RT O -@cityofcalgary O -: O -Free O -swimming O -and O -golf O -tomorrow O -for O -@cbc O -Sports B-other -Day I-other -in O -Canada B-geo-loc -#yyc O -#sportsday O -http://ow.ly/2G4sf O - -today O -in O -a O -nutshell O -; O -abusing O -blind O -people O -, O -seducing O -teachers O -, O -chinese O -takeaways O -, O -gatt B-person -holly I-person -, O -phil B-person -collins I-person -, O -the O -skin O -of O -a O -goblins O -nose O -, O -lush B-company - -IT'S O -FRIDAY O -I'M O -( O -NOT O -) O -IN O -LOVEEE O - -If O -you're O -a O -guy O -& O -I O -keep O -hurting O -your O -" O -feelings O -" O -all O -the O -time O -, O -something O -ain't O -right O -. O -Chin O -up O -nigga O -DAMN O -. O -So O -unattractive O -. O - -Free O -Speech O -Friday O -underway O -. O -Anything O -on O -your O -mind O -is O -fair O -game O -.. O -radio.woai.com O -k/w O -pags O - -SWEETY O -IM O -GONNA O -GIVe O -YOu O -THAT O -FUCKING O -50000 O -WHEN O -I O -GET O -IT O -! O -XD O - -Amazon O -U.K. O -Offering O -HTC B-product -Desire I-product -Z I-product -Unlocked O -October O -11 O -: O -We O -just O -got O -official O -word O -of O -the O -HTC B-product -Desire I-product -Z I-product -earlier O -in O -Lo O -... O -http://bit.ly/bsyz9H O - -Watching O -The B-movie -Room I-movie -for O -like O -the O -fifth O -time O -. O -It O -'s O -a O -classic O -. O -Tommy B-person -WIseau I-person -is O -a O -genius O -. O -#theroom O -#tommywiseau O - -@CHRIS_Daughtry O -I O -knew O -it O -was O -a O -joke O -;) O -That O -'s O -why O -I O -was O -ROTFLMAO O -when O -you O -posted O -that O -! O -;) O -Sorry O -some O -people O -just O -can't O -take O -a O -joke O -. O -:) O - -RT O -@SdotYoung O -: O -@MRsPRETTyKANDi O -If O -i O -could O -be O -with O -u O -for O -our O -b'days O -that O -would O -be O -amazin O -... O -< O -< O -ii O -DEF O -agree O -Boo O -.. O -we O -should O -do O -our O -best O -to O - -@x_yesenia O -me O -too O -! O -i O -cant O -wait O -for O -the O -new O -season O -to O -start O -this O -week O -( O -: O - -RT O -@lil_jeezy_85 O -: O -Going O -live O -tonight O -and O -2mmrow O -< O -what O -you O -performing O -somewhere O -? O - -@JulyzOwn_23 O -TODAY O -BESTIE O -!!! O -He O -was O -at O -my O -school O -, O -and O -I O -saw O -him O -on O -my O -way O -to O -practice O -!! O - -So O -happy O -this O -hellish O -week O -is O -over O -! O -Nothing O -that O -some O -sushi O -and O -sake O -can O -fix O -;) O - -Summer O -has O -come O -and O -past O -, O -the O -innocent O -can O -never O -last O -. O -Wake O -me O -up O -when O -September O -ends O -. O - -Recording O -vocals O -tonight O -! O -Can't O -wait O -for O -the O -song O -to O -be O -done O -! O - -must O -be O -a O -slow O -news O -day O -for O -everybody O -2 O -b O -talkin O -bout O -a O -known O -druggie/acoholic O -bi O -actress O -testin O -pos O -. O -for O -caine O - -Every O -seven O -years O -you O -lose O -around O -half O -of O -your O -friends O -, O -and O -replace O -them O -with O -new O -friends O -. O - -Maybe O -it O -'s O -compulsary O -.. O -when O -you're O -over O -the O -age O -of O -30 O -and O -an O -unemployed O -wrestler O -you O -have O -to O -act O -like O -a O -total O -tool O -. O -http://fb.me/GpyFjTgp O - -Jessum B-person -'s O -Question O -of O -teh O -Day O -: O -who O -'s O -you O -favorite O -sidekick O -? O -Yesterday O -'s O -results O -: O -http://yfrog.com/mmeoxj O - -@zedohhee O -hehehe-ahh O -I O -love O -the O -trolling O -XD O -I'm O -sure O -you O -can O -spell O -propah O -guud O -( O -when O -you O -try O -really O -hard O -) O -:) O - -inspired O -yet O -again O -to O -write O -.. O -cant O -wait O -to O -share O -my O -testimony O -.. O -cant O -wait O -for O -the O -day O -when O -im O -taken O -underwater O -brought O -back O -up O -.. O -BORN O -AGAIN O - -#JenniferAniston O -quote O -of O -the O -day O -: O -I O -don't O -have O -a O -religio O -n.I O -believe O -in O -a O -Go O -d.I O -don't O -know O -what O -it O -looks O -like O -( O -cont O -) O -http://tl.gd/63av0t O - -wore O -socks O -for O -second O -day O -in O -a O -row O -... O -RIP O -Summer O - -RT O -@ZodiacFacts O -: O -#ZodiacFacts O -As O -a O -#Pisces O -you O -may O -find O -your O -thoughts O -racing O -so O -fast O -you O -can't O -keep O -up O -with O -them O -. O - -Today O -'s O -is O -@Elyse_D O -'s O -last O -day O -before O -she O -goes O -on O -Maternity O -leave O -. O -Please O -take O -a O -moment O -to O -wish O -her O -well O -. O -We'll O -miss O -you O -!!! O - -RT O -@KaspervanKooten O -Naast O -de O -theatertour O -die O -komende O -week O -weer O -aanvangt O -ziet O -u O -mijn O -pan O -ook O -op O -tv O -: O -ma O -. O -Co-ass O -., O -di O -. O -Opium O -en O -do O -. O -Koffi O -... O - -@GaryLuvin O -still O -hot O -... O -But O -excited O -about O -tomorrow O -going O -to O -see O -Lady B-person -Gaga I-person -:) O - -@till1025 O -how O -did O -you O -miss O -that O -?! O -I've O -had O -it O -set O -to O -record O -for O -a O -week O -. O -I'll O -save O -it O -for O -when O -you O -come O -to O -cali O - -So O -much O -work O -yet O -to O -do O -before O -heading O -to O -Grand B-other -Masquerade I-other -next O -week O -. O - -RT O -@CarolynsOpinion O -: O -Daytime O -'s O -oldest O -soap O -went O -off O -the O -air O -today O -after O -54 O -years O -, O -so O -the O -WORLD O -stopped O -turning O -for O -3 O -million O -viewers O -. O -Sh O -... O - -@virtualpatti O -- O -Well O -there O -were O -4 O -interviews O -( O -diff O -gov O -jobs O -) O -i O -got O -the O -1 O -i O -wanted O -. O -Glad O -i O -found O -out O -today O -, O -it O -would O -be O -a O -long O -weekend O -if O -not O - -On O -the O -bus O -. O -Full O -of O -people O -- O -thank O -goodness O -it O -'s O -Friday O -!! O - -@ClaireMcD22 O -I O -honestly O -don't O -know O -how O -she O -does O -it O -, O -she O -works O -from O -8am O -till O -9pm O -at O -least O -4 O -days O -a O -week O -. O -She O -'s O -off O -mon/tues O -, O - -Thanks O -and O -I O -will O -RT O -@InnerGizerBunni O -@kizfoprez_TR O -Happy O -Berlated O -Birthday O -! O -I O -hope O -u O -have O -fun O -& O -live O -it O -up O -tonight O -.. O -Enjoy O -& O -b O -safe O -! O - -@JennyVeronica92 O -thats O -whassup O -. O -when O -u O -posed O -to O -be O -comin O -out O -here O - -Our O -Favorite O -YouTube B-company -Videos O -This O -Week O -: O -The O -Drama O -Edition O -: O -Had O -any O -epic O -meltdowns O -this O -week O -? O -You O -know O -, O -of O -the O -cryin O -... O -http://bit.ly/cuy0yt O - -Wtf O -do O -i O -have O -a O -stupid O -3hr O -shift O -tonight O -!! O -I O -wanna O -go O -out O -and O -have O -fun O -:( O -Oh O -well O -time O -to O -watch O -some O -Jersey B-tvshow -Shore I-tvshow -haha O - -the O -current O -library O -plan O -is O -to O -go O -Monday O -morning O -. O -first O -books O -on O -shelves O -, O -then O -holds O -, O -then O -check O -bookstore O -on O -way O -home O -for O -The B-product -Replacement I-product -. O - -ASPCA O -PETA O -HSUS O -.. O -Animal O -rescue O -or O -BIG O -BUSINESS O -? O -http://tl.gd/63aeok O -CK O -OUT O -THIS O -SERIES O -OF O -LINKS O -BEFORE O -YOU O -DONATE O - -Today O -is O -a O -logistical O -nightmare O -! O -Committed O -to O -a O -b'day O -party O -attendance O -but O -discover O -I O -have O -2 O -kids O -, O -no O -car O -& O -only O -plastic O -money O -. O -#amplotting O - -About O -online O -dietician O -: O -A O -thinner O -stool O -than O -usual O -can O -be O -identified O -and O -removed O -before O -they O -get O -are O -2 O -weeks O -ago O -J O -... O -http://bit.ly/cYGBdz O - -@AppleJoey_ O -Buut O -. O -Ill O -have O -to O -get O -on O -my O -phone O -. O -Im O -going O -to O -some O -thing O -with O -my O -friends O -tonight O -. O -D O -: O - -Ha O -! O -RT O -@julie_cook O -: O -thanks O -port B-company -city I-company -java I-company -for O -the O -caffeine O -. O -not O -so O -sure O -my O -coworkers O -will O -be O -as O -pleased O -when O -i O -start O -shooting O -off O -emails O -at O - -RT O -@GreatestQuotes O -: O -" O -When O -everything O -seems O -to O -be O -going O -against O -you O -, O -remember O -that O -the O -airplane O -takes O -off O -against O -the O -wind O -, O -not O -with O -it O -." O -... O - -RT O -@pinkbikeralph O -: O -Working O -at O -BAR B-facility -tonight O -. O -1309 O -sansom O -st O -. O -More O -secrets O -than O -Copa O -. O -Shhhhh O -... O - -@dolemite4 O -haha O -. O -I O -might O -be O -comin O -into O -town O -next O -week O -! O - -YOU O -! O -@STARLIGHTWALTZ O -! O -Do O -you O -want O -to O -go O -to O -art O -showing O -with O -me O -? O -At O -7 O -? O -Tomorrow O -? O -Please O -? O -< O -3 O -you O - -Fuckin O -FRIDAY O -NIGHT O -, O -Tweople O -( O -RIDE O -IT) O -! O -http://www.youtube.com/watch?v=A_Zi-YSW3aQ O - -The O -Cheap O -and O -Choosy O -: O -Play O -All O -Day O -Fitted O -Diaper O -Review O -& O -$15 O -EcoMom O -Gif O -... O -http://t.co/OH9z9zq O -via O -@blogger O - -Bonfire O -tonite O -. O -All O -are O -welcome O -, O -joe B-person -included O - -total O -9 O -for O -Clark B-person -Duke I-person -RT O -@Naathan_B O -: O -Just O -watched O -hot O -tub O -time O -machine O -. O -Funny O -film O -! O -Quite O -enjoyable O -! O -Hmm O -7.5 O -/10 O - -Want O -to O -see O -the O -view O -from O -our O -floor O -of O -yesterday O -'s O -police O -car O -mayhem O -during O -the O -shooting O -incident O -? O -http://wp.me/11Azj O - -@dragonjones O -couldn't O -help O -but O -notice O -you O -look O -uncomfortable O -when O -you O -walk O -. O -I O -am O -guessing O -you O -have O -a O -back O -problem O -. O - -Happy O -birfday O -to O -the O -only O -p-i-m-p O -I O -know O -... O -@amcdavid O -! O -We're O -getting O -" O -white O -boy O -wasted O -" O -tonight O -! O - -RT O -@USArmy O -: O -#USArmy O -recognizes O -today O -as O -National B-other -POW/MIA I-other -Recognition I-other -Day I-other -. O -Visit O -http://www.dtic.mil/dpmo/pow_day/ O -to O -read O -the O -Presidenta O -... O - -Anybody O -who O -waits O -until O -AFTER O -the O -last O -item O -is O -scanned O -and O -the O -total O -is O -calculated O -to O -get O -out O -a O -checkbook O -or O -d O -... O -http://tinyurl.com/2c4wqjv O - -Win O -$100 O -Visa B-company -card O -! O -http://www.alimartell.com/index.php/2010/09/14/its-the-most-wonderful-time-of-the-year-and-im-about-to-make-it-100-better O - -@TempestBeauty O -Ive O -heard O -of O -that O -. O -Hadn't O -heard O -it O -called O -that O -before O -. O -I O -considered O -it O -but O -my O -hair O -is O -so O -oily O -, O -I O -think O -I O -would O -look O -dirty O -. O -:) O - -Is O -Joaquin B-person -Phoenix I-person -Our O -Modern O -Day O -Andy B-person -Kaufman I-person -?: O -... O -the O -bizarre O -events O -leading O -up O -to O -this O -moment O -, O -the O -movie O -could O -... O -http://bit.ly/aUZ2on O - -DUDE O -I O -CAN O -WATCH O -JENNIFERS B-movie -BODY I-movie -:O O -I O -KNOW O -WHAT O -IMMA O -BE O -DOING O -ALL O -WEEK O -xD O -I O -LOVE O -THESE O -MOVIES O -! O - -Nice O -and O -easy O -as O -I O -take O -my O -time O -. O - -Uhmm O -. O -Looks O -like O -I'm O -not O -leaving O -my O -house O -tonight O -! O - -RT O -@InspiredRobin O -: O -We O -have O -so O -much O -time O -and O -so O -little O -to O -do O -. O -Strike O -that O -, O -reverse O -it O -. O --Roald B-person -Dahl I-person - -RT O -@Sexstrology O -: O -Pisces O -tend O -to O -escape O -into O -fantasy O -and O -day O -dreams O -. O -There O -they O -are O -free O -. O -< O -yesss O -#wavesindexfingerintheair O -#AGREED O -! O -^10thpower O - -RT O -@JAMESINREHAB O -: O -@4094jij05b O -it O -'s O -in O -England B-geo-loc -. O -Will O -tweet O -tomorrow O -if O -they O -let O -me O -... O - -.. O -@Davman91 O -Thinks O -I'm O -Funny O -When O -I O -Tell O -The O -Punch O -Line O -Wrong O -... O -;) O - -whats O -the O -point O -in O -being O -on O -the O -computer O -if O -i O -can O -just O -be O -mobile O -all O -the O -time O -? O -exactly O -. O - -@Sophieeander O -will O -.. O -Dont O -go O -!! O -Cause O -its O -your O -last O -year O -i O -know O -the O -school O -suck O -but O -you O -should O -go O -to O -school O -^^ O -" O -HK O -can O -wait O -for O -u O -.. O -!! O - -@bowlerhatlover O -Been O -trying O -to O -find O -a O -copy O -of O -" O -Nostalgia B-product -" O -for O -years O -. O -http://hollisframpton.org.uk/ O - -I O -love O -listening O -to O -lies O -when O -i O -know O -the O -truth O - -RT O -@CoreyTaylorRock O -: O -Chinese O -Proverb O -: O -one O -must O -first O -look O -foolish O -before O -he O -looks O -brilliant O -. O - -Apparently O -#turningpoint O -has O -a O -hash-tag O -. O -And O -@RoundhouseLDN O -has O -a O -Twitter B-company -account O -. O -So O -with O -the O -formalities O -over O -... O -see O -you O -tomorrow O -. O - -photographing O -the O -midway O -tonight O -.. O -should O -be O -fun O -.. O -lots O -of O -fun O -lights O -!! O -makes O -for O -great O -photos O -.. O -like O -this O -one O -.. O -http://twitpic.com/2pdvsw O - -Just O -casually O -stalking O -Cheryl B-person -AND O -Dermot B-person -tomorrow O -.... O -NO O -BIGGIE O - -@Robo_Robb O -I O -see O -you O -#FF O -' O -ing O -me O -. O -:O O -( O -Also O -Matt B-person -will O -be O -saying O -thanks O -when O -he O -'s O -at O -home O -:P O -) O - -RT O -@NROcorner O -: O -Why O -Constitution B-other -Day I-other -Matters O -: O - -5 O -Things O -You O -Need O -To O -Do O -Before O -Contacting O -A O -Professional O -Photographer O -http://goo.gl/fb/I7rBo O -#inourhead O - -@DEVEY2G O -no O -I O -liked O -the O -one O -u O -had O -had O -that O -time O -but O -I'm O -sure O -I O -cood O -make O -one O -wit O -my O -bro O -possibly O - -Alright O -yall O -im O -done O -add O -me O -on O -bbm O -again O -, O -this O -the O -last O -time O -pin O -: O -22AF9362 O - -RT O -@Slijterijmeisje O -: O -Kreeg O -net O -een O -bruikbare O -tip O -van O -iemand O -die O -vorige O -week O -was O -begonnen O -met O -een O -whiskydieet O -, O -hij O -was O -nu O -al O -3 O -dagen O -kwijt O - -It O -is O -the O -80 O -' O -weekend O -! O -I O -started O -today O -with O -some O -wonderful O -videos O -; O -I'll O -be O -sharing O -with O -all O -of O -you O -more O -wonderful O -... O -http://fb.me/G3Nzs97h O - -@KatiesPizza O -On O -the O -menu O -at O -#playwithyourfood O -this O -Sunday O -: O -hot O -, O -fresh O -ricotta O -donuts O -with O -blackberries O -and O -honey O -. O -http://tiny.cc/6dshg O - -Wat O -a O -day O -today O -. O -But O -oh O -well O -i O -love O -her O -*Danielle O -ily* O - -I O -just O -took O -" O -When O -your O -mom O -makes O -you O -go O -live O -with O -your O -dad O -Scooter B-person -Braun I-person -, O -your O -life O -turns O -..." O -and O -got O -: O -Part O -2 O -! O -Try O -it O -: O -http://bit.ly/clxrMy O - -We O -are O -tied O -as O -the O -5th O -band O -to O -compete O -in O -South B-company -107 I-company -'s O -Battle O -of O -the O -Bands O -next O -Friday O -for O -the O -spot O -to O -open O -for O -... O -http://fb.me/IxhGnUhs O - -John B-person -Acuff I-person -joined O -@daveramseys O -team O -! O -sweet O -! O -I O -hope O -Dave B-person -gives O -him O -his O -own O -show O -one O -day O -, O -but O -i'm O -excited O -for O -JA B-person -and O -wish O -him O -all O -the O -best O - -@sporty191 O -MUAH O -!!!!! O -Hope O -you O -had O -a O -gr8 O -day O -today O -! O -XO O - -Had O -a O -Helluva O -Schedule O -Yesterday O -, O -Today O -and O -Tomorrow O -.. O -Finally O -get O -to O -rest O -on O -Sunday O -.. O -Unless O -there O -'s O -work O -to O -do O -out O -there O -o_O O -#ImJustSaying O - -RT O -@OMGwhatateen O -: O -RT O -if O -you O -are O -happy O -it O -'s O -friday O -. O -#OMGwhatateen O - -I O -hate O -it O -when O -I'm O -tired O -at O -about O -8 O -but O -by O -11 O -I O -seem O -to O -be O -awake O -. O -- O -. O -- O - -RT O -@ZodiacFacts O -: O -#ZodiacFacts O -As O -a O -#Cancer O -you O -may O -find O -that O -your O -energy O -is O -just O -right O -for O -something O -really O -big O -. O - -Dues O -can O -still O -be O -paid O -on O -monday O -! O -Better O -get O -at O -it O -@its_lizzyN O -@katzamora O -@cindyyobitch_ O -@6ixty_nine O -!! O - -ANW O -: O -Update O -: O -TMZ B-company -Reporting O -Lindsay B-person -Lohan I-person -Just O -Failed O -Her O -Drug O -Test O -: O -We O -just O -congratulated O -Lindsay B-person -an O -hour O -ago O -on O -h O -... O -http://bit.ly/a2lhpa O - -Resources O -- O -Tweets O -of O -the O -Week O -( O -9.13 O -. O -10 O -- O -9.17 O -. O -10) O -: O -This O -weeks O -Tweets O -of O -the O -Week O -has O -some O -cool O -inspiration O -, O -coupl O -... O -http://bit.ly/95X8G5 O - -Hope O -to O -see O -you O -tonight O -at O -Morgan B-facility -Le I-facility -Fays I-facility -!! O -Great O -time O -:) O - -#lindsaylohan O -US B-geo-loc -- O -Lindsay B-person -Lohan I-person -: O -Failed O -Drug O -Test O -Report O -Is O -" O -Nuts O -": O -After O -it O -'s O -reported O -that O -she O -flunked O -a O -drug O -... O -http://dlvr.it/5RfK7 O - -RT O -@LilTwist O -: O -RT O -this O -if O -you O -want O -me O -to O -go O -back O -live O -on O -Ustream B-company -later O -tonight O - -says O -it O -'s O -Saturday O -! O -I'm O -wearing O -my O -Weekend O -! O -:) O - -Football O -game O -tonight O -!!!!! O -V-I-K-I-N-G-S B-sportsteam -!!!!!!! O - -Is O -sad O -that O -she O -'s O -missing O -Cowboy B-musicartist -Mouth I-musicartist -tonight O -:( O - -@AishaHanii2 O -@starflower1204 O -Ahhh O -okay O -. O -I've O -never O -ordered O -a O -CD O -before O -so O -I O -have O -NO O -idea O -. O -just O -say O -I O -NEED O -IT O -TO O -GET O -THROUGH O -THE O -WEEKEND O -!! O -xD O - -Political O -Wire O -: O -Bonus O -Quote O -of O -the O -Day O -http://bit.ly/9UiOqY O - -My O -all O -time O -favourite O -socks O -are O -the O -ones O -I O -knitted O -myself O -. O -So O -comfy O -. O -Must O -knit O -some O -more O -. O - -Damn O -all O -mi O -niggas O -havin O -babies O -on O -meh O -. O -Nope O -nope O -! O -I O -not O -da O -step O -mommy O -. O -Time O -for O -a O -new O -team O -. O -I'm O -talkin O -#allstarlineup O - -@mychiller O -Must O -be O -before O -the O -coffee O -right O -@erichthewebguy O - -RT O -@Queen_UK O -: O -Pope B-person -insists O -he O -left O -his O -cigarettes O -at O -Holyroodhouse B-facility -. O -One O -'s O -asked O -the O -staff O -to O -have O -a O -look O -around O -. O -The O -DoE B-other -may O -well O -have O -p O -... O - -what O -to O -do O -today O -? O - -i O -dunno O -why O -people O -still O -type O -'' O -sad O -face''.....''smiley O -face''...''raised O -eyebrow''...when O -its O -alot O -more O -easier O -to O -make O -it O - -@JohnJames_BBuk O -look O -at O -this O -picture O -of O -you O -when O -your O -like O -14 O -... O -on O -Twitpic O -http://t.co/hDq4cXW O -via O -@AddThis O - -Drycleaners O -then O -shape O -up O -. O -Get O -fresh O -then O -off O -to O -d O -red O -carrots O -tonight O -. O -Holla O -at O -ur O -boi O - -i O -was O -surprised O -when O -she O -sent O -me O -a O -request O -on O -facebook B-company -... O -we O -hadnt O -taLked O -in O -a O -few O -years O -:( O - -does O -any O -one O -kno O -the O -right O -address O -to O -the O -walmart B-company -that O -@Treysongz O -is O -suppose O -to O -be O -at O -tomorrow O -in O -maryland B-geo-loc -?? O - -Now O -time O -too O -do O -my O -hair O -! O -New O -look O -on O -deck O -! O - -RT O -@robynsuave O -shout O -out O -@OhNoIts_JoJo O -#teamlesbian O -#teamstud O -ayyy O -< O --- O -all O -day O -everyday O -fam O -!!!! O - -'I O -just O -got O -confirmed O -that O -me O -and O -Jorge B-person -will O -be O -playing O -2 O -songs O -from O -" O -The B-other -Devils I-other -Art I-other -" O -at O -Words B-other -and I-other -Wine I-other -September O -... O -http://fb.me/AyezVfH3' O - -@modernpaper O -@alamodestuff O -what O -a O -fabulous O -photo O -of O -you O -two O -! O -it O -looks O -like O -you O -were O -fast O -friends O -, O -and O -had O -an O -amazing O -time O -. O - -Playboy B-company -- O -10 O -October O -2010 O -Slovenia B-geo-loc -: O -Playboy B-company -- O -10 O -October O -2010 O -SloveniaSlovene O -| O -138 O -pages O -| O -PDF O -| O -39.45 O -Mb O -http://bit.ly/aJILb4 O - -@themayorpete O -I O -split O -my O -time O -now O -btwn O -SF B-geo-loc -and O -San B-geo-loc -Mateo I-geo-loc - -Time O -magazine O -wrote O -an O -article O -saying O -the O -rumor O -touch O -is O -for O -fags O - -dear O -youtube B-company -why O -does O -it O -take O -so O -long O -to O -upload O -a O -video O -gggrrrrr O - -@welovepop O -much O -prefer O -a O -GA B-geo-loc -announcement O -that O -they O -are O -recording O -soon O -..... O -it O -is O -really O -a O -bad O -time O -for O -GA B-geo-loc -fans O -at O -the O -moment O -.... O -do O -not O -like O -.. O - -ASPCA O -PETA O -HSUS O -.. O -Animal O -rescue O -or O -BIG O -BUSINESS O -? O -http://tl.gd/63aeok O -CK O -OUT O -THIS O -SERIES O -OF O -LINKS O -BEFORE O -YOU O -DONATE O - -Hey O -Listeners O -we're O -recording O -Sunday O -for O -episode O -28 O -& O -it O -'s O -a O -retrospective O -on O -the O -Halo B-product -franchise O -along O -with O -an O -ass O -load O -of O -other O -goodness O -! O - -If O -I O -had O -one O -wish O -it O -would O -be O -to O -drive O -bugatti B-product -V I-product -for O -a O -day O -! O -Pretty O -pls O -fix O -it O -for O -me O -jim O -! O -Xxx O - -the O -group O -Maunalua B-musicartist -will O -be O -playing O -tomorrow O -@wfmkahala O -at O -Noon O -! O - -Anyone O -know O -what O -days/times O -that O -you O -can O -smoke O -hookah O -at O -the O -mix(cma O -center O -) O -in O -corbin B-geo-loc -. O - -Willow B-person -Smith I-person -is O -not O -9 O -years O -old O -.... O -she O -got O -that O -Lil O -Mama/ O -Benjamin B-person -Button I-person -disease O - -( O -via O -POPSUGAR O -) O -Sarah B-person -Jessica I-person -Parker I-person -and O -Gwen B-person -Stefani I-person -Wrap O -Up O -Another O -Successful O -New B-other -York I-other -Fashion I-other -Week I-other -: O -New B-geo-loc -York I-geo-loc -Fa O -... O -http://bit.ly/aMaJNB O - -my O -friend O -and O -me O -were O -filmed O -and O -interviewed O -about O -sex O -in O -cologne O -:D O -gosh O -thats O -so O -crazy O -.. O -hope O -they O -show O -it O -on O -tv O -on O -monday O -:P O -#RTLextra O - -It O -'s O -supposed O -to O -rain O -in O -The B-geo-loc -Bay I-geo-loc -on O -Sunday O -... O -well O -, O -weather.com O -says O -" O -showers O -". O -Whatever O -, O -same O -thing O -. O - -Sony/Bud O -Light O -3D O -tailgate O -parties O -on O -Saturday O -- O -Clemson B-geo-loc -party O -at O -http://bit.ly/cds8Pd O -; O -Auburn B-geo-loc -party O -at O -http://bit.ly/9X1KP8 O -#sony3d O -@espn O - -about O -to O -get O -dress O -hangin O -wit O -the O -big O -sis O -Abby B-person -tonite O - -@2kjdream O -good O -morning O -! O -And O -hope O -today O -you O -will O -enjoy O -your O -day~ O -:D O - -RT O -@iMakeChicksCry O -: O -But O -when O -ur O -not O -actually O -alone O -then O -forever O -doesn't O -seem O -so O -bad O -... O - -RT O -@KhadijahC O -RT O -@SuchaMFGentlemn O -wonder O -when O -@sexinini O -is O -gon O -lt O -me O -put O -the O -8==== O -=D O -on O -her O -? O -< O -lmfao O -wooooooow O -( O -nini B-person -knws O -wsup O -ha O -u O -next O -lol O -) O - -Niiiiice O -. O -http://www.pbs.org/wgbh/pages/frontline/ O -Watched O -The B-tvshow -Persuaders I-tvshow -in O -class O -today O -, O -super O -stupid O -example O -but O -super O -good O -. O - -@Ravenwithclaws O -last O -time O -we O -spoke O -you O -were O -very O -... O -Opinionated O -? O -Lol O -:) O -and O -im O -good O -too O -, O -sorry O -i O -have O -not O -been O -on O -: O --S O - -@TheWon_A O -Crooked B-musicartist -is O -in O -town O -October O -7th O -!! O - -Business B-company -Alliance I-company -will O -save O -you O -time O -and O -money O -and O -most O -importantly O -, O -match O -you O -with O -the O -right O -franchise O -. O -check O -out O -at O -http://ow.ly/2kt2M O - -no O -matter O -how O -long O -I'll O -wait O - -@KimberleyW1983 O -Thanks O -, O -c4 O -is O -going O -on O -! O -Lol O -. O -DVD O -Out O -25th O -October O -! O - -Whoo O -who O -ready O -for O -some O -Seahawk B-sportsteam -football O -? O -Can't O -wait O -to O -watch O -my O -dirtbags O -on O -sunday O -. O - -will O -go O -at O -my O -B-day O -party O -. O -Fucking O -can't O -believe O -it O -! O - -May O -everyone O -who O -is O -celebrating O -Yom B-other -Kippur I-other -have O -an O -easy O -and O -meaningful O -fast O -! O -#notaboutbirthcontrol O - -only O -on O -the O -#LateLate O -can O -i O -find O -respect O -for O -#desbishop O -, O -he O -'s O -actually O -pretty O -funny O -, O -never O -liked O -him O -before O -this O -point O -. O - -@borneobaby O -Me O -neither O -. O -I O -will O -think O -of O -witty O -replies O -a O -week O -later O -! O -My O -mind O -will O -be O -a O -complete O -blank O -I O -know O -it O -! O - -RT O -@claywsyx6 O -: O -THE O -FOOTBALL O -FEVER O -: O -Ohio B-geo-loc -head O -coach O -Frank B-person -Solich I-person -says O -Ohio B-sportsteam -State I-sportsteam -knows O -they O -have O -a O -special O -team O -and O -season O -underway O -. O -Long O -... O - -Always O -wondered O -which O -species O -had O -the O -highest O -biomass O -RT O -@NatGeoSociety O -: O -Friday O -Fact O -: O -The O -world O -'s O -termites O -outweigh O -the O -world O -'s O -people O - -< O -3 O -it O -RT O -@Djcheapshot O -: O -Tonite O -I O -m O -DJing O -at O -Mai B-facility -Tai I-facility -in O -Long B-geo-loc -Beach I-geo-loc -. O -I'm O -considering O -wearing O -MY O -TIE O -!! O -Get O -it O -? O -My O -tie O -= O -Mai O -Tai O -? O -No O -? O -Sorry O -. O -Bye O -. O - -French O -IV O -embarrassed O -me O -today O -.. O -never O -again O -will O -I O -go O -into O -a O -battle O -unprepared O -. O - -@BPadalecki1990 O -Do O -you O -have O -any O -pic O -'s O -from O -your O -wedding O -day O -hun O -I O -would O -like O -to O -see O -them O - -Looks O -like O -we O -will O -be O -living O -in O -the O -studio O -for O -the O -next O -couple O -of O -months O - -Happy O -Day O -!!!.... O -:) O - -@yanxfan24 O -We O -should O -win O -but O -I O -said O -that O -on O -Tuesday O -. O -These O -games O -are O -normally O -pretty O -poor O -though O -. O -Probably O -0-0 O -lol O - -You'll O -come O -back O -when O -they O -call O -you O -... O -No O -need O -to O -say O -goodbye O -. O - -@lauramcblain94_ O -lololololol O -tonight O -was O -worse O -than O -stressful O -! O -xo O - -#FF O -THE O -FUTURE O -OF O -THE O -@WWE O -@JTG1284 O -HE O -#SofaKingFlyy O -HE O -ONE O -OF O -MY O -ALL O -TIME O -FAVS O -.... O -#ReallyDoe O - -@DavidHayemaker O -Liked O -your O -spot O -on O -Radio B-other -1 I-other -the O -other O -day O -. O - -RT O -@ContrarianDave O -: O -Here O -'s O -my O -story O -on O -today O -'s O -Forensic O -Science O -Comm O -. O -meeting O -, O -where O -Willingham B-geo-loc -probe O -took O -a O -remarkable O -turn O -. O -http://bi O -... O - -RT O -@MATTHARDYBRAND O -: O -Thx O -to O -everyone O -who O -wished O -my O -Dad O -, O -" O -The O -Legend O -" O -a O -Happy O -88th O -B-Day O -just O -a O -couple O -days O -ago O -.. O -He O -'s O -my O -inspiration O -& O -me O -... O - -@imRockey O -I O -will O -let O -you O -know O -when O -you O -can O -take O -another O -visit O -; O -today O -was O -a O -little O -quiet O -. O - -@tonygarcha O -Hurray O -! O -Enjoy O -your O -time O -in O -Harrison B-geo-loc -. O - -Off O -work O -I O -guess O -? O -Told O -to O -leave O -an O -hour O -and O -half O -before O -I O -was O -scheduled O -to O -. O -My O -paycheck O -is O -going O -to O -be O -weak O -. O -Not O -happy O -. O - -Yup O -, O -it O -is O -:) O -| O -RT O -@iSuboohi O -: O -- O -When O -someone/anyone O -makes O -you O -feel O -special O -, O -it O -'s O -sheer O -bliss O -:) O - -RT O -@Quotealicious O -: O -When O -ever O -i O -see O -a O -cute O -love O -story O -movie O -, O -It O -'s O -always O -you O -that O -comes O -into O -my O -mind O -#Quotealicious O - -I O -had O -a O -wonderful O -day O -today O -and O -i O -hope O -u O -all O -did O -, O -i O -wish O -u O -all O -good O -night O -and O -sweet O -dreams O -xoxoxo O - -Theres O -nothing O -funny O -about O -testicles O -Cooper B-person -as O -you O -will O -find O -out O -in O -my O -office O -tomorrow O -. O -Lmfao O -. O - -Welcome O -Saturday O - -@darthsunshine O -Wheeee O -!! O -Okay O -- O -we O -could O -get O -down O -there O -by O -like O -9:30 O -if O -we O -want O -some O -shopping O -time O -and O -lunch O -before O -class O -. O -@CaMeaux O - -every O -day O -I O -see O -my O -dream O -every O -day O -I O -see O -my O -dream O -every O -day O -I O -see O -my O -dream O -every O -day O -I O -see O -my O -dream O -every O -day O -I O -see O -my O -dream O - -@_paaaul10 O -- O -haha O -six O -? O -; O -o O -woaahh O -lol O -, O -well O -you O -can O -go O -buy O -some O -when O -you O -get O -outta O -work O -x O -D O - -Foooooootball O -game O -toooonight O -. O -then O -auburn B-geo-loc -game O -tomorrow O -!!!! O -war O -damn O -eagle O -!!! O - -RT O -@CHRIS_Daughtry O -: O -We're O -playing O -a O -show O -with O -KISS B-musicartist -tomorrow O -. O -Can't O -say O -I O -saw O -that O -coming O -5 O -years O -ago O -... O - -FUUCCCKKKK O -. O -I O -WAS O -SO O -FUCKING O -READY O -FOR O -MY O -GOD O -DAMN O -ALONE O -TIME O -, O -AND O -IT O -TURNS O -OUT O -FUCKING O -MADI B-person -HAS O -AN O -ILLNESS O -AND O -IS O -HOME O -W/DAD O -. O -GOD O -. O -DAMN O -. O - -What O -it O -is O -twiggas O -it O -'s O -Friday O -in O -this O -bissh O -!!! O -What O -'s O -y'all O -niggas O -plans O -???? O -Betta O -be O -at O -Elements B-facility -tonight O -BOGO O -drinks O -all O -night O -!! O -Nuff O -Said O - -@PaulJones3 O -delicioso O -. O -hows O -your O -week O -? O - -Your O -paycheck O -may O -shrink O -next O -year O -, O -at O -least O -temporarily O -, O -if O -a O -vote O -on O -extending O -the O -Bush B-person -tax O -cuts O -slips O -past O -November O -. O -http://bit.ly/9y8fdS O - -'Was O -going O -to O -stay O -until O -Sunday O -but O -my O -" O -mother O -" O -has O -already O -made O -me O -mad O -furthermore O -hair O -appointment O -tomrrw O -and O -back O -to O -Kent B-geo-loc -!' O - -That O -'s O -college O -tuition O -. O -That O -'s O -two O -average O -cars O -. O -That O -'s O -a O -year O -'s O -worth O -of O -someone O -'s O -salary O -. O -That O -'s O -insaneeee O -. O - -@lukeymoore O -you O -not O -playing O -tomorrow O -? O -Dear O -me O -, O -is O -it O -just O -me O -and O -pete B-person -, O -is O -he O -trying O -to O -rape O -me O - -RT O -@askyfullofstars O -: O -AstronomersWithoutBorders B-other -has O -a O -list O -of O -live O -#InOMN10 O -broadcasters O -for O -online O -Moon O -viewing O -Sept O -18 O -http://is.gd/ffDFE O - -@poscas O -I O -said O -to O -him O -' O -the O -impression O -I O -had O -of O -early O -80s O -NYC B-geo-loc -was O -if O -you O -went O -to O -Central B-geo-loc -Park I-geo-loc -after O -dark O -you'd O -be O -lucky O -to O -come O -out O -alive'... O - -@KatherineRamos O -it O -'s O -cool O -also O -I'm O -not O -sure O -. O -If O -I O -were O -you O -I'd O -renew O -right O -away O -before O -but O -if O -not O -I'd O -still O -try O -. O -Best O -of O -luck O - -@TomCruise O -I'm O -from O -Brazil B-geo-loc -.... O -and O -I O -love O -you O -. O -You're O -my O -favorite O -actor O -! O -Knight B-movie -and I-movie -Day I-movie -is O -so O -cool O -... O -My O -dream O -is O -to O -meet O -you O -! O - -made O -sure O -im O -off O -next O -friday O - -What O -fuckin O -luck O -walk O -in O -to O -pop-eyes O -the O -the O -fuckin O -line O -is O -full O -of O -I O -don't O -know O -what O -fuc O -I O -want O -people O -. O -By O -the O -time O -I'm O -up O -I O -have O -to O -wait O -1 O - -@franchiseking O -Do O -you O -have O -time O -to O -offer O -advice O -to O -this O -franchise O -? O -http://ow.ly/2G4MN O - -Good B-facility -Day I-facility -Sunshine I-facility -http://bit.ly/cK4PB8 O -is O -available O -to O -rent O -off O -season O -! O -Great O -surfing O -, O -fishing O -, O -hot-tubbing O -! O -I O -< O -3 O -fall O -in O -Rodanthe B-geo-loc -@OBXcom O - -tea O -time O -. O - -Whats O -the O -plan O -tonight O - -watching O -the O -VMAs B-other -Eminem B-person -is O -BeAsT O -and O -is O -smexy O -!!; O -)And O -Chelsea B-person -Handler I-person -is O -hilarious O -! O -Finally O -got O -a O -boyfren O -he O -gave O -me O -the O -cutest O -thing O -today O - -RT O -@ZodiacFacts O -: O -#ZodiacFacts O -As O -a O -#Gemini O -your O -energy O -is O -best O -spent O -delegating O -responsibilities O -and O -being O -there O -if O -or O -when O -people O -need O -you O -. O - -@Lissa_Lise O -they're O -looking O -pretty O -good O -. O -i O -dnt O -think O -ima O -play O -tho O -. O -I'm O -playing O -on O -a O -co O -rec O -team O -too O -. O -I O -won't O -have O -time O -to O -breathe O -. O - -Don B-person -Mattingly I-person -will O -replace O -Joe B-person -Torre I-person -as O -LA B-sportsteam -Dodgers I-sportsteam -manager O -after O -this O -season O - -You O -remeber O -we O -were O -sitting O -there O -by O -the O -water O -you O -put O -your O -arm O -around O -me O -for O -the O -first O -time O -you O -made O -a O -rebel O -careless O -mans O -careful O -daughter O - -@FATJEW O -: O -I O -watch O -porn O -movies O -on O -rewind O -because O -I O -love O -when O -sex O -ends O -and O -the O -girl O -immediately O -puts O -on O -her O -clothes O -( O -cont O -) O -http://tl.gd/63asgi O - -Takin O -' O -ur O -hapiness O -before O -mine O - -@OhSoJosie O -shit O -get O -me O -tight O -when O -I O -go O -to O -the O -beach O -n O -everybody O -in O -the O -sand O --_- O - -I O -remember O -the O -days O -in O -school O -when O -I O -lost/forgot O -my O -lunch O -tickets O -. O -#PureTragedy O -You O -know O -, O -the O -bright O -yellow/dark O -blue O -ones O -. O - -It O -'s O -official O -!!! O -Vegas B-geo-loc -for O -the O -weekend O -! O -Starting O -tonight O -!! O -#nohands O - -RT O -@thirdday O -: O -RT O -@macpowell O -: O -Just O -landed O -in O -DC B-geo-loc -. O -Is O -anybody O -coming O -to O -the O -Awakening B-other -Festival I-other -tonight O -? O -I'm O -thinking O -about O -rocking O -it O -. O - -RT O -@KdotDeeZy O -: O -Yes O -we O -do O -!!! O -Is O -part O -2 O -in O -the O -making O -? O -RT O -@GOTEMSAYNBABY O -remember O -when O -i O -was O -on O -twitter B-company -violating O -some O -girl O -in O -the O -summer O -? O - -@sardesairajdeep O -I O -can O -see O -three O -kinds O -of O -reactions O -after O -Ayodhya B-person -verdict O -. O - -We O -were O -all O -enjoying O -a O -glass O -of O -wine O -in O -the O -office O -when O -a O -fudge O -delivery O -showed O -up O -. O -I O -love O -my O -job O -. O -And O -I O -love O -Fridays O -. O - -@briancag O -"' O -Brian B-person -.' O -Isn't O -available O -right O -now O -..." O -hahahaha O -that O -'s O -what O -I O -hear O -when O -I O -call O -your O -saynow O -. O - -A O -compliment O -from O -a O -stranger O -on O -my O -outfit O -as O -I O -walked O -in O -to O -work O -= O -best O -way O -to O -start O -the O -day O -! O - -Chicago B-sportsteam -Blackhawks I-sportsteam -general O -manager O -Stan B-person -Bowman I-person -said O -Cristobal B-person -Huet I-person -will O -be O -gone O -in O -two O -weeks O -... O -http://fan.ac/Ydv O -#NHL O - -@justinbieber O -i O -just O -wanna O -say O -you O -make O -me O -smile O -everyday O -:) O -thanks O -for O -smiling O -because O -when O -u O -smile O -i O -smile O -! O -:) O - -Oh O -beautiful O -day O -! O -What O -'s O -everyone O -doing O -tonight O -! O -:] O - -Very O -busy O -weekend O -and O -TWO O -articles O -due O -for O -publishing O -on O -Monday O -morning O -. O -As O -such O -, O -I'm O -afraid O -the O -Jup B-other -Force I-other -PvP I-other -event O -is O -delayed O -. O -( O -more O -) O - -What O -do O -a O -prostitute O -and O -a O -bungee O -jump O -have O -in O -common O -? O -they O -cost O -the O -same O -, O -last O -as O -long O -, O -and O -if O -the O -rubber O -breaks O -you're O -screwed O -. O - -Kristen B-person -is O -MY O -LIFE O -. O -She O -made O -me O -who O -i O -am O -today O -, O -I O -was O -a O -fucker O -before O -her O -, O -she O -fixed O -me O -. O -And O -so O -did O -Robert B-person -. O - -Feeling O -really O -good O -after O -great O -week O -in O -our O -SF B-geo-loc -and O -LA B-geo-loc -offices O -. O -Glad O -to O -kick O -back O -on O -AMerican B-company -flight O -back O -to O -NYC B-geo-loc - -Opted O -for O -pants O -over O -shorts O -today O -... O -summer O -is O -officially O -over O -. O -Bring O -on O -the O -pumpkin O -beers O -! O - -RT O -@LightCMS O -: O -Day O -6 O -: O -Win O -1 O -of O -10 O -iPads O -from O -@LightCMS O -- O -RT O -to O -enter O -or O -read O -more O -http://speaklight.com/iPad O - -Ah O -crap O -. O -No O -#MCRchat O -tonight O -for O -me O -, O -I O -guess O -: O -| O -Damn O -I O -was O -so O -excited O -:' O -( O - -I'm O -off O -I O -shall O -tweet O -when O -I'm O -home O -:D O -:D O -xxxxxx O - -UPDATE O -1-Cricket-Gul O -takes O -six O -wickets O -as O -Pakistan B-geo-loc -beat O -England B-geo-loc -: O -LONDON B-geo-loc -, O -Sept O -17 O -( O -Reuters B-company -) O -- O -Umar B-person -Gul I-person -took O -... O -http://bit.ly/ckNyMG O -#Cricket O - -Football O -game O -tonight O -:) O -yeeah O -boilers B-sportsteam -.. O -oh O -wait O -, O -we O -suck O -. O -haha O - -No O -tasting O -today O -due O -to O -my O -being O -out O -Sick O -... O -ugh O -. O -But O -next O -Friday O -we O -will O -have O -Mark B-person -Rowan I-person -from O -Novel B-company -Wines I-company -here O -... O -http://fb.me/G2CLHL1H O - -its O -friday O -. O -but O -instead O -of O -partying O -or O -doing O -homework O -. O -I'm O -going O -to O -tumblr B-company -for O -a O -bit O -and O -sleep O -XD O - -@mynameisraven O -get O -on O -skype B-company -or O -ichat O -when O -you O -see O -this O -. O - -RT O -@Sn00ki O -: O -Haha O -yes O -!!! O -I O -love O -that O -you O -knew O -that O -:) O -RT O -@trishamelissa O -@Sn00ki O -Is O -phenomenal O -the O -word O -of O -the O -day O -? O - -Also O -, O -I O -learned O -at O -school O -today O -that O -everyone O -, O -in O -fact O -, O -knows O -@penguin1124 O -. O -( O -I O -learned O -this O -, O -no O -joke O -, O -during O -choir O -. O -) O -#fact O - -@zfarmville O -funnyordie O -: O -Text O -messages O -are O -like O -Gremlins O -. O -Keep O -them O -away O -from O -drinks O -and O -proceed O -with O -caution O -after O -midnight O -. O -http:///5dkkZ O - -@OGOchoCinco O -ocho O -you O -know O -your O -talent O -, O -I O -have O -agreed O -with O -every O -statement O -you O -have O -said O -over O -the O -past O -2 O -years O - -Just O -got O -all O -caught O -up O -on O -my O -flagged O -emails O -from O -the O -week O -. O -Now O -I O -feel O -good O -about O -leaving O -the O -office O -for O -the O -weekend O -. O -TGIF O -! O - -RT O -@AllTeensThings O -: O -Christmas B-other -was O -so O -much O -better O -when O -there O -was O -a O -santa O -:( O -#allteensthings O - -@26Promo O -YOU O -CAN O -DO O -IT O -! O -Just O -start O -with O -one O -task O -at O -a O -time O -. O - -@aszreal O -SO O -PROUD O -. O -also O -I O -haven't O -written O -a O -word O -since O -Sunday O -night O -. O -=( O -I O -wrote O -500 O -on O -the O -plane O -. O -( O -Yes O -, O -chapter O -five O -! O -@thegypsyscribe O -lol O -;) O - -RT O -@LilTwist O -: O -RT O -this O -if O -you O -want O -me O -to O -go O -back O -live O -on O -Ustream B-company -later O -tonight O - -'I O -just O -took O -" O -When O -your O -mom O -makes O -you O -go O -live O -with O -your O -dad O -Scooter B-person -Braun I-person -, O -your O -life O -turns O -..." O -and O -got O -: O -Part O -1 O -! O -Try O -it O -: O -http://bit.ly/ch09f2' O - -It O -'s O -time O -to O -CELEBRATE O -!!!!!! O -YESSSSS O -!!! O -We O -going O -in O -tonight O -!!!! O - -is O -there O -actually O -anything O -on O -UK B-geo-loc -tv O -tonight O -! O - -I'll O -be O -gone O -till O -november O -, O -but O -I O -ain't O -even O -trippin O -cause O -I O -know O -drizzy O -gonna O -kill O -em O -! O - -@uppercasem O -@cecinestpastom O -pig O -pinata O -for O -the O -big O -day O -? O -http://j.mp/9ack1V O - -i'm O -off O -to O -bed O -! O -tomorrow O -i'll O -go O -to O -Nijverdal B-geo-loc -and O -meet O -@ElineEpica O -there O -, O -on O -her O -sweeeet16 O -#partyyy O - -check O -out O -@140hours O -auction O -starts O -tonight O -! O -Dreaming O -Storm O -http://bit.ly/5Bq0Qm O - -argrgrgr O -hy O -is O -it O -already O -midway O -through O -sep O -!!!! O -:( O - -tonight O -at O -The B-facility -Lodge I-facility -: O -braised O -bison O -shortribs O -, O -kabocha O -squash O -risotto O -http://twitpic.com/2pdu4g O - -Thanks O -for O -all O -you O -do O -: O -@marketinghabit O -, O -@girlfriendgroup O -, O -@cjhayden O -, O -@KadenaSimon O -. O -You O -bolstered O -my O -week O -. O - -@borneobaby O -THAT O -is O -exactly O -how O -I O -am O -feelin O -! O -Just O -under O -1 O -hour O -2 O -go O -& O -still O -unsure O -. O -Havent O -slept O -in O -days O -thinkin O -about O -wat O -I O -wd O -wear O -and O -say O -! O - -i O -decided O -im O -gonna O -sleep O -at O -my O -mommys O -tonight O -and O -hang O -out O -there O -after O -filming O -tomorrow O -morning O -till O -i O -work O -. O -oh O -the O -sacrafices O -i O -make O -. O - -@darynjones O -imma O -tell O -you O -one O -time O -! O -JUTHTIN B-person -BEAVERRRR I-person -!!!! O - -All O -Night O -Skate O -Session O -tomorrow O -night O -! O -Hope O -to O -see O -you O -all O -there O -! O - -sunny O -friday O -:) O - -@SheSo_Major O -they O -giving O -the O -nigga O -a O -realllllll O -hard O -time O -about O -it O -lmfaoo O -as O -they O -should O -and O -now O -his O -feelings O -are O -hurt O -... O -i O -like O -him O -tho O -LMAO O - -@TexusMade O -lol O -. O -Try O -cudos O -! O -I O -neva O -been O -round O -this O -time O -doe O -. O - -#cl O -CamelBak B-product -BPA-Free I-product -Better I-product -Bottle I-product -with I-product -Bite I-product -Valve I-product -by O -Camelbak B-company -93 O -days O -in O -the O -top O -100 O -( O -494)Buy O -new O -: O -$ O -4.99 O -- O -$1 O -... O -http://bit.ly/9YA597 O - -Shoutout O -to O -@HarryReid O -for O -his O -help O -with O -DADT B-other -. O -Thanks O -for O -following O -. O -Good O -luck O -on O -Tuesday O -! O - -@TeemoneeNYC O -lmao O -Sunday O -can O -be O -done O - -@LewisDixon O -Trust O -me O -! O -im O -gonna O -be O -bringing O -out O -music O -like O -theres O -no O -tomorrow O -, O -Be O -doing O -pure O -blog O -videos O -& O -freestyle O -videos O -#Moesh O -! O - -#SIUC O -Whats O -the O -Plan O -For O -tonight O -?? O -Whos O -Goin O -to O -the B-facility -BLAST I-facility - -I O -should O -not O -be O -awake O -at O -this O -hour O -on O -a O -Saturday O -. O -Blerghhhhh O -. O -Good O -cause O -though O -. O -I O -hope O -. O - -Baseball O -Tips O -On O -Hitting O --- O -How O -To O -Approach O -The O -Flame O -Throwers O -! O -http://t.co/vaVT5LW O -#mlb O -select O -league O -#baseball O -#FF O -Follow O -Friday O -.. O - -@weezywiii O -yes O -i O -do O -.. O -scouts O -dont O -smoke O -week O - -I O -ain't O -necessary O -listenin O -' O -to O -the O -lyrics O -but O -the O -beat O -got O -head O -rockin'. O -" O -It B-other -'s I-other -Gucci I-other -Time I-other -!" O -#nodisrespect O -I O -fucks O -with O -every O -now O -& O -then O -... O - -Do O -you O -ever O -have O -fights O -with O -your O -running O -shoes O -? O -You O -know O -, O -when O -your O -shoes O -want O -to O -exercise O -but O -you O -don't. O -Well O -... O -http://fb.me/xKyLBInS O - -10 O -Things O -You O -May O -Have O -Missed O -On O -TV O -This O -Week O -[ O -Mixed O -Bag O -] O -http://bit.ly/9vZOUc O - -@amandaguido O -i O -didnt O -sseee O -you O -today O -:( O - -I'm O -shattered O -!! O -I'm O -looking O -forward O -to O -having O -a O -nice O -long O -lay O -in O -, O -in O -the O -morning O -:D O - -@Johnterrysbabe O -lol O -I'm O -not O -up O -to O -much O -I'm O -just O -off O -to O -bed O -lol O -had O -a O -long O -day O -cht O -tumoz O -:) O - -On O -the O -amusing O -side O -....: O -When O -we O -- O -with O -dozens O -of O -other O -journalists O -- O -were O -at O -the O -event O -waiting O -to O -see O -if O -... O -http://fxn.ws/aAnazd O - -@joeymcintyre O -I O -heart O -you O -. O -Even O -if O -I O -haven't O -seen O -u O -in O -months O -... O -SEND O -A O -PIC O -! O - -Lederhosen O -, O -lederhosen O -, O -you're O -so O -delicious O -, O -I O -love O -you O -more O -than O -... O -all O -the O -other O -foreign O -pants O -that O -I O -need O -to O -borrow O -for O -a O -single O -day O -. O - -After O -work O -nap O -time O -; O -Shhhhhhh O -*Do O -not O -disturb* O - -#aberdeen O -RT O -@flook_firehose2010Polar O -Bear O -http://flook.it/c/1H1HZq O -Sun O -, O -17 O -Oct O -2010 O -at O -10:28 O -am O -The O -Tunnels O -Carnegies O -Brae O -Aberdeen O -Un O -... O - -After O -work O -I'm O -breaking O -glass O -against O -my O -house O -and O -then O -doing O -yoga O -. O - -RT O -@ctvedmonton O -: O -surprise O -, O -surprise O -... O -Frost O -WARNINGS O -in O -effect O -for O -pretty O -much O -all O -of O -Ctl O -& O -N O -Alta O -again O -tonight O -. O -that O -includes O -the O -c O -... O - -have O -to O -sleep O -now O -! O -gym O -, O -facial O -, O -then O -SUPAHFEST B-other -tonight O -i O -need O -my O -kuya O -'s O -driver O -... O -MOSH O -PIT O -VIP O -common O - -'WHEN O -HE O -SAY O -" O -iS O -YOU O -CHEATiN O -ON O -ME O -" O -THAT O -JUST O -#REALLYMEANS O -, O -HE O -ALREADY O -CHEATiN O -!' O - -'" O -We O -still O -need O -eachother O -when O -we O -stumble O -and O -fall O -.." O -" O -We O -both O -know O -our O -heart O -is O -breaking O -, O -can O -we O -learn O -from O -our O -mistakess O -?"' O - -#sp O -Facebook B-company -Job-Hunting O -App O -BranchOut B-product -Raises O -$6 O -Million O -From O -Accel B-company -And O -Super O -Angels O -: O -When O -you O -want O -to O -hang O -out O -... O -http://migre.me/1kO1K O - -@TheTiniestVole O -It O -is O -. O -It O -'s O -getting O -across O -London B-geo-loc -that O -'s O -the O -killer O -. O -Still O -miss O -it O -, O -though O -. O -Lived O -there O -for O -20 O -years O -. O - -im O -doin O -the O -most O -Homework O -on O -a O -friday O -#blown O - -NEW B-geo-loc -ORLEANS I-geo-loc -! O -- O -Tonight O -@damionyancy O -at O -Republic B-facility -! O - -How O -many O -times O -. O -Can O -you O -possibly O -. O -Make O -an O -excuse O -. O -To O -play O -a O -goddamn O -banjo O -. O -In O -an O -hour-long O -show O -. O -A O -show O -that O -has O -nothing O -to O -do O -with O -banjos O -. O - -@Kalyro O -LOL O -PROBABLY O -. O -I O -JUST O -SPENT O -A O -FEW O -MINUTES O -SHELTER-STALKING O -BEFORE O -REALIZING O -THAT O -I O -HAVE O -3 O -LEGENDARY/NOVELTIES O -ALREADY O -. O - -GuitarNews B-other -: O -RockHouse B-other -: O -The O -Weekend O -Read O -For O -Sept O -. O -17th O -2010 O -- O -Guitar O -Lessons O -, O -Articles O -and O -More O -: O -http://bit.ly/bmHnln O - -CAFE B-facility -NINE I-facility -TONIGHT O -AT O -11 O -!! O - -Information B-other -Underground I-other -: O -This O -Week O -on O -Information B-other -Underground I-other -http://fb.me/JGgSgWBg O - -para O -com O -isso O -ce O -o O -neymar O -ser O -afastado O -o O -time O -acaba O - -Good O -prospects O -for O -dabigatran B-product -on O -Monday O -. O -RT O -@DrJennyK O -: O -FDA B-other -Suggests O -#Afib O -Drug O -Okay O -Even O -Before O -Vote O -http://bit.ly/dpOphU O -#FDACV O - -Whomever O -said O -, O -" O -M&M B-product -'s O -melt O -in O -your O -mouth O -, O -not O -in O -your O -hand O -" O -never O -had O -a O -1/4 O -of O -a O -bag O -in O -their O -hand O -at O -one O -time O -#myhandismulicolored O -#Pretty O - -@BadAssAlexander O -*sighs* O -Studying O -for O -a O -exam O -coming O -up O -soon O -. O -*huffs O -before O -smiling* O -Wanna O -go O -out O -tonight O -? O -( O -LMAO O -I O -honestly O -thought O -today O -- O - -@1STLADYNHEELS O -Alright O -, O -I'll O -get O -there O -around O -that O -time O -. O -I'll O -call/text O -you O -when O -I O -arrive O -. O - -Did O -@kanyewest O -drop O -his O -Good B-other -Friday I-other -song O -yet O -today O -? O - -@BenArch O -100- O -Tim B-person -Bulmer I-person -had O -a O -100% O -likelihood O -of O -getting O -his O -YPF O -detail O -form O -in O -this O -week O -. O -Punctuality O -. O - -headin O -to O -get O -shitty O -in O -a O -few O -... O -1st O -n O -summit O -... O -then O -rehab O -... O -my O -life O -will O -be O -a O -blur O -after O -this O -night O - -You O -think O -I'm O -pretty O -without O -any O -makeup O -on O -, O -you O -think O -I'm O -funny O -when O -I O -tell O -the O -punchline O -wrong O -. O -Know O -you O -get O -me O -so O -I O -let O -my O -walls O -come O -down O - -RT O -@ThePoochStore O -: O -One O -reason O -a O -dog O -can O -be O -such O -a O -comfort O -when O -you're O -feeling O -blue O -is O -that O -he O -doesn't O -try O -to O -find O -out O -why O -. O -~Author O -Unknown O - -RT O -@dsteel O -If O -you O -feel O -as O -though O -your O -company O -is O -having O -problems O -generating O -sales O -, O -it O -might O -be O -time O -to O -bring O -in O -a O -sales O -training O -speaker O -. O - -'Science O -and O -the O -media O -: O -11 O -- O -17 O -September O -: O -Under O -the O -headline O -" O -China B-geo-loc -needs O -to O -elaborate O -on O -plans O -to O -modernize O -its O -f O -... O -http://bit.ly/bVaPzc' O - -e O -tem O -mais O -.... O -Robinho O -no O -meu O -time O -eh O -banco O -, O -mesmo O -com O -o O -Pato O -machucado O -! O -Tenho O -dito O -! O - -@RiffTrax O -I've O -suffered O -through O -the O -era O -of O -HSM O -popularity O -. O -I've O -wanted O -a O -riff O -for O -the O -damn O -thing O -for O -years O -. O -You've O -made O -me O -very O -happy O - -estonia B-geo-loc -tomorrow O -with O -@MyiaOnFyia O -and O -the O -rest O -of O -our O -team O -! O -ready O -for O -some O -good O -practice O -games O -! O -:) O - -" O -I O -gotta O -admit O -, O -Alex B-person -sounds O -hot O -when O -he O -talks O -in O -spanish O -during O -the O -' O -Alejandro B-other -' O -Cover O -" O --via O -someone O -'s O -tumblr B-company - -@OGOchoCinco O -depends O -what O -Leo B-person -'s O -revamped O -D O -can O -do O -to O -him O -on O -Sunday O -. O -Go O -Lions B-sportsteam -!! O - -@BeLIEveNcRisS O -omg O -yayyy O -I O -got O -mine O -today O -too O -!!!!! O -:D O -didn't O -have O -time O -to O -try O -it O -on O -tho O -!! O -I O -was O -running O -out O -the O -door O -to O -go O -to O -the O -concert O -!: O -) O - -Change O -of O -Plans O -on O -Saturday O -! O -We'll O -be O -@ O -Glover B-facility -Park I-facility -farmers O -market O -, O -9-1 O -in O -DC B-geo-loc -. O -Come O -by O -and O -try O -some O -old O -favorites O -& O -crazy O -new O -flavors O -! O - -@KathyReid O -have O -a O -fun O -day O -of O -freedom O - -California B-geo-loc -Woman O -Dies O -in O -Accident O -Hours O -Before O -DUI O -Class O -: O -Woman O -was O -drinking O -until O -early O -morning O -before O -court O -orde O -... O -http://bit.ly/aiHEvj O - -@JoxVox O -Just O -stressing O -about O -the O -fitted O -ones O -though O -- O -opro O -were O -in O -school O -today O -and O -I O -had O -forgotten O -to O -register O -him O -for O -fitting O -... O -whoops O - -@buffystar O -Turn O -your O -phone O -off O -tomorrow O -! O - -RT O -@ddockett O -: O -I O -got O -another O -account O -... O -someone O -else O -will O -tweet O -when O -they O -think O -I O -would O -! O -#fact O -| O -Although O -@NFL O -will O -probably O -figure O -that O -out O - -$$ O -#forex O -#fx O -: O -Forex B-company -Articles O -for O -the O -Weekend O -September O -18lo O -http://www.forexcrunch.com/forex-articles-for-the-weekend-september-18lo/ O - -Ok O -yall O -work O -day O -over O -Damn O -do O -I O -ever O -sleep O -Naw O -fuck O -dat O -when O -I O -die O -I'll O -sleep O -4ever O -until O -than O -sleep O -money O -don't O -( O -cont O -) O -http://tl.gd/63b142 O - -@divacoachdabney O -you O -made O -Nina B-person -'s O -day O -. O -Sweet O -surprises O -for O -you O -. O - -@mandyjiroux O -Mandy B-person -when O -you O -and O -Miley B-person -will O -upgrade O -Miley B-tvshow -and I-tvshow -Mandy I-tvshow -show I-tvshow -? O -I O -miss O -the O -videos O -. O - -@RealWizKhalifa O -I O -will O -be O -smacked O -when O -I O -see O -u O -tonite O -:) O - -Quick O -Question O -: O -Chink-A-Link O -Have O -You O -Ever O -Scratched O -Before O -? O -Lls O - -@FerLiebe O -:D O -how O -is O -fer O -today O -?? O - -Sooo O -plans O -for O -tomorrow O -night O -? O -I O -gotta O -do O -something O -. O -I O -need O -a O -night O -out O -. O - -RT O -IF O -U O -GETTN O -FUCKED O -UP O -TONIGHT O -.... O - -argh O -my O -hands O -are O -dying O -. O -i O -definitely O -need O -to O -take O -gloves O -next O -time O -. O - -@planetjedward O -were O -'s O -John B-person -and O -Edward B-person -... O -there O -Tweets O -make O -my O -day O -... O -now O -what O -am O -i O -supposed O -to O -do O -:' O -( O -COME O -BACK O -TO O -USSS O -< O -3 O - -It O -'s O -Friday O -... O -and O -it O -'s O -ScuttleButton O -time O -! O -http://tinyurl.com/39tkyfh O - -Analogy O -time O -! O -Loyola B-geo-loc -Chicago B-geo-loc -: O -Alexei B-person -:: O -Philadelphia B-sportsteam -Eagles I-sportsteam -: O -Donovan B-person -McNabb I-person -( O -even O -b4 O -the O -trade) O -. O -#nfl O -#fb O - -@lessafelipe O -Vai O -no O -Garotos O -Podres O -+ O -No O -Milk O -Today O -hoje O -? O - -@CoryFerrari O -test O -came O -out O -positive O -? O -Sorry O -lil O -buddy O -..... O -At O -least O -medicine O -these O -days O -can O -keep O -it O -in O -control O -. O - -Don't O -forget O -to O -come O -and O -see O -us O -at O -the O -football O -game O -tomorrow O -afternoon O -! O -Go O -Reivers B-sportsteam -! O - -RT O -@ItsROWEtime O -: O -Y O -does O -Pinky B-person -have O -a O -music O -video O -? O -Thats O -3 O -porn O -star O -rappers O -in O -the O -last O -month O -. O -This O -is O -y O -rap O -is O -doing O -so O -bad O -, O -ppl O -actual O -... O - -EVENT O --- O -> O -http://schmap.it/bP7bEZ O -- O -Tonight O -at O -Primal B-facility -Its O -@Roscoedash O -, O -& O -@DJDrama O -Taking O -Over O -! O -TxT O -Names O -to O -678-223-3699 O -by O -6:59 O -pm O - -Buffs O -FB O -: O -Giddings B-geo-loc -faces O -Elgin B-geo-loc -tonite O -@ O -Wildcat B-facility -Stadium I-facility -@ O -7:30 O -. O -The B-sportsteam -Buffs I-sportsteam -will O -try O -to O -re-group O -after O -two O -straight O -losses O -. O -Updates O -on O -LCL B-company - -@robbieanthony87 O -yey O -:D O -thank O -you O -so O -much O -. O -How're O -you O -? O -I O -proper O -need O -a O -day O -in O -town O -shopping O -haha O -xx O - -EuroVPS B-company -celebrates O -its O -6 O -Year O -Anniversary O -with O -special O -Promotion O -. O -Buy O -1 O -VPS O -and O -get O -a O -second O -VPS O -FREE O -! O -http://fb.me/FZAfqgWn O - -Damn O -. O -Is O -he O -in O -town O -tonite O -? O - -Am O -thinking O -i O -may O -steal O -this O -awesome O -idea O -from O -@TheFrontBurner O -http://j.mp/dBTwTr O -. O -I O -think O -it O -would O -be O -awesome O -in O -our O -stairwells O -! O - -@XrisLastRights O -yep O -well O -.. O -i O -know O -mcfly B-person -told O -me O -they O -were O -going O -to O -come O -back O -here O -when O -i O -met O -em O -' O -but O -anyway O -:) O -so O -, O -how O -was O -your O -day O -? O - --Been O -Sore O -For O -The O -Past O -Days O -From O -Workin O -Out O -. O - -what O -to O -do O -, O -what O -to O -do O -... O -gross O -sushi O -for O -dinner O -but O -time O -with O -family O -from O -delaware B-geo-loc -, O -or O -leftover O -pizza O -and O -alone O -time O -to O -write O -and O -relax O -. O - -RT O -@NAT3OH3 O -: O -VMB B-other -awards I-other -last O -night O -were O -super O -fun O -- O -we O -won O -best O -bossa O -nova O -act O -! O -off O -to O -HSBC B-facility -center I-facility -to O -soundcheck O -for O -epic O -show O -tonight O -.. O -... O - -I O -wished O -I'd O -school O -tomorrow O -so O -I O -could O -get O -outta O -this O -house O -. O - -@SITSGirls O -would O -have O -started O -mine O -a O -year O -earlier- O -2002ish O -, O -when O -I O -first O -got O -to O -college O - -@_Flik_ O -awww O -he O -'s O -in O -the O -rocky B-movie -horror I-movie -show I-movie -next O -week O -which O -means O -he O -'s O -gotta O -wear O -stockings O -an O -suspenders O -lol O -:P O - -Thanks O -to O -all O -who O -tuned O -in O -to O -my O -show O -on O -http://kplu.org O -the O -last O -two O -days O -. O -Ending O -with O -Herbie B-person -& O -Freddie B-person -! O - -@RealWendyHall O -@piccoz O -It O -didn't O -stop O -me O -when O -there O -was O -4 O -of O -yous O -at O -me O -!!! O -Quite O -enjoyed O -it O -tbh O -!!! O -Lol O - -3 O -tour O -coaches O -with O -a O -police O -escort O -in O -Chapel B-geo-loc -Hill I-geo-loc -... O -game O -tonight O -? O - -Our O -Favorite O -YouTube B-company -Videos O -This O -Week O -: O -The O -Drama O -Edition O -http://dlvr.it/5RfHy O - -Small B-other -Biz I-other -Tech I-other -Tour I-other -2010 I-other -Launches O -Five O -City O -Tour O -MONTCLAIR B-geo-loc -N.J. B-geo-loc -...: O -The O -all O -day O -event O -features O -America's... O -http://tinyurl.com/28hd9fu O -#fb O - -WIN O -FREE O -PAMPERS B-company -FOR O -A O -YEAR O -!!! O -And O -more O -... O -http://nblo.gs/80qJu O - -Preventing O -Public O -Health O -Disasters O -[ O -The O -Pump O -Handle] O -: O -Earlier O -this O -week O -, O -FDA B-other -Commissioner O -Margaret B-person -Hamburg I-person -present O -... O -http://bit.ly/9i1AEg O - -RT O -@robmoysey O -Eyeopener B-sportsteam -vs O -. O -Ryerson B-sportsteam -Quidditch I-sportsteam -team I-sportsteam -this O -Sunday O -at O -4 O -p.m. O -Anyone O -know O -where O -to O -get O -cheap O -brooms O -? O -#Ryerson O -@RUQuidditch O -#Rams O - -RT O -@ZodiacFacts O -: O -#ZodiacFacts O -As O -an O -#Aries O -romance O -is O -n't O -about O -what O -you O -'re O -after O --- O -it O -'s O -about O -taking O -chances O -, O -exploring O -and O -keeping O -an O -... O - -I O -have O -lost O -everything O -importing O -in O -my O -life O -within O -the O -past O -week O -anything O -that O -i O -loved O -is O -now O -gone O - -RT O -@SonyElectronics O -: O -Sony/Bud O -Light O -3D O -tailgate O -parties O -on O -Saturday O -- O -Clemson B-geo-loc -party O -at O -http://bit.ly/cds8Pd O -; O -Auburn B-geo-loc -party O -at O -http://bit O -. O -... O - -RT O -@kanyewest O -: O -You O -basically O -can O -say O -anything O -to O -someone O -on O -an O -email O -or O -text O -as O -long O -as O -you O -put O -LOL O -at O -the O -end O - -@Zerinaakers O -talking O -about O -as O -far O -as O -shooting O -for O -location O -its O -going O -to O -be O -on O -#Vh1 B-other -so O -by O -the O -end O -of O -the O -month O -i O -should O -know O -something O - -I O -'ve O -got O -a O -theory O -that O -if O -you O -give O -100% O -all O -of O -the O -time O -, O -somehow O -things O -will O -work O -out O -in O -the O -end O -. O --Larry B-person -Bird I-person - -@bendingoutward O -What O -about O -the O -site O -could O -be O -changed O -in O -order O -to O -reduce O -the O -time O -required O -for O -you O -to O -provide O -solutions O -? O - -@winashbrown O -If O -that O -auto O -URL O -shortening O -could O -work O -I O -might O -stick O -with O -SD2 B-product -for O -a O -while O -- O -that O -saves O -me O -3 O -or O -4 O -steps O -when O -I O -am O -tweeting O -links O - -Kk O -its O -22:48 O -friday O -nyt O -:D O -really O -tired O -so O -imma O -go O -to O -sleep O -:) O -good O -nyt O -x O -god O -bles O -xxxxx O - -And O -now O -the O -wait O -til O -the O -games O -get O -started O -for O -the O -day O -. O - -belles B-facility -mansion I-facility -4 O -bday O -dinner O -. O -eventhough O -its O -a O -day O -early O -. O -*N*I*K*K*I* O - -Kahlua B-product -< O -Coffee O -over O -looking O -the O -lake O -is O -just O -what O -the O -doctor O -ordered O -.... O -hope O -everyone O -has O -a O -great O -Friday O -night O -! O - -I O -just O -took O -" O -After O -getting O -trampled O -at O -a O -Justin B-person -Bieber I-person -concert O -, O -yoiu O -wake O -up O -and O -..." O -and O -got O -: O -Part O -6 O -:) O -! O -Try O -it O -: O -http://tinyurl.com/26zeju5 O - -Gotta O -love O -the O -bumper O -stickers O -of O -Fayette B-geo-loc -County I-geo-loc -: O -" O -Ever O -wonder O -if O -there O -is O -life O -after O -death O -? O -Touch O -my O -truck O -and O -'ll O -find O -out O -." O - -Going O -to O -start O -#HelloMornings O -on O -Oct O -1 O -- O -Check O -it O -out O -on O -@michellebygrace O - -Pick O -up O -here O -. O -Deliver O -on O -Dayton B-geo-loc -NJ I-geo-loc -tomorrow O -(@ O -Ball B-company -Metal I-company -Container I-company -) O - -RT O -@Mickey__Rourke O -: O -58 O -years O -ago O -today O -I O -tumbled O -from O -my O -mother O -'s O -womb O -with O -a O -cigarette O -in O -my O -mouth O -and O -a O -chihuahua O -puppy O -under O -each O -arm O -... O - -No O -I O -'m O -not O -saying O -I O -'m O -sorry O -One O -day O -, O -maybe O -'ll O -meet O -again O -No O -No O -No O -No O -( O -8 O -) O - -Companies O -fixing O -a O -problem O -that O -'s O -wholly O -their O -fault O -and O -calling O -it O -a O -" O -one-time O -courtesy O -" O -is O -insult O -to O -injury O -. O -They O -'re O -clearly O -not O -sorry O -! O - -Do O -n't O -Have O -Time O -To O -Stop O -In O -??? O -Then O -, O -Check O -Out O -Our O -Quick O -Full O -Service O -Drive O -Thru O -Window O -:) O - -today O -seems O -like O -a O -good O -day O -to O -chill O -:) O - -I O -am O -living O -my O -dream O -, O -winning O -at O -life O -and O -being O -respected O -for O -every O -thing O -I O -do O -. O -And O -its O -about O -fucking O -time O -. O -#dontsettle O -#EVER O - -@toukie_s O -i'mgoing O -to O -sleep O -, O -not O -feeling O -very O -well O -, O -see O -you O -tomorrow O - -Soulja B-person -Boy I-person -| O -TheDeAndreWay O -. O -com O -- O -The O -DeAndre O -Way O -in O -stores O -Nov O -2 O -, O -2010 O -souljaboytellem-iga.ning.com O -via O -@SouljaBoy O -! O -#Blessed O - -#woot O -I'd O -like O -to O -buy O -this O -, O -but O -i O -have O -a O -hard O -time O -doing O -a O -sit O -up O -... O - -RT O -@ItsTheTeenLife O -: O -The O -exact O -day O -that O -I O -wear O -shorts O -and O -a O -tee O -shirt O -everyone O -shows O -up O -wearing O -hoodies O -and O -jeans O -. O --_- O -#ItsTheTeenLife O - -RT O -@BieberPledge O -: O -When O -I O -Get O -Old O -, O -Imma O -Still O -Be O -Rockin O -Out O -To O -' O -Baby'...Even O -If O -I O -Break O -A O -Hip O -;) O - -Stephen B-person -, O -the O -Intellectual O -. O -@bbhess O -I O -bet O -ppl O -that O -design O -book O -covers O -for O -a O -living O -hate O -when O -people O -say O -do O -n't O -judge O -a O -book O -by O -its O -cover O - -Not O -feeling O -up O -to O -par O -today O -. O -#stressing O - -Wanna O -see O -ya O -bro O -, O -I O -miss O -u O -U O -scared O -me O -to O -hell O -yesterday O -, O -cant O -live O -without O -u O -U O -'re O -more O -than O -a O -bro O -u O -'re O -Like O -my O -twin O -Need O -u O -, O -come O -home O -Luv O -u O -< O -3 O - -Man O -i O -been O -eatin O -fastfood O -everyday O -im O -so O -glad O -my O -mommy O -cookin O -today O -... O - -It O -might O -just O -be O -me O -but O -when O -males O -text O -" O -K O -" O -or O -cross O -there O -legs O -while O -sitting O -ai O -n't O -cool O -wit O -me O -. O - -Have O -DJ B-other -Got I-other -Us I-other -Fallin I-other -in I-other -Love I-other -in O -my O -head O -. O -" O -Cause O -baby O -tonight O -, O -The O -dj O -got O -us O -fallin O -in O -love O -again O -" O - -@_AsToldByBV O -well O -i O -wrote O -you O -like O -6 O -times O -this O -week O -! O -urgh O -. O -RACHELLE B-person -im O -gonnauh O -kill O -YOU O -tonite O -! O - -Before O -I O -proceed O -into O -the O -paradise O -, O -let O -'s O -not O -forget O -the O -Princess B-movie -Lover I-movie -OVA I-movie -1 I-movie -teaser O -pic O -, O -SFW O -http://yfrog.com/0fg2kfj O - -I O -see O -her O -tomorrow O -: O -3 O -so O -yeah~ O -ca O -n't O -wait O - -Pope B-person -Benedict I-person -trip O -: O -Why O -move O -John B-person -Henry I-person -Newman I-person -toward O -sainthood O -?: O -Pope B-person -Benedict I-person -XVI I-person -plans O -on O -Sunday O -during O -his O -sta O -... O -http://bit.ly/d7tM0r O - -When O -you O -know O -what O -you O -want O -, O -and O -want O -it O -bad O -enough O -, O -you O -will O -find O -a O -way O -to O -get O -it O -. O -- O -Jim B-person -Rohn I-person -RT O -@TheSCICoach O - -last O -day O -of O -sorting O -pope B-person -visit O -to O -birmingham B-geo-loc -stuff O -out O -..... O -hope O -it O -goes O -ok O -on O -sunday O -!! O - -Is O -Joaquin B-person -Phoenix I-person -Our O -Modern O -Day O -Andy B-person -Kaufman I-person -?: O -... O -the O -bizarre O -events O -leading O -up O -to O -this O -moment O -, O -the O -movie O -could O -... O -http://bit.ly/aUZ2on O - -My O -first O -assignment O -was O -a O -medical O -illustration O -job O -through O -a O -friend O -. O -After O -meeting O -with O -the O -surgeon O -, O -I O -comple O -... O - -@MATHHOFFA O -@darealjustjuice O -yo O -I O -put O -that O -bitch O -boy O -in O -his O -place O -the O -other O -day O -. O -Do O -n't O -even O -waste O -ya O -time O -wit O -clowns O - -@justinbieber O -I O -have O -chance O -with O -you O -? O -webcam O -enters O -Sunday O -7:00 O -pm O -? O -please O -? O -to O -talk O -right O -! O -kisses O -on O -the O -chin O - -RT O -@BiebersFairies O -: O -the O -day O -Justin B-person -Bieber I-person -was O -born O -was O -a O -rainy O -day O -, O -but O -it O -was O -no O -rain O -, O -NO O -! O -the O -heaven O -was O -crying O -cause O -he O -lost O -his O -most O -... O - -@JessicaLynne6 O -where O -you O -takin O -me O -tonight O -after O -u O -get O -off O -? O -i O -know O -u O -tryna O -kick O -it O -.. O -lol O - -@besocialonline O -dank O -je O -Gert O -Jan O - -Exclusive O -: O -Rep O -. O -Steve B-person -King I-person -on O -ObamaCare O -, O -Tea O -Party O -, O -and O -Constitution B-other -Day I-other -: O -The O -inclusion O -of O -the O -Tenth O -Amendment O -in O -... O -http://bit.ly/cYITA8 O - -Long O -18k O -run O -done O -and O -in O -the O -books O -! O -Beauty O -day O -. O -Til O -now O -, O -at O -dentist O -, O -and O -lady O -next O -to O -me O -spilling O -over O -to O -my O -chair O -.. O -Just O -belched O -. O -Loudly O -. O -+1 O - -Lingerie B-other -football I-other -league I-other -come O -on O -tonight O - -... O -four O -fucking O -years O -I O -'ve O -been O -configuring O -iBGP B-other -and O -eBGP B-other -on O -Cisco B-company -AND O -Juniper B-company -routers O -for O -America B-geo-loc -'s O -largest O -wireless O -ISP O -... O -and O -I O -get O -a O -612 O - -looking O -forward O -to O -a O -weekend O -resting O -foot O -!! O -oh O -and O -the O -irish O -on O -sunday O -. O - -RT O -@zahrock12 O -: O -Fuck O -yeah O -its O -FRIDAY O -!! O - -Going O -now O -. O -Be O -back O -later O -tonight O -. O -Thanks O -for O -all O -the O -Friday O -Fucking O -, O -appreciate O -it O -. O -Make O -love O -, O -not O -war O -: O -condoms O -are O -cheaper O -than O -guns O -. O -XOXO O - -Day O -at O -T O -. O -epic O -interview O -and O -photo O -shoot O -with O -Marie B-company -Clair I-company -. O -@OHEMMGEEZY O - -Sleeping O -later O -and O -later O -everyday O -, O -waking O -up O -earlier O -and O -earlier O -day O -by O -day O -. O -My O -eyes O -are O -getting O -smaller O -and O -smaller O -. O - -She O -should O -nom O -me O -when O -she O -gets O -back O -! O -If O -she O -'s O -not O -tired O -that O -is O -. O - -RT O -@TomsTop5 O -: O -Check O -us O -out O -- O -we O -'re O -FEATURED O -on O -iTunes O -this O -week O -! O -Make O -sure O -to O -subscribe O -so O -Jobs O -knows O -you O -love O -us O -http://twitpic.com/2pdr8h O - -@ashhleybrookee O -it O -may O -be O -nasty O -but O -... O -it O -works O -. O - -nightmare B-movie -before I-movie -christmas I-movie -!!! O - -USM B-facility -get O -ready O -yours O -truly O -tonight O -at O -frat B-facility -house I-facility -Hattiesburg I-facility -on O -the O -1s O -n O -2s O -!! O - -Lots O -of O -Merlot B-product -on O -Thursday O -night O -means O -tired O -London O -Ladies O -are O -in O -bed O -by O -23.19 O -on O -the O -Friday O -. O -Listening O -to O -the O -arts O -show O -on O -@BBCRadio4 O -. O - -.... O -today O -is O -better O -than O -yesterday O -, O -but O -I O -hope O -tomorrow O -is O -better O -than O -today O -, O -because O -today O -still O -somewhat O -- O -SUCKS O -. O - -@IzzieZac O -Told O -you O -!! O -:o O -)) O -When O -'s O -she O -back O -again O -? O - -Observances O -mark O -9/11 O -anniversary O -: O -Observances O -in O -Temecula B-geo-loc -and O -elsewhere O -throughout O -the O -region O -last O -week O -marked O -the O -... O -http://dlvr.it/5Rckw O - -Honored O -Double O -Diaomond O -as O -well O -: O -Jan B-person -Bloom I-person -... O -her O -two O -beautiful O -daughters O -surprised O -her O -on O -stage O -, O -it O -was O -just O -sooo O -... O -http://fb.me/JLLhbDl0 O - -RT O -@Quotealicious O -: O -Today O -, O -I O -saw O -a O -guy O -driving O -a O -Pepsi B-company -truck O -, O -drinking O -a O -Coke B-product -. O -MLIA O -#Quotealicious O - -pulling O -an O -all O -nighter O -tonight O -.... O -good O -times O -are O -approaching O - -@bookpage O -We O -should O -'ve O -scheduled O -a O -meeting O -with O -Trisha B-person -in O -Paris B-geo-loc -for O -this O -week O -... O -#deau O - -Who O -loves O -Friday O -? O -At O -the O -gym O -now O -! O -Excited O -for O -weekend O -plans O -! O - -Time O -to O -have O -#sex O - -The B-movie -Town I-movie -might O -be O -one O -of O -the O -best O -movies O -I O -have O -seen O -all O -year O -. O -So O -, O -so O -good O -. O -And O -do O -n't O -worry O -Ben B-person -, O -we O -already O -forgave O -you O -for O -Gigli B-movie -. O -Really O -. O - -@Jsmoothhh O -it O -'s O -not O -supposed O -to O -rain O -till O -Sunday O -night O -. O - -Going O -to O -the O -country O -fair O -today O -:) O - -Pope B-person -says O -religion O -' O -marginalized': O -That O -'s O -why O -he O -ca O -n't O -get O -a O -headline O -printed O -anywhere O -, O -because O -religion O -is O -so O -mar O -... O -http://bit.ly/cBvt1u O - -http://bit.ly/aTTQYq O -When O -Pepsi B-company -to O -ring O -usually O -confirm O -to O -, O -winning O -a O -Nokia B-product -5800 I-product -? O - -Just O -tried O -to O -watch O -The B-tvshow -Inbetweeners I-tvshow -for O -the O -first O -time O -, O -not O -entirely O -sure O -about O -it O -. O - -But O -what O -happens O -when O -karam O -turns O -right O -around O -and O -bites O -you O -? O -And O -everything O -you O -stand O -for O -turns O -on O -you O -despite O -you O -? O - -Today O -I O -justt O -felt O -special O -forr O -no O -reason O -< O -3 O -. O -It O -wass O -cool O -thou( O -: O - -i O -think O -it O -'s O -better O -to O -go O -to O -bed O -after O -this O -bad O -day O -... O - -JV B-sportsteam -soccer I-sportsteam -leads O -4-1 O -after O -Benitez B-person -scores O -again O -, O -less O -than O -7 O -mins O -to O -play O - -@princessmariam9 O -i O -know O -.. O -i O -need O -to O -stop O -before O -my O -room O -is O -gonna O -get O -filled O -with O -water O -. O - -Goin O -to O -my O -BEST O -friends O -birthday O -party O -2day O -. O -As O -u O -can O -see O -her O -name O -is O -taylor B-person -and O -i O -love O -her O -like O -a O -sister O -! O -~HAPPY O -B-DAY O -TAYLOR B-person -!!! O -LUVZ O -YA~ O - -Our O -Favorite O -YouTube B-company -Videos O -This O -Week O -: O -The O -Drama O -Edition O -: O -Had O -any O -epic O -meltdowns O -this O -week O -? O -You O -know O -, O -of O -the O -cryin O -... O -http://bit.ly/cuy0yt O - -Pedigree B-company -Donates O -Dog O -Food O -for O -Blog O -Posts O -: O -These O -days O -, O -big O -brands O -usually O -look O -to O -the O -big O -social O -networks O -like O -Face O -... O -http://bit.ly/bYWzrq O - -A O -day O -late O -but O -thank O -you O -= O -) O -I O -love O -it O - -@ O -home O -chilling O -guess O -I O -'m O -staying O -in O -2day O -2 O -rest O -this O -week O -was O -crazy O -but O -I O -'m O -ready O -for O -dis O -weekend O - -Peace O -green B-product -tea I-product -time O -!! O -Happyzone O -!!!! O -:) O -)))) O - -LADY B-person -GAGA I-person -IS O -BETTER O -THE O -5th O -TIME O -OH O -BABY( O -: O - -Good O -start O -for O -sharks B-sportsteam -tonight O -can O -only O -get O -better O -. O -Played O -with O -short O -bench O -tonight O -. O -Thought O -Rocks B-sportsteam -well O -in O -game O -early O -though O -until O -' O -unseen O -' O - -@sarahk47 O -Okay O -The O -doc O -'s O -saying O -Keep O -it O -tamped O -down O -a O -coupla O -days O -then O -bring O -her O -out O -early O -? O - -holy O -FUCK O -THIS O -IS O -THE O -MOST O -BORING O -DAY O -EVER O -. O - -RT O -@SteevoSupremo O -: O -#The1906andPearlsShow O -THIS O -MONDAY O -@ O -7:06 O -: O -08pm O -!!!!! O -#MAINBUILDING O -ROOM O -. O -104 O -!!! O -http://twitpic.com/2oeark O -SPECIAL O -PERF O -... O - -In O -bed O -watching O -Day O -51 O -HLs O -. O -( O -Awaiting O -Marcus B-person -Bentley I-person -! O -) O -#BB11 B-tvshow - -@Starmaker73 O -ya O -but O -it O -would O -have O -to O -go O -down O -Saturday O -morning O -and O -tht O -would O -ntake O -all O -day O -lol O -so O -id O -be O -cutting O -it O -close O -lol O - -I O -got O -a O -very O -, O -very O -fun O -item O -from O -FedEx B-company -today O -. O -I'm O -so O -excited O -to O -add O -it O -to O -my O -demo O -bag O -! O -#PassionParties O -. O -#WAHM O - -RT O -@LightCMS O -: O -Day O -6 O -: O -Win O -1 O -of O -10 O -iPads O -from O -@LightCMS O -- O -RT O -to O -enter O -or O -read O -more O -http://speaklight.com/iPad O - -SCOTT B-person -WEILAND I-person -Announces O -Memoir O -Release O -- O -Music B-other -News I-other -Net I-other -: O -MUSIC O -NEWS O -- O -Simon B-other -& I-other -Schuster I-other -has O -announced O -a O -March O -8 O -, O -20 O -... O -http://bit.ly/ayLf9C O - -The B-musicartist -Maccabees I-musicartist -are O -the O -love O -of O -my O -life O -. O -one O -day O -i O -will O -be O -married O -to O -all O -of O -them O -. O - -RT O -@justingodsey O -: O -When O -the O -eagles O -fly O -high O -, O -they O -make O -all O -the O -smaller O -birds O -jealous O -. O - -@ShelAndPushPlay O -Yeah O -man O -, O -u O -got O -it O -. O -It O -'s O -only O -a O -matter O -of O -time O -. O - -Do O -any O -of O -you O -take O -the O -time O -to O -look O -up O -at O -the O -stars O -at O -night O -? O -#Anybody O -? O - -@cprieboy O -At O -the O -end O -of O -the O -year O -? O -Sometime O -in O -May O -. O -They O -are O -just O -starting O -at O -the O -beginning O -of O -August O -now O -instead O -of O -the O -middle O -. O - -Eastern B-geo-loc -Bloc I-geo-loc -25th O -b'day O -party O -kicking O -off O -now O -! O -Get O -your O -tweets O -up O -, O -using O -the O -tag O -#botweast O - -@LightBringer87 O -I'm O -too O -busy O -asking O -@leestanleyinnqy O -formspring O -questions O -, O -I O -may O -get O -around O -to O -you O -later O -;) O - -looking O -forward O -to O -a O -weekend O -resting O -foot O -!! O -oh O -and O -the O -irish O -on O -sunday O -. O - -@alealshinn O -@BambingBling O -~~~ O -@sunchips O -this O -is O -the O -second O -time O -they O -have O -messed O -up O -.... O -about O -to O -give O -up O -on O -them O - -Of O -course O -, O -I O -was O -stunned O -. O -This O -, O -after O -all O -, O -was O -an O -inanimate O -object O -. O - -30 O -Mosques O -in O -30 O -Days O -: O -A O -Trip O -Through O -Islamic O -America O -http://dlvr.it/5RdYv O - -@jdub79 O -We O -must O -have O -been O -in O -the O -same O -bathroom O -today O -. O -#losers O -#youusedtoknowhowtoflush O -#followthrough O - -RT O -@BeliebInCodyyy O -: O -@TheCodySimpson O -every O -minute O -, O -every O -second O -, O -every O -hour O -of O -the O -day O -iyiyi O -think O -of O -you143 O -< O -3 O - -@lovable_sin O -OMG O -OMG O -OMG O -! O -Thank O -you O -for O -" O -tumblring O -" O -it O -to O -me O -, O -I O -so O -wasn't O -expecting O -them O -today O -. O -OMG O -! O - -@Maike_84 O -Flikken B-tvshow -is O -geweldig O -! O -Ik O -kan O -niet O -wachten O -tot O -de O -nieuwe O -serie O -in O -november O -.. O -(geloof O -ik O -) O -Dit O -jaar O -alles O -gezien O -via O -uitzendinggemist O -. O - -RT O -@_MyNameBrandon_ O -@Mr_Marcell O -lol O -! O -yeah O -aint O -nothing O -there O -but O -my O -family O -. O -< O -thaz O -really O -it O -now O -.. O -to O -b O -honest O -... O -when O -u O -coming O -dwn O -to O -sam O - -RT O -@ThePredator48 O -: O -I O -need O -all O -my O -followers O -2 O -bring O -a O -new O -children O -'s O -book O -to O -the O -game O -this O -Sunday O -for O -the O -@WRFoundation O -Redskins O -read O -boo O -... O - -@majornelson O -I'm O -out O -of O -town O -this O -weekend O -! O -dagnabbit O -! O -Why O -does O -all O -the O -cool O -stuff O -happen O -when O -I O -leave O -? O - -Dollar O -steady O -versus O -yen O -but O -market O -wary O -of O -intervention O -| O -Money O -...: O -The O -dollar O -held O -near O -its O -highest O -in O -a O -month O -... O -http://bit.ly/a0F3dO O - -before O -the O -season O -even O -starts O -i O -will O -not O -respond O -to O -any O -new O -found O -miami B-sportsteam -heat I-sportsteam -fans O -... O - -IF O -YOU O -HAVEN'T O -HEARD O -... O -THERE O -IS O -AN O -AMAZING O -SHOW O -GOING O -ON O -TONIGHT O -AT O -JANNUS B-facility -LIVE I-facility -... O -SUENALO B-musicartist -! O -FROM O -MIAMI B-geo-loc -W/ O -SHAKEMODE B-musicartist -AND O -INDIGENOUS B-musicartist -... O - -:( O -RT O -@themaine O -Who O -is O -coming O -to O -the O -show O -tomorrow O -in O -Hawaii B-geo-loc -? O - -@atlgc17 O -Im O -16 O -and O -a O -freshmen O -again O -. O -I O -fluncked O -once O -in O -elementry O -school O -and O -I O -fluncked O -last O -year O -because O -I O -never O -did O -my O -work O -. O - -When O -your O -database O -of O -all O -staff O -is O -on O -a O -flash O -drive O -that O -goes O -missing O -#flash O -#database O -#Security O -http://bit.ly/b1lWKW O - -Today O -in O -art O -this O -kid O -was O -like O -Are O -you O -going O -to O -homecoming O -tonight O -? O -Other O -kid O -: O -No O -, O -I'm O -going O -to O -be O -at O -home O -... O -coming O -. O -RT O -If O -you O -get O -it O -! O - -This O -is O -one O -of O -my O -favorite O -days O -on O -twitter B-company -ever O -. O -@radhallman O -and O -@foofmcewan O -kudos O - -@Strigy O -got O -mine O -in O -bbt O -aintree B-geo-loc -today O -. O -Played O -table O -tennis O -on O -it O -in O -store O -! O -V O -impressed O -. O -Did O -you O -get O -analogue O -controller O -2 O -? O - -RT O -@foxyscot30 O -: O -RT O -@_CorruptedAngel O -RT O -@tomcoates O -: O -Bit O -grumpy O -about O -the O -bloody O -Pope B-person -clogging O -up O -London B-geo-loc -tomorrow O -. O -I O -suggest O -we O -take O -Gay O -P O -... O - -@flossyflop O -It O -'s O -a O -slightly O -off O -night O -tonight O -. O -Lots O -of O -requests O -I O -wouldn't O -normally O -play O -cos O -they're O -too O -slow O -. O - -my O -heart O -stops O -when O -you O -look O -at O -me O -, O -just O -one O -touch O -now O -baby O -i O -believe O -< O -33 O - -bahahah O -i O -think O -i'm O -over O -hyper O -today O -xD O - -Today O -'s O -cheer O -went O -from O -awful O -to O -awesome O -from O -the O -moment O -I O -realized O -I O -did O -not O -have O -class O -today O -. O - -RT O -@johan0394 O -: O -Soulja B-person -Boy I-person -| O -TheDeAndreWay O -. O -com O --- O -The B-other -DeAndre I-other -Way I-other -in O -stores O -Nov O -2 O -, O -2010 O -http://t.co/eoELod9 O -via O -@SouljaBoy O - -#blackholidays O -TD B-other -Jakes I-other -Day I-other - -@SneakyLix O -thx O -! O -btw O -i'll O -text O -u O -in O -a O -bit O -! O -i O -just O -got O -home O -, O -trying O -to O -dL O -some O -songs O -, O -and O -gonna O -head O -to O -my O -bf's. O -i'll O -text O -u O -when O -i O -get O -there O - -@dancindeac O -bet O -the O -vino O -tastes O -especially O -good O -this O -week O -. O -;-) O - -RT O -@aplusk O -: O -This O -made O -me O -laugh O -today O -http://bit.ly/bjOhom O -< O ---- O -courtesy O -of O -splurb O -. O -What O -made O -you O -laugh O -? O - -Goodmorning O -? O -Just O -woke O -up O -.. O -Gonna O -study O -again O -for O -today O -'s O -exams..that O -'s O -okay O -because O -later O -no O -more O -examinations O -;) O - -Performing O -on O -Thursday O -, O -Sept O -. O -23 O -at O -11:00 O -a.m. O -in O -the O -Student B-facility -Centre I-facility -at O -Centennial B-other -College I-other -( O -Progress B-geo-loc -Campus I-geo-loc -) O -in O -Scarborough B-geo-loc -. O -@ccsai O - -No O -Good O -Punk O -: O -Thug O -knocks O -84-year-old O -to O -the O -ground O -, O -steals O -her O -purse O -http://lsnlw.com/t/4188153892/ O - -@XrisLastRights O -yep O -... O -we O -don't O -have O -anything O -here O -... O -( O -ok O -, O -we've O -had O -mcfly O -one O -year O -ago O -but O -.. O -that O -'s O -it O -) O - -I O -favorited O -a O -YouTube B-company -video O --- O -Yoav B-person -" O -We B-other -All I-other -Are I-other -Dancing I-other -" O -( O -Copenhagen B-geo-loc -March O -2010 O -) O -http://youtu.be/xi8qJ3IKK8U?a O - -Thank O -you O -@ZUNE O -for O -todays O -Album O -of O -the O -Day O -. O -Good O -choice O -that O -fits O -perfectly O -. O - -@MiggleM O -We'll O -catch O -up O -some O -time O -... O -*knows O -this O -won't O -happen* O - -@Laylaloves O -Well O -Im O -Irish O -and O -I O -couldnt O -stand O -her O -in O -the O -programme O -even O -before O -" O -waterfight/braless O -gate O -"!! O -She O -jus O -seemd O -sniddy O -! O - -@jaredleto O -... O -ive O -been O -to O -13 O -30stm B-musicartist -concerts O -now O -and O -you O -never O -got O -me O -on O -stage O -during/before O -kings B-musicartist -and I-musicartist -queens I-musicartist -:( O -(( O - -havent O -been O -on O -facebook B-company -for O -a O -day O -. O -go O -on O -and O -have O -0 O -notifications O -#foreveralone O - -@rattleknackers O -amusing O -? O -This O -is O -some O -of O -the O -greatest O -music O -of O -all O -time O -, O -my O -foot O -hasn't O -stopped O -tapping O -all O -night O - -@godsgirl8494 O -I O -believe O -in O -the O -sun O -, O -even O -when O -it O -is O -not O -shining O -. O -I O -believe O -in O -love O -, O -even O -when O -I O -do O -not O -feel O -it O -. O -:) O - -@lorymichael O -Good O -Night O -and O -God O -Bless O -. O -Sleep O -well O -honey O -See O -you O -tomorrow O -Bye O -bye O -I O -love O -you O -dear O - -RT O -@DaisyCottageIRL O -: O -#FridayTwiz O -Q10 O -What O -is O -a O -Cruciverbalist O -? O -Answer O -: O -someone O -with O -WAY O -too O -much O -time O -on O -their O -hands O -. O -#ShouldTakeUpDarning O - -@HeartLessVick O -Lol O -, O -was O -george B-person -with O -carmen B-person -today O -;D O - -RT O -@therealadamwest O -: O -Sunday O -is O -my O -birthday O -. O -82 O -years O -young O -! O -What O -should O -I O -wear O -? O -( O -don't O -say O -birthday O -suit O -! O -) O - -haha O -dia O -25 O -vou O -ver O -a O -@bandahydrogen O -less O -than O -20 O -seconds O -ago O -via O -web O - -Is O -so O -excited O -to O -get O -some O -dancing O -done O -tonight O -with O -@jdsamson O -/MEN O -at O -The B-facility -Echo I-facility -. O - -Use O -My O -Shopping O -Genie O -before O -?? O -How O -did O -it O -work O -for O -you O -? O -Try O -it O -out O -here O -http://bit.ly/cawTJF O - -@dena33 O -have O -a O -good O -weekend O -! O -Next O -time O -I'm O -at O -Gardens B-facility -we O -are O -joining O -eatery O -forces O -. O - -@ZhouMaharet O -I O -haven't O -for O -a O -long O -ass O -time O - -omg O -tomorrow O -we O -meet O -, O -it O -will O -be O -good O -to O -see O -you O -again O - -Soulja B-person -Boy I-person -| O -TheDeAndreWay O -. O -com O -- O -The B-other -DeAndre I-other -Way I-other -in O -stores O -Nov O -2 O -, O -2010 O -http://t.co/ifJBMTR O -via O -@SouljaBoy O - -@AddieWilliams O -Dat O -'s O -what O -I'm O -talkin O -' O -' O -bout O -. O -In O -20 O -years O -who O -knows O -. O -Maybe O -Mr O -. O -and O -Mrs O -... O -? O - -Photo O -: O -Travie O -hanging O -out O -with O -fans O -at O -103.5 O -in O -Chicago B-geo-loc -today O -http://tumblr.com/xvliuc7q5 O - -RT O -@shan7792 O -: O -its O -annoying O -tht O -ppl O -ask O -me O -what O -race O -i O -am O -before O -they O -ask O -me O -my O -name O --_- O - -My O -few O -followers O -..! O -What O -'s O -up O -..? O -( O -: O -I O -kind O -of O -got O -in O -trouble O -today O -lol O -... O -Just O -helps O -me O -remember O -I'm O -still O -a O -human O -. O -Imperfectly O -perfect O -..!! O -( O -: O - -RT O -@ItsTheTeenLife O -: O -" O -HAHA O -, O -remember O -when O -you O -guys O -dated!'' O -'' O -Shut O -up O -! O -NEVER O -EVER O -bring O -that O -up O -again!'' O -#ItsTheTeenLife O - -@coulditbeJenG O -It O -was O -20 O -years O -ago O -. O -They're O -coming O -after O -me O -, O -I O -just O -know O -it O -. O - -RT O -@JR_Swish O -: O -Every O -time O -I O -look O -@NICKIMINAJ O -I O -just O -be O -lost O -for O -words O -damn O -! O -#shhhhhhh O - -Train O -with O -us O -for O -the O -HCA B-other -8K I-other -! O -We O -start O -tomorrow O -morning O -at O -7:30 O -am O -at O -the O -showroom O -! O - -PeopleSearchAffiliates O -. O -com O -: O -#1 O -For O -Over O -2 O -Years O -: O -Make O -money O -today O -! O -Most O -lucrative O -people O -search O -products O -on O -CB O -for O -... O -http://bit.ly/aVU9RU O - -going O -to O -the O -mall O -with O -my O -momma O -, O -then O -church O -after O -this O -is O -a O -big'o O -challenge O -for O -the O -next O -3 O -days O -encounter O -here O -I O -come O -lets O -see O -what O -happens O - -of O -my O -days O -like O -paris B-person -hilton I-person -doing O -blowjobs O -on O -camera O -to O -earn O -enough O -money O -to O -feed O -my O -angry O -birds O -habbit O -until O -i O -die O - -@AlineLemos O -T.T O -Ainda O -vou O -fazer O -o O -Caroleta O -'s O -Day O -[ O -? O -] O - -Anja B-person -Rubik I-person -Model O -in O -Lingerie O -of O -the O -Day O -http://f.ast.ly/DCaEM O - -Right O -arm O -Rousseff B-person -, O -Erenice B-other -War I-other -, O -51 O -years O -, O -became O -chief O -minister O -of O -the O -Civil B-other -House I-other -in O -April O -this O -year O - -" O -In O -business O -, O -Tuesday O -is O -usually O -the O -most O -productive O -day O -. O -For O -Twitter B-company -, O -is O -when O -people O -tweet O -the O -most O -" O -- O -Ironic O -, O -isn't O -it O -? O - -thank O -god O -its O -fucking O -friday O - -NEW O -BLOG O -- O -' O -Badass B-other -Babes I-other -Savin I-other -' I-other -The I-other -Day I-other -' O --- O -CHIX O -6 O -, O -The O -ROXPERIENCE O -WEEK O -5 O -: O -BWW O -is O -excited O -to O -present O -a O -new O -blog O -, O -... O -http://bit.ly/9QkhUC O - -@JustBmoss O -hey O -hub O -how O -u O -been O -haven't O -seen O -u O -since O -my O -Love O -days O -lol O - -I O -madw O -my O -email O -when O -i O -was O -10 O -and O -yeah O -its O -really O -embarissing O -lol O - -RT O -@TheValarium O -: O -Online O -ticket O -sales O -for O -Ghostland B-musicartist -Observatory I-musicartist -extended O -until O -6 O -PM O -EST O -due O -to O -high O -demand O -. O -Get O -them O -before O -they O -sell O -out O -... O - -RT O -@WScottsdale O -: O -What O -'s O -happening O -at O -@WScottsdale O -the O -rest O -of O -the O -month O -? O -Check O -out O -our O -September O -Happenings O -! O -http://bit.ly/WHapps O - -Not O -only O -is O -it O -#BattlestarGalactica O -'s O -32nd O -Anniversary O -, O -tonite O -is O -the O -3rd O -season O -premiere O -of O -#StarWars O -#TheCloneWars O -!! O - -JV B-sportsteam -soccer I-sportsteam -leads O -4-1 O -after O -Benitez B-person -scores O -again O -, O -less O -than O -7 O -mins O -to O -play O - -RT O -@BadAssNigerian O -: O -Chillin O -w O -| O -@Brownin21 O -.... O -n O -gettin O -pierced O -this O -week O -< O -WHOOOOP O -!!! O - -RT O -@CobraCommander O -: O -When O -your O -precious O -country O -collapses O -, O -just O -remember O -, O -every O -pair O -of O -Crocs B-company -you O -bought O -contributed O -to O -the O -downfall O -. O - -" O -Count O -your O -age O -by O -#friends O -, O -not O -years O -. O -Count O -your O -life O -by O -#smiles O -, O -not O -tears O -." O -#quote O -John B-person -Lennon I-person -#quotes O - -next O -few O -weeks O -are O -going O -to O -be O -hectic O -between O -traveling O -and O -knee O -surgery O -when O -i O -get O -back O - -@BornThisWayBaby O -thankk O -yhuu O -#monsterlove O -u O -just O -made O -my O -day O -:D O - -Lol O -girrrrrrl O -. O -I'm O -very O -lucky O -but O -everything O -is O -work O -. O -Yours O -will O -come O -when O -its O -supposed O -to O -RT O -@_katesit_ O -Can't O -( O -cont O -) O -http://tl.gd/63aq2c O - -UT O -: O -John B-person -Chiles I-person -out O -, O -Mike B-person -Davis I-person -in O -: O -Texas B-geo-loc -wide O -receiver O -John B-person -Chiles I-person -has O -been O -ruled O -out O -of O -Saturday O -night O -'s O -game O -wit O -... O -http://es.pn/aBLrXN O - -Fuckk O -man O -! O -I O -fuckinn O -missed O -@yelyahwilliams O -concert O -:( O -#shizz O -! O -I O -wonder O -if O -they're O -coming O -to O -Arizona B-geo-loc -next O -year O -?? O - -today O -just O -doesn't O -feel O -like O -a O -Friday O - -@wonderwhygal O -I O -was O -trying O -to O -be O -sly O -today O -. O -I O -started O -a O -new O -hat O -, O -and O -have O -a O -pink O -one O -done O -4 O -@loongirl O -My O -goal O -is O -5 O -. O - -@basboeit O -Een O -groeiproces O -in O -de O -ruimste O -zin O -van O -het O -woord O -. O -Ik O -arriveerde O -toen O -5 O -kilo O -. O -Gelukkig O -viel O -ik O -er O -deze O -week O -evenzoveel O -weer O -uit O -. O - -Why O -do O -that O -to O -begin O -with O -? O -lols O -. O -@mggykills O -@dieSIBdie O -When O -on O -vacation O -in O -Cebu B-geo-loc -, O -you O -do O -not O -sniff O -your O -roommate O -'s O -boxers O -. O -#rulesbetweenmen O - -RT O -@WholeFoodsATX O -: O -Anyone O -out O -there O -getting O -excited O -4 O -' O -Dazed B-movie -& I-movie -Confused I-movie -' O -on O -our O -rooftop O -plaza O -tonight O -? O -Movie O -starts O -at O -sundown O -! O -#30thBday O - -@cosmicblend O -but O -you O -ARE O -coming O -tonight O -, O -right O -? O - -Edmonton B-geo-loc -dealers O -raise O -$320000 O -for O -Special B-other -Olympics I-other -: O -By O -Tim B-person -Yip I-person -, O -Edmonton B-other -Journal I-other -September O -17 O -, O -2010 O -2:03 O -PM O -Chari O -... O -http://bit.ly/b0CYjb O - -Photo O -: O -Chillo O -?? O -Trying O -out O -for O -the O -1st O -time O -this O -shit O -!! O -http://tumblr.com/x0jiuemje O - -RT O -@fredthompson O -: O -WH O -rejects O -" O -global O -warming O -", O -favors O -term O -" O -global O -climate O -disruption O -". O -Ya O -know O -, O -I O -remember O -back O -when O -we O -used O -to O -call O -it O -... O - -New O -Trailer O -For O -Platinum O -Games O -' O -Vanquish B-product -for O -PS3 B-product -, O -Xbox B-product -360 I-product -: O -by O -Widro B-company -- O -September O -17 O -, O -2010 O -| O -Email O -the O -author O -Direc O -... O -http://bit.ly/aCPM2x O - -RT O -@ArtVanFurniture O -: O -Mr B-person -. I-person -Van I-person -sure O -is O -busy O -today O -. O -Here O -, O -he O -takes O -time O -to O -talk O -with O -a O -crew O -from O -@wilxTV O -. O -http://twitpic.com/2pb3i4 O - -RT O -@BLKICE3 O -: O -Free O -before O -10:30 O -.... O -Wear O -All O -Black O -People O -!!!! O -http://twitpic.com/2pd1vc O - -RT O -@GreatestQuotes O -: O -" O -When O -everything O -seems O -to O -be O -going O -against O -you O -, O -remember O -that O -the O -airplane O -takes O -off O -against O -the O -wind O -, O -not O -with O -it O -." O -... O - -Report O -: O -Michael B-person -Vick I-person -Will O -Start O -for O -Eagles B-sportsteam -on O -Sunday O -http://t.co/UqXcSXT O - -RT O -@DubOnDaBeatz O -: O -People O -who O -gossip O -remind O -me O -of O -reporters O -#nshit O -always O -got O -da O -latest O -info O -on O -somebody O -! O -Time O -for O -you O -loose O -lips O -to O -chan O -... O - -no O -mins O -ha O -i O -guess O -i O -aint O -goin O -to O -town O -today O -ha O -just O -gonna O -stay O -and O -ball O -.... O - -Thank O -you O -so O -very O -much O -EVERYONE O -for O -the O -lovely O -Friday O -Follows O -today O -- O -you O -are O -all O -too O -lovely O -:) O -Sorry O -I've O -not O -had O -time O -to O -reciprocate O -xXx O - -RT O -@BitchImYerp O -: O -all O -the O -badd O -biddies O -gon O -have O -lingerie O -on O -tonite O -at O -#obsessions O -so O -ima O -wear O -my O -cheetah O -print O -thong O -! O -lmfao O - -Hey O -. O -#dmv O -. O -There O -is O -a O -march O -. O -10-2-10 O -onenationworkingtogether.org O -for O -more O -info O - -Good O -start O -for O -sharks B-sportsteam -tonight O -can O -only O -get O -better O -. O -Played O -with O -short O -bench O -tonight O -. O -Thought O -Rocks O -well O -in O -game O -early O -though O -until O -' O -unseen O -' O - -tonight O -was O -pretty O -good O -:) O -but O -soo O -coldd O -:( O - -Bringing O -this O -one O -back O -. O -From O -last O -March O -. O -On O -tackles O -. O -On O -intent O -. O -More O -to O -say O -but O -for O -lack O -of O -time O -, O -here O -'s O -this O -: O -http://ow.ly/2G4Mb O -( O -Colin B-person -) O - -Saturday O -Night O -, O -Knighttime B-facility -Billiards I-facility -with O -4D O -! O -Lets O -help O -Bonita B-person -celebrate O -her O -Birthday O -! O -Come O -early O -and O -make O -a O -toast O -! O - -RT O -@DouchebagMovie O -: O -Come O -to O -" O -DOUCHEBAG B-other -LOS I-other -ANGELES I-other -OPENING I-other -WEEKEND I-other -EXTRAVAGANZA I-other -" O -Friday O -, O -October O -8 O -at O -7:30 O -pm O -until O -< O -br O -/ O -> O -Saturday O -... O - -my O -past O -was O -the O -reason O -i O -tried O -to O -kill O -myself O -the O -other O -day O -, O -and O -i'll O -gladly O -do O -it O -again O -. O - -Mr B-person -Ehrlich I-person -uses O -bd O -language O -after O -school O - -@masalaskeptic O -that O -'s O -how O -I O -got O -this O -nickname O -. O -Told O -I O -looked O -like O -the O -muppet O -. O -And O -I O -kind O -of O -did O -the O -same O -jom O -when O -I O -did O -theatre O -work O -. O - -oh O -snap O -i O -got O -#blackandyellow O -on O -today O -.. O -just O -for O -wiz O -aha O -! O - -Oh O -wow O -. O -After O -like O -half O -a O -year O -of O -misaddressing O -them O -, O -Game B-company -Informer I-company -FINALLY O -corrected O -my O -label O -printing O -issues O -. O -Hope O -this O -one O -'s O -good O -. O - -Really O -wish O -I O -can O -see O -a O -rocket O -to O -the O -moon O -today O -. O -They O -would O -totally O -make O -my O -day O -. O - -preciso O -de O -um O -BG O -do O -all O -time O -low O -ou O -do O -the O -maine O -( O -: O - -@FaeNathara O -Yes O -, O -well O -a O -HOOT O -absoroootly O -because O -I'm O -secretly O -a O -hoot O -, O -the O -rest O -of O -the O -time O -I'm O -serious O -... O -don't O -tell O -anyone O - -@cdellz11 O -yeah O -. O -But O -it O -'s O -been O -so O -long O -. O -I O -miss O -home O -. O -But O -u O -still O -on O -ur O -grind O -tho O -. O -That O -'s O -wassup O -. O - -party O -today O -< O -3 O -:) O -happy O -birthday O -florence B-person -< O -3 O - -@keevaguilfoyle O -Wow O -congrats O -on O -your O -engagement O -Keeva B-person -, O -delighted O -for O -you O -& O -Dave B-person -!! O -( O -Was O -a O -bit O -worried O -after O -his O -initial O -muted O -response O -;-) O -) O - -Finna O -Chill O -Tonite O -!! O -Dont O -Feel O -Like O -Doinq O -Shxt O -My O -Mouth O -Hurts O -!! O - -RT O -@ZodiacFacts O -: O -#ZodiacFacts O -As O -an O -#Aries O -romance O -isn't O -about O -what O -you're O -after O --- O -it O -'s O -about O -taking O -chances O -, O -exploring O -and O -keeping O -an O -... O - -Not O -awful O -... O -going O -to O -start O -fresh O -this O -week O -:-) O -Soo O -hungry O -! O - -RT O -@TalibanTwon O -: O -RT O -@bigmatt3288 O -: O -The O -Birthday O -Celebration O -for O -The O -Boy O -@Talibantwon O -*Tonight* O -+ O -#LaylaFridays O -= O -ZOOOVIE O - -J O -shaken O -hands O -w/santana O -and/fatt O -lady O -like O -no O -otherQuote O -" O -this O -ought O -to O -get O -my O -ratins O -up O -before O -Conan B-tvshow -Get O -back-Big O -Jack O -Off O -show-1 O -4 O -me O -" O - -Mostly O -clear O -tonight O -, O -low O -63 O -( O -17 O -C) O -. O -Sunny O -Saturday O -, O -high O -near O -86 O -( O -30 O -C) O -. O - -Tomorrow O -'s O -the O -big O -day O -! O -Our O -Pet O -Spa O -and O -Adoption O -Event O -with O -Evergreen B-company -Subaru I-company -is O -from O -10am O -to O -1pm O -and O -we O -can't... O -http://fb.me/I3aLAgtp O - -Because O -maybe O -You're O -gonna O -be O -the O -one O -who O -saves O -me O -? O -And O -after O -all O -You're O -my O -wonderwall O - -Lohan B-person -fails O -drug O -test O -: O -reports O -: O -Oh O -no O -, O -LiLo B-person -! O -Several O -gossip O -websites O -reported O -Friday O -afternoon O -that O -Lindsay B-person -Lohan I-person -... O -http://bit.ly/aKHoZc O - -@iansomerhalder O -before O -seeing O -you O -, O -I O -did O -not O -believe O -in O -the O -ancient O -gods O -! O -Well O -, O -now O -I O -change O -my O -idea O -! O -you O -are O -the O -God O -of O -perfection O -!!!! O - -@rachelshaps O -- O -dang O -! O -I O -didn't O -know O -you O -were O -giving O -shoutouts O -! O -Haha O -oh O -well O -. O -They're O -is O -always O -next O -friday O -:) O - -Reading O -: O -Tips O -: O -Long-press O -address O -bar O -to O -share O -URL O -: O -Shared O -by O -Renee B-person -Or O -as O -I'd O -describe O -it O -, O -a O -shortcut O -to O -the O -" O -N O -... O -http://bit.ly/cPZU6A O - -Ducks B-sportsteam -sign O -LW B-person -Beleskey I-person -to O -2-year O -extension O -- O -San B-other -Jose I-other -Mercury I-other -News I-other -http://dlvr.it/5RcvP O -#ANADucks O - -Beginning O -what O -I O -hope O -will O -be O -a O -long O -and O -wonderful O -relationship O -with O -it B-tvshow -'s I-tvshow -always I-tvshow -sunny I-tvshow -. O -Since O -I O -do O -own O -season O -1 O -and O -2 O -on O -DVD O -already O -. O - -@LesColyer O -Who O -do O -you O -film O -for O -? O -I O -was O -on O -SKY B-other -news I-other -2 O -weeks O -ago O -#checkmeout O - -@snufflesgirl25 O -@mo9x O -Well O -. O -just O -beg O -@100monkeysmusic O -.. O -PLEASE O -come O -to O -the O -#SBLeurope O -Next O -year O -!. O -We O -need O -you O -there O -! O -*puss O -in O -boots O -eyes* O - -omg O -i O -get O -paid O -today O -yesss O - -RT O -@Leonsays O -: O -Need O -a O -nice O -chill O -spot O -tonight O -, O -no O -hype O -... O -Any O -suggestions O -? O - -so O -far O -the O -herring O -is O -a O -success O -. O -definitely O -needs O -a O -couple O -more O -days O -though O - -Kk O -its O -22:48 O -friday O -nyt O -:D O -really O -tired O -so O -imma O -go O -to O -sleep O -:) O -good O -nyt O -x O -god O -bles O -xxxxx O - -mothers B-other -day I-other -greeting O -cards O -:) O -Surrounded O -by O -Mothers B-other -Day I-other -and O -Dreading O -It O -? O -http://bit.ly/a7d7tg O - -@ConorMc_Ginty O -GaGa B-person -played O -here O -a O -few O -weeks O -back O -. O -She O -spoke O -out O -against O -SB1070 B-other -during O -the O -show O -. O -God O -bless O -her O -for O -that O -. O - -RT O -@jaye403 O -: O -@BlackCanseco O -you O -know O -it O -is O -funny O -how O -those O -ppl O -want O -their O -heros O -remembered O -... O -when O -the O -truth O -is O -are O -less O -than O -worthy O - -Goodnight O -and O -may O -peace O -be O -upon O -you O -and O -yours O -. O -< O -3 O - -RT O -@MarioBB9 O -: O -Pope B-person -says O -atheists O -pick O -and O -choose O -their O -morals O -. O -Correct O -. O -Today O -I O -will O -be O -frowning O -on O -child O -abuse O -and O -not O -having O -a O -proble O -... O - -@TMCphotographs O -I O -know O -, O -but O -there O -are O -plenty O -of O -mid O -week O -programmes O -that O -I O -can O -take O -the O -piss O -out O -of O -now O -the O -winter O -is O -almost O -upon O -us O -. O -xx O - -@miaturner O -OOC O -: O -Feeling O -a O -little O -volent O -today O -, O -huh O -? O - -It O -sucks O -when O -you O -waiting O -for O -the O -bus O -with O -your O -bike O -, O -then O -it O -shows O -up O -with O -two O -bikes O -already O -on O -it O -. O - -Lines O -for O -Tonight O -'s O -game O -9:00 O -pm O -at O -Burnaby B-geo-loc -8Rinks O -. O -Please O -note O -, O -these O -are O -not O -the O -lines O -for O -showering O -tonight O -! O -http://twitpic.com/2pducs O - -@TrustL33 O -word O -im O -definitely O -gettin O -that O -the O -day O -it O -drops O - -RT O -@cinnzcocky_yep O -" O -I O -got O -lots O -of O -gualllluhh O -I O -spend O -it O -" O --- O -> O -drinks O -on O -u O -next O -time O - -I O -just O -took O -" O -After O -getting O -trampled O -at O -a O -Justin B-person -Bieber I-person -concert O -, O -yoiu O -wake O -up O -and O -..." O -and O -got O -: O -Part O -6 O -:) O -! O -Try O -it O -: O -http://tinyurl.com/233u5lu O - -como O -saben O -que O -danger O -days O -es O -el O -nombre O -del O -disco O -? O - -Hurry O -up O -! O -Brandy B-person -will O -be O -Leaving O -in O -2 O -days O -! O -- O -#Eskorte O -and O -#Massasje O -i O -#Norge O - -#DiggyFollows50 O -@Diggy_simmons O -FOLLOW O -ME O -PLZ O -CAUSE O -IM O -HAVIN O -A O -BAD O -DAY O -AND O -WOULD O -BE O -SO O -MUCH O -BETTER O -IF O -U O -FOLLOWD O -ME O -. O -BUMPING O -AIRBORNE O -RIGHT O -NOW O - -RT O -@LoveQuotesx O -: O -Have O -you O -ever O -noticed O -that O -the O -worst O -way O -to O -miss O -someone O -is O -when O -they O -are O -right O -beside O -you O -and O -you O -can O -never O -have O -them O -? O - -Having O -movie O -night O -tonight O -at O -the O -crib O -and O -ur O -not O -invited O -*BBM O -sticks O -tongue O -out O -smiley* O - -FRIDAY O -.... O -home O -alone O -! O -xD O -wanna O -join O -me O -?? O - -I O -just O -had O -to O -step O -into O -my O -office O -( O -front O -porch O -) O -again O -. O -Lol O -. O -I O -feel O -like O -everything O -happening O -today O -is O -highly O -unusual O -. O -I O -like O -it O -a O -lot O -! O - -@morgancharlie O -mmm O -deffo O -one O -of O -' O -those O -' O -thought O -provoking O -days O -:/ O -Never O -mind O -, O -onwards O -& O -upwards O -we O -shall O -go O -!! O - -If O -I O -had O -known O -what O -I O -know O -now O -about O -truck O -drivers O -, O -I O -don't O -think O -I O -would O -have O -asked O -them O -to O -honk O -as O -much O -when O -I O -was O -younger O -. O - -Yay O -. O -@penguin O -is O -coming O -to O -see O -me O -next O -week O -... O -plotting O -my O -cooking O -now O -! O - -@KaulitzClub O -Hahaha O -! O -We O -dance O -, O -eat O -, O -drink O -, O -and O -talk O -EVERYTIME O -we O -get O -together O -. O -And O -it O -'s O -been O -this O -way O -for O -like O -years O -now O -! O - -was O -on O -club B-product -penguin I-product -with O -@twilightmarisa O -till O -she O -decided O -to O -log O -of O -to O -go O -bed O -and O -leave O -me O -loool O -reminded O -me O -of O -my O -old O -cp O -days O -lol O - -ohh O -right O -... O -im O -an O -actor O -... O -back O -to O -work O -tonight O -WOOT O - -Surely O -mags O -will O -line O -up O -to O -feature O -. O -RT O -@peoplemag O -: O -Lindsay B-person -Lohan I-person -fails O -court-mandated O -drug O -test O -, O -faces O -more O -jail O -time O -http://ow.ly/2G3R2 O - -RT O -@JDay_Sept15 O -: O -RT O -@Ashanie_di_Don O -: O -@JDay_Sept15 O -lol O -i O -see O -someone O -hyped O -and O -ready O -for O -#FlavaFridays O -tonight O -< O -every O -week O -. O -Lets O -get O -it O -! O - -Our O -Favorite O -YouTube B-company -Videos O -This O -Week O -: O -The B-other -Drama I-other -Edition I-other -- O -Had O -any O -epic O -meltdowns O -this O -week O -? O -You O -know O -, O -of O -the O -crying O -... O -http://ow.ly/193oP5 O - -Doing O -" O -spring O -cleaning O -", O -found O -photo O -storage O -tank O -that O -is O -5x O -size O -of O -my O -iPhone B-product -with O -less O -capacity O -than O -iPhone4 B-product -... O -4 O -years O -old O -, O -technology O -! O - -e O -o O -q O -vai O -ser O -hj O -? O -cerveja O -foi O -ontem O -... O -que O -tal O -um O -vinho O -pra O -variar O -um O -pouco O -!! O -friday O -'s O -celebrate O -!!! O -yupppppp O - -#asdrakesays O -" O -what O -'s O -a O -star O -when O -his O -most O -important O -fan O -is O -missing O -?" O - -What O -a O -productive O -day O -. O -Not O -. O - -Fellas O -Don't O -just O -lick O -the O -PussyY O -, O -LOVE O -IT O -! O -enjoy O -it O -. O -like O -u O -just O -got O -married O -& O -the O -preacher O -said O -u O -may O -kiss O -the O -bride O - -You O -all O -need O -to O -know O -justin B-person -isnt O -the O -innocent O -16 O -year O -old O -boy O -you O -think O -he O -is O -he O -has O -to O -be O -like O -that O -and O -' O -love O -' O -his O -fans O -so O -... O - -What O -game O -come O -on O -tonight O -? O - -Power O -nap O -. O -I O -need O -it O -. O -Its O -been O -a O -stressful O -week O -. O -I'm O -excited O -for O -xmas B-other -, O -haha O -. O -Goodnight O -. O -:) O - -@LH_TCC O -I O -hope O -for O -All O -Of O -The O -Lights O -. O -I O -really O -do O -. O -CDQ O -of O -Runaway O -better O -have O -those O -samples O -from O -when O -he O -did O -it O -Live O -too O -. O - -@Viic156 O -I'm O -Ocutta O -Time O -ps O -tambn O -la O -amooo O -peroo O -la O -otra O -tambn O -me O -gustaa O - -RT O -@yikes77 O -: O -@DonnieWahlberg O -I O -have O -a O -feeling O -that O -when O -it O -comes O -to O -you O -, O -you O -need O -more O -than O -just O -ONE O -date O -. O -- O -YUP O -!!!! O - -@Dazzx O -I O -think O -you O -may O -have O -mentioned O -it O -. O -Once O -or O -twice O -. O -:) O - -Beddy O -bye O -for O -sleepy O -time O -xx O - -@Dishfunctional O -yeah O -. O -big O -time O -dancer O -. O -i O -might O -worship O -her O -a O -bit O - -@erutan O -Stanford B-other -'s O -Robert B-person -Laughlin I-person -( O -Nobel O -Physics1998 O -) O -has O -a O -better O -explanation O -than O -I O -have O -time O -to O -provide O -rt O -now O -. O -http://bit.ly/9k1L4N O - -@MsAustinTaylor O -whats O -been O -going O -on O -today O -? O - -Next O -time O -you'll O -show O -restraint O -you O -don't O -get O -another O -chance O -life O -is O -no O -nintendo B-company -game O -but O -you O -lied O -again O -now O -you O -get O -to O -watch O -her O -leave O - -Not O -feeling O -up O -to O -par O -today O -. O -#stressing O - -The B-sportsteam -Wolves I-sportsteam -to O -host O -the B-sportsteam -Lions I-sportsteam -for O -game O -time O -! O -Follow O -us O -for O -scores O -and O -updates O -throughout O -the O -game O -! O - -@stefaniwashburn O -Me O -too O -! O -I O -always O -get O -so O -full O -with O -chips O -and O -salsa O -before O -my O -entree O -comes O -? O -I O -like O -the O -Verde O -salsa O -the O -best O -! O - -Done O -teaching O -in O -Viera B-geo-loc -time O -for O -some O -eats O -and O -back O -to O -orlando B-geo-loc -, O -possibly O -swing O -dance O -tonight O -! O - -@davidcushman O -David B-person -, O -I O -just O -realised O -the O -other O -day O -you're O -no O -longer O -at O -Brando B-company -? O -How O -come O -? O -Doing O -your O -own O -thing O -?! O -( O -ps O -yeah O -it O -sucks O -! O -) O - -@SammieLynnsMom O -@tg10781 O -they O -will O -be O -all O -done O -by O -Sunday O -trust O -me O -*wink* O - -RT O -@hificlub O -Come O -get O -your O -blood O -flowing O -with O -@waxromeo O -& O -@fordinho O -at O -The B-facility -Heartbeat I-facility -tonight O -, O -or O -as O -we O -call O -it O -Hide-and-Go O -Sikh O -! O - -complete O -kiddie O -goodness O -today O -. O -first O -a O -kids O -party O -followed O -by O -a O -night O -at O -the O -circus O -followed O -by O -another O -kiddie O -party O -tomorrow O -. O - -RT O -@TheLoveStories O -: O -When O -people O -are O -mad O -at O -you O -, O -don't O -ever O -forget O -what O -they O -say O -, O -because O -they O -MEAN O -every O -single O -word O -they O -say O -! O -#TLS O - -@Zerinaakers O -talking O -about O -as O -far O -as O -shooting O -for O -location O -its O -going O -to O -be O -on O -#Vh1 B-company -so O -by O -the O -end O -of O -the O -month O -i O -should O -know O -something O - -@raytida O -Travelling O -light O -ftw O -. O -When O -I O -came O -here O -first O -, O -I O -had O -one O -half-empty O -books-based O -suitcase O -and O -one O -clothes-based O -suitcase O -. O -That O -'s O -all O - -At O -7pm O -we'll O -show O -the O -Awarded O -movie O -" O -Agora B-movie -" O -by O -Spanish O -director O -Alex B-person -Amenabar I-person -who O -directed O -" O -Mar B-movie -Adentro I-movie -" O -( O -" O -The B-movie -Sea I-movie -Inside I-movie -" O -) O -. O -W/Rachel B-person -Weisz I-person -. O - -@AyeoKitty O -ill O -txt O -you O -soon O -in O -a O -few O -minutes O -when O -i O -get O -a O -hold O -of O -my O -phone O - -Friday O -deal O -! O -Save O -30% O -on O -select O -new O -arrivals O -by O -@MizMooz O -. O -Check O -it O -out O -! O -http://bit.ly/cDXN4 O - -Today O -the O -mall O -was O -full O -of O -moms O -who O -love O -scrapbooking O -. O -kill O -me O -. O - -RT O -@OMBieberJokes O -: O -Little O -Girl O -: O -Santa B-person -Is O -So O -Awesome O -! O -Me O -: O -No O -Little O -Girl O -... O -You O -Only O -Use O -The O -Word O -" O -Awesome O -" O -When O -You O -Are O -Referring O -To O -Just O -... O - -U O -accused O -me O -of O -doin O -something O -. O -So O -u O -ain't O -talkin O -to O -me O -till O -Wednesday O -. O -Ok O -. O -Imma O -get O -on O -twitter B-company -since O -u O -say O -I O -did O -. O -Now O -u O -mad O -I'm O -on O -twitter B-company -! O - -super B-product -nintendo I-product -sega B-product -genesis I-product -, O -when O -i O -was O -dead O -broke O -man O -i O -couldn't O -picture O -this O -! O -#nowplaying O -Juicy O -by O -Biggie O - -@zombie_killer94 O -aaahh O -sorry O -i O -posted O -the O -link O -wrong O -:( O -basically O -their O -record O -company O -confirmed O -the O -title O -& O -it O -comes O -out O -nov O -23 O -XD O - -My O -mum O -just O -had O -ago O -at O -me O -for O -not O -having O -a O -job O -when O -ive O -missed O -2 O -years O -of O -school O -, O -and O -tbh O -if O -im O -gonna O -catch O -up O -i O -dont O -have O -time O -4 O -a O -job O -! O - -I O -only O -just O -found O -out O -yesterday O -that O -KRISTEN B-person -BELL I-person -is O -the O -voice O -of O -#GG O -: O -l O - -@Angie_Jerez O -will O -u O -b O -attending O -to O -da O -Mexican O -parade O -in O -5th O -ave O -dis O -Sunday O -? O - -RT O -@ChuckMetcalf O -: O -There O -will O -likely O -not O -be O -a O -proper O -episode O -this O -week O -, O -but O -we'll O -give O -you O -an O -update O -on O -the O -future O -of O -the O -show O -. O - -dude O -got O -gucci B-company -symbol O -tatted O -all O -over O -his O -face O -http://mediacheckout.blogspot.com/2010/09/nuh-uhhhhhhh-yesterday-we-showed-you.html O - -@JusSimplyMee O -I O -think O -i'm O -going O -to O -start O -buying O -chipotle B-product -for O -ONE O -lucky O -girl O -every O -week O -lol O - -RT O -@WakaFlocka1017 O -: O -SO O -WHEN O -I O -GET O -ON O -106 O -N O -JOKE O -AROUND O -YALL O -TALK O -ABOUT O -ME O -LIKE O -A O -DOG O -SHAWTY O -BUT O -WHEN O -I O -BUY O -30,000 O -WORTH O -SCHOOL O -SUPPLY O -N O -... O - -ahhh O -the O -disapointment O -, O -after O -all O -them O -awkward O -bait O -moments O -. O - -we O -need O -some O -time O -alone O -we O -need O -to O -let O -it O -BREATH O - -RT O -@Purpl3_Kiss3s O -: O -when O -a O -female O -thinks O -she O -'s O -in O -love O -u O -cant O -tell O -her O -shit O -! O - -Jackie B-person -joins O -Twitter B-company -50 O -years O -after O -JFK B-person -campaign O -[ O -AP O -: O -US O -News O -] O -http://bzbx.us/uEf O -#50 O -#Campaign O -via O -buzzbox O -. O -com O - -APO B-other -disaster O -relief O -tomorrow O -10-12 O -. O -Sign O -up O -on O -the O -website O -! O - -@xensweetie O -Ohhh O -we O -should O -do O -some O -shopping O -do O -you O -know O -what O -your O -wearing O -on O -Friday O -yet O -? O - -get O -ready O -to O -shop O -local O -!! O -RT O -@uniqueLA O -: O -BIG O -NEWS O -to O -start O -your O -weekend O -: O -Oct O -22 O -@UniqueLA O -will O -be O -opening O -a O -store O -at O -@HDbuttercupLA O -. O - -Safety O -Day O -tomorrow O -at O -North B-facility -Phoenix I-facility -ATA I-facility -. O -Kids O -Self-Defense O -class O -from O -12-1 O -pm O -, O -Safety O -Presentation O -from O -1-2 O -, O -Adult O -Self-Defense O -from O -2-3 O -. O - -@xoLaurenParkxo O -Are O -you O -watching O -S2 O -? O -He O -doesn't O -get O -one O -after O -all O -. O - -Did O -somebody O -lose O -their O -shades O -? O -Today O -'s O -#cutepic O -winner O -has O -them O -! O -http://twitpic.com/2pbm1d O -Congrats O -to O -@MissMarisa22 O -! O -( O -DM O -us O -your O -addy O -) O - -Just O -changed O -my O -twitter B-company -background O -, O -check O -it O -out O -! O -Found O -it O -at O -http://twitrounds.com O -.. O -September O -17 O -, O -2010 O -, O -2:52 O -pm O - -RT O -@sweetwaterbrew O -: O -1st O -batch O -of O -Exodus B-product -Porter I-product -went O -into O -the O -brew O -house O -today O -! O -going O -exclusively O -into O -the O -winter O -edition O -of O -the O -tackle O -... O - -@BiteMe_iimKeema O -OMG O -... O -BUT O -HOW O -LONG O -AGO O -U O -GOING O -OUT O -TONIGHT O - -: O -Dan B-person -is O -using O -his O -new O -course O -and O -made O -12'000 O -Gold O -in O -2 O -Weeks O -! O -You O -can O -do O -it O -to O -! O -spon O -http://tinyurl.com/364pkdn O -http://bit.ly/cJfTJ8 O - -@MakeDaPussyDrip O -ALL O -DAY O - -@SashaGrey O -i O -am O -your O -big O -fan O -but O -i O -live O -too O -far O -, O -when O -do O -you O -come O -in O -orlando B-geo-loc -one O -day O -let O -me O -know O -ok O - -Mayday O -Parade O -& O -The O -Maine O -in O -like O -16 O -days O -, O -excitedddd O -. O -All O -time O -low O -next O -year O -, O -maybe O -. O - -Shot O -with O -a O -Jaybilizer O -today O -... O -my O -teacher O -designed O -this O -, O -totally O -sweet O -! O -http://jaybilizer.com/products/jaybilizer-hdslr/ O - -Check O -this O -video O -out O --- O -General B-tvshow -Hospital I-tvshow -Spoilers O -Week O -9-27-10 O -& O -10-4-10 O -http://t.co/GciGiuz O -via O -@youtube O -.. O -ahhh O -OMG O -! O -Can't O -wait O -to O -see O -this O - -@aaronpfenning O -is O -there O -any O -chance O -we'll O -be O -able O -to O -buy O -some O -of O -your O -music O -by O -the O -time O -you O -start O -touring O -again O -? O - -If O -Conn B-person -is O -less O -than O -impressed O -with O -chevre O -, O -he O -has O -a O -loyal O -ally O -in O -his O -grandscruffy O -, O -who O -finds O -it O -great O -when O -it O -'s O -good O --- O -which O -is O -rarely O -. O - -#obsessions O -tonight O -! O -Ima O -do O -some O -new O -tatts O -on O -my O -self O -I O -feel O -like O -showing O -off O -! O - -@littlesusie12 O -@4mike4ever O -I O -sending O -a O -goodnight O -to O -you O -that O -I O -love O -, O -to O -you O -that O -every O -day O -you O -give O -me O -so O -much O -... O -love O - -anyway O -tonight O -was O -awesome O -but O -@cammieahch O -left O -really O -early O -:/ O - -It O -'s O -the O -fescue O -turf O -grass O -aeration O -& O -overseeding O -season O -for O -Atlanta B-geo-loc -& O -N O -. O -Georgia B-geo-loc -. O -Do O -some O -evaluation O -& O -planning O -beforehand O -, O -there O -'s O -time O - -@maureenjohnson O -: O -Zombies O -. O -You O -always O -have O -to O -be O -careful O -because O -you O -never O -know O -when O -they'll O -strike O -. O - -I O -ain O -wear O -makeup O -in O -almost O -2 O -weeks O - -@MWgirl O -I O -have O -a O -date O -tonight O -, O -otherwise O -I O -would O -! O -Expect O -a O -call O -or O -text O -from O -me O -later O -, O -though O -;-) O - -@justinbieber O -concert O -tonight O -! O -< O -3 O -so O -pumped O -!! O -:D O -going O -to O -leave O -the O -concert O -being O -his O -favorite O -girl O -! O -And O -I O -will O -be O -one O -less O -lonely O -girl O -! O -< O -3 O - -@ls_n O -perhaps O -, O -but O -folks O -may O -find O -something O -in O -the O -gallery O -that O -is O -helpful O -in O -their O -day-to-day O -work O -as O -well O -. O -Even O -just O -to O -use O -it O -. O - -George B-person -N I-person -. I-person -Parks I-person -, O -UMass B-other -band O -director O -, O -dies O -after O -performance O -in O -Ohio B-geo-loc -: O -George B-person -N I-person -. I-person -Parks I-person -, O -for O -33 O -years O -the O -dire O -... O -http://tinyurl.com/2femvgq O - -@Thuyenth_JB O -i O -made O -plans O -for O -ur O -day O -, O -so O -no O -worries O -. O -and O -u O -keep O -witnessing O -my O -lil O -frustration O -moments O -. O - -anyone O -else O -ever O -have O -a O -problem O -moving O -a O -Hyper B-other -V I-other -guest O -from O -a O -server B-product -2008 I-product -to O -server B-product -2008 I-product -R2 I-product -host O -? O -Having O -strange O -lock O -up O -after O -the O -move O -.. O - -How O -to O -make O -your O -home O -ready O -for O -gadgets O -of O -tomorrow O -http://goo.gl/fb/2jPag O -#digg O -#Popular O - -well O -poolnewsfeed O -when O -you're O -good O -, O -you're O -good O -! O - -Haha O -watching O -@littleradge O -' O -s O -first O -blog O -. O -" O -Probably O -won't O -keep O -it O -up O -" O -You O -lied O -big O -time O -Liam B-person -! O - -im O -in O -a O -bar O -and O -ckecking O -my O -twit O -acc O -so O -i O -guess O -its O -time O -to O -go O -home O -. O - -When O -Lari B-person -and O -I O -went O -to O -Futoosh B-facility -, O -the O -owner O -gave O -us O -our O -drinks O -for O -free O -! O - -@iKutiePie O -coolen O -.. O -i O -aint O -speak O -to O -my O -lil O -baby O -all O -day O -.. O -had O -to O -check O -up O -on O -u O - -its O -a O -good O -year O -for O -a O -murder O -... O - -RT O -@Jasmynebieberr O -: O -Teenage B-other -Dream I-other -is O -on O -the O -radio O -! O -My O -day O -just O -got O -a O -little O -better O -. O - -@YourboyH O -cool O -I'll O -check O -it O -out O -when O -I O -get O -home O -. O - -Reminder O -: O -One-day O -tax O -workshops O -for O -small O -& O -mid-size O -tax O -exempt O -orgs O -. O -First O -one O -is O -Sept O -. O -22 O -in O -Detroit B-geo-loc -. O -Register O -now O -! O -http://bit.ly/a2hvAv O - -I O -might O -be O -drunk O -again O -tonight O - -Bar O -hopping O -in O -buckhead O -tonight O -. O - -ok O -im O -so O -late O -but O -unpacking O -finally O -from O -when O -i O -was O -away O -in O -July O -lol O -..... O - -como O -chama O -o O -careca O -do O -All O -Time O -Low O -, O -@joeylazzari O -?? O - -Not O -much O -Twitter B-company -action O -here O -tonight O -or O -morning O -from O -you O -southern O -lot O -. O - -RT O -@joejonas O -: O -Woke O -up O -in O -Sacramento B-geo-loc -. O -The O -CA B-geo-loc -weather O -feels O -great O -. O -Good O -workout O -. O -Good O -Subway B-company -sandwich O -. O -Nice O -New O -shoes O -. O -Ready O -for O -tonight O -. O -... O - -Road O -snow O -is O -expected O -today O -on O -Milford B-other -Rd I-other -, O -Lindis B-geo-loc -Pass I-geo-loc -and O -Arthurs B-geo-loc -Pass I-geo-loc -today O -http://ow.ly/2G4Re O -No O -warnings O -for O -Desert B-other -Rd I-other -, O -Lewis B-geo-loc -, O -Rimutakas B-geo-loc - -@Nolicious O -have O -a O -great O -time O -tm O -x O -o O -x O -you O -will O -have O -so O -much O -fun O -x O -o O -x O - -Man O -i O -hate O -when O -people O -carry O -ragedy O -luggage O -.. O -ima O -just O -rip O -it O -up O -more O -with O -the O -belt O -loader O -#itaintmines O - -@tanyahowse O -ok O -this O -may O -sound O -stupid O -but O -can O -u O -tell O -them O -jedsweemonster O -says O -hi O -:' O -) O - -@ShawanaA O -It O -did O -me O -t O -o.I O -was O -so O -sympathtic O -to O -her O -when O -I O -frst O -hrd O -this O -. O -When O -she O -blmd O -a O -AA O -for O -it O -realy O -pis'd O -me O -off O -. O -Someone O -copied O -her O -hoax O - -IG O -| O -Nieuws O -: O -Football O -Manager O -2011 O -komt O -op O -5 O -november O -( O -PC O -) O -http://bit.ly/aKe1HL O - -We O -thank O -you O -for O -your O -interest O -and O -consideration O -and O -we O -look O -forward O -to O -working O -with O -you O -this O -year O -at O -the O -20 O -... O -http://tinyurl.com/27ypv4o O - -I O -jus O -saw O -his O -ass O -lol O -RT O -@BLikeHer O -Ima O -get O -my O -lil O -brother O -wasted O -tonight O -. O - -I O -love O -seeing O -the O -U.S. B-facility -Capitol I-facility -building I-facility -in O -my O -rearview O -mirror O -when O -I'm O -driving O -home O -from O -work O -. O -#ILoveDC O - -Going O -to O -this O -fish O -fry O -after O -work O -, O -even O -though O -I'm O -tired O -as O -hell O -. O -Food O -trumps O -sleep O -though O -. O - -@vivarocks777 O -just O -giving O -a O -hard O -time O -so O -don't O -hurt O -me O - -I O -hope O -the O -weeks O -keep O -flying O -. O -It O -'s O -actually O -fantastic O -the O -way O -none O -of O -the O -days O -dragged O -this O -week O -.... O -like O -NONE O -. O -:D O - -@WutieBooty O -her O -album O -just O -debuted O -in O -May O -2010 O -& O -is O -getting O -more O -popular O -each O -day O -. O -Wait O -until O -after O -the O -Glee B-tvshow -premiere O -& O -Charice B-person -'s O -will O -soar O -!! O - -Top O -10 O -Best O -Personal O -Finance O -Books O -of O -All O -Time O -! O -| O -Personal O -Finance O -... O -http://bit.ly/cAEI7h O - -@NiccoleG O -when O -you O -coming O -to O -grandma O -house O -? O - -Tonight O -is O -Theeeeeeeeeee O -niiiiiiiiiight O -;) O -. O -*Gwen B-person -Stefani I-person -voice* O -:] O -. O - -Vanilla B-product -vodka I-product -infusion O -is O -done O -. O -Two O -weeks O -from O -now O -so O -will O -the O -apple O -:) O - -struggled O -in O -locating O -parent O -room O -there O -and O -found O -out O -it O -was O -closed O -when O -we O -were O -there O -finally O -. O - -@hannahhhh_ O -hehee O -love O -for O -them O -. O -I O -have O -undying O -love O -for O -a O -lot O -of O -people O -today O -. O -Love O -turns O -my O -world O -around O -. O - -Can't O -trust O -anyone O -these O -days O -:/ O - -RT O -@Desbishop O -: O -Just O -about O -to O -go O -on O -the O -Late O -Late O -. O -After O -some O -angel O -bullshit O - -All O -day O -http://plixi.com/p/45666421 O - -@usnOOzin O -Ahh O -Steve B-person -, O -you're O -so O -good O -to O -me O -, O -every O -week O -. O -#FF O -. O -Thanks O -so O -much O -. O -How O -'s O -the O -wife O -recovering O -??? O - -@WEConVERSUS O -Sick O -fight O -! O -I'll O -take O -my O -boy O -#aldo O -all O -day O -! O - -Goodnight O -twitterland O -!! O -seee O -you O -tomorrow O -:) O -Michael B-person -I O -love O -you O -more O -than O -my O -life O -... O -the O -biggest O -kiss O -for O -you O -my O -love O -!!! O -:D O -L O -. O -O O -. O -V O -. O -E O - -too O -lazy O -to O -go O -to O -school O -today O -.. O -oh O -well O -, O -i O -have O -no O -choice O -.. O - -Comets O -circle O -the O -universe O -and O -every O -few O -hundred O -yrs O -it O -takes O -photos O -of O -progress O -Time O -out O -there O -id O -different O -than O -close O -up O - -You O -know O -, O -we O -got O -to O -thinking O -. O -We've O -been O -ragging O -on O -poor O -M B-person -. I-person -Night I-person -for O -weeks O -now O -and O -perhaps O -, O -just O -perhaps O -, O -we're... O -http://fb.me/v87NJfkO O - -Who O -'s O -going O -to O -win O -one O -of O -our O -travel O -mugs O -? O -There O -'s O -still O -time O -to O -enter O -but O -now O -much O -~ O -http://ht.ly/2G4tn O -#win O -#giveaway O - -RT O -@cityroom O -: O -The O -Week O -in O -Pictures O -for O -Sept O -. O -17 O -http://nyti.ms/9SCejx O - -@daxx_d24 O -best O -feeling O -in O -the O -world O -twice O -this O -week O -. O -Got O -3 O -more O -on O -the O -way O -lmao O -i O -cant O -stop O - -Let O -'s O -go O -Yanks B-sportsteam -! O -Sad O -that O -I O -can't O -go O -to O -the O -game O -tonight O -... O -but O -super O -excited O -to O -spend O -the O -weekend O -in O -Happy B-geo-loc -Valley I-geo-loc -! O - -Comment O -of O -the O -Day O -: O -The O -Depths O -of O -Poverty O -[ O -We O -Read O -You] O -: O - -@jimmyfallon O -is O -following O -me O -! O -OMG O -! O -My O -life O -is O -now O -complete O -! O -I O -heart O -you O -JF O -and O -have O -for O -years O -! O -Thank O -you O -for O -making O -me O -laugh O -everyday O -! O - -A O -FUN O -movie O -for O -the O -kids- O -Alpha B-movie -& I-movie -Omega I-movie -( O -PG O -) O -Friday O -5:00 O -, O -7:00 O -, O -8:45 O -, O -Saturday- O -1:30 O -, O -3:15 O -, O -5:00 O -, O -7:00 O -, O -8:45 O -,... O -http://fb.me/IiraJpe3 O - -Great O -week O -in O -the O -Optimise B-company -office O -, O -another O -new O -client O -on O -board O -and O -we O -are O -close O -to O -signing O -a O -new O -team O -member O - -'On O -set O -filming O -Chris B-person -" O -Rainy B-other -Days I-other -" O -video O -. O -This O -is O -the O -final O -scene O -then O -we O -can O -finally O -edit O -it O -.' O - -The O -ppl O -who O -organized O -the O -dept O -. O -picnic O -at O -1 O -on O -Sunday O -clearly O -didn't O -want O -me O -to O -go O -or O -they O -wouldn't O -have O -scheduled O -it O -during O -a O -Packer B-sportsteam -game O -. O - -Ok O -, O -I O -lied O -about O -the O -Cachupas B-product -... O -they O -didn't O -get O -made O -... O -tomorrow O -... O -maybe O -( O -need O -I O -remind O -you O -that O -we O -reserve O -the O -... O -http://fb.me/I0ogMBll O - -Ew O -wait O -baq O -da O -fuq O -up O -. O -I O -have O -to O -look O -after O -Michael B-person -and O -his O -friend O -... O -JOY O -. O --___-op O - -@jenilynnsimpson O -next O -time O -we'll O -share O -a O -bottle O - -That O -'s O -when O -you O -start O -to O -laugh O -. O -Saying O -I O -just O -want O -what O -I O -can't O -have O -. O -Won't O -even O -give O -me O -a O -chance O -. O - -I O -favorited O -a O -YouTube B-company -video O --- O -Water B-other -by O -Testing B-musicartist -Tomorrow I-musicartist -http://youtu.be/htAM7lgcelw?a O - -So O -tired O -! O -Someone O -said O -I O -have O -no O -life O -today O -:( O -they O -said O -they O -were O -joking O -but O -still O -it O -made O -me O -mad O -lol O - -Why O -is O -it O -that O -I O -can O -sleep O -when O -I O -shouldn't O -be O -but O -not O -when O -I O -should O -be O -??? O - -Whats O -wrong O -with O -all O -these O -girls O -tonight O -!!! O - -RT O -@mashable O -Our O -Favorite O -YouTube B-company -Videos O -This O -Week O -: O -The O -Drama O -Edition O -http://bit.ly/9duvYG O -#SMM O - -RT O -@oceanbutterfly7 O -: O -Sweet O -spirited O -: O -U O -brighten O -my O -day O -!! O -@kalnok O -@Realtorkeita O -@SaintDC O -@angelgrace226 O -@Anita316 O -@Leters4theLord O -@GlyndiA O -... O - -I'm O -a O -little O -too O -excited O -to O -be O -a O -social O -media O -goy O -tomorrow O -. O - -Cleveland B-sportsteam -Browns I-sportsteam -( O -Official O -Site O -) O -> O -> O -Delhomme B-geo-loc -doubtful O -for O -Sunday O -http://buzztap.com/-tgXgig O - -OH-OH O -It O -seems O -Lindsay B-person -Lohan I-person -maybe O -be O -heading O -back O -to O -jail O -. O -The O -actress O -has O -failed O -a O -court O -mandated O -drug O -test O -& O -could O -face O -30 O -days O -in O -prison O - -@valovanity O -hahah O -. O -Speaking O -of O -, O -when O -are O -you O -planning O -the O -savannah O -trip O -? O -Like O -, O -budgeting O -it O -out O -and O -whatnot O -. O - -'RT O -@cobraxcobrax O -: O -Today O -is O -the O -last O -day O -!! O -" O -Like O -" O -us O -on O -Facebook B-company -, O -get O -entered O -to O -win O -3TB O -using O -USB O -3.0 O -. O -Easy O -. O -http://ow.ly/2FXM2 O -#tech' O - -RT O -@RespectJustinB O -: O -t.v O -: O -text O -Justin O -to O -28 O -28 O -2 O -to O -get O -all O -Justin B-person -features O -! O -me O -: O -*pics O -my O -phone O -up* O -t.v O -: O -just O -9.99 O -$ O -a O -month O -. O -me O -: O -*puts O -ph O -... O - -Just O -waking O -up O -and O -about O -2 O -clean O -4 O -my O -mom O -'s O -pre-wedding O -dinner O -or O -rehearsal O -dinner O -whatever O -it O -call O -this O -weekend O -. O -Then O -on2 O -study O -time O -... O - -@plzsaythekittie O -your O -hair O -is O -long O -as O -fuck O -and O -i O -didnt O -get O -to O -pull O -that O -shit O -yet O - -i O -hate O -when O -i O -dont O -have O -any O -change O -for O -the O -customers O -.. O - -RT O -@fucking_way O -: O -A O -gente O -acostumou O -a O -escrever O -I O -Brought O -You O -My O -Bullets O -I O -Brought O -You O -Me O -Your O -Love O -, O -Danger O -Days O -:: O -The O -True O -Lives O -Of O -The O -Fa O -... O - -Icecream O -time O -! O -#baskinrobbins O - -@TulsaTornado O -Yeah O -, O -I'm O -not O -worried O -. O -Yet O -. O -When O -I O -hit O -30 O -, O -maybe O -I'll O -be O -worried O -then O -:) O - -Bloggers O -: O -Who O -are O -you O -writing O -for O -? O -| O -Social O -Media O -Today O -http://bit.ly/9mY0NK O - -In O -the O -day O -of O -prosperity O -be O -joyful O -, O -but O -in O -the O -day O -of O -adversity O -consider O -. O -Ecclesiastes B-other -7:14 I-other -#JESUS O -#TRUTH O -#jesustweeters O - -First B-other -Day I-other -of I-other -Autumn I-other -Networking I-other -Mixer I-other -at O -Dick B-facility -'s I-facility -Carpet I-facility -next O -T O -... O -http://conta.cc/ap95gL O -via O -#constantcontact O - -Men O -'s O -soccer O -vs O -. O -Crown B-sportsteam -College I-sportsteam -at O -home O -tomorrow O -at O -2 O -pm O -. O -Come O -support O -your O -Eagles B-sportsteam -! O -Praying O -for O -you O -guys O -! O - -RT O -@IlliniCampusRec O -: O -Orange B-other -& I-other -Blue I-other -Skate I-other -tonight O -, O -7:30 O -- O -9:30 O -pm O -! O -Free O -for O -UI B-other -faculty/staff O -& O -their O -immediate O -families O -. O -http://bit.ly/c1M8dr O - -In O -the O -last O -7 O -days O -, O -I O -lost O -10 O -followers O -and O -I O -caught O -them O -all O -with O -http://who.unfollowed.me O -#whounfollowedme O - -I O -couldn't O -even O -make O -it O -to O -Sunday O -http://twitpic.com/2pe22y O - -BORDER B-musicartist -LINEA I-musicartist -IS O -TONIGHT O -! O -9 O -p.m. O -to O -2 O -a.m. O -NO O -COVER O -. O -Come O -by O -and O -see O -local O -musicians O -and O -artists O -showcased O -at O -Limelight B-facility -on O -St B-geo-loc -. I-geo-loc -Mary's. I-geo-loc - -I O -feel O -like O -there O -was O -something O -i O -as O -suppossed O -to O -do O -today O -.... O - -Amazing O -. O -And O -encouraging O -. O -RT O -@SusannahFox O -: O -36,000 O -#bluebutton O -downloads O -for O -VA O -health O -records O -in O -1st O -10 O -days O -! O - -So O -tired O -.. O -Goodnight O -twitter B-company -! O -tty O -tomorrow O - -AI O -: O -DEMAND O -RELEASE O -FOR O -SYRIAN O -BLOGGER O -: O -17-9-2010Tal O -al-Mallohi B-person -, O -a O -19-year-old O -Syrian O -woman O -, O -has O -been O -held O -incommun O -... O -http://bit.ly/aDG3ub O - -RT O -@jjburdett O -: O -Most O -Important O -Election O -in O -years O -. O -RECLAMATION O -! O -@marcorubio O -@sharronangle O -@seanbielat O - -So O -today O -at O -the O -doctor O -, O -I O -learned O -I O -gained O -muscle O -weight O -. O -I O -swear O -it O -has O -to O -be O -all O -in O -my O -ass O -! O - -@SeanKingston O -Hey O -sweety O -have O -a O -great O -, O -relaxing O -time O -:) O -Hugs O -to O -you O -! O - -@BSaltzberg O -hahahahaha O -, O -first O -vacation O -in O -probably O -20 O -years O -that O -she O -left O -her O -pillows O -at O -home O -! O -SHOCKING O -! O - -Selling O -a O -home O -: O -Strategies O -for O -selling O -in O -a O -slow O -market O -: O -by O -Denise B-person -Calaman I-person -The O -July O -2010 O -housing O -market O -sales O -repo O -... O -http://bit.ly/d1xAoc O - -Had O -a O -little O -bit O -of O -a O -panic O -attack O -at O -the O -end O -of O -my O -shift O -today- O -I O -was O -cleaning O -at O -the O -bar O -and O -then O -a O -bigass O -spider O -appeared O -out O -of O -nowhere O -. O - -@syriusgemini O -Those O -were O -the O -days O -.... O -sadly O -it O -turned O -into O -#killthecheerleader O -#saveaviewer O - -Band B-facility -hall I-facility -. O -Game O -time O -. O -Lets O -get O -it O -. O - -Made O -it O -home O -. O -How O -long O -will O -I O -last O -before O -I O -fall O -asleep O -? O - -Every O -time O -I O -watch O -Donnie B-person -Hathaway I-person -'s O -Unsung B-other -I O -just O -get O -speechless O -in O -amazement O -. O -He O -would O -be O -sooooo O -proud O -of O -his O -children O -if O -he O -were O -alive O - -Dollar O -steady O -versus O -yen O -but O -market O -wary O -of O -intervention O -| O -Money O -...: O -The O -dollar O -held O -near O -its O -highest O -in O -a O -month O -... O -http://bit.ly/a0F3dO O - -St B-other -. I-other -Luke I-other -'s I-other -emergency O -department O -wait O -time O -is O -approximately O -12 O -minutes O -. O - -Why O -is O -twitter B-company -dead O -? O -Oh O -yeah O -, O -that O -'s O -right O -, O -other O -people O -have O -lives O -on O -Friday O -nights O -. O - -I O -had O -a O -substitute O -today O -that O -looked O -like O -@thedillon O -from O -the O -side O -of O -his O -face O -. O -Haha O - -I O -was O -rotfl O -at O -lunch O -today O -. O -xD O -My O -BFF O -& O -I O -sat O -next O -to O -these O -2 O -boys O -, O -Shane B-person -& O -Jonah B-person -. O -And O -this O -girl O -, O -Claire B-person -. O -( O -cont O -) O - -RT O -@Gina_says_xo O -: O -Football O -and O -partiesss O -. O -This O -is O -the O -only O -thing O -I O -love O -about O -the O -school O -year O -. O -< O -3 O - -Shirt O -before O -the O -shirt O - -Lelystad B-geo-loc -- O -za O -18 O -sep O -- O -Schrijfster O -en O -tekenaar O -van O -Vos O -en O -Haas O -in O -FlevoMeer O -Bibliotheek O -Lelystad O -: O -#boomagenda O -http://bit.ly/9KzGNu O - -@EBArchDesign O -Thanks O -for O -the O -#FF O -! O -I O -hope O -you O -have O -a O -great O -time O -tonight O -! O - -Snooth O -Talk O -: O -Zinfandel O -Port O -: O -This O -week O -is O -all O -about O -Zinfandel O -, O -and O -just O -last O -night O -I O -released O -a O -post O -on O -Zinfandel O -... O -http://bit.ly/972Sik O - -@keevaguilfoyle O -you O -know O -you're O -loved O -when O -@shabbyofficial O -gets O -a O -new O -hat O -in O -your O -honor O -. O - -@therowdydog O -Me O -neither O -. O -It O -'s O -tough O -to O -get O -away O -. O -There O -is O -one O -Tuesday O -in O -AUS B-geo-loc -and O -the O -one O -in O -RR B-geo-loc -on O -Friday O -. O -We O -should O -go O -so O -I O -have O -a O -friend O -! O - -RT O -@DGCLEAN O -: O -This O -weeks O -daily O -Deal O -is O -a O -twofer O -1 O -: O -window O -cleaning O -$4 O -per O -window O -with O -a O -$100 O -minimum O -or O -as O -an O -add O -on O -to O -a O -...... O - -Ah O -a O -Friday O -night O -free O -! O -Avin O -a O -drink O -with O -friends O -. O -Lent O -the O -T4 O -to O -friend O -to O -camp O -in O -. O -Off O -camping O -to O -Robinhoods B-geo-loc -bay I-geo-loc -In O -Jasmin B-geo-loc -. O -Good O -weekend O - -How O -sweet O -! O -RT O -@midnight_sky33 O -Seeing O -my O -son O -face O -when O -his O -bus O -driver O -gave O -him O -a O -positive O -bus O -behavior O -token O -the O -other O -day O -was O -#pricelesstome O - -@Elcartoonifyer O -really O -? O -Jeff B-person -told O -me O -he O -was O -going O -there O -tonight O -too O -... O - -A O -week O -in O -New B-geo-loc -Jersey I-geo-loc -, O -of O -all O -places O -, O -and O -I O -never O -want O -to O -hear O -a O -*Boston* B-geo-loc -accent O -again O -. O - -RT O -@nickjonas O -: O -Just O -arrived O -in O -Sacramento B-geo-loc -! O -What O -a O -beautiful O -day O -. O - -On O -our O -way O -to O -@IF3 O -!!! O -This O -won't O -be O -a O -party O -for O -juniors O -tonight O -! O - -Lesson O -Learned O -for O -today O -. O -Sometimes O -making O -changes O -is O -unnecessary O -. O -Some O -people O -don't O -necessarily O -need O -to O -be O -taken O -out O -of O -their O -comfort O -zone O - -Showering O -real O -reallll O -quick O -. O -This O -tweet O -is O -for O -anyone O -that O -tries O -to O -text O -me O -when O -I'm O -in O -the O -shower O - -Field O -trip O -today O -was O -awesome O -! O -:D O - -RT O -@BunBTrillOG O -: O -Okay O -#teamtrill O -time O -to O -show O -them O -our O -power O -! O -#BunB106andPark O -needs O -to O -trend O -now O -! O -RT O -til O -it O -hurts O -! O -I O -got O -ya O -twitter B-company -jail O -... O - -iz O -gettin O -a O -eye O -full O -of O -pussys O -tonight O -. O - -Support O -Your O -Local O -Op O -Shop O -- O -Only O -4 O -days O -left O -! O -http://post.ly/yCqH O - -RT O -@el_frawg O -: O -I'm O -not O -really O -good O -at O -history O -. O -I O -once O -went O -back O -in O -time O -and O -killed O -Charlie B-person -Chaplin I-person -. O - -@GirlinCali79 O -tape O -him O -. O -that O -would O -be O -great O -for O -YEARS O -later O -!!! O - -RT O -@Hatshepsutely O -: O -@adamlambert O -please O -, O -oh O -please O -wear O -the O -infamous O -beach O -hat O -tonight O -during O -your O -encore O -( O -in O -lieu O -of O -a O -rasta O -wig) O -. O -< O -3333 O - -some O -freshies O -sat O -at O -our O -lunch O -table O -today O -. O -so O -i O -sat O -with O -them O -and O -talked O -with O -them O -about O -.. O -stuff O -. O - -moving O -to O -the O -UK B-geo-loc -in O -approx O -12 O -days O - -@tsykoduk O -that O -was O -yesterday O -and O -@RandomGemini O -he O -already O -has O -one O -. O -:-) O - -1 O -week O -til O -my O -birthday O -!! O - -@Mandiiemars O -she O -is O -asleep O -will O -do O -when O -she O -wakes O -up O -lol O -i O -already O -miss O -u O -:( O - -A O -Twisted O -two O -nighter O -in O -London B-geo-loc -NOVEMBER O -18 O -... O -Younger B-musicartist -Brother I-musicartist -( O -Live) O -, O -Shpongle B-musicartist -( O -Simon B-musicartist -Posford I-musicartist -DJ O -set O -) O -and O -other O -... O -http://fb.me/Hqwb7kzG O - -'RT O -@ItsTheTeenLife O -: O -I O -remember O -when O -myspace B-company -was O -a O -big O -" O -who O -has O -the O -most O -free O -time O -on O -their O -hands O -to O -make O -their O -page O -look O -the O -coolest O -" O -comp O -...' O - -taken O -a O -few O -minutes O -ago O -what O -do O -u O -think O -??? O -:) O -http://twitpic.com/2pdsi9 O - -@LulaBurlesque O -You're O -in O -our O -mosaic O -! O -Be O -sure O -to O -watch O -the O -premiere O -show O -Monday O -9/13 O -. O -Find O -yourself O -here O -http://bit.ly/EllenMosaic O - -@emmarose99 O -hows O -ur O -day O -? O - -Approximately O -two-thirds O -of O -people O -tip O -their O -head O -to O -the O -right O -when O -they O -kiss O - -BoiRevolution O -: O -After O -Dark O -! O -Mondays O -@ O -11pm/EST O -. O -Only O -on O -www.blogtalkradio.com/boi-revolution O -( O -RETWEET O -PLEASE O -) O - -#MuchLove O -to O -my O -baby O -girl O -! O -10.20 O -. O -09 O -!! O -One O -year O -here O -we O -come O -! O - -i'm O -definitely O -eating O -tacos O -for O -dinner O -. O -the O -update O -for O -green O -may O -or O -may O -not O -have O -had O -something O -to O -do O -with O -it O -... O - -Been O -a O -rough O -day O -. O -On O -my O -third O -Dortmunder B-product -Gold I-product -. O - -'i O -really O -wanna O -see O -@MyBrodasKeepa_ O -today O -" O -/' O - -Son O -popping O -of O -to O -school O -, O -anybody O -want O -a O -shoutout O -before O -i O -go O -? O -just O -ask O -, O -and O -ill O -provide O -. O - -#futbol O -> O -Newcastle B-sportsteam -Jets I-sportsteam -Youth I-sportsteam -v O -AIS B-sportsteam -U21 I-sportsteam -@ O -18 O -Sep O -04:00 O -- O -precios O -actualizados O -! O -http://bit.ly/dBJacr O -#apuestas O - -Show O -tonight O -!!! O - -Is O -this O -rubbish O -or O -what O -!? O -441 O -free O -texts O -, O -588mins O -and O -only O -two O -days O -to O -use O -' O -em O -. O -http://twitpic.com/2pdxm4 O - -Ahhhh O -! O -Omg O -. O -Just O -saw O -a O -preview O -for O -episode O -one O -of O -season O -six O -of O -Criminal B-tvshow -Minds I-tvshow -! O -Can't O -wait O -for O -next O -week O -! O - -Find O -us O -at O -Tipitina B-facility -'s O -tonight O -and O -mention O -that O -you're O -following O -us O -on O -Twitter B-company -and O -get O -a O -FREE O -Fried O -Mac O -N O -' O -Cheese O -ball O -with O -any O -order O -! O - -RT O -@WeScreamAlex O -: O -LET'S O -ALL O -GET O -WASTED O -TONIGHT O -OK O -? O - -@ImBieberGuy O -If O -you O -would O -die O -tomorrow O -what O -would O -you O -do O -on O -your O -last O -day O -here O -on O -earth O -ahaha O -? O -What O -a O -dramatic O -question O -:p O - -@joejonas O -Yay O -, O -I'm O -so O -glad O -you're O -here O -! O -I O -can't O -wait O -to O -see O -you O -& O -the O -Bros O -tonight O -! O -It O -'s O -my O -sons O -' O -first O -concert O -!!! O -Sing O -to O -SECTION O -204 O -!!! O - -#BlackHoliday O -Marvin B-person -Gaye I-person -Day O - -RT O -@TeenThings O -: O -I O -love O -waking O -up O -in O -the O -middle O -of O -the O -night O -knowing O -i O -have O -more O -time O -to O -sleep O -. O -#teenthings O -#comedownwithlove O - -Okay O -, O -I'm O -kinda O -proud O -. O -I O -finished O -the O -first O -8 O -books O -of O -the O -True B-tvshow -Blood I-tvshow -novels O -in O -8 O -days O -. O - -I O -don't O -get O -when O -people O -say O -when O -Justin B-person -gets O -a O -gf O -that O -should O -be O -me O -will O -be O -#1 O -I'm O -pretty O -sure O -almost O -all O -bieber B-person -fans O -have O -it O -already O -. O -8 O -) O - -RT O -@INTERMIX O -: O -Shopping O -on O -the O -High O -Holy O -Days O -. O -Retail O -, O -repent O -, O -retweet O -. O - -business O -technology O -today O -http://bit.ly/9cugk1 O -Cyber-Ark B-company -to O -Showcase O -Business O -and O -Compliance O -Advantages O -of O -Privileged O -Identity O -Managem O -... O -;O O - -Wake B-person -, O -Davidson B-person -, O -Charlotte B-person -and O -NC B-other -St I-other -. O -have O -all O -been O -in O -to O -check O -out O -2012 O -pg O -Tyler B-person -Lewis I-person -since O -contact O -pd O -began O -. O -UNC B-other -will O -be O -there O -next O -week O -. O - -'Come O -to O -" O -MRB B-other -@ I-other -61 I-other -Roadhouse I-other -BBQ I-other -" O -Sunday O -, O -October O -10 O -from O -12:00 O -pm O -to O -4:00 O -pm O -. O -We O -are O -playing O -for O -the O -61 O -Roadhouse O -... O -http://fb.me/G1FbJGw1' O - -its O -time O -for O -the O -devil O -to O -dance O -. O -. O -. O - -@colinake O -Have O -a O -safe O -trip O -home O -. O -hopefully O -next O -time O -you O -will O -be O -able O -to O -see O -some O -of O -the O -really O -good O -stuff O -at O -KSC B-geo-loc -. O - -Today O -was O -amazing( O -: O -go O -Jackets O -! O -I O -want O -sum O -steak O -! O - -George B-person -Hitchcock I-person -, O -Kayak B-person -Magazine I-person -Founder O -, O -Dies O -at O -96 O -: O -George B-person -Parks I-person -Hitchcock I-person -was O -born O -on O -June O -2 O -, O -1914 O -, O -in O -Ho O -... O -http://tinyurl.com/249srz6 O - -I O -think O -i O -might O -get O -8,000 O -tweets O -today O - -RT O -@dengshot O -Mochi O -waffles O -! O -@ O -new B-facility -Teaze I-facility -cafe I-facility -by O -the O -library O -! O -Try O -two O -for O -$4 O -w/ O -today O -'s O -#IndulgeLivingVA O -deal O -! O -http://tinyurl.com/2fd6yba O - -#thiswomaniscrazy O -she O -speeding O -on O -the O -highway O -tryna O -catch O -to O -a O -car O -that O -cut O -her O -off O -10mins O -ago O -while O -waving O -a O -bat O -! O - -With O -Kirsten B-person -( O -: O -going O -to O -Dicks B-facility -tonight O -. O - -A O -guy O -at O -work O -calls O -me O -Larry B-person -Bird I-person -when O -I O -shoot O -hoops O -in O -the O -break O -room O -. O -Should O -I O -call O -him O -Joel B-person -Ward I-person -if O -he O -'s O -playing O -air O -hockey O -? O -#profiling O -. O - -@LinnySmit O -Linny O -Linny O -Linny O -. O -U O -are O -something O -lady O -... O -Well O -that O -story O -has O -been O -on O -hold O -for O -weeks O -now O -hasn't O -it O -? O -I O -want O -to O -get O -on O -it O -this O -> O -> O - -Tonight O -! O -Homemade O -ice O -cream O -flights O --- O -try O -3 O -of O -chef O -Tim B-person -'s O -boozy O -ice O -cream O -creations O -! O - -And O -#nowplaying O -With B-other -You I-other -- O -Delirious B-musicartist -? O -[ O -World O -Service O -] O -May O -our O -Lord B-person -Jesus I-person -be O -with O -u O -, O -watching O -over O -& O -directing O -u O -. O -Have O -a O -lovely O -night O -, O -all O -! O - -going O -to O -have O -to O -go O -to O -bed O -before O -i O -cry O -. O -wish O -you O -would O -make O -an O -effort O -to O -be O -there O -for O -me O -like O -i O -am O -for O -you O -. O - -RT O -@WakaFlocka1017 O -: O -SO O -WHEN O -I O -GET O -ON O -106 O -N O -JOKE O -AROUND O -YALL O -TALK O -ABOUT O -ME O -LIKE O -A O -DOG O -SHAWTY O -BUT O -WHEN O -I O -BUY O -30,000 O -WORTH O -SCHOOL O -SUPPLY O -N O -... O - -@bougiemagazine O -You O -are O -really O -showing O -out O -today O -lol O -. O - -@barbieryan O -I O -get O -to O -tomorrow O -! O -Oh O -wait....I'm O -getting O -my O -hair O -cut O -... O -fail O - -Ceremony O -Sunday O -to O -mark O -Battle B-other -of I-other -Britain I-other -http://dlvr.it/5RhwW O - -What O -do O -you O -do O -when O -you're O -stuck O -because O -the O -one O -that O -you O -love O -Has O -pushed O -you O -away O -, O -And O -you O -can't O -deal O -with O -the O -pain O -. O - -@MixedUpMatty O -he O -should O -be O -out O -wednesday O -.. O - -@carolermp O -;) O -did O -you O -read O -about O -my O -mealworm O -prank O -the O -other O -day O -? O -;) O - -@jenniehales O -wat O -u O -up O -to O -tonite O -. O -My O -mob O -won't O -let O -me O -txt O -fs O -. O -Xxx O - -GUYS O -IF O -YOU O -WNNA O -BE O -IN O -AN O -AWESOME O -FAN O -BOOK O -. O -TWEET O -@JedwardsAngel O -AND O -WE'LL O -SORT O -IT O -OUT O -. O -WE'VE O -GOT O -15COOOOL O -IDEAS O -WHEN O -WE O -SEE O -THEM O -!: O -D O - -RT O -@iSplashInYoMouf O -: O -RE*TWEET O -IF O -YOU O -DRINKING O -TONIGHT O -? O - -Serious B-company -Eats I-company -: O -Leftovers O -: O -The O -Day O -'s O -Stray O -Links O -#Food O -http://portfo.li/t/7/rjE O - -RT O -@Lexus4u O -: O -OK O -SO O -WE O -ARE O -HAVING O -A O -POOL O -PARTY O -TONITE O -, O -HOPEFULLY O -I O -CAN O -C O -SUM O -EYE O -CANDY O -! O -LOL O - -My O -mama O -just O -surprised O -me O -on O -ring O -day O -!! O -Ahh O -today O -just O -got O -so O -much O -better O -! O - -Day O -27 O -- O -Day O -27- O -Why O -are O -you O -doing O -this O -30 O -day O -challenge O -im O -doing O -this O -because O -i O -am O -bored O -:) O -http://tumblr.com/xooiuell3 O - -@Wes0010 O -good O -idea O -! O -i O -have O -some O -i O -found O -in O -the O -golf O -bag O -i O -just O -received O -from O -my O -aunt O -who O -died O -21 O -years O -ago O -! O - -Its O -3 O -o'clock O -on O -my O -first O -day O -in O -Vegas B-geo-loc -and O -I O -am O -already O -drunk O -and O -just O -sang O -meatloaf B-person -at O -karoke O -. O - -@Emmas76 O -have O -i O -missed O -anything O -these O -last O -2 O -weeks- O -how O -'s O -music O -practice O -going O -? O - -@DudeImSuchATeen O -Ooo O -I O -do O -!! O -It O -would O -make O -THiS O -day O -better O -:-/ O - -@xiaomai_ O -Kom O -deze O -maand O -denk O -ik O -niet O -, O -next O -week O -Parijs O -, O -en O -allemaal O -toetsen O -en O -presentaties O -enzo O -> O -. O -< O -'.... O - -Well O -had O -the O -weigh O -in O -today O -. O -I've O -lost O -a O -pound O -!!! O -I've O -had O -no O -alcohol O -, O -no O -puddings O -, O -no O -crisps O -, O -no O -chocolate O -& O -I've O -only O -lost O -a O -pound O -!!!!! O - -@maeband O -I'm O -so O -excited O -to O -see O -you O -guys O -on O -Oct O -. O -12th O -at O -the O -Southgate B-facility -House I-facility -! O -I've O -been O -listening O -to O -you O -since O -D O -: O -B O -. O -Your O -so O -amazing O -!! O - -been O -listenin O -to O -trey B-person -alllll O -week O -... O -can O -u O -luv O -someone O -u O -never O -met O -?? O -bcuz O -i O -think O -im O -in O -luv O -yeeuuuuppp O -!!! O - -Last O -day O - -#birdgang O -in O -full O -force O -!!! O -Sunday O -10:00 O -am O -get O -yo O -popcorn O -ready O -@ddockett O -will O -dominate O -! O - -well O -my O -700th O -tweet O -was O -3 O -tweets O -ago O -. O - -gym O -tonight O -because O -I O -was O -a O -slacker O -last O -night O -and O -went O -out O -to O -dinner O -instead O -of O -working O -out O -#fb O - -'" O -come O -on O -its O -Friday O -night O -... O -shes O -dreaming O -whoa O -oh O -oh O -oh O -dreaming O -of O -a O -weekend O -whoa O -oh O -oh O -oh O -"' O - -@terryronald O -Hello O -mate O -hows O -you O -? O -Long O -time O -no O -tweet O -What O -you O -been O -up O -to O -? O -Hows O -the O -book O -? O - -Bouncing O -my O -ass O -like O -thrs O -no O -tomorrow O - -RT O -@iLL87WiLL O -: O -When O -trey B-person -songz I-person -was O -a O -kid O -his O -mother O -probably O -went O -did O -you O -do O -your O -homework O -? O -And O -he O -probably O -went O -#YUPPP O -lol O - -Dear O -sun O -, O -it O -'s O -approaching O -the O -end O -of O -September O -and O -seriously O -, O -you're O -still O -unbearable O -. O -Give O -Arizona B-geo-loc -a O -break O -. O -Love O -, O -Mackenna B-person - -@since1938 O -@Kimmiecoomd O -Kudos O -to O -them O -, O -Lois B-person -was O -always O -" O -there O -'s O -something O -about O -you O -.. O -whatever O -tell O -me O -when O -you're O -ready O -" O -thing O -. O - -RT O -@MirandaBuzz O -: O -Last O -day O -of O -this O -season O -!!! O -Next O -season O -I O -hope O -I'm O -driving O -myself O -to O -work O -! O -Gonna O -miss O -everybody O -like O -crazy O -:) O - -@Mys_Perdita O -ha O -ye O -theyr O -may O -be O -severe O -consiquences O -of O -our O -meeting O -like O -the O -waypeople O -talk O -bout O -theday O -sum1 O -told O -adolf O -he O -wasnt O -blond O -enuf O -: O -S O - -@SofieB_ O -Hah O -! O -That O -'s O -the O -one O -programme O -my O -dad O -and O -I O -watch O -together O -every O -week O -! O - -Are O -you O -ready O -for O -the O -shield O -game O -tonight O -at O -7 O -? O - -Friday O -night O -is O -the O -best O -cus O -u O -know O -the O -nest O -day O -u O -can O -rest O -!!!!! O -:D O - -Honestly O -what O -is O -wrong O -with O -kids O -today O -. O -On O -a O -packed O -t O -and O -there O -are O -2 O -older O -ppl O -( O -1 O -w/ O -a O -cane O -mind O -you O -) O -and O -nobody O -gets O -up O -to O -let O -them O -sit O -!! O - -I'm O -being O -stalked O -by O -@thisisiceland O -. O -I O -saw O -this O -when O -I O -opened O -flipboard B-company -just O -now O -. O -http://yfrog.com/eir70oj O - -Our O -Favorite O -YouTube B-company -Videos O -This O -Week O -: O -The O -Drama O -Edition O -http://bit.ly/aYSci6 O -| O -mashable O - -Very O -bad O -day O -. O - -For O -the O -late O -Friday O -p.m. O -crowd O -{ O -all O -3 O -of O -you} O -! O -On O -the O -blog O -today O -... O -Creative O -invitation O -wording O -for O -different O -situations O -! O -http://ow.ly/2G4Bm O - -Thank O -you O -for O -all O -your O -support O -. O -Best O -wishes O -from O -DUKETERS'S..have O -a O -nice O -day O - -'I O -just O -took O -" O -After O -getting O -trampled O -at O -a O -Justin B-person -Bieber I-person -concert O -, O -yoiu O -wake O -up O -and O -..." O -and O -got O -: O -Part O -1 O -:) O -! O -Try O -it O -: O -http://tinyurl.com/3alx5up' O - -cant O -believe O -her O -brother O -is O -leaving O -for O -uni O -tomorrow O -! O -He O -'s O -taking O -all O -the O -best O -dvds O -! O - -You O -know O -you O -should O -be O -living O -somewhere O -when O -you O -know O -that O -place O -front O -and O -back O -without O -living O -there O -. O - -Yay O -, O -just O -ordered O -new O -tragus O -bars O -:) O -Hopefully O -they'll O -arrive O -on O -Monday O - -'@jennkorducki O -http://twitpic.com/2pcj44 O -- O -Ah O -this O -made O -my O -national O -" O -holy O -crap O -" O -day O -even O -better O -:)' O - -If O -this O -beer O -list O -doesn't O -make O -your O -head O -spin O -, O -it O -will O -tomorrow O -when O -we O -ADD O -MORE O -!... O -http://fb.me/GVhHzbMR O - -'@Lovelylanvin O -Oh O -ugh O -. O -Should O -never O -do O -#ff O -before O -coffee O -. O -You O -should O -have O -been O -in O -the O -" O -tweeps O -who O -are O -friends O -IRL O -" O -Sorry O -!' O - -Oh O -before O -I O -forget O -good O -luck O -also O -to O -the O -cherry O -and O -whites O -against O -London B-sportsteam -Irish I-sportsteam -. O -Only O -a O -win O -will O -do O -. O -Gives O -me O -more O -excuses O -to O -celebrate O -! O - -@glasgirl O -Lol O -. O -How O -were O -things O -today O -, O -love O -? O - -Oh O -my O -goodness O -, O -I O -thought O -by O -Friday O -evening O -one O -'s O -supposed O -to O -breath O -easy O -, O -but O -at O -the O -moment O -this O -is O -sadly O -not O -the O -case O -. O - -Here O -we O -go O -, O -Friday O -night O -craziness O - -Just O -as O -i O -have O -pointed O -out O -to O -our O -readers- O -The O -Canadian O -Markets O -offer O -so O -much O -opportunity O -. O -Gabriel B-company -Resources I-company -shares O -shoot O -up O -after O -Romania B-geo-loc -mo O - -I O -stopped O -listening O -to O -this O -lecture O -like O -20mins O -ago O -... O -#sad O - -David B-person -'s O -new O -season O -celery O -@OrfordMarket O -tomorrow O -, O -plus O -local O -apples O -from O -High B-company -House I-company -Farm I-company -= O -mighty O -fine O -local O -Waldorf O -Salad O -. O -Yes O -please O -. O - -@xxTeamJosiexx O -Do O -you O -know O -when O -its O -on O -babe O -? O -x O - -Seeyas O -all O -in O -a O -week O -. O -Off O -to O -( O -hopefully O -) O -a O -slightly O -warmer O -place O -... O -or O -not O -. O -x O - -@x_mayy_x O -tu O -vai O -no O -show O -may O -? O - -wow O -i O -just O -noticed O -i O -always O -feel O -different O -everyday O -yesterday O -i O -was O -depressed O -, O -and O -today O -im O -happy O -i O -wonder O -wat O -tom B-person -. O -is O -...... O -random O -much O -? O - -Our O -Favorite O -YouTube B-company -Videos O -This O -Week O -: O -The O -Drama O -Edition O -: O -Had O -any O -epic O -meltdowns O -this O -week O -? O -You O -know O -, O -of O -the O -cryin O -... O -http://bit.ly/bwop5U O - -@ChristineCaine O -please O -pray O -for O -my O -hubby O -& O -I O -, O -u O -spoke O -a O -promise O -over O -us O -years O -ago O -, O -I O -pray O -that O -come O -to O -pass O -and O -my O -hubby O -stay O -rooted O -in O -JC O -x O - -looks O -like O -its O -my O -byza-bedtime O -! O -hope O -to O -fuck O -that O -ema O -is O -in O -tomorrow O -! O - -two O -auditions O -in O -one O -day O -, O -it&#39 O -; O -s O -almost O -as O -if O -I O -am O -a O -real O -actor O - -The O -last O -time O -I O -chatted O -with O -Kyle B-person -was O -June O -. O -The O -last O -time O -we O -emailed O -was O -August O -. O - -RT O -@ZodiacFacts O -: O -#ZodiacFacts O -As O -an O -#Aries O -romance O -isn't O -about O -what O -you're O -after O --- O -it O -'s O -about O -taking O -chances O -, O -exploring O -and O -keeping O -an O -... O - -@jemelehill O -I O -hit O -that O -JL B-person -joint O -when O -it O -came O -out O -, O -he O -does O -Teddy B-person -P I-person -proud O -on O -Wake O -Up O -! O - -@milaceccato O -hahah O -yeah O -! O -My O -bestie O -& O -I O -met O -his O -dad O -too O -& O -were O -seeing O -him O -I O -think O -on O -Monday O -were O -not O -sure O -yet( O -; O - -Why O -Your O -Homepage O -May O -Matter O -Less O -Than O -You O -Think O -http://t.co/dNyFfPP O -via O -@TMGmedia O - -RT O -@MARLONLWAYANS O -: O -Happy O -Yom B-other -Kippur I-other -to O -all O -my O -Jewish O -friends O -. O -Black O -people O -need O -a O -Leroy B-other -Jenkins I-other -Day I-other -. O -We O -need O -some O -days O -off O -too O - -volleybaltraining O -was O -super O -leuk O -! O -volgende O -week O -1e O -wedstrijd O - -RT O -@WakaFlocka1017 O -: O -SO O -WHEN O -I O -GET O -ON O -106 O -N O -JOKE O -AROUND O -YALL O -TALK O -ABOUT O -ME O -LIKE O -A O -DOG O -SHAWTY O -BUT O -WHEN O -I O -BUY O -30,000 O -WORTH O -SCHOOL O -SUPPLY O -N O -... O - -RT O -@Sexstrology O -: O -Pisces O -tend O -to O -escape O -into O -fantasy O -and O -day O -dreams O -. O -There O -they O -are O -free O -. O - -RT O -@LeadToday O -: O -Authentic O -leaders O -don't O -blame O -other O -people O -when O -they O -make O -a O -mistake O -. O - -RT O -@kiera16_x O -: O -#Twitition O -HELP O -us O -meet O -Justin B-person -Bieber I-person -on O -November O -6 O -( O -: O -THANKYOU O -http://twitition.com/oxm5g O -@MariaMdn O - -i O -swear O -when O -you O -miss O -1 O -day O -of O -school O -, O -feels O -like O -you O -missed O -a O -whole O -year O -! O -dont O -even O -have O -homwork O -today O -, O -suprising O -? O - -Thrilled O -about O -this O -@HPC//RT@DeLynnRizzo O -: O -girls O -, O -register4 O -#livethedream O -@joycemeyer O -, O -J B-person -John I-person -, O -@kerriweems O -oct O -7-9 O -. O -livethedreamconference O -. O -com O - -Stop O -by O -the O -COLLEGE B-other -REPUBLICAN I-other -TAILGATE I-other -tomorrow O -@ O -9:30 O -in O -Yellow O -Lot O -17 O -for O -FOOD O -before O -the O -game O - -RT O -@OscarBlandi O -: O -That O -time O -again O -! O -#FreebieFriday O -Be O -the O -15th O -RT O -< O -get O -your O -hands O -on O -a O -free O -travel O -size O -pronto O -invisible O -dry O -shampoo O -! O - -Oh O -yeah O -! O -Probably O -hairdresser O -tomorrow O -, O -and O -for O -sure O -meet O -Levin B-person -, O -Hien B-person -< O -3 O -and O -Fabi B-person -. O -'ll O -have O -a O -great O -daaaaay O -< O -33 O - -good O -friday O -whatchu O -got O -for O -me O -@kanyewest O - -I O -think O -that O -today O -the O -party O -has O -been O -canceled O -makes O -too O -much O -air O -in O -the O -street O -loll O -. O -miss O -u O -twitfriends O -. O -xx O - -I O -may O -have O -been O -wrong O -about O -Murkowski B-person -, O -look O -like O -she O -'s O -going O -to O -turn O -Friday O -news O -dump O -on O -its O -head O -. O -three-way O -race O -reportedly O -ahead O -in O -AK B-geo-loc -. O - -@rattleknackers O -amusing O -? O -This O -is O -some O -of O -the O -greatest O -music O -of O -all O -time O -, O -my O -foot O -has O -n't O -stopped O -tapping O -all O -night O - -Salty O -i O -broke O -one O -of O -my O -favorite O -Betsey B-person -Johnson I-person -necklaces O -today O -!! O - -cant O -wait O -to O -see O -my O -beautiful O -neice O -nx O -week O -, O -gonna O -spoil O -her O -rotton O - -@snufflesgirl25 O -@mo9x O -Well O -. O -just O -beg O -@100monkeysmusic O -.. O -PLEASE O -come O -to O -the O -#SBLeurope O -Next O -year O -!. O -We O -need O -you O -there O -! O -*puss O -in O -boots O -eyes* O - -Korean B-other -War I-other -spy O -rivalries O -persist O -60 O -years O -later O -: O -Far O -be O -it O -that O -the O -week O -should O -pass O -without O -noting O -that O -it O -was O -60 O -... O -http://bit.ly/cCjFBm O - -@ConorMc_Ginty O -GaGa B-person -played O -here O -a O -few O -weeks O -back O -. O -She O -spoke O -out O -against O -SB1070 B-product -during O -the O -show O -. O -God O -bless O -her O -for O -that O -. O - -CHILLIN O -@LIVE O -TONIGHT O -! O - -by O -my O -lonesome O -tonight O -.... O -#obsessions O - -Football O -game O -tonight O -with O -mariaelena B-person -, O -sarah B-person -, O -and O -brittany B-person -! O -Mood O -: O -excited O -!! O -GOO O -WB B-sportsteam -WILDCATS I-sportsteam -! O - -@k8_walsh O -I O -'ve O -been O -out O -of O -the O -loop O -, O -but O -is O -Private B-tvshow -Practice I-tvshow -coming O -back O -this O -year O -? O - -I O -HATE O -MY O -MOTHER O -!!!!!!!!!!!... O -Where O -is O -Dee B-person -when O -I O -need O -him O - -3 O -tests O -and O -an O -essay O -today O -. O -> O -__ O -> O -also O -i O -lost O -my O -backpackkk O -c O -: O -but O -then O -i O -found O -it O -. O - -RT O -@DaymiCarolina O -: O -Yayaayayay O -kings B-musicartist -of I-musicartist -leon I-musicartist -tonight O -!!! O - -Amazon B-company -U.K. I-company -Offering O -HTC B-product -Desire I-product -Z I-product -Unlocked O -October O -11 O -: O -We O -just O -got O -official O -word O -of O -the O -HTC B-product -Desire I-product -Z I-product -earlier O -in O -Lo O -... O -http://bit.ly/bsyz9H O - -@lauraluanabeth O -'ll O -be O -back O -in O -LA B-geo-loc -tomorrow O -night O -! O -yaaaay O -! O - -Lol O -lucky O -me O -, O -u O -remembered O -me O -:D O -... O -When O -u O -c O -her O -again O -tell O -her O -I O -say O -hi O -x O -) O - -RT O -@JDay_Sept15 O -: O -RT O -@Ashanie_di_Don O -: O -@JDay_Sept15 O -lol O -i O -see O -someone O -hyped O -and O -ready O -for O -#FlavaFridays O -tonight O -< O -every O -week O -. O -Lets O -get O -it O -! O - -Everything O -shouldber O -cleared O -up O -by O -the O -week O -end O -. O - -the O -last O -group O -dalam O -lab O -, O -for O -the O -first O -time O - -@pjames O -Add O -a O -little O -Belanger O -, O -and O -you O -'ve O -got O -it O -! O -Of O -course O -... O -after O -his O -household O -items O -arrive O -from O -D O -. O -C O -. O - -RT O -@dropolo O -Headed O -to O -da O -gump O -today O -alabama B-geo-loc -here O -I O -come O -< O -< O -come O -to O -shut O -it O -down O -broski O -.. O -fuck O -wit O -me..Parlae O -'s O -in O -house O -producer O - -@Miss_deadpool O -May O -'s O -costume O -is O -the O -giant O -spider O -on O -her O -upper O -body O -. O -I O -'m O -confused O -. O - -@PERSONGUY6661 O -I O -have O -n't O -unfortunately O -:( O -I O -missed O -the O -chance O -when O -theycame O -to O -the O -uk B-geo-loc -this O -year O -, O -you O -seen O -them O -? O - -QUAKE O -: O -Mag O -4.5 O -, O -Saturday O -, O -September O -18 O -2010 O -at O -10:03 O -am O -( O -NZST) O -, O -20 O -km O -south-west O -of O -Christchurch B-geo-loc -. O -http://tinyurl.com/265n4mx O -#eqnz O - -RT O -@Eeenie_Meenie O -: O -RT O -if O -you O -are O -happy O -that O -today O -is O -FRIDAY O -! O -:) O - -Is O -all O -done O -getting O -things O -set O -up O -for O -the O -Ultimate B-other -Magic I-other -playtest O -that O -starts O -on O -Monday O -. O -Ca O -n't O -wait O -to O -see O -what O -people O -think O -. O - -Gotta O -call O -mom O -2 O -let O -her O -know O -it O -'s O -almost O -3 O -. O -Yli O -gets O -out O -in O -15 O -. O -Mom O -took O -the O -antenna O -home O -today O -< O -connected O -it O -at O -home O -, O -finally O -. O - -DJ O -set O -at O -The B-facility -Standard I-facility -in O -LA B-geo-loc -tonight O -! O -http://fb.me/I91IUaEq O - -RT O -@TRAEABN O -Headed O -to O -Shreveport B-geo-loc -then O -Lake B-geo-loc -Charles I-geo-loc --might O -as O -well O -shoot O -through O -dat O -#BMT O -after O -the O -slab O -show O -in O -Lake B-geo-loc -Charles I-geo-loc -.. O - -LIVESTRONG B-other -Day I-other -is O -10-02-10 O -around O -the O -world O -. O - -@CWWatson O -one O -of O -the O -complainers O -is O -a O -child O -minder O -who O -brings O -kids O -to O -the O -school O -then O -looks O -after O -other O -kids O -thro O -the O -day O -. O -Beggers O -belief O - -ahaha O -that O -just O -made O -my O -day O -xDlmfao O -!!!! O - -Amber B-person -Le I-person -Bon I-person -is O -to O -walk O -in O -Giles B-person -Deacon I-person -'s O -Monday O -LFW O -show O - -Someone O -just O -started O -following O -me O -today O -so O -I O -better O -post O -something O -. O -There O -. O - -@endlesskies O -ik O -me O -2 O -. O -At O -night O -I O -'m O -wide O -awake O -but O -the O -daytime O -, O -I O -'m O -so O -tired O -my O -day O -drags O - -LOL O -A-W-E-S-O-M-E O -!! O -btw O -, O -totally O -agree O -RT O -@fanoffob O -Fall B-musicartist -Out I-musicartist -Boy I-musicartist -is O -gay O -#reallymeans O -I O -'m O -secretly O -a O -big O -time O -fan O -. O - -even O -better O -than O -cocktail O -friday O -! O -http://is.gd/ffDb9 O - -RT O -@GreatestQuotes O -: O -" O -When O -everything O -seems O -to O -be O -going O -against O -you O -, O -remember O -that O -the O -airplane O -takes O -off O -against O -the O -wind O -, O -not O -with O -it O -." O -... O - -RT O -@AKingPNCRadio O -: O -Shout O -out O -to O -@Laureluxe O -for O -a O -dope O -show O -today O -on O -@TheLuxeHour O -w/special O -guest O -@cheridennis O -... O -audio O -droppin O -later O -tod O -... O - -DITR O -REI O -Update O -: O -California B-geo-loc -Home O -Sales O -Decline O -Year-Over-Year O -: O -By O -Michael B-person -Kraus I-person -on O -September O -17 O -... O -http://bit.ly/96Zavf O -#realestate O -#invest O - -RT O -@TheOutlawz O -: O -EVERYBODY O -WHO O -WANT O -A O -FOLLOW O -FROM O -ME O -FOR O -FOLLOW O -FRIDAY O -LET O -ME O -KNOW O -!! O -WE O -AINT O -LIKE O -THESE O -HOLLYWOOD B-geo-loc -AZZ O -RAPPERS O -WE O -FOLLOW O -BACK O -! O - -@Foxy_Shoe_Thief O -*purrs O -long O -on O -the O -inside O -, O -moving O -stealthy O -like O -my O -pard O -taught O -me O -to O -do O -.. O -ears O -pulled O -back O -, O -claws O -out O -long* O -@MicahPardKing O - -Israel B-geo-loc -grinds O -to O -a O -halt O -for O -Yom B-other -Kippur I-other -fasting O -day O -( O -wwrn.org O -) O -http://bit.ly/cqlwCZ O - -Tidyin O -my O -room O -. O -Need O -to O -do O -my O -hair O -. O -its O -soo O -nasty O -when O -its O -naturally O -curly O -. O - -RT O -@FTFDband O -NEW O -SONG O -COMING O -THIS O -SUNDAY O -! O -:D O - -@Hollly_ O -16 O -b O -17 O -in O -feb O - -to O -all O -my O -girls O -in O -london B-geo-loc -or O -travelling O -up O -tomorrow O -i O -love O -and O -miss O -you O -wish O -i O -could O -be O -there O -! O -NEXT O -YEAR O -I O -WILL O -! O - -#Astros B-sportsteam -lineup O -for O -tonight O -. O -Keppinger B-person -sits O -, O -Downs B-person -plays O -2B O -, O -CJ B-person -bats O -5th O -. O -@alysonfooter O -http://bit.ly/bHvgCS O - -Yahoo B-company -: O -Jackie B-person -joins O -Twitter B-company -50 O -years O -after O -JFK B-person -campaign O -: O -Had O -Twitter B-company -been O -an O -option O -in O -1960 O -, O -a O -pregnant O -Jacquelin B-person -... O -http://yhoo.it/9VAD4A O - -my O -past O -was O -the O -reason O -i O -tried O -to O -kill O -myself O -the O -other O -day O -, O -and O -i O -'ll O -gladly O -do O -it O -again O -. O - -#Random O -But O -Why O -Do O -Woman O -Say O -The O -Want O -A O -Big O -D@ck O -< O -When O -They O -Get O -It O -They O -Favorite O -Line O -Is O -" O -where O -is O -that O -suppose O -to O -go O -" O - -Rep O -. O -Bono B-person -Mack I-person -to O -criticize O -fellow O -desert O -dweller O -Barbara B-person -Boxer I-person -at O -event O -today O -. O -http://tinyurl.com/2bhyuq7 O - -My O -town O -! O -RT O -@Etsy O -: O -This O -week O -'s O -Dinner O -< O -a O -Video O -: O -Take O -a O -visit O -to O -one O -of O -the O -largest O -antique O -shows O -in O -the O -country O -. O -http://etsy.me/9tTkop O - -Think O -I O -'m O -going O -to O -give O -in O -and O -pull O -the O -boots O -out O -tonight O -! O -#bittersweet O - -Halo B-product -Reach I-product -was O -a O -bit O -crap O -tonight O - -No O -. O -The O -Pope B-person -asked O -me O -to O -stay O -after O -class O -. O -#moviedialoguewithpopesubstitute O - -I O -called O -my O -sister O -and O -she O -asked O -why O -i O -want O -a O -mcdonalds B-company -at O -this O -time O -of O -nigh O -t.i O -said O -' O -blame O -rose B-person -' O -she O -said O -' O -who O -'s O -rose B-person -?' O -lmao O - -Driving O -, O -driving O -, O -driving O -away O -to O -Phil B-geo-loc -. O -Tasty O -dinner O -tonight O -with O -the O -Society B-other -of I-other -Mining I-other -and I-other -Metallurgy I-other -Engineers I-other -. O - -All O -night O -long O -RT O -@FauxSuhlenaa O -: O -I O -rape O -Nick B-person -Jonas I-person -. O - -@irishhooligan O -or O -the O -best O -is O -just O -walking O -along O -the O -road O -when O -a O -clump O -of O -it O -just O -appears O -to O -be O -waiting O -in O -front O -of O -you O -all O -that O -time O -#icky O - -RT O -@jimjonescapo O -: O -Som1 O -ask O -whn O -do O -I O -sleep O -: O -when O -God B-person -tel O -me O -to O -cum O -home O -is O -when O -I O -wil O -sleep O -til O -thn O -Im O -chasin O -n O -makin O -this O -money O -to O -leav O -... O - -Working O -at O -my O -old O -office O -tomorrow O -to O -catch O -the O -bookkeeping O -up O -. O -Money O -to O -do O -easy O -stuff O -when O -I O -would O -just O -be O -sitting O -around O -anyway O -? O -Yes O -plz O -. O - -I O -liked O -a O -YouTube B-company -video O --- O -Family B-tvshow -Guy I-tvshow -- O -Songs O -Named O -After O -a O -Girl O -http://youtu.be/ErPsx8zdnX4?a O - -No O -loss O -is O -greater O -than O -the O -loss O -of O -a O -thought O -process O -before O -you O -'ve O -had O -the O -time O -to O -write O -it O -down O -. O - -@jimmy722 O -surgery O -or O -not O -, O -the O -most O -important O -thing O -is O -that O -'ll O -be O -all O -okay O -after O -that O -! O - -Mmmm O -wings O -and O -beer O -. O -Damn O -that O -means O -extra O -gym O -time O -2mar O - -RT O -@Huddy85 O -: O -@Mz_Twilightxxx O -*kisses O -your O -ass**sneezes O -after* O -Lol O - -Day O -One O -: O -Ten O -things O -you O -want O -to O -say O -to O -ten O -different O -people O -right O -now O -- O -1 O -) O -Eduardo B-person -Surita I-person -: O -your O -a O -freaking O -... O -http://tumblr.com/xmciuda0t O - -RT O -@DesignerDepot O -: O -Minimalist O -Web O -Design O -: O -When O -Less O -is O -More O -- O -http://ow.ly/2FwyX O - -RT O -@JeremiahBonds O -: O -who O -thinks O -they O -deserve O -a O -#ff O -follow O -friday O -? O -lol O -< O -< O -@Ms_GudBitch O -Duhhh O -Lol O -:) O - -@lexi_nicole2010 O -That O -'s O -not O -the O -story O -by O -a O -long O -shot O -biotch O -! O - -Columbus B-geo-loc -, O -GA B-geo-loc -: O -@TimmNintey146 O -and O -'ll O -be O -mixing O -an O -80 O -'s O -and O -other O -throwbacks O -dance O -party O -tonight O -at O -eighty-five O -. O -Come O -kickit O -! O - -I O -want O -it O -NOW O -! O -;) O -RT O -: O -@corsairartisan O -Pumpkin B-product -Moonshine I-product -has O -arrived O -in O -Nashville B-geo-loc --- O -Give O -it O -a O -week O -to O -get O -it O -in O -stores O -. O -KY B-geo-loc -ships O -next O -week O -! O - -I O -'ve O -just O -watched O -first O -four O -episodes O -of O -K-On B-tvshow -for O -the O -first O -time O -! O -: O -3 O - -#blackholidays O -" O -National O -forget O -the O -he O -say O -she O -say O -... O -what O -do O -God B-person -say O -day O -" O - -@CULTOFMIKEY O -It O -'s O -too O -long O -, O -and O -it O -just O -... O -I O -do O -n't O -know O -, O -it O -sounds O -cliche O -. O -And O -dumb O -. O -My O -sister O -wants O -to O -cry O -she O -hates O -it O -so O -much O -. O - -@DaylySpecialz O -im O -doin O -good O -, O -just O -maxin O -< O -relaxin O -on O -my O -day O -off O -... O -i O -have O -a O -4 O -day O -stetch O -startin O -tomorrow O -. O - -@mekamoe927 O -I O -'ve O -had O -one O -before O -, O -but O -it O -was O -a O -short O -experience O -( O -double O -entendre O -intended) O -. O - -the O -day O -you O -go O -shirtless O -in O -school O -is O -the O -day O -I O -give O -birth O -to O -a O -pig O - -RT O -@ChrystallK O -: O -Yo O -, O -artists O -! O -ASU B-facility -Step I-facility -Gallery I-facility -wants O -your O -" O -socially O -relevant O -" O -comic O -book O -cover O -designs O -for O -a O -Nov O -. O -show O -http://bit.ly/8YLSw4 O - -Ugh O -I O -DO O -N'T O -HAVE O -MY O -EARPHONES O -I O -GUESS O -I O -WO O -N'T O -B O -TALKING O -AND O -DRIVING O -TODAY O -LOL O -.... O -Excuses O -Excuses O -Excuses O -SMH O - -There O -was O -just O -a O -fire O -at O -work O -. O -Today O -is O -looking O -up O -. O - -@angelajames O -@roxannestclaire O -I O -have O -n't O -tried O -MAC B-company -. O -Next O -time O -! O - -@peasmom3 O -and O -as O -if O -I O -was O -n't O -obsessed O -with O -AFL O -already O -, O -after O -Sunday O -I O -'ve O -gotten O -even O -more O -so O -!! O -I O -'m O -so O -obsessed O -with O -his O -sexiness O -!!!! O - -Kid B-person -Cudi I-person -Opens O -Up O -About O -Cocaine O -Use O -, O -Failed O -Lady O -Gaga O -Tour O -: O -by O -Mariel B-person -Concepcion I-person -, O -NY B-geo-loc -| O -September O -1 O -... O -http://bit.ly/cZWGDi O - -@Yeweezii O -Jst O -lock O -her O -outside O -4 O -d O -nite O -. O -It O -workz O -al O -d O -time O -:-D O - -@NeilPen O -I O -position O -to O -Manchester B-geo-loc -tomorrow O -on O -BA O -2910 O -and O -fly O -to O -Barbados B-geo-loc -on O -Sunday O - -I O -was O -15 O -minutes O -late O -yesterday O -so O -I O -left O -20 O -min O -earlier O -today O -. O -I O -will O -be O -20 O -minutes O -late O -today O -. O -#lifeispointless O - -@isaccnewton7 O -call O -me O -when O -u O -go O -out O -to O -practice O - -Was O -hard O -work O -going O -to O -the O -gym O -on O -my O -own O -tonight O -but O -managed O -a O -30 O -min O -run O -and O -plenty O -of O -weights O -. O -Catching O -up O -on O -ufc B-company -now O - -RT O -@Sexstrology O -: O -Sagittarius O -is O -turned O -off O -when O -you O -play O -hard O -to O -get O -. O -They O -want O -it O -now O -. O - -RT O -@daviddesrosiers O -: O -Happy O -birthday O -@chuckcomeau O -! O -have O -fun O -in O -vancouver B-geo-loc -tonight O -! O - -I O -swear O -that O -i`ll O -hold O -on O -to O -that O -promise O -i O -made O -on O -the O -17 O -of O -March O -2010 O -. O - -Engadget B-company -: O -Sky B-company -3D I-company -enlists O -Stephen B-person -Fry I-person -to O -explain O -the O -technology O -ahead O -of O -its O -October O -1 O -launch O -( O -video) O -: O -With O -the O -of O -... O -http://bit.ly/abRALO O - -... O -if O -I O -didnt O -have O -to O -worry O -about O -$ O -I'd O -move O -to O -VT B-geo-loc -and O -buy O -some O -hogs O -and O -spend O -my O -days O -dropping O -LSD O -and O -taking O -long O -walks O -in O -gucci B-company -shoes O -... O - -Relieved O -that O -the O -first O -full O -week O -of O -school O -is O -over O -! O -So O -happy O -I O -can O -sleep O -in O -tomorrow O -! O - -RT O -@angelfrmcanada O -: O -RT O -@kraftfoods O -Each O -time O -you O -RT O -this O -& O -include O -#KraftFightsHunger O -, O -we'll O -donate O -a O -meal O -to O -@FeedingAmerica O -Give O -us O -a O -... O - -@millyTW__ O -night O -:) O -I'll O -have O -more O -for O -you O -to O -dream O -tomorrow O -I'm O -in O -tha O -mood O -for O -writing O -;) O - -If O -you O -can O -smile O -when O -things O -go O -wrong O -, O -you O -have O -someone O -in O -mind O -to O -blame O -. O - -I O -hope O -you O -will O -have O -a O -blessed O -and O -nice O -time O -off O -this O -weekend O -. O -Love O -your O -children O -, O -love O -your O -pets O -and O -enjoy O -all O -the O -goodness O -in O -your O -life O -. O - -Shooting O -sunset O -in O -#btv O -, O -time-lapse O -5 O -sec O -. O -Intervals O -from O -Champlain B-facility -campus I-facility -. O -Pretty O -. O - -It O -sucks O -when O -you O -eat O -old O -cookies O -for O -lunch O -and O -they O -make O -you O -feel O -barfy O -. O - -@CHRIS_Daughtry O -Aw O -ur O -cute O -when O -u O -get O -mad O -ur O -songs O -r O -Killing O -me O -I O -just O -Love O -Ur O -Voice O -! O -even O -my O -bros O -do O -which O -is O -weird O -they O -don't O -like O -my O -stuff O - -Tomorrow O -- O -London B-geo-loc -- O -Hyde B-facility -Park I-facility -Corner I-facility -- O -1.30 O -pm O -. O -If O -you O -cannot O -be O -there O -- O -send O -out O -supportive O -thought O -waves O -! O - -@MrsTiaJ O -no O -you're O -the O -jerk-o O -for O -lying O -. O -Plus O -I O -had O -just O -decided O -like O -10 O -mins O -before O -then O -that O -I O -wasn't O -coming O - -Yahoo B-company -: O -Jackie B-person -joins O -Twitter B-company -50 O -years O -after O -JFK B-person -campaign O -: O -Had O -Twitter B-company -been O -an O -option O -in O -1960 O -, O -a O -pregnant O -Jacquelin B-person -... O -http://yhoo.it/9VAD4A O - -@aprildenay O -Quit O -messing O -with O -ut O -... O -every O -time O -you O -take O -the O -cotton O -out O -you O -break O -the O -clots O -keeping O -it O -from O -bleeding O -goober O -head O -! O - -@Nikitajonesxo O -mhm O -fo O -sho O -! O -i O -like O -being O -able O -to O -walk O -over O -when O -im O -bored O -ha O - -RT O -@CantBeliebIt O -: O -When O -Justin B-person -gets O -a O -girlfriend O -That O -Should O -Be O -Me O -is O -gonna O -be O -#1 O -on O -iTunes B-product -. O - -15 O -million O -children O -die O -of O -hunger O -every O -year O -. O -Heed O -the O -call O -to O -end O -#world O -#hunger O -: O -http://linkbee.com/D8RKT O -via O -@here4cause O - -When O -glass O -breaks O -, O -the O -cracks O -move O -faster O -than O -3,000 O -miles O -per O -hour O -. O -To O -photograph O -it O -, O -a O -camera O -must O -shoot O -at O -a O -millionth O -of O -a O -second O -! O - -When O -the O -last O -time O -you O -ran O -into O -Rick B-person -Ross I-person -and O -Drake B-person -twice O -in O -the O -same O -day O -at O -2 O -diff O -video O -shoot O -locations O -. O -Today O -I O -did O -! O - -Imma O -get O -your O -heart O -racing O -in O -my O -skin-tight O -jeans O -be O -your O -teenage O -dream O -tonight O -.... O - -RT O -@itsJuliaHardy O -@WanderingRyu O -I O -noticed O -a O -Ryu O -shaped O -hole O -in O -the O -show O -... O -;) O -Def O -tune O -in O -next O -week O -if O -u O -can O -. O -XX O - -PLANO B-other -BALLOON I-other -FESTIVAL I-other -2010 I-other -TO O -START O -TODAY O -: O -The B-other -31st I-other -annual I-other -Plano I-other -Balloon I-other -Festival I-other -, O -Plano B-other -Balloon I-other -Festival I-other -2 O -... O -http://tinyurl.com/22kg6uz O - -i'll O -miss O -yooouuu O -my O -aunty O -tersayangg O -.... O -See O -U O -next O -Time O -yah O -... O -Mmuah O -: O -* O - -HPU O -pau O -for O -the O -day O -! O -Happy O -Aloha O -Friday O -!!! O - -My O -horrible O -doctor O -was O -featured O -on O -Oh B-other -Canada I-other -Team I-other -'s O -blog O -: O -http://ohcanadateam.blogspot.com/2010/09/friday-finds-doctor-horrible.html O - -@Cuddle_Factory O -will O -send O -again O -tomorrow O -honey O -too O - -RT O -@GraceThorson O -: O -I O -enjoy O -reading O -, O -because O -I O -can O -visit O -different O -places/time O -periods O -, O -and O -I O -always O -learn O -something O -from O -it O -. O -@ChristianScroll O - -@chazzy00 O -what O -we O -on O -tonight O -friend O - -And O -we O -had O -some O -amazing O -dessert O -wines O -too O -! O -First O -time O -I've O -tried O -that O -, O -but O -now O -a O -convert O -. O -Will O -have O -to O -add O -wine O -to O -dessert O -from O -now O -on O -! O -Yum O -! O - -This O -week O -I O -have O -peed O -a O -lot O -and O -have O -eaten O -very O -well O -. O -Weekend O -, O -please O -don't O -kill O -me O -. O - -i O -have O -a O -feeling O -that O -this O -weekend O -will O -be O -amazing O -... O -or O -at O -least O -tonight O -will O -be O -. O - -RT O -@JBieber_isHOT O -: O -When O -Justin B-person -gets O -a O -girlfriend O -... O -its O -going O -to O -be O -World B-other -War I-other -3 I-other -!!!!!! O - -@Iamjustinbieb O -i O -know O -that O -today O -is O -follow O -friday O -. O -so O -im O -guessing O -thats O -what O -i O -means O -. O -but O -im O -not O -suree O -lmaoo O - -Copa B-other -Sudamericana I-other -#2010 O -: O -Thursday O -Round-Up O -- O -http://newzfor.me/?754c O - -RT O -@PrincePoppycock O -: O -A O -gentleman O -would O -never O -get O -his O -lipstick O -on O -a O -lady O -'s O -hand O -when O -kissing O -it O -! O - -@keoghsie O -It O -'s O -been O -ages O -!! O -How O -are O -you O -? O -We've O -been O -doing O -non-Twitter O -stuff O -for O -a O -while O -, O -but O -now O -have O -time O -for O -yapping O -again O -! O -HUrrah O -!! O - -Driving O -, O -driving O -, O -driving O -away O -to O -Phil B-geo-loc -. O -Tasty O -dinner O -tonight O -with O -the O -Society B-other -of I-other -Mining I-other -and I-other -Metallurgy I-other -Engineers I-other -. O - -This O -. O -RT O -@bethanyshondark O -: O -RT O -@duchess_rebecca O -: O -Twitter B-company -is O -going O -to O -be O -less O -fun O -tonight O -without O -the O -snarky O -Joos O -. O -Aw O -!! O - -TOP O -EVENT O --- O -> O -http://schmap.it/98hCOt O -- O -And O -tomorrow O -the O -@BaristaThing O -Event O -goes O -down O -- O -open O -to O -pros O -, O -home O -baristas O -, O -c O -... O -( O -via O -@CoffeeGeek O -) O - -@kajelly_milk O -( O -: O -but O -hey O -.. O -its O -all O -good O -. O -i O -like O -him O -haha O -&omg O -jason O -kept O -touching O -me O -today O -D O -; O -i O -was O -like O -man O -stop O -touching O -O O -: O - -@x_Jamelia_x O -When O -r O -u O -gona O -realise O -we O -dont O -want O -u O -on O -our O -tvs O -?? O -To O -prove O -my O -point O -enter O -ur O -name O -into O -search O -.. O -Uve O -had O -ur O -15mins O -! O - -I O -liked O -a O -YouTube B-company -video O --- O -Testing B-musicartist -Tomorrow I-musicartist -http://youtu.be/aOYInsel7Qw?a O - -I O -love O -to O -see O -a O -child O -'s O -face O -when O -they O -think O -of O -the O -fairies O -. O -Like O -a O -fairy O -their O -little O -faces O -light O -up O -:) O - -The O -forced O -ads O -in O -ngmoco B-company -games O -are O -outstandingly O -annoying O -! O -Looks O -like O -it O -'s O -time O -to O -delete O -all O -their O -apps O -from O -my O -iOS B-product -devices O -! O - -RT O -@LightCMS O -: O -Day O -6 O -: O -Win O -1 O -of O -10 O -iPads O -from O -@LightCMS O -- O -RT O -to O -enter O -or O -read O -more O -http://speaklight.com/iPad O - -RT O -@KarlaRay O -: O -What O -a O -day O -in O -#Collier O -! O -Watch O -@NBC2 O -at O -6 O -for O -details O -on O -the O -latest O -growhouse O -bust O -and O -the O -county O -'s O -first-ever O -West O -Nile O -d O -... O - -I O -played O -this O -out O -not O -too O -long O -ago O -lol O -- O -http://www.youtube.com/watch?v=aWISNFAvGkw O -I O -really O -do O -like O -it O -even O -though O -it O -'s O -utter O -shit O -. O - -" O -You O -may O -have O -given O -up O -on O -me O -, O -but O -I'm O -not O -given O -up O -on O -you O -." O --The O -Boy O -You O -Use O -To O -Love O - -Artful O -Pumpkins O -Ladies O -Long O -Sleeve O -: O -Artful O -Pumpkins O -Ladies O -Long O -Sleeve O -. O -Altered O -vintage O -design O -. O -Backgroun O -... O -http://bit.ly/9RRYOI O -#tshirts O - -June B-other -Fest I-other -2010 I-other -~ O -Forgotten B-facility -Door I-facility -LIVE O -! O -http://fb.me/G1pof3lO O - -@marciaforbes O -When O -U O -dig O -grave O -always O -dig O -2 O -. O -Like O -he O -wanted O -to O -diminish O -Brady B-person -& O -end O -up O -doing O -that O -to O -himself O -< O -> O -True O -.... O -Dat O - -I'm O -tired O -after O -school O -today O -! O - -" O -smell O -. O -( O -the O -lack O -of O -could O -only O -help O -when O -forced O -to O -go O -cowtipping O -with O -ram O -. O -) O -" O -- O -@jenist O -wins O -! O -your O -mineral O -water O -is O -in O -the O -mail O -. O - -four O -months O -< O -3 O - -Rain O -rain O -go O -away O -come O -back O -when O -it O -'s O -not O -my O -dads O -birthday O -. O - -@justinbieber O -When O -you O -smile O -I O -smile O -... O -seriously O -I O -do O -!!!! O - -@Surfrdan O -to O -be O -fair O -though O -it O -'s O -roughly O -the O -millionth O -time O -he O -'s O -played O -Tony B-person -Blair I-person -. O - -@jenniferehle O -: O -Did O -you O -get O -heavy O -rains O -up O -there O -yesterday O -? O - -Sweet O -! O -RT O -@SimoneSOliver O -: O -Here O -'s O -the O -London B-other -Fashion I-other -Week I-other -schedule O -: O -http://nyti.ms/bW0N3u O - -RT O -@davehodg O -: O -Pope B-person -says O -atheists O -pick O -& O -choose O -their O -morals O -. O -Correct O -. O -Today O -I O -will O -be O -frowning O -on O -child O -abuse O -& O -not O -having O -a O -problem O -wit O -... O - -@DeezyWest O -raiders O -get O -there O -first O -win O -this O -sunday O -#Predictions O - -WHATS O -GOODIE O -EVERY1 O -COOLIN O -COOLIN O -FEELIN O -THE O -BREEZE O -#FRIDAY#MANIA O - -@valdary O -sorry O -i O -meant O -my O -dad O -in O -the O -first O -tweet O -, O -my O -mums O -been O -dead O -5 O -years O -. O - -Great O -week O -for O -music O -with O -@blindoldfreak O -, O -UNKLE B-musicartist -& O -@trent_reznor O -all O -putting O -out O -new O -releases O -. O -Amazing O -stuff O -. O -**Support O -decent O -music** O - -@victorcajiao O -So O -can O -I O -call O -in O -sick O -on O -my O -last O -day O -on O -the O -job O -so O -I O -can O -go O -out O -there O -and O -look O -at O -it O -? O -:-) O - -Thank O -God O -it O -'s O -finally O -Friday O -! O -I O -really O -needed O -this O -. O -:-) O - -Telly O -was O -junk O -last O -week O - -Love O -me O -when O -I O -least O -deserve O -it O -, O -because O -that O -'s O -when O -I O -need O -it O -the O -most O -. O - -I O -really O -feel O -like O -staying O -up O -all O -night O -listening O -to O -my O -CDs O -. O -The O -neighbours O -and O -my O -boss O -at O -work O -tomorrow O -would O -complain O -, O -so O -no O -can O -do O -:( O - -@SweetBee_90 O -what O -'s O -the O -look O -for O -tonight O -? O - -volunteering O -for O -the O -Rotary B-facility -Club I-facility -at O -the O -PoMo O -Terry B-person -Fox I-person -run O -. O -Should O -be O -an O -excellent O -time O -: O -come O -down O -and O -check O -it O -out O -on O -Sunday O -am O -! O - -I O -distinctly O -remember O -someone O -giving O -me O -chocolates O -last O -week O -. O -I O -don't O -recall O -eating O -a O -single O -piece O -. O -They're O -nowhere O -to O -be O -found O -of O -course O -. O - -RT O -@Durell_G O -: O -When O -life O -throws O -u O -lemons O -... O -Throw O -them O -back O -.. O -< O -make O -lemonade O -or O -@giapo O -sorbet O -? O - -Coach B-person -Germano I-person -, O -Albin B-person -, O -Haines B-person -and O -Burrow B-person -each O -had O -home O -damage O -from O -the O -storm O -yesterday O -, O -according O -to O -@JasonCorriher O -. O - -Madden B-product -tonite O -!!! O -the O -homie O -going O -down O -! O -Better O -get O -then O -clips O -ready O -! O -For O -my O -free O -cut O -!! O -Ha O -ha O - -@OmegaWife5 O -exhaustingly O -long O -lol O -:) O -how O -has O -yours O -been O -? O - -sexy O -azz O -@RICCOBARRINO O -tweeted O -me O -but O -can O -i O -get O -a O -follow O -baby O -??? O -and O -i O -got O -you O -i O -will O -let O -my O -folks O -know O -... O -when O -is O -the O -new O -album O -?? O -i O -willget O - -RT O -@BettyCrocker O -: O -Less O -than O -25 O -followers O -to O -go O -! O -Let O -'s O -reach O -11,000 O -before O -the O -weekend O -! O -:) O -http://ht.ly/2G4sQ O -#sweepstakes O - -RT O -@ItsTheTeenLife O -: O -The O -exact O -day O -that O -I O -wear O -shorts O -and O -a O -tee O -shirt O -everyone O -shows O -up O -wearing O -hoodies O -and O -jeans O -. O --_- O -#ItsTheTeenLife O - -@LOOPYisLINDSAY O -hahaha O -. O -Nice O -. O -How O -was O -ur O -day O -2day O -? O - -good O -day O -. O -bad O -day O -. O -good O -day O -? O -merek O -minuman O -sachet O - -Time O -to O -go O -, O -hihi O -! O -Beijinhos O -, O -seus O -viciados O -: O -* O - -She O -Love O -Me O -Long O -Timeeeee O - -RT O -@Ashanie_di_Don O -: O -@JDay_Sept15 O -lol O -i O -see O -someone O -hyped O -and O -ready O -for O -#FlavaFridays O -tonight O -< O -every O -week O -. O -Lets O -get O -it O -! O - -@chrislhayes O -That O -'s O -nothing O -compared O -to O -the O -LaserDiscs O -I O -picked O -up O -at O -goodwill O -the O -other O -day O -. O -Completely O -useless O -those O -. O - -ahhh O -that O -made O -my O -day O -( O -: O -i O -would O -say O -something O -stupid O -like O -that O -:D O - -September O -dates O -to O -remember O -: O -http://fb.me/zUJIeN0U O - -Internet O -. O -My O -MOM O -got O -a O -tattoo O -today O -. O -Which O -, O -if O -one O -knows O -her O -, O -is O -so O -out O -of O -character O -. O -I O -find O -it O -awesome O -. O -Go O -ma O -! O - -Checking O -out O -this O -weeks O -tell O -' O -em O -Steve B-person -Dave I-person -Podcast O - -@BundleHunt O -Is O -it O -being O -released O -today O -or O -soon O -? O - -Its O -crazy O -that O -one O -day O -I O -have O -energy O -to O -do O -laundry O -, O -mop O -floors O -, O -scrub O -everything O -down O -and O -the O -next O -I O -don't O -even O -feel O -like O -doing O -anything O -! O - -Why O -hate O -on O -somebody O -for O -doing O -well O -when O -you O -can O -simply O -say O -great O -job O -and O -move O -on O -.. O -[Haters O -u O -can O -kill O -yourself O -] O -cuz O -ur O -losers O - -@urbanturbanguy O -Ooh O -, O -I O -could O -be O -your O -long O -lost O -Mexican O -sister O -for O -this O -. O - -its O -time O -to O -have O -some O -fun O -tonight O -: O -) O - -@danibuono O -yet O -, O -once O -I O -get O -my O -iPhone B-product -that O -'s O -when O -I'ma O -scream O -TeamiPhone O -RT O -your_commander O -: O -_Shomari O -lol O -why O -u O -say O -u O -guess O -? O -http:///T0UZh O - -Saturday O -September O -18th O -2010 O -8pm O -Hotel B-facility -Elegante I-facility -Steve B-person -" I-person -Stylez I-person -" I-person -A.K.A. O -Desperado B-person -Birthday O -Bash O -! O -" O -Headrush O -in O -da O -building O -" O - -sfitzy93 B-person -asked O -: O -No O -problem O -( O -: O -awesome O -! O -toast O -= O -life O -:P O -.. O -You O -doing O -anything O -for O -the O -day O -? O -http://tumblr.com/xkdiufb81 O - -The B-movie -Town I-movie -might O -be O -one O -of O -the O -best O -movies O -I O -have O -seen O -all O -year O -. O -So O -, O -so O -good O -. O -And O -don't O -worry O -Ben B-person -, O -we O -already O -forgave O -you O -for O -Gigli B-movie -. O -Really O -. O - -Wow O -, O -I'd O -forgotten O -when O -Jenny B-person -was O -actually O -really O -nice O -lol O -#theLword O - -Pretty O -much O -any O -time O -I O -get O -an O -email O -from O -someone O -who O -puts O -" O -PhD O -" O -in O -the O -from O -field O -, O -I O -know O -there O -'s O -going O -to O -be O -trouble O - -signing O -along O -to O -Remembering O -Sunday O - -@TakeMeDownCHRIS O -he O -didnt O -say O -when O -.. O -he O -just O -said O -he O -workin O -on O -it O -& O -gonna O -release O -the O -full O -version O -to O -yeah3x O -soon O -! O - -was O -WAITING O -for O -this O -day O -for O -specific O -reasons O -. O -now O -im O -leaving O -again O -& O -will O -have O -to O -try O -this O -bullshit O -AGAIN O -next O -friday O -. O -dawg O -wasted O -my O -time O - -Might O -have O -an O -early O -night O -got O -to O -help O -Rob B-person -finish O -the O -decorating O -tomorrow O - -@shhaunna_btm O -i'm O -not O -ignoring O -you O -doll..i'm O -doing O -my O -friday O -follows O - -@wildflower94 O -hw O -=/ O -i O -cant O -wate O -til O -summer O -!!! O -This O -year O -us O -guna O -b O -a O -mess O - -RT O -@relentless1323 O -: O -I O -hate O -when O -a O -nigga O -shows O -me O -a O -video O -of O -him O -on O -his O -phone O -, O -gettin O -some O -head O -from O -a O -shorty O -. O -Im O -Like O -yo O -... O -U O -just O -showed O -... O - -For O -code O -I've O -written O -since O -Sept O -' O -09 O -, O -the O -delay O -between O -commit O -to O -SCM O -and O -running O -on O -customer O -hardware O -has O -been O -about O -10 O -days O -. O - -Thank O -u O -! O -xo O -RT O -@Kadzyq O -: O -Happy O -Bday O -!! O -From O -all O -ur O -fans O -in O -the O -Middle B-geo-loc -East I-geo-loc -:) O -hope O -to O -c O -u O -n O -the O -voices O -with O -Yanni B-person -in O -concert O -here O -some O -day O -:) O - -@Jee_agustino O -:O O -seriously O -even O -when O -you O -do O -not O -want O -? O - -Im O -starting O -to O -hate O -verizon B-company -. O -I O -normally O -have O -one O -bar O -or O -no O -bars O -. O -But O -i O -sure O -have O -4 O -bars O -for O -ev O -all O -the O -time O -.. O -. O -. O -. O -Stupid O - -Gets O -To O -See O -Lindsay B-person -Tonight O -! O -:D O -:D O -: O -LD O -< O -3 O -Love O -YOu O -Beautiful O -!! O - -@vincentsmojo O -You O -think O -I O -secretly O -love O -dresses O -lol O -Only O -when O -Lilia B-person -wears O -them O -;) O - -@paulwalk O -It O -'s O -the O -view O -from O -where O -I'm O -living O -for O -two O -weeks O -. O -Empire B-facility -State I-facility -Building I-facility -= O -ESB B-facility -. O -Pretty O -bad O -storm O -here O -last O -evening O -. O - -Lucid B-musicartist -Dementia I-musicartist -Playing O -in O -Austin B-geo-loc -Tonite O -at O -The B-geo-loc -Scoot I-geo-loc -Inn I-geo-loc -at O -1am O -! O - -#jobs O -#careers O -#Greenville O -AT&T B-company -Full O -Time O -Retail O -Sales O -Consultant(s O -) O -- O -Greenville B-geo-loc -, O -AL B-geo-loc -http://bit.ly/dflARA O - -@hannibal_flow O -I O -got O -miami B-geo-loc -pics O -for O -days O -lol O - -8hr O -shift O -today O -yuck O -lol O - -Buck B-geo-loc -mountain I-geo-loc -tomorrow O -. O -#adirondacks O -#hiking O - -ALL O -TIME O -LOW O -ARE O -COMMING O -TO O -GLASGOW B-geo-loc -!!!!!!!!!!!!!!!!!!! O - -RT O -@GaidaAbuSaleh O -: O -my O -1997th O -tweet O -goes O -to O -....... O -@LIDIAABUSALEH O -, O -i O -would O -like O -to O -thank O -lidia O -, O -for O -being O -born O -on O -the O -1997th O -year O -. O -thank O -you O -. O - -RT O -@AniCoronado O -: O -#ZodiacFacts O -As O -a O -#Taurus O -it O -may O -seem O -like O -things O -are O -falling O -into O -place O -, O -and O -this O -feeling O -gives O -you O -a O -surge O -of O -energy O -. O - -RT O -@Madslanger O -: O -Performing O -in O -Naples B-geo-loc -tomorrow O -with O -N.E.R.D. B-musicartist -, O -Maroon B-musicartist -5 I-musicartist -and O -others O -at O -the O -MTV B-other -show O -. O -Tonight O -Stars B-facility -in O -Vordingborg B-geo-loc -- O -few O -tic O -... O - -RT O -@robmoysey O -Eyeopener B-sportsteam -vs O -. O -Ryerson B-sportsteam -Quidditch I-sportsteam -team O -this O -Sunday O -at O -4 O -p.m. O -Anyone O -know O -where O -to O -get O -cheap O -brooms O -? O -#Ryerson O -@RUQuidditch O -#Rams O - -He O -said O -Rihanna B-person -all O -low O -toned O -and O -shit O -!! O -Prolly O -the O -first O -time O -he O -said O -her O -names O -in O -AGES O -!!! O -LMFAOOOOO O - -Today O -is O -my O -oldest O -daughters O -13th O -birthday O -. O -Really O -don't O -know O -what O -to O -think O -about O -her O -being O -a O -" O -teenager O -". O -Except O -that O -I O -am O -really O -getting O -old O -. O - -Comment O -of O -the O -Day O -: O -The O -Depths O -of O -Poverty O -[ O -We O -Read O -You] O -: O - -@leeboardman O -and O -three O -points O -on O -sunday O -..... O -na O -night O -!! O - -omg O -@wethekings O -is O -playing O -a O -show O -at O -Stetson B-facility -in O -November O -.. O -if O -it O -'s O -students O -only O -, O -I O -will O -be O -pissed O - -@MinkyMoo O -I O -really O -didn't O -want O -one O -either O -but O -I O -literally O -laughed O -out O -loud O -when O -I O -read O -it O -. O -I'm O -sure O -that O -is O -not O -the O -reaction O -she O -wanted O -;) O - -i O -may O -have O -failed O -, O -but O -yeah O -i O -loved O -you O -from O -the O -start O -- O -i O -should O -continue O -editing O -and O -stop O -quoting O -. O - -BBL B-other -started O -tonight O -, O -EBL B-other -tmrw O -, O -Euroleague B-other -qualifiers O -tues O -but O -damn O -, O -i O -can O -haz O -NBA B-other -now O -? O -League B-tvshow -Pass I-tvshow -withdrawal O -symptoms O -...... O - -All O -caught O -up O -on O -@SHO_weeds O -! O -After O -eating O -a O -full O -meal O -that O -was O -the O -next O -thing O -on O -my O -list O -to O -do O -after O -graduating O -! O - -Had O -3 O -doctors O -appts O -today O -... O -fun O -stuff O -. O -leaving O -for O -the O -bahamas B-geo-loc -tomorrow O -though O -!! O - -RT O -@MirandaBuzz O -: O -Were O -working O -til O -midnight O -tonight O -! O -Sleepover O -on O -set O -! O - -you O -know O -its O -bad O -when O -you O -do O -that O -, O -but O -you O -don't O -care O -. O - -I O -don't O -understand O -why O -some O -dudes O -get O -mad O -when O -you O -flirt O -with O -someone O -else O -... O - -@soapsindepthcbs O -Disagree O -, O -disagree O -, O -no O -, O -no O -. O -When O -it O -'s O -a O -finale O -, O -you O -can't O -really O -argue O -about O -stories/etc O -. O -ATWT O -went O -out O -great O -... O -splendidly O -. O - -Join O -Anya B-person -Kamenetz I-person -on O -Monday O -'s O -#Mozilla O -#Drumbeat O -community O -call O -. O -DIY O -U O -and O -the O -future O -of O -education O -. O -http://mzl.la/azQO2g O -@anya1anya O - -next O -Friday O -, O -the O -24th O -both O -my O -bands O -are O -playing O -at O -Swayzes B-facility -Venue I-facility -. O -Please O -come O -support O -!!! O - -RT O -@iSplashInYoMouf O -: O -RE*TWEET O -IF O -YOU O -SMOKING O -TONIGHT O -? O - -What O -my O -#Followers O -Doing O -tonight O -? O - -@TimothyJMoore O -Thnx O -, O -I'm O -glad O -they O -weren't O -totally O -ignored O -, O -but O -one O -isn't O -many O -when O -you O -think O -how O -many O -there O -are O -in O -the O -CofE O -! O - -@Miss_SarahBaby O -hmmm O -.. O -u O -kno O -when O -happy O -hour O -ends O -? O - -Grammar B-other -Boot I-other -Camp I-other -starts O -on O -Monday O -! O -7th O -graders O -should O -be O -ready O -to O -GO-GO-GO O -!!!! O -8th O -Graders O -have O -a O -big O -project O -due O -next O -Friday O -... O -be O -ready O -! O - -RT O -@TeamShaneDawson O -: O -RT O -If O -you O -think O -@AntiShaneDawson O -is O -a O -10 O -year O -old O -without O -a O -penis O -to O -play O -with O -so O -they O -hate O -on O -Shane B-person -instead O -:( O -:' O -) O - -On O -my O -way O -to O -my O -dentist O -appointment O -ugh O -! O -i O -hate O -the O -dentist O -!! O -remember O -kids O -brush O -3 O -times O -a O -day O -and O -floss O -twice O -and O -u'll O -have O -a O -great O -#smile O - -Asprin O -, O -check O -, O -cup O -of O -tea O -, O -check O -, O -pillow O -, O -check O -, O -warm O -sleeping O -bag O -, O -check O -, O -fanfiction O -on O -the O -laptop O -, O -check O -. O -Time O -to O -settle O -down O -and O -relax O - -havnt O -lost O -a O -game O -in O -open O -gym O -all O -week O -.. O -now O -to O -go O -get O -dat O -fresh O -cut O - -Tomorrows O -plan O -is O -a O -little O -skirt O -along O -the O -coast O -then O -a O -stop O -in O -Glenveagh B-facility -national I-facility -park I-facility -, O -should O -be O -a O -picturesque O -day O -. O -Est O -85 O -mile O -of O -riding O - -New O -Comic O -List O -is O -out O -for O -Next O -Week O -( O -September O -22 O -, O -2010 O -) O -and O -don't O -forget O -to O -stop O -by O -Today O -from O -5-8pm O -for O -Happy O -... O -http://fb.me/JLaJCWdu O - -Fashion B-other -week I-other -ends O -with O -Ralph B-person -Lauren I-person -, O -Oscar B-person -de I-person -la I-person -Renta I-person -, O -Gwen B-person -Stefani I-person -& O -a O -Weir O -...: O --Coco B-person -Chanel I-person -" O -I O -design O -for O -the O -... O -http://bit.ly/beBG9e O - -@TheGayDragon O -@RealmOfMachias O -Sir O -.. O -I O -SWEAR O -I O -had O -nothing O -to O -do O -with O -it O -this O -time O -.. O -honest O -.. O -please O -don't O -hurt O -me O -.. O - -@JBsHOTNESS O -i O -do O -talk O -about O -him O -every O -second O -she O -like O -his O -songs O -for O -a O -week O -:/ O -@clarebieberx O -np O -! O -@FutreTeenStar O -sure O -!! O - -@bakontrack O -I O -was O -on O -a O -successful O -business O -trip O -!!! O -I O -had O -no O -time O -to O -tweet O -!!! O - -took O -the O -combine O -out O -for O -a O -spin O -today O -, O -but O -the O -barley O -is O -still O -a O -tad O -on O -the O -wet O -side O -. O - -OK O -IMMA O -ASK O -THIS O -AGAIN O -HOW O -LONG O -DO O -YOU O -TALK O -TO O -A O -GIRL O -BEFORE O -U O -MAKE O -IT O -OFFICIAL O -& O -START O -GOIN O -OUT O -WITH O -HER O -???? O - -Branching O -out O -from O -Lincoln B-product -park I-product -after I-product -dark I-product -... O -Hello O -" O -Russian B-product -navy I-product -" O -it O -'s O -like O -the O -same O -thing O -but O -with O -glitter O -! O - -' O -Free O -' O -' O -Day O -26 O -' O -'' O -#nowplaying O -http://cpwr.me/c9GNpt O - -What O -A O -Great O -Day O -! O -Except O -For O -Another O -Schedule O -Change O -:/ O -Dance O -Tonightt O -! O -( O -: O - -Our O -Favorite O -YouTube B-company -Videos O -This O -Week O -: O -The O -Drama O -Edition O -: O -Had O -any O -epic O -meltdowns O -this O -week O -? O -You O -know O -, O -of O -the O -cr O -... O -http://bit.ly/cuy0yt O -#in O - -RT O -@LilTwist O -: O -RT O -this O -if O -you O -want O -me O -to O -go O -back O -live O -on O -Ustream B-company -later O -tonight O - -Institutional O -buying O -lifts O -UFlex B-company -30% O -in O -a O -month O -http://bit.ly/bOAk9y O - -Nothing O -like O -a O -3pm O -Friday O -meeting O -! O - -RT O -@ssssab O -: O -Mariano B-person -: O -she O -used O -to O -be O -a O -very O -nice O -girl O -, O -before O -she O -discovered O -macdonalds B-product - -@playavolta O -haha O -it O -'s O -not O -that O -bad O -now O -but O -when O -i O -was O -little O -i O -used O -to O -freak O -out O -so O -i O -would O -use O -the O -chewables O -that O -tasted O -like O -chalk O -. O - -@NTRLDisaster O -well O -I'm O -sure O -u O -could O -do O -it O -one O -at O -a O -time O -with O -Paco B-person -( O -Thats O -my O -cars O -name O -) O -plust O -there O -'s O -a O -roof O -rack O -on O -top O -u O -can O -tie O -stuff O -too O -? O - -@FritzTitz O -it O -took O -me O -2 O -tries O -, O -but O -after O -the O -2nd O -he O -became O -one O -of O -my O -favorite O -. O - -Take O -this O -time O -to O -discover O -the O -treasures O -within O -you O -! O - -So O -far O -this O -day O -hasn' O -; O -t O -gone O -to O -well O -, O -hopefully O -my O -night O -will O -be O -better O -! O - -Read O -so O -much O -on O -India B-geo-loc -, O -looks O -like O -a O -cert O -for O -future O -vacation O -, O -when O -I'm O -retired O -probably O -- O -http://bit.ly/b9vMjW O - -Balloon O -glowing O -? O -We'd O -love O -to O -see O -you O -before O -or O -after O -at O -Pi B-geo-loc -! O -CWE B-geo-loc -and O -Delmar B-geo-loc -are O -around O -the O -corner O -, O -Kirkwood B-geo-loc -& O -... O -http://fb.me/I6SZ52nW O - -Tonight O -is O -going O -to O -be O -romantic O - -@glacerie O -It O -'s O -in O -my O -tweets O -a O -year O -ago O -I'll O -pull O -up O -the O -story O -later O -on O -. O -Just O -hope O -the O -film O -helps O -Shange B-person -sell O -more O -books O -. O - -Today O -'s O -post O -is O -about O -3D O -graffitti O -... O -http://francescoandreone.blogspot.com/2010/09/banksy-makes-social-commentary-art.html O - -@BieberEgypt O -I've O -got O -some O -bad O -news O -, O -Yeah O -I O -cant O -come O -to O -the O -meeting O -tomorrow O -. O -: O -S O - -@The_TDawson O -what O -were O -yu O -talking O -about O -yesterday O -? O - -@aplusk O -What O -made O -you O -laugh O -? O -This O -did O -, O -courtesy O -of O -@rainnwilson O -http://www.officetally.com/rainn-wilson-and-friends-seattle-oct-23 O - -Stairs O -: O -One O -more O -chance O -to O -win O -Roger B-person -Waters I-person -tickets O -on O -KLH B-other -tonight O -between O -6:15 O -& O -6:45 O -!! O -http://fb.me/Jjp2pz4I O - -The O -British O -have O -a O -remarkable O -talent O -for O -keeping O -calm O -, O -even O -when O -there O -is O -no O -crisis O -. O - -Tonight O -@AZUKARlounge O -Baka B-musicartist -Boyz I-musicartist -LIVE O -w/ O -DJ B-musicartist -Elements I-musicartist -103.5 O -& O -Soreal O -. O -$150 O -VIP O -booths O -tonight O -. O -Text O -916.960 O -. O -8120 O -for O -NO O -cover O -glist O -!! O - -First O -week O -of O -school O -has O -been O -crazy O -. O -Everyone O -is O -very O -moody O -and O -tired O -! O - -Dems B-other -to O -voters O -: O -You O -may O -hate O -us O -, O -but O -GOP B-other -is O -worse O -( O -AP O -) O -AP O -- O -With O -just O -six O -weeks O -to O -avoid O -a O -possible O -election O -catastrophe O -, O -http://tiny.ly/wc5 O - -RT O -@Sexstrology O -: O -Virgos O -need O -to O -learn O -how O -to O -relax O -at O -the O -end O -of O -the O -day O -. O -You O -tend O -to O -stress O -yourself O -out O -due O -to O -your O -high O -standards O -. O - -RT O -@BieberPledge O -: O -When O -I O -Get O -Old O -, O -Imma O -Still O -Be O -Rockin O -Out O -To O -' O -Baby'...Even O -If O -I O -Break O -A O -Hip O -;) O - -@thevoiceoverguy O -9 O -days O -then O -Sydney B-geo-loc - -" O -why O -is O -it O -you O -always O -look O -seriously O -stoned O -when O -i O -see O -you O -? O -" O -- O -- O -> O -Danny B-person -. O -lol O -good O -god O -! O -( O -: O - -I O -just O -took O -" O -When O -your O -mom O -makes O -you O -go O -live O -with O -your O -dad O -Scooter B-person -Braun I-person -, O -your O -life O -..." O -and O -got O -: O -Part O -7 O -! O -Try O -it O -: O -http://tinyurl.com/27r2pdz O - -@ChefGuyFieri O -Pls O -RT O -- O -NIGHT O -golf O -tournament O -9/25 O -to O -benefit O -3 O -year O -old O -Penny B-person -of O -Mesa B-geo-loc -, O -AZ B-geo-loc -who O -had O -heart O -transplant O -. O -www.flancers.com O -4 O -info O - -The O -end O -of O -another O -week O -for O -MTS B-company -, O - -http://sk.mu/aEnsGunwfr9 O -: O -Above&Beyond B-other -- O -Trance B-other -Around I-other -The I-other -World I-other -338 I-other -( I-other -Wippenberg I-other -Guestmix I-other -) I-other -( O -17 O -September O -2010 O -) O -#Trance O -#Trancefamily O -#TATW O - -Mac O -Sync O -Completed O -- O -September O -17 O -, O -2010 O -, O -5:00 O -pm O - -football O -game O -tonight O -! O -ahh O -im O -so O -nervous O -about O -our O -dance O -!! O - -Today O -is O -friday O -. O -Crap O -forgot O -XD O - -not O -sure O -how O -i O -feel O -about O -the O -rain O -right O -now O -... O -but O -what O -a O -wonderful O -day O -it O -has O -been O -... O -can't O -wait O -for O -later O -tonight O -!!! O - -Man O -i O -been O -eatin O -fastfood O -everyday O -im O -so O -glad O -my O -mommy O -cookin O -today O -... O - -Gotta O -call O -mom O -2 O -let O -her O -know O -it O -'s O -almost O -3 O -. O -Yli B-person -gets O -out O -in O -15 O -. O -Mom O -took O -the O -antenna O -home O -today O -& O -connected O -it O -at O -home O -, O -finally O -. O - -Chicago B-geo-loc -Weekend O -Events O -: O -Lebowski B-other -Fest I-other -, O -Dave B-person -Matthews I-person -, O -Latin O -Music O -And O -More O -: O -The O -lively O -weekend O -( O -well O -, O -Friday O -throu O -... O -http://bit.ly/cLTnyl O - -@jeffpulver O -@wildman94 O -@MusicIsGood4U O -thanks O -for O -hitting O -me O -and O -@shoewolf O -up O -. O -Let O -me O -know O -when O -that O -DM O -with O -all O -the O -info O -has O -been O -sent O -. O - -holy O -cow O -- O -I'm O -almost O -ready O -for O -sessions O -! O -Walls O -painted O -, O -lights O -hung O -, O -backdrop O -system O -up O -and O -working O -! O -Tomorrow O -... O -http://fb.me/t8hzqbAZ O - -CyberNation O -Sky B-company -3D I-company -enlists O -Stephen B-person -Fry I-person -to O -explain O -the O -technology O -ahead O -of O -its O -October O -1 O -launch O -( O -video) O -: O -With O -the O -o O -... O -http://bit.ly/aWoRmo O - -I O -just O -took O -" O -( O -ALL O -PARTS O -) O -Your O -a O -normal O -belieber O -.... O -what O -happens O -when O -you O -find O -J O -..." O -and O -got O -: O -Part O -1 O -: O -Holidays O -!! O -Try O -it O -: O -http://bit.ly/aOcpG6 O - -u O -kno O -that O -day O -when O -the O -wanted O -will O -go O -2 O -a O -radio O -station O -to O -find O -out O -if O -there O -number O -1 O -with O -heart O -vacancy O -, O -when O -will O -that O -be O -? O - -RT O -@WakaFlocka1017 O -: O -SO O -WHEN O -I O -GET O -ON O -106 O -N O -JOKE O -AROUND O -YALL O -TALK O -ABOUT O -ME O -LIKE O -A O -DOG O -SHAWTY O -BUT O -WHEN O -I O -BUY O -30,000 O -WORTH O -SCHOOL O -SUPPLY O -N O -... O - -Gonna O -for O -vocal O -solo O -on O -tuesday O -. O -T_T O - -@CWWatson O -one O -of O -the O -complainers O -is O -a O -child O -minder O -who O -brings O -kids O -to O -the O -school O -then O -looks O -after O -other O -kids O -thro O -the O -day O -. O -Beggers O -belief O - -@Wale O -I O -just O -heard O -attention O -deficit O -today O -. O -Waay O -underrated O -but O -I O -still O -like O -more O -about O -nothing O -better O - -RT O -@Mirandreamer O -: O -" O -I O -tried O -to O -walk O -into O -target B-company -yesterday O -, O -I O -missed O -." O - -Boo O -Boo O -, O -Ta O -and O -I O -had O -a O -girls O -day O -. O -She O -is O -such O -a O -wonderful O -baby O -. O - -@Beauty_NsideOut O -when O -i O -TOLD O -you O -i O -was O -goin O -back O -to O -sleep O -! O -#Crazy O - -Action O -is O -the O -foundational O -key O -to O -all O -success O -!.... O -take O -massive O -action O -today O -my O -friends O -...... O -and O -watch O -synchronicities O -happen O - -Salt O -tasting O -today O -http://yfrog.com/07kicyj O - -@_MissMarika O -haha O -I O -need O -to O -update O -my O -tan O -I O -feel O -so O -white O -. O -My O -face O -! O -I O -have O -been O -tanning O -myself O -this O -month O -, O -hurry O -up O -summer O -! O - -Can O -your O -CMS O -guarantee O -pixel-precise O -output O -when O -compared O -to O -our O -MS-Paint O -designs O -? O -#cmsretractions O - -@endlesskies O -ik O -me O -2 O -. O -At O -night O -I'm O -wide O -awake O -but O -the O -daytime O -, O -I'm O -so O -tired O -my O -day O -drags O - -@peterc83 O -night O -night O -good O -luck O -tomorrow O -x O - -it O -'s O -friday O -it O -'s O -friday O -i'm O -so O -happy O -it O -'s O -friday O -:: O -whooo O -yooo O -doing O -dance O -and O -takin O -a O -chance O -la O -la O -la O -lalala O -:: O - -@Mayuko_115 O -You're O -welcome O -! O -Yes O -, O -it O -saved O -a O -lot O -of O -time O -, O -although O -i O -still O -had O -a O -few O -twits O -i O -wanted O -to O -#ff O -that O -weren't O -on O -the O -@ffhelper O -list O - -@BEE_LYNN_BAYBEE O -Funniest O -shit O -of O -the O -day O -" O -Im O -officially O -blown O -. O -Dont O -wanna O -to O -talk O -about O -it O -" O -lls O - -RT O -@pastorbrady O -: O -Remember O -, O -every O -Sunday O -morning O -gathering O -at O -New B-facility -Life I-facility -can O -be O -watched O -live O -at O -9am O -and O -11am O -- O -www.NLCLive.com O - -@edryall O -@sjo_m O -you O -been O -23 O -for O -a O -few O -years O -now O -#really27 O - -After O -2 O -months O -not O -even O -a O -word O -. O -Guess O -ure O -fine O -.. O -thanks O -for O -being O -nothing O -but O -a O -fake O -. O -Seriously O -cycling O -? O - -Said O -it O -once O -and O -I O -will O -say O -it O -again O -, O -when O -someone O -shows O -you O -who O -they O -are O -, O -BELIEVE O -THEM O -!! O - -@TheEllenShow O -#AskEllen O -Hi O -Ellen B-person -. O -Can O -you O -ask O -Portia B-person -when O -she O -'s O -going O -to O -make O -a O -Twitter B-company -account O -? O -Love O -you O -both O -so O -much O -! O -Xo O - -The B-movie -Town I-movie -was O -a O -really O -good O -movie O -!!! O -Now O -passing O -the O -time O -watching O -" O -Easy B-movie -A I-movie -" O -until O -" O -Devil B-movie -" O -starts O -! O - -@BieberPaycee O -in O -like O -3 O -days O -I O -think O -... O -Well O -that O -'s O -when O -the O -single O -'s O -released O -:P O - -this O -is O -what O -we O -do O -when O -we O -have O -no O -picture O -ideas O -. O -http://twitpic.com/2pdzm0 O - -@lilwhitebear O -@GrouchoDuke O -I'm O -not O -kidding O -! O -I O -used O -a O -book O -which O -was O -published O -in O -2008 O -( O -last O -year) O -... O -it O -was O -an O -awesome O -book O -! O - -realy O -bored O -smack O -down O -tonite O -yay O -!! O -( O -: O -mood O -: O -bored-_- O -ha O -! O - -going O -to O -talk O -vill O -into O -gettinhg O -reach O -today O -. O -want O -it O -in O -time O -for O -sundays O -reacharound O -gamenight O - -Going O -to O -the O -country O -fair O -today O -:) O - -@demhot O -I O -hate O -it O -here O -, O -lol O -. O -i'm O -moving O -to O -america B-geo-loc -when O -i'm O -at O -least O -23 O -. O -no O -joke O - -im O -sick O -had O -a O -bad O -day O -hope O -thee O -game O -us O -good O - -foi O -quando O -eu O -te O -encontrei O -, O -ouvindo O -o O -som O -do O -mar O -( O -8 O - -I O -went O -to O -old B-company -navy I-company -today O -and O -bought O -two O -pairs O -of O -pants O -. O -Now O -I O -have O -three O -pairs O -of O -pants O -! O - -Ha O -ha O -i O -try O -for O -you O -really O -hm O -lmao O -lame O -pretty O -day O - -@HotwifeKay O -after O -Big O -Maxxx O -@killerCram O -" O -left O -his O -mark O -" O -on O -my O -hottie O -SlutWife O -: O -http://tinyurl.com/25gxuvq O - -@SweetTartelette O -Glad O -to O -hear O -they O -are O -okay O -. O -A O -drunk O -ran O -into O -the O -back O -of O -my O -son O -'s O -car O -this O -week O -. O -He O -said O -it O -was O -frightening O -. O -N O - -@IZONModels O -we O -all O -need O -it O -from O -time O -to O -time O -. O -:) O - -#uknouugly O -when O -#TeamFollowBack O -dnt O -follow O -you O -back O - -@jeffstinco O -or O -@chuckcomeau O -follow O -me O -for O -make O -this O -bad O -day O -a O -HAPPY O -day O -:D O -love O -you O -guys O -!!! O - -@sarahXtinchyBBK O -looool O -u O -hav O -made O -me O -want O -mcdz O -again O -an O -im O -tryin O -2 O -stay O -off O -that O -had O -it O -all O -week O -an O -sounds O -nice O -:D O -x O - -... O -around O -all O -day O -isnt O -good O -for O -you O -, O -least O -i O -get O -home-relaxing O -time O -, O -thursday O -and O -frees O -to O -keeep O -me O -sane O -:) O - -RT O -@PHANTHANHstic O -" O -Good O -Morning O -! O -Have O -a O -pleasant O -and O -happy O -day O -!" O - -Hey O -.... O -my O -birthday O -is O -in O -exactly O -one O -month O -!!! O -:D O - -@KaiyhaBADDass O -for O -you O -? O -when O -I O -see O -you O -... O -seriously O -... O - -@Romanthropy O -oi O -u O -there O -'s O -a O -#MCRchat O -on O -tonight O -. O -No O -idea O -what O -it O -entails O -but O -if O -it O -'s O -new O -album O -convos O -were O -in O -!! O - -miren O -el O -video0 O -one O -time O -.... O -ESTA O -SIENDO O -ATACADO O -X O -LOS O -NO O -ME O -GUSTA O -D O -: O - -RT O -@chloezxy O -: O -had O -a O -great O -time O -hanging O -out O -and O -dinner-ing O -with O -DL3 O -peeps O -!! O -:) O -they O -are O -really O -awesome O -people O -!! O -:D O - -im O -thinking O -jalepeno O -poppers O -tonight O -:] O -] O - -Today O -is O -goin O -to O -slow O -at O -work O -... O -Can't O -wait O -to O -get O -this O -weekend O -started O -!!! O - -@johngirvin O -you've O -been O -working O -on O -that O -all O -night O -! O -I O -suspect O -you O -may O -even O -have O -opened O -a O -git B-product -manual O -. O - -@joereist O -hey O -Joe B-person -. O -I'm O -down O -the O -road O -in O -Ft B-geo-loc -Mitchell I-geo-loc -. O -No O -time O -to O -smoke O -but O -waving O -. O -Did O -you O -get O -the O -book O -? O - -Congrats O -to O -@BigBlueSpruce O -and O -their O -record O -release O -today O -! O -Join O -us O -to O -celebrate O -with O -them O -at O -@worldcafelive O -Get O -your O -CD O -& O -tees O -8PM O -today O -! O - -Dearrykas O -#1 O -rule O -of O -lifenever O -date O -someone O -who O -lives O -by O -you O -it O -only O -causes O -troublr O -when O -you O -break O -up O - -FRIDAY O -! O -yesshh O -!! O -FiNaLlY O - -Just O -ordered O -pizza O -. O -First O -time O -since O -May O -to O -order O -Pizza B-company -Hut I-company -. O -Definitely O -not O -a O -fave O -, O -but O -Pizza O -by O -the O -Chef O -doesn't O -deliver O -five O -hours O -away/ O - -Drop O -by O -the O -sweat O -check O -I'll O -be O -there O -volunteering O -RT O -@EricJacksch O -Ah O -, O -the O -weekend O -. O -See O -you O -at O -the O -Army B-other -Run I-other -on O -Sunday O -? O -#Ottawa O - -RT O -@TCPB_McDreamy O -: O -Gonna O -study O -for O -a O -while O -before O -iTake O -this O -quiz O -... O -wish O -me O -luck O -=) O -( O -luck O -) O - -This O -week O -only O -$5 O -off O -custom O -blogger O -design O -! O -http://www.bonanzle.com/LoisCollis O - -@sfgiantsfan55 O -omg O -Todo B-person -Cabio I-person -has O -been O -my O -fav O -since O -like O -`07 O -i O -still O -listen O -to O -their O -CD O -all O -the O -time O -i O -want O -the O -new O -one O -. O -alejate O -de O -mi O -... O - -I O -know O -u O -like O -that O -turn O -you O -into O -a O -star O -I O -got O -it O -like O -that O -baby O -don't O -fight O -it O -cus O -when O -I O -miss O -your O -call O -I O -hit O -u O -right O -back O - -@HackinTimSeeley O -A O -world O -without O -bare O -legs O -is O -a O -terrible O -place O -indeed O -! O -Here O -'s O -to O -6+ O -more O -years O -of O -bare O -legged O -cassie O - -RT O -@ewangri O -: O -It O -would O -be O -lovely O -if O -I O -could O -get O -on O -my O -laptop O -and O -do O -some O -work O -before O -I O -go O -out O -, O -but O -@emmams88 O -is O -flirting O -with O -some O -boy O -. O - -Was O -thinking O -today O -that O -I O -am O -fortunate O -o O -know O -so O -many O -incredible O -people O -in O -this O -world O -. O - -This O -is O -also O -the O -time O -of O -year O -where O -@jaketapper O -'s O -kittehs O -are O -all O -, O -" O -U O -FASTIN O -? O -LOLZ O -, O -WE O -IZ O -UP O -IN O -OUR O -FOODZ O -." O -#caturday O -#feedthesenses O - -Belief O -happens O -for O -most O -from O -the O -" O -gut'-Oh O -there O -may O -be O -some O -mental O -stimulus O -.. O -But O -feeling O -often O -has O -more O -to O -do O -with O -it O -@WestonLockley O - -RT O -@AwwBieber O -: O -Boyfriend O -: O -Ive O -got O -dinner O -planned O -for O -Valentines B-other -Day I-other -. O -Me O -: O -Sorry O -, O -i O -wont O -have O -time O -for O -u O -or O -your O -dinner O -..... O -Im O -Watching O -Bie O -... O - -@Sexstrology O -lol O -im O -a O -sargittarius O -so O -dat O -means O -i O -wanna O -have O -a O -good O -time O -2 O -?? O - -@FLAM3Z130 O -half O -the O -time O -in O -inglewood B-geo-loc -the O -other O -half O -in O -mv B-geo-loc -... O -where O -u O -been O - -wake O -me O -up O -when O -I O -have O -a O -reason O -to O -stay O -up O -! O - -@ZachGarmoe O -AH O -! O -Concert O -in O -2 O -days O -! O -So O -excited O -! O - -Okay O -, O -I O -now O -don't O -have O -plans O -for O -tonight O -. O -Let O -me O -know O -if O -you O -wanna O -hang O -:) O - -@saarbei O -ooh O -! O -as O -long O -as O -food O -was O -had O -to O -celebrate O -. O -you O -HAVE O -to O -celebrate O -all O -occasions O -with O -good O -food O -! O - -5:59 O -and O -the O -train O -comes O -to O -a O -halt O -. O -Here O -we O -go O -again O -I O -do O -not O -think O -so O -. O -There O -'s O -sun O -this O -time O -! O -What O -'s O -the O -deal O -, O -LIRR O -? O -OK O -, O -back O -on O -the O -move O -. O - -Work O -from O -Home O -try O -a O -FREE O -90 O -Day O -Membership O -, O -We O -have O -Clients O -waiting O -for O -You O -at O -: O -http://www.dewpointe.com/JoinMyAppointmentSetter O - -The O -#Forex O -Revolution O -http://bit.ly/cLjJPO O -Jump O -onboard O -and O -earn O -60% O -commission O -today O -! O -Hot O -#Earner O -http://bit.ly/cL O -... O -http://sns.ly/Kfl55 O - -Mike B-person -vick O -back O -in O -dat O -line O -up O -its O -bout O -time O - -@darrencyrus O -ee O -it O -wasnt O -lambrini O -actually O -LOL O -:) O -was O -posh O -proper O -wine O -my O -mam O -got O -today O -from O -work O -off O -someone O -, O -it O -was O -RANK O -down O -the O -sink O -LOL O - -I O -had O -a O -good O -day O -:) O -, O -how O -y'all O -doing O -? O - -It O -'s O -about O -that O -time O -! O - -@CJ___ O -you'll O -see O -when O -you O -catch O -up O -in O -the O -thread O -. O -He O -was O -bitching O -, O -I O -asked O -if O -he'd O -be O -happier O -in O -another O -league O -and O -he O -said O -replace O -him O - -@Kybil O -Hmm O -... O -I O -probably O -would O -. O -I O -don't O -know O -if O -I O -can O -find O -a O -visual O -for O -R'lyeh, B-other -though O -. O -I O -may O -have O -to O -bust O -out O -my O -copy O -of O -the O -story O -... O - -#np O -never O -too O -late O -- O -three O -days O -grace O -< O -3 O - -U O -shoulda O -known O -! O -Its O -a O -Friday O -! O -RT O -@BoRnStar88 O -Tutoring O -ppl O -all O -day O -at O -work O -on O -a O -Friday O -is O -already O -a O -bore O -then O -the O -students O -don't O -show O -up O -!!. O - -O O -happy O -day O -. O -My O -local O -grocery O -carries O -Hoegaarden B-product -. O - -After B-product -Eventide I-product -( O -Kindle B-product -Edition)By O -Catherine B-person -Olson I-person -Buy O -new O -: O -$ O -6.49 O -Customer O -R O -... O -http://tinyurl.com/25e59fk O -#twilight O - -cuz O -this O -isnt O -a O -confusing O -phrasing O -of O -this O -... O -LOL O -r O -u O -busy O -the O -next O -day O -? O -;) O -RT O -@DEWsAtlaWinner O -: O -@JRKandDDubLuva O -l O -busy O -day O -the O -next O - -Catching O -up O -with O -old O -friends O -is O -sound O -haven't O -seen O -ben O -for O -like O -4 O -years O - -RT O -@jeyyounit11 O -: O -i O -was O -watching O -all O -the O -footage O -on O -my O -camera O -from O -the O -past O -few O -months O -and O -if O -i'd O -actually O -finish O -what O -i O -was O -talking O -abou O -... O - -is O -going O -to O -be O -at O -HoveLive B-other -Festival I-other -tomorrow O -in O -Belgium B-geo-loc -! O -http://fb.me/v5CUZkEY O - -RT O -@CB_Gorka O -: O -Here O -'s O -the O -video O -for O -tonight O -'s O -Cahill B-geo-loc -event O -: O -http://youtu.be/KX5jNnDMfxA O -#magov O - -@SaviiISJAPAN O -nooo O -- O -i O -have O -when O -people O -ask O -me O -so O -i O -dont O -ask O -people O -lool O -. O - -#plan O -Land B-other -of I-other -Talk I-other -- O -Minneapolis B-geo-loc -( O -First O -Avenue O -and O -7th O -St O -Entry O -) O -Wed O -, O -Sep O -29 O -, O -2010 O -http://planca.st/AwQ O - -Today O -= O -Morton B-product -'s I-product -Salt I-product -motto O -+ O -Murphy B-other -'s I-other -Law I-other - -RT O -@TechZader O -: O -iPad B-product -Refresh O -Due O -in O -January O -http://bit.ly/cw730s O - -@number2jake O -wanna O -go O -to O -Liffey B-geo-loc -valley O -tomorrow O -? O -:) O -< O -3 O - -@elwxnino O -@clausw O -@ManDee4 O -The O -#comcast O -outages O -have O -been O -increasing O -for O -the O -last O -year O -and O -yet O -the O -prices O -haven't O -gone O -down O -. O - -It O -'s O -still O -Friday O -here O -so O -my O -first O -#FF O -goes O -to O -@rockmybones O -- O -its O -her O -birthday O -today O -and O -@Hungryforbones O -- O -thank O -you O -:) O - -RT O -@medeadly O -: O -50 B-person -tyson I-person -droppin O -a O -video O -before O -you O -bumass O -rappers O -.... O -i O -bet O -they O -hella O -mad O -... O -yall O -niggas O -been O -rappin O -for O -years O -and O -still O -a O -... O - -RT O -@anabekoa O -: O -http://plixi.com/p/45606791 O -You O -may O -say O -that O -I'm O -a O -dreamer O -But O -I'm O -not O -the O -only O -one O -I O -hope O -someday O -you'll O -join O -( O -cont O -) O -http O -... O - -REFINERY29 O -STALKS O -LA'S O -MOST O -FASHIONABLE O -FEMMES O -FROM O -LAST O -WEEK O -VB'S O -PARISIAN O -BREAKFAST O -. O -MERCI O -, O -MERCI O -! O -XX O -http://ow.ly/2G4KX O - -I O -hate O -it O -when O -my O -[ O -HONK O -! O -] O -gets O -stuck O -in O -my O -[ O -WHOOPWHOOP] O -. O - -Season O -opener O -for O -Bradley B-sportsteam -Soccer I-sportsteam -tonight O -! O - -Getting O -ready O -, O -& O -then O -out O -for O -the O -nighttttt O -:) O -With O -my O -boyfriend O -tomorrow-Sunday O -< O -3 O - -what O -happens O -when O -your O -happy O -but O -heartbroken O -? O -this O -. O - -Heading O -to O -Herbivore B-facility -tonight O -for O -dinner O -for O -the O -first O -time O -. O -Any O -favorites O -or O -must O -trys O -? O - -@evarley O -Yes O -, O -thanks O -. O -We're O -more O -traumatised O -than O -he O -is O -... O -;) O -I O -hate O -A&E B-other -< O -shudders O -> O -Now O -it O -'s O -time O -to O -catch O -up O -with O -the O -day O -... O - -i O -just O -woke O -up O -from O -the O -worst O -nap O -eva O -.. O -arggghhh O -! O -now O -its O -time O -for O -work O -.. O -double O -arggghhh O -!! O - -When O -TIFF B-other -is O -in O -Tdot B-geo-loc -, O -ALWAYS O -have O -ur O -camera O -in O -purse O -! O -Managed O -to O -get O -a O -pic O -of O -EVA B-person -MENDES I-person -! O -Didn't O -know O -she O -was O -signing O -autographs O -@ O -the O -mall O -. O - -Does O -anyone O -else O -hate O -when O -authors O -spend O -the O -first O -half O -of O -each O -book O -chapter O -telling O -you O -what O -the O -rest O -of O -the O -chapter O -is O -going O -to O -be O -about O -? O - -RT O -@KaiWayne O -: O -I O -think O -Big O -Sam B-person -was O -misquoted O -when O -he O -said O -he O -could O -manage O -Real B-sportsteam -. O -What O -he O -actually O -said O -was O -he O -could O -manage O -a O -real O -ale O -. O - -#NowPlaying O -Tomorrow O -always O -comes O -- O -@samantharonson O -#ILikeIt O -#NiceSong O -( O -: O - -@SpankyDuhh O -well O -same O -thing O -goes O -to O -me O -duhh O -i O -need O -up O -to O -800 O -more O -follower O -before O -i O -drop O -my O -album O -... O - -Wats O -good O -for O -tonight O -any O -partys O - -@suggsygirl O -Have O -a O -lovely O -day O -and O -take O -lots O -of O -inappropriate O -photos O -! O -*bg* O - -Rsn O -out O -of O -gas O -. O -Walking O -to O -the O -gas O -station O -this O -time O -. O -This O -is O -supposed O -to O -happen O -!!! O - -'RT O -@amandacarolinne O -: O -" O -Danger B-other -Days I-other -: I-other -The I-other -True I-other -Lives I-other -Of I-other -The I-other -Fabulous I-other -Killjoys I-other -"' O - -I O -hate O -when O -people O -ask O -me O -" O -what O -are O -you O -, O -like O -your O -race O -?" O -cuz O -I O -don't O -be O -knowing O -what O -to O -say O -. O -I O -just O -say O -I'm O -black O -and O -stuff O -. O -lol O - -watching O -camp B-movie -rock I-movie -2 I-movie -again O -... O -all O -the O -way O -through O -this O -time O -:P O -. O - -'RT O -@ItsTheTeenLife O -: O -People O -that O -txt O -back O -" O -K O -." O -20 O -minutes O -after O -the O -convo O -already O -ended O -. O -Three O -words O -for O -you O -[ O -F%#* O -- O -You O -- O -Loser O -] O -#Itsthe O -...' O - -Well O -, O -after O -a O -fast-paced O -week O -full O -of O -prayer O -from O -family O -and O -friends O -, O -I O -now O -have O -a O -full O -time O -job O -and O -I O -pretty O -much O -start O -Monday O -:-) O - -Beginners O -Guides O -to O -Blogging O -and O -Twitter B-company -: O -Two O -amazing O -products O -from O -Mike B-person -'s O -Life O -. O -The O -60 O -day O -Beginner O -'s O -Guide O -to O -Bl O -... O -http://bit.ly/99wlho O - -@_AlexLight_ O -WHAAAA O -? O -And O -you O -actually O -played O -? O -When O -it O -wasn't O -with O -the O -Wolstenbeast O -?! O - -@LightCMS O -over O -6000 O -followers O -! O -Told O -you O -, O -you O -would O -get O -a O -lot O -, O -you O -had O -about O -2000 O -on O -the O -first O -day O -. O -Less O -and O -less O -likely O -I O -will O -win O -lol O - -Dylan B-person -refuses O -to O -take O -a O -nap O -and O -he O -was O -up O -before O -7am O -, O -now O -he O -will O -crash O -late O -and O -stay O -up O -half O -the O -night O - -@KSSchro O -When O -I O -run O -over O -an O -animal O -I O -think O -of O -Disney B-company -movies O -& O -that O -I O -just O -ran O -over O -someones O -brother O -, O -mom O -or O -dad O -. O -The O -full O -life O -montage O -helps O - -RT O -@refuel_bar O -: O -Annual O -Whole B-other -Hog I-other -Dinner I-other -Wed O -Oct O -27 O -; O -3 O -seatings O -, O -communal O -tables O -, O -family O -style O -$49 O -; O -http://bit.ly/62Gs3X O -for O -details O -. O - -oh O -and O -by O -the O -way O -i O -don't O -want O -to O -leave O -tomorrow O -:( O - -@Vivianna_loves O -I O -just O -bought O -Dior B-product -mascara O -for O -the O -first O -time O -..... O -its O -the O -only O -mascara O -that O -doesn't O -make O -me O -wanna O -wear O -lashes O -. O - -I've O -never O -laughed O -so O -much O -in O -my O -entire O -life O -as O -i O -have O -tonight O -. O -my O -eyes O -are O -watering O -, O -it O -hurts O -to O -move O -and O -i O -actually O -fell O -off O -my O -chair O -once O - -/Lebowski'd O -RT O -@RebellionPR O -: O -When O -I'm O -pope B-person -the O -first O -thing O -I'm O -going O -to O -do O -is O -shit O -in O -the O -woods O -. O -#pope O - -@thedgoddess O -I O -can O -be O -... O -when O -I O -want O -to O -be O -. O -RT O -@thedgoddess O -: O -You O -are O -so O -benevolent O -, O -oh O -kind O -master O -. O - -RT O -@DevoRiEr O -: O -Aye O -@Jus1Nyt O -how O -about O -u O -catch O -a O -red O -eye O -out O -tonight O -so O -u O -can O -come O -out O -tonight O -? O -LMaO O - -@halbpro O -it O -'s O -simple O -to O -get O -two O -films O -worth O -of O -filming O -done O -in O -a O -year O -. O -3 O -pushes O -it O -a O -bit O -, O -but O -is O -possible O -. O -4 O -is O -on O -the O -limit O -. O -depends O -though O - -You O -Get O -What O -You O -Give O -~ O -Zac B-musicartist -Brown I-musicartist -Band I-musicartist -40 O -days O -in O -the O -top O -100 O -Release O -Date O -: O -September O -21 O -, O -2010Buy O -new O -: O -$ O -18.98 O -$ O -... O -http://amzn.to/9Cfkpc O - -This O -time O -tomorow O -i'll O -be O -in O -Taorminaa B-geo-loc -:D O -DD O -^^ O - -hmm O -, O -I O -always O -get O -a O -bit O -scared O -when O -I O -update O -my O -Blackberry B-company -.... O - -Good O -day O -today O -:) O -) O - -What O -the O -:\ O -after O -tweeting O -' O -monkey O -donkey O -' O -my O -first O -account O -got O -limit O -#twitterjail O - -Only O -about O -1/3 O -done O -w/ O -ch O -6 O -Looks O -like O -those O -of O -u O -who O -told O -me O -u O -still O -need O -to O -catch O -up O -will O -have O -time O -2 O -Will O -get O -to O -review O -replies O -soon O -!!! O - -@lehoop18 O -not O -going O -out O -tonight O -, O -but O -I O -love O -you O -. O -productive O -room O -cleaning O -being O -done O -! O - -Maybe O -if O -I'd O -told O -you O -the O -right O -words O -At O -the O -right O -time O -You'd O -be O -mine O - -Added O -a O -few O -people O -tonight O -so O -if O -I O -added O -you O -i'm O -not O -a O -stalker O -, O -just O -following O -@scotsman7 O -amd O -his O -#FF O -ty O -:) O - -'ive O -been O -driving O -all O -over O -the O -place O -today O -and O -this O -is O -the O -7th O -time O -ive O -heard O -" O -dynamite B-other -" O -on O -the O -radio O -. O -#loveit O -though' O - -RT O -@papalote415 O -: O -Homemade O -PAPALOTE O -Vegan O -Soyrizo O -Tamales O -and O -Soyrizo O -Tamales O -at O -OFF B-facility -the I-facility -GRID I-facility -Fort B-facility -Mason I-facility -at O -5PM O -TODAY O -!! O -< O -< O -salsa O -, O -salsa O -, O -my O -fav O - -I O -fully O -expect O -that O -my O -shrimp O -will O -be O -delicious O -tonight O -, O -if O -not O -extra O -buttery O -from O -the O -oil O -spills O -. O -#redlobster O - -RT O -@Queen_UK O -: O -Having O -repaired O -500 O -years O -of O -religious O -division O -and O -set O -an O -example O -of O -tolerance O -and O -leadership O -to O -the O -Christian O -world O -, O -one O -... O - -RT O -@Softscrub O -: O -This O -week O -'s O -#FF O -has O -started O -! O -Follow O -us O -for O -a O -chance O -to O -win O -a O -free O -bottle O -of O -#SoftScrub O -! O - -@iFckd_Hannah O -wow O -, O -sounds O -like O -ur O -pretty O -busy O -these O -days O -... O -twitpic B-company -ur O -new O -haircut O -hehe O -:) O -so O -ur O -havin O -a O -relaxin O -friday O -, O -no O -parties O -? O - -I O -spent O -my O -day O -Photoshopping O -Kerri B-person -russel I-person -into O -a O -mutant O -headed O -tree O -hugger O -, O -Now O -that O -thats O -done O -. O -Im O -off O -to O -my O -late O -night O -job O -, O -Sittin O -' O -BABIES O - -lalala O -south O -game O -tonight O -!!!! O -Go O -us O -. O -http://bit.ly/b351o9 O - -yupp O -! O -RT O -@TrentShelton O -have O -u O -ever O -met O -some1 O -that O -when O -u O -tell O -a O -story O -THEY O -HAVE O -to O -tell O -their O -story O -2 O -overshadow O -yours O -to O -feel O -superior O - -Yes O -sir O -, O -@Digeratii O -is O -on O -a O -customer O -service O -roll O -today O -. O -Good O -man O -that O -Josh B-person -. O - -Beer O -after O -golf O -?? O -Give O -me O -a O -hell O -yeah O -!! O -(@ O -White B-facility -Eagle I-facility -) O -http://4sq.com/b5Wloc O - -RT O -@JodyModel O -: O -#Blackholidays O -Pookie O -Release O -From O -Prison O -Day O - -@wellydogdesigns O -aww O -shame O -he O -'s O -not O -full O -of O -beans O -too O -! O -ah O -well O -, O -as O -long O -as O -you're O -having O -fun O -;-) O -hmm O -I O -fancy O -a O -drink O -now O -:) O -) O - -today O -everything O -will O -gonna O -be O -okay O -, O -i O -know O -it O -. O -Haaaa O -, O -the O -secret O -. O -I O -know O -I O -know O -it O -, O -I O -will O -meet O -very O -much O -cool O -people O -. O -Yes O -yes O -yes O -. O -IKNOWIT O - -'::: O -Flashback O -Friday O -- O -" O -Dancing B-other -Queen I-other -" O -http://bit.ly/d49VXU' O - -RT O -@HeyThatsSoTrue O -: O -When O -I O -was O -little O -, O -I O -had O -a O -huge O -imagination O -. O -Now O -I O -can't O -even O -make O -up O -a O -story O -for O -english O -class O -. O -#HeyThatsSoTrue O - -I O -KNEW O -IT O -RT O -@stuartwashere O -: O -Awh O -... O -@AaronShoutNever O -still O -manages O -to O -be O -nice O -when O -he O -'s O -drunk O -. O -: O -L O - -@diggy_simmons O -#diggyfollows50 O -diggy B-person -should O -follow O -me O -because O -i O -saw O -him O -at O -@justinbieber O -'s O -concert O -on O -september O -third O -:D O - -We O -bought O -Riley B-person -a O -onesie O -today O -. O -:) O - -@daraobriain O -hmmm O -. O -Cant O -wait O -. O -Comin O -on O -Thursday O -. O -First O -time O -to O -the B-facility -Apollo I-facility -. O - -Well O -, O -I O -was O -gonna O -buy O -a O -Zune B-product -HD I-product -... O -but O -now O -that O -the O -@Rally4Sanity O -exists O -, O -I O -must O -go O -to O -D.C. B-geo-loc -next O -month O -. O -YESSSSSS O -. O - -So O -I'm O -off O -to O -the O -Mumpreneur B-other -Awards I-other -tomorrow O -and O -leaving O -daughter O -@itsonlymestupid O -to O -talk O -to O -press O -& O -buyers O -on O -our O -stand O -@LondonFashionWk O - -I'm O -trying O -to O -figure O -out O -if O -I O -wanna O -do O -something O -tonight O - -now O -my O -instructor O -says O -that O -when O -he O -picks O -me O -up O -I'm O -just O -gonna O -get O -straight O -into O -the O -drivers O -seat O -! O -: O -L O -I O -think O -I'll O -cope O -!! O -lol O -x O - -@highs_are_low O -cause O -I O -was O -too O -busy O -singing O -my O -heart O -out O -and O -all O -. O -It O -'s O -hard O -to O -go O -crazy O -when O -you're O -trying O -to O -record O -something O -:p O - -td O -bem O -que O -as O -meninas O -do O -outro O -time O -eram O -mt O -brutas O -e O -cavalas O -, O -maais O -foi O -bom O -, O -porq O -. O -ganhamos O - -RT O -@Sexstrology O -: O -How O -many O -Sagittarians O -does O -it O -take O -to O -change O -a O -lightbulb O -? O -The O -sun O -is O -shining O -, O -the O -day O -is O -young O -and O -youre O -worrying O -about O -... O - -r O -rated O -http://bit.ly/aGV3kX O -Weekend O -Movie O -News O -Wrap O -Up O -: O -July O -11 O -, O -2010 O -Screen O -Rant O - -Got O -my O -office O -all O -reranged O -from O -how O -I O -put O -it O -yesterday O -! O -=) O - -@stephen_james O -May O -I O -ask O -what O -is O -it O -with O -your O -severe O -hatred O -for O -the O -word O -' O -bro'? O - -@FashionDivvah O -I O -know O -right O -.. O -he O -'s O -my O -baby O -cos O -he O -'s O -way O -younger O -than O -me O -... O -like O -4 O -years O -junior O -... O -awwww O -I O -had O -to O -sharply O -spill O -! O -LOL O -! O - -RT O -@tabloidwatch O -: O -Daily B-company -Mail I-company -'s O -not-at-all O -predictable O -front O -page O -headline O -tomorrow O -: O -Pope B-person -'s O -battle O -to O -save O -Christmas B-other -. O - -I'm O -almost O -halfway O -through O -my O -#cleaneating O -challenge O -! O -http://bit.ly/9mVsCQ O -Getting O -takeout O -for O -the O -first O -time O -tonight O -- O -what O -to O -get O -?! O - -Tarantula O -in O -my O -lounge O -last O -night O -, O -hoping O -i O -dont O -get O -a O -repeat O -tonight O - -Chevron B-company -gouging O -consumers O -by O -claiming O -payments O -are O -late O -when O -in O -reality O -statement O -delivery O -is O -very O -poor O -turnaround O -time O -. O -charging O -hefty O -fee O -'s O - -DING O -DONG O -DING O -DONG O -guess O -who O -'s O -comin O -dinner O -tonight O -? O -U O -gonna O -be O -part O -of O -my O -meal O -tonight O -....... O - -@TylerHilton O -One O -thing O -about O -twitteris O -, O -We O -all O -have O -plenty O -of O -time O -... O -LOL O -:-) O - -@jaredleto O -i O -saw O -chelsea B-person -today O -u O -weren't O -there O -? O -WTF O - -#ff O -@eops O -to O -cheer O -him O -up O -, O -he O -'s O -not O -feeling O -so O -hot O -today O -. O -(and O -send O -him O -some O -virtual O -soup O -) O - -Is O -ur O -date O -interested O -in O -a O -hook-up O -or O -long-term O -relationship O -? O -Answer O -lies O -in O -what O -he O -'s O -looking O -at-your O -face O -or O -your O -bod O -! O -http://ow.ly/2FPLk O - -Jupiter B-geo-loc -: O -Closest O -Approach O -in O -Nearly O -50 O -years O -. O -Catch O -it O -nxt O -week O -. O -It O -wnt O -B O -that O -big O -or O -bright O -again O -til O -2022 O -http://bit.ly/d5Bzx7 O - -An O -Entire O -Facebook B-company -Photo O -Album O -Captured O -in O -a O -Single O -Long O -Exposure O -Photograph O -[ O -Photography] O -: O - -@Carr0t O -aye O -been O -tonight O -- O -excellent O - -@bornofosichris O -ok O -I'm O -gonna O -fb O -message O -you O -deets O -when O -I O -get O -a O -chance O -! O - -game O -tonight O -!!!!!! O - -I O -haven't O -driven O -to O -bc B-geo-loc -in O -years O -, O -and O -I O -am O -just O -stunned O -by O -how O -beautiful O -the O -drive O -is O -. O - -@GottaLaff O -don't O -do O -the O -crime O -if O -you O -can't O -serve O -the O -time O -. O -;) O - -eh O -? O -horner O -'s O -on O -the O -#uspro O -start O -list O -: O -RT O -@hornerakg O -: O -Heading O -to O -@clarkscornerca O -for O -Q&A O -. O -Back O -in O -Ione B-company -to O -ride O -tomorrow O -! O -http://ow.ly/2G4ti O - -new O -office O -furniture- O -check O -, O -paintings O -of O -hubby- O -check O -, O -neuro O -drink O -stock- O -check O -, O -finished O -giving O -interview- O -check O -. O -time O -2 O -go O -home O -. O - -The O -week O -ahead O -- O -http://newzfor.me/?7jfc O - -G'mar O -chatima O -tova O -, O -internet O -! O - -'RT O -@ihatequotes O -: O -For O -anyone O -having O -a O -bad O -day O -, O -just O -remember O -: O -" O -If O -God B-person -brings O -you O -to O -it O -, O -He O -will O -bring O -you O -through O -it O -!" O --@KimKardashian O -#i O -...' O - -@Alleycat17 O -HAPPY O -BIRTHDAY O -! O -:D O -. O -Hope O -you're O -having O -an O -amazingrific O -day O -!!! O -:D O -. O - -RT O -@LUNGevity O -: O -This O -is O -going O -to O -be O -an O -amazing O -weekend O -w/3 O -events O -on O -the O -same O -day O -benefiting O -lung O -cancer O -research O -( O -Sun/Sept O -19 O -) O -!... O -http O -... O - -ellwood B-person -'s O -sushi O -, O -a O -glass O -of O -pinot O -, O -" O -strokes B-other -of I-other -genius I-other -" O -by O -john B-person -wertheim I-person -, O -play O -at O -barksdale B-facility -in O -a O -bit O -, O -lovely O -friday O -night O -:) O - -RT O -@Kiara_TheGR8 O -: O -Someone O -please O -wake O -me O -up O -when O -May O -7 O -, O -2011 O -gets O -here O -! O -#thanks O - -@henryandfriends O -ooh O -that O -sounds O -lovely O -:) O -we O -had O -lots O -sunshine O -2- O -hope O -it O -stays O -4 O -tomo O -when O -we O -going O -to O -do O -the O -doggie O -show O -*paws O -x-d* O -! O -x O -x O - -Haven't O -tweeted O -since O -june O -wow O -....................... O -but O -I O -did O -get O -a O -facebook B-company -so O -who O -cares O -about O -twitter B-company - -'I O -just O -took O -" O -Justin B-person -Bieber I-person -takes O -2 O -months O -off O -career O -to O -finish O -school O -, O -H O -..." O -and O -got O -: O -Part O -25 O -: O -They O -got O -my O -back O -.! O -Try O -it O -: O -http://bit.ly/bcqNCs' O - -So O -here O -in O -Belgium B-geo-loc -its O -now O -7minutes O -before O -midnight O -,, O -Tell O -me O -how O -late O -it O -is O -were O -you O -live O -:D O - -RT O -@eljmayes O -: O -Ladbrokes O -Labour O -Leadership O -Market- O -http://bit.ly/cD3Rn8 O -Ed B-person -Miliband I-person -'s O -odds O -have O -shortened O -significantly O -in O -the O -last O -week O -. O - -Ha O -I'm O -lucky O -. O -I O -got O -in O -the O -car O -like O -five O -seconds O -before O -it O -started O -POURING O -rain O -. O - -Always O -kinda O -awkward O -when O -you O -get O -the O -chat O -group O -wrong O -in O -WoW B-product -. O -Sometimes O -you O -don't O -want O -the O -tank O -to O -know O -you're O -dissin O -' O -him O -. O -Oops O -. O - -he O -likes O -prince B-person -, O -paul B-person -simon I-person -, O -and O -o.d.b. B-person -i O -may O -have O -found O -my O -soul O -mate O -. O - -'@stephaniebriggs O -Jumped O -in O -the O -truck O -, O -flipped O -the O -iPod B-product -over O -to O -shuffle O -, O -and O -it O -gives O -me O -" O -goodbye B-other -moon I-other -". O -Probably O -a O -year O -since O -last O -heard O -it O -!' O - -RT O -@rainnwilson O -: O -Hey O -, O -Seattle-ites O -! O -Check O -it O -: O -http://www.officetally.com/rainn-wilson-and-friends-seattle-oct-23 O - -Think O -the O -article O -should O -be O -ready O -to O -resubmit O -next O -week O -. O -Quite O -excited O -, O -but O -also O -rather O -scared O -. O - -RT O -@ezraklein O -: O -It O -'s O -Yom B-other -Kippur I-other -eve O -, O -so O -it O -'s O -the O -time O -to O -apologize O -to O -anyone O -I've O -wronged O -. O -Hopefully O -, O -you're O -on O -Twitter B-company -. O -Sorry O -! O - -FREE O -: O -Air B-product -Music I-product -Jump I-product -. O -if O -you O -like O -Rock B-product -Band I-product -or O -Guitar B-product -Hero I-product -, O -you O -may O -also O -like O -Music B-product -Jump I-product -. O -http://appj.mp/AirMusicJump O - -@Cortney_plus2 O -ding O -ding O -ding O -! O -front O -, O -back O -and O -let O -'s O -just O -say O -I'll O -be O -putting O -soph O -'s O -hair O -in O -a O -bun O -before O -the O -next O -nap O -time O -. O - -Back O -at O -work O -, O -waiting O -for O -Sunday O -when O -Mike B-person -Vick I-person -get O -his O -shine O -on O -Vs O -. O -the B-sportsteam -Lions I-sportsteam - -Conflicts B-product -of I-product -Law I-product -: I-product -Cases I-product -and I-product -Materials I-product -: O -R O -. O -Lea B-person -Brilmayer I-person -, O -Jack B-person -L I-person -. O -Goldsmith[May O -01 O -, O -2011 O -] O -http://amzn.to/bnabQu O - -What O -a O -day O -< O -3 O - -@Kianerzzz O -showering O -sleeping O -and O -tanning O -always O -make O -me O -feel O -better O -when O -I'm O -sick O -. O - -Hope O -to O -see O -a O -lot O -of O -people O -at O -game O -night O -at O -@gathering_umc O -tonight O -. O -Plenty O -of O -games O -to O -choose O -from O -just O -from O -my O -family O -. O -:) O - -RT O -@jakeandamir O -: O -If O -you O -would O -have O -told O -me O -20 O -years O -ago O -that O -YOM B-other -KIPPUR I-other -would O -be O -trending O -on O -twitter B-company -I'd O -be O -all O -like O -" O -what O -'s O -twitter B-company -?" O - -so O -much O -for O -trying O -to O -do O -my O -h/w O -before O -my O -bday O -weekend O -. O -working O -til O -close O -:/ O - -RT O -@its_QuiB O -@jperiod1015 O -cooking O -next O -week O -!!: O -)) O -< O -> O -lol O -bet O -! O - -RT O -@Quotealicious O -: O -A O -guy O -& O -girl O -can O -be O -friends O -. O -But O -sooner O -or O -later O -, O -one O -will O -fall O -for O -the O -other O -. O -Maybe O -too O -early O -, O -maybe O -too O -LATE O -or O -may O -... O - -@P_Rezzphere O -: O -uh O -... O -Fruit O -this O -time O -.. O -Gotta O -stay O -in O -shape O -my O -brotha O -... O - -@CHAMBERSfever O -i O -used O -to O -have O -fishes O -but O -they O -lived O -like O -5 O -days O -what O -pet O -would O -you O -like O -to O -have O -? O -i'm O -deeply O -in O -love O -with O -Eli B-person - -After O -the O -Storm O -- O -http://newzfor.me/?7r7c O -[ O -Like O -it O -? O -http://bit.ly/cWAfsq O -] O - -@JayDubshow O -it O -showed O -you O -Clapping O -on O -TV O -after O -the O -Swagger/Kofi B-other -Match I-other -Lol O - -RT O -@PeterRabbitt O -: O -HAPPY O -BIRTHDAY O -TO O -@DaDaDaphne O -WISHIN O -YOU O -THE O -BEST O -& O -HOPE O -YOU O -HAVE O -A O -WONDERFUL O -DAY O - -@becky_89 O -Oh O -dear O -, O -well O -i'll O -cheer O -you O -up O -on O -Wednesday O -! O -xx O - -Today O -'s O -Vuelter B-other -blog I-other -: O -http://bit.ly/choe8Y O - -if O -i O -hit O -tweet O -limit O -again O -today O -, O -i'm O -gonna O -be O -so O -frickin O -' O -pissed O -. O - -@eatzombiebabies O -haha O -maybe O -we'll O -dance O -off O -in O -the O -online O -stream O -tomorrow O -! O -lol O - -@George_Hill3 O -u O -made O -my O -day O -:) O - -" O -When O -you O -don't O -get O -what O -you O -want O -, O -you O -suffer O -. O -If O -you O -get O -it O -, O -you O -suffer O -too O -since O -you O -can't O -hold O -on O -to O -it O -forever O -." O -~ O -Peaceful O -Warrior O - -RT O -@iSplashInYoMouf O -: O -RE*TWEET O -IF O -YOU O -GOING O -OUT O -TONIGHT O -? O - -Ready O -for O -Friday O -night O -at O -The B-facility -Pub I-facility -! O -DUDE O -Jones B-person -playing O -inside O -and O -DJ B-musicartist -Chris I-musicartist -L I-musicartist -in O -Loft O - -'RT O -@chambermusician O -Sprezzatura O -Time O -( O -" O -But O -, O -I O -could O -play O -it O -in O -practice O -" O -) O -#music O -#musicians O -http://su.pr/3iJ0RE' O - -@luvgh62 O -next O -sun O -on O -stelly O -day-boo O -! O - -found O -some O -really O -funny O -pictures O -of O -1st O -year O -: O -L O -going O -to O -put O -them O -on O -facebook B-company -now O -!! O - -@bubzeh O -What O -are O -you O -up O -to O -today O -? O - -'or O -even O -in O -a O -69 O -. O -smother O -me O -w/ O -it O -and O -hold O -my O -dick O -like O -a O -joystick O -when O -u O -do O -it O -. O -shit O -... O -say O -some O -shit O -like O -" O -suck O -this O -pussy O -" O -i O -wont O -laugh O -.' O - -I O -love O -it O -, O -I O -can O -torment O -my O -friends O -and O -their O -kids O -long O -distance O -via O -fb B-company -and O -twitter B-company -. O -Mwahaha O -! O - -Need O -it O -to O -be O -slammed O -at O -the O -haus O -tonight O - -@Jfordj12 O -Wee O -need O -u O -to O -make O -it O -happen O -on O -Sunday O - -Best O -#free O -#livemusic O -in O -#Denver B-geo-loc -tonight O -with O -the O -return O -of O -Breaking B-musicartist -& I-musicartist -Entering I-musicartist -. O -Be O -sure O -to O -come O -down O -to O -Hi B-facility -Pac I-facility -and O -check O -it O -out O -! O - -Xbox B-product -360 I-product -: O -Enslaved B-product -: I-product -Odyssey I-product -to I-product -the I-product -West I-product -Demo O -Coming O -to O -PlayStation B-product -Network I-product -on O -September O -... O -http://bit.ly/9gFTHn O - -I'm O -at O -Bowl B-facility -Long I-facility -Island I-facility -( O -138 O -West O -Ave O -, O -Patchogue) B-geo-loc -. O -http://4sq.com/aX6N26 O - -@superanne O -Can O -I O -get O -a O -Twitter B-company -software O -to O -spew O -random O -bullshit O -about O -airports/coffee/conferences O -? O -Cause O -that O -would O -free O -up O -some O -time O -. O - -Woot O -! O -My O -Oakland B-geo-loc -line O -made O -the O -list O -. O -| O -MT O -@LaphamsQuart O -We've O -posted O -the O -best O -of O -yesterday O -'s O -#mycityis O -definitions O -. O -http://bit.ly/dffSJG O - -Game O -tonight O -with O -Tessa B-person -. O -I O -get O -to O -see O -Seanzie B-person -if O -he O -goes O -:) O -. O -I O -get O -to O -see O -my O -boyfriend O -too O -< O -3 O -c O -: O - -Twenty O -five O -after O -Three O - -im O -tired O -of O -doing O -this O -short O -hair O -thing O -. O -i O -need O -pay O -day O -to O -come O -alreadyyyy O -!! O - -'RT O -@veganbotanicals O -: O -alright O -#vegans O -! O -ANY O -purchase O -from O -our O -etsy O -shop O -is O -1/2 O -off O -when O -you O -type O -" O -go O -#vegan O -" O -in O -the O -message O -to O -seller O -at O -c O -...' O - -Let O -'s O -all O -take O -a O -shot O -for O -Lindsay B-person -Lohan I-person -tonight O -. O -Maybe O -snort O -a O -line O -if O -you're O -a O -real O -dedicated O -fan O -. O - -@yamyamjones O -haha O -! O -Thts O -actually O -quite O -useful O -x O -lmao O -x O -iv O -never O -thought O -bout O -tht O -before O -x O -I'm O -glad O -it O -made O -yu O -smile O -x O -:D O - -Gettin O -it O -in O -tonight O -! O -Shoutsout O -to O -the O -homie O -Jarred B-person -Gidley I-person -! O - -I O -just O -remembered O -this O -week O -is O -The B-facility -Heatwave I-facility -7th O -birthday O -... O -meant O -to O -make O -tonight O -'s O -Brixton B-geo-loc -bashment O -our O -birthday O -party O -but O -I O -forgot O -LOL O - -I O -want O -2 O -talk O -2 O -some O -people O -before O -i O -go O -to O -sleep O -:( O -http://tinychat.com/welovejustinbieber O - -@leahmrb O -Same O -time O -as O -you O -silly O -!! O -#lushy O - -Free O -Romeo B-other -& I-other -Julliet I-other -under O -the O -stars O -tonight O -thru O -Sunday O -in O -South B-geo-loc -Park- I-geo-loc -please O -retweet O -please O -come O -! O -http://is.gd/ff482 O - -' O -Breaking B-movie -Dawn I-movie -' O -Returns O -to O -Vancouver B-geo-loc -on O -January O -11th O -http://bit.ly/dbDMs8 O - -@ShayalKumar O -GooD O -MorNinG O -:) O -HaVe O -A O -GrEaT O -DaY O -:) O - -@RotaryCarClub O -r O -you O -driving O -this O -time O -? O - -Good O -morning O -! O -Today O -'s O -Software B-other -Freedom I-other -Day I-other -. O -Join O -us O -at O -UP B-geo-loc -Diliman I-geo-loc -for O -a O -day O -of O -great O -events O -. O -Free O -admission O -! O -http://ow.ly/2FM1n O -#sfdphils O - -Castle B-person -goes O -down O -, O -Murkowski B-person -goes O -write-in O -. O -Probably O -not O -the O -NRSC B-other -'s O -best-ever O -week O -. O - -Every O -Saturday O -night O -Casitas B-facility -presents O -Luna B-facility -Lounge I-facility -, O -the O -perfect O -setting O -to O -linger O -under O -the O -moonlight O -http://twitpic.com/2pdt30 O - -The O -latest O -App O -news O -: O -iTunes B-company -Instant O -: O -Search O -iTunes B-company -at O -high O -speed O -thanks O -to O -15-year-old O -developer O -: O -If O -you've O -hear O -... O -http://bit.ly/cy1wNb O - -RT O -@rocsidiaz O -: O -@Wale O -ill O -be O -in O -DC B-geo-loc -tonight O -where O -u O -at O -?? O -Ill O -be O -at O -muse B-facility -717 O -6th O -St O -Nw O -Dc O -to O -party O - -I O -don't O -know O -, O -how O -to O -be O -fine O -when O -i'm O -not O -. O -And O -i O -don't O -know O -how O -to O -make O -a O -feelin O -stop O -. O - -'" O -When O -I O -think O -of O -the O -Fearless B-other -Tour I-other -, O -I O -think O -of O -how O -this O -is O -one O -of O -the O -golden O -ages O -of O -my O -life O -. O -High O -school O -..." O -http://tumblr.com/xbhiucqso' O - -The O -babe O -'s O -first O -day O -of O -co-op O -preschool O -. O -http://plixi.com/p/45663997 O - -time O -to O -get O -into O -the O -PJ O -Bottoms O -;D O - -#YouKnowHow O -when O -you O -see O -little O -kids O -and O -u O -feel O -like O -u O -have O -to O -watch O -them O -cuz O -their O -parents O -aren't? O -Lol O - -RT O -@PolarBeverages O -: O -Before O -Hurricane B-other -Earl I-other -comes O -, O -stock O -up O -on O -Polar O -Orange O -Dry O -! O -:) O - -Soulja B-person -Boy I-person -| O -TheDeAndreWay O -. O -com O -- O -The B-other -DeAndre I-other -Way I-other -in O -stores O -Nov O -2 O -, O -2010 O -http://t.co/8XvVWHz O -via O -@SouljaBoy O - -RT O -@MyPhilaEagles O -: O -Is O -it O -Sunday O -yet O -? O -#Eagles O - -Best O -wishes O -to O -everyone O -as O -you O -travel O -to O -your O -respective O -football O -games O -tonight O -, O -and O -this O -weekend O -. O -Go O -band O -! O - -Blades B-sportsteam -trade O -Travis B-person -Toomey I-person -to O -Seattle B-geo-loc -for O -4th O -Rd O -Pick O -in O -' O -11 B-other -Bantam I-other -Draft I-other -. O -7 O -20-year O -olds O -remain O -. O -#yxe O -#Blades O -#WHL O - -Just O -when O -you O -turned O -your O -back O -!: O -http://wp.me/pSFZQ-9w O - -@lennaraye O -i O -know O -its O -awful O -. O -i O -hate O -the O -feeling O -of O -being O -passed O -even O -when O -im O -going O -a O -little O -bit O -over O -speed O -limit O - -OMFresh O -man O -he O -has O -a O -body O -yes O -i O -finally O -saw O -it O -today O -lolz O -during O -gym O -he O -was O -lifting O -up O -his O -shirt O -;) O -#TeamWonder O - -RT O -@MsSingandPlay O -: O -@bruindude92 O -Aww O -: O -0 O -( O -When O -do O -you O -guys O -start O -filming O -for O -season O -5 O -? O -( O -because O -there O -just O -HAS O -to O -be O -an O -S5 O -or O -I'll O -cry O -) O - -@mamasoxfanVI26 O -It O -has O -been O -3 O -years O -and O -never O -an O -answer O -. O -It O -just O -drove O -me O -nuts O -, O -but O -i'm O -not O -mad O -. O -Just O -thinkin O -not O -again O -ya O -know O -. O - -'FREEGAME O -FRIDAY O -!!! O -text O -in O -the O -word O -" O -FREEGAME O -" O -to O -59925 O -for O -a O -FREE O -1hr O -timeplay O -gamecard O -!' O - -On B-other -My I-other -Own I-other -Time I-other -- O -Gym B-musicartist -Class I-musicartist -Heroes I-musicartist - -New O -boyz O -have O -come O -a O -long O -way O -in O -the O -game O -and O -still O -going O -strong O -! O -#muchlove O - -@jasonhand O -Have O -fun O -;) O -I O -expect O -updates O -on O -your O -drunk O -status O -since O -I'm O -working O -tonight O -! O - -@Desrea_JazzMane O -L O -O O -L O -aww O -i O -will O -give O -you O -some O -next O -friday O -to O -( O -: O -x O - -Conv O -between O -sisters O -pals O -. O -Charlene B-person -; O -it O -'s O -weird O -to O -think O -Jill B-person -will O -have O -a O -baby O -after O -christmas B-other -. O -Jemma B-person -; O -they O -already O -have O -a O -baby O -charlene B-person -. O -: O -L O - -Damn O -. O -I O -do O -NOT O -know O -how O -to O -work O -this O -phone O -today O -. O -I'm O -pushing O -all O -kinds O -of O -buttons O -that O -shouldn't O -be O -pushed O -. O -#storyofmylife O - -@TheRealDavidgs O -How O -kind O -I O -greatly O -enhanced O -my O -confidence O -for O -success O -after O -attending O -this O -seminar O -. O -http://lz.ly/2bIR O -Fair O - -Q O -: O -If O -you O -could O -spend O -an O -entire O -day O -with O -one O -... O -A O -: O -Metallica B-musicartist -.: O -http://chatter.com/ask-me-anything/kt2yo O - -RT O -@BieberInAus O -: O -WAIT O -I O -THHINK O -@JUSTINBIEBER O -IS O -STILL O -ON O -BECAUSE O -HE O -WAS O -JUST O -ON O -FACEBOOK B-company -1 O -MIN O -AGO O -.. O -CREEPER O -! O - -I O -didn't O -know O -if O -you O -rub O -your O -eye O -for O -a O -long O -time O -you O -can O -get O -cock O -eyed O - -Im O -scared O -that O -every O -girl O -i O -care O -for O -will O -find O -a O -better O -woman O -and O -end O -up O -happier O -in O -the O -long O -run O -... O - -its O -saturday O -;) O -i O -think O -so O -: O -L O -< O -3 O -< O -for O -the O -competition O -xxxx O -(@PaigeLouiseRyan O -live O -on O -http://twitcam.com/2237v O -) O - -i O -see O -some O -ppl O -celebrating O -their O -birthdays O -like O -2-3 O -times O -a O -year O -... O -WTF O -?! O - -@TRARONEdwards O -tuesday O -is O -perfecy O - -@MiSS_SOTO O -I O -think O -that O -'s O -when O -I O -'m O -gonna O -be O -there O - -On O -Thanksgiving B-other -after O -you O -done O -eating O -its O -#TimeToGetOut O -unless O -you O -wanna O -help O -with O -the O -dishes O - -omg O -simone B-person -is O -coming O -over O -then O -2morrow O -we O -foin O -2 O -da O -fall O -festival O -cant O -wait O -4 O -GAC O -2night O -! O -an O -Justin B-person -Bieber I-person -concert O -in O -Dec O -.!!!!!!! O -< O -3 O - -Costa O -Rican O -group O -CocoFunka B-musicartist -power O -this O -week O -'s O -Indiesent B-tvshow -Exposure I-tvshow -http://ht.ly/2G4nS O -by O -@fuseboxradio O -on O -@planetill O - -ThAnK O -gOd O -ItS O -fRiDaY O -!! O - -@mfeige O -it O -'s O -on O -a O -Saturday O -though O -. O -So O -that O -'s O -a O -thing O -. O - -Personal O -Twitter B-company -is O -now O -ago O -. O -People O -can O -now O -keep O -track O -of O -me O -instead O -of O -just O -my O -films O -and O -production O -company O -. O - -@Bills_Ears O -WELL O -SORRRY O -!! O -I O -did O -n't O -know O -you O -YEARS O -AGO O -! O - -@aplusk O -http://www.youtube.com/watch?v=eLMui7zBiXo O -we O -beat O -kilkenny B-geo-loc -after O -they O -beat O -us O -for O -the O -last O -4 O -years O -in O -the O -hurling O -. O -Woo O -!!! O - -@MakethisRalate O -good O -luck O -at O -Elliot B-facility -Miner I-facility -:D O -yous O -were O -greaaaat O -tonight O -< O -3 O -xx O - -Hit O -word O -count O -! O -Woo-hoo O -! O -*throwing O -confetti* O -Now O -I O -can O -goof O -off O -tonight O -and O -read O -. O -*happy O -writer O -dance* O -#amwriting O -#havewritten-yay O -! O - -RT O -@therbertson O -: O -Just O -saw O -the O -worst O -movie O -in O -years O -... O -Piranha B-movie -3D I-movie -just O -exceeded O -the O -limits O -of O -crappyness O -. O -| O -Glad O -you O -enjoyed O -it O -. O - -It O -is O -Friday O -afternoon O -adn O -I O -could O -n't O -be O -happier O -. O -Ending O -a O -crappy O -week O -on O -a O -good O -note O -is O -always O -nice O -. O - -But O -in O -any O -case O -I O -suppose O -you O -will O -not O -let O -it O -away O -for O -some O -days O -? O - -So O -this O -will O -be O -more O -lhs B-facility -games O -then O -i O -went O -to O -then O -when O -i O -went O -to O -lhs B-facility - -@ToriVicotia O -aaahhh O -! O -Me O -too O -!!!! O -Its O -so O -much O -better O -than O -when O -you O -play O -it O -yourself O -! O -Haha O -! O - -lush O -when O -ya O -download O -limit O -has O -been O -succeeded O -like O -all O -night O -: O -L O - -I O -just O -took O -" O -After O -getting O -trampled O -at O -a O -Justin B-person -Bieber I-person -concert O -, O -yoiu O -wake O -up O -and O -..." O -and O -got O -: O -Part O -6 O -:) O -! O -Try O -it O -: O -http://tinyurl.com/233u5lu O - -; O -I O -can O -upgrade O -to O -a O -BlackBerry B-product -in O -November O -!! O -Yesss O -. O -< O -3 O -BB B-product -Bold I-product -, O -here O -I O -come O -! O - -@_Kinghoopa O -the O -one O -thats O -out O -now O -is O -free O -... O -Father O -to O -tha O -Game O -is O -droppin O -in O -a O -month O -or O -so O -.. O - -I O -need O -to O -draw O -FASTER O -. O -I O -'m O -gonna O -scan O -two O -weeks O -worth O -of O -doodles O -to O -get O -going O -, O -lol O -. O - -@FeeninforPretty O -making O -something O -to O -eat O -, O -aint O -ate O -all O -day O - -RT O -@m_candelaria O -: O -The O -amazing O -Follow O -Friday O -Train O -: O -@Sketchjobs O -@lollieshopping O -@philadelphiabn O -@elainebiss O -@TableMatters10 O -@STALKmyPRETT O -... O - -RT O -@itsPLG O -: O -Congrats O -to O -@bobburnquist O -for O -qualifying O -in O -1st O -place O -today O -!!! O -Yeah O -son O -!!! O - -Still O -on O -my O -@teganandsara O -kick O -! O -It O -'s O -been O -a O -solid O -2 O -weeks O -! O - -Chilling O -wit O -my O -baby O -tonight O -! O - -What O -a O -productive O -day O -. O -Not O -. O - -RT O -@OMGwhatateen O -: O -RT O -if O -you O -are O -happy O -it O -'s O -friday O -. O -#OMGwhatateen O - -@Dc_Luvs_Swift O -randomly O -that O -just O -made O -me O -smile O -:D O -do O -n't O -ring O -the O -bell O -tweet O -me O -when O -ur O -here O -'ll O -let O -u O -in O -:D O -... O -that O -def O -would O -be O -awesome O -:D O - -@justinbieber O -I O -could O -sing O -with O -you O -what O -I`ll O -do O -o O -show O -in O -november O -in O -my O -city O -oh O -my O -dream O -and O -sing O -one O -time O - -The O -Basic O -Step O -Before O -You O -Even O -Start O -Thinking O -Of O -Making O -Your O -...: O -Keyword O -research O -is O -a O -well O -known O -subject O -, O -yet O -so O -... O -http://bit.ly/9XQgSr O - -Thanks O -and O -I O -will O -RT O -@InnerGizerBunni O -@kizfoprez_TR O -Happy O -Berlated O -Birthday O -! O -I O -hope O -u O -have O -fun O -< O -live O -it O -up O -tonight O -.. O -Enjoy O -< O -b O -safe O -! O - -RT O -@DonnieWahlberg O -: O -Soldiers O -... O -Familia O -... O -BH's... O -NK O -Fam O -... O -Homies O -... O -Etc O -. O -Etc O -. O -Etc O -.... O -I O -'m O -gonna O -need O -some O -company O -next O -Friday O -in O -NYC B-geo-loc -... O - -I O -'ve O -never O -found O -a O -happy O -way O -to O -preserve O -it O -but O -it O -'s O -usually O -affordable O -most O -of O -the O -year O -. O -We O -eat O -a O -lot O -2 O -, O -grilled O -saute O -bake O -or O -fried O - -RT O -@BuffSabresGrl63 O -: O -RT O -@LindyRuffsTie O -: O -RT O -@FakeDarcy O -: O -No O -Derek B-person -, O -when O -guys O -get O -hurt O -, O -" O -kissing O -the O -boo-boo O -" O -will O -not O -make O -it O -better O -. O -#sab O -... O - -Must O -say O -iPhone B-product -is O -more O -accurate O -. O -Wasnt O -brought O -up O -here O -though O -, O -Wrawby B-geo-loc -is O -where O -I O -spent O -18 O -years O -of O -my O -life O -. O - -@Littlesapling O -You O -said O -that O -last O -week O -Ash B-person -. O -I O -think O -you O -need O -a O -12 O -step O -program O -. O - -How O -to O -Write O -a O -Resume O -After O -Being O -Fired O -: O -Dealing O -with O -how O -to O -write O -a O -resume O -after O -being O -fired O -is O -something O -many O -of O -... O -http://bit.ly/amRxFJ O - -RT O -@NickSilly O -: O -Fun O -! O -RT O -@JackFMDFW O -: O -Put O -on O -your O -Boogie O -Shoes O -and O -Get O -Down O -Tonight O -with O -KC B-musicartist -and I-musicartist -The I-musicartist -Sunshine I-musicartist -Band I-musicartist -. O -#Dallas B-geo-loc -#concerts O -http O -:/ O -... O - -@zombie_killer94 O -aaahh O -sorry O -i O -posted O -the O -link O -wrong O -:( O -basically O -their O -record O -company O -confirmed O -the O -title O -< O -it O -comes O -out O -nov O -23 O -XD O - -Happy O -birfday O -to O -the O -only O -p-i-m-p O -I O -know O -... O -@amcdavid O -! O -We O -'re O -getting O -" O -white O -boy O -wasted O -" O -tonight O -! O - -Lmao O -.. O -They O -#Hurrt O -! O -RT O -@_sofucKENNrude O -y O -every O -nigga O -tryna O -grow O -they O -hair O -out O -. O -should O -of O -did O -that O -a O -long O -time O -ago O -. O --_- O -skimp O -ass O -braids O -. O - -Half B-movie -baked I-movie -, O -next O -how B-movie -high I-movie -, O -grandma B-movie -boys I-movie -, O -puff B-movie -puff I-movie -pass I-movie -, O -and O -4 O -hours O -of O -friday O -. O -< O -DEAD O -GREEN O -HARTS O -> O - -@mcflysandra O -haha O -yes O -:) O -have O -to O -go O -speak O -tomorrow O -:) O -good O -night O -xxx O - -@MikePortnoy O -I O -got O -slapped O -in O -the O -face O -the O -other O -day O -by O -a O -few O -DT B-musicartist -fans O -because O -I O -said O -you O -were O -awesome O -for O -joining O -A7X O -. O -So O -worth O -it O -. O -foREVer O -! O - -@2kjdream O -Good O -morning O -! O -Have O -a O -Good O -day O -:) O -from O -JPN B-geo-loc -. O - -Good O -golly O -. O -Who O -left O -the O -candy O -in O -the O -lunchroom O -. O -May O -set O -a O -world O -record O -for O -the O -most O -Sour B-product -Patch I-product -kids I-product -eaten O -by O -an O -individual O -! O - -Hey O -.... O -my O -birthday O -is O -in O -exactly O -one O -month O -!!! O -:D O - -Do O -n't O -miss O -our O -Half O -Way O -to O -St B-other -. I-other -Patty I-other -'s I-other -Day I-other -Party O -Tonight O -! O -We O -'re O -having O -crazy O -drink O -Specials O -too O -.. O -tex O -980-333-3923 O -to O -get O -on O -the O -guest O -list O -! O - -RT O -@rockerfuckerak O -: O -What O -'s O -done O -is O -done O -, O -Just O -leave O -it O -alone O -, O -and O -do O -n't O -regret O -. O -Sometimes O -somethings O -turn O -in-to O -dumb O -things O -. O -And O -that O -'s O -when O -... O - -@ShelAndPushPlay O -Yeah O -man O -, O -u O -got O -it O -. O -It O -'s O -only O -a O -matter O -of O -time O -. O - -@jaredleto O -... O -ive O -been O -to O -13 O -30stm B-musicartist -concerts O -now O -and O -you O -never O -got O -me O -on O -stage O -during/before O -kings B-musicartist -and I-musicartist -queens I-musicartist -:( O -(( O - -Reduced O -Sodium O -labels O -may O -only O -have O -25% O -less O -sodium O -than O -the O -original O -.. O -go O -for O -" O -Low O -Sodium O -" O -labels O -which O -have O -no O -more O -than O -140 O -mg O -per O -serving O - -Power O -nap O -. O -I O -need O -it O -. O -Its O -been O -a O -stressful O -week O -. O -I O -'m O -excited O -for O -xmas B-other -, O -haha O -. O -Goodnight O -. O -:) O - -today O -just O -does O -n't O -feel O -like O -a O -Friday O - -HA O -! O -RT O -@ew32766 O -I O -love O -it O -when O -my O -mom O -posts O -status O -updates O -on O -FB B-company -, O -they O -are O -usually O -personal O -messages O -she O -meant O -2write O -on O -someone O -'s O -wall O -. O -;) O - -Awesome O -tennis O -practice O -today~ O -So O -happy O -to O -play O -again O -:) O - -Dollar O -steady O -versus O -yen O -but O -market O -wary O -of O -intervention O -| O -Money O -...: O -The O -dollar O -held O -near O -its O -highest O -in O -a O -month O -... O -http://bit.ly/a0F3dO O - -Knowing O -that O -I O -have O -people O -who O -love O -me O -and O -I O -love O -them O -back O -makes O -my O -day O - -@DrHubaEvaluator O -Not O -long O -ago O -I O -responded O -to O -a O -angry O -cardiologist O -whom O -I O -have O -known O -yrs O -. O -when O -he O -spit O -venom O -to O -the O -newspaper O -but O -I O -did O -it O -+ O - -fckn O -emily B-person -rodriguez I-person -did O -n't O -bring O -my O -cookies O -today O -!! O -lil O -brat O -. O - -@hdscc O -Yo O -Man O -, O -just O -won O -$1000 O -tonight O -. O -100% O -Bonus O -, O -Bonus O -Code O -: O -win2bet O -! O -Fantastic O -Place O -to O -Play O -http://bit.ly/aoDEls?=njq4 O - -RT O -@mrdaveyd O -: O -PG<E O -donated O -35G O -to O -state O -committe O -chairman O -investigating O -them O -1day O -after O -explosion O -in O -#sanBrunofire O --Who O -is O -still O -defendi O -... O - -@jonronson O -But O -a O -mess O -on O -one O -day O -may O -not O -a O -mess O -eternally O -be O -. O - -@GiaAllemand O -soon O -, O -as O -long O -as O -@chrislambton13 O -peels O -himself O -away O -from O -the O -duckies O -, O -and O -get O -on O -his O -way O -. O -but O -we O -want O -more O -pics O -dont O -we O -? O - -Have O -a O -practice O -session O -@Cromwell B-facility -Field I-facility -today O -at O -6pm O -. O -Hope O -i O -shine O -well O -and O -start O -the O -next O -game O -. O -:) O - -#yesterday O - -ugh O -so O -this O -chick O -onFB O -jus O -wrote O -this O -long O -status O -cussin O -me O -out O -, O -but O -im O -not O -even O -gonna O -stoop O -to O -her O -leve O -but O -i O -wanna O -bomb O -her O -ass O -out O -so O -bad O - -It O -'s O -5pm O -on O -Friday O -! O -Wish O -you O -guys O -have O -a O -fabulous O -evening O -!!! O -And O -have O -a O -wonderful O -weekend O -< O -3 O -< O -3 O -http://fb.me/xLeN7MAe O - -It O -'s O -official O -. O -Next O -book O -club O -selection O -is O -" O -Not B-other -a I-other -Genuine I-other -Black I-other -Man I-other -" O -by O -@BrianCopie O -I O -saw O -the O -show O -a O -few O -years O -ago O -and O -it O -was O -fantastic O -. O - -I O -'m O -tired O -after O -school O -today O -! O - -@FirstLadyEve O -have O -a O -dating O -show O -with O -@ogochocinco O -evry O -week O -'ll O -compete O -with O -sum O -hawt O -dude O -2 O -keep O -ur O -intrest O -like O -@willdemps O -my$ O -on O -will O - -Greek B-other -Festival I-other -at O -St B-geo-loc -Johns I-geo-loc -before O -ASPEN B-geo-loc - -RT O -@ajc O -: O -Today O -'s O -the O -102nd O -birthday O -of O -the O -American B-other -Professional I-other -Football I-other -Association I-other -( O -we O -call O -it O -the O -NFL) B-other -. O -First O -draft O -choice O -: O -Brett B-person -Favre I-person -. O - -So O -far O -this O -day O -xxx O -; O -t O -gone O -to O -well O -, O -hopefully O -my O -night O -will O -be O -better O -! O - -@SoccerByIves O -Let O -'s O -hope O -the O -Serie B-other -A I-other -continues O -to O -be O -on O -the O -tv O -schedule O -next O -week O -. O -This O -strike O -has O -fans O -of O -Calcio B-geo-loc -worried O -.... O - -@SweetBee_90 O -what O -'s O -the O -look O -for O -tonight O -? O - -going O -now O -guiseeeee O -... O -if O -i O -dont O -see O -you O -through O -the O -week O -ill O -see O -you O -through O -the O -window O -:) O -:) O - -Beautiful O -day O -in O -Chicago B-geo-loc -! O -Nice O -to O -get O -away O -from O -the O -Florida B-geo-loc -heat O -. O - -College O -Student O -- O -Trusty O -House O -and O -Pet O -Sitter O -( O -Barefoot O -Student) O -: O -I O -'m O -a O -22 O -year O -old O -first O -year O -graduate O -student O -at O -... O -http://bit.ly/cDYeqQ O - -@alealshinn O -@BambingBling O -~~~ O -@sunchips O -this O -is O -the O -second O -time O -they O -have O -messed O -up O -.... O -about O -to O -give O -up O -on O -them O - -Love O -how O -I O -ca O -n't O -wear O -my O -shorts O -to O -the O -party O -tonight O -, O -so O -I O -have O -to O -wear O -sweat O -pants O -over O -them O -and O -change O -when O -I O -get O -there O -. O -SmackMyHead O -. O - -Aggressive O -Kids O -With O -ADHD B-other -May O -Not O -Need O -Antipsychotic O -Meds O -http://t.co/JfGm0uH O - -Oh O -lord O -here O -we O -go O -again O -Paranormal B-movie -Activity I-movie -2 I-movie -http://j.mp/ascgZr O -I O -'m O -not O -requesting O -it O -in O -my O -area O -this O -time O -. O -Are O -you O -? O - -RT O -@HippoArmy O -: O -Thousands O -of O -angry O -masturbators O -marched O -against O -Christine B-person -O'Donnell I-person -today O -http://bit.ly/9nTWQw O -Seriously O -. O - -Man O -gets O -20 O -years O -in O -murder O -plot O -against O -judge O -http://bit.ly/agB9iL O - -@godsgirl8494 O -can O -u O -please O -follow O -me O -? O -you O -will O -make O -my O -day O -< O -3 O -8 O - -@joejonas O -@nickjonas O -@kevinjonas O -@papajonas O -@greggarbo O -@johnlloydtaylor O -Rock O -to O -SECTION O -204 O -tonight O -!!!! O - -in O -tthe O -Car O -:/ O -Today O -wasz O -Fun O -cusz O -anna B-person -Came O -juss O -for O -me O -< O -3( O -: O -hahaha O - -man O -my O -twin O -wanna O -act O -fake O -today O -but O -its O -okay O -bcuz O -i O -still O -love O -you O - -Tryna O -Get O -Into O -Something O -Tonight O - -RT O -@LilTwist O -: O -RT O -this O -if O -you O -want O -me O -to O -go O -back O -live O -on O -Ustream B-company -later O -tonight O - -Bestfriend O -HouseTonight O -Til O -Sunday O -Route O -66 O -Tomarrow O -Wit O -The O -Cuhzans O -, O -Bestfriend<Ride O -Or O -Die O -. O -Fuxx O -Widd O -IHT O -MODEL'$$ O -Repp O -:) O - -Good O -day O -looking O -for O -software O -n O -usb O -drivers O -http://f.ast.ly/DfxRf O - -VIDEO O -: O -Showbiz B-tvshow -Tonight I-tvshow -- O -Is O -TV O -Too O -Gay O -?: O -http://bit.ly/aAZMD6 O - -[ O -GigaOM O -] O -DRM O -FAIL O -: O -Five O -Broken O -Copy O -Protection O -Schemes O -: O -This O -week O -, O -we O -learned O -that O -the O -HDCP O -copy O -protection O -scheme O -... O -http://bit.ly/blWf0e O - -@Jessica_Chobot O -did O -you O -see O -the O -yakuza B-other -vs O -zombies O -.... O -smh O -but O -cool O -at O -the O -same O -time O - -@Syniq O -yeah O -, O -after O -a O -long O -week O -, O -I O -think O -we O -need O -an O -early O -night O -, O -so O -are O -heading O -to O -bed O -shortly O -. O -We'll O -pop O -over O -tomorrow O -afternoon O -tho O -' O - -twenty-four O -days O -for O -Costa B-other -do I-other -Sauipe I-other -*-* O - -@italylogue O -Was O -it O -you O -who O -sent O -out O -the O -Ginger O -Peach O -jam O -recipe O -? O -Making O -some O -tonight O -w O -our O -fab O -#Colorado O -Palisades O -peaches O -. O -Mmmm O -.... O - -Spotted O -: O -Kanye B-person -West I-person -Celebrates O -LAMB B-product -With O -Gwen B-person -Stefani I-person -: O -New B-other -York I-other -Fashion I-other -Week I-other -is O -coming O -to O -a O -close O -, O -but O -not O -before O -... O -http://bit.ly/cSyZUi O - -Getting O -the O -10am O -bus O -tomorrow O -#thethingsido O - -Happy O -B-Day O -Jimmie B-person -Johnson I-person -FLYING O -LIZARD O - -It O -'s O -near O -! O -RT O -@skinnyemmie O -: O -RT O -@shesafitchick O -: O -989 O -followers O -!! O -RT O -to O -help O -me O -hit O -1000 O -today O -please O -! O -:) O -#fitblog O -#hls O -#31DBBB O - -@SinfulSalvatore O -{ O -drinks O -slowly O -from O -my O -cup O -observing O -your O -actions O -before O -clearing O -my O -throat O -} O -is O --this- O -your O -plans O -for O -today O -? O - -I'm O -so O -excited O -about O -going O -out O -tonight O -no O -work O -yea O -.... O -Badd O -Badd O -Badd O -Badd O -girls O -. O -Let O -'s O -gooooooooooo O - -MD B-geo-loc -Jobs O -| O -Executive O -Pharmacist-Full O -Time O -Float-Ellicott B-geo-loc -City I-geo-loc -, O -MD B-geo-loc -Job O -( O -MD B-geo-loc -) O -http://bit.ly/daNH6v O -#Job O -#Hiring O -#MDJobs O - -The O -only O -problem O -with O -taxing O -ur O -car O -online O -is O -its O -no O -fun O -not O -paying O -anything O -for O -a O -5litre O -car O -when O -its O -a O -classic O -:) O - -Let O -'s O -go O -to O -New B-geo-loc -York I-geo-loc -tomorrow O -with O -Blakehurst B-other -. O -Contact O -us O -about O -your O -fall/winter O -trave O -@ O -410-336-3234 O - -Year O -10 O -is O -a O -bitch O - -@alyaffair O -Lol O -, O -that O -'s O -not O -going O -to O -solve O -your O -problems O -. O -When O -you O -sober O -up O -again O -, O -they're O -going O -to O -back O -. O - -starting O -to O -be O -temped O -to O -get O -an O -android B-product -device O -, O -but O -prob O -next O -gen O -after O -desire/desirehd/galaxy O -s O -. O -critical O -mass/commercial O - -My O -teacher O -turned O -on O -the O -radio O -in O -class O -and O -all O -i O -heard O -was O -@justinbieber O -U O -smile O -I O -smile O -that O -made O -my O -day O -:D O - -Gonna O -be O -on O -national O -television O -monday O -and O -on O -the O -internet O -tuesday O -. O -Interview O -' O -cause O -of O -the O -text O -I O -wrote O -on O -the O -day O -against O -homophobia O - -RT O -@ArianaGrande O -: O -Follow O -Friday O -@lushcosmetics O -... O -Just O -got O -some O -yummyyyyy O -bubble O -bath O -goodies O -there O -. O -:) O -xxxxxx O -#FF O - -Avast B-product -AntiVirus I-product -4 I-product -8 I-product -PROFIONAL I-product -Full O -Life O -Time O -Key O -: O -Avast B-product -AntiVirus I-product -4.8 I-product -PROFESIONAL I-product -Full O -with O -Life O -Time O -Keygen O -! O ----- O -... O -http://bit.ly/blPqIH O - -Jets B-sportsteam -coach O -Rex B-person -Ryan I-person -pleased O -with O -the O -offense O -this O -week O -in O -practice O -http://bit.ly/9GkyjU O - -Over O -1 O -, O -5 O -week O -sta O -ik O -in O -de O -keuken O -bij O -Delphi B-company -... O -ik O -word O -toch O -ooit O -nog O -kok O -! O - -@niamh_schnapps O -fuckin O -yeaaaaaa O -march O -1st O -omgomg O -best O -news O -!!!! O - -#October O -Iron B-movie -Man I-movie -2 I-movie -( O -Three-Disc O -Blu-ray/DVD O -Combo O -+ O -Digital O -Copy O -) O -Starring O -: O -Robert B-person -Downey I-person -Jr I-person -., O -Mickey B-person -Rourke I-person -Direc O -... O -http://bit.ly/a6iCPn O - -@jesus_army O -:-( O -Mikes O -been O -at O -A&E B-other -all O -day O -and O -is O -now O -comfortable O -in O -a O -hospital O -bed O -:-( O -Hope O -you O -are O -both O -better O -soon O -. O - -RT O -@DeepakChopra O -: O -Split O -me O -, O -make O -me O -nothing O -. O -Fling O -me O -across O -the O -fabric O -of O -space O -and O -time O -. O -From O -nothing O -once O -again O -, O -make O -me O -everything O --Rumi B-person - -I O -have O -made O -the O -tough O -decisions O -, O -always O -with O -an O -eye O -toward O -the O -bottom O -line O -. O -Perhaps O -it O -'s O -time O -America B-geo-loc -was O -run O -like O -a O -business O -. O -Donald B-person -Trump I-person - -The B-musicartist -Get I-musicartist -Up I-musicartist -Kids I-musicartist -, O -Sunny B-musicartist -Day I-musicartist -Real I-musicartist -Estate I-musicartist -, O -and O -Cap'n B-musicartist -Jazz I-musicartist -are O -quite O -possibly O -the O -greatest O -( O -original O -) O -emo O -bands O -to O -ever O -exist O -. O -Ever O -. O -So O -listen O -. O - -Morning O -....! O -I O -really O -never O -enjoy O -to O -sleep O -... O -hhmmm O -.. O -Have O -a O -nice O -saturday O -all O -..! O -Hehehehehe O - -I O -don't O -mean O -to O -sound O -old O -, O -but O -today O -'s O -music O -is O -wack O -. O - -Lmaao O -! O -RT O -@TheHomieJoseph O -I O -hate O -when O -people O -try O -to O -have O -a O -convo O -& O -ask O -" O -what O -'s O -your O -name O -" O -lol O -com'on O -now O -its O -right O -there O - -Sitting O -here O -feeling O -awful O -with O -hayfever/headcold/sinusinfection O -but O -the O -sunshine O -is O -lovely O -. O -Can't O -feel O -too O -bad O -with O -such O -a O -nice O -day O -. O - -MOTOGP B-other -: O -Round O -13 O -, O -Motoland O -of O -Aragon B-geo-loc -, O -Spain B-geo-loc -, O -day O -one O -. O -Marlboro B-company -Ducati I-company -'s O -Casey B-person -Stoner I-person -wasted O -no O -time O -in O -taking O -... O -http://fb.me/HSTaAyrb O - -@hunleyd O -What O -is O -taking O -them O -sooo O -long O -? O - -@TATTEDUP_FLYTY O -lol O -Hmmm O -.. O -man O -u O -kno O -when O -u O -tripped O -LMao O - -RT O -@momswithapps O -: O -A O -family-friendly O -app O -, O -FREE O -every O -Friday O -: O -http://bit.ly/97opLN O -( O -this O -week O -'s O -app O -by O -@snowglobemaker O -) O -#appfriday O - -I've O -been O -waiting O -for O -band O -camp O -for O -a O -year O -but O -I'm O -not O -going O -. O -How O -ironic O -. O - -Role O -of O -a O -Real O -Estate O -Lawyer O -in O -FSBO O -| O -eHowcom O -: O -Role O -of O -a O -Real O -Estate O -Lawyer O -in O -FSBO O -. O -When O -you O -purchase O -property O -i O -... O -http://bit.ly/cGsTy1 O - -Consider O -the O -world O -'s O -top-ranked O -golfer O -, O -Tiger B-person -Woods I-person -. O -If O -a O -guy O -who O -can O -rake O -in O -a O -billion O -dollars O -in O -a O -year O -can O -also O -... O -http://bit.ly/94sBNr O - -when O -a O -woman O -says O -, O -" O -we O -need O -some O -time O -, O -we O -need O -a O -break O -" O -that O -#reallymeans O -" O -i O -am O -about O -to O -leave O -you O -, O -do O -something O -for O -me O -" O - -@JoshenReborn O -let O -me O -know O -if O -or O -when O -you O -are O -coming O -. O -#FullHouse O - -#cricket O -Lancashire B-geo-loc -let O -Powell B-person -go O -early O -: O -Lancashire B-geo-loc -have O -released O -West B-geo-loc -Indies I-geo-loc -fast O -bowler O -Daren B-person -Powell I-person -a O -year O -befo O -... O -http://bit.ly/aLxTis O - -Have O -a O -practice O -session O -@Cromwell O -Field O -today O -at O -6pm O -. O -Hope O -i O -shine O -well O -and O -start O -the O -next O -game O -. O -:) O - -#reallymeans O -to O -love- O -can't O -get O -her O -outta O -my O -head O -wann O -spend O -every O -day O -with O -herz O -! O - -time O -to O -get O -some O -sleep O -. O -been O -out O -from O -4pm O -till O -now O -. O - -Costa O -Rican O -group O -CocoFunka B-musicartist -power O -this O -week O -'s O -Indiesent B-tvshow -Exposure I-tvshow -http://ht.ly/2G4nS O -by O -@fuseboxradio O -on O -@planetill O - -Omfg O -I'm O -bailing O -out O -of O -the O -car O -right O -now O -my O -mom O -slapped O -me O -when O -I O -changed O -the O -radio O -off O -this O -Justin B-person -Bieber I-person -you O -smile O -I O -smile O -fucking O -song O - -and O -I O -wonder O -if O -I O -ever O -cross O -your O -mind O -... O -For O -me O -it O -happens O -all O -the O -time O - -@yungtybrownski O -just O -weak O -lol O -. O -You O -gone O -go O -Hamm B-geo-loc -next O -Thursday O -on O -team O -darkness O - -@JustinxTyler O -i'm O -excited O -!! O -see O -you O -tonight O -! O - -All O -The O -Time O - -@Komanapalli O -people O -believe O -what O -you O -say O -when O -you O -demonstrate O -reliability O -as O -a O -source O -of O -knowledge O -. O - -@ItsMariaSmith O -@PaddiStone O -hahah O -ohkayy O -when O -? O - -Saw O -some O -people O -from O -my O -past O -over O -at O -the O -school O -today O -. O -They O -all O -remembered O -me O -. O -:) O - -RT O -@kktv11news O -: O -Woman O -Gets O -Year O -In O -Prison O -in O -Dog O -'s O -Dragging O -Death O -http://goo.gl/fb/WcjLV O - -Chinese O -symbols O -Sotheby B-company -| I-company -' I-company -s I-company -Hong B-geo-loc -Kong I-geo-loc -twentieth O -century O -Chinese O -art O -sales O -fall O -in O -October O -http://bit.ly/aFLHax O - -RT O -@ChicagoFire O -: O -Join O -the O -TIFO B-other -CREW I-other -says O -@Section8Chicago O -'s O -Dan B-person -Martin I-person -in O -this O -week O -'s O -S8 B-other -blog I-other -: O -http://ow.ly/2G43u O - -Prophet B-person -Muhammad I-person -[ O -peace O -& O -blessings O -of O -Allah B-person -be O -upon O -him O -] O -said O -:" O -Take O -advantage O -of O -five O -matters O -before O -five O -other O -... O -http://fb.me/A25CPleD O - -RT O -@TeamVA O -: O -81 O -Days O -Till O -Last B-other -Sacrifice I-other - -Fave O -Looks O -from O -SS B-other -2011 I-other -Fashion I-other -Week I-other -: O -Jason B-person -Wu I-person -read O -more O -http://bit.ly/brsV4V O - -Doctor O -'s O -appointment O -and O -blood O -test O -today O -. O -%#$&@%$&%&@$ O -!!!!!!! O - -@philmacaulay O -Recently O -appreciated O -how O -amazing O -night O -time O -is O -:D O - -If O -Partner O -left O -me O -and O -BB B-person -I O -would O -not O -hunt O -him O -down O -. O -I'd O -take O -his O -calls O -but O -it O -would O -be O -his O -job O -to O -make O -sure O -he O -spent O -time O -with O -BB B-person -. O - -Peppers B-facility -with O -Kathy B-person -... O -where O -we O -always O -go O -when O -we O -don't O -know O -where O -to O -go O -. O - -YEAH O -! O -No O -jury O -duty O -. O -Yes O -class O -on O -Monday O -. O -still O -willing O -to O -send O -out O -study O -guides O -to O -those O -who O -send O -me O -an O -email O -request O -. O -sorgelaw@yahoo O -. O -com O - -What O -should O -you O -do O -when O -you're O -sued O -. O -Here O -are O -a O -few O -tips O -. O -http://www.calitigationblog.com/2010/09/articles/what-to-do-when-you-are-sued/ O - -Got O -my O -phone O -taken O -up O -today O -. O -Guess O -why O -. O -@justinbieber O -. O -That O -'s O -why O -. O - -My O -eyes O -are O -supeeer O -dark O -today O -.. O -john B-person -says O -its O -cuz O -im O -overtired O -.. O -:/ O - -RT O -@HRosenfelder O -- O -Educate O -yourself O -about O -long-term O -care O -by O -going O -to O -http://www.ltcfp.com O -. O -#financial O - -Lindsay B-person -Lohan I-person -allegedly O -failed O -her O -drug O -test O -. O -So O -not O -fetch O -. O -Quick O -, O -let O -'s O -all O -put O -on O -our O -shocked O -faces O -that O -23 O -days O -of O -rehab O -didn't O -work O -! O - -Hydrating O -for O -the O -big O -game O -tomorrow O -:) O - -Been O -working O -like O -crazy O -these O -past O -couple O -dayzz O --__- O -... O -well O -at O -least O -for O -a O -high O -school O -student O -... O -got O -home O -today O -and O -CRASHED O -=D O - -Logging O -off O -for O -today O -. O -About O -to O -go O -enjoy O -my O -#wkndread O -Sandman B-product -Slim I-product -by O -Richard B-person -Kadrey I-person -. O -Love O -#wkndreads O -:) O - -" O -You'll O -find O -that O -when O -you O -seek O -to O -understand O -first O -you O -won't O -feel O -a O -need O -to O -be O -understood O -as O -much O -." O --@TonyGaskins O - -I O -remember O -when O -you O -all O -were O -thiiis O -big O -, O -you O -know O -? O - -RT O -@bobbyhundreds O -: O -After O -a O -few O -episodes O -, O -I O -think O -I've O -figured O -out O -how O -to O -catch O -a O -predator O -. O -It O -seems O -you O -just O -ask O -them O -to O -take O -a O -seat O -. O - -RT O -@OMGTotallyLegit O -: O -Having O -a O -dream O -about O -someone O -then O -feeling O -weird O -about O -them O -the O -next O -day O -. O -#OMGTotallyLegit O - -@ALiNA_BlahxD O -nein O -, O -das O -ist O -die O -von O -meinem O -bruder O -. O -aber O -bei O -ihm O -staubt O -die O -nur O -ein O -:D O -jetzt O -kann O -ich O -schon O -one O -time O -von O -justin B-person -bieber I-person -und O - -@GOBLUE_FUCKosu O -it O -was O -our O -10th O -grade O -year O -. O -smh O - -@leesalulu O -perfect O -round O -for O -us O -this O -week O -.... O -and O -yes O -, O -I'm O -convincing O -myself O -it O -'s O -as O -hard O -as O -any O -other O -week O -! O - -Got O -some O -close O -friends O -from O -back O -home O -over O -tonight O -. O -Might O -get O -a O -little O -carried O -away O -woot O - -There O -is O -like O -no O -mayo O -on O -this O -sandwich O -! O -Ugh O -.... O -next O -time O -Im O -going O -with O -my O -dad O - -@snarkobabble O -I'll O -look O -for O -you O -tonight O -! O -#wineparty O - -@EssieLovesYou O -yesterday O -i O -had O -a O -kings B-musicartist -of I-musicartist -leon I-musicartist -marathon O -. O -i O -slept O -happy O -ll O -! O - -Public O -encouraged O -to O -attend O -Saturday O -'s O -memorial O -for O -jazz O -great O -Caliman B-person -at O -First B-facility -Baptist I-facility -on O -Harvard B-geo-loc -http://bit.ly/bsnOkT O -Musical O -celebration O - -@drance O -just O -wait O -til O -you O -discover O -that O -all O -tableviews O -scroll O -up O -when O -you O -tap O -the O -top O -bar O -where O -the O -clock O -is O -:) O - -#Offline O -For O -along O -time O -... O -maybe O -not O -until O -tomorrow O - -RT O -@EckoMMA O -: O -CRAZY O -#FF O -DEAL O -!!! O -We'll O -give O -away O -50 O -free O -tees O -to O -followers O -if O -we O -hit O -10K O -followers O -today O -. O -You O -must O -be O -following O -& O -must O -RT O -! O - -RT O -@Rammsies O -: O -haha O -thats O -awesome O -RT O -: O -@aplusk O -This O -made O -me O -laugh O -today O -http://bit.ly/bjOhom O -< O ---- O -courtesy O -of O -splurb O -. O -What O -made O -you O -laugh O -? O - -@batonabike O -I O -feel O -really O -sorry O -, O -the O -ratbags O -, O -maybe O -in O -a O -few O -years O -when O -you O -are O -straight O -and O -enjoying O -it O -all O -, O -you O -will O -forget O - -#stupidnews O -An O -Entire O -Facebook B-company -Photo O -Album O -Captured O -in O -a O -Single O -Long O -Exposure O -Photograph O -[ O -Photography O -] O -http://gizmo.do/9GeY0q O - -@i_r_squared O -In O -December O -, O -I O -would O -advise O -against O -that O -. O - -I'm O -feelin O -better O -than O -some O -head O -on O -a O -Sunday O -afternoon O -, O -better O -than O -a O -chick O -that O -said O -yes O -too O -soon O -. O - -Jessica B-person -Simpson I-person -is O -PREGNANT O -!?!: O -Jessica B-person -Simpson I-person -is O -pregnant O -?? O -Simpson B-person -is O -pregnant O -with O -her O -barely O -4-month O -boyfrien O -... O -http://bit.ly/aoempS O - -Click O -4 O -Details O -> O -> O -> O -http://schmap.it/u046mn O -SEDUCTION B-other -SATURDAYS I-other -@ O -CALABASH B-facility -LOUNGE I-facility -SEP O -25TH O -#DABOSS O -#CALABASH O -#WJE O - -Most O -of O -time O -his O -love O -to O -us O -is O -one O -way O -. O -I O -always O -complained O -that O -I O -can't O -feel O -where O -he O -is O -. O -But O -the O -problem O -was O -not O -him O -, O -it O -was O -me O -. O - -New O -show O -announced O -in O -Orillia B-geo-loc -, O -ON B-geo-loc -at O -Leacock B-facility -Museum I-facility -on O -October O -24 O -, O -2010 O -http://artistdata.com/a/26up O - -Y B-musicartist -La I-musicartist -Bamba I-musicartist -: O -Playing O -a O -show O -in O -Portland B-geo-loc -, O -OR O -at O -9:00 O -PM O -today O -at O -Mississippi B-facility -Studios I-facility -http://artistdata.com/a/1kdx O - -Oh O -sneaks O -, O -how O -I O -love O -turning O -on O -my O -computer O -( O -or O -well O -my O -dad O -'s O -as O -case O -may O -be O -) O -and O -finding O -me O -some O -sneaks O -. O -Can't O -wait O -for O -Thursday O -!! O - -RT O -@wkeving O -: O -Check O -out O -@slidedeck O -powerful O -Web O -Slider O -. O -Follow O -+ O -RT O -to O -enter O -for O -a O -Free O -Pro O -version O -each O -day O -in O -Sept O -http://bit.ly/SDcontest O - -@thelazymarmot O -ohh O -really O -? O -but O -well O -you O -saw O -most O -of O -the O -concert O -right O -? O -have O -you O -seen O -them O -before O -?? O - -@cassidyhaley O -ah O -, O -sorry O -hun O -. O -I'm O -not O -on O -call O -tonight O -, O -but O -I O -am O -a O -crisis O -worker O -, O -so O -you O -can O -call O -me O -anytime O -:) O - -@shaikhamah O -oh O -yeah O -30th O -. O -Sorry O -I O -am O -lost O -in O -time O -, O -my O -apologies O -m( O -. O -_ O -. O -)m O - -Been O -a O -long O -week O -. O -Tired O -. O -Sleep O -now O -please O -. O - -# O -howtohustle.net O -Prodigy O -Will O -Be O -Released O -In O -February O -http://bit.ly/c3yxYy O - -6 O -- O -close O -tonight O -. O -Its O -gonna O -be O -a O -longg O -evening O -! O - -Latest O -Fsbo O -Auctions O -| O -No O -Qualifying O -Loan O -: O -US O -$ O -32.00 O -. O -End O -Date O -: O -Tuesday O -Oct-12-2010 O -20:30 O -: O -22 O -PDT O -Buy O -It O -Now O -for O -on O -... O -http://bit.ly/9IV2ex O - -Made O -it O -back O -home O -to O -GA B-geo-loc -. O -It O -sucks O -not O -to O -be O -at O -Disney B-facility -world I-facility -, O -but O -its O -good O -to O -be O -home O -. O -Time O -to O -start O -planning O -the O -next O -Disney B-facility -World I-facility -trip O -. O - -Haha O -I O -seen O -a O -ped O -egg O -called O -a O -foot O -cheese O -grater O -today O -. O -I O -found O -it O -proper O -hilarious O -xD O - -SATURDAY O -NIGHT O -CAST O -PARTY O -INVITATION O -http://conta.cc/csqjCc O -via O -#constantcontact O - -Stop O -by O -teh O -Nex-Tech B-company -and O -Nex-Tech B-company -Wireless I-company -tent O -at O -TailGreat B-other -tomorrow O -evening O -for O -Free O -ice O -cream O -, O -bottles O -of O -water O -and O -an O -FHSU B-sportsteam -foam O -finger O -. O - -tomorow O -on O -day O -off O -!! O -youppppiiieeee O - -< O -3 O -@CamrynRocks O -always O -brightens O -up O -my O -day O -. O -you O -should O -follow O -her O -and O -listen O -to O -her O -wonderful O -music O -. O -< O -3 O - -Excited O -to O -spend O -time O -with O -my O -best O -friend O -and O -relax O -this O -weekend O -! O -Been O -a O -crazy O -intense O -but O -GOOD O -week O -! O - -ok O -bed O -time O -. O -I O -think O -i'ma O -curl O -up O -and O -watch O -Sabrina B-tvshow -!!! O -Thanks O -@louisha O -!!! O -x O - -Today O -was O -a O -good O -day O -, O -enjoyed O -myself O -:) O - -@jimmiebjr O -Nope O -. O -I O -think O -you O -believe O -she O -'s O -a O -time O -bomb O -and O -should O -be O -sacrificed O -if O -necessary O -. O - -I O -wanna O -go O -to O -the O -mall O -SO O -bad O -. O -Not O -enough O -hours O -in O -a O -day O - -CampaignMonitor B-other -denied O -list O -of O -< O -600 O -names O -: O -client O -can't O -prove O -names O -not O -harvested O -fr O -Twitter/FB O -. O -Email'd O -to O -this O -same O -list O -when O -it O -was O -< O -500 O - -Oh O -and O -who O -'s O -fixin O -' O -to O -go O -to O -MAiZE B-facility -tomorrow O -night O -with O -some O -people O -and O -I O -? O - -@PageVGP O -I'm O -pretty O -sure O -it O -doesn't O -exist O -.. O -I O -said O -it O -when O -I O -was O -really O -young O -, O -and O -I O -still O -say O -it O -now O -.. O - -Small O -Biz O -Tech O -Tour O -2010 O -Launches O -Five O -City O -Tour O -MONTCLAIR B-geo-loc -N.J. B-geo-loc -...: O -The O -all O -day O -event O -features O -America's... O -http://tinyurl.com/28hd9fu O -#fb O - -@jonronson O -Ask O -yourself O -what O -the O -Mail O -on O -Sunday O -would O -do O -, O -and O -do O -the O -opposite O -. O - -Season O -3 O -of O -Friday B-tvshow -Night I-tvshow -Lights I-tvshow -... O -It O -seems O -that O -no O -one O -at O -Dillion B-facility -High I-facility -School I-facility -graduates O -. O - -Let O -'s O -go O -to O -the B-facility -funny I-facility -bone I-facility -to O -see O -@deraydavis O -RT O -@MzT_If_UNasty O -: O -So O -wat O -we O -doing O -tonight O -? O -@coley_baby O -@chelsea_fay11 O -@hoggy_allday O - -weekend O -saturday O -- O -c O -daij O -sunday O -church O -saturday O -evening O -- O -get O -sht O -faced O -sunday O -evenin O -home O -work O -wat O -a O -life O -monday O -school O -hbu O -? O - -When O -you're O -dreaming O -with O -a O -broken O -heart O -Then O -waking O -up O -is O -the O -hardest O -part O - -OMG O -! O -Miley B-person -Cyrus I-person -Has O -A O -14-Year-Old O -Stalker O -!: O -First O -Paris B-person -Hilton I-person -, O -now O -Miley B-person -Cyrus I-person -! O -Cops O -responded O -to O -a O -break O -in O -cal O -... O -http://bit.ly/cd6OPC O - -@Jsmoothhh O -it O -'s O -not O -supposed O -to O -rain O -till O -Sunday O -night O -. O - -My O -boobs O -look O -HUGE O -today O -! O -=D O - -RT O -@mari_dj O -: O -e O -nem O -joguei O -direito O -. O -no O -outro O -time O -estava O -o O -monstro O -do O -vitor O -D O -: O -ushuahsahs O - -@princessmariam9 O -i O -know O -.. O -i O -need O -to O -stop O -before O -my O -room O -is O -gonna O -get O -filled O -with O -water O -. O - -RT O -@BomadeBeverage O -#TEAMBOMADE O -everywhere O -! O -today O -from O -7-10pm O -Golf O -Mixer O -at O -Trophy B-facility -Club I-facility -Gwinnett I-facility -3254 O -Clu O -... O -http://schmap.it/YoLAXI?a O -< O --- O -MAP O - -Next O -week O -is O -Spirit B-other -Week I-other -at O -school O -... O -Homecoming B-other -Week I-other -... O -And O -Picture O -day O -on O -Tueday O -=\ O - -I O -think O -that O -today O -the O -party O -has O -been O -canceled O -makes O -too O -much O -air O -in O -the O -street O -loll O -. O -miss O -u O -twitfriends O -. O -xx O - -RT O -@josielovesmcr O -: O -Danger B-other -Days I-other -: I-other -True I-other -Lives I-other -of I-other -The I-other -Fabulous I-other -Killjoys I-other -#mychemicalromance O - -Yesterday O -my O -son O -forgot O -his O -jacket O -at O -school O -. O -Today O -he O -remembered O -to O -bring O -home O -the O -jacket O -, O -but O -forgot O -his O -lunchbox O -. O - -Please O -, O -I O -wanna O -apologize O -in O -advance O -for O -my O -boundless O -verbosity O -tonight O -. O -I'm O -UI(as O -in O -DUI O -) O -feel O -me O -? O - -This O -Time O -'s O -fundraiser O -ends O -9/20 O -. O -Incentives O -include O -raffles O -& O -access O -to O -the O -video O -shoot O -ustream O -- O -http://www.cassidyhaley.com/thistime.php O - -@BadgerBlogger O -LOL O -! O -Yes O -it O -would O -have O -. O -" O -Let O -'s O -have O -one O -every O -dang O -day O -!" O - -@aplusk O -http://www.youtube.com/watch?v=eLMui7zBiXo O -we O -beat O -kilkenny B-geo-loc -after O -they O -beat O -us O -for O -the O -last O -4 O -years O -in O -the O -hurling O -. O -Woo O -!!! O - -@jeffstinco O -where O -are O -you O -??? O -=D O -hey O -when O -a O -new O -videochat O -on O -spcrew O -??!!! O -;) O - -I O -need O -a O -place O -to O -work O -out O -with O -free O -weights O -for O -the O -day O -. O -somebody O -hook O -that O -up O -. O -#SwollOn O - -@MakethisRalate O -good O -luck O -at O -Elliot O -Miner O -:D O -yous O -were O -greaaaat O -tonight O -< O -3 O -xx O - -someone O -may O -need O -to O -take O -away O -my O -mind O -, O -to O -stop O -me O -constantly O -drowning O -in O -my O -thoughts O -. O - -I O -just O -took O -" O -Justin B-person -Bieber I-person -takes O -2 O -months O -off O -career O -to O -finish O -school O -, O -He O -is O -a O -huge O -..." O -and O -got O -: O -Part O -3 O -! O -Try O -it O -: O -http://tinyurl.com/246946v O - -every O -time O -u O -smile O -=) O - -RT O -@tommcfly O -: O -Working O -on O -some O -final O -Super O -Site O -stuff O -all O -day O -. O -Can't O -believe O -the O -Super B-company -City I-company -is O -nearly O -open O -! O - -i've O -had O -a O -wicked O -week O - -Time O -to O -fire O -up O -the O -grill O -again O -. O -I O -do O -believe O -it O -will O -be O -chicken O -. O - -Facebook B-company -Job-Hunting O -App O -BranchOut O -Raises O -$6 O -Million O -From O -Accel B-company -And O -Super B-company -Angels I-company -: O -When O -you O -want O -to O -hang O -out O -with O -... O -http://bit.ly/aLTo95 O - -Only O -a O -few O -short O -weeks O -until O -the O -Great B-other -Pumpkin I-other -rises O -! O -Maybe O -he O -will O -bring O -me O -an O -iPad B-product -! O -http://grtpmpkin.com/4fj O - -I've O -listened O -to O -Tenshi B-other -ni I-other -Fureta I-other -yo I-other -so O -many O -times O -that O -I'm O -starting O -to O -memorize O -the O -lyrics O -, O -and O -it O -'s O -only O -been O -out O -for O -4 O -days O -! O -o O -. O -O O - -@DaylySpecialz O -im O -doin O -good O -, O -just O -maxin O -& O -relaxin O -on O -my O -day O -off O -... O -i O -have O -a O -4 O -day O -stetch O -startin O -tomorrow O -. O - -@JazzLuvsJedward O -Yeah O -tomorrow O -'s O -grand O -( O -: O -= O -yeah O -just O -email O -me O -cause O -I O -never O -go O -on O -msn B-company -! O -I'll O -dm O -you O -< O -3 O - -Shopping O -day O -with O -JC B-person -- O -so O -excited O -! O - -I O -just O -took O -" O -After O -getting O -trampled O -at O -a O -Justin B-person -Bieber I-person -concert O -, O -yoiu O -wake O -up O -and O -..." O -and O -got O -: O -Part O -6 O -:) O -! O -Try O -it O -: O -http://tinyurl.com/26zeju5 O - -@CarolynAWebster O -I'm O -okay O -. O -Had O -better O -days O -. O - -@lovemj41042 O -yes O -agreed O -there O -are O -pro O -and O -cons O -in O -everything O -we O -do O -in O -life O -, O -and O -this O -is O -after O -all O -the O -greatest O -Human O -to O -have O -ever O -lived O - -@k8_walsh O -I've O -been O -out O -of O -the O -loop O -, O -but O -is O -Private B-tvshow -Practice I-tvshow -coming O -back O -this O -year O -? O - -@Jacksonter O -Haha O -, O -i O -laugh O -every O -time O -at O -the O -memory O -of O -your O -facial O -expression O -transforming O -: O -L O - -RT O -@eliseven O -: O -RT O -@yehudaberg O -: O -join O -me O -& O -thousands O -of O -people O -around O -the O -world O -tomorrow O -in O -a O -synchronized O -prayer O -4 O -peace O -. O -4pm O -PST O -. O -http:// O -... O - -@frankenteen O -So O -excited O -! O -But O -.... O -I O -just O -called O -into O -work O -Tuesday O -night...it O -'s O -horrible O -but O -at O -the O -same O -time O -really O -important O -to O -work O -. O - -Adam B-person -Beyer I-person -: O -Swedish O -Techno O -Pioneer O -: O -When O -it O -comes O -to O -his O -own O -DJing O -and O -sound O -, O -he O -'s O -slightly O -more O -diverse O -and O -likes O -... O -http://bit.ly/c1E0I1 O - -RT O -@Mickey__Rourke O -: O -58 O -years O -ago O -today O -I O -tumbled O -from O -my O -mother O -'s O -womb O -with O -a O -cigarette O -in O -my O -mouth O -and O -a O -chihuahua O -puppy O -under O -each O -arm O -... O - -I O -just O -took O -" O -( O -All O -Parts O -) O -+ O -(* O -New O -Season O -2 O -, O -All O -Parts*) O -You've O -been O -fri O -..." O -and O -got O -: O -PART O -10 O -, O -A O -MILLION O -YEARS O -!! O -Try O -it O -: O -http://bit.ly/bRCD9M O - -Time O -to O -begin O -looking O -for O -somewhere O -to O -live O -around O -Coolum B-geo-loc -. O -Sun O -, O -sand O -, O -surf O -and O -bikinis O -. O -Looking O -forward O -to O -my O -new O -phase O -in O -life O -. O - -RT O -@MyLifeAsLiz_Liz O -: O -When O -did O -being O -a O -nerd O -become O -cool O -& O -why O -wasn't O -it O -when O -I O -was O -in O -high O -school O -? O -I O -would've O -been O -the O -most O -popular O -chick O -... O - -@josiery O -Hope O -that O -I O -can O -make O -it O -on O -time O -. O -Wish O -that O -I'll O -be O -an O -' O -S.S O -' O -in O -the O -end O -of O -the O -year O -:) O - -@ethernat O -You O -know O -me O -, O -if O -I O -can O -be O -kind O -and O -guilt O -you O -at O -the O -same O -time O -, O -well O -, O -my O -job O -is O -done O -:) O - -Costco B-company -Irvine B-geo-loc -has O -fresh O -sushi O -samples O -today O -. O -Awesome O -. O - -RT O -@PhilKeoghan O -: O -Private O -sneak O -peak O -of O -' O -The B-movie -Ride I-movie -' O -my O -ride O -across O -USA B-geo-loc -, O -tonight O -only O -, O -San B-geo-loc -Francisco I-geo-loc -6pm O -contact O -Jennifer B-person -. O -Gainza@nmss O -. O -org O - -Wonder O -what O -the O -new O -Green B-musicartist -Day I-musicartist -album O -will O -called O -, O -Tre O -'s O -Penis O -? O - -RT O -@m_candelaria O -: O -The O -amazing O -Follow O -Friday O -Train O -: O -@Sketchjobs O -@lollieshopping O -@philadelphiabn O -@elainebiss O -@TableMatters10 O -@STALKmyPRETT O -... O - -" O -Always O -play O -them O -before O -they O -can O -play O -you O -" O -- O -Joy B-person -Huhn I-person - -RT O -@_RANGO_ O -: O -Hey O -y'all O -" O -East B-other -Nu I-other -," O -round O -Two O -tonight O -. O -See O -y'all O -early O -! O -(shit O -!!!! O -I O -should've O -stayed O -lol O - -@colintattum O -It O -'s O -already O -tomorrow O -in O -Australia B-geo-loc -so O -can O -you O -tell O -just O -me O -now O -?! O - -@YFarahat O -@n_atalla O -I O -stopped O -buying O -bottled O -water O -for O -like O -2 O -years O -:) O -installed O -a O -water O -filter O -and O -it O -'s O -great O -. O -Water O -taste O -like O -water O -now O -:) O - -@Tink3rbell94 O -going O -to O -GA B-geo-loc -, O -don't O -know O -why O -but O -when O -I O -thought O -about O -it O -ATL B-geo-loc -stood O -out O -in O -my O -mind O -. O -I O -have O -time O -to O -plan O -it O -properly O -though O -:) O - -The O -pope B-person -isn't O -really O -making O -much O -of O -an O -effort O -. O -He O -'s O -wearing O -the O -same O -clothes O -as O -yesterday O -. O - -2 O -days O -till O -#BoardwalkEmpire O - -@JodiWonderland O -@LeighWonderland O -@sharwonderland O -@kaseywonderland O -@CiCiWonderland O -You O -ladies O -were O -great O -today O -. O -Have O -a O -wicked O -weekend O -. O -xx O - -Justin B-person -Timberlake I-person -ft O -. O -Beyonce B-person -- O -Until B-other -the I-other -End I-other -of I-other -Time I-other -http://t.co/kUe6djJ O -via O -@youtube O - -@ahmong O -I'm O -thinking O -they O -trade O -him O -before O -the O -deadline O -and O -try O -to O -blow O -it O -up O -so O -they O -don't O -lose O -him O -for O -nothing O -. O - -@glorybeful O -Well O -... O -thats O -why O -they O -invented O -... O -tomorrow O -! O -:o O -D O - -omg O -, O -i'm O -sooooo O -ready O -for O -tonight O -! O -< O -3 O - -Any O -1 O -know O -when O -season O -3 O -True B-tvshow -Blood I-tvshow -is O -back O -on O -the O -telly O -? O - -@TripleThreat99 O -lol O -where O -u O -headed O -tonight O -? O - -RT O -@SavingGorillas O -: O -Kwiruka B-person -left O -her O -group O -several O -times O -after O -death O -of O -infant-unknown O -causes-late O -July O -; O -last O -seen O -alive O -Aug O -24 O -. O - -RT O -@DollarVanDemos O -: O -#BROOKLYN O -! O -Stay O -up O -late O -tonight O -to O -catch O -the O -1 O -hr O -special O -of O -Demos B-tvshow -@ O -3AM O -on O -BCAT B-other -feat O -@BlissyCakes O -@YaGirlNicolette O -... O - -Second-year O -CBs O -Jerraud B-person -Powers I-person -and O -Jacob B-person -Lacey I-person -don't O -remember O -much O -about O -Manning B-other -Bowl I-other -I I-other -. O -Both O -were O -college O -... O -http://bit.ly/at71uZ O - -ooh O -didn't O -know O -we O -was O -at O -home O -tomorrow O -. O -Silly O -billy B-person - -Ok O -off O -to O -bed O -BUT O -WHEN O -I O -WAKE O -UP O -I O -BETTER O -HAVE O -WON O -A O -T-SHIRT O - -RT O -@ATeenageThought O -: O -i O -hate O -it O -when O -my O -parents O -want O -me O -to O -come O -home O -early O -from O -a O -party O -, O -but O -i O -wanna O -stay O -really O -late O -#ATeenageThought O - -Yesterday O -'s O -shoot O -was O -unreal O -. O -Today O -is O -just O -as O -sick O -! O -Is O -that O -even O -possible O -?! O - -no O -work O -tonight O -. O -going O -to O -a O -sweet O -sixteen O -. O -shirt O -and O -tie O -. O -free O -food O -. O -why O -not O -. O - -Ah O -, O -that O -has O -seriously O -made O -my O -day O -. O -:D O -@chrisbrown O -, O -iloveyousomuch&hearts O -; O - -Totes O -just O -pissed O -off O -the O -Vista B-geo-loc -del I-geo-loc -Lago I-geo-loc -Eagles B-sportsteam -, O -well O -some O -of O -em O -by O -saying O -" O -Go O -Lancers B-sportsteam -!!" O -teehee O -. O -They're O -playing O -each O -other O -tonite O -! O -lol O - -RT O -@Georg_Grey O -: O -Man O -:" O -Is O -there O -any O -way O -for O -a O -long O -life O -?" O -Doctor O -: O -" O -Get O -married O -." O -Man O -:" O -Will O -it O -help O -?" O -Doctor O -:" O -No O -, O -but O -the O -thought O -of O -long O -li O -... O - -USM B-other -get O -ready O -yours O -truly O -tonight O -at O -frat B-facility -house I-facility -Hattiesburg I-facility -on O -the O -1s O -n O -2s O -!! O - -@Daniim92 O -tks O -:D O -obviously O -its O -one O -of O -the O -happiest O -day O -in O -my O -life O -lol O - -@DahlDana O -ooooh O -when O -'s O -he O -releasing O -it O -? O - -When O -I O -go O -to O -that O -musical O -I'm O -staying O -near O -old O -people O -so O -they'll O -cry O -w/me O -like O -Les B-movie -Miserables I-movie -:' O -) O - -Exhausted O -after O -a O -surprisingly O -busy O -( O -and O -boozy O -day O -) O -with O -@joemcelderry91 O -http://ow.ly/i/3TYe O -@whitneytweets O -@matthewlawton O -and O -Wonder B-person -Woman I-person -! O - -Hendrix B-person -died O -40 O -years O -ago O -... O -but O -his O -studio O -lives O -on O -. O -A O -look O -at O -NYC B-geo-loc -'s O -Electric B-company -Lady I-company -Studios I-company -, O -past O -and O -present O -: O -http://ow.ly/2G49m O - -RT O -@Yarcom O -: O -It O -'s O -Guinness B-company -O'clock. O -< O --- O -it O -'s O -almost O -that O -time O -over O -here O -on O -the O -west O -coast O -too O -. O - -Really O -hope O -I O -can O -get O -to O -@glasgowfilm O -for O -Winter B-movie -'s I-movie -Bone I-movie -- O -need O -to O -get O -on O -with O -job O -applications O -tonight O -then O -! O -Trailer O -: O -http://bit.ly/bhUlum O - -RT O -@J_Dizzle18 O -: O -11.04 O -. O -10 O -is O -gonna O -be O -a O -good O -day//yes O -.. O -I O -turn O -18 O -lol O - -Although O -, O -three O -months O -? O -I O -may O -well O -be O -wrong O -on O -this O -. O -#cantdecide O -#wilty O - -@MajorBerry O -Praying O -with O -ya O -that O -he O -answers O -& O -will O -be O -in O -Sweden B-geo-loc -, O -so O -you O -can O -meet O -him O -! O -I O -met O -him O -3 O -weeks O -ago O -& O -still O -thinking O -about O -it O -lol O -. O - -Ready O -for O -tonight O - -smh O -i O -love O -my O -male O -friends O -...... O -my O -female O -friends O -well O -i O -like O -yall O -most O -of O -the O -time O -lol O - -RT O -@jerilthompson O -: O -WaPo O -: O -renewing O -Bush B-person -tax O -cuts O -would O -" O -deprive O -" O -Treasury B-other -of O -$4T O -. O -No O -. O -Treasury B-other -deprived O -of O -$4T O -when O -Obama B-person -spent O -it O -all O -. O -# O -... O - -@write_as_rain O -you O -need O -to O -listen O -to O -that O -green B-musicartist -day I-musicartist -song O -. O - -were O -too O -much O -. O -MJ B-person -still O -had O -it O -but O -he O -was O -50 O -years O -old O -at O -time O -. O -AEG B-company -just O -wanted O -more O -money O -off O -his O -name O -like O -everyone O -else O -@JackieJackson5 O - -@RaniLovezYou O -Yeah O -, O -he O -'s O -had O -a O -few O -shows O -over O -here O -. O -Great O -chef O -, O -glad O -I O -wasn't O -disappointed O -! O -His O -wife O -gave O -birth O -a O -couple O -of O -days O -ago O -, O -aww O -. O - -Anyone O -is O -the O -valley O -want O -" O -Around B-product -The I-product -World I-product -Twice I-product -" O -written O -by O -me O -and O -Kathie O -? O -Just O -got O -a O -box O -half O -are O -gone O -won't O -last O -long O -. O -Inbox O -me O -:) O - -@rishi_syd O -hey O -, O -I'm O -fine O -!! O -Thanx O -:) O -u O -too O -have O -a O -great O -day O -;) O - -HAVE O -YOU O -HEARD O -DJ B-musicartist -STRATEGY I-musicartist -IS O -NOW O -AT O -VISIONS B-facility -LOUNGE I-facility -IN O -HICKORY B-geo-loc -, O -NC B-geo-loc -ON O -WEDNESDAY O -; O -S O -NIGHTS O -... O -WOW O -!! O -DONT O -MISS O -IT O -! O - -FAN O -Art O -in O -Club B-product -Penguin I-product -:) O -: O -Here O -are O -this O -weeks O -Club B-product -Penguin I-product -FUN O -Art O -Drawings O -:) O -The O -Snow O -Fort O -is O -a O -Super O -FUN O -Plac O -... O -http://bit.ly/bCc2MS O - -I O -was O -there O -on O -Tuesday O -!! O -heheRT O -@devialicious O -: O -otw O -pelabuhan O -ratu O - -Today O -I O -must O -make O -full O -use O -of O -the O -filter O -.. O -Please O -don't O -fail O -me O -today O -! O - -imma O -get O -my O -homework O -done O -today O -:) O - -Find O -out O -Why O -An O -Affiliate O -Home O -Business O -Is O -Better O -Than O -A O -Part O -Time O -Job O -: O -http://EzineArticles.com/4985692 O -EzineArticles O - -Every O -night O -& O -every O -day O -!! O - -I O -straightened O -my O -hair O -today O -i O -feel O -all O -purdy O -and O -junk O -:) O - -I O -just O -took O -" O -Justin B-person -Bieber I-person -takes O -2 O -months O -off O -career O -to O -finish O -school O -, O -He O -is O -a O -huge O -player O -..." O -and O -got O -: O -Part O -8 O -! O -Try O -it O -: O -http://bit.ly/cNarLp O - -@rach22uk_OM O -Aaaaah O -, O -us O -and O -Baveh B-person -Nate I-person -... O -meant O -to O -BE O -!! O -He O -was O -nice O -that O -day O -, O -couldn't O -fucking O -get O -rid O -of O -him O -and O -his O -inappropriate O -touching O - -Who O -'s O -excited O -for O -#UNM O -homecoming O -?! O -Check O -out O -the O -COP B-other -tailgate O -... O -65 O -years O -is O -definitely O -something O -to O -celebrate O -about O -. O -Oct O -. O -2 O -@ O -1 O -p O -. O -m O -. O - -spending O -time O -with O -my O -mom#priceless O - -Ahaha O -, O -the O -look O -on O -@__nancy O -' O -s O -face O -when O -someone O -asks O -' O -What O -part O -of O -the B-geo-loc -States I-geo-loc -are O -you O -from O -?' O -is O -PRICELESS O -. O - -@godsgirl8494 O -can O -u O -please O -follow O -me O -? O -you O -will O -make O -my O -day O -< O -3 O -8 O - -@WildLives O -I O -do O -. O -I O -expect O -it O -. O -It O -'s O -a O -lovely O -part O -of O -the O -day O -listening O -to O -a O -background O -of O -playground O -noises O -. O - -@singingislife15 O -Follow O -Friday O -:) O - -@spookytown O -omg O -was O -it O -Today O -?? O -ill O -go O -to O -bed O -soon O -so O -ill O -read O -tomorrow O -. O -Looking O -forward O -hehe O - -Coding O -typo O -of O -the O -day O -: O -" O -resluts O -" O -- O -> O -" O -results O -" O - -Confronting O -my O -hoarder O -tendencies O -. O -If O -anyone O -wants O -17 O -pairs O -of O -used O -size O -9 O -mens O -Nikes O -from O -10 O -years O -ago O -or O -60 O -blank O -cassette O -tapes O -... O - -Where O -in O -the O -world O -is O -Gelato B-person -Gilberto I-person -? O -Tonight O -the O -cart O -will O -be O -at O -Burning B-facility -Bush I-facility -Grille I-facility -, O -13206 O -W O -Highway O -42 O -,... O -http://fb.me/JIlT65a5 O - -even O -better O -than O -cocktail O -friday O -! O -http://is.gd/ffDb9 O - -@_AsToldByBV O -well O -i O -wrote O -you O -like O -6 O -times O -this O -week O -! O -urgh O -. O -RACHELLE B-person -im O -gonnauh O -kill O -YOU O -tonite O -! O - -Check O -it O -out O -!! O -$1497-$3497 O -per O -day O -!! O -WWW O -. O -bigbucksonline O -. O -info O - -For O -women O -the O -best O -aphrodisiacs O -are O -words O -. O -The O -G-spot O -is O -in O -the O -ears O -. O -He O -who O -looks O -for O -it O -below O -there O -is O -wasting O -his O -time O -. O - -@onephotojourney O -Thanks O -for O -the O -follow O -friday O -bump O -! O -:) O - -@EimearJedward O -auditioning O -on O -sunday O -:) O -xo O - -I O -want O -it O -NOW O -! O -;) O -RT O -: O -@corsairartisan O -Pumpkin B-product -Moonshine I-product -has O -arrived O -in O -Nashville B-geo-loc --- O -Give O -it O -a O -week O -to O -get O -it O -in O -stores O -. O -KY B-geo-loc -ships O -next O -week O -! O - -Headed O -to O -Davidson B-sportsteam -vs O -Blount B-sportsteam -with O -Bennett- B-person -love O -spending O -time O -with O -my O -son O -! O - -" O -compare O -where O -you O -are O -to O -where O -you O -wanna O -be O -and O -you'll O -get O -nowhere O -". O -Thanks O -for O -the O -reminder O -, O -Sara B-person -B I-person -. I-person -Focusing O -on O -the O -good O -. O -Show O -tonight O -! O - -hai O -@ayuhastari O -sukses O -ya O -untuk O -ngehostnya O -today O -di O -inbox O -, O -sy O -gak O -bisa O -liat O -nih O -wajib O -ngantor O -. O - -@MoochieBad O -I O -don't O -have O -none O -! O -What O -are O -urs O -? O -I O -have O -2 O -days O -with O -nothing O -to O -do O -! O - -RT O -@TheOutlawz O -: O -EVERYBODY O -WHO O -WANT O -A O -FOLLOW O -FROM O -ME O -FOR O -FOLLOW O -FRIDAY O -LET O -ME O -KNOW O -!! O -WE O -AINT O -LIKE O -THESE O -HOLLYWOOD B-geo-loc -AZZ O -RAPPERS O -WE O -FOLLOW O -BACK O -! O - -I O -know O -when O -my O -mom O -saw O -a O -picture O -of O -justin B-person -bieber I-person -she O -said O -that O -he O -was O -cute O -. O -She O -said O -she O -didn't. O -But O -, O -she O -did O -. O -Haha O -. O -:) O - -i O -love O -when O -good O -things O -happen O -to O -deserving O -people O -. O -Fills O -me O -with O -happiness O - -EXA-OIL O -. O -COM O -Deep B-company -Sea I-company -Intervention I-company -opens O -Singapore B-geo-loc -facility O -: O -September O -17,201 O -0 O --- O -Deep B-company -Sea I-company -Intervention I-company -has O -opened O -... O -http://bit.ly/cZZGj7 O - -I O -may O -have O -an O -extra O -ticket O -for O -the O -Jonas B-musicartist -Bros I-musicartist -this O -Sunday O -, O -if O -anyone O -wants O -it O -or O -wants O -me O -to O -take O -ur O -kiddo O -w/me O -:) O - -RT O -@sandiegozoo O -: O -Fossil O -dig O -update O -: O -found O -the O -flippers O -! O -Looks O -like O -a O -pretty O -complete O -3 O -million-year-old O -whale O -fossil O -. O -YG O - -The O -#gothic O -Daily O -is O -out O -- O -read O -this O -Twitter B-company -newspaper O -on O -http://bit.ly/aQOoSP O -( O -22 O -contributions O -today O -) O - -Starstruck B-company -Promo O -Special O -: O -Unlimited O -email O -blast(s O -) O -for O -$ O -350 O -a O -month O -. O -DM O -Me O -or O -send O -a O -email O -to O -ineedpromo@starstruckpromo O -. O -com O - -@journalproject O -For O -example O -, O -when O -younger O -, O -she O -won O -trophy O -, O -but O -results O -were O -messed O -up O -& O -some1 O -else O -really O -one O -. O -She O -refused O -to O -give O -it O -back O -. O - -@JennieGoingWest O -DO O -IT O -! O -it O -'s O -better O -with O -dirty O -hair O -too O -. O -when O -my O -hair O -is O -too O -clean O -it O -'s O -nearly O -impossible O -to O -do O -nything O -w O -it O -!!! O - -OCTOBER O -2ND O -1988 O -A O -STAR O -WAS O -BORN O -... O - -@artichoke_diep O -hey O -man O -we O -should O -hang O -out O -some O -time O -soon O -! O - -'I O -just O -took O -" O -You O -and O -justin B-person -bieber I-person -love O -story O -( O -long O -) O -" O -and O -got O -: O -you O -run O -away O -together O -! O -Try O -it O -: O -http://tinyurl.com/2brsuxs' O - -btw O -, O -i O -called O -him O -kitty O -boy O -' O -cause O -my O -friend O -always O -says O -" O -that O -'s O -maa O -kitty O -" O -when O -she O -sees O -him O -:D O -it O -'s O -kinda O -weard O -but O -cute O -at O -the O -same O -time O - -RT O -@FamousWomen O -: O -When O -you O -choose O -to O -forgive O -those O -who O -have O -hurt O -you O -, O -you O -take O -away O -their O -power O -. O - -Game O -@ O -Widener B-geo-loc -tonight O -at O -7:30 O - -I O -love O -and O -hate O -today O -, O -I O -swear O -I O -just O -got O -a O -flashback O -to O -my O -sophomore O -year O -. O - -Maikes O -hair O -. O -School O -. O -Webinar O -at O -the O -first O -job O -. O -Changed O -... O -ti O -second O -job O -... O -then O -time O -with O -the O -boy O -! O -What O -a O -day O -.; O -) O - -@_mcintyre O -agreed O -lolzaa O -also O -hate O -school O -for O -making O -a O -long O -lie O -10:30 O -. O -:( O -xx O - -@nickjonas O -i O -should O -write O -this O -before O -but O -i O -couldn O -t O -happy O -birthday O -:) O -i O -hope O -you O -enjoyed O -a O -lot O -yesterday O - -A O -slow O -yes O -beats O -a O -fast O -no O -7 O -days O -a O -week O -... O -dont O -rush O -people O -when O -dey O -make'n O -choices O -u O -might O -get O -told O -no O -just O -cuz O -dey O -want O -u O -2 O -shut O -up O - -Major O -Muslim O -organisations O -throw O -weight O -behind O -6 O -Nov O -demo O -against O -racism O -, O -fascism O -and O -Islamophobia O -http://uaf.org.uk/1465 O - -@sefronia O -we O -haven't O -had O -slugs O -in O -the O -house O -for O -a O -few O -days O -and O -found O -a O -toad O -by O -our O -front O -door O -this O -morning O -, O -you O -need O -pet O -toad O -. O - -Mostly O -Cloudy O -and O -64 O -F O -at O -Buffalo B-geo-loc -Municipal I-geo-loc -Airport I-geo-loc -, O -MN B-geo-loc -Winds O -are O -Calm O -. O -The O -humidity O -is O -68% O -. O -Last O -Updated O -on O -Sep O -17 O -20 O -http://s1z.us/yb.htm O - -Tomorrow O -is O -Saturday O -. O -Aka O -#dreadlock O -day O -. O -Bring O -on O -the O -twist O -n O -rip O -... O -#TnR O -#reggae O -#rasta O -#marley O -#bobmarley O - -A O -BLISSFUL O -MORNING O -TO O -ALL O -! O -It O -'s O -a O -brand O -new O -day O -to O -start O -with O -. O -Hope O -you O -have O -a O -great O -day O -ahead O -! O -Keep O -safe O -. O -Take O -care O -. O -God O -bless O -you O -! O - -If O -I O -could O -sleep O -well O -tonight O -that O -would O -be O -great O -thanks O -. O - -@luvtotalk2u O -you O -have O -cracked O -me O -up O -with O -your O -tweets O -today O -babe O -. O -Love O -it O -!! O -X O - -Used O -a O -lot O -of O -Sellotape B-product -today O -:) O -printing O -on O -Monday O -;D O - -its O -a O -good O -day O -today O -! O - -RT O -@WakaFlocka1017 O -: O -SO O -WHEN O -I O -GET O -ON O -106 O -N O -JOKE O -AROUND O -YALL O -TALK O -ABOUT O -ME O -LIKE O -A O -DOG O -SHAWTY O -BUT O -WHEN O -I O -BUY O -30,000 O -WORTH O -SCHOOL O -SUPPLY O -N O -... O - -http://bit.ly/9sFpBN O -Last O -#Reach O -video O -today O -, O -I O -promise O -. O -This O -one O -is O -for O -everyone O -complaining O -about O -the O -sword O -lunge O -being O -gone O -. O -Sprint O -! O - -right O -. O -Five O -Star O -Day O -came O -out O -in O -may O -this O -year O -. O -why O -haven't O -ANY O -trailers O -come O -out O -here O -in O -Scotland B-geo-loc -yet O -? O -i've O -waited O -about O -a O -year O -for O -this O -! O - -@ScottBourne O -Oh O -hi O -, O -I O -swear O -I O -can O -scroll O -down O -past O -actual O -text O -on O -a O -page O -. O -I'll O -definitely O -pick O -that O -up O -this O -next O -week O -. O -Thanks O -. O -:) O - -18 O -Sep O -10 O -07:50 O -, O -Speed O -: O -6.0 O -, O -Gust O -: O -22.0 O -, O -Lull O -: O -0.0 O -, O -Dir O -: O -WNW O -, O -Temp O -: O -4.0 O - -@Jeaworld O -Happy O -Birthday O -JeA B-person -Unnie I-person -Have O -lots O -of O -birthday O -fun O -May O -your O -birthday O -wishes O -all O -come O -true O -luv O -ya O -& O -take O -care O - -@swavelbean13 O -:D O -How O -was O -your O -day O -? O -:) O - -#fathers O -day O -Burl O -Fish O -: O -Fathers B-other -Day I-other -Trip O -: O -Fathers B-other -Day I-other -Trip O -. O -So O -the O -fishing O -has O -been O -super O -good O -this O -summer O -. O -I'... O -http://bit.ly/au121g O -#dad O - -'RT O -@keidence O -" O -Good O -Morning O -! O -Have O -a O -pleasant O -and O -happy O -day O -!" O -http://dlvr.it/5RcZC' O - -Cowboy O -fans O -remember O -when O -Da B-sportsteam -Bears I-sportsteam -demolished O -you O -guys O -44-0 O -in O -your O -own O -home O -in O -1985 O -?? O -same O -year O -we O -won O -the O -superbowl O -! O -#BEARDOWN O - -When O -you O -judge O -another O -you O -don't O -define O -them O -, O -you O -define O -yourself O -. O -~Wayne B-person -Dyer I-person - -@BEHINDpleasure O -October O -29th O - -forget O -homework O -, O -kick O -off O -your O -weekend O -the O -right O -way O -with O -The B-tvshow -Weekend I-tvshow -Grind I-tvshow -from O -8-10pm O -tonight O -! O - -2 O -MORE O -WEEKS O -! O -XD O - -@XboxSupport O -after O -making O -any O -changes O -to O -an O -avatar O -whole O -system O -chugs O -along O -and O -when O -booting O -up O -a O -game O -it O -freeze O -the O -system O -up O -. O -250g O -360-s O - -Music O -& O -More O -Industry O -Wednesday O -- O -Sep O -23,201 O -0 O -http://goo.gl/fb/5CBjZ O -#FABreality O - -'" O -Two O -ravens O -in O -the O -old O -oak O -tree O -And O -one O -for O -you O -and O -one O -for O -me O -And O -bluebells O -in O -the O -late O -December O -I O -see O -..." O -http://tumblr.com/xx9iucxjw' O - -Part O -2 O -to O -my O -day O -is O -about O -to O -begin O -.... O -hair O -stylist O -flow O -... O - -'RT O -@JustinsHairFlip O -: O -Justin B-person -was O -TOTALLY O -prancing O -around O -the O -room O -when O -he O -saw O -my O -username O -& O -he O -was O -screaming O -" O -OMG O -I O -HAVE O -A O -TWITTER B-company -FOR O -MA O -...' O - -'Rickey B-person -Smiley I-person -had O -me O -laughing O -all O -day O -today O -. O -" O -Um O -yes O -, O -Do O -you O -serve O -boiled O -rottweiler O -lips O -?"' O - -@jacobvanhorn O -, O -if O -our O -children O -are O -dressed O -in O -UT B-sportsteam -gear O -tomorrow O -, O -you O -will O -be O -sleeping O -on O -the O -sofa O -. O - -I O -hate O -that O -mini O -heart O -attack O -you O -get O -when O -you O -lose O -your O -phone O -and O -its O -on O -silent O - -@DesiiDanii4 O -nou O -ze O -is O -al O -een O -week O -best O -wel O -erg O -ziek O -, O -echt O -heel O -zielig O -!: O - -i O -have O -pride O -in O -myself O -that O -one O -day O -i O -will O -be O -#1 O -girl O -of O -the O -week O -and O -u O -have O -pride O -in O -your O -self O -that O -your O -dream O -will O -come O -true O -ok O -love O -u O -! O - -time O -#ONFRITA O - -RT O -@KenzieComersMum O -: O -" O -He B-other -'s I-other -Just I-other -A I-other -Boy I-other -" O -music O -video O -will O -be O -uploaded O -on O -to O -@mckenziecomer O -you B-company -tube I-company -channel O -today O -5 O -pm O -AEST O -Sat O -18th O -Sept O -, O -... O - -RT O -@botdfmusic O -: O -We O -will O -set O -up O -meet O -and O -greets O -for O -#theepictour O -before O -and O -after O -the O -shows O -. O -So O -everyone O -will O -get O -autographs O -and O -pictures O -... O - -@matthewstaylor O -yeah O -you O -will O -. O -you O -gonna O -mosh O -tonight O -? O - -Waiting O -for O -my O -mom O -to O -fucken O -pick O -me O -up O -the O -ganna O -go O -eat O -after O -off O -to O -garden B-geo-loc -grove I-geo-loc - -The B-facility -Metro I-facility -Sept O -26 O -Mannie B-person -Fresh I-person -and O -Jermaine B-person -Dupri I-person -fa O -da O -after O -party O -, O -holla O -at O -me O -fa O -ur O -tickets O - -Justice B-person -Breyer I-person -'s O -About O -Face O -: O -Koran-Burning O -Is O -Constitutionally O -Protected O -After O -All O -via O -Atlas B-other -Shrugs I-other -http://tinyurl.com/39wg73o O - -New O -Teeshirts O -ordered O -with O -all O -new O -designs O -... O -I O -hope O -people O -will O -like O -! O -October O -9th O -2010 O -... O -Remington B-other -'s I-other -Annual I-other -Octoberfest I-other -!! O - -Dinner O -tonight O -: O -Gnocchi B-product -with O -Italian B-product -Sausage I-product -, O -Swiss B-product -Chard I-product -and O -Ripe O -Tomatoes B-product -! O - -" O -her O -lips O -, O -her O -lips O -i O -could O -kiss O -them O -all O -day O -if O -she'd O -let O -me O -... O -hahahaha O -" O -lol O -sooooooooo O -cute O -< O -3 O - -RT O -@SonyElectronics O -: O -Sony/Bud O -Light O -3D O -tailgate O -parties O -on O -Saturday O -- O -Clemson B-geo-loc -party O -at O -http://bit.ly/cds8Pd O -; O -Auburn B-geo-loc -party O -at O -http://bit O -. O -... O - -cant O -believe O -im O -going O -to O -have O -to O -get O -up O -at O -half O -8 O -on O -sunday O -:( O -ahhhhh O -. O -should O -be O -a O -laugh O -though O -:) O - -@keshiaford O -how O -'s O -ur O -day O -going O -? O - -ok O -, O -starting O -BIG O -crash O -diet O -this O -week O -, O -just O -getting O -an O -exercise O -bike O -now O -& O -I O -will O -be O -at O -least O -2 O -dress O -sizes O -smaller O -by O -New O -year O -! O - -#FF O -@Ashley_Akalei O -Love O -her O -. O -She`s O -a O -sweetie O -when O -she`s O -in O -a O -good O -mood O -. O -Lol O -. O - -Bio B-product -Spot I-product -6 O -Month O -Spot O -On O -Flea O -& O -Tick O -Cats O -5 O -lbs O -& O -Over O -#E_BAY O -#forsale O -#supplies O -#pet O -#SFO O -http://3taps.com/w/ML2EQ4 O - -2 O -weeks O -until O -@Eurogamer_Expo O -. O -Am O -I O -excited O -? O -YOU O -BET O -:D O - -RT O -if O -you O -think O -that O -@justinbieber O -is O -sexy O -but O -at O -the O -same O -time O -very O -talented O -< O -3 O - -Looking O -to O -list O -another O -property O -tomorrow O -in O -Greenville B-geo-loc -, O -OH B-geo-loc - -On O -my O -way O -to O -my O -grandparents O -. O -And O -after O -a O -good O -nights O -sleep O -i O -head O -to O -the O -Tournament B-other -of I-other -Champions I-other -!!!!!!! O -Super O -excited O - -RT O -@RealJudgeJules O -: O -Playing O -the O -world O -exclusive O -of O -@SanderVanDoorn O -remix O -of O -@swedishousemfia O -- O -Miami B-other -2 I-other -Ibiza I-other -on O -the O -show O -tonight O -. O -Tune O -i O -... O - -@StarryEyedJoeJ O -like O -after O -I O -download O -a O -PSD O -from O -@ O -CherryPSDs O -what O -do O -I O -do O -?! O - -@dj279 O -do O -you O -have O -any O -new O -kasha O -this O -week O -? O - -@BeNFranKCaptivE O -yea O -yea O -yea O -... O -How O -was O -your O -day O -? O - -RT O -@TMZ O -: O -Lindsay B-person -Lohan I-person -tested O -positive O -for O -cocaine O -last O -week O -and O -that O -is O -why O -she O -failed O -her O -drug O -test O -... O -sources O -tell O -TMZ B-company -.... O -http://bi O -... O - -'MUSIC O -CHOICE O -JUST O -HIT O -ME O -SO O -IF O -YOU O -ON O -DEMAND O -TONITE O -" O -GOTTA B-other -BODY I-other -" O -MUST O -LISTEN O -( O -FREEKEY O -ZEKEY O -FT O -JIM B-person -JONES I-person -)' O - -Who O -'s O -going O -to O -be O -Club O -Champ O -this O -year O -?? O - -I'm O -mad O -coach O -d O -want O -me O -to O -go O -to O -workouts O -at O -9am O -tomorrow O -and O -I O -can't O -even O -play O - -@SaveLakeAlbert O -work O -both O -days O -. O -I'm O -not O -stressed O -. O -Will O -send O -photos O -of O -the O -view O -later O -. O - -@preciousweapons O -#filthyglaMOUR O -arrived O -at O -my O -school O -today O -http://twitpic.com/2pai07 O -LOOOK O -please O -39 O - -Im O -' O -performing O -tonight O -at O -8 O -Bond O -Street O -in O -Soho B-geo-loc -. O -If O -your O -in O -the O -city O -, O -hit O -me O -up O -, O -and O -then O -swing O -through O -around O -10ish O -! O - -@jessesco O -gosh O -... O -lots O -that O -i O -dont O -tweet O -about O -. O -tough O -few O -days O -. O -but O -things O -happen O -for O -a O -reason O -and O -life O -goes O -on O -:) O -I O -hope O -youve O -been O -well O -. O - -When O -I O -said O -good O -night O -, O -I O -ended O -up O -watching O -movies O -on O -my O -laptop O -. O -Yep O -. O -No O -sleep O -again O -. O -Time O -to O -take O -a O -bath O -now O -! O -It O -'s O -gonna O -be O -a O -looong O -day O -. O - -MITE O -SLEEP O -IN O -MY O -LIVINGROOM O -AINT O -DONE O -THAT O -FOR O -TIME O -! O -#TRANSPORTER O -#E4 O - -#MedicalJobs O -CT/Rad O -Tech O -- O -PRN O -: O -TX-Fort O -Worth O -, O -When O -physicians O -own O -the O -hospital O -, O -the O -latest O -advances O -in O -medical O -s O -... O -http://bit.ly/cVPhNE O - -After O -a O -great O -two O -days O -of O -meetings O -with O -Houston B-geo-loc -, O -Fes B-person -is O -back O -in O -Salt B-geo-loc -Lake I-geo-loc -. O - -@jess_robbo O -Haha O -I'm O -watching O -it O -too O -now O -and O -not O -for O -the O -first O -time O -, O -I O -love O -that O -programme O -:D O - -HAPPY O -FRIDAY O -! O -~madi O - -@sjonrefur O -Tuesday-Fight O -Night O -, O -Wednesday-MvC3 O -Fight B-facility -Club I-facility -in O -Chicago B-geo-loc -. O -Pringles B-product -, O -baby O -! O -Hope O -you O -get O -better O -by O -then O -! O - -I'm O -making O -Thai O -food O -tonight O - -My O -#twitter O -age O -is O -470 O -days O -13 O -hours O -18 O -minutes O -9 O -seconds O -. O -Find O -out O -yours O -at O -http://twitter.seocoder.org/ O -#twittertime O - -been O -reading O -shit O -about O -ppl O -have'n O -neon O -shit O -and O -black'n O -out O -after O -a O -night O -of O -#4lokos O -lol O -iown O -kno O -if O -I O -can O -trust O -it O -lol O -@JUICEBOXX513 O - -CCU B-other -exercise O -science O -program O -participates O -in O -national O -Walk B-other -at I-other -Work I-other -Day I-other -http://bit.ly/cJ0wpo O - -'Do O -it O -footsoldiers O -! O -RT O -@Jones_Drew32 O -: O -And O -speaking O -of O -rule O -#2 O -tonite O -" O -running B-tvshow -wit I-tvshow -mjd I-tvshow -" O -will O -be O -on O -live O -from O -7- O -9pm O -eastern O -sirius B-company -211 O -. O -Xm O -147' O - -@wackeychan O -Awesome O -! O -First O -episode O -goes O -up O -two O -weeks O -from O -Monday O -. O -Hope O -it O -meets O -your O -expectations O -. O - -just O -whooped O -st O -. O -francis O -preps O -asssss O -. O -and O -i O -scored O -a O -goal O -:) O -and O -its O -friday O -. O -and O -i O -have O -no O -homeworkkk O -. O -SICK O -LIFEEEE O - -RT O -@ichloegebbie O -: O -in O -december O -drinking O -horchata O -, O -I'd O -look O -psychotic O -in O -a O -balaclava O -( O -8 O -) O - -@daddiee_nini O -No O -... O -I'll O -try O -not O -too O -. O -I've O -missed O -too O -many O -days O -already O -... O - -'Listen O -to O -my O -new O -freestyle O -" O -September O -Freestyle O -".. O -http://www.youtube.com/watch?v=VLuGJgf-w3Y O -make O -sure O -you O -give O -me O -feedback O -on O -that O -...' O - -@AMILLI_SHEDIDDY O -it O -came O -just O -in O -time O -. O -And O -how O -you O -been O -. O -How O -'s O -work O -coming O -along O -?? O - -#MuchLove O -to O -my O -baby O -it O -will O -be O -3 O -months O -in O -a O -couple O -days O - -RT O -@BorisKodjoe O -: O -Okaaay O -! O -RT O -@NBC_Undercovers O -: O -Want O -a O -chance O -2 O -meet O -Boris B-person -Kodjoe I-person -? O -He'll O -be O -in O -NYC B-geo-loc -for O -a O -meet O -n O -greet O -next O -Wednesday O -! O -http O -... O - -Its O -clear O -and O -104&#xB0 O -; O -F O -- O -Humidity O -: O -7&#x25 O -; O -25 O -- O -Dew O -Pnt O -: O -28&#xB0 O -; O -F O -- O -Wind O -: O -WNW O -at O -9mph O -- O -Rain O -so O -far O -today O -: O -0.00 O -- O -http://cvwx.com O -#wxob O - -@WeLoveMay O -Heyy O -, O -how O -are O -u O -today O -? O -;) O -follow O -4 O -follow O -? O - -Radio O -Ga O -Ga O -: O -Collector O -fills O -his O -house O -with O -1,200 O -radio O -sets O -spanning O -more O -than O -100 O -years O -http://bit.ly/dgpvp1 O - -It O -makes O -me O -#aggy O -when O -I O -c O -#OneoFMYfollowErs O -retweet O -her O -crock O -of O -shyt O -disguised O -as O -a O -tweet O -lol O - -@PersonalSelena O -can O -you O -follow O -me O -pretty O -please O -. O -that O -would O -make O -my O -day O -. O -Peace O -! O - -I O -get O -high O -with O -love O -when O -I'm O -helping O -and O -being O -of O -service O -to O -you O -. O -Thank O -you O -for O -allowing O -. O - -#news O -Dems B-other -to O -voters O -: O -You O -may O -hate O -us O -, O -but O -GOP B-other -is O -worse O -( O -AP B-other -) O -( O -Yahoo B-company -! O -) O -: O -Share O -With O -Friends O -: O -| O -Latest O -Top O -Ne O -... O -http://adpro.co/aQxQtY O - -Social O -networking O -site O -Facebook B-company -officially O -has O -500 O -million O -users O -, O -the O -company O -announced O -21 O -July O -http://ow.ly/20ATc O - -Gak O -bobok O -lg O -aja O -? O -:D O -RT O -@RatihNurma O -: O -Knp O -may O -? O -Ayo O -bangun O -yok O -, O -kita O -lari2 O -biar O -ga O -sedih O -:D O -RT O -@mayas_24 O -: O -Ya O -Allah O -.. O -pgn O -nangis O -.. O - -My O -#twitter O -age O -is O -538 O -days O -13 O -hours O -14 O -minutes O -38 O -seconds O -. O -Find O -out O -yours O -at O -http://twitter.seocoder.org/ O -#twittertime O - -Lohan B-person -Failed O -A O -Drug O -Test O -: O -Lindsay B-person -Lohan I-person -may O -be O -in O -trouble O -once O -again O -. O -The O -actress O -, O -who O -recently O -completed O -a O -stint O -... O -http://bit.ly/9GEfEa O - -How O -is O -it O -that O -the O -same O -ppl O -who O -complain O -about O -crime O -and O -potholes O -r O -outraged O -@ O -the O -idea O -of O -paying O -an O -extra O -$2/month O -to O -improve O -those O -things O -? O - -@rachel_luttrell O -love O -it O -when O -people O -can O -find O -good O -in O -a O -bad O -situation O -. O -Thanks O -for O -being O -an O -inspiration O -to O -me O -again O -. O -:) O - -datjayycarterkidd O -asked O -: O -omg O -hey O -sir O -?? O -omg O -what O -color O -are O -oyu O -eyes O -?? O -hit O -up O -the O -ask O -box O -when O -you O -can O -!! O -http://tumblr.com/xn3iud2q8 O - -I O -pray O -an O -give O -God B-person -glory O -even O -when O -im O -in O -pain O -, O -hurting O -, O -or O -crying O -. O -24/7 O -God B-person -is O -great O -!!! O - -RT O -@atlantamade1986Shorts O -or O -leggings O -tonight O -??" O -Leggings O -. O -It O -'s O -sumthin O -bout O -em O -I O -like O -on O -a O -woman O -... O -cuz O -u O -can O -c O -da O -cameltoe O -that O -'s O -why O -.. O -lol O - -I'm O -not O -even O -tired O -but O -i O -have O -to O -go O -to O -sleep O -or O -i O -will O -be O -like O -a O -zombie O -at O -work O -tomorrow O - -RT O -@iRespectFemales O -R O -E O -T O -W O -E O -E O -T O -if O -you O -stare O -at O -the O -same O -text O -for O -a O -long O -time O -because O -you O -can't O -figure O -out O -how O -to O -reply O -to O -it O - -meet O -the O -luckiest O -guy O -in O -america B-geo-loc -... O -soooo O -jealous O -!: O -RT@vincewelch O -My O -1st O -trip O -to O -Fenway B-geo-loc -tonight O -http://twitpic.com/2pdwoo O - -@JuicylyJas O -iiqht O -im O -bout O -to O -look O -into O -sunday O -for O -.. O - -@jasonderulo O -jason B-person -, O -coming O -to O -see O -you O -at O -Newcastle-Upon-Tyne B-geo-loc -in O -Feb O -, O -cant O -wait O -!! O -How O -are O -you O -? O -xx O - -@drugmonkey O -First-time O -commenters O -automatically O -go O -to O -moderation O -, O -so O -I'll O -need O -to O -publish O -it O -by O -hand O -, O -but O -I'm O -watching O -for O -it O -. O - -i O -didnt O -know O -people O -were O -so O -out O -of O -touch O -. O -before O -debates O -im O -just O -gonna O -start O -asking O -questions O -about O -where O -they O -got O -their O -info O -first O - -is O -up O -and O -ready O -for O -his O -last O -day O -on O -the O -punt O -until O -Stakes B-other -Day I-other -... O -#sadbuttrue O - -@Johnn_Dillinger O -nothing O -man O -. O -Tryna O -see O -was O -good O -for O -today O -. O -Was O -good O -with O -you O -? O - -So O -apperently O -there O -is O -a O -dance O -and O -game O -tonight O -..... O -I O -wonder O -if O -kierra O -will O -go O -with O -me O --hinthint- O -I O -hope O -she O -gets O -this O -to O -her O -phone O -e_e O - -I O -don't O -know O -what O -criteria O -does O -MTV B-company -use O -to O -give O -music O -awards O -these O -days O -. O - -'I O -just O -took O -" O -After O -getting O -trampled O -at O -a O -Justin B-person -Bieber I-person -concert O -, O -yoiu O -wake O -up O -and O -find O -J O -..." O -and O -got O -: O -part O -5 O -:) O -! O -Try O -it O -: O -http://bit.ly/9Uqiu5' O - -@nes1983 O -as O -a O -personal O -experience O -, O -I O -can O -tell O -that O -it O -takes O -a O -day O -before O -you O -can O -form O -hypotheses O -. O -Many O -don't O -even O -get O -that O -far O - -created O -some O -great O -rum O -based O -sig O -cocktails O -for O -a O -tasting O -today O -: O -Figgy B-other -Soda I-other -w/ O -a O -fresh O -fig O -puree O -& O -Rose B-product -Press I-product -w/ O -rose O -water O -& O -edible O -flowers O - -1 O -MO O -DAY O -TO O -GO O ------- O -> O -http://yfrog.com/jwoq9uj O - -@bduenas O -@HotCARLL O -@MxBIEson O -#Reach O -4v4 O -! O -Today O -BIackMaglc O - -'@krtgrphr O -it O -was O -" O -Oo O -Harii O -" O -once O -upon O -a O -time O -.. O -and O -the O -666 O -depict O -moodu O -namalu O -..' O - -@NeonGrizzly O -I'll O -call O -u O -after O -class O -pooky O -;D O - -- O -famous O -people O -looking O -at O -there O -followers O -going O -up O -in O -100s O -and O -thousands O -, O -me O -looking O -at O -my O -couple O -a O -day O -.. O - -@KrisReyes O -Have O -an O -awesome O -time O -on O -your O -travels!I'll O -miss O -seeing O -u O -on O -CityTV B-other -! O - -#icandowithout O -you O -, O -when O -I O -say O -it O -I O -will O -do O -it O -. O - -Top O -M&A O -news O -of O -the O -week O -- O -#India O -#Infoline O -- O -#Mauritius O -- O -#World O -> O -http://youreader.com/a.php?i=90133 O - -Work O -330-1130 O -come O -home O -shower O -bed O -up O -early O -for O -buckboardday B-other -parade I-other -then O -sanchez B-person -family O -reunion O -then O -betos B-person -gbye O -party O -so O -much O -for O -a O -day O -off O - -back O -from O -Verona B-geo-loc -:) O -Looking O -forward O -to O -come O -back O -in O -Paris B-geo-loc -next O -week O -. O -We O -plan O -a O -drink O -together O -friends O -? O - -@thisdog O -Two O -tracks O -- O -RGA O -& O -DGA O -ads O -came O -out O -at O -same O -time O -- O -neither O -candidate O -has O -control O -of O -those O -org O -'s O -Mitchell B-person -ad O -as O -@ASFried O -1/2 O - -Happy O -Constitution B-other -Day I-other -! O - -the O -day O -@justinbieber O -gets O -a O -girlfriend O -is O -the O -day O -world O -war O -3 O -starts O -lol O -:) O -true O -story O - -When O -too O -much O -choice O -is O -a O -bad O -thing O -, O -Joel B-person -on O -Technology O -http://bit.ly/9KKobr O - -" O -Just O -when O -I O -think O -I'm O -out O -, O -they O -pull O -me O -back O -in O -" O --Summer O -#Vancouver O -#summerstill O - -May O -have O -overdone O -it O -today O -... O -My O -back O -hurts O -... O -:-( O - -@TheAndyKaufman O -A O -couple O -weeks O -ago O -? O -It O -caught O -my O -attention O -, O -because O -my O -dad O -drove O -truck O -for O -Wal-mart/swift O -a O -few O -months O -back O -. O - -'RT O -@JReyez O -: O -The O -" O -Say O -Aah O -" O -crew O -in O -Toronto B-geo-loc -? O -November O -? O -Hahaha' O - -ahhh O -yes O -, O -a O -friday O -:) O -just O -chilled O -with O -madison B-person -! O -< O -3 O - -RT O -@Teacher_Spirit O -: O -know O -this O -today O -. O -no O -thing O -will O -ever O -come O -upon O -you O -and O -overtake O -you O -. O -the O -love O -of O -Jesus B-person -Christ I-person -will O -overtake O -it O -. O -He O -is O -... O - -3:12 O -am O -tweet O -just O -planned O -the O -day O -after O -reading O -loads O -of O -NEWS O -... O -tommorrow O -is O -a O -big O -day O -wipro B-company -'s O -interview O -checking O -my O -pronunciation O -..! O - -'RT O -@SFAC O -: O -Paint O -on O -a O -" O -free O -wall O -" O -with O -@chorboogie O -on O -Sunday O -thanks O -to O -@SFAC O -and O -@sfdpw O -. O -( O -via O -@SFist O -) O -http://bit.ly/bFGfwL' O - -Hurry O -up O -! O -Santy B-person -will O -be O -Leaving O -in O -2 O -days O -! O -- O -#Eskorte O -and O -#Massasje O -i O -#Norge O - -@nickjonas O -I O -just O -have O -to O -say O -that O -the O -photos O -that O -I've O -seen O -of O -you O -and O -Elvis B-person -on O -stage O -yesterday O -is O -just O -about O -the O -most O -adorable O -thing O -ever O -. O - -I O -am O -waiting O -for O -UOEM O -to O -finally O -wrapped O -before O -giving O -it O -a O -go O -again O -, O -I O -can't O -deal O -with O -it O -'s O -continous O -heartfail O -.. O - -Today O -'s O -health O -service O -lecture O -was O -actually O -interesting O -to O -me O -x O -) O - -Today O -is O -the O -COMPLETE O -OPPOSITE O -of O -yesterday O -- O -. O -- O - -Have O -a O -feeling O -my O -phone O -bill O -will O -be O -high O -this O -month O - -haha O -i O -luv O -weekends O -when O -i O -actually O -have O -plans O -, O -lol O -! O - -Salem B-geo-loc -Daily O -Dish// O -Chef O -'s O -Choice O -Menu O -at O -Spoons B-facility -NW I-facility -Bistro I-facility -for O -tonight O -...: O -Caramalized O -Onion O -Tarthousemade O -tart O -wit O -... O -http://bit.ly/98QmI9 O - -@karensuluay O -Hyna B-person -the O -Brad B-person -paisley I-person -concert O -we O -were O -supposed O -to O -go O -to O -is O -tonight O -*tear* O - -TODAY O -IS O -THE O -TEACHER'S B-other -DAY I-other -2 I-other -! O -HAPPY O -TEACHER'S B-other -DAY I-other -2 I-other -ME O -!!! O -YEAAAAHHHH O -!!! O -AGUANT O -LA O -DOCENSIA O -!!! O - -http://bit.ly/aqinZ2 O -wala O -na O -tlga O -c O -smarhelp O -naman O -: O -pa O -help O -nman O -jan O -mga O -kapatid O -anu O -proxy O -ng O -om O -http://bit.ly/9cAe9y O - -@NatalieRooney O -had O -nice O -day O -, O -this O -weather O -could O -be O -better O -. O - -[ O -Reuters O -Govt O -Reg O -] O -UPDATE O -1-Tighter O -rules O -for O -market O -makers O -post-'flash O -crash': O -NEW B-geo-loc -YORK I-geo-loc -, O -Sept O -17 O -( O -Reuters B-company -) O -- O -U O -. O -S O -.... O -http://bit.ly/bWUyBR O - -'RT O -@TheGhostOfYou_ O -: O -Adeus O -noites O -bem O -dormidas O -RT O -@Vivi_Venom O -" O -Danger B-other -Days I-other -: I-other -The I-other -True I-other -Lives I-other -Of I-other -The I-other -Fabulous I-other -Killjoys I-other -." O -22nd O -november' O - -First O -, O -I O -had O -these O -crazy O -dreams O -all O -last O -night O -. O -Then O -I O -sleep O -all O -day O -today O -... O -I O -hope O -nobody O -put O -some O -hoodoo O -on O -me O -! O - -If O -my O -calculation O -is O -right O -, O -Christmas B-other -is O -only O -98 O -days O -away O -... O - -@tach47 O -Did O -not O -know O -I O -would O -be O -moving O -there O -til O -Wednesday O -, O -had O -no O -advanced O -notice O -or O -else O -I O -would O -have O -it O -on O -and O -working O -Saturday O -morning O -! O - --CHANGE O -OF O -ROOM O -! O -- O -FATHER'S O -DAY O -auditions O -Sept O -25 O -CANWEST B-facility -Center I-facility -for O -Theater O -and O -Film O -U B-geo-loc -of I-geo-loc -W I-geo-loc -ROOM O -0T15 O -11:30 O -am O -- O -4:00 O -PM O -. O - -CLUB B-facility -BLU I-facility -tonite O -...... O -90 O -'s O -music O -.. O -oldskool O -night O -wiith O -dj B-musicartist -finese I-musicartist - -As O -women O -pay O -homage O -to O -masculinity O -they O -avoid O -their O -own O -power O -. O -It O -doesn't O -help O -anyone O -. O -Men O -need O -our O -leadership O -even O -when O -they O -don't O -like O -it O -. O - -OMG-Fact O -#14906 O -: O -Jellyfish O -have O -existed O -for O -over O -500 O -million O -years O -. O -Back O -in O -2007 O -, O -paleontologists O -discovered O -jell O -... O -http://bit.ly/b9yY3L O - -@FH14 O -I O -ironed O -mine O -just O -fine O -. O -You O -may O -want O -to O -wash O -it O -first O -, O -though O -, O -so O -it O -gets O -really O -soft O -. O -If O -you O -want O -to O -be O -extra O -safe O -do O -on O -the O -reverse O - -@LillaRebellato O -bah O -! O -Read O -the O -last O -one O -before O -the O -one O -before O -. O -Thought O -I O -was O -in O -then O -. O - -@iPukeRKOs O -I O -cant O -You'll O -have O -to O -Tune O -into O -the O -show O -this O -Sunday O -. O -Ill O -give O -ya O -a O -hint O -He O -'s O -a O -wrestler O -& O -has O -been O -in O -the O -business O -for O -20 O -+ O -yrs O - -Is O -making O -me O -purchase O -windows B-product -, O -antivirus O -and O -office B-product -when O -i O -buy O -a O -laptop O -not O -third O -line O -forcing O -? O - -Last O -film O -I O -can O -watch O -on O -a O -decent O -telly O -for O -a O -while O -tonight O -. O -Kick-Ass B-movie -is O -very O -tempting O -indeed O -. O - -A O -Few O -Clouds O -and O -69 O -F O -at O -Islip O -, O -Long B-geo-loc -Island I-geo-loc -Mac I-geo-loc -Arthur I-geo-loc -Airport I-geo-loc -, O -NY B-geo-loc -Winds O -are O -North O -at O -16.1 O -MPH O -( O -14 O -KT) O -. O -The O -pressure O -is O -http://s1z.us/vf.htm O - -#DiggyFollows50 O -right O -after O -his O -daddy O -stops O -tweeting O -via O -tub O - -You O -blink O -when O -you O -breathe O -and O -you O -breathe O -when O -you O -lie O -. O -You O -blink O -when O -you O -lie O -. O - -I'm O -pleased O -to O -have O -introduced O -@TheSmokingGunn O -to O -twitter B-company -. O -May O -he O -become O -as O -inane O -as O -me O -. O - -changed O -mii O -twitta O -name O -i O -wass O -really O -on O -mii O -nerd O -floww O -todaii O -them O -glasses O -are O -the O -best O -...... O -nerd O -glasses O -on O -deck O -... O -now O -time O -for O -church O - -RT O -@TLyric O -: O -@slimthugga O -when O -dat O -Gangsta O -video O -droppin O -bruh O -< O --- O -u O -late O -Lil O -bro O - -todaii O -was O -such O -a O -long O -day O - -The O -bank O -teller O -I O -always O -go O -to O -wants O -me O -. O -Lol O -. O -No O -shit O -. O -She O -left O -me O -wide O -open O -for O -tomorrow O -. O -Too O -bad O -I O -already O -have O -fucking O -plans O -! O - -Anyone O -up O -for O -some O -late O -night O -trivia O -tonight O -? O -10pm O -at O -Summits B-facility -. O -Let O -me O -know O -! O - -@bieberarmy_asia O -hey O -i O -gotta O -ask O -you O -something O -if O -i O -can O -:) O -please O -tweet O -me O -when O -you O -be O -online O -and O -i'll O -send O -you O -DM O -. O -okkk O -? O -thank O -you O - -RT O -@obsidianchao O -: O -OF O -FUCKING O -COURSE O -. O -I O -GET O -HOME O -AND O -MY O -BROTHER O -IS O -ON O -THE O -MOTHER O -FUCKING O -XBOX B-product -. O -Worst O -fucking O -day O -ever O -. O - -Interesting O -1st O -week O -of O -work O -( O -during O -training O -) O -1 O -of O -the O -6 O -ppl O -decided O -the O -job O -wasnt O -for O -her O -, O -and O -the O -firm O -decided O -to O -let O -go O -another O -guy O -. O - -2 O -years O -99 O -days O -3 O -hours O -36 O -minutes O -and O -20 O -seconds O -Until O -Mayan B-other -Doomsday I-other -, O -DEC O -22 O -2012 O -AD O - -@taqui_jade O -lol O -I O -snapped O -so O -quick O -too O -lmao O -some O -people O -just O -piss O -me O -off O -instantly O -when O -they O -open O -they O -mouth O -lol O - -Can O -have O -any O -girl O -I O -choose O -but O -as O -time O -goes O -by O -baby O -im O -goin O -home O -wit O -u O - -RT O -@jettathatsme O -: O -#nowplaying- O -This B-other -Is I-other -Our I-other -Someday I-other -- O -Big B-musicartist -Time I-musicartist -Rush I-musicartist -< O -3 O -:) O - -Home O -made O -cod O -& O -chips O -+ O -bottle O -of O -Microsoft B-company -provided O -champagne O -= O -top O -class O -Friday O -;-) O - -http://yfrog.com/n6cdhzj O -Just O -picked O -my O -dog O -up O -from O -his O -posh O -day O -spa O -. O -Actually O -he O -can't O -run O -under O -vet O -orders O -. O -He O -swims O -here O -occasionally O - -Google B-product -Music I-product -Rumors O -: O -Labels O -Happy O -About O -iTunes B-company -Alternative O -? O -- O -Techland B-company -- O -TIME O -. O -com O -http://bit.ly/a4kSfV O - -@Kieran_Doyle O -ew O -suga O -on O -tea O -! O -wen O -i O -was O -yungr O -nd O -we O -adta O -giv O -stuf O -up O -4 O -lent O -i O -gav O -up O -suga O -on O -ma O -tea O -... O -afta O -da O -month O -i O -didnt O -lyk O -it O -nymor O -lol O - -@Quintessence_T O -I O -COME O -OUT O -HAIRY O -FACED O -ONLY O -DES O -DAYS O -IMMA O -TAKE O -SUM O -NEW O -PIX O -AIGHT O - -@DonnieWahlberg O -Hey O -hun O -... O -We O -are O -all O -here O -tonight O -. O -lol O -. O -Friday O -. O -oh O -yeah O -... O -lol O - -tell O -ur O -dad2bring O -the O -ypp O -back O -in O -Hayes O -we O -sorted O -it O -out O -last O -time O -I'm O -like O -yea O -I'll O -tell O -him O -*covers O -eyes*wat O -informing O -am O -I O -doing O -#llowit O - -@abydauz O -good O -luck O -with O -the O -audition O -Saturday O -. O -Remember O -positive O -thoughts O -!!!!! O - -RT O -@NPR O -: O -Song O -of O -the O -Day O -: O -Blonde B-musicartist -Redhead I-musicartist -: O -A B-other -Different I-other -Kind I-other -Of I-other -Ache I-other -http://n.pr/9O8yXE O - -@krtgrphr O -Arre O -no O -. O -After O -experiencing O -loos O -in O -Japan B-geo-loc -, O -I'm O -spoiled O -forever O -. O -Now O -everything O -is O -SulahShauchalay O -baraabar O -. O - -Iwebslog O -. O -com O -Jupiter B-geo-loc -making O -closest O -approach O -in O -nearly O -50 O -years O -( O -AP) O -: O -AP O -- O -Better O -catch O -Jupiter B-geo-loc -next O -... O -http://l.mytr.in/aQn2Kg O -#Blog O - -New O -#VideoRecorder O -#7 O -: O -Flip B-product -MinoHD I-product -Video O -Camera O --- O -8 O -GB O -, O -2 O -Hour O -Record O -Time O -: O -Flip B-product -MinoHD I-product -Video O -Camera O --- O -... O -http://dlvr.it/5P5kV O -#cheaptweet O - -Look O -n O -feel O -like O -a O -complete O -heffa O -today O -. O -FML O -. O - -Just O -bought O -my O -Newcastle B-geo-loc -K B-other -! I-other -Tour I-other -ticket O -! O -XD O -Can't O -wait O -for O -February O -! O - -Massive O -B-stock O -list O -! O -While O -supplies O -last O -. O -Check O -it O -out O -here O -: O -http://soniccircus.com/September-B-Stock-List O - -@Kirstyjarvie O -yesss O -!!!! O -I'm O -off O -after O -6 O -:) O - -really O -needs O -to O -buckle O -down O -and O -get O -some O -work O -done O -before O -she O -heads O -home O -... O - -RT O -@chelseakent O -: O -Going O -to O -eat O -at O -Loveless B-facility -Cafe I-facility -tonight O -! O -:) O - -Omg O -i O -just O -found O -Roy B-person -'s O -and O -Greg B-person -'s O -old O -band O -'s O -website O -! O -but O -when O -I O -clicked O -on O -it O -, O -it O -wouldn't O -work:'( O - -International B-other -Observe I-other -the I-other -Moon I-other -Night I-other -( O -9/18/2010 O -) O -| O -Saturday O -, O -Sep O -18 O -, O -06:00 O -PM O -| O -http://bit.ly/bVOdBF O -#cuboulder O - -Blegh O -, O -that O -fell O -through O -. O -Staffing O -agency O -had O -a O -miscommunication O -w\the O -client O -. O -So O -two O -week O -temp O -assignment O -fell O -through O -unfortunately O -. O - -Well O -I O -was O -gonna O -be O -a O -good O -girl O -today O -but O -@Luscious_Liz_ O -changed O -my O -mine O -#FuckeryFriday O -http://yfrog.com/mr8xqj O - -ooo O -@claireymarsh O -where O -are O -you O -when O -i O -need O -you O -.. O - -@DebVRuns O -hi O -pal O -!! O -How O -'s O -Hawaii B-geo-loc -?? O -when O -are O -you O -heading O -home O -! O - -Is O -counting O -down O -the O -days O -to O -Oct O -23rd O -and O -the O -Wright B-other -family I-other -Halloween B-other -bash O -!! O -It O -'s O -gonna O -be O -amazing O -.. O -they O -go O -all O -out O -for O -it O -! O - -@LisaMack324 O -yep O -, O -we O -are O -. O -hopefully O -we'll O -be O -done O -in O -time O -... O -> O -. O -< O - -@matttraynor O -ME O -! O -the O -november O -15th O -show O -( O -: O - -@webcowgirl O -You're O -supposed O -to O -fast O -after O -sundown O -, O -silly O -! O - -I O -hate O -when O -someone O -ask O -you O -" O -what O -'s O -wrong O -" O -& O -u O -say O -" O -long O -story O -" O -then O -they O -say O -" O -I've O -got O -time O -" O -okay O -I O -obviously O -don't O -want O -to O -tell O -you O -! O - -#nowplaying O -when B-other -im I-other -gone I-other -eminem B-person -:) O - -i O -saw O -@TATTEDUP_FLYTY O -one O -day O -walkin O -2 O -the O -store O -.. O -and O -this O -foo O -tripped O -and O -tried O -2 O -play O -it O -off O -LOL O -!!!! O - -going O -to O -tucson B-geo-loc -tonight O -:/ O - -iPad B-product -Refresh O -Due O -in O -January O -: O -Display O -manufacturers O -tip O -the O -world O -to O -the O -timing O -of O -the O -next O -iPad B-product -. O -IPad B-product -... O -http://bit.ly/9gplXp O - -Posted O -a O -new O -blog O -today O -about O -the O -new O -Lounge22 B-product -Powered O -by O -CORT B-company -collection O -! O -The O -furniture O -is O -divine O -: O -http://go.cort.com/5f O - -Just O -puffed O -on O -sour O -d O -unintentionally O -... O -I'm O -so O -high O -Idk O -when O -I O -will O -come O -back O -to O -earth O - -Win O -tix O -2 O -KELE B-person -and O -DOES B-musicartist -IT I-musicartist -OFFEND I-musicartist -YOU I-musicartist -, I-musicartist -YEAH I-musicartist -? I-musicartist -Sept O -21st O -@ O -Music B-facility -Box I-facility -: O -contest@ O -dimmak.com O -| O -Afterparty O -at O -Dim B-facility -Mak I-facility -Tuesdays O -@CinespaceLA O - -I O -wanna O -go O -to O -a O -SHS B-sportsteam -football O -game O -cuz O -I O -ain't O -been O -to O -a O -high O -school O -game O -in O -years O -. O -@themystikSAIDso O -make O -it O -happen O - -Follow O -Friday O -: O -Once O -again O -, O -I O -suggest O -you O -tell O -everyone O -you O -know O -to O -FOLLOW O -ME O -! O -Let O -'s O -get O -me O -over O -200 O -followers O -! O -Porn O -robots O -welcome O -! O -#ff O - -@diaryOFcrazy_T O -still O -trying O -to O -warm O -up O -my O -toes O -are O -cold O -as O -:) O -but O -just O -chillin O -no O -running O -today O -. O - -Stoked O -about O -the O -awesome O -date O -night O -im O -about O -to O -have O -with O -@sssmash O -and O -also O -very O -stoked O -about O -the O -bro O -time O -tomorrow O -with O -@jeffsyourhero O - -@MJay615 O -joining O -the O -awsm O -before O -I'm O -interested O -to O -see O -what O -they're O -bringing O -to O -it O - -Sleeping O -time O -. O -Morgenochtend O -maar O -even O -de O -Koperdoos O -een O -schoonmaakbeurtje O -geven O -... O - -About O -to O -get O -ready O -for O -work...don't O -have O -a O -good O -feeling O -about O -tonight O -#famouslastwords O - -... O -and O -the O -rules O -are O -? O -#corny O -RT O -@barzangi O -: O -I've O -said O -it O -before O -and O -I'll O -say O -it O -again O -: O -Indian B-geo-loc -food O -RULES O -. O - -@jodieboulger O -Ehe O -good O -:) O -see O -you O -tomorrow O -dude O -! O - -Tomorrow O -is O -another O -day O -! O - -Parking B-other -Day I-other -- O -Parking B-other -Day I-other -hosted O -by O -the O -Hudson B-other -TMA I-other -at O -Town B-facility -Square I-facility -Place I-facility -on O -Pavonia B-geo-loc -Avenue I-geo-loc -in O -the O -Newport B-geo-loc -... O -http://tumblr.com/xmyiud0zl O - -Five O -Bullpens O -to O -Stay O -Away O -From O -in O -MLB B-other -Odds O -: O -When O -you O -think O -of O -the O -worst O -teams O -in O -baseball O -, O -you O -think O -of O -teams O -tha O -... O -http://bit.ly/9R73CK O - -New O -Dorky O -Stuff O -: O -Friday O -Review O -Rundown O -! O -Sept O -17th O -Easy O -A O -vs O -. O -Devil O -vs O -. O -A O -Woman O -, O -A O -Gun O -, O -and O -A O -Noodleshop O -http://ow.ly/193oCy O - -Oh O -PEI B-person -, O -i O -always O -forget O -how O -beautiful O -you O -are O -. O -Especially O -when O -you O -stop O -hanging O -out O -with O -tourists O -. O - -workin O -at O -the O -ol O -Sunset B-facility -Bistro I-facility -tonight O -... O -come O -get O -some O -grub O - diff --git a/nlp_class2/ner_baseline.py b/nlp_class2/ner_baseline.py deleted file mode 100644 index 572332c8..00000000 --- a/nlp_class2/ner_baseline.py +++ /dev/null @@ -1,125 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python - -# data from https://github.com/aritter/twitter_nlp/blob/master/data/annotated/ner.txt -# data2 from http://schwa.org/projects/resources/wiki/Wikiner#WikiGold - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from sklearn.utils import shuffle -from pos_baseline import LogisticRegression - -def get_data(split_sequences=False): - word2idx = {} - tag2idx = {} - word_idx = 0 - tag_idx = 0 - Xtrain = [] - Ytrain = [] - currentX = [] - currentY = [] - for line in open('ner.txt'): - line = line.rstrip() - if line: - r = line.split() - word, tag = r - word = word.lower() - if word not in word2idx: - word2idx[word] = word_idx - word_idx += 1 - currentX.append(word2idx[word]) - - if tag not in tag2idx: - tag2idx[tag] = tag_idx - tag_idx += 1 - currentY.append(tag2idx[tag]) - elif split_sequences: - Xtrain.append(currentX) - Ytrain.append(currentY) - currentX = [] - currentY = [] - - if not split_sequences: - Xtrain = currentX - Ytrain = currentY - - print("number of samples:", len(Xtrain)) - Xtrain, Ytrain = shuffle(Xtrain, Ytrain) - Ntest = int(0.3*len(Xtrain)) - Xtest = Xtrain[:Ntest] - Ytest = Ytrain[:Ntest] - Xtrain = Xtrain[Ntest:] - Ytrain = Ytrain[Ntest:] - print("number of classes:", len(tag2idx)) - return Xtrain, Ytrain, Xtest, Ytest, word2idx, tag2idx - - -# def get_data2(split_sequences=False): -# word2idx = {} -# tag2idx = {} -# word_idx = 0 -# tag_idx = 0 -# Xtrain = [] -# Ytrain = [] -# for line in open('../large_files/aij-wikiner-en-wp3'): -# # each line is a full sentence -# currentX = [] -# currentY = [] -# line = line.rstrip() -# if not line: -# continue -# triples = line.split() -# for triple in triples: -# word, _, tag = triple.split('|') -# if word not in word2idx: -# word2idx[word] = word_idx -# word_idx += 1 -# currentX.append(word2idx[word]) - -# if tag not in tag2idx: -# tag2idx[tag] = tag_idx -# tag_idx += 1 -# currentY.append(tag2idx[tag]) - -# Xtrain.append(currentX) -# Ytrain.append(currentY) - -# if not split_sequences: -# Xtrain = np.concatenate(Xtrain) -# Ytrain = np.concatenate(Ytrain) - -# print("number of samples:", len(Xtrain)) -# Xtrain, Ytrain = shuffle(Xtrain, Ytrain) -# Ntest = int(0.3*len(Xtrain)) -# Xtest = Xtrain[:Ntest] -# Ytest = Ytrain[:Ntest] -# Xtrain = Xtrain[Ntest:] -# Ytrain = Ytrain[Ntest:] -# print("number of classes:", len(tag2idx)) -# return Xtrain, Ytrain, Xtest, Ytest, word2idx, tag2idx - - -def main(): - Xtrain, Ytrain, Xtest, Ytest, word2idx, tag2idx = get_data() - - V = len(word2idx) - print("vocabulary size:", V) - K = len(tag2idx) - - # train and score - model = LogisticRegression() - model.fit(Xtrain, Ytrain, V=V, K=K, epochs=5) - print("training complete") - print("train score:", model.score(Xtrain, Ytrain)) - print("train f1 score:", model.f1_score(Xtrain, Ytrain)) - print("test score:", model.score(Xtest, Ytest)) - print("test f1 score:", model.f1_score(Xtest, Ytest)) - -if __name__ == '__main__': - main() diff --git a/nlp_class2/ner_rnn.py b/nlp_class2/ner_rnn.py deleted file mode 100644 index 026914ef..00000000 --- a/nlp_class2/ner_rnn.py +++ /dev/null @@ -1,26 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -from ner_baseline import get_data -from pos_rnn import RNN - -def main(): - Xtrain, Ytrain, Xtest, Ytest, word2idx, tag2idx = get_data(split_sequences=True) - V = len(word2idx) - K = len(tag2idx) - rnn = RNN(10, [10], V, K) - rnn.fit(Xtrain, Ytrain, epochs=70) - print("train score:", rnn.score(Xtrain, Ytrain)) - print("test score:", rnn.score(Xtest, Ytest)) - print("train f1 score:", rnn.f1_score(Xtrain, Ytrain)) - print("test f1 score:", rnn.f1_score(Xtest, Ytest)) - - -if __name__ == '__main__': - main() diff --git a/nlp_class2/ner_tf.py b/nlp_class2/ner_tf.py deleted file mode 100644 index 7f8fa2c1..00000000 --- a/nlp_class2/ner_tf.py +++ /dev/null @@ -1,224 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -import tensorflow as tf -import os -import sys -sys.path.append(os.path.abspath('..')) -from pos_baseline import get_data -from sklearn.utils import shuffle -from util import init_weight -from datetime import datetime -from sklearn.metrics import f1_score - -from tensorflow.contrib.rnn import static_rnn as get_rnn_output -from tensorflow.contrib.rnn import BasicRNNCell, GRUCell - - - -def get_data(split_sequences=False): - word2idx = {} - tag2idx = {} - word_idx = 1 - tag_idx = 1 - Xtrain = [] - Ytrain = [] - currentX = [] - currentY = [] - for line in open('ner.txt'): - line = line.rstrip() - if line: - r = line.split() - word, tag = r - word = word.lower() - if word not in word2idx: - word2idx[word] = word_idx - word_idx += 1 - currentX.append(word2idx[word]) - - if tag not in tag2idx: - tag2idx[tag] = tag_idx - tag_idx += 1 - currentY.append(tag2idx[tag]) - elif split_sequences: - Xtrain.append(currentX) - Ytrain.append(currentY) - currentX = [] - currentY = [] - - if not split_sequences: - Xtrain = currentX - Ytrain = currentY - - print("number of samples:", len(Xtrain)) - Xtrain, Ytrain = shuffle(Xtrain, Ytrain) - Ntest = int(0.3*len(Xtrain)) - Xtest = Xtrain[:Ntest] - Ytest = Ytrain[:Ntest] - Xtrain = Xtrain[Ntest:] - Ytrain = Ytrain[Ntest:] - print("number of classes:", len(tag2idx)) - return Xtrain, Ytrain, Xtest, Ytest, word2idx, tag2idx - - - -def flatten(l): - return [item for sublist in l for item in sublist] - - - -# get the data -Xtrain, Ytrain, Xtest, Ytest, word2idx, tag2idx = get_data(split_sequences=True) -V = len(word2idx) + 2 # vocab size (+1 for unknown, +1 for pad) -K = len(set(flatten(Ytrain)) | set(flatten(Ytest))) + 1 # num classes - - -# training config -epochs = 5 -learning_rate = 1e-2 -mu = 0.99 -batch_size = 32 -hidden_layer_size = 10 -embedding_dim = 10 -sequence_length = max(len(x) for x in Xtrain + Xtest) - - - -# pad sequences -Xtrain = tf.keras.preprocessing.sequence.pad_sequences(Xtrain, maxlen=sequence_length) -Ytrain = tf.keras.preprocessing.sequence.pad_sequences(Ytrain, maxlen=sequence_length) -Xtest = tf.keras.preprocessing.sequence.pad_sequences(Xtest, maxlen=sequence_length) -Ytest = tf.keras.preprocessing.sequence.pad_sequences(Ytest, maxlen=sequence_length) -print("Xtrain.shape:", Xtrain.shape) -print("Ytrain.shape:", Ytrain.shape) - - - -# inputs -inputs = tf.placeholder(tf.int32, shape=(None, sequence_length)) -targets = tf.placeholder(tf.int32, shape=(None, sequence_length)) -num_samples = tf.shape(inputs)[0] # useful for later - -# embedding -We = np.random.randn(V, embedding_dim).astype(np.float32) - -# output layer -Wo = init_weight(hidden_layer_size, K).astype(np.float32) -bo = np.zeros(K).astype(np.float32) - -# make them tensorflow variables -tfWe = tf.Variable(We) -tfWo = tf.Variable(Wo) -tfbo = tf.Variable(bo) - -# make the rnn unit -rnn_unit = GRUCell(num_units=hidden_layer_size, activation=tf.nn.relu) - - -# get the output -x = tf.nn.embedding_lookup(tfWe, inputs) - -# converts x from a tensor of shape N x T x D -# into a list of length T, where each element is a tensor of shape N x D -x = tf.unstack(x, sequence_length, 1) - -# get the rnn output -outputs, states = get_rnn_output(rnn_unit, x, dtype=tf.float32) - - -# outputs are now of size (T, N, M) -# so make it (N, T, M) -outputs = tf.transpose(outputs, (1, 0, 2)) -outputs = tf.reshape(outputs, (sequence_length*num_samples, hidden_layer_size)) # NT x M - -# Linear activation, using rnn inner loop last output -logits = tf.matmul(outputs, tfWo) + tfbo # NT x K -predictions = tf.argmax(logits, 1) -predict_op = tf.reshape(predictions, (num_samples, sequence_length)) -labels_flat = tf.reshape(targets, [-1]) - -cost_op = tf.reduce_mean( - tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=logits, - labels=labels_flat - ) -) -train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost_op) - - - - -# init stuff -sess = tf.InteractiveSession() -init = tf.global_variables_initializer() -sess.run(init) - - -# training loop -costs = [] -n_batches = len(Ytrain) // batch_size -for i in range(epochs): - n_total = 0 - n_correct = 0 - - t0 = datetime.now() - Xtrain, Ytrain = shuffle(Xtrain, Ytrain) - cost = 0 - - for j in range(n_batches): - x = Xtrain[j*batch_size:(j+1)*batch_size] - y = Ytrain[j*batch_size:(j+1)*batch_size] - - # get the cost, predictions, and perform a gradient descent step - c, p, _ = sess.run( - (cost_op, predict_op, train_op), - feed_dict={inputs: x, targets: y}) - cost += c - - # calculate the accuracy - for yi, pi in zip(y, p): - # we don't care about the padded entries so ignore them - yii = yi[yi > 0] - pii = pi[yi > 0] - n_correct += np.sum(yii == pii) - n_total += len(yii) - - # print stuff out periodically - if j % 10 == 0: - sys.stdout.write( - "j/N: %d/%d correct rate so far: %f, cost so far: %f\r" % - (j, n_batches, float(n_correct)/n_total, cost) - ) - sys.stdout.flush() - - # get test acc. too - p = sess.run(predict_op, feed_dict={inputs: Xtest, targets: Ytest}) - n_test_correct = 0 - n_test_total = 0 - for yi, pi in zip(Ytest, p): - yii = yi[yi > 0] - pii = pi[yi > 0] - n_test_correct += np.sum(yii == pii) - n_test_total += len(yii) - test_acc = float(n_test_correct) / n_test_total - - print( - "i:", i, "cost:", "%.4f" % cost, - "train acc:", "%.4f" % (float(n_correct)/n_total), - "test acc:", "%.4f" % test_acc, - "time for epoch:", (datetime.now() - t0) - ) - costs.append(cost) - -plt.plot(costs) -plt.show() - - diff --git a/nlp_class2/neural_network.py b/nlp_class2/neural_network.py deleted file mode 100644 index d44c6f52..00000000 --- a/nlp_class2/neural_network.py +++ /dev/null @@ -1,141 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt -import random -from datetime import datetime - -import os -import sys -sys.path.append(os.path.abspath('..')) -from rnn_class.util import get_wikipedia_data -from rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx - -from markov import get_bigram_probs - - -if __name__ == '__main__': - # load in the data - # note: sentences are already converted to sequences of word indexes - # note: you can limit the vocab size if you run out of memory - sentences, word2idx = get_sentences_with_word2idx_limit_vocab(2000) - # sentences, word2idx = get_sentences_with_word2idx() - - # vocab size - V = len(word2idx) - print("Vocab size:", V) - - # we will also treat beginning of sentence and end of sentence as bigrams - # START -> first word - # last word -> END - start_idx = word2idx['START'] - end_idx = word2idx['END'] - - - # a matrix where: - # row = last word - # col = current word - # value at [row, col] = p(current word | last word) - bigram_probs = get_bigram_probs(sentences, V, start_idx, end_idx, smoothing=0.1) - - - # train a shallow neural network model - D = 100 - W1 = np.random.randn(V, D) / np.sqrt(V) - W2 = np.random.randn(D, V) / np.sqrt(D) - - losses = [] - epochs = 1 - lr = 1e-2 - - def softmax(a): - a = a - a.max() - exp_a = np.exp(a) - return exp_a / exp_a.sum(axis=1, keepdims=True) - - # what is the loss if we set W = log(bigram_probs)? - W_bigram = np.log(bigram_probs) - bigram_losses = [] - - t0 = datetime.now() - for epoch in range(epochs): - # shuffle sentences at each epoch - random.shuffle(sentences) - - j = 0 # keep track of iterations - for sentence in sentences: - # convert sentence into one-hot encoded inputs and targets - sentence = [start_idx] + sentence + [end_idx] - n = len(sentence) - inputs = np.zeros((n - 1, V)) - targets = np.zeros((n - 1, V)) - inputs[np.arange(n - 1), sentence[:n-1]] = 1 - targets[np.arange(n - 1), sentence[1:]] = 1 - - # get output predictions - hidden = np.tanh(inputs.dot(W1)) - predictions = softmax(hidden.dot(W2)) - - # do a gradient descent step - W2 = W2 - lr * hidden.T.dot(predictions - targets) - dhidden = (predictions - targets).dot(W2.T) * (1 - hidden * hidden) - W1 = W1 - lr * inputs.T.dot(dhidden) - - # keep track of the loss - loss = -np.sum(targets * np.log(predictions)) / (n - 1) - losses.append(loss) - - # keep track of the bigram loss - # only do it for the first epoch to avoid redundancy - if epoch == 0: - bigram_predictions = softmax(inputs.dot(W_bigram)) - bigram_loss = -np.sum(targets * np.log(bigram_predictions)) / (n - 1) - bigram_losses.append(bigram_loss) - - - if j % 10 == 0: - print("epoch:", epoch, "sentence: %s/%s" % (j, len(sentences)), "loss:", loss) - j += 1 - - print("Elapsed time training:", datetime.now() - t0) - plt.plot(losses) - - # plot a horizontal line for the bigram loss - avg_bigram_loss = np.mean(bigram_losses) - print("avg_bigram_loss:", avg_bigram_loss) - plt.axhline(y=avg_bigram_loss, color='r', linestyle='-') - - - # plot smoothed losses to reduce variability - def smoothed_loss(x, decay=0.99): - y = np.zeros(len(x)) - last = 0 - for t in range(len(x)): - z = decay * last + (1 - decay) * x[t] - y[t] = z / (1 - decay ** (t + 1)) - last = z - return y - - plt.plot(smoothed_loss(losses)) - plt.show() - - # plot W and bigram probs side-by-side - # for the most common 200 words - plt.subplot(1,2,1) - plt.title("Neural Network Model") - plt.imshow(np.tanh(W1).dot(W2)) - plt.subplot(1,2,2) - plt.title("Bigram Probs") - plt.imshow(W_bigram) - plt.show() - - - - diff --git a/nlp_class2/neural_network2.py b/nlp_class2/neural_network2.py deleted file mode 100644 index 159dc571..00000000 --- a/nlp_class2/neural_network2.py +++ /dev/null @@ -1,164 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt -import random -from datetime import datetime - -import os -import sys -sys.path.append(os.path.abspath('..')) -from rnn_class.util import get_wikipedia_data -from rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx - -from markov import get_bigram_probs - - -if __name__ == '__main__': - # load in the data - # note: sentences are already converted to sequences of word indexes - # note: you can limit the vocab size if you run out of memory - sentences, word2idx = get_sentences_with_word2idx_limit_vocab(2000) - # sentences, word2idx = get_sentences_with_word2idx() - - # vocab size - V = len(word2idx) - print("Vocab size:", V) - - # we will also treat beginning of sentence and end of sentence as bigrams - # START -> first word - # last word -> END - start_idx = word2idx['START'] - end_idx = word2idx['END'] - - - # a matrix where: - # row = last word - # col = current word - # value at [row, col] = p(current word | last word) - bigram_probs = get_bigram_probs(sentences, V, start_idx, end_idx, smoothing=0.1) - - - # train a shallow neural network model - D = 100 - W1 = np.random.randn(V, D) / np.sqrt(V) - W2 = np.random.randn(D, V) / np.sqrt(D) - - losses = [] - epochs = 1 - lr = 1e-2 - - def softmax(a): - a = a - a.max() - exp_a = np.exp(a) - return exp_a / exp_a.sum(axis=1, keepdims=True) - - # what is the loss if we set W = log(bigram_probs)? - W_bigram = np.log(bigram_probs) - bigram_losses = [] - - t0 = datetime.now() - for epoch in range(epochs): - # shuffle sentences at each epoch - random.shuffle(sentences) - - j = 0 # keep track of iterations - for sentence in sentences: - # do not one-hot encoded inputs and targets - sentence = [start_idx] + sentence + [end_idx] - n = len(sentence) - inputs = sentence[:n-1] - targets = sentence[1:] - - # get output predictions - hidden = np.tanh(W1[inputs]) - predictions = softmax(hidden.dot(W2)) - - # keep track of the loss - loss = -np.sum(np.log(predictions[np.arange(n - 1), targets])) / (n - 1) - losses.append(loss) - - # do a gradient descent step - # do it after loss since the calculation of doutput will overwrite predictions - # we don't want to make a copy because it would be slow - doutput = predictions # N x V - doutput[np.arange(n - 1), targets] -= 1 - W2 = W2 - lr * hidden.T.dot(doutput) # (D x N) (N x V) - dhidden = doutput.dot(W2.T) * (1 - hidden * hidden) # (N x V) (V x D) * (N x D) - # # for reference: - # # original: W1 = W1 - lr * inputs.T.dot(dhidden) # VxN NxD --> VxD - - # fastest way - W1_copy = W1.copy() - np.subtract.at(W1, inputs, lr * dhidden) - - # vs this - # W1_test = W1_copy.copy() - # oh_inputs = np.zeros((n - 1, V)) - # oh_inputs[np.arange(n - 1), sentence[:n-1]] = 1 - # W1_test = W1_test - lr * oh_inputs.T.dot(dhidden) - # assert(np.allclose(W1_test, W1)) - - # vs this - # W1_test = W1_copy.copy() - # i = 0 - # for w in inputs: # don't include end token - # W1_test[w] = W1_test[w] - lr * dhidden[i] - # i += 1 - # assert(np.allclose(W1_test, W1)) - - # keep track of the bigram loss - # only do it for the first epoch to avoid redundancy - if epoch == 0: - bigram_predictions = softmax(W_bigram[inputs]) - bigram_loss = -np.sum(np.log(bigram_predictions[np.arange(n - 1), targets])) / (n - 1) - bigram_losses.append(bigram_loss) - - - if j % 100 == 0: - print("epoch:", epoch, "sentence: %s/%s" % (j, len(sentences)), "loss:", loss) - j += 1 - - - print("Elapsed time training:", datetime.now() - t0) - plt.plot(losses) - - # plot a horizontal line for the bigram loss - avg_bigram_loss = np.mean(bigram_losses) - print("avg_bigram_loss:", avg_bigram_loss) - plt.axhline(y=avg_bigram_loss, color='r', linestyle='-') - - - # plot smoothed losses to reduce variability - def smoothed_loss(x, decay=0.99): - y = np.zeros(len(x)) - last = 0 - for t in range(len(x)): - z = decay * last + (1 - decay) * x[t] - y[t] = z / (1 - decay ** (t + 1)) - last = z - return y - - plt.plot(smoothed_loss(losses)) - plt.show() - - # plot W and bigram probs side-by-side - # for the most common 200 words - plt.subplot(1,2,1) - plt.title("Neural Network Model") - plt.imshow(np.tanh(W1).dot(W2)) - plt.subplot(1,2,2) - plt.title("Bigram Probs") - plt.imshow(W_bigram) - plt.show() - - - - diff --git a/nlp_class2/pmi.py b/nlp_class2/pmi.py deleted file mode 100644 index b321e91f..00000000 --- a/nlp_class2/pmi.py +++ /dev/null @@ -1,328 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python -# https://www.udemy.com/data-science-natural-language-processing-in-python - -# Author: http://lazyprogrammer.me -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import os, sys -import string -import numpy as np -import matplotlib.pyplot as plt -from scipy.sparse import lil_matrix, csr_matrix, save_npz, load_npz -from scipy.spatial.distance import cosine as cos_dist -from sklearn.metrics.pairwise import pairwise_distances -from glob import glob -from datetime import datetime - - -# input files -files = glob('../large_files/enwiki*.txt') - - -# unfortunately these work different ways -def remove_punctuation_2(s): - return s.translate(None, string.punctuation) - -def remove_punctuation_3(s): - return s.translate(str.maketrans('','',string.punctuation)) - -if sys.version.startswith('2'): - remove_punctuation = remove_punctuation_2 -else: - remove_punctuation = remove_punctuation_3 - - -# max vocab size -V = 2000 - -# context size -context_size = 10 - -# word counts -all_word_counts = {} - -# get the top V words -num_lines = 0 -num_tokens = 0 -for f in files: - for line in open(f): - # don't count headers, structured data, lists, etc... - if line and line[0] not in ('[', '*', '-', '|', '=', '{', '}'): - num_lines += 1 - for word in remove_punctuation(line).lower().split(): - num_tokens += 1 - if word not in all_word_counts: - all_word_counts[word] = 0 - all_word_counts[word] += 1 -print("num_lines:", num_lines) -print("num_tokens:", num_tokens) - - -# words I really want to keep -keep_words = [ - 'king', 'man', 'queen', 'woman', - 'heir', 'heiress', 'prince', 'princess', - 'nephew', 'niece', 'uncle', 'aunt', - 'husband', 'wife', 'brother', 'sister', - 'tokyo', 'beijing', 'dallas', 'texas', - 'january', 'february', 'march', - 'april', 'may', 'june', - 'july', 'august', 'september', - 'october', 'november', 'december', - 'actor', 'actress', - 'rice', 'bread', 'miami', 'florida', - 'walk', 'walking', 'swim', 'swimming', -] -for w in keep_words: - all_word_counts[w] = float('inf') - - -# sort in descending order -all_word_counts = sorted(all_word_counts.items(), key=lambda x: x[1], reverse=True) - -# keep just the top V words -# save a slot for -V = min(V, len(all_word_counts)) -top_words = [w for w, count in all_word_counts[:V-1]] + [''] -# TODO: try it without UNK at all - -# reverse the array to get word2idx mapping -word2idx = {w:i for i, w in enumerate(top_words)} -unk = word2idx[''] - -# for w in ('king', 'man', 'queen', 'woman', 'france', 'paris', \ -# 'london', 'england', 'italy', 'rome', \ -# 'france', 'french', 'english', 'england', \ -# 'japan', 'japanese', 'chinese', 'china', \ -# 'italian', 'australia', 'australian' \ -# 'japan', 'tokyo', 'china', 'beijing'): -# assert(w in word2idx) - - -if not os.path.exists('pmi_counts_%s.npz' % V): - # init counts - wc_counts = lil_matrix((V, V)) - - ### make PMI matrix - # add counts - k = 0 - # for line in open('../large_files/text8'): - for f in files: - for line in open(f): - # don't count headers, structured data, lists, etc... - if line and line[0] not in ('[', '*', '-', '|', '=', '{', '}'): - line_as_idx = [] - for word in remove_punctuation(line).lower().split(): - if word in word2idx: - idx = word2idx[word] - # line_as_idx.append(idx) - else: - idx = unk - # pass - line_as_idx.append(idx) - - for i, w in enumerate(line_as_idx): - # keep count - k += 1 - if k % 10000 == 0: - print("%s/%s" % (k, num_tokens)) - - start = max(0, i - context_size) - end = min(len(line_as_idx), i + context_size) - for c in line_as_idx[start:i]: - wc_counts[w, c] += 1 - for c in line_as_idx[i+1:end]: - wc_counts[w, c] += 1 - print("Finished counting") - - save_npz('pmi_counts_%s.npz' % V, csr_matrix(wc_counts)) - -else: - wc_counts = load_npz('pmi_counts_%s.npz' % V) - - -# context counts get raised ^ 0.75 -c_counts = wc_counts.sum(axis=0).A.flatten() ** 0.75 -c_probs = c_counts / c_counts.sum() -c_probs = c_probs.reshape(1, V) - - -# PMI(w, c) = #(w, c) / #(w) / p(c) -# pmi = wc_counts / wc_counts.sum(axis=1) / c_probs # works only if numpy arrays -pmi = wc_counts.multiply(1.0 / wc_counts.sum(axis=1) / c_probs).tocsr() -# this operation changes it to a coo_matrix -# which doesn't have functions we need, e.g log1p() -# so convert it back to a csr -print("type(pmi):", type(pmi)) -logX = pmi.log1p() # would be logX = np.log(pmi.A + 1) in numpy -print("type(logX):", type(logX)) -logX[logX < 0] = 0 - - -### do alternating least squares - - -# latent dimension -D = 100 -reg = 0.1 - - -# initialize weights -W = np.random.randn(V, D) / np.sqrt(V + D) -b = np.zeros(V) -U = np.random.randn(V, D) / np.sqrt(V + D) -c = np.zeros(V) -mu = logX.mean() - - -costs = [] -t0 = datetime.now() -for epoch in range(10): - print("epoch:", epoch) - delta = W.dot(U.T) + b.reshape(V, 1) + c.reshape(1, V) + mu - logX - # cost = ( delta * delta ).sum() - cost = np.multiply(delta, delta).sum() - # * behaves differently if delta is a "matrix" object vs "array" object - costs.append(cost) - - ### partially vectorized updates ### - # update W - # matrix = reg*np.eye(D) + U.T.dot(U) - # for i in range(V): - # vector = (logX[i,:] - b[i] - c - mu).dot(U) - # W[i] = np.linalg.solve(matrix, vector) - - # # update b - # for i in range(V): - # numerator = (logX[i,:] - W[i].dot(U.T) - c - mu).sum() - # b[i] = numerator / V #/ (1 + reg) - - # # update U - # matrix = reg*np.eye(D) + W.T.dot(W) - # for j in range(V): - # vector = (logX[:,j] - b - c[j] - mu).dot(W) - # U[j] = np.linalg.solve(matrix, vector) - - # # update c - # for j in range(V): - # numerator = (logX[:,j] - W.dot(U[j]) - b - mu).sum() - # c[j] = numerator / V #/ (1 + reg) - - - ### vectorized updates ### - # vectorized update W - matrix = reg*np.eye(D) + U.T.dot(U) - vector = (logX - b.reshape(V, 1) - c.reshape(1, V) - mu).dot(U).T - W = np.linalg.solve(matrix, vector).T - - # vectorized update b - b = (logX - W.dot(U.T) - c.reshape(1, V) - mu).sum(axis=1) / V - - # vectorized update U - matrix = reg*np.eye(D) + W.T.dot(W) - vector = (logX - b.reshape(V, 1) - c.reshape(1, V) - mu).T.dot(W).T - U = np.linalg.solve(matrix, vector).T - - # vectorized update c - c = (logX - W.dot(U.T) - b.reshape(V, 1) - mu).sum(axis=0) / V - - -print("train duration:", datetime.now() - t0) - -plt.plot(costs) -plt.show() - - - - -### test it -king = W[word2idx['king']] -man = W[word2idx['man']] -queen = W[word2idx['queen']] -woman = W[word2idx['woman']] - -vec = king - man + woman - -# find closest -# closest = None -# min_dist = float('inf') -# for i in range(len(W)): -# dist = cos_dist(W[i], vec) -# if dist < min_dist: -# closest = i -# min_dist = dist - -# set word embedding matrix -# W = (W + U) / 2 - -distances = pairwise_distances(vec.reshape(1, D), W, metric='cosine').reshape(V) -idx = distances.argsort()[:10] - -print("closest 10:") -for i in idx: - print(top_words[i], distances[i]) - -print("dist to queen:", cos_dist(W[word2idx['queen']], vec)) - - - -def analogy(pos1, neg1, pos2, neg2): - # don't actually use pos2 in calculation, just print what's expected - print("testing: %s - %s = %s - %s" % (pos1, neg1, pos2, neg2)) - for w in (pos1, neg1, pos2, neg2): - if w not in word2idx: - print("Sorry, %s not in word2idx" % w) - return - - p1 = W[word2idx[pos1]] - n1 = W[word2idx[neg1]] - p2 = W[word2idx[pos2]] - n2 = W[word2idx[neg2]] - - vec = p1 - n1 + n2 - - distances = pairwise_distances(vec.reshape(1, D), W, metric='cosine').reshape(V) - idx = distances.argsort()[:10] - - # pick the best that's not p1, n1, or n2 - best_idx = -1 - keep_out = [word2idx[w] for w in (pos1, neg1, neg2)] - for i in idx: - if i not in keep_out: - best_idx = i - break - - print("got: %s - %s = %s - %s" % (pos1, neg1, top_words[best_idx], neg2)) - print("closest 10:") - for i in idx: - print(top_words[i], distances[i]) - - print("dist to %s:" % pos2, cos_dist(p2, vec)) - - -analogy('king', 'man', 'queen', 'woman') -analogy('miami', 'florida', 'dallas', 'texas') -# analogy('einstein', 'scientist', 'picasso', 'painter') -analogy('china', 'rice', 'england', 'bread') -analogy('man', 'woman', 'he', 'she') -analogy('man', 'woman', 'uncle', 'aunt') -analogy('man', 'woman', 'brother', 'sister') -analogy('man', 'woman', 'husband', 'wife') -analogy('man', 'woman', 'actor', 'actress') -analogy('man', 'woman', 'father', 'mother') -analogy('heir', 'heiress', 'prince', 'princess') -analogy('nephew', 'niece', 'uncle', 'aunt') -analogy('france', 'paris', 'japan', 'tokyo') -analogy('france', 'paris', 'china', 'beijing') -analogy('february', 'january', 'december', 'november') -analogy('france', 'paris', 'italy', 'rome') -analogy('paris', 'france', 'rome', 'italy') -analogy('france', 'french', 'england', 'english') -analogy('japan', 'japanese', 'china', 'chinese') -analogy('japan', 'japanese', 'italy', 'italian') -analogy('japan', 'japanese', 'australia', 'australian') -analogy('walk', 'walking', 'swim', 'swimming') diff --git a/nlp_class2/pos_baseline.py b/nlp_class2/pos_baseline.py deleted file mode 100644 index c33bd039..00000000 --- a/nlp_class2/pos_baseline.py +++ /dev/null @@ -1,224 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python - -# You can get the data from this URL: https://www.clips.uantwerpen.be/conll2000/chunking/ -# If above URL does not work, try this: -# https://drive.google.com/file/d/0BxGV7C-8DTe5QmF2MTFwN3JjWGc/view?usp=sharing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import os, sys -import numpy as np -import theano -import theano.tensor as T -import matplotlib.pyplot as plt -from sklearn.utils import shuffle -from sklearn.metrics import f1_score -from sklearn.tree import DecisionTreeClassifier - - -class LogisticRegression: - def __init__(self): - pass - - def fit(self, X, Y, V=None, K=None, D=50, lr=1e-1, mu=0.99, batch_sz=100, epochs=6): - if V is None: - V = len(set(X)) - if K is None: - K = len(set(Y)) - N = len(X) - - W = np.random.randn(V, K) / np.sqrt(V + K) - b = np.zeros(K) - self.W = theano.shared(W) - self.b = theano.shared(b) - self.params = [self.W, self.b] - - thX = T.ivector('X') - thY = T.ivector('Y') - - py_x = T.nnet.softmax(self.W[thX] + self.b) - prediction = T.argmax(py_x, axis=1) - - cost = -T.mean(T.log(py_x[T.arange(thY.shape[0]), thY])) - grads = T.grad(cost, self.params) - dparams = [theano.shared(p.get_value()*0) for p in self.params] - self.cost_predict_op = theano.function( - inputs=[thX, thY], - outputs=[cost, prediction], - allow_input_downcast=True, - ) - - updates = [ - (p, p + mu*dp - lr*g) for p, dp, g in zip(self.params, dparams, grads) - ] + [ - (dp, mu*dp - lr*g) for dp, g in zip(dparams, grads) - ] - train_op = theano.function( - inputs=[thX, thY], - outputs=[cost, prediction], - updates=updates, - allow_input_downcast=True - ) - - costs = [] - n_batches = N // batch_sz - for i in range(epochs): - X, Y = shuffle(X, Y) - print("epoch:", i) - for j in range(n_batches): - Xbatch = X[j*batch_sz:(j*batch_sz + batch_sz)] - Ybatch = Y[j*batch_sz:(j*batch_sz + batch_sz)] - - c, p = train_op(Xbatch, Ybatch) - costs.append(c) - if j % 200 == 0: - print( - "i:", i, "j:", j, - "n_batches:", n_batches, - "cost:", c, - "error:", np.mean(p != Ybatch) - ) - plt.plot(costs) - plt.show() - - def score(self, X, Y): - _, p = self.cost_predict_op(X, Y) - return np.mean(p == Y) - - def f1_score(self, X, Y): - _, p = self.cost_predict_op(X, Y) - return f1_score(Y, p, average=None).mean() - - -def get_data(split_sequences=False): - if not os.path.exists('chunking'): - print("Please create a folder in your local directory called 'chunking'") - print("train.txt and test.txt should be stored in there.") - print("Please check the comments to get the download link.") - exit() - elif not os.path.exists('chunking/train.txt'): - print("train.txt is not in chunking/train.txt") - print("Please check the comments to get the download link.") - exit() - elif not os.path.exists('chunking/test.txt'): - print("test.txt is not in chunking/test.txt") - print("Please check the comments to get the download link.") - exit() - - word2idx = {} - tag2idx = {} - word_idx = 0 - tag_idx = 0 - Xtrain = [] - Ytrain = [] - currentX = [] - currentY = [] - for line in open('chunking/train.txt'): - line = line.rstrip() - if line: - r = line.split() - word, tag, _ = r - if word not in word2idx: - word2idx[word] = word_idx - word_idx += 1 - currentX.append(word2idx[word]) - - if tag not in tag2idx: - tag2idx[tag] = tag_idx - tag_idx += 1 - currentY.append(tag2idx[tag]) - elif split_sequences: - Xtrain.append(currentX) - Ytrain.append(currentY) - currentX = [] - currentY = [] - - if not split_sequences: - Xtrain = currentX - Ytrain = currentY - - # load and score test data - Xtest = [] - Ytest = [] - currentX = [] - currentY = [] - for line in open('chunking/test.txt'): - line = line.rstrip() - if line: - r = line.split() - word, tag, _ = r - if word in word2idx: - currentX.append(word2idx[word]) - else: - currentX.append(word_idx) # use this as unknown - currentY.append(tag2idx[tag]) - elif split_sequences: - Xtest.append(currentX) - Ytest.append(currentY) - currentX = [] - currentY = [] - if not split_sequences: - Xtest = currentX - Ytest = currentY - - return Xtrain, Ytrain, Xtest, Ytest, word2idx - -def main(): - Xtrain, Ytrain, Xtest, Ytest, word2idx = get_data() - - # convert to numpy arrays - Xtrain = np.array(Xtrain) - Ytrain = np.array(Ytrain) - - # convert Xtrain to indicator matrix - N = len(Xtrain) - V = len(word2idx) + 1 - print("vocabulary size:", V) - # Xtrain_indicator = np.zeros((N, V)) - # Xtrain_indicator[np.arange(N), Xtrain] = 1 - - # decision tree - dt = DecisionTreeClassifier() - - # without indicator - dt.fit(Xtrain.reshape(N, 1), Ytrain) - print("dt train score:", dt.score(Xtrain.reshape(N, 1), Ytrain)) - p = dt.predict(Xtrain.reshape(N, 1)) - print("dt train f1:", f1_score(Ytrain, p, average=None).mean()) - - # with indicator -- too slow!! - # dt.fit(Xtrain_indicator, Ytrain) - # print("dt score:", dt.score(Xtrain_indicator, Ytrain)) - - # train and score - model = LogisticRegression() - model.fit(Xtrain, Ytrain, V=V) - print("training complete") - print("lr train score:", model.score(Xtrain, Ytrain)) - print("lr train f1:", model.f1_score(Xtrain, Ytrain)) - - - Ntest = len(Xtest) - Xtest = np.array(Xtest) - Ytest = np.array(Ytest) - # convert Xtest to indicator - # Xtest_indicator = np.zeros((Ntest, V)) - # Xtest_indicator[np.arange(Ntest), Xtest] = 1 - - # decision tree test score - print("dt test score:", dt.score(Xtest.reshape(Ntest, 1), Ytest)) - p = dt.predict(Xtest.reshape(Ntest, 1)) - print("dt test f1:", f1_score(Ytest, p, average=None).mean()) - # print("dt test score:", dt.score(Xtest_indicator, Ytest)) # too slow! - - # logistic test score -- too slow!! - print("lr test score:", model.score(Xtest, Ytest)) - print("lr test f1:", model.f1_score(Xtest, Ytest)) - -if __name__ == '__main__': - main() diff --git a/nlp_class2/pos_hmm.py b/nlp_class2/pos_hmm.py deleted file mode 100644 index e3065cd2..00000000 --- a/nlp_class2/pos_hmm.py +++ /dev/null @@ -1,92 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt - -import os -import sys -sys.path.append(os.path.abspath('..')) -from hmm_class.hmmd_scaled import HMM - -from pos_baseline import get_data -from sklearn.utils import shuffle -from datetime import datetime -from sklearn.metrics import f1_score - - -def accuracy(T, Y): - # inputs are lists of lists - n_correct = 0 - n_total = 0 - for t, y in zip(T, Y): - n_correct += np.sum(t == y) - n_total += len(y) - return float(n_correct) / n_total - - -def total_f1_score(T, Y): - # inputs are lists of lists - T = np.concatenate(T) - Y = np.concatenate(Y) - return f1_score(T, Y, average=None).mean() - - -# def flatten(l): -# return [item for sublist in l for item in sublist] - - -def main(smoothing=1e-1): - # X = words, Y = POS tags - Xtrain, Ytrain, Xtest, Ytest, word2idx = get_data(split_sequences=True) - V = len(word2idx) + 1 - - # find hidden state transition matrix and pi - M = max(max(y) for y in Ytrain) + 1 #len(set(flatten(Ytrain))) - A = np.ones((M, M))*smoothing # add-one smoothing - pi = np.zeros(M) - for y in Ytrain: - pi[y[0]] += 1 - for i in range(len(y)-1): - A[y[i], y[i+1]] += 1 - # turn it into a probability matrix - A /= A.sum(axis=1, keepdims=True) - pi /= pi.sum() - - # find the observation matrix - B = np.ones((M, V))*smoothing # add-one smoothing - for x, y in zip(Xtrain, Ytrain): - for xi, yi in zip(x, y): - B[yi, xi] += 1 - B /= B.sum(axis=1, keepdims=True) - - hmm = HMM(M) - hmm.pi = pi - hmm.A = A - hmm.B = B - - # get predictions - Ptrain = [] - for x in Xtrain: - p = hmm.get_state_sequence(x) - Ptrain.append(p) - - Ptest = [] - for x in Xtest: - p = hmm.get_state_sequence(x) - Ptest.append(p) - - # print results - print("train accuracy:", accuracy(Ytrain, Ptrain)) - print("test accuracy:", accuracy(Ytest, Ptest)) - print("train f1:", total_f1_score(Ytrain, Ptrain)) - print("test f1:", total_f1_score(Ytest, Ptest)) - -if __name__ == '__main__': - main() diff --git a/nlp_class2/pos_ner_keras.py b/nlp_class2/pos_ner_keras.py deleted file mode 100644 index 7a1335e1..00000000 --- a/nlp_class2/pos_ner_keras.py +++ /dev/null @@ -1,224 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -import os -import sys -sys.path.append(os.path.abspath('..')) -from pos_baseline import get_data -from sklearn.utils import shuffle -from util import init_weight -from datetime import datetime -from sklearn.metrics import f1_score - -from keras.models import Model -from keras.layers import Input, Dense, Embedding, LSTM, GRU -from keras.preprocessing.sequence import pad_sequences -from keras.preprocessing.text import Tokenizer -from keras.optimizers import Adam - - -MAX_VOCAB_SIZE = 20000 -MAX_TAGS = 100 - - - -def get_data_pos(split_sequences=False): - if not os.path.exists('chunking'): - print("Please create a folder in your local directory called 'chunking'") - print("train.txt and test.txt should be stored in there.") - print("Please check the comments to get the download link.") - exit() - elif not os.path.exists('chunking/train.txt'): - print("train.txt is not in chunking/train.txt") - print("Please check the comments to get the download link.") - exit() - elif not os.path.exists('chunking/test.txt'): - print("test.txt is not in chunking/test.txt") - print("Please check the comments to get the download link.") - exit() - - Xtrain = [] - Ytrain = [] - currentX = [] - currentY = [] - for line in open('chunking/train.txt'): - line = line.rstrip() - if line: - r = line.split() - word, tag, _ = r - currentX.append(word) - - currentY.append(tag) - elif split_sequences: - Xtrain.append(currentX) - Ytrain.append(currentY) - currentX = [] - currentY = [] - - if not split_sequences: - Xtrain = currentX - Ytrain = currentY - - # load and score test data - Xtest = [] - Ytest = [] - currentX = [] - currentY = [] - for line in open('chunking/test.txt'): - line = line.rstrip() - if line: - r = line.split() - word, tag, _ = r - currentX.append(word) - currentY.append(tag) - elif split_sequences: - Xtest.append(currentX) - Ytest.append(currentY) - currentX = [] - currentY = [] - if not split_sequences: - Xtest = currentX - Ytest = currentY - - return Xtrain, Ytrain, Xtest, Ytest - - -def get_data_ner(split_sequences=False): - Xtrain = [] - Ytrain = [] - currentX = [] - currentY = [] - for line in open('ner.txt'): - line = line.rstrip() - if line: - r = line.split() - word, tag = r - word = word.lower() - currentX.append(word) - currentY.append(tag) - elif split_sequences: - Xtrain.append(currentX) - Ytrain.append(currentY) - currentX = [] - currentY = [] - - if not split_sequences: - Xtrain = currentX - Ytrain = currentY - - print("number of samples:", len(Xtrain)) - Xtrain, Ytrain = shuffle(Xtrain, Ytrain) - Ntest = int(0.3*len(Xtrain)) - Xtest = Xtrain[:Ntest] - Ytest = Ytrain[:Ntest] - Xtrain = Xtrain[Ntest:] - Ytrain = Ytrain[Ntest:] - return Xtrain, Ytrain, Xtest, Ytest - - - - -# get the data -Xtrain, Ytrain, Xtest, Ytest = get_data_ner(split_sequences=True) - - -# convert the sentences (strings) into integers -tokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE) -tokenizer.fit_on_texts(Xtrain) -Xtrain = tokenizer.texts_to_sequences(Xtrain) -Xtest = tokenizer.texts_to_sequences(Xtest) - -# get word -> integer mapping -word2idx = tokenizer.word_index -print('Found %s unique tokens.' % len(word2idx)) -vocab_size = min(MAX_VOCAB_SIZE, len(word2idx) + 1) - - -# convert the tags (strings) into integers -tokenizer2 = Tokenizer(num_words=MAX_TAGS) -tokenizer2.fit_on_texts(Ytrain) -Ytrain = tokenizer2.texts_to_sequences(Ytrain) -Ytest = tokenizer2.texts_to_sequences(Ytest) - -# get tag -> integer mapping -tag2idx = tokenizer2.word_index -print('Found %s unique tags.' % len(tag2idx)) -num_tags = min(MAX_TAGS, len(tag2idx) + 1) - - -# pad sequences -sequence_length = max(len(x) for x in Xtrain + Xtest) -Xtrain = pad_sequences(Xtrain, maxlen=sequence_length) -Ytrain = pad_sequences(Ytrain, maxlen=sequence_length) -Xtest = pad_sequences(Xtest, maxlen=sequence_length) -Ytest = pad_sequences(Ytest, maxlen=sequence_length) -print("Xtrain.shape:", Xtrain.shape) -print("Ytrain.shape:", Ytrain.shape) - - -# one-hot the targets -Ytrain_onehot = np.zeros((len(Ytrain), sequence_length, num_tags), dtype='float32') -for n, sample in enumerate(Ytrain): - for t, tag in enumerate(sample): - Ytrain_onehot[n, t, tag] = 1 - -Ytest_onehot = np.zeros((len(Ytest), sequence_length, num_tags), dtype='float32') -for n, sample in enumerate(Ytest): - for t, tag in enumerate(sample): - Ytest_onehot[n, t, tag] = 1 - - - -# training config -epochs = 30 -batch_size = 32 -hidden_layer_size = 10 -embedding_dim = 10 - - - - -# build the model -input_ = Input(shape=(sequence_length,)) -x = Embedding(vocab_size, embedding_dim)(input_) -x = GRU(hidden_layer_size, return_sequences=True)(x) -output = Dense(num_tags, activation='softmax')(x) - - -model = Model(input_, output) -model.compile( - loss='categorical_crossentropy', - optimizer=Adam(lr=1e-2), - metrics=['accuracy'] -) - - -print('Training model...') -r = model.fit( - Xtrain, - Ytrain_onehot, - batch_size=batch_size, - epochs=epochs, - validation_data=(Xtest, Ytest_onehot) -) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='acc') -plt.plot(r.history['val_accuracy'], label='val_acc') -plt.legend() -plt.show() - diff --git a/nlp_class2/pos_rnn.py b/nlp_class2/pos_rnn.py deleted file mode 100644 index 714297f3..00000000 --- a/nlp_class2/pos_rnn.py +++ /dev/null @@ -1,169 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -import theano -import theano.tensor as T -import os -import sys -sys.path.append(os.path.abspath('..')) -from rnn_class.gru import GRU -from pos_baseline import get_data -from sklearn.utils import shuffle -from util import init_weight -from datetime import datetime -from sklearn.metrics import f1_score - - -class RNN: - def __init__(self, D, hidden_layer_sizes, V, K): - self.hidden_layer_sizes = hidden_layer_sizes - self.D = D - self.V = V - self.K = K - - def fit(self, X, Y, learning_rate=1e-4, mu=0.99, epochs=30, show_fig=True, activation=T.nnet.relu, RecurrentUnit=GRU, normalize=False): - D = self.D - V = self.V - N = len(X) - - We = init_weight(V, D) - self.hidden_layers = [] - Mi = D - for Mo in self.hidden_layer_sizes: - ru = RecurrentUnit(Mi, Mo, activation) - self.hidden_layers.append(ru) - Mi = Mo - - Wo = init_weight(Mi, self.K) - bo = np.zeros(self.K) - - self.We = theano.shared(We) - self.Wo = theano.shared(Wo) - self.bo = theano.shared(bo) - self.params = [self.Wo, self.bo] - for ru in self.hidden_layers: - self.params += ru.params - - thX = T.ivector('X') - thY = T.ivector('Y') - - Z = self.We[thX] - for ru in self.hidden_layers: - Z = ru.output(Z) - py_x = T.nnet.softmax(Z.dot(self.Wo) + self.bo) - - testf = theano.function( - inputs=[thX], - outputs=py_x, - ) - testout = testf(X[0]) - print("py_x.shape:", testout.shape) - - prediction = T.argmax(py_x, axis=1) - - cost = -T.mean(T.log(py_x[T.arange(thY.shape[0]), thY])) - grads = T.grad(cost, self.params) - dparams = [theano.shared(p.get_value()*0) for p in self.params] - - dWe = theano.shared(self.We.get_value()*0) - gWe = T.grad(cost, self.We) - dWe_update = mu*dWe - learning_rate*gWe - We_update = self.We + dWe_update - if normalize: - We_update /= We_update.norm(2) - - updates = [ - (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads) - ] + [ - (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads) - ] + [ - (self.We, We_update), (dWe, dWe_update) - ] - - self.cost_predict_op = theano.function( - inputs=[thX, thY], - outputs=[cost, prediction], - allow_input_downcast=True, - ) - - self.train_op = theano.function( - inputs=[thX, thY], - outputs=[cost, prediction], - updates=updates - ) - - costs = [] - sequence_indexes = range(N) - n_total = sum(len(y) for y in Y) - for i in range(epochs): - t0 = datetime.now() - sequence_indexes = shuffle(sequence_indexes) - n_correct = 0 - cost = 0 - it = 0 - for j in sequence_indexes: - c, p = self.train_op(X[j], Y[j]) - cost += c - n_correct += np.sum(p == Y[j]) - it += 1 - if it % 200 == 0: - sys.stdout.write( - "j/N: %d/%d correct rate so far: %f, cost so far: %f\r" % - (it, N, float(n_correct)/n_total, cost) - ) - sys.stdout.flush() - print( - "i:", i, "cost:", cost, - "correct rate:", (float(n_correct)/n_total), - "time for epoch:", (datetime.now() - t0) - ) - costs.append(cost) - - if show_fig: - plt.plot(costs) - plt.show() - - def score(self, X, Y): - n_total = sum(len(y) for y in Y) - n_correct = 0 - for x, y in zip(X, Y): - _, p = self.cost_predict_op(x, y) - n_correct += np.sum(p == y) - return float(n_correct) / n_total - - def f1_score(self, X, Y): - P = [] - for x, y in zip(X, Y): - _, p = self.cost_predict_op(x, y) - P.append(p) - Y = np.concatenate(Y) - P = np.concatenate(P) - return f1_score(Y, P, average=None).mean() - - -def flatten(l): - return [item for sublist in l for item in sublist] - - -def main(): - Xtrain, Ytrain, Xtest, Ytest, word2idx = get_data(split_sequences=True) - V = len(word2idx) + 1 - K = len(set(flatten(Ytrain)) | set(flatten(Ytest))) - rnn = RNN(10, [10], V, K) - rnn.fit(Xtrain, Ytrain) - print("train score:", rnn.score(Xtrain, Ytrain)) - print("test score:", rnn.score(Xtest, Ytest)) - print("train f1:", rnn.f1_score(Xtrain, Ytrain)) - print("test f1:", rnn.f1_score(Xtest, Ytest)) - - -if __name__ == '__main__': - main() diff --git a/nlp_class2/pos_tf.py b/nlp_class2/pos_tf.py deleted file mode 100644 index 974453b6..00000000 --- a/nlp_class2/pos_tf.py +++ /dev/null @@ -1,252 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -import tensorflow as tf -import os -import sys -sys.path.append(os.path.abspath('..')) -from pos_baseline import get_data -from sklearn.utils import shuffle -from util import init_weight -from datetime import datetime -from sklearn.metrics import f1_score - -from tensorflow.contrib.rnn import static_rnn as get_rnn_output -from tensorflow.contrib.rnn import BasicRNNCell, GRUCell - - - -def get_data(split_sequences=False): - if not os.path.exists('chunking'): - print("Please create a folder in your local directory called 'chunking'") - print("train.txt and test.txt should be stored in there.") - print("Please check the comments to get the download link.") - exit() - elif not os.path.exists('chunking/train.txt'): - print("train.txt is not in chunking/train.txt") - print("Please check the comments to get the download link.") - exit() - elif not os.path.exists('chunking/test.txt'): - print("test.txt is not in chunking/test.txt") - print("Please check the comments to get the download link.") - exit() - - word2idx = {} - tag2idx = {} - word_idx = 1 - tag_idx = 1 - Xtrain = [] - Ytrain = [] - currentX = [] - currentY = [] - for line in open('chunking/train.txt'): - line = line.rstrip() - if line: - r = line.split() - word, tag, _ = r - if word not in word2idx: - word2idx[word] = word_idx - word_idx += 1 - currentX.append(word2idx[word]) - - if tag not in tag2idx: - tag2idx[tag] = tag_idx - tag_idx += 1 - currentY.append(tag2idx[tag]) - elif split_sequences: - Xtrain.append(currentX) - Ytrain.append(currentY) - currentX = [] - currentY = [] - - if not split_sequences: - Xtrain = currentX - Ytrain = currentY - - # load and score test data - Xtest = [] - Ytest = [] - currentX = [] - currentY = [] - for line in open('chunking/test.txt'): - line = line.rstrip() - if line: - r = line.split() - word, tag, _ = r - if word in word2idx: - currentX.append(word2idx[word]) - else: - currentX.append(word_idx) # use this as unknown - currentY.append(tag2idx[tag]) - elif split_sequences: - Xtest.append(currentX) - Ytest.append(currentY) - currentX = [] - currentY = [] - if not split_sequences: - Xtest = currentX - Ytest = currentY - - return Xtrain, Ytrain, Xtest, Ytest, word2idx - - -def flatten(l): - return [item for sublist in l for item in sublist] - - - -# get the data -Xtrain, Ytrain, Xtest, Ytest, word2idx = get_data(split_sequences=True) -V = len(word2idx) + 2 # vocab size (+1 for unknown, +1 b/c start from 1) -K = len(set(flatten(Ytrain)) | set(flatten(Ytest))) + 1 # num classes - - -# training config -epochs = 20 -learning_rate = 1e-2 -mu = 0.99 -batch_size = 32 -hidden_layer_size = 10 -embedding_dim = 10 -sequence_length = max(len(x) for x in Xtrain + Xtest) - - - -# pad sequences -Xtrain = tf.keras.preprocessing.sequence.pad_sequences(Xtrain, maxlen=sequence_length) -Ytrain = tf.keras.preprocessing.sequence.pad_sequences(Ytrain, maxlen=sequence_length) -Xtest = tf.keras.preprocessing.sequence.pad_sequences(Xtest, maxlen=sequence_length) -Ytest = tf.keras.preprocessing.sequence.pad_sequences(Ytest, maxlen=sequence_length) -print("Xtrain.shape:", Xtrain.shape) -print("Ytrain.shape:", Ytrain.shape) - - - -# inputs -inputs = tf.placeholder(tf.int32, shape=(None, sequence_length)) -targets = tf.placeholder(tf.int32, shape=(None, sequence_length)) -num_samples = tf.shape(inputs)[0] # useful for later - -# embedding -We = np.random.randn(V, embedding_dim).astype(np.float32) - -# output layer -Wo = init_weight(hidden_layer_size, K).astype(np.float32) -bo = np.zeros(K).astype(np.float32) - -# make them tensorflow variables -tfWe = tf.Variable(We) -tfWo = tf.Variable(Wo) -tfbo = tf.Variable(bo) - -# make the rnn unit -rnn_unit = GRUCell(num_units=hidden_layer_size, activation=tf.nn.relu) - - -# get the output -x = tf.nn.embedding_lookup(tfWe, inputs) - -# converts x from a tensor of shape N x T x M -# into a list of length T, where each element is a tensor of shape N x M -x = tf.unstack(x, sequence_length, 1) - -# get the rnn output -outputs, states = get_rnn_output(rnn_unit, x, dtype=tf.float32) - - -# outputs are now of size (T, N, M) -# so make it (N, T, M) -outputs = tf.transpose(outputs, (1, 0, 2)) -outputs = tf.reshape(outputs, (sequence_length*num_samples, hidden_layer_size)) # NT x M - -# final dense layer -logits = tf.matmul(outputs, tfWo) + tfbo # NT x K -predictions = tf.argmax(logits, 1) -predict_op = tf.reshape(predictions, (num_samples, sequence_length)) -labels_flat = tf.reshape(targets, [-1]) - -cost_op = tf.reduce_mean( - tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=logits, - labels=labels_flat - ) -) -train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost_op) - - - - -# init stuff -sess = tf.InteractiveSession() -init = tf.global_variables_initializer() -sess.run(init) - - -# training loop -costs = [] -n_batches = len(Ytrain) // batch_size -for i in range(epochs): - n_total = 0 - n_correct = 0 - - t0 = datetime.now() - Xtrain, Ytrain = shuffle(Xtrain, Ytrain) - cost = 0 - - for j in range(n_batches): - x = Xtrain[j*batch_size:(j+1)*batch_size] - y = Ytrain[j*batch_size:(j+1)*batch_size] - - # get the cost, predictions, and perform a gradient descent step - c, p, _ = sess.run( - (cost_op, predict_op, train_op), - feed_dict={inputs: x, targets: y}) - cost += c - - # calculate the accuracy - for yi, pi in zip(y, p): - # we don't care about the padded entries so ignore them - yii = yi[yi > 0] - pii = pi[yi > 0] - n_correct += np.sum(yii == pii) - n_total += len(yii) - - # print stuff out periodically - if j % 10 == 0: - sys.stdout.write( - "j/N: %d/%d correct rate so far: %f, cost so far: %f\r" % - (j, n_batches, float(n_correct)/n_total, cost) - ) - sys.stdout.flush() - - # get test acc. too - p = sess.run(predict_op, feed_dict={inputs: Xtest, targets: Ytest}) - n_test_correct = 0 - n_test_total = 0 - for yi, pi in zip(Ytest, p): - yii = yi[yi > 0] - pii = pi[yi > 0] - n_test_correct += np.sum(yii == pii) - n_test_total += len(yii) - test_acc = float(n_test_correct) / n_test_total - - print( - "i:", i, "cost:", "%.4f" % cost, - "train acc:", "%.4f" % (float(n_correct)/n_total), - "test acc:", "%.4f" % test_acc, - "time for epoch:", (datetime.now() - t0) - ) - costs.append(cost) - -plt.plot(costs) -plt.show() - - diff --git a/nlp_class2/pretrained_glove.py b/nlp_class2/pretrained_glove.py deleted file mode 100644 index b5e60463..00000000 --- a/nlp_class2/pretrained_glove.py +++ /dev/null @@ -1,143 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python -# https://www.udemy.com/data-science-natural-language-processing-in-python - -# Author: http://lazyprogrammer.me -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -# WHERE TO GET THE VECTORS: -# GloVe: https://nlp.stanford.edu/projects/glove/ -# Direct link: http://nlp.stanford.edu/data/glove.6B.zip - -import numpy as np -from sklearn.metrics.pairwise import pairwise_distances - - -def dist1(a, b): - return np.linalg.norm(a - b) -def dist2(a, b): - return 1 - a.dot(b) / (np.linalg.norm(a) * np.linalg.norm(b)) - -# pick a distance type -dist, metric = dist2, 'cosine' -# dist, metric = dist1, 'euclidean' - - -## more intuitive -# def find_analogies(w1, w2, w3): -# for w in (w1, w2, w3): -# if w not in word2vec: -# print("%s not in dictionary" % w) -# return - -# king = word2vec[w1] -# man = word2vec[w2] -# woman = word2vec[w3] -# v0 = king - man + woman - -# min_dist = float('inf') -# best_word = '' -# for word, v1 in iteritems(word2vec): -# if word not in (w1, w2, w3): -# d = dist(v0, v1) -# if d < min_dist: -# min_dist = d -# best_word = word -# print(w1, "-", w2, "=", best_word, "-", w3) - - -## faster -def find_analogies(w1, w2, w3): - for w in (w1, w2, w3): - if w not in word2vec: - print("%s not in dictionary" % w) - return - - king = word2vec[w1] - man = word2vec[w2] - woman = word2vec[w3] - v0 = king - man + woman - - distances = pairwise_distances(v0.reshape(1, D), embedding, metric=metric).reshape(V) - idxs = distances.argsort()[:4] - for idx in idxs: - word = idx2word[idx] - if word not in (w1, w2, w3): - best_word = word - break - - print(w1, "-", w2, "=", best_word, "-", w3) - - -def nearest_neighbors(w, n=5): - if w not in word2vec: - print("%s not in dictionary:" % w) - return - - v = word2vec[w] - distances = pairwise_distances(v.reshape(1, D), embedding, metric=metric).reshape(V) - idxs = distances.argsort()[1:n+1] - print("neighbors of: %s" % w) - for idx in idxs: - print("\t%s" % idx2word[idx]) - - - -# load in pre-trained word vectors -print('Loading word vectors...') -word2vec = {} -embedding = [] -idx2word = [] -with open('../large_files/glove.6B/glove.6B.50d.txt', encoding='utf-8') as f: - # is just a space-separated text file in the format: - # word vec[0] vec[1] vec[2] ... - for line in f: - values = line.split() - word = values[0] - vec = np.asarray(values[1:], dtype='float32') - word2vec[word] = vec - embedding.append(vec) - idx2word.append(word) -print('Found %s word vectors.' % len(word2vec)) -embedding = np.array(embedding) -V, D = embedding.shape - - -find_analogies('king', 'man', 'woman') -find_analogies('france', 'paris', 'london') -find_analogies('france', 'paris', 'rome') -find_analogies('paris', 'france', 'italy') -find_analogies('france', 'french', 'english') -find_analogies('japan', 'japanese', 'chinese') -find_analogies('japan', 'japanese', 'italian') -find_analogies('japan', 'japanese', 'australian') -find_analogies('december', 'november', 'june') -find_analogies('miami', 'florida', 'texas') -find_analogies('einstein', 'scientist', 'painter') -find_analogies('china', 'rice', 'bread') -find_analogies('man', 'woman', 'she') -find_analogies('man', 'woman', 'aunt') -find_analogies('man', 'woman', 'sister') -find_analogies('man', 'woman', 'wife') -find_analogies('man', 'woman', 'actress') -find_analogies('man', 'woman', 'mother') -find_analogies('heir', 'heiress', 'princess') -find_analogies('nephew', 'niece', 'aunt') -find_analogies('france', 'paris', 'tokyo') -find_analogies('france', 'paris', 'beijing') -find_analogies('february', 'january', 'november') -find_analogies('france', 'paris', 'rome') -find_analogies('paris', 'france', 'italy') - -nearest_neighbors('king') -nearest_neighbors('france') -nearest_neighbors('japan') -nearest_neighbors('einstein') -nearest_neighbors('woman') -nearest_neighbors('nephew') -nearest_neighbors('february') -nearest_neighbors('rome') diff --git a/nlp_class2/pretrained_w2v.py b/nlp_class2/pretrained_w2v.py deleted file mode 100644 index 730d3134..00000000 --- a/nlp_class2/pretrained_w2v.py +++ /dev/null @@ -1,82 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-natural-language-processing-in-python -# https://www.udemy.com/data-science-natural-language-processing-in-python - -# Author: http://lazyprogrammer.me -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -from gensim.models import KeyedVectors - - -# warning: takes quite awhile -# https://code.google.com/archive/p/word2vec/ -# direct link: https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing -# 3 million words and phrases -# D = 300 -word_vectors = KeyedVectors.load_word2vec_format( - '../large_files/GoogleNews-vectors-negative300.bin', - binary=True -) - - -# convenience -# result looks like: -# [('athens', 0.6001024842262268), -# ('albert', 0.5729557275772095), -# ('holmes', 0.569324254989624), -# ('donnie', 0.5690680742263794), -# ('italy', 0.5673537254333496), -# ('toni', 0.5666348338127136), -# ('spain', 0.5661854147911072), -# ('jh', 0.5661597847938538), -# ('pablo', 0.5631559491157532), -# ('malta', 0.5620371103286743)] -def find_analogies(w1, w2, w3): - r = word_vectors.most_similar(positive=[w1, w3], negative=[w2]) - print("%s - %s = %s - %s" % (w1, w2, r[0][0], w3)) - -def nearest_neighbors(w): - r = word_vectors.most_similar(positive=[w]) - print("neighbors of: %s" % w) - for word, score in r: - print("\t%s" % word) - - -find_analogies('king', 'man', 'woman') -find_analogies('france', 'paris', 'london') -find_analogies('france', 'paris', 'rome') -find_analogies('paris', 'france', 'italy') -find_analogies('france', 'french', 'english') -find_analogies('japan', 'japanese', 'chinese') -find_analogies('japan', 'japanese', 'italian') -find_analogies('japan', 'japanese', 'australian') -find_analogies('december', 'november', 'june') -find_analogies('miami', 'florida', 'texas') -find_analogies('einstein', 'scientist', 'painter') -find_analogies('china', 'rice', 'bread') -find_analogies('man', 'woman', 'she') -find_analogies('man', 'woman', 'aunt') -find_analogies('man', 'woman', 'sister') -find_analogies('man', 'woman', 'wife') -find_analogies('man', 'woman', 'actress') -find_analogies('man', 'woman', 'mother') -find_analogies('heir', 'heiress', 'princess') -find_analogies('nephew', 'niece', 'aunt') -find_analogies('france', 'paris', 'tokyo') -find_analogies('france', 'paris', 'beijing') -find_analogies('february', 'january', 'november') -find_analogies('france', 'paris', 'rome') -find_analogies('paris', 'france', 'italy') - -nearest_neighbors('king') -nearest_neighbors('france') -nearest_neighbors('japan') -nearest_neighbors('einstein') -nearest_neighbors('woman') -nearest_neighbors('nephew') -nearest_neighbors('february') -nearest_neighbors('rome') \ No newline at end of file diff --git a/nlp_class2/recursive_tensorflow.py b/nlp_class2/recursive_tensorflow.py deleted file mode 100644 index 02f02360..00000000 --- a/nlp_class2/recursive_tensorflow.py +++ /dev/null @@ -1,235 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -# data is from: http://nlp.stanford.edu/sentiment/ -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import sys -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt - -from sklearn.utils import shuffle -from datetime import datetime -from util import init_weight, get_ptb_data, display_tree - -if tf.__version__.startswith('2'): - tf.compat.v1.disable_eager_execution() - - -def get_labels(tree): - # must be returned in the same order as tree logits are returned - # post-order traversal - if tree is None: - return [] - return get_labels(tree.left) + get_labels(tree.right) + [tree.label] - - -class TNN: - def __init__(self, V, D, K, activation): - self.D = D - self.f = activation - - # word embedding - We = init_weight(V, D) - - # linear terms - W1 = init_weight(D, D) - W2 = init_weight(D, D) - - # bias - bh = np.zeros(D) - - # output layer - Wo = init_weight(D, K) - bo = np.zeros(K) - - # make them tensorflow variables - self.We = tf.Variable(We.astype(np.float32)) - self.W1 = tf.Variable(W1.astype(np.float32)) - self.W2 = tf.Variable(W2.astype(np.float32)) - self.bh = tf.Variable(bh.astype(np.float32)) - self.Wo = tf.Variable(Wo.astype(np.float32)) - self.bo = tf.Variable(bo.astype(np.float32)) - self.params = [self.We, self.W1, self.W2, self.Wo] - - def fit(self, trees, lr=1e-1, mu=0.9, reg=0.1, epochs=5): - train_ops = [] - costs = [] - predictions = [] - all_labels = [] - i = 0 - N = len(trees) - print("Compiling ops") - for t in trees: - i += 1 - sys.stdout.write("%d/%d\r" % (i, N)) - sys.stdout.flush() - logits = self.get_output(t) - labels = get_labels(t) - all_labels.append(labels) - - cost = self.get_cost(logits, labels, reg) - costs.append(cost) - - prediction = tf.argmax(input=logits, axis=1) - predictions.append(prediction) - - train_op = tf.compat.v1.train.MomentumOptimizer(lr, mu).minimize(cost) - train_ops.append(train_op) - - # save for later so we don't have to recompile - self.predictions = predictions - self.all_labels = all_labels - self.saver = tf.compat.v1.train.Saver() - - init = tf.compat.v1.initialize_all_variables() - actual_costs = [] - per_epoch_costs = [] - correct_rates = [] - with tf.compat.v1.Session() as session: - session.run(init) - - for i in range(epochs): - t0 = datetime.now() - - train_ops, costs, predictions, all_labels = shuffle(train_ops, costs, predictions, all_labels) - epoch_cost = 0 - n_correct = 0 - n_total = 0 - j = 0 - N = len(train_ops) - for train_op, cost, prediction, labels in zip(train_ops, costs, predictions, all_labels): - _, c, p = session.run([train_op, cost, prediction]) - epoch_cost += c - actual_costs.append(c) - n_correct += np.sum(p == labels) - n_total += len(labels) - - j += 1 - if j % 10 == 0: - sys.stdout.write("j: %d, N: %d, c: %f\r" % (j, N, c)) - sys.stdout.flush() - - print( - "epoch:", i, "cost:", epoch_cost, - "elapsed time:", (datetime.now() - t0) - ) - - per_epoch_costs.append(epoch_cost) - correct_rates.append(n_correct / float(n_total)) - - self.saver.save(session, "recursive.ckpt") - - plt.plot(actual_costs) - plt.title("cost per train_op call") - plt.show() - - plt.plot(per_epoch_costs) - plt.title("per epoch costs") - plt.show() - - plt.plot(correct_rates) - plt.title("correct rates") - plt.show() - - def get_cost(self, logits, labels, reg): - cost = tf.reduce_mean( - input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=logits, - labels=labels - ) - ) - rcost = sum(tf.nn.l2_loss(p) for p in self.params) - cost += reg*rcost - return cost - - # list_of_logits is an output! - # it is added to using post-order traversal - def get_output_recursive(self, tree, list_of_logits, is_root=True): - if tree.word is not None: - # this is a leaf node - x = tf.nn.embedding_lookup(params=self.We, ids=[tree.word]) - else: - # this node has children - x1 = self.get_output_recursive(tree.left, list_of_logits, is_root=False) - x2 = self.get_output_recursive(tree.right, list_of_logits, is_root=False) - x = self.f( - tf.matmul(x1, self.W1) + - tf.matmul(x2, self.W2) + - self.bh) - - logits = tf.matmul(x, self.Wo) + self.bo - list_of_logits.append(logits) - - return x - - def get_output(self, tree): - logits = [] - # try: - self.get_output_recursive(tree, logits) - # except Exception as e: - # display_tree(tree) - # raise e - return tf.concat(logits, 0) - - def score(self, trees): - if trees is None: - predictions = self.predictions - all_labels = self.all_labels - else: - # just build and run the predict_op for each tree - # and accumulate the total - predictions = [] - all_labels = [] - - i = 0 - N = len(trees) - print("Compiling ops") - for t in trees: - - i += 1 - sys.stdout.write("%d/%d\r" % (i, N)) - sys.stdout.flush() - - logits = self.get_output(t) - labels = get_labels(t) - all_labels.append(labels) - - prediction = tf.argmax(input=logits, axis=1) - predictions.append(prediction) - - n_correct = 0 - n_total = 0 - with tf.compat.v1.Session() as session: - self.saver.restore(session, "recursive.ckpt") - for prediction, y in zip(predictions, all_labels): - p = session.run(prediction) - n_correct += (p[-1] == y[-1]) # we only care about the root - n_total += len(y) - - return float(n_correct) / n_total - - -def main(): - train, test, word2idx = get_ptb_data() - - train = train[:100] - test = test[:100] - - V = len(word2idx) - D = 80 - K = 5 - - model = TNN(V, D, K, tf.nn.relu) - model.fit(train) - print("train accuracy:", model.score(None)) - print("test accuracy:", model.score(test)) - - -if __name__ == '__main__': - main() diff --git a/nlp_class2/recursive_theano.py b/nlp_class2/recursive_theano.py deleted file mode 100644 index 2241999b..00000000 --- a/nlp_class2/recursive_theano.py +++ /dev/null @@ -1,298 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import sys -import numpy as np -import matplotlib.pyplot as plt -import theano -import theano.tensor as T - -from sklearn.utils import shuffle -from util import init_weight, get_ptb_data, display_tree -from datetime import datetime - - -def adagrad(cost, params, lr, eps=1e-10): - grads = T.grad(cost, params) - caches = [theano.shared(np.ones_like(p.get_value())) for p in params] - new_caches = [c + g*g for c, g in zip(caches, grads)] - - c_update = [(c, new_c) for c, new_c in zip(caches, new_caches)] - g_update = [ - (p, p - lr*g / T.sqrt(new_c + eps)) for p, new_c, g in zip(params, new_caches, grads) - ] - updates = c_update + g_update - return updates - - -class RecursiveNN: - def __init__(self, V, D, K): - self.V = V - self.D = D - self.K = K - - def fit(self, trees, learning_rate=3*1e-3, mu=0.99, reg=1e-4, epochs=15, activation=T.nnet.relu, train_inner_nodes=False): - D = self.D - V = self.V - K = self.K - self.f = activation - N = len(trees) - - We = init_weight(V, D) - Wh = np.random.randn(2, D, D) / np.sqrt(2 + D + D) - bh = np.zeros(D) - Wo = init_weight(D, K) - bo = np.zeros(K) - - self.We = theano.shared(We) - self.Wh = theano.shared(Wh) - self.bh = theano.shared(bh) - self.Wo = theano.shared(Wo) - self.bo = theano.shared(bo) - self.params = [self.We, self.Wh, self.bh, self.Wo, self.bo] - - words = T.ivector('words') - parents = T.ivector('parents') - relations = T.ivector('relations') - labels = T.ivector('labels') - - def recurrence(n, hiddens, words, parents, relations): - w = words[n] - # any non-word will have index -1 - # if T.ge(w, 0): - # hiddens = T.set_subtensor(hiddens[n], self.We[w]) - # else: - # hiddens = T.set_subtensor(hiddens[n], self.f(hiddens[n] + self.bh)) - hiddens = T.switch( - T.ge(w, 0), - T.set_subtensor(hiddens[n], self.We[w]), - T.set_subtensor(hiddens[n], self.f(hiddens[n] + self.bh)) - ) - - r = relations[n] # 0 = is_left, 1 = is_right - p = parents[n] # parent idx - # if T.ge(p, 0): - # # root will have parent -1 - # hiddens = T.set_subtensor(hiddens[p], hiddens[p] + hiddens[n].dot(self.Wh[r])) - hiddens = T.switch( - T.ge(p, 0), - T.set_subtensor(hiddens[p], hiddens[p] + hiddens[n].dot(self.Wh[r])), - hiddens - ) - return hiddens - - hiddens = T.zeros((words.shape[0], D)) - - h, _ = theano.scan( - fn=recurrence, - outputs_info=[hiddens], - n_steps=words.shape[0], - sequences=T.arange(words.shape[0]), - non_sequences=[words, parents, relations], - ) - - # shape of h that is returned by scan is TxTxD - # because hiddens is TxD, and it does the recurrence T times - # technically this stores T times too much data - py_x = T.nnet.softmax(h[-1].dot(self.Wo) + self.bo) - - prediction = T.argmax(py_x, axis=1) - - rcost = reg*T.mean([(p*p).sum() for p in self.params]) - if train_inner_nodes: - # won't work for binary classification - cost = -T.mean(T.log(py_x[T.arange(labels.shape[0]), labels])) + rcost - else: - # print "K is:", K - # premean = T.log(py_x[-1]) - # target = T.zeros(K) - # target = T.set_subtensor(target[labels[-1]], 1) - # cost = -T.mean(target * premean) - - cost = -T.mean(T.log(py_x[-1, labels[-1]])) + rcost - # grads = T.grad(cost, self.params) - # dparams = [theano.shared(p.get_value()*0) for p in self.params] - - # updates = [ - # (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads) - # ] + [ - # (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads) - # ] - - updates = adagrad(cost, self.params, lr=8e-3) - - self.cost_predict_op = theano.function( - inputs=[words, parents, relations, labels], - outputs=[cost, prediction], - allow_input_downcast=True, - ) - - self.train_op = theano.function( - inputs=[words, parents, relations, labels], - outputs=[h, cost, prediction], - updates=updates - ) - - costs = [] - sequence_indexes = range(N) - if train_inner_nodes: - n_total = sum(len(words) for words, _, _, _ in trees) - else: - n_total = N - for i in range(epochs): - t0 = datetime.now() - sequence_indexes = shuffle(sequence_indexes) - n_correct = 0 - cost = 0 - it = 0 - for j in sequence_indexes: - words, par, rel, lab = trees[j] - _, c, p = self.train_op(words, par, rel, lab) - - if np.isnan(c): - print("Cost is nan! Let's stop here. \ - Why don't you try decreasing the learning rate?") - exit() - cost += c - if train_inner_nodes: - n_correct += np.sum(p == lab) - else: - n_correct += (p[-1] == lab[-1]) - it += 1 - if it % 1 == 0: - sys.stdout.write("j/N: %d/%d correct rate so far: %f, cost so far: %f\r" % (it, N, float(n_correct)/n_total, cost)) - sys.stdout.flush() - print( - "i:", i, "cost:", cost, - "correct rate:", (float(n_correct)/n_total), - "time for epoch:", (datetime.now() - t0) - ) - costs.append(cost) - - plt.plot(costs) - plt.draw() # don't block later code - - def score(self, trees, idx2word=None): - n_total = len(trees) - n_correct = 0 - for words, par, rel, lab in trees: - _, p = self.cost_predict_op(words, par, rel, lab) - n_correct += (p[-1] == lab[-1]) - # if idx2word: - # print_sentence(words, idx2word) - # print("label:", lab[-1], "pred:", p[-1]) - print("n_correct:", n_correct, "n_total:", n_total, end=" ") - return float(n_correct) / n_total - - -def add_idx_to_tree(tree, current_idx): - # post-order labeling of tree nodes - if tree is None: - return current_idx - current_idx = add_idx_to_tree(tree.left, current_idx) - current_idx = add_idx_to_tree(tree.right, current_idx) - tree.idx = current_idx - current_idx += 1 - return current_idx - - -def tree2list(tree, parent_idx, is_binary=False, is_left=False, is_right=False): - if tree is None: - return [], [], [], [] - - w = tree.word if tree.word is not None else -1 - if is_left: - r = 0 - elif is_right: - r = 1 - else: - r = -1 - words_left, parents_left, relations_left, labels_left = tree2list(tree.left, tree.idx, is_binary, is_left=True) - words_right, parents_right, relations_right, labels_right = tree2list(tree.right, tree.idx, is_binary, is_right=True) - - words = words_left + words_right + [w] - parents = parents_left + parents_right + [parent_idx] - relations = relations_left + relations_right + [r] - if is_binary: - if tree.label > 2: - label = 1 - elif tree.label < 2: - label = 0 - else: - label = -1 # we will eventually filter these out - else: - label = tree.label - labels = labels_left + labels_right + [label] - - return words, parents, relations, labels - - -# def get_sentence(tree): -# if tree is None: -# return [] -# w = [tree.word] if tree.word is not None else [] -# return get_sentence(tree.left) + get_sentence(tree.right) + w - - -def print_sentence(words, idx2word): - # sentence = ' '.join(get_sentence(tree)) - # print(sentence, "label:", tree.label) - for w in words: - if w >= 0: - print(idx2word[w], end=" ") - - -def main(is_binary=True): - train, test, word2idx = get_ptb_data() - - for t in train: - add_idx_to_tree(t, 0) - train = [tree2list(t, -1, is_binary) for t in train] - if is_binary: - train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels - - # sanity check - # check that last node has no parent - # for t in train: - # assert(t[1][-1] == -1 and t[2][-1] == -1) - - for t in test: - add_idx_to_tree(t, 0) - test = [tree2list(t, -1, is_binary) for t in test] - if is_binary: - test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels - - train = shuffle(train) - # train = train[:2000] - n_pos = sum(t[3][-1] for t in train) - # print("num pos train:", n_pos) - # idx2word = {v:k for k, v in word2idx.items()} - # for i in range(4): - # words, _, _, labels = train[i] - # print_sentence(words, idx2word) - # print("label:", labels[-1]) - test = shuffle(test) - test = test[:1000] - - V = len(word2idx) - print("vocab size:", V) - D = 10 - K = 2 if is_binary else 5 - - model = RecursiveNN(V, D, K) - model.fit(train, learning_rate=1e-2, reg=1e-2, mu=0, epochs=20, activation=T.tanh, train_inner_nodes=False) - print("train accuracy:", model.score(train)) - print("test accuracy:", model.score(test)) - - # make sure program doesn't end until we close the plot - plt.show() - - -if __name__ == '__main__': - main() diff --git a/nlp_class2/rntn_tensorflow.py b/nlp_class2/rntn_tensorflow.py deleted file mode 100644 index 77b563f2..00000000 --- a/nlp_class2/rntn_tensorflow.py +++ /dev/null @@ -1,254 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -# data is from: http://nlp.stanford.edu/sentiment/ - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import sys -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt - -from sklearn.utils import shuffle -from util import init_weight, get_ptb_data, display_tree - - -def tensor_mul(d, x1, A, x2): - A = tf.reshape(A, [d, d*d]) - # (1 x d) x (d x dd) - tmp = tf.matmul(x1, A) - # (1 x dd) - tmp = tf.reshape(tmp, [d, d]) - # (d x d) - tmp = tf.matmul(tmp, tf.transpose(x2)) - # (d x 1) - return tf.reshape(tmp, [1, d]) - - -def get_labels(tree): - # must be returned in the same order as tree logits are returned - # post-order traversal - if tree is None: - return [] - return get_labels(tree.left) + get_labels(tree.right) + [tree.label] - - -class RNTN: - def __init__(self, V, D, K, activation): - self.D = D - self.f = activation - - # word embedding - We = init_weight(V, D) - - # quadratic terms - W11 = np.random.randn(D, D, D) / np.sqrt(3*D) - W22 = np.random.randn(D, D, D) / np.sqrt(3*D) - W12 = np.random.randn(D, D, D) / np.sqrt(3*D) - - # linear terms - W1 = init_weight(D, D) - W2 = init_weight(D, D) - - # bias - bh = np.zeros(D) - - # output layer - Wo = init_weight(D, K) - bo = np.zeros(K) - - # make them tensorflow variables - self.We = tf.Variable(We.astype(np.float32)) - self.W11 = tf.Variable(W11.astype(np.float32)) - self.W22 = tf.Variable(W22.astype(np.float32)) - self.W12 = tf.Variable(W12.astype(np.float32)) - self.W1 = tf.Variable(W1.astype(np.float32)) - self.W2 = tf.Variable(W2.astype(np.float32)) - self.bh = tf.Variable(bh.astype(np.float32)) - self.Wo = tf.Variable(Wo.astype(np.float32)) - self.bo = tf.Variable(bo.astype(np.float32)) - self.params = [self.We, self.W11, self.W22, self.W12, self.W1, self.W2, self.Wo] - - def fit(self, trees, lr=1e-2, mu=0.9, reg=1e-1, epochs=5): - train_ops = [] - costs = [] - predictions = [] - all_labels = [] - i = 0 - N = len(trees) - print("Compiling ops") - for t in trees: - i += 1 - sys.stdout.write("%d/%d\r" % (i, N)) - sys.stdout.flush() - logits = self.get_output(t) - labels = get_labels(t) - all_labels.append(labels) - - cost = self.get_cost(logits, labels, reg) - costs.append(cost) - - prediction = tf.argmax(logits, 1) - predictions.append(prediction) - - train_op = tf.train.MomentumOptimizer(lr, mu).minimize(cost) - train_ops.append(train_op) - - # save for later so we don't have to recompile if we call score - self.predictions = predictions - self.all_labels = all_labels - - self.saver = tf.train.Saver() - init = tf.initialize_all_variables() - actual_costs = [] - per_epoch_costs = [] - correct_rates = [] - with tf.Session() as session: - session.run(init) - - for i in range(epochs): - train_ops, costs, predictions, all_labels = shuffle(train_ops, costs, predictions, all_labels) - epoch_cost = 0 - n_correct = 0 - n_total = 0 - j = 0 - N = len(train_ops) - for train_op, cost, prediction, labels in zip(train_ops, costs, predictions, all_labels): - _, c, p = session.run([train_op, cost, prediction]) - epoch_cost += c - actual_costs.append(c) - n_correct += np.sum(p == labels) - n_total += len(labels) - - j += 1 - if j % 10 == 0: - sys.stdout.write("j: %d, N: %d, c: %f\r" % (j, N, c)) - sys.stdout.flush() - - if np.isnan(c): - exit() - - per_epoch_costs.append(epoch_cost) - correct_rates.append(n_correct / float(n_total)) - - self.save_path = self.saver.save(session, "tf_model.ckpt") - - plt.plot(actual_costs) - plt.title("cost per train_op call") - plt.show() - - plt.plot(per_epoch_costs) - plt.title("per epoch costs") - plt.show() - - plt.plot(correct_rates) - plt.title("correct rates") - plt.show() - - def get_cost(self, logits, labels, reg): - cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels)) - rcost = sum(tf.nn.l2_loss(p) for p in self.params) - cost += reg*rcost - return cost - - # list_of_logits is an output! - # it is added to using post-order traversal - def get_output_recursive(self, tree, list_of_logits, is_root=True): - if tree.word is not None: - # this is a leaf node - x = tf.nn.embedding_lookup(self.We, [tree.word]) - else: - # if tree.left is None or tree.right is None: - # print("This tree node has no word but also has no children:") - # display_tree(tree) - # print("") - # this node has children - x1 = self.get_output_recursive(tree.left, list_of_logits, is_root=False) - x2 = self.get_output_recursive(tree.right, list_of_logits, is_root=False) - x = self.f( - tensor_mul(self.D, x1, self.W11, x1) + - tensor_mul(self.D, x2, self.W22, x2) + - tensor_mul(self.D, x1, self.W12, x2) + - tf.matmul(x1, self.W1) + - tf.matmul(x2, self.W2) + - self.bh) - - logits = tf.matmul(x, self.Wo) + self.bo - list_of_logits.append(logits) - - return x - - def get_output(self, tree): - logits = [] - try: - self.get_output_recursive(tree, logits) - except Exception as e: - display_tree(tree) - raise e - return tf.concat(0, logits) - - def score(self, trees): - if trees is None: - predictions = self.predictions - all_labels = self.all_labels - else: - # just build and run the predict_op for each tree - # and accumulate the total - predictions = [] - all_labels = [] - - i = 0 - N = len(trees) - print("Compiling ops") - for t in trees: - - i += 1 - sys.stdout.write("%d/%d\r" % (i, N)) - sys.stdout.flush() - - logits = self.get_output(t) - labels = get_labels(t) - all_labels.append(labels) - - prediction = tf.argmax(logits, 1) - predictions.append(prediction) - - n_correct = 0 - n_total = 0 - with tf.Session() as session: - self.saver.restore(session, "tf_model.ckpt") - - for prediction, y in zip(predictions, all_labels): - p = session.run(prediction) - # print("pred:", p) - # print("label:", y) - # n_correct += np.sum(p == y) - n_correct += (p[-1] == y[-1]) # we only care about the root - n_total += len(y) - - return float(n_correct) / n_total - - -def main(): - train, test, word2idx = get_ptb_data() - - train = train[:100] - test = test[:100] - - V = len(word2idx) - D = 80 - K = 5 - - model = RNTN(V, D, K, tf.nn.relu) - model.fit(train) - print("train accuracy:", model.score(None)) - print("test accuracy:", model.score(test)) - - -if __name__ == '__main__': - main() diff --git a/nlp_class2/rntn_tensorflow_rnn.py b/nlp_class2/rntn_tensorflow_rnn.py deleted file mode 100644 index 816ff4a2..00000000 --- a/nlp_class2/rntn_tensorflow_rnn.py +++ /dev/null @@ -1,340 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import sys -import numpy as np -import matplotlib.pyplot as plt -import tensorflow as tf - -from sklearn.utils import shuffle -from util import init_weight, get_ptb_data, display_tree -from datetime import datetime -from sklearn.metrics import f1_score - -if tf.__version__.startswith('2'): - tf.compat.v1.disable_eager_execution() - - - -class RecursiveNN: - def __init__(self, V, D, K, activation=tf.tanh): - self.V = V - self.D = D - self.K = K - self.f = activation - - def fit(self, trees, test_trees, reg=1e-3, epochs=8, train_inner_nodes=False): - D = self.D - V = self.V - K = self.K - N = len(trees) - - We = init_weight(V, D) - W11 = np.random.randn(D, D, D) / np.sqrt(3*D) - W22 = np.random.randn(D, D, D) / np.sqrt(3*D) - W12 = np.random.randn(D, D, D) / np.sqrt(3*D) - W1 = init_weight(D, D) - W2 = init_weight(D, D) - bh = np.zeros(D) - Wo = init_weight(D, K) - bo = np.zeros(K) - - self.We = tf.Variable(We.astype(np.float32)) - self.W11 = tf.Variable(W11.astype(np.float32)) - self.W22 = tf.Variable(W22.astype(np.float32)) - self.W12 = tf.Variable(W12.astype(np.float32)) - self.W1 = tf.Variable(W1.astype(np.float32)) - self.W2 = tf.Variable(W2.astype(np.float32)) - self.bh = tf.Variable(bh.astype(np.float32)) - self.Wo = tf.Variable(Wo.astype(np.float32)) - self.bo = tf.Variable(bo.astype(np.float32)) - self.weights = [self.We, self.W11, self.W22, self.W12, self.W1, self.W2, self.Wo] - - - words = tf.compat.v1.placeholder(tf.int32, shape=(None,), name='words') - left_children = tf.compat.v1.placeholder(tf.int32, shape=(None,), name='left_children') - right_children = tf.compat.v1.placeholder(tf.int32, shape=(None,), name='right_children') - labels = tf.compat.v1.placeholder(tf.int32, shape=(None,), name='labels') - - # save for later - self.words = words - self.left = left_children - self.right = right_children - self.labels = labels - - def dot1(a, B): - return tf.tensordot(a, B, axes=[[0], [1]]) - - def dot2(B, a): - return tf.tensordot(B, a, axes=[[1], [0]]) - - def recursive_net_transform(hiddens, n): - h_left = hiddens.read(left_children[n]) - h_right = hiddens.read(right_children[n]) - return self.f( - dot1(h_left, dot2(self.W11, h_left)) + - dot1(h_right, dot2(self.W22, h_right)) + - dot1(h_left, dot2(self.W12, h_right)) + - dot1(h_left, self.W1) + - dot1(h_right, self.W2) + - self.bh - ) - - - def recurrence(hiddens, n): - w = words[n] - # any non-word will have index -1 - - h_n = tf.cond( - pred=w >= 0, - true_fn=lambda: tf.nn.embedding_lookup(params=self.We, ids=w), - false_fn=lambda: recursive_net_transform(hiddens, n) - ) - hiddens = hiddens.write(n, h_n) - n = tf.add(n, 1) - return hiddens, n - - - def condition(hiddens, n): - # loop should continue while n < len(words) - return tf.less(n, tf.shape(input=words)[0]) - - - hiddens = tf.TensorArray( - tf.float32, - size=0, - dynamic_size=True, - clear_after_read=False, - infer_shape=False - ) - - hiddens, _ = tf.while_loop( - cond=condition, - body=recurrence, - loop_vars=[hiddens, tf.constant(0)], - parallel_iterations=1 - ) - h = hiddens.stack() - logits = tf.matmul(h, self.Wo) + self.bo - - prediction_op = tf.argmax(input=logits, axis=1) - self.prediction_op = prediction_op - - rcost = reg*sum(tf.nn.l2_loss(p) for p in self.weights) - if train_inner_nodes: - # filter out -1s - labeled_indices = tf.compat.v1.where(labels >= 0) - - cost_op = tf.reduce_mean( - input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=tf.gather(logits, labeled_indices), - labels=tf.gather(labels, labeled_indices), - ) - ) + rcost - else: - cost_op = tf.reduce_mean( - input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=logits[-1], - labels=labels[-1], - ) - ) + rcost - - train_op = tf.compat.v1.train.AdagradOptimizer(learning_rate=8e-3).minimize(cost_op) - # train_op = tf.train.MomentumOptimizer(learning_rate=8e-3, momentum=0.9).minimize(cost_op) - - # NOTE: If you're using GPU, InteractiveSession breaks - # AdagradOptimizer and some other optimizers - # change to tf.Session() if so. - self.session = tf.compat.v1.Session() - init_op = tf.compat.v1.global_variables_initializer() - self.session.run(init_op) - - - costs = [] - sequence_indexes = range(N) - for i in range(epochs): - t0 = datetime.now() - sequence_indexes = shuffle(sequence_indexes) - n_correct = 0 - n_total = 0 - cost = 0 - it = 0 - for j in sequence_indexes: - words_, left, right, lab = trees[j] - # print("words_:", words_) - # print("lab:", lab) - c, p, _ = self.session.run( - (cost_op, prediction_op, train_op), - feed_dict={ - words: words_, - left_children: left, - right_children: right, - labels: lab - } - ) - if np.isnan(c): - print("Cost is nan! Let's stop here. \ - Why don't you try decreasing the learning rate?") - for p in self.params: - print(p.get_value().sum()) - exit() - cost += c - n_correct += (p[-1] == lab[-1]) - n_total += 1 - - it += 1 - if it % 10 == 0: - sys.stdout.write( - "j/N: %d/%d correct rate so far: %f, cost so far: %f\r" % - (it, N, float(n_correct)/n_total, cost) - ) - sys.stdout.flush() - - - # calculate the test score - n_test_correct = 0 - n_test_total = 0 - for words_, left, right, lab in test_trees: - p = self.session.run(prediction_op, feed_dict={ - words: words_, - left_children: left, - right_children: right, - labels: lab - }) - n_test_correct += (p[-1] == lab[-1]) - n_test_total += 1 - - - print( - "i:", i, "cost:", cost, - "train acc:", float(n_correct)/n_total, - "test acc:", float(n_test_correct)/n_test_total, - "time for epoch:", (datetime.now() - t0) - ) - costs.append(cost) - - plt.plot(costs) - plt.show() - - def predict(self, words, left, right, lab): - return self.session.run( - self.prediction_op, - feed_dict={ - self.words: words, - self.left: left, - self.right: right, - self.labels: lab - } - ) - - - def score(self, trees): - n_total = len(trees) - n_correct = 0 - for words, left, right, lab in trees: - p = self.predict(words, left, right, lab) - n_correct += (p[-1] == lab[-1]) - return float(n_correct) / n_total - - def f1_score(self, trees): - Y = [] - P = [] - for words, left, right, lab in trees: - p = self.predict(words, left, right, lab) - Y.append(lab[-1]) - P.append(p[-1]) - return f1_score(Y, P, average=None).mean() - - -def add_idx_to_tree(tree, current_idx): - # post-order labeling of tree nodes - if tree is None: - return current_idx - current_idx = add_idx_to_tree(tree.left, current_idx) - current_idx = add_idx_to_tree(tree.right, current_idx) - tree.idx = current_idx - current_idx += 1 - return current_idx - - -def tree2list(tree, parent_idx, is_binary=False): - if tree is None: - return [], [], [], [] - - words_left, left_child_left, right_child_left, labels_left = tree2list(tree.left, tree.idx, is_binary) - words_right, left_child_right, right_child_right, labels_right = tree2list(tree.right, tree.idx, is_binary) - - if tree.word is None: - w = -1 - left = tree.left.idx - right = tree.right.idx - else: - w = tree.word - left = -1 - right = -1 - - words = words_left + words_right + [w] - left_child = left_child_left + left_child_right + [left] - right_child = right_child_left + right_child_right + [right] - - if is_binary: - if tree.label > 2: - label = 1 - elif tree.label < 2: - label = 0 - else: - label = -1 # we will eventually filter these out - else: - label = tree.label - labels = labels_left + labels_right + [label] - - return words, left_child, right_child, labels - - -def main(is_binary=True): - train, test, word2idx = get_ptb_data() - - for t in train: - add_idx_to_tree(t, 0) - train = [tree2list(t, -1, is_binary) for t in train] - if is_binary: - train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels - - for t in test: - add_idx_to_tree(t, 0) - test = [tree2list(t, -1, is_binary) for t in test] - if is_binary: - test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels - - - - train = shuffle(train) - # train = train[:5000] - # n_pos = sum(t[3][-1] for t in train) - # print("n_pos train:", n_pos) - test = shuffle(test) - smalltest = test[:1000] - # n_pos = sum(t[3][-1] for t in test) - # print("n_pos test:", n_pos) - - V = len(word2idx) - print("vocab size:", V) - D = 10 - K = 2 if is_binary else 5 - - model = RecursiveNN(V, D, K) - model.fit(train, smalltest, reg=1e-3, epochs=20, train_inner_nodes=True) - print("train accuracy:", model.score(train)) - print("test accuracy:", model.score(test)) - print("train f1:", model.f1_score(train)) - print("test f1:", model.f1_score(test)) - - -if __name__ == '__main__': - main() diff --git a/nlp_class2/rntn_theano.py b/nlp_class2/rntn_theano.py deleted file mode 100644 index 3949a3c8..00000000 --- a/nlp_class2/rntn_theano.py +++ /dev/null @@ -1,357 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import sys -import numpy as np -import matplotlib.pyplot as plt -import theano -import theano.tensor as T - -from sklearn.utils import shuffle -from util import init_weight, get_ptb_data, display_tree -from datetime import datetime -from sklearn.metrics import f1_score - - -# helper for adam optimizer -# use tensorflow defaults -# def adam(cost, params, lr0=1e-4, beta1=0.9, beta2=0.999, eps=1e-8): -# grads = T.grad(cost, params) -# updates = [] -# time = theano.shared(0) -# new_time = time + 1 -# updates.append((time, new_time)) -# lr = lr0*T.sqrt(1 - beta2**new_time) / (1 - beta1**new_time) -# for p, g in zip(params, grads): -# m = theano.shared(p.get_value() * 0.) -# v = theano.shared(p.get_value() * 0.) -# new_m = beta1*m + (1 - beta1)*g -# new_v = beta2*v + (1 - beta2)*g*g -# new_p = p - lr*new_m / (T.sqrt(new_v) + eps) -# updates.append((m, new_m)) -# updates.append((v, new_v)) -# updates.append((p, new_p)) -# return updates - - -# def momentum_updates(cost, params, learning_rate=1e-3, mu=0.99): -# # momentum changes -# dparams = [theano.shared(p.get_value() * 0.) for p in params] - -# updates = [] -# grads = T.grad(cost, params) -# for p, dp, g in zip(params, dparams, grads): -# dp_update = mu*dp - learning_rate*g -# p_update = p + dp_update - -# updates.append((dp, dp_update)) -# updates.append((p, p_update)) -# return updates - - -# def rmsprop(cost, params, lr=1e-3, decay=0.999, eps=1e-10): -# grads = T.grad(cost, params) -# caches = [theano.shared(np.ones_like(p.get_value())) for p in params] -# new_caches = [decay*c + (1. - decay)*g*g for c, g in zip(caches, grads)] - -# c_update = [(c, new_c) for c, new_c in zip(caches, new_caches)] -# g_update = [ -# (p, p - lr*g / T.sqrt(new_c + eps)) for p, new_c, g in zip(params, new_caches, grads) -# ] -# updates = c_update + g_update -# return updates - - -def adagrad(cost, params, lr, eps=1e-10): - grads = T.grad(cost, params) - caches = [theano.shared(np.ones_like(p.get_value())) for p in params] - new_caches = [c + g*g for c, g in zip(caches, grads)] - - c_update = [(c, new_c) for c, new_c in zip(caches, new_caches)] - g_update = [ - (p, p - lr*g / T.sqrt(new_c + eps)) for p, new_c, g in zip(params, new_caches, grads) - ] - updates = c_update + g_update - return updates - - -class RecursiveNN: - def __init__(self, V, D, K, activation=T.tanh): - self.V = V - self.D = D - self.K = K - self.f = activation - - def fit(self, trees, test_trees, reg=1e-3, epochs=8, train_inner_nodes=False): - D = self.D - V = self.V - K = self.K - N = len(trees) - - We = init_weight(V, D) - W11 = np.random.randn(D, D, D) / np.sqrt(3*D) - W22 = np.random.randn(D, D, D) / np.sqrt(3*D) - W12 = np.random.randn(D, D, D) / np.sqrt(3*D) - W1 = init_weight(D, D) - W2 = init_weight(D, D) - bh = np.zeros(D) - Wo = init_weight(D, K) - bo = np.zeros(K) - - self.We = theano.shared(We) - self.W11 = theano.shared(W11) - self.W22 = theano.shared(W22) - self.W12 = theano.shared(W12) - self.W1 = theano.shared(W1) - self.W2 = theano.shared(W2) - self.bh = theano.shared(bh) - self.Wo = theano.shared(Wo) - self.bo = theano.shared(bo) - self.params = [self.We, self.W11, self.W22, self.W12, self.W1, self.W2, self.bh, self.Wo, self.bo] - - lr = T.scalar('learning_rate') - words = T.ivector('words') - left_children = T.ivector('left_children') - right_children = T.ivector('right_children') - labels = T.ivector('labels') - - def recurrence(n, hiddens, words, left, right): - w = words[n] - # any non-word will have index -1 - hiddens = T.switch( - T.ge(w, 0), - T.set_subtensor(hiddens[n], self.We[w]), - T.set_subtensor(hiddens[n], - self.f( - hiddens[left[n]].dot(self.W11).dot(hiddens[left[n]]) + - hiddens[right[n]].dot(self.W22).dot(hiddens[right[n]]) + - hiddens[left[n]].dot(self.W12).dot(hiddens[right[n]]) + - hiddens[left[n]].dot(self.W1) + - hiddens[right[n]].dot(self.W2) + - self.bh - ) - ) - ) - return hiddens - - hiddens = T.zeros((words.shape[0], D)) - - h, _ = theano.scan( - fn=recurrence, - outputs_info=[hiddens], - n_steps=words.shape[0], - sequences=T.arange(words.shape[0]), - non_sequences=[words, left_children, right_children], - ) - - py_x = T.nnet.softmax(h[-1].dot(self.Wo) + self.bo) - - prediction = T.argmax(py_x, axis=1) - - rcost = reg*T.sum([(p*p).sum() for p in self.params]) - if train_inner_nodes: - relevant_labels = labels[labels >= 0] - cost = -T.mean(T.log(py_x[labels >= 0, relevant_labels])) + rcost - else: - cost = -T.mean(T.log(py_x[-1, labels[-1]])) + rcost - - - updates = adagrad(cost, self.params, lr) - - self.cost_predict_op = theano.function( - inputs=[words, left_children, right_children, labels], - outputs=[cost, prediction], - allow_input_downcast=True, - ) - - self.train_op = theano.function( - inputs=[words, left_children, right_children, labels, lr], - outputs=[cost, prediction], - updates=updates - ) - - lr_ = 8e-3 # initial learning rate - costs = [] - sequence_indexes = range(N) - # if train_inner_nodes: - # n_total = sum(len(words) for words, _, _, _ in trees) - # else: - # n_total = N - for i in range(epochs): - t0 = datetime.now() - sequence_indexes = shuffle(sequence_indexes) - n_correct = 0 - n_total = 0 - cost = 0 - it = 0 - for j in sequence_indexes: - words, left, right, lab = trees[j] - c, p = self.train_op(words, left, right, lab, lr_) - if np.isnan(c): - print("Cost is nan! Let's stop here. \ - Why don't you try decreasing the learning rate?") - for p in self.params: - print(p.get_value().sum()) - exit() - cost += c - n_correct += (p[-1] == lab[-1]) - n_total += 1 - it += 1 - if it % 10 == 0: - sys.stdout.write( - "j/N: %d/%d correct rate so far: %f, cost so far: %f\r" % - (it, N, float(n_correct)/n_total, cost) - ) - sys.stdout.flush() - - # calculate the test score - n_test_correct = 0 - n_test_total = 0 - for words, left, right, lab in test_trees: - _, p = self.cost_predict_op(words, left, right, lab) - n_test_correct += (p[-1] == lab[-1]) - n_test_total += 1 - - print( - "i:", i, "cost:", cost, - "train acc:", float(n_correct)/n_total, - "test acc:", float(n_test_correct)/n_test_total, - "time for epoch:", (datetime.now() - t0) - ) - costs.append(cost) - - plt.plot(costs) - plt.show() - - def score(self, trees): - n_total = len(trees) - n_correct = 0 - for words, left, right, lab in trees: - _, p = self.cost_predict_op(words, left, right, lab) - n_correct += (p[-1] == lab[-1]) - return float(n_correct) / n_total - - def f1_score(self, trees): - Y = [] - P = [] - for words, left, right, lab in trees: - _, p = self.cost_predict_op(words, left, right, lab) - Y.append(lab[-1]) - P.append(p[-1]) - return f1_score(Y, P, average=None).mean() - - -def add_idx_to_tree(tree, current_idx): - # post-order labeling of tree nodes - if tree is None: - return current_idx - current_idx = add_idx_to_tree(tree.left, current_idx) - current_idx = add_idx_to_tree(tree.right, current_idx) - tree.idx = current_idx - current_idx += 1 - return current_idx - - -def tree2list(tree, parent_idx, is_binary=False): - if tree is None: - return [], [], [], [] - - words_left, left_child_left, right_child_left, labels_left = tree2list(tree.left, tree.idx, is_binary) - words_right, left_child_right, right_child_right, labels_right = tree2list(tree.right, tree.idx, is_binary) - - if tree.word is None: - w = -1 - left = tree.left.idx - right = tree.right.idx - else: - w = tree.word - left = -1 - right = -1 - - words = words_left + words_right + [w] - left_child = left_child_left + left_child_right + [left] - right_child = right_child_left + right_child_right + [right] - - if is_binary: - if tree.label > 2: - label = 1 - elif tree.label < 2: - # else: - label = 0 - else: - label = -1 # we will eventually filter these out - else: - label = tree.label - labels = labels_left + labels_right + [label] - - return words, left_child, right_child, labels - - -def main(is_binary=True): - train, test, word2idx = get_ptb_data() - - for t in train: - add_idx_to_tree(t, 0) - train = [tree2list(t, -1, is_binary) for t in train] - if is_binary: - train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels - - for t in test: - add_idx_to_tree(t, 0) - test = [tree2list(t, -1, is_binary) for t in test] - if is_binary: - test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels - - # check imbalance - # pos = 0 - # neg = 0 - # mid = 0 - # label_counts = np.zeros(5) - # for t in train + test: - # words, left_child, right_child, labels = t - # # for l in labels: - # # if l == 0: - # # neg += 1 - # # elif l == 1: - # # pos += 1 - # # else: - # # mid += 1 - # for l in labels: - # label_counts[l] += 1 - # # print("pos / total:", float(pos) / (pos + neg + mid)) - # # print("mid / total:", float(mid) / (pos + neg + mid)) - # # print("neg / total:", float(neg) / (pos + neg + mid)) - # print("label proportions:", label_counts / label_counts.sum()) - # exit() - - - train = shuffle(train) - # train = train[:5000] - # n_pos = sum(t[3][-1] for t in train) - # print("n_pos train:", n_pos) - test = shuffle(test) - smalltest = test[:1000] - # n_pos = sum(t[3][-1] for t in test) - # print("n_pos test:", n_pos) - - V = len(word2idx) - print("vocab size:", V) - D = 20 - K = 2 if is_binary else 5 - - model = RecursiveNN(V, D, K) - model.fit(train, smalltest, epochs=20, train_inner_nodes=True) - print("train accuracy:", model.score(train)) - print("test accuracy:", model.score(test)) - print("train f1:", model.f1_score(train)) - print("test f1:", model.f1_score(test)) - - -if __name__ == '__main__': - main() diff --git a/nlp_class2/tfidf_tsne.py b/nlp_class2/tfidf_tsne.py deleted file mode 100644 index 55bd4ce5..00000000 --- a/nlp_class2/tfidf_tsne.py +++ /dev/null @@ -1,108 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import json -import numpy as np -import matplotlib.pyplot as plt -from sklearn.utils import shuffle -from sklearn.manifold import TSNE -from sklearn.decomposition import TruncatedSVD, PCA, KernelPCA -from datetime import datetime - -import os -import sys -sys.path.append(os.path.abspath('..')) -from rnn_class.util import get_wikipedia_data -from rnn_class.brown import get_sentences_with_word2idx_limit_vocab, get_sentences_with_word2idx - -from util import find_analogies -from sklearn.feature_extraction.text import TfidfTransformer - - -def main(): - analogies_to_try = ( - ('king', 'man', 'woman'), - ('france', 'paris', 'london'), - ('france', 'paris', 'rome'), - ('paris', 'france', 'italy'), - ) - - ### choose a data source ### - # sentences, word2idx = get_sentences_with_word2idx_limit_vocab(n_vocab=1500) - sentences, word2idx = get_wikipedia_data(n_files=3, n_vocab=2000, by_paragraph=True) - # with open('tfidf_word2idx.json', 'w') as f: - # json.dump(word2idx, f) - - notfound = False - for word_list in analogies_to_try: - for w in word_list: - if w not in word2idx: - print("%s not found in vocab, remove it from \ - analogies to try or increase vocab size" % w) - notfound = True - if notfound: - exit() - - - # build term document matrix - V = len(word2idx) - N = len(sentences) - - # create raw counts first - A = np.zeros((V, N)) - print("V:", V, "N:", N) - j = 0 - for sentence in sentences: - for i in sentence: - A[i,j] += 1 - j += 1 - print("finished getting raw counts") - - transformer = TfidfTransformer() - A = transformer.fit_transform(A.T).T - - # tsne requires a dense array - A = A.toarray() - - # map back to word in plot - idx2word = {v:k for k, v in iteritems(word2idx)} - - # plot the data in 2-D - tsne = TSNE() - Z = tsne.fit_transform(A) - plt.scatter(Z[:,0], Z[:,1]) - for i in range(V): - try: - plt.annotate(s=idx2word[i].encode("utf8").decode("utf8"), xy=(Z[i,0], Z[i,1])) - except: - print("bad string:", idx2word[i]) - plt.draw() - - ### multiple ways to create vectors for each word ### - # 1) simply set it to the TF-IDF matrix - # We = A - - # 2) create a higher-D word embedding - tsne = TSNE(n_components=3) - We = tsne.fit_transform(A) - - # 3) use a classic dimensionality reduction technique - # svd = KernelPCA(n_components=20, kernel='rbf') - # We = svd.fit_transform(A) - - for word_list in analogies_to_try: - w1, w2, w3 = word_list - find_analogies(w1, w2, w3, We, word2idx, idx2word) - - plt.show() # pause script until plot is closed - - -if __name__ == '__main__': - main() diff --git a/nlp_class2/util.py b/nlp_class2/util.py deleted file mode 100644 index f2a79888..00000000 --- a/nlp_class2/util.py +++ /dev/null @@ -1,204 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import os -import numpy as np -from sklearn.metrics.pairwise import pairwise_distances - - - -def init_weight(Mi, Mo): - return np.random.randn(Mi, Mo) / np.sqrt(Mi + Mo) - - -# slow version -# def find_analogies(w1, w2, w3, We, word2idx): -# king = We[word2idx[w1]] -# man = We[word2idx[w2]] -# woman = We[word2idx[w3]] -# v0 = king - man + woman - -# def dist1(a, b): -# return np.linalg.norm(a - b) -# def dist2(a, b): -# return 1 - a.dot(b) / (np.linalg.norm(a) * np.linalg.norm(b)) - -# for dist, name in [(dist1, 'Euclidean'), (dist2, 'cosine')]: -# min_dist = float('inf') -# best_word = '' -# for word, idx in iteritems(word2idx): -# if word not in (w1, w2, w3): -# v1 = We[idx] -# d = dist(v0, v1) -# if d < min_dist: -# min_dist = d -# best_word = word -# print("closest match by", name, "distance:", best_word) -# print(w1, "-", w2, "=", best_word, "-", w3) - -# fast version -def find_analogies(w1, w2, w3, We, word2idx, idx2word): - V, D = We.shape - - king = We[word2idx[w1]] - man = We[word2idx[w2]] - woman = We[word2idx[w3]] - v0 = king - man + woman - - for dist in ('euclidean', 'cosine'): - distances = pairwise_distances(v0.reshape(1, D), We, metric=dist).reshape(V) - # idx = distances.argmin() - # best_word = idx2word[idx] - idx = distances.argsort()[:4] - best_idx = -1 - keep_out = [word2idx[w] for w in (w1, w2, w3)] - for i in idx: - if i not in keep_out: - best_idx = i - break - best_word = idx2word[best_idx] - - - print("closest match by", dist, "distance:", best_word) - print(w1, "-", w2, "=", best_word, "-", w3) - - -class Tree: - def __init__(self, word, label): - self.left = None - self.right = None - self.word = word - self.label = label - - -def display_tree(t, lvl=0): - prefix = ''.join(['>']*lvl) - if t.word is not None: - print("%s%s %s" % (prefix, t.label, t.word)) - else: - print("%s%s -" % (prefix, t.label)) - # if t.left is None or t.right is None: - # raise Exception("Tree node has no word but left and right child are None") - if t.left: - display_tree(t.left, lvl + 1) - if t.right: - display_tree(t.right, lvl + 1) - - -current_idx = 0 -def str2tree(s, word2idx): - # take a string that starts with ( and MAYBE ends with ) - # return the tree that it represents - # EXAMPLE: "(3 (2 It) (4 (4 (2 's) (4 (3 (2 a) (4 (3 lovely) (2 film))) (3 (2 with) (4 (3 (3 lovely) (2 performances)) (2 (2 by) (2 (2 (2 Buy) (2 and)) (2 Accorsi))))))) (2 .)))" - # NOTE: not every node has 2 children (possibly not correct ??) - # NOTE: not every node has a word - # NOTE: every node has a label - # NOTE: labels are 0,1,2,3,4 - # NOTE: only leaf nodes have words - # s[0] = (, s[1] = label, s[2] = space, s[3] = character or ( - - # print "Input string:", s, "len:", len(s) - - global current_idx - - label = int(s[1]) - if s[3] == '(': - t = Tree(None, label) - # try: - - # find the string that represents left child - # it can include trailing characters we don't need, because we'll only look up to ) - child_s = s[3:] - t.left = str2tree(child_s, word2idx) - - # find the string that represents right child - # can contain multiple ((( ))) - # left child is completely represented when we've closed as many as we've opened - # we stop at 1 because the first opening paren represents the current node, not children nodes - i = 0 - depth = 0 - for c in s: - i += 1 - if c == '(': - depth += 1 - elif c == ')': - depth -= 1 - if depth == 1: - break - # print "index of right child", i - - t.right = str2tree(s[i+1:], word2idx) - - # except Exception as e: - # print "Exception:", e - # print "Input string:", s - # raise e - - # if t.left is None or t.right is None: - # raise Exception("Tree node has no word but left and right child are None") - return t - else: - # this has a word, so it's a leaf - r = s.split(')', 1)[0] - word = r[3:].lower() - # print "word found:", word - - if word not in word2idx: - word2idx[word] = current_idx - current_idx += 1 - - t = Tree(word2idx[word], label) - return t - - -def get_ptb_data(): - # like the wikipedia dataset, I want to return 2 things: - # word2idx mapping, sentences - # here the sentences should be Tree objects - - if not os.path.exists('../large_files/trees'): - print("Please create ../large_files/trees relative to this file.") - print("train.txt and test.txt should be stored in there.") - print("Please download the data from http://nlp.stanford.edu/sentiment/") - exit() - elif not os.path.exists('../large_files/trees/train.txt'): - print("train.txt is not in ../large_files/trees/train.txt") - print("Please download the data from http://nlp.stanford.edu/sentiment/") - exit() - elif not os.path.exists('../large_files/trees/test.txt'): - print("test.txt is not in ../large_files/trees/test.txt") - print("Please download the data from http://nlp.stanford.edu/sentiment/") - exit() - - word2idx = {} - train = [] - test = [] - - # train set first - for line in open('../large_files/trees/train.txt'): - line = line.rstrip() - if line: - t = str2tree(line, word2idx) - # if t.word is None and t.left is None and t.right is None: - # print "sentence:", line - # display_tree(t) - # print "" - train.append(t) - # break - - # test set - for line in open('../large_files/trees/test.txt'): - line = line.rstrip() - if line: - t = str2tree(line, word2idx) - test.append(t) - return train, test, word2idx - -# get_ptb_data() diff --git a/nlp_class2/visualize_countries.py b/nlp_class2/visualize_countries.py deleted file mode 100644 index 9d0a44e8..00000000 --- a/nlp_class2/visualize_countries.py +++ /dev/null @@ -1,39 +0,0 @@ -# Course URL: -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import json -import numpy as np -import matplotlib.pyplot as plt -from sklearn.manifold import TSNE - -def main(we_file='glove_model_50.npz', w2i_file='glove_word2idx_50.json'): - words = ['japan', 'japanese', 'england', 'english', 'australia', 'australian', 'china', 'chinese', 'italy', 'italian', 'french', 'france', 'spain', 'spanish'] - - with open(w2i_file) as f: - word2idx = json.load(f) - - npz = np.load(we_file) - W = npz['arr_0'] - V = npz['arr_1'] - We = (W + V.T) / 2 - - idx = [word2idx[w] for w in words] - # We = We[idx] - - tsne = TSNE() - Z = tsne.fit_transform(We) - Z = Z[idx] - plt.scatter(Z[:,0], Z[:,1]) - for i in range(len(words)): - plt.annotate(s=words[i], xy=(Z[i,0], Z[i,1])) - plt.show() - - -if __name__ == '__main__': - main() diff --git a/nlp_class2/w2v_model.npz b/nlp_class2/w2v_model.npz deleted file mode 100644 index 79dc4349..00000000 Binary files a/nlp_class2/w2v_model.npz and /dev/null differ diff --git a/nlp_class2/w2v_word2idx.json b/nlp_class2/w2v_word2idx.json deleted file mode 100644 index 1fb8a968..00000000 --- a/nlp_class2/w2v_word2idx.json +++ /dev/null @@ -1 +0,0 @@ -{"limited": 575, "writings": 1340, "four": 171, "consists": 1061, "oldest": 1493, "whose": 544, "paris": 734, 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726, "minutes": 1841, "display": 1854, "supreme": 1162, "deaths": 1756, "universal": 1476, "abbot": 1749, "aristotle": 611, "education": 499, "autism": 720, "functions": 1182, "compared": 840, "variety": 642, "percent": 715, "40": 1710, "book": 187, "branch": 1521, "canada": 758, "star": 850, "june": 519, "ultimately": 1837, "amphibians": 1583, "friends": 1410, "exposure": 1829, "\u2013": 202, "\u2014": 1879, "bce": 1655, "factors": 1159, "rule": 999, "write": 1243} \ No newline at end of file diff --git a/nlp_class2/word2vec.py b/nlp_class2/word2vec.py deleted file mode 100644 index ba92e68c..00000000 --- a/nlp_class2/word2vec.py +++ /dev/null @@ -1,358 +0,0 @@ -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import json -import numpy as np -import matplotlib.pyplot as plt -from scipy.special import expit as sigmoid -from sklearn.utils import shuffle -from datetime import datetime -# from util import find_analogies - -from scipy.spatial.distance import cosine as cos_dist -from sklearn.metrics.pairwise import pairwise_distances - - -from glob import glob - -import os -import sys -import string - -sys.path.append(os.path.abspath('..')) -from rnn_class.brown import get_sentences_with_word2idx_limit_vocab as get_brown - - - -# unfortunately these work different ways -def remove_punctuation_2(s): - return s.translate(None, string.punctuation) - -def remove_punctuation_3(s): - return s.translate(str.maketrans('','',string.punctuation)) - -if sys.version.startswith('2'): - remove_punctuation = remove_punctuation_2 -else: - remove_punctuation = remove_punctuation_3 - - - - -def get_wiki(): - V = 20000 - files = glob('../large_files/enwiki*.txt') - all_word_counts = {} - for f in files: - for line in open(f): - if line and line[0] not in '[*-|=\{\}': - s = remove_punctuation(line).lower().split() - if len(s) > 1: - for word in s: - if word not in all_word_counts: - all_word_counts[word] = 0 - all_word_counts[word] += 1 - print("finished counting") - - V = min(V, len(all_word_counts)) - all_word_counts = sorted(all_word_counts.items(), key=lambda x: x[1], reverse=True) - - top_words = [w for w, count in all_word_counts[:V-1]] + [''] - word2idx = {w:i for i, w in enumerate(top_words)} - unk = word2idx[''] - - sents = [] - for f in files: - for line in open(f): - if line and line[0] not in '[*-|=\{\}': - s = remove_punctuation(line).lower().split() - if len(s) > 1: - # if a word is not nearby another word, there won't be any context! - # and hence nothing to train! - sent = [word2idx[w] if w in word2idx else unk for w in s] - sents.append(sent) - return sents, word2idx - - - - -def train_model(savedir): - # get the data - sentences, word2idx = get_wiki() #get_brown() - - - # number of unique words - vocab_size = len(word2idx) - - - # config - window_size = 5 - learning_rate = 0.025 - final_learning_rate = 0.0001 - num_negatives = 5 # number of negative samples to draw per input word - epochs = 20 - D = 50 # word embedding size - - - # learning rate decay - learning_rate_delta = (learning_rate - final_learning_rate) / epochs - - - # params - W = np.random.randn(vocab_size, D) # input-to-hidden - V = np.random.randn(D, vocab_size) # hidden-to-output - - - # distribution for drawing negative samples - p_neg = get_negative_sampling_distribution(sentences, vocab_size) - - - # save the costs to plot them per iteration - costs = [] - - - # number of total words in corpus - total_words = sum(len(sentence) for sentence in sentences) - print("total number of words in corpus:", total_words) - - # for subsampling each sentence - threshold = 1e-5 - p_drop = 1 - np.sqrt(threshold / p_neg) - - - # train the model - for epoch in range(epochs): - # randomly order sentences so we don't always see - # sentences in the same order - np.random.shuffle(sentences) - - # accumulate the cost - cost = 0 - counter = 0 - t0 = datetime.now() - for sentence in sentences: - # keep only certain words based on p_neg - sentence = [w for w in sentence \ - if np.random.random() < (1 - p_drop[w]) - ] - if len(sentence) < 2: - continue - - - # randomly order words so we don't always see - # samples in the same order - randomly_ordered_positions = np.random.choice( - len(sentence), - size=len(sentence),#np.random.randint(1, len(sentence) + 1), - replace=False, - ) - - - for pos in randomly_ordered_positions: - # the middle word - word = sentence[pos] - - # get the positive context words/negative samples - context_words = get_context(pos, sentence, window_size) - neg_word = np.random.choice(vocab_size, p=p_neg) - targets = np.array(context_words) - - # do one iteration of stochastic gradient descent - c = sgd(word, targets, 1, learning_rate, W, V) - cost += c - c = sgd(neg_word, targets, 0, learning_rate, W, V) - cost += c - - counter += 1 - if counter % 100 == 0: - sys.stdout.write("processed %s / %s\r" % (counter, len(sentences))) - sys.stdout.flush() - # break - - - # print stuff so we don't stare at a blank screen - dt = datetime.now() - t0 - print("epoch complete:", epoch, "cost:", cost, "dt:", dt) - - # save the cost - costs.append(cost) - - # update the learning rate - learning_rate -= learning_rate_delta - - - # plot the cost per iteration - plt.plot(costs) - plt.show() - - - # save the model - if not os.path.exists(savedir): - os.mkdir(savedir) - - with open('%s/word2idx.json' % savedir, 'w') as f: - json.dump(word2idx, f) - - np.savez('%s/weights.npz' % savedir, W, V) - - # return the model - return word2idx, W, V - - -def get_negative_sampling_distribution(sentences, vocab_size): - # Pn(w) = prob of word occuring - # we would like to sample the negative samples - # such that words that occur more often - # should be sampled more often - - word_freq = np.zeros(vocab_size) - word_count = sum(len(sentence) for sentence in sentences) - for sentence in sentences: - for word in sentence: - word_freq[word] += 1 - - # smooth it - p_neg = word_freq**0.75 - - # normalize it - p_neg = p_neg / p_neg.sum() - - assert(np.all(p_neg > 0)) - return p_neg - - -def get_context(pos, sentence, window_size): - # input: - # a sentence of the form: x x x x c c c pos c c c x x x x - # output: - # the context word indices: c c c c c c - - start = max(0, pos - window_size) - end_ = min(len(sentence), pos + window_size) - - context = [] - for ctx_pos, ctx_word_idx in enumerate(sentence[start:end_], start=start): - if ctx_pos != pos: - # don't include the input word itself as a target - context.append(ctx_word_idx) - return context - - -def sgd(input_, targets, label, learning_rate, W, V): - # W[input_] shape: D - # V[:,targets] shape: D x N - # activation shape: N - # print("input_:", input_, "targets:", targets) - activation = W[input_].dot(V[:,targets]) - prob = sigmoid(activation) - - # gradients - gV = np.outer(W[input_], prob - label) # D x N - gW = np.sum((prob - label)*V[:,targets], axis=1) # D - - V[:,targets] -= learning_rate*gV # D x N - W[input_] -= learning_rate*gW # D - - # return cost (binary cross entropy) - cost = label * np.log(prob + 1e-10) + (1 - label) * np.log(1 - prob + 1e-10) - return cost.sum() - - -def load_model(savedir): - with open('%s/word2idx.json' % savedir) as f: - word2idx = json.load(f) - npz = np.load('%s/weights.npz' % savedir) - W = npz['arr_0'] - V = npz['arr_1'] - return word2idx, W, V - - - -def analogy(pos1, neg1, pos2, neg2, word2idx, idx2word, W): - V, D = W.shape - - # don't actually use pos2 in calculation, just print what's expected - print("testing: %s - %s = %s - %s" % (pos1, neg1, pos2, neg2)) - for w in (pos1, neg1, pos2, neg2): - if w not in word2idx: - print("Sorry, %s not in word2idx" % w) - return - - p1 = W[word2idx[pos1]] - n1 = W[word2idx[neg1]] - p2 = W[word2idx[pos2]] - n2 = W[word2idx[neg2]] - - vec = p1 - n1 + n2 - - distances = pairwise_distances(vec.reshape(1, D), W, metric='cosine').reshape(V) - idx = distances.argsort()[:10] - - # pick one that's not p1, n1, or n2 - best_idx = -1 - keep_out = [word2idx[w] for w in (pos1, neg1, neg2)] - # print("keep_out:", keep_out) - for i in idx: - if i not in keep_out: - best_idx = i - break - # print("best_idx:", best_idx) - - print("got: %s - %s = %s - %s" % (pos1, neg1, idx2word[best_idx], neg2)) - print("closest 10:") - for i in idx: - print(idx2word[i], distances[i]) - - print("dist to %s:" % pos2, cos_dist(p2, vec)) - - -def test_model(word2idx, W, V): - # there are multiple ways to get the "final" word embedding - # We = (W + V.T) / 2 - # We = W - - idx2word = {i:w for w, i in word2idx.items()} - - for We in (W, (W + V.T) / 2): - print("**********") - - analogy('king', 'man', 'queen', 'woman', word2idx, idx2word, We) - analogy('king', 'prince', 'queen', 'princess', word2idx, idx2word, We) - analogy('miami', 'florida', 'dallas', 'texas', word2idx, idx2word, We) - analogy('einstein', 'scientist', 'picasso', 'painter', word2idx, idx2word, We) - analogy('japan', 'sushi', 'germany', 'bratwurst', word2idx, idx2word, We) - analogy('man', 'woman', 'he', 'she', word2idx, idx2word, We) - analogy('man', 'woman', 'uncle', 'aunt', word2idx, idx2word, We) - analogy('man', 'woman', 'brother', 'sister', word2idx, idx2word, We) - analogy('man', 'woman', 'husband', 'wife', word2idx, idx2word, We) - analogy('man', 'woman', 'actor', 'actress', word2idx, idx2word, We) - analogy('man', 'woman', 'father', 'mother', word2idx, idx2word, We) - analogy('heir', 'heiress', 'prince', 'princess', word2idx, idx2word, We) - analogy('nephew', 'niece', 'uncle', 'aunt', word2idx, idx2word, We) - analogy('france', 'paris', 'japan', 'tokyo', word2idx, idx2word, We) - analogy('france', 'paris', 'china', 'beijing', word2idx, idx2word, We) - analogy('february', 'january', 'december', 'november', word2idx, idx2word, We) - analogy('france', 'paris', 'germany', 'berlin', word2idx, idx2word, We) - analogy('week', 'day', 'year', 'month', word2idx, idx2word, We) - analogy('week', 'day', 'hour', 'minute', word2idx, idx2word, We) - analogy('france', 'paris', 'italy', 'rome', word2idx, idx2word, We) - analogy('paris', 'france', 'rome', 'italy', word2idx, idx2word, We) - analogy('france', 'french', 'england', 'english', word2idx, idx2word, We) - analogy('japan', 'japanese', 'china', 'chinese', word2idx, idx2word, We) - analogy('china', 'chinese', 'america', 'american', word2idx, idx2word, We) - analogy('japan', 'japanese', 'italy', 'italian', word2idx, idx2word, We) - analogy('japan', 'japanese', 'australia', 'australian', word2idx, idx2word, We) - analogy('walk', 'walking', 'swim', 'swimming', word2idx, idx2word, We) - - - -if __name__ == '__main__': - word2idx, W, V = train_model('w2v_model') - # word2idx, W, V = load_model('w2v_model') - test_model(word2idx, W, V) - diff --git a/nlp_class2/word2vec_tf.py b/nlp_class2/word2vec_tf.py deleted file mode 100644 index d272b003..00000000 --- a/nlp_class2/word2vec_tf.py +++ /dev/null @@ -1,458 +0,0 @@ -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import json -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt -from scipy.special import expit as sigmoid -from sklearn.utils import shuffle -from datetime import datetime -# from util import find_analogies - -from scipy.spatial.distance import cosine as cos_dist -from sklearn.metrics.pairwise import pairwise_distances - - -from glob import glob - -import os -import sys -import string - -if tf.__version__.startswith('2'): - tf.compat.v1.disable_eager_execution() - - - -# unfortunately these work different ways -def remove_punctuation_2(s): - return s.translate(None, string.punctuation) - -def remove_punctuation_3(s): - return s.translate(str.maketrans('','',string.punctuation)) - -if sys.version.startswith('2'): - remove_punctuation = remove_punctuation_2 -else: - remove_punctuation = remove_punctuation_3 - - -def download_text8(dst): - pass - - -def get_text8(): - # download the data if it is not yet in the right place - path = '../large_files/text8' - if not os.path.exists(path): - download_text8(path) - - words = open(path).read() - word2idx = {} - sents = [[]] - count = 0 - for word in words.split(): - if word not in word2idx: - word2idx[word] = count - count += 1 - sents[0].append(word2idx[word]) - print("count:", count) - return sents, word2idx - - -def get_wiki(): - V = 20000 - files = glob('../large_files/enwiki*.txt') - all_word_counts = {} - for f in files: - for line in open(f): - if line and line[0] not in '[*-|=\{\}': - s = remove_punctuation(line).lower().split() - if len(s) > 1: - for word in s: - if word not in all_word_counts: - all_word_counts[word] = 0 - all_word_counts[word] += 1 - print("finished counting") - - V = min(V, len(all_word_counts)) - all_word_counts = sorted(all_word_counts.items(), key=lambda x: x[1], reverse=True) - - top_words = [w for w, count in all_word_counts[:V-1]] + [''] - word2idx = {w:i for i, w in enumerate(top_words)} - unk = word2idx[''] - - sents = [] - for f in files: - for line in open(f): - if line and line[0] not in '[*-|=\{\}': - s = remove_punctuation(line).lower().split() - if len(s) > 1: - # if a word is not nearby another word, there won't be any context! - # and hence nothing to train! - sent = [word2idx[w] if w in word2idx else unk for w in s] - sents.append(sent) - return sents, word2idx - - - - -def train_model(savedir): - # get the data - sentences, word2idx = get_wiki() #get_text8() - - - # number of unique words - vocab_size = len(word2idx) - - - # config - window_size = 10 - learning_rate = 0.025 - final_learning_rate = 0.0001 - num_negatives = 5 # number of negative samples to draw per input word - samples_per_epoch = int(1e5) - epochs = 20 - D = 50 # word embedding size - - # learning rate decay - learning_rate_delta = (learning_rate - final_learning_rate) / epochs - - # distribution for drawing negative samples - p_neg = get_negative_sampling_distribution(sentences) - - - # params - W = np.random.randn(vocab_size, D).astype(np.float32) # input-to-hidden - V = np.random.randn(D, vocab_size).astype(np.float32) # hidden-to-output - - - # create the model - tf_input = tf.compat.v1.placeholder(tf.int32, shape=(None,)) - tf_negword = tf.compat.v1.placeholder(tf.int32, shape=(None,)) - tf_context = tf.compat.v1.placeholder(tf.int32, shape=(None,)) # targets (context) - tfW = tf.Variable(W) - tfV = tf.Variable(V.T) - # biases = tf.Variable(np.zeros(vocab_size, dtype=np.float32)) - - def dot(A, B): - C = A * B - return tf.reduce_sum(input_tensor=C, axis=1) - - # correct middle word output - emb_input = tf.nn.embedding_lookup(params=tfW, ids=tf_input) # 1 x D - emb_output = tf.nn.embedding_lookup(params=tfV, ids=tf_context) # N x D - correct_output = dot(emb_input, emb_output) # N - # emb_input = tf.transpose(emb_input, (1, 0)) - # correct_output = tf.matmul(emb_output, emb_input) - pos_loss = tf.nn.sigmoid_cross_entropy_with_logits( - labels=tf.ones(tf.shape(input=correct_output)), logits=correct_output) - - # incorrect middle word output - emb_input = tf.nn.embedding_lookup(params=tfW, ids=tf_negword) - incorrect_output = dot(emb_input, emb_output) - # emb_input = tf.transpose(emb_input, (1, 0)) - # incorrect_output = tf.matmul(emb_output, emb_input) - neg_loss = tf.nn.sigmoid_cross_entropy_with_logits( - labels=tf.zeros(tf.shape(input=incorrect_output)), logits=incorrect_output) - - # total loss - loss = tf.reduce_mean(input_tensor=pos_loss) + tf.reduce_mean(input_tensor=neg_loss) - - # output = hidden.dot(tfV) - - # loss - # neither of the built-in TF functions work well - # per_sample_loss = tf.nn.nce_loss( - # # per_sample_loss = tf.nn.sampled_softmax_loss( - # weights=tfV, - # biases=biases, - # labels=tfY, - # inputs=hidden, - # num_sampled=num_negatives, - # num_classes=vocab_size, - # ) - # loss = tf.reduce_mean(per_sample_loss) - - # optimizer - # train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) - train_op = tf.compat.v1.train.MomentumOptimizer(0.1, momentum=0.9).minimize(loss) - # train_op = tf.train.AdamOptimizer(1e-2).minimize(loss) - - # make session - session = tf.compat.v1.Session() - init_op = tf.compat.v1.global_variables_initializer() - session.run(init_op) - - - # save the costs to plot them per iteration - costs = [] - - - # number of total words in corpus - total_words = sum(len(sentence) for sentence in sentences) - print("total number of words in corpus:", total_words) - - - # for subsampling each sentence - threshold = 1e-5 - p_drop = 1 - np.sqrt(threshold / p_neg) - - - # train the model - for epoch in range(epochs): - # randomly order sentences so we don't always see - # sentences in the same order - np.random.shuffle(sentences) - - # accumulate the cost - cost = 0 - counter = 0 - inputs = [] - targets = [] - negwords = [] - t0 = datetime.now() - for sentence in sentences: - - # keep only certain words based on p_neg - sentence = [w for w in sentence \ - if np.random.random() < (1 - p_drop[w]) - ] - if len(sentence) < 2: - continue - - - # randomly order words so we don't always see - # samples in the same order - randomly_ordered_positions = np.random.choice( - len(sentence), - # size=np.random.randint(1, len(sentence) + 1), - size=len(sentence), - replace=False, - ) - - - for j, pos in enumerate(randomly_ordered_positions): - # the middle word - word = sentence[pos] - - # get the positive context words/negative samples - context_words = get_context(pos, sentence, window_size) - neg_word = np.random.choice(vocab_size, p=p_neg) - - - n = len(context_words) - inputs += [word]*n - negwords += [neg_word]*n - # targets = np.concatenate([targets, targets_]) - targets += context_words - - # _, c = session.run( - # (train_op, loss), - # feed_dict={ - # tf_input: [word], - # tf_negword: [neg_word], - # tf_context: targets_, - # } - # ) - # cost += c - - - if len(inputs) >= 128: - _, c = session.run( - (train_op, loss), - feed_dict={ - tf_input: inputs, - tf_negword: negwords, - tf_context: targets, - } - ) - cost += c - - # reset - inputs = [] - targets = [] - negwords = [] - - counter += 1 - if counter % 100 == 0: - sys.stdout.write("processed %s / %s\r" % (counter, len(sentences))) - sys.stdout.flush() - # break - - - # print stuff so we don't stare at a blank screen - dt = datetime.now() - t0 - print("epoch complete:", epoch, "cost:", cost, "dt:", dt) - - # save the cost - costs.append(cost) - - # update the learning rate - learning_rate -= learning_rate_delta - - - # plot the cost per iteration - plt.plot(costs) - plt.show() - - # get the params - W, VT = session.run((tfW, tfV)) - V = VT.T - - # save the model - if not os.path.exists(savedir): - os.mkdir(savedir) - - with open('%s/word2idx.json' % savedir, 'w') as f: - json.dump(word2idx, f) - - np.savez('%s/weights.npz' % savedir, W, V) - - # return the model - return word2idx, W, V - - -def get_negative_sampling_distribution(sentences): - # Pn(w) = prob of word occuring - # we would like to sample the negative samples - # such that words that occur more often - # should be sampled more often - - word_freq = {} - word_count = sum(len(sentence) for sentence in sentences) - for sentence in sentences: - for word in sentence: - if word not in word_freq: - word_freq[word] = 0 - word_freq[word] += 1 - - # vocab size - V = len(word_freq) - - p_neg = np.zeros(V) - for j in range(V): - p_neg[j] = word_freq[j]**0.75 - - # normalize it - p_neg = p_neg / p_neg.sum() - - assert(np.all(p_neg > 0)) - return p_neg - - -def get_context(pos, sentence, window_size): - # input: - # a sentence of the form: x x x x c c c pos c c c x x x x - # output: - # the context word indices: c c c c c c - - start = max(0, pos - window_size) - end_ = min(len(sentence), pos + window_size) - - context = [] - for ctx_pos, ctx_word_idx in enumerate(sentence[start:end_], start=start): - if ctx_pos != pos: - # don't include the input word itself as a target - context.append(ctx_word_idx) - return context - - - -def load_model(savedir): - with open('%s/word2idx.json' % savedir) as f: - word2idx = json.load(f) - npz = np.load('%s/weights.npz' % savedir) - W = npz['arr_0'] - V = npz['arr_1'] - return word2idx, W, V - - - -def analogy(pos1, neg1, pos2, neg2, word2idx, idx2word, W): - V, D = W.shape - - # don't actually use pos2 in calculation, just print what's expected - print("testing: %s - %s = %s - %s" % (pos1, neg1, pos2, neg2)) - for w in (pos1, neg1, pos2, neg2): - if w not in word2idx: - print("Sorry, %s not in word2idx" % w) - return - - p1 = W[word2idx[pos1]] - n1 = W[word2idx[neg1]] - p2 = W[word2idx[pos2]] - n2 = W[word2idx[neg2]] - - vec = p1 - n1 + n2 - - distances = pairwise_distances(vec.reshape(1, D), W, metric='cosine').reshape(V) - idx = distances.argsort()[:10] - - # pick one that's not p1, n1, or n2 - best_idx = -1 - keep_out = [word2idx[w] for w in (pos1, neg1, neg2)] - for i in idx: - if i not in keep_out: - best_idx = i - break - - print("got: %s - %s = %s - %s" % (pos1, neg1, idx2word[idx[0]], neg2)) - print("closest 10:") - for i in idx: - print(idx2word[i], distances[i]) - - print("dist to %s:" % pos2, cos_dist(p2, vec)) - - -def test_model(word2idx, W, V): - # there are multiple ways to get the "final" word embedding - # We = (W + V.T) / 2 - # We = W - - idx2word = {i:w for w, i in word2idx.items()} - - for We in (W, (W + V.T) / 2): - print("**********") - - analogy('king', 'man', 'queen', 'woman', word2idx, idx2word, We) - analogy('king', 'prince', 'queen', 'princess', word2idx, idx2word, We) - analogy('miami', 'florida', 'dallas', 'texas', word2idx, idx2word, We) - analogy('einstein', 'scientist', 'picasso', 'painter', word2idx, idx2word, We) - analogy('japan', 'sushi', 'england', 'bread', word2idx, idx2word, We) - analogy('man', 'woman', 'he', 'she', word2idx, idx2word, We) - analogy('man', 'woman', 'uncle', 'aunt', word2idx, idx2word, We) - analogy('man', 'woman', 'brother', 'sister', word2idx, idx2word, We) - analogy('man', 'woman', 'husband', 'wife', word2idx, idx2word, We) - analogy('man', 'woman', 'actor', 'actress', word2idx, idx2word, We) - analogy('man', 'woman', 'father', 'mother', word2idx, idx2word, We) - analogy('heir', 'heiress', 'prince', 'princess', word2idx, idx2word, We) - analogy('nephew', 'niece', 'uncle', 'aunt', word2idx, idx2word, We) - analogy('france', 'paris', 'japan', 'tokyo', word2idx, idx2word, We) - analogy('france', 'paris', 'china', 'beijing', word2idx, idx2word, We) - analogy('february', 'january', 'december', 'november', word2idx, idx2word, We) - analogy('france', 'paris', 'germany', 'berlin', word2idx, idx2word, We) - analogy('week', 'day', 'year', 'month', word2idx, idx2word, We) - analogy('week', 'day', 'hour', 'minute', word2idx, idx2word, We) - analogy('france', 'paris', 'italy', 'rome', word2idx, idx2word, We) - analogy('paris', 'france', 'rome', 'italy', word2idx, idx2word, We) - analogy('france', 'french', 'england', 'english', word2idx, idx2word, We) - analogy('japan', 'japanese', 'china', 'chinese', word2idx, idx2word, We) - analogy('china', 'chinese', 'america', 'american', word2idx, idx2word, We) - analogy('japan', 'japanese', 'italy', 'italian', word2idx, idx2word, We) - analogy('japan', 'japanese', 'australia', 'australian', word2idx, idx2word, We) - analogy('walk', 'walking', 'swim', 'swimming', word2idx, idx2word, We) - - - -if __name__ == '__main__': - word2idx, W, V = train_model('w2v_tf') - # word2idx, W, V = load_model('w2v_tf') - test_model(word2idx, W, V) - diff --git a/nlp_class2/word2vec_theano.py b/nlp_class2/word2vec_theano.py deleted file mode 100644 index 50e3efd5..00000000 --- a/nlp_class2/word2vec_theano.py +++ /dev/null @@ -1,409 +0,0 @@ -# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python -# https://udemy.com/natural-language-processing-with-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import json -import numpy as np -import matplotlib.pyplot as plt -from scipy.special import expit as sigmoid -from sklearn.utils import shuffle -from datetime import datetime -# from util import find_analogies - -from scipy.spatial.distance import cosine as cos_dist -from sklearn.metrics.pairwise import pairwise_distances - - -from glob import glob - -import os -import sys -import string - -import theano -import theano.tensor as T - - - -# unfortunately these work different ways -def remove_punctuation_2(s): - return s.translate(None, string.punctuation) - -def remove_punctuation_3(s): - return s.translate(str.maketrans('','',string.punctuation)) - -if sys.version.startswith('2'): - remove_punctuation = remove_punctuation_2 -else: - remove_punctuation = remove_punctuation_3 - - - -def get_wiki(): - V = 20000 - files = glob('../large_files/enwiki*.txt') - all_word_counts = {} - for f in files: - for line in open(f): - if line and line[0] not in '[*-|=\{\}': - s = remove_punctuation(line).lower().split() - if len(s) > 1: - for word in s: - if word not in all_word_counts: - all_word_counts[word] = 0 - all_word_counts[word] += 1 - print("finished counting") - - V = min(V, len(all_word_counts)) - all_word_counts = sorted(all_word_counts.items(), key=lambda x: x[1], reverse=True) - - top_words = [w for w, count in all_word_counts[:V-1]] + [''] - word2idx = {w:i for i, w in enumerate(top_words)} - unk = word2idx[''] - - sents = [] - for f in files: - for line in open(f): - if line and line[0] not in '[*-|=\{\}': - s = remove_punctuation(line).lower().split() - if len(s) > 1: - # if a word is not nearby another word, there won't be any context! - # and hence nothing to train! - sent = [word2idx[w] if w in word2idx else unk for w in s] - sents.append(sent) - return sents, word2idx - - - - -def train_model(savedir): - # get the data - sentences, word2idx = get_wiki() #get_text8() - - - # number of unique words - vocab_size = len(word2idx) - - - # config - window_size = 5 - learning_rate = 0.025*128 - final_learning_rate = 0.0001*128 - num_negatives = 5 # number of negative samples to draw per input word - samples_per_epoch = int(1e5) - epochs = 1 - D = 50 # word embedding size - - - # learning rate decay - learning_rate_delta = (learning_rate - final_learning_rate) / epochs - # learning_rate_delta = 0 - - - # params - W = np.random.randn(vocab_size, D) / np.sqrt(D + vocab_size) # input-to-hidden - V = np.random.randn(D, vocab_size) / np.sqrt(D + vocab_size) # hidden-to-output - - - # theano variables - thW = theano.shared(W) - thV = theano.shared(V) - - # theano placeholders - th_pos_word = T.ivector('pos_word') - th_neg_word = T.ivector('neg_word') - th_context = T.ivector('context') - th_lr = T.scalar('learning_rate') - - # get the output and loss - input_words = T.concatenate([th_pos_word, th_neg_word]) - W_subset = thW[input_words] - dbl_context = T.concatenate([th_context, th_context]) - V_subset = thV[:, dbl_context] - logits = W_subset.dot(V_subset) - out = T.nnet.sigmoid(logits) - - n = th_pos_word.shape[0] - th_cost = -T.log(out[:n]).mean() - T.log(1 - out[n:]).mean() - - - # specify the updates - gW = T.grad(th_cost, W_subset) - gV = T.grad(th_cost, V_subset) - W_update = T.inc_subtensor(W_subset, -th_lr*gW) - V_update = T.inc_subtensor(V_subset, -th_lr*gV) - updates = [(thW, W_update), (thV, V_update)] - - # full update - # gW, gV = T.grad(th_cost, [thW, thV]) - # vW = theano.shared(np.zeros_like(W)) - # vV = theano.shared(np.zeros_like(V)) - # new_vW = 0.9*vW - th_lr*gW - # new_vV = 0.9*vV - th_lr*gV - # W_update = thW + new_vW - # V_update = thV + new_vV - # updates = [(thW, W_update), (thV, V_update), (vW, new_vW), (vV, new_vV)] - - # make callable functions - cost_op = theano.function( - inputs=[th_pos_word, th_neg_word, th_context], - outputs=th_cost, - # allow_input_downcast=True - ) - cost_train_op = theano.function( - inputs=[th_pos_word, th_neg_word, th_context, th_lr], - outputs=th_cost, - updates=updates, - # allow_input_downcast=True - ) - - - # distribution for drawing negative samples - p_neg = get_negative_sampling_distribution(sentences, vocab_size) - - - # save the costs to plot them per iteration - costs = [] - - - # number of total words in corpus - total_words = sum(len(sentence) for sentence in sentences) - print("total number of words in corpus:", total_words) - - - # keep only certain words based on p_neg - threshold = 1e-5 - p_drop = 1 - np.sqrt(threshold / p_neg) - - - # train the model - for epoch in range(epochs): - # randomly order sentences so we don't always see - # sentences in the same order - np.random.shuffle(sentences) - - # accumulate the cost - cost = 0 - counter = 0 - inputs = [] - targets = [] - negwords = [] - t0 = datetime.now() - for sentence in sentences: - # keep only certain words based on p_neg - sentence = [w for w in sentence \ - if np.random.random() < (1 - p_drop[w]) - ] - if len(sentence) < 2: - continue - - # randomly order words so we don't always see - # samples in the same order - randomly_ordered_positions = np.random.choice( - len(sentence), - size=len(sentence), - replace=False, - ) - - - for pos in randomly_ordered_positions: - # the middle word - word = sentence[pos] - - # get the positive context words/negative samples - context_words = get_context(pos, sentence, window_size) - neg_word = np.random.choice(vocab_size, p=p_neg) - - n = len(context_words) - inputs += [word]*n - negwords += [neg_word]*n - targets += context_words - - - if len(inputs) >= 128: - c = cost_train_op(inputs, negwords, targets, learning_rate) - cost += c - - if np.isnan(c): - print("c is nan:", c) - exit() - - # reset - inputs = [] - targets = [] - negwords = [] - - counter += 1 - if counter % 100 == 0: - sys.stdout.write("processed %s / %s, cost: %s\r" % (counter, len(sentences), c)) - sys.stdout.flush() - - - # print stuff so we don't stare at a blank screen - dt = datetime.now() - t0 - print("epoch complete:", epoch, "cost:", cost, "dt:", dt) - - # save the cost - costs.append(cost) - - # update the learning rate - learning_rate -= learning_rate_delta - - - # plot the cost per iteration - plt.plot(costs) - plt.show() - - - # save the model - if not os.path.exists(savedir): - os.mkdir(savedir) - - with open('%s/word2idx.json' % savedir, 'w') as f: - json.dump(word2idx, f) - - # don't forget to extract the weights from theano - W, V = thW.get_value(), thV.get_value() - np.savez('%s/weights.npz' % savedir, W, V) - - # return the model - return word2idx, W, V - - -def get_negative_sampling_distribution(sentences, vocab_size): - # Pn(w) = prob of word occuring - # we would like to sample the negative samples - # such that words that occur more often - # should be sampled more often - - word_freq = np.zeros(vocab_size) - word_count = sum(len(sentence) for sentence in sentences) - for sentence in sentences: - for word in sentence: - word_freq[word] += 1 - - # smooth it - p_neg = word_freq**0.75 - - # normalize it - p_neg = p_neg / p_neg.sum() - - assert(np.all(p_neg > 0)) - return p_neg - - -def get_context(pos, sentence, window_size): - # input: - # a sentence of the form: x x x x c c c pos c c c x x x x - # output: - # the context word indices: c c c c c c - - start = max(0, pos - window_size) - end_ = min(len(sentence), pos + window_size) - - context = [] - for ctx_pos, ctx_word_idx in enumerate(sentence[start:end_], start=start): - if ctx_pos != pos: - # don't include the input word itself as a target - context.append(ctx_word_idx) - return context - # return np.concatenate([sentence[start:pos], sentence[pos+1:end_]]) - - -def load_model(savedir): - with open('%s/word2idx.json' % savedir) as f: - word2idx = json.load(f) - npz = np.load('%s/weights.npz' % savedir) - W = npz['arr_0'] - V = npz['arr_1'] - return word2idx, W, V - - - -def analogy(pos1, neg1, pos2, neg2, word2idx, idx2word, W): - V, D = W.shape - - # don't actually use pos2 in calculation, just print what's expected - print("testing: %s - %s = %s - %s" % (pos1, neg1, pos2, neg2)) - for w in (pos1, neg1, pos2, neg2): - if w not in word2idx: - print("Sorry, %s not in word2idx" % w) - return - - p1 = W[word2idx[pos1]] - n1 = W[word2idx[neg1]] - p2 = W[word2idx[pos2]] - n2 = W[word2idx[neg2]] - - vec = p1 - n1 + n2 - - distances = pairwise_distances(vec.reshape(1, D), W, metric='cosine').reshape(V) - idx = distances.argsort()[:10] - - # pick one that's not p1, n1, or n2 - best_idx = -1 - keep_out = [word2idx[w] for w in (pos1, neg1, neg2)] - # print("keep_out:", keep_out) - for i in idx: - if i not in keep_out: - best_idx = i - break - # print("best_idx:", best_idx) - - print("got: %s - %s = %s - %s" % (pos1, neg1, idx2word[best_idx], neg2)) - print("closest 10:") - for i in idx: - print(idx2word[i], distances[i]) - - print("dist to %s:" % pos2, cos_dist(p2, vec)) - - -def test_model(word2idx, W, V): - # there are multiple ways to get the "final" word embedding - # We = (W + V.T) / 2 - # We = W - - idx2word = {i:w for w, i in word2idx.items()} - - for We in (W, (W + V.T) / 2): - print("**********") - - analogy('king', 'man', 'queen', 'woman', word2idx, idx2word, We) - analogy('king', 'prince', 'queen', 'princess', word2idx, idx2word, We) - analogy('miami', 'florida', 'dallas', 'texas', word2idx, idx2word, We) - analogy('einstein', 'scientist', 'picasso', 'painter', word2idx, idx2word, We) - analogy('japan', 'sushi', 'germany', 'bratwurst', word2idx, idx2word, We) - analogy('man', 'woman', 'he', 'she', word2idx, idx2word, We) - analogy('man', 'woman', 'uncle', 'aunt', word2idx, idx2word, We) - analogy('man', 'woman', 'brother', 'sister', word2idx, idx2word, We) - analogy('man', 'woman', 'husband', 'wife', word2idx, idx2word, We) - analogy('man', 'woman', 'actor', 'actress', word2idx, idx2word, We) - analogy('man', 'woman', 'father', 'mother', word2idx, idx2word, We) - analogy('heir', 'heiress', 'prince', 'princess', word2idx, idx2word, We) - analogy('nephew', 'niece', 'uncle', 'aunt', word2idx, idx2word, We) - analogy('france', 'paris', 'japan', 'tokyo', word2idx, idx2word, We) - analogy('france', 'paris', 'china', 'beijing', word2idx, idx2word, We) - analogy('february', 'january', 'december', 'november', word2idx, idx2word, We) - analogy('france', 'paris', 'germany', 'berlin', word2idx, idx2word, We) - analogy('week', 'day', 'year', 'month', word2idx, idx2word, We) - analogy('week', 'day', 'hour', 'minute', word2idx, idx2word, We) - analogy('france', 'paris', 'italy', 'rome', word2idx, idx2word, We) - analogy('paris', 'france', 'rome', 'italy', word2idx, idx2word, We) - analogy('france', 'french', 'england', 'english', word2idx, idx2word, We) - analogy('japan', 'japanese', 'china', 'chinese', word2idx, idx2word, We) - analogy('china', 'chinese', 'america', 'american', word2idx, idx2word, We) - analogy('japan', 'japanese', 'italy', 'italian', word2idx, idx2word, We) - analogy('japan', 'japanese', 'australia', 'australian', word2idx, idx2word, We) - analogy('walk', 'walking', 'swim', 'swimming', word2idx, idx2word, We) - - - -if __name__ == '__main__': - word2idx, W, V = train_model('w2v_model') - # word2idx, W, V = load_model('w2v_model') - test_model(word2idx, W, V) - diff --git a/nlp_class3/attention.py b/nlp_class3/attention.py deleted file mode 100644 index 19ba3066..00000000 --- a/nlp_class3/attention.py +++ /dev/null @@ -1,500 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-advanced-nlp -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import os, sys - -from keras.models import Model -from keras.layers import Input, LSTM, GRU, Dense, Embedding, \ - Bidirectional, RepeatVector, Concatenate, Activation, Dot, Lambda -from keras.preprocessing.text import Tokenizer -from keras.preprocessing.sequence import pad_sequences -import keras.backend as K - -import numpy as np -import matplotlib.pyplot as plt - -try: - import keras.backend as K - if len(K.tensorflow_backend._get_available_gpus()) > 0: - from keras.layers import CuDNNLSTM as LSTM - from keras.layers import CuDNNGRU as GRU -except: - pass - - -# make sure we do softmax over the time axis -# expected shape is N x T x D -# note: the latest version of Keras allows you to pass in axis arg -def softmax_over_time(x): - assert(K.ndim(x) > 2) - e = K.exp(x - K.max(x, axis=1, keepdims=True)) - s = K.sum(e, axis=1, keepdims=True) - return e / s - - - -# config -BATCH_SIZE = 64 -EPOCHS = 30 -LATENT_DIM = 400 -LATENT_DIM_DECODER = 400 # idea: make it different to ensure things all fit together properly! -NUM_SAMPLES = 20000 -MAX_SEQUENCE_LENGTH = 100 -MAX_NUM_WORDS = 20000 -EMBEDDING_DIM = 100 - - - - -# Where we will store the data -input_texts = [] # sentence in original language -target_texts = [] # sentence in target language -target_texts_inputs = [] # sentence in target language offset by 1 - - -# load in the data -# download the data at: http://www.manythings.org/anki/ -t = 0 -for line in open('../large_files/translation/spa.txt'): - # only keep a limited number of samples - t += 1 - if t > NUM_SAMPLES: - break - - # input and target are separated by tab - if '\t' not in line: - continue - - # split up the input and translation - input_text, translation, *rest = line.rstrip().split('\t') - - # make the target input and output - # recall we'll be using teacher forcing - target_text = translation + ' ' - target_text_input = ' ' + translation - - input_texts.append(input_text) - target_texts.append(target_text) - target_texts_inputs.append(target_text_input) -print("num samples:", len(input_texts)) - - - - - - -# tokenize the inputs -tokenizer_inputs = Tokenizer(num_words=MAX_NUM_WORDS) -tokenizer_inputs.fit_on_texts(input_texts) -input_sequences = tokenizer_inputs.texts_to_sequences(input_texts) - -# get the word to index mapping for input language -word2idx_inputs = tokenizer_inputs.word_index -print('Found %s unique input tokens.' % len(word2idx_inputs)) - -# determine maximum length input sequence -max_len_input = max(len(s) for s in input_sequences) - -# tokenize the outputs -# don't filter out special characters -# otherwise and won't appear -tokenizer_outputs = Tokenizer(num_words=MAX_NUM_WORDS, filters='') -tokenizer_outputs.fit_on_texts(target_texts + target_texts_inputs) # inefficient, oh well -target_sequences = tokenizer_outputs.texts_to_sequences(target_texts) -target_sequences_inputs = tokenizer_outputs.texts_to_sequences(target_texts_inputs) - -# get the word to index mapping for output language -word2idx_outputs = tokenizer_outputs.word_index -print('Found %s unique output tokens.' % len(word2idx_outputs)) - -# store number of output words for later -# remember to add 1 since indexing starts at 1 -num_words_output = len(word2idx_outputs) + 1 - -# determine maximum length output sequence -max_len_target = max(len(s) for s in target_sequences) - - - - -# pad the sequences -encoder_inputs = pad_sequences(input_sequences, maxlen=max_len_input) -print("encoder_data.shape:", encoder_inputs.shape) -print("encoder_data[0]:", encoder_inputs[0]) - -decoder_inputs = pad_sequences(target_sequences_inputs, maxlen=max_len_target, padding='post') -print("decoder_data[0]:", decoder_inputs[0]) -print("decoder_data.shape:", decoder_inputs.shape) - -decoder_targets = pad_sequences(target_sequences, maxlen=max_len_target, padding='post') - - - - - - -# store all the pre-trained word vectors -print('Loading word vectors...') -word2vec = {} -with open(os.path.join('../large_files/glove.6B/glove.6B.%sd.txt' % EMBEDDING_DIM)) as f: - # is just a space-separated text file in the format: - # word vec[0] vec[1] vec[2] ... - for line in f: - values = line.split() - word = values[0] - vec = np.asarray(values[1:], dtype='float32') - word2vec[word] = vec -print('Found %s word vectors.' % len(word2vec)) - - - - -# prepare embedding matrix -print('Filling pre-trained embeddings...') -num_words = min(MAX_NUM_WORDS, len(word2idx_inputs) + 1) -embedding_matrix = np.zeros((num_words, EMBEDDING_DIM)) -for word, i in word2idx_inputs.items(): - if i < MAX_NUM_WORDS: - embedding_vector = word2vec.get(word) - if embedding_vector is not None: - # words not found in embedding index will be all zeros. - embedding_matrix[i] = embedding_vector - - - - -# create embedding layer -embedding_layer = Embedding( - num_words, - EMBEDDING_DIM, - weights=[embedding_matrix], - input_length=max_len_input, - # trainable=True -) - - - - - - -# create targets, since we cannot use sparse -# categorical cross entropy when we have sequences -decoder_targets_one_hot = np.zeros( - ( - len(input_texts), - max_len_target, - num_words_output - ), - dtype='float32' -) - -# assign the values -for i, d in enumerate(decoder_targets): - for t, word in enumerate(d): - if word > 0: - decoder_targets_one_hot[i, t, word] = 1 - - - - - - -##### build the model ##### - -# Set up the encoder - simple! -encoder_inputs_placeholder = Input(shape=(max_len_input,)) -x = embedding_layer(encoder_inputs_placeholder) -encoder = Bidirectional(LSTM( - LATENT_DIM, - return_sequences=True, - # dropout=0.5 # dropout not available on gpu -)) -encoder_outputs = encoder(x) - - -# Set up the decoder - not so simple -decoder_inputs_placeholder = Input(shape=(max_len_target,)) - -# this word embedding will not use pre-trained vectors -# although you could -decoder_embedding = Embedding(num_words_output, EMBEDDING_DIM) -decoder_inputs_x = decoder_embedding(decoder_inputs_placeholder) - - - - -######### Attention ######### -# Attention layers need to be global because -# they will be repeated Ty times at the decoder -attn_repeat_layer = RepeatVector(max_len_input) -attn_concat_layer = Concatenate(axis=-1) -attn_dense1 = Dense(10, activation='tanh') -attn_dense2 = Dense(1, activation=softmax_over_time) -attn_dot = Dot(axes=1) # to perform the weighted sum of alpha[t] * h[t] - -def one_step_attention(h, st_1): - # h = h(1), ..., h(Tx), shape = (Tx, LATENT_DIM * 2) - # st_1 = s(t-1), shape = (LATENT_DIM_DECODER,) - - # copy s(t-1) Tx times - # now shape = (Tx, LATENT_DIM_DECODER) - st_1 = attn_repeat_layer(st_1) - - # Concatenate all h(t)'s with s(t-1) - # Now of shape (Tx, LATENT_DIM_DECODER + LATENT_DIM * 2) - x = attn_concat_layer([h, st_1]) - - # Neural net first layer - x = attn_dense1(x) - - # Neural net second layer with special softmax over time - alphas = attn_dense2(x) - - # "Dot" the alphas and the h's - # Remember a.dot(b) = sum over a[t] * b[t] - context = attn_dot([alphas, h]) - - return context - - -# define the rest of the decoder (after attention) -decoder_lstm = LSTM(LATENT_DIM_DECODER, return_state=True) -decoder_dense = Dense(num_words_output, activation='softmax') - -initial_s = Input(shape=(LATENT_DIM_DECODER,), name='s0') -initial_c = Input(shape=(LATENT_DIM_DECODER,), name='c0') -context_last_word_concat_layer = Concatenate(axis=2) - - -# Unlike previous seq2seq, we cannot get the output -# all in one step -# Instead we need to do Ty steps -# And in each of those steps, we need to consider -# all Tx h's - -# s, c will be re-assigned in each iteration of the loop -s = initial_s -c = initial_c - -# collect outputs in a list at first -outputs = [] -for t in range(max_len_target): # Ty times - # get the context using attention - context = one_step_attention(encoder_outputs, s) - - # we need a different layer for each time step - selector = Lambda(lambda x: x[:, t:t+1]) - xt = selector(decoder_inputs_x) - - # combine - decoder_lstm_input = context_last_word_concat_layer([context, xt]) - - # pass the combined [context, last word] into the LSTM - # along with [s, c] - # get the new [s, c] and output - o, s, c = decoder_lstm(decoder_lstm_input, initial_state=[s, c]) - - # final dense layer to get next word prediction - decoder_outputs = decoder_dense(o) - outputs.append(decoder_outputs) - - -# 'outputs' is now a list of length Ty -# each element is of shape (batch size, output vocab size) -# therefore if we simply stack all the outputs into 1 tensor -# it would be of shape T x N x D -# we would like it to be of shape N x T x D - -def stack_and_transpose(x): - # x is a list of length T, each element is a batch_size x output_vocab_size tensor - x = K.stack(x) # is now T x batch_size x output_vocab_size tensor - x = K.permute_dimensions(x, pattern=(1, 0, 2)) # is now batch_size x T x output_vocab_size - return x - -# make it a layer -stacker = Lambda(stack_and_transpose) -outputs = stacker(outputs) - -# create the model -model = Model( - inputs=[ - encoder_inputs_placeholder, - decoder_inputs_placeholder, - initial_s, - initial_c, - ], - outputs=outputs -) - - -def custom_loss(y_true, y_pred): - # both are of shape N x T x K - mask = K.cast(y_true > 0, dtype='float32') - out = mask * y_true * K.log(y_pred) - return -K.sum(out) / K.sum(mask) - - -def acc(y_true, y_pred): - # both are of shape N x T x K - targ = K.argmax(y_true, axis=-1) - pred = K.argmax(y_pred, axis=-1) - correct = K.cast(K.equal(targ, pred), dtype='float32') - - # 0 is padding, don't include those - mask = K.cast(K.greater(targ, 0), dtype='float32') - n_correct = K.sum(mask * correct) - n_total = K.sum(mask) - return n_correct / n_total - - -# compile the model -model.compile(optimizer='adam', loss=custom_loss, metrics=[acc]) -# model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['acc']) - -# train the model -z = np.zeros((len(encoder_inputs), LATENT_DIM_DECODER)) # initial [s, c] -r = model.fit( - [encoder_inputs, decoder_inputs, z, z], decoder_targets_one_hot, - batch_size=BATCH_SIZE, - epochs=EPOCHS, - validation_split=0.2 -) - - - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='acc') -plt.plot(r.history['val_accuracy'], label='val_acc') -plt.legend() -plt.show() - - - -##### Make predictions ##### -# As with the poetry example, we need to create another model -# that can take in the RNN state and previous word as input -# and accept a T=1 sequence. - -# The encoder will be stand-alone -# From this we will get our initial decoder hidden state -# i.e. h(1), ..., h(Tx) -encoder_model = Model(encoder_inputs_placeholder, encoder_outputs) - -# next we define a T=1 decoder model -encoder_outputs_as_input = Input(shape=(max_len_input, LATENT_DIM * 2,)) -decoder_inputs_single = Input(shape=(1,)) -decoder_inputs_single_x = decoder_embedding(decoder_inputs_single) - -# no need to loop over attention steps this time because there is only one step -context = one_step_attention(encoder_outputs_as_input, initial_s) - -# combine context with last word -decoder_lstm_input = context_last_word_concat_layer([context, decoder_inputs_single_x]) - - - - -# lstm and final dense -o, s, c = decoder_lstm(decoder_lstm_input, initial_state=[initial_s, initial_c]) -decoder_outputs = decoder_dense(o) - - -# note: we don't really need the final stack and tranpose -# because there's only 1 output -# it is already of size N x D -# no need to make it 1 x N x D --> N x 1 x D - - - -# create the model object -decoder_model = Model( - inputs=[ - decoder_inputs_single, - encoder_outputs_as_input, - initial_s, - initial_c - ], - outputs=[decoder_outputs, s, c] -) - - - -# map indexes back into real words -# so we can view the results -idx2word_eng = {v:k for k, v in word2idx_inputs.items()} -idx2word_trans = {v:k for k, v in word2idx_outputs.items()} - - - - - -def decode_sequence(input_seq): - # Encode the input as state vectors. - enc_out = encoder_model.predict(input_seq) - - # Generate empty target sequence of length 1. - target_seq = np.zeros((1, 1)) - - # Populate the first character of target sequence with the start character. - # NOTE: tokenizer lower-cases all words - target_seq[0, 0] = word2idx_outputs[''] - - # if we get this we break - eos = word2idx_outputs[''] - - - # [s, c] will be updated in each loop iteration - s = np.zeros((1, LATENT_DIM_DECODER)) - c = np.zeros((1, LATENT_DIM_DECODER)) - - - # Create the translation - output_sentence = [] - for _ in range(max_len_target): - o, s, c = decoder_model.predict([target_seq, enc_out, s, c]) - - - # Get next word - idx = np.argmax(o.flatten()) - - # End sentence of EOS - if eos == idx: - break - - word = '' - if idx > 0: - word = idx2word_trans[idx] - output_sentence.append(word) - - # Update the decoder input - # which is just the word just generated - target_seq[0, 0] = idx - - return ' '.join(output_sentence) - - - - -while True: - # Do some test translations - i = np.random.choice(len(input_texts)) - input_seq = encoder_inputs[i:i+1] - translation = decode_sequence(input_seq) - print('-') - print('Input sentence:', input_texts[i]) - print('Predicted translation:', translation) - print('Actual translation:', target_texts[i]) - - ans = input("Continue? [Y/n]") - if ans and ans.lower().startswith('n'): - break - diff --git a/nlp_class3/bilstm_mnist.py b/nlp_class3/bilstm_mnist.py deleted file mode 100644 index 3da99920..00000000 --- a/nlp_class3/bilstm_mnist.py +++ /dev/null @@ -1,108 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-advanced-nlp -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import os -from keras.models import Model -from keras.layers import Input, LSTM, GRU, Bidirectional, GlobalMaxPooling1D, Lambda, Concatenate, Dense -import keras.backend as K -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -try: - import keras.backend as K - if len(K.tensorflow_backend._get_available_gpus()) > 0: - from keras.layers import CuDNNLSTM as LSTM - from keras.layers import CuDNNGRU as GRU -except: - pass - - -def get_mnist(limit=None): - if not os.path.exists('../large_files'): - print("You must create a folder called large_files adjacent to the class folder first.") - if not os.path.exists('../large_files/train.csv'): - print("Looks like you haven't downloaded the data or it's not in the right spot.") - print("Please get train.csv from https://www.kaggle.com/c/digit-recognizer") - print("and place it in the large_files folder.") - - print("Reading in and transforming data...") - df = pd.read_csv('../large_files/train.csv') - data = df.values - np.random.shuffle(data) - X = data[:, 1:].reshape(-1, 28, 28) / 255.0 # data is from 0..255 - Y = data[:, 0] - if limit is not None: - X, Y = X[:limit], Y[:limit] - return X, Y - - - - -# get data -X, Y = get_mnist() - -# config -D = 28 -M = 15 - - -# input is an image of size 28x28 -input_ = Input(shape=(D, D)) - -# up-down -rnn1 = Bidirectional(LSTM(M, return_sequences=True)) -x1 = rnn1(input_) # output is N x D x 2M -x1 = GlobalMaxPooling1D()(x1) # output is N x 2M - -# left-right -rnn2 = Bidirectional(LSTM(M, return_sequences=True)) - -# custom layer -permutor = Lambda(lambda t: K.permute_dimensions(t, pattern=(0, 2, 1))) - -x2 = permutor(input_) -x2 = rnn2(x2) # output is N x D x 2M -x2 = GlobalMaxPooling1D()(x2) # output is N x 2M - -# put them together -concatenator = Concatenate(axis=1) -x = concatenator([x1, x2]) # output is N x 4M - -# final dense layer -output = Dense(10, activation='softmax')(x) - -model = Model(inputs=input_, outputs=output) - -# testing -# o = model.predict(X) -# print("o.shape:", o.shape) - -# compile -model.compile( - loss='sparse_categorical_crossentropy', - optimizer='adam', - metrics=['accuracy'] -) - -# train -print('Training model...') -r = model.fit(X, Y, batch_size=32, epochs=10, validation_split=0.3) - - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='acc') -plt.plot(r.history['val_accuracy'], label='val_acc') -plt.legend() -plt.show() - diff --git a/nlp_class3/bilstm_test.py b/nlp_class3/bilstm_test.py deleted file mode 100644 index 2f31b489..00000000 --- a/nlp_class3/bilstm_test.py +++ /dev/null @@ -1,41 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-advanced-nlp -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.models import Model -from keras.layers import Input, LSTM, GRU, Bidirectional -import numpy as np -import matplotlib.pyplot as plt - -try: - import keras.backend as K - if len(K.tensorflow_backend._get_available_gpus()) > 0: - from keras.layers import CuDNNLSTM as LSTM - from keras.layers import CuDNNGRU as GRU -except: - pass - - -T = 8 -D = 2 -M = 3 - - -X = np.random.randn(1, T, D) - - -input_ = Input(shape=(T, D)) -# rnn = Bidirectional(LSTM(M, return_state=True, return_sequences=True)) -rnn = Bidirectional(LSTM(M, return_state=True, return_sequences=False)) -x = rnn(input_) - -model = Model(inputs=input_, outputs=x) -o, h1, c1, h2, c2 = model.predict(X) -print("o:", o) -print("o.shape:", o.shape) -print("h1:", h1) -print("c1:", c1) -print("h2:", h2) -print("c2:", c2) \ No newline at end of file diff --git a/nlp_class3/cnn_toxic.py b/nlp_class3/cnn_toxic.py deleted file mode 100644 index f0c55604..00000000 --- a/nlp_class3/cnn_toxic.py +++ /dev/null @@ -1,163 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-advanced-nlp -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import os -import sys -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from keras.preprocessing.text import Tokenizer -from keras.preprocessing.sequence import pad_sequences -from keras.layers import Dense, Input, GlobalMaxPooling1D -from keras.layers import Conv1D, MaxPooling1D, Embedding -from keras.models import Model -from sklearn.metrics import roc_auc_score - - -# Download the data: -# https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge -# https://lazyprogrammer.me/course_files/toxic_comment_train.csv -# Download the word vectors: -# http://nlp.stanford.edu/data/glove.6B.zip - - -# some configuration -MAX_SEQUENCE_LENGTH = 100 -MAX_VOCAB_SIZE = 20000 -EMBEDDING_DIM = 100 -VALIDATION_SPLIT = 0.2 -BATCH_SIZE = 128 -EPOCHS = 10 - - - -# load in pre-trained word vectors -print('Loading word vectors...') -word2vec = {} -with open(os.path.join('../large_files/glove.6B/glove.6B.%sd.txt' % EMBEDDING_DIM)) as f: - # is just a space-separated text file in the format: - # word vec[0] vec[1] vec[2] ... - for line in f: - values = line.split() - word = values[0] - vec = np.asarray(values[1:], dtype='float32') - word2vec[word] = vec -print('Found %s word vectors.' % len(word2vec)) - - - -# prepare text samples and their labels -print('Loading in comments...') - -train = pd.read_csv("../large_files/toxic-comment/train.csv") -sentences = train["comment_text"].fillna("DUMMY_VALUE").values -possible_labels = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"] -targets = train[possible_labels].values - - - -# convert the sentences (strings) into integers -tokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE) -tokenizer.fit_on_texts(sentences) -sequences = tokenizer.texts_to_sequences(sentences) -# print("sequences:", sequences); exit() - - -print("max sequence length:", max(len(s) for s in sequences)) -print("min sequence length:", min(len(s) for s in sequences)) -s = sorted(len(s) for s in sequences) -print("median sequence length:", s[len(s) // 2]) - -print("max word index:", max(max(seq) for seq in sequences if len(seq) > 0)) - - -# get word -> integer mapping -word2idx = tokenizer.word_index -print('Found %s unique tokens.' % len(word2idx)) - -# exit() - - -# pad sequences so that we get a N x T matrix -data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH) -print('Shape of data tensor:', data.shape) - - - -# prepare embedding matrix -print('Filling pre-trained embeddings...') -num_words = min(MAX_VOCAB_SIZE, len(word2idx) + 1) -embedding_matrix = np.zeros((num_words, EMBEDDING_DIM)) -for word, i in word2idx.items(): - if i < MAX_VOCAB_SIZE: - embedding_vector = word2vec.get(word) - if embedding_vector is not None: - # words not found in embedding index will be all zeros. - embedding_matrix[i] = embedding_vector - - - -# load pre-trained word embeddings into an Embedding layer -# note that we set trainable = False so as to keep the embeddings fixed -embedding_layer = Embedding( - num_words, - EMBEDDING_DIM, - weights=[embedding_matrix], - input_length=MAX_SEQUENCE_LENGTH, - trainable=False -) - - -print('Building model...') - -# train a 1D convnet with global maxpooling -input_ = Input(shape=(MAX_SEQUENCE_LENGTH,)) -x = embedding_layer(input_) -x = Conv1D(128, 3, activation='relu')(x) -x = MaxPooling1D(3)(x) -x = Conv1D(128, 3, activation='relu')(x) -x = MaxPooling1D(3)(x) -x = Conv1D(128, 3, activation='relu')(x) -x = GlobalMaxPooling1D()(x) -x = Dense(128, activation='relu')(x) -output = Dense(len(possible_labels), activation='sigmoid')(x) - -model = Model(input_, output) -model.compile( - loss='binary_crossentropy', - optimizer='rmsprop', - metrics=['accuracy'] -) - -print('Training model...') -r = model.fit( - data, - targets, - batch_size=BATCH_SIZE, - epochs=EPOCHS, - validation_split=VALIDATION_SPLIT -) - - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='acc') -plt.plot(r.history['val_accuracy'], label='val_acc') -plt.legend() -plt.show() - -# plot the mean AUC over each label -p = model.predict(data) -aucs = [] -for j in range(6): - auc = roc_auc_score(targets[:,j], p[:,j]) - aucs.append(auc) -print(np.mean(aucs)) diff --git a/nlp_class3/convert_twitter.py b/nlp_class3/convert_twitter.py deleted file mode 100644 index 477c20fd..00000000 --- a/nlp_class3/convert_twitter.py +++ /dev/null @@ -1,23 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-advanced-nlp -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -# each output line should be: -# INPUTRESPONSE -with open('../large_files/twitter_tab_format.txt', 'w') as f: - prev_line = None - # data source: https://github.com/Phylliida/Dialogue-Datasets - for line in open('../large_files/TwitterLowerAsciiCorpus.txt'): - line = line.rstrip() - - if prev_line and line: - f.write("%s\t%s\n" % (prev_line, line)) - - # note: - # between conversations there are empty lines - # which evaluate to false - - prev_line = line diff --git a/nlp_class3/extra_reading.txt b/nlp_class3/extra_reading.txt deleted file mode 100644 index bc94c5e6..00000000 --- a/nlp_class3/extra_reading.txt +++ /dev/null @@ -1,47 +0,0 @@ -https://deeplearningcourses.com/c/deep-learning-advanced-nlp - -Bidirectional Recurrent Neural Networks -https://maxwell.ict.griffith.edu.au/spl/publications/papers/ieeesp97_schuster.pdf - -Translation Modeling with Bidirectional Recurrent Neural Networks -http://emnlp2014.org/papers/pdf/EMNLP2014003.pdf - -Sequence to Sequence Learning with Neural Networks -https://arxiv.org/abs/1409.3215 - -A Neural Conversational Model -https://arxiv.org/abs/1506.05869v3 - -Neural Machine Translation by Jointly Learning to Align and Translate (Attention) -https://arxiv.org/abs/1409.0473 - -Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems (Simplified Attention) -https://arxiv.org/abs/1512.08756 - -Memory Networks -https://arxiv.org/abs/1410.3916 - -Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks -http://arxiv.org/abs/1502.05698 - -End-To-End Memory Networks -http://arxiv.org/abs/1503.08895 - -Ask Me Anything: Dynamic Memory Networks for Natural Language Processing -https://arxiv.org/abs/1506.07285 - -WaveNet -https://deepmind.com/blog/wavenet-generative-model-raw-audio/ - -Tacotron -https://google.github.io/tacotron/ - -Tacotron 2 -https://research.googleblog.com/2017/12/tacotron-2-generating-human-like-speech.html - -An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling -https://arxiv.org/abs/1803.01271 -(just released March 2018!) - -Relational recurrent neural networks -https://arxiv.org/abs/1806.01822 diff --git a/nlp_class3/lstm_toxic.py b/nlp_class3/lstm_toxic.py deleted file mode 100644 index 113c3c5d..00000000 --- a/nlp_class3/lstm_toxic.py +++ /dev/null @@ -1,159 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-advanced-nlp -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import os -import sys -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from keras.models import Model -from keras.layers import Dense, Embedding, Input -from keras.layers import LSTM, Bidirectional, GlobalMaxPool1D, Dropout -from keras.preprocessing.text import Tokenizer -from keras.preprocessing.sequence import pad_sequences -from keras.optimizers import Adam -from sklearn.metrics import roc_auc_score - -import keras.backend as K -# if len(K.tensorflow_backend._get_available_gpus()) > 0: -# from keras.layers import CuDNNLSTM as LSTM -# from keras.layers import CuDNNGRU as GRU - - -# Download the data: -# https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge -# https://lazyprogrammer.me/course_files/toxic_comment_train.csv -# Download the word vectors: -# http://nlp.stanford.edu/data/glove.6B.zip - - -# some configuration -MAX_SEQUENCE_LENGTH = 100 -MAX_VOCAB_SIZE = 20000 -EMBEDDING_DIM = 50 -VALIDATION_SPLIT = 0.2 -BATCH_SIZE = 128 -EPOCHS = 5 - - - -# load in pre-trained word vectors -print('Loading word vectors...') -word2vec = {} -with open(os.path.join('../large_files/glove.6B/glove.6B.%sd.txt' % EMBEDDING_DIM)) as f: - # is just a space-separated text file in the format: - # word vec[0] vec[1] vec[2] ... - for line in f: - values = line.split() - word = values[0] - vec = np.asarray(values[1:], dtype='float32') - word2vec[word] = vec -print('Found %s word vectors.' % len(word2vec)) - - - -# prepare text samples and their labels -print('Loading in comments...') - -train = pd.read_csv("../large_files/toxic-comment/train.csv") -sentences = train["comment_text"].fillna("DUMMY_VALUE").values -possible_labels = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"] -targets = train[possible_labels].values - - - - -# convert the sentences (strings) into integers -tokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE) -tokenizer.fit_on_texts(sentences) -sequences = tokenizer.texts_to_sequences(sentences) - - - -# get word -> integer mapping -word2idx = tokenizer.word_index -print('Found %s unique tokens.' % len(word2idx)) - - -# pad sequences so that we get a N x T matrix -data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH) -print('Shape of data tensor:', data.shape) - - - -# prepare embedding matrix -print('Filling pre-trained embeddings...') -num_words = min(MAX_VOCAB_SIZE, len(word2idx) + 1) -embedding_matrix = np.zeros((num_words, EMBEDDING_DIM)) -for word, i in word2idx.items(): - if i < MAX_VOCAB_SIZE: - embedding_vector = word2vec.get(word) - if embedding_vector is not None: - # words not found in embedding index will be all zeros. - embedding_matrix[i] = embedding_vector - - - -# load pre-trained word embeddings into an Embedding layer -# note that we set trainable = False so as to keep the embeddings fixed -embedding_layer = Embedding( - num_words, - EMBEDDING_DIM, - weights=[embedding_matrix], - input_length=MAX_SEQUENCE_LENGTH, - trainable=False -) - - - -print('Building model...') - -# create an LSTM network with a single LSTM -input_ = Input(shape=(MAX_SEQUENCE_LENGTH,)) -x = embedding_layer(input_) -x = LSTM(15, return_sequences=True)(x) -# x = Bidirectional(LSTM(15, return_sequences=True))(x) -x = GlobalMaxPool1D()(x) -output = Dense(len(possible_labels), activation="sigmoid")(x) - -model = Model(input_, output) -model.compile( - loss='binary_crossentropy', - optimizer=Adam(lr=0.01), - metrics=['accuracy'], -) - - -print('Training model...') -r = model.fit( - data, - targets, - batch_size=BATCH_SIZE, - epochs=EPOCHS, - validation_split=VALIDATION_SPLIT -) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='acc') -plt.plot(r.history['val_accuracy'], label='val_acc') -plt.legend() -plt.show() - -p = model.predict(data) -aucs = [] -for j in range(6): - auc = roc_auc_score(targets[:,j], p[:,j]) - aucs.append(auc) -print(np.mean(aucs)) - diff --git a/nlp_class3/memory_network.py b/nlp_class3/memory_network.py deleted file mode 100644 index 52f4c291..00000000 --- a/nlp_class3/memory_network.py +++ /dev/null @@ -1,431 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-advanced-nlp -from __future__ import print_function, division -from builtins import range, input - - -import numpy as np -import keras.backend as K -import matplotlib.pyplot as plt -import re -import tarfile - -from keras.models import Model -from keras.layers import Dense, Embedding, Input, Lambda, Reshape, add, dot, Activation -from keras.preprocessing.sequence import pad_sequences -from keras.optimizers import Adam, RMSprop -from keras.utils.data_utils import get_file - - - -# get the data and open the compressed file using the tarfile library -# https://research.fb.com/downloads/babi/ -path = get_file( - 'babi-tasks-v1-2.tar.gz', - origin='https://s3.amazonaws.com/text-datasets/babi_tasks_1-20_v1-2.tar.gz') -tar = tarfile.open(path) - - - -# relevant data in the tar file -# there's lots more data in there, check it out if you want! -challenges = { - # QA1 with 10,000 samples - 'single_supporting_fact_10k': 'tasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_{}.txt', - # QA2 with 10,000 samples - 'two_supporting_facts_10k': 'tasks_1-20_v1-2/en-10k/qa2_two-supporting-facts_{}.txt', -} - - - -def tokenize(sent): - '''Return the tokens of a sentence including punctuation. - - >>> tokenize('Bob dropped the apple. Where is the apple?') - ['Bob', 'dropped', 'the', 'apple', '.', 'Where', 'is', 'the', 'apple', '?'] - ''' - return [x.strip() for x in re.split('(\W+?)', sent) if x.strip()] - - - - -def get_stories(f): - # data will return a list of triples - # each triple contains: - # 1. a story - # 2. a question about the story - # 3. the answer to the question - data = [] - - # use this list to keep track of the story so far - story = [] - - # print a random story, helpful to see the data - printed = False - for line in f: - line = line.decode('utf-8').strip() - - # split the line number from the rest of the line - nid, line = line.split(' ', 1) - - # see if we should begin a new story - if int(nid) == 1: - story = [] - - # this line contains a question and answer if it has a tab - # questionanswer - # it also tells us which line in the story is relevant to the answer - # Note: we actually ignore this fact, since the model will learn - # which lines are important - # Note: the max line number is not the number of lines of the story - # since lines with questions do not contain any story - # one story may contain MULTIPLE questions - if '\t' in line: - q, a, supporting = line.split('\t') - q = tokenize(q) - - # numbering each line is very useful - # it's the equivalent of adding a unique token to the front - # of each sentence - story_so_far = [[str(i)] + s for i, s in enumerate(story) if s] - - # uncomment if you want to see what a story looks like - # if not printed and np.random.rand() < 0.5: - # print("story_so_far:", story_so_far) - # printed = True - data.append((story_so_far, q, a)) - story.append('') - else: - # just add the line to the current story - story.append(tokenize(line)) - return data - - -# recursively flatten a list -def should_flatten(el): - return not isinstance(el, (str, bytes)) - -def flatten(l): - for el in l: - if should_flatten(el): - yield from flatten(el) - else: - yield el - - - - - - -# convert stories from words into lists of word indexes (integers) -# pad each sequence so that they are the same length -# we will need to re-pad the stories later so that each story -# is the same length -def vectorize_stories(data, word2idx, story_maxlen, query_maxlen): - inputs, queries, answers = [], [], [] - for story, query, answer in data: - inputs.append([[word2idx[w] for w in s] for s in story]) - queries.append([word2idx[w] for w in query]) - answers.append([word2idx[answer]]) - return ( - [pad_sequences(x, maxlen=story_maxlen) for x in inputs], - pad_sequences(queries, maxlen=query_maxlen), - np.array(answers) - ) - - - -# this is like 'pad_sequences' but for entire stories -# we are padding each story with zeros so every story -# has the same number of sentences -# append an array of zeros of size: -# (max_sentences - num sentences in story, max words in sentence) -def stack_inputs(inputs, story_maxsents, story_maxlen): - for i, story in enumerate(inputs): - inputs[i] = np.concatenate( - [ - story, - np.zeros((story_maxsents - story.shape[0], story_maxlen), 'int') - ] - ) - return np.stack(inputs) - - - - - -# make a function to get the data since -# we want to load both the single supporting fact data -# and the two supporting fact data later -def get_data(challenge_type): - # input should either be 'single_supporting_fact_10k' or 'two_supporting_facts_10k' - challenge = challenges[challenge_type] - - - # returns a list of triples of: - # (story, question, answer) - # story is a list of sentences - # question is a sentence - # answer is a word - train_stories = get_stories(tar.extractfile(challenge.format('train'))) - test_stories = get_stories(tar.extractfile(challenge.format('test'))) - - - # group all the stories together - stories = train_stories + test_stories - - # so we can get the max length of each story, of each sentence, and of each question - story_maxlen = max((len(s) for x, _, _ in stories for s in x)) - story_maxsents = max((len(x) for x, _, _ in stories)) - query_maxlen = max(len(x) for _, x, _ in stories) - - # Create vocabulary of corpus and find size, including a padding element. - vocab = sorted(set(flatten(stories))) - vocab.insert(0, '') - vocab_size = len(vocab) - - # Create an index mapping for the vocabulary. - word2idx = {c:i for i, c in enumerate(vocab)} - - # convert stories from strings to lists of integers - inputs_train, queries_train, answers_train = vectorize_stories( - train_stories, - word2idx, - story_maxlen, - query_maxlen - ) - inputs_test, queries_test, answers_test = vectorize_stories( - test_stories, - word2idx, - story_maxlen, - query_maxlen - ) - - # convert inputs into 3-D numpy arrays - inputs_train = stack_inputs(inputs_train, story_maxsents, story_maxlen) - inputs_test = stack_inputs(inputs_test, story_maxsents, story_maxlen) - print("inputs_train.shape, inputs_test.shape", inputs_train.shape, inputs_test.shape) - - - # return model inputs for keras - return train_stories, test_stories, \ - inputs_train, queries_train, answers_train, \ - inputs_test, queries_test, answers_test, \ - story_maxsents, story_maxlen, query_maxlen, \ - vocab, vocab_size - - -# get the single supporting fact data -train_stories, test_stories, \ - inputs_train, queries_train, answers_train, \ - inputs_test, queries_test, answers_test, \ - story_maxsents, story_maxlen, query_maxlen, \ - vocab, vocab_size = get_data('single_supporting_fact_10k') - - - - -##### create the model ##### -embedding_dim = 15 - - -# turn the story into a sequence of embedding vectors -# one for each story line -# treating each story line like a "bag of words" -input_story_ = Input((story_maxsents, story_maxlen)) -embedded_story = Embedding(vocab_size, embedding_dim)(input_story_) -embedded_story = Lambda(lambda x: K.sum(x, axis=2))(embedded_story) -print("input_story_.shape, embedded_story.shape:", input_story_.shape, embedded_story.shape) - - -# turn the question into an embedding -# also a bag of words -input_question_ = Input((query_maxlen,)) -embedded_question = Embedding(vocab_size, embedding_dim)(input_question_) -embedded_question = Lambda(lambda x: K.sum(x, axis=1))(embedded_question) - -# add a "sequence length" of 1 so that it can -# be dotted with the story later -embedded_question = Reshape((1, embedding_dim))(embedded_question) -print("inp_q.shape, emb_q.shape:", input_question_.shape, embedded_question.shape) - - -# calculate the weights for each story line -# embedded_story.shape = (N, num sentences, embedding_dim) -# embedded_question.shape = (N, 1, embedding_dim) -x = dot([embedded_story, embedded_question], 2) -x = Reshape((story_maxsents,))(x) # flatten the vector -x = Activation('softmax')(x) -story_weights = Reshape((story_maxsents, 1))(x) # unflatten it again to be dotted later -print("story_weights.shape:", story_weights.shape) - - - -x = dot([story_weights, embedded_story], 1) -x = Reshape((embedding_dim,))(x) # flatten it again -ans = Dense(vocab_size, activation='softmax')(x) - -# make the model -model = Model([input_story_, input_question_], ans) - -# compile the model -model.compile( - optimizer=RMSprop(lr=1e-2), - loss='sparse_categorical_crossentropy', - metrics=['accuracy'] -) - -# train the model -r = model.fit( - [inputs_train, queries_train], - answers_train, - epochs=4, - batch_size=32, - validation_data=([inputs_test, queries_test], answers_test) -) - - -# Check how we weight each input sentence given a story and question -debug_model = Model([input_story_, input_question_], story_weights) - -# choose a random story -story_idx = np.random.choice(len(train_stories)) - -# get weights from debug model -i = inputs_train[story_idx:story_idx+1] -q = queries_train[story_idx:story_idx+1] -w = debug_model.predict([i, q]).flatten() - -story, question, ans = train_stories[story_idx] -print("story:\n") -for i, line in enumerate(story): - print("{:1.5f}".format(w[i]), "\t", " ".join(line)) - -print("question:", " ".join(question)) -print("answer:", ans) - - -# pause so we can see the output -input("Hit enter to continue\n\n") - - - -##### two supporting facts ##### - - -# get the two supporting fact data -train_stories, test_stories, \ - inputs_train, queries_train, answers_train, \ - inputs_test, queries_test, answers_test, \ - story_maxsents, story_maxlen, query_maxlen, \ - vocab, vocab_size = get_data('two_supporting_facts_10k') - - - -##### create the model ##### -embedding_dim = 30 - - -# make a function for this so we can use it again -def embed_and_sum(x, axis=2): - x = Embedding(vocab_size, embedding_dim)(x) - x = Lambda(lambda x: K.sum(x, axis))(x) - return x - -# define the inputs -input_story_ = Input((story_maxsents, story_maxlen)) -input_question_ = Input((query_maxlen,)) - - -# embed the inputs -embedded_story = embed_and_sum(input_story_) -embedded_question = embed_and_sum(input_question_, 1) - - -# final dense will be used in each hop -dense_layer = Dense(embedding_dim, activation='elu') - - -# define one hop -# the "query" can be the question, or the answer from the previous hop -def hop(query, story): - # query.shape = (embedding_dim,) - # story.shape = (num sentences, embedding_dim) - x = Reshape((1, embedding_dim))(query) # make it (1, embedding_dim) - x = dot([story, x], 2) - x = Reshape((story_maxsents,))(x) # flatten it for softmax - x = Activation('softmax')(x) - story_weights = Reshape((story_maxsents, 1))(x) # unflatten for dotting - - # makes a new embedding - story_embedding2 = embed_and_sum(input_story_) - x = dot([story_weights, story_embedding2], 1) - x = Reshape((embedding_dim,))(x) - x = dense_layer(x) - return x, story_embedding2, story_weights - - -# do the hops -ans1, embedded_story, story_weights1 = hop(embedded_question, embedded_story) -ans2, _, story_weights2 = hop(ans1, embedded_story) - -# get the final answer -ans = Dense(vocab_size, activation='softmax')(ans2) - - -# build the model -model2 = Model([input_story_, input_question_], ans) - -# compile the model -model2.compile( - optimizer=RMSprop(lr=5e-3), - loss='sparse_categorical_crossentropy', - metrics=['accuracy'] -) - -# fit the model -r = model2.fit( - [inputs_train, queries_train], - answers_train, - epochs=30, - batch_size=32, - validation_data=([inputs_test, queries_test], answers_test) -) - - -### print story line weights again ### -debug_model2 = Model( - [input_story_, input_question_], - [story_weights1, story_weights2] -) - -# choose a random story -story_idx = np.random.choice(len(train_stories)) - -# get weights from debug model -i = inputs_train[story_idx:story_idx+1] -q = queries_train[story_idx:story_idx+1] -w1, w2 = debug_model2.predict([i, q]) -w1 = w1.flatten() -w2 = w2.flatten() - -story, question, ans = train_stories[story_idx] -print("story:\n") -for j, line in enumerate(story): - print("{:1.5f}".format(w1[j]), "\t", "{:1.5f}".format(w2[j]), "\t", " ".join(line)) - -print("question:", " ".join(question)) -print("answer:", ans) -print("prediction:", vocab[ np.argmax(model2.predict([i, q])[0]) ]) - - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='acc') -plt.plot(r.history['val_accuracy'], label='val_acc') -plt.legend() -plt.show() diff --git a/nlp_class3/poetry.py b/nlp_class3/poetry.py deleted file mode 100644 index 74fac502..00000000 --- a/nlp_class3/poetry.py +++ /dev/null @@ -1,227 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-advanced-nlp -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import os -import sys -import string -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from keras.models import Model -from keras.layers import Dense, Embedding, Input, LSTM -from keras.preprocessing.text import Tokenizer -from keras.preprocessing.sequence import pad_sequences -from keras.optimizers import Adam, SGD - -try: - import keras.backend as K - if len(K.tensorflow_backend._get_available_gpus()) > 0: - from keras.layers import CuDNNLSTM as LSTM - from keras.layers import CuDNNGRU as GRU -except: - pass - - -# some configuration -MAX_SEQUENCE_LENGTH = 100 -MAX_VOCAB_SIZE = 3000 -EMBEDDING_DIM = 50 -VALIDATION_SPLIT = 0.2 -BATCH_SIZE = 128 -EPOCHS = 2000 -LATENT_DIM = 25 - -# load in the data -input_texts = [] -target_texts = [] -for line in open('../hmm_class/robert_frost.txt'): - line = line.rstrip() - if not line: - continue - - input_line = ' ' + line - target_line = line + ' ' - - input_texts.append(input_line) - target_texts.append(target_line) - - -all_lines = input_texts + target_texts - -# convert the sentences (strings) into integers -tokenizer = Tokenizer(num_words=MAX_VOCAB_SIZE, filters='') -tokenizer.fit_on_texts(all_lines) -input_sequences = tokenizer.texts_to_sequences(input_texts) -target_sequences = tokenizer.texts_to_sequences(target_texts) - -# find max seq length -max_sequence_length_from_data = max(len(s) for s in input_sequences) -print('Max sequence length:', max_sequence_length_from_data) - - -# get word -> integer mapping -word2idx = tokenizer.word_index -print('Found %s unique tokens.' % len(word2idx)) -assert('' in word2idx) -assert('' in word2idx) - - -# pad sequences so that we get a N x T matrix -max_sequence_length = min(max_sequence_length_from_data, MAX_SEQUENCE_LENGTH) -input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_length, padding='post') -target_sequences = pad_sequences(target_sequences, maxlen=max_sequence_length, padding='post') -print('Shape of data tensor:', input_sequences.shape) - - - -# load in pre-trained word vectors -print('Loading word vectors...') -word2vec = {} -with open(os.path.join('../large_files/glove.6B/glove.6B.%sd.txt' % EMBEDDING_DIM)) as f: - # is just a space-separated text file in the format: - # word vec[0] vec[1] vec[2] ... - for line in f: - values = line.split() - word = values[0] - vec = np.asarray(values[1:], dtype='float32') - word2vec[word] = vec -print('Found %s word vectors.' % len(word2vec)) - - - -# prepare embedding matrix -print('Filling pre-trained embeddings...') -num_words = min(MAX_VOCAB_SIZE, len(word2idx) + 1) -embedding_matrix = np.zeros((num_words, EMBEDDING_DIM)) -for word, i in word2idx.items(): - if i < MAX_VOCAB_SIZE: - embedding_vector = word2vec.get(word) - if embedding_vector is not None: - # words not found in embedding index will be all zeros. - embedding_matrix[i] = embedding_vector - - - -# one-hot the targets (can't use sparse cross-entropy) -one_hot_targets = np.zeros((len(input_sequences), max_sequence_length, num_words)) -for i, target_sequence in enumerate(target_sequences): - for t, word in enumerate(target_sequence): - if word > 0: - one_hot_targets[i, t, word] = 1 - - - -# load pre-trained word embeddings into an Embedding layer -embedding_layer = Embedding( - num_words, - EMBEDDING_DIM, - weights=[embedding_matrix], - # trainable=False -) - - - -print('Building model...') - -# create an LSTM network with a single LSTM -input_ = Input(shape=(max_sequence_length,)) -initial_h = Input(shape=(LATENT_DIM,)) -initial_c = Input(shape=(LATENT_DIM,)) -x = embedding_layer(input_) -lstm = LSTM(LATENT_DIM, return_sequences=True, return_state=True) -x, _, _ = lstm(x, initial_state=[initial_h, initial_c]) # don't need the states here -dense = Dense(num_words, activation='softmax') -output = dense(x) - -model = Model([input_, initial_h, initial_c], output) -model.compile( - loss='categorical_crossentropy', - # optimizer='rmsprop', - optimizer=Adam(lr=0.01), - # optimizer=SGD(lr=0.01, momentum=0.9), - metrics=['accuracy'] -) - -print('Training model...') -z = np.zeros((len(input_sequences), LATENT_DIM)) -r = model.fit( - [input_sequences, z, z], - one_hot_targets, - batch_size=BATCH_SIZE, - epochs=EPOCHS, - validation_split=VALIDATION_SPLIT -) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='acc') -plt.plot(r.history['val_accuracy'], label='val_acc') -plt.legend() -plt.show() - - - -# make a sampling model -input2 = Input(shape=(1,)) # we'll only input one word at a time -x = embedding_layer(input2) -x, h, c = lstm(x, initial_state=[initial_h, initial_c]) # now we need states to feed back in -output2 = dense(x) -sampling_model = Model([input2, initial_h, initial_c], [output2, h, c]) - - -# reverse word2idx dictionary to get back words -# during prediction -idx2word = {v:k for k, v in word2idx.items()} - - -def sample_line(): - # initial inputs - np_input = np.array([[ word2idx[''] ]]) - h = np.zeros((1, LATENT_DIM)) - c = np.zeros((1, LATENT_DIM)) - - # so we know when to quit - eos = word2idx[''] - - # store the output here - output_sentence = [] - - for _ in range(max_sequence_length): - o, h, c = sampling_model.predict([np_input, h, c]) - - # print("o.shape:", o.shape, o[0,0,:10]) - # idx = np.argmax(o[0,0]) - probs = o[0,0] - if np.argmax(probs) == 0: - print("wtf") - probs[0] = 0 - probs /= probs.sum() - idx = np.random.choice(len(probs), p=probs) - if idx == eos: - break - - # accuulate output - output_sentence.append(idx2word.get(idx, '' % idx)) - - # make the next input into model - np_input[0,0] = idx - - return ' '.join(output_sentence) - -# generate a 4 line poem -while True: - for _ in range(4): - print(sample_line()) - - ans = input("---generate another? [Y/n]---") - if ans and ans[0].lower().startswith('n'): - break diff --git a/nlp_class3/simple_rnn_test.py b/nlp_class3/simple_rnn_test.py deleted file mode 100644 index e6f84430..00000000 --- a/nlp_class3/simple_rnn_test.py +++ /dev/null @@ -1,86 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-advanced-nlp -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -from keras.models import Model -from keras.layers import Input, LSTM, GRU -import numpy as np -import matplotlib.pyplot as plt - -try: - import keras.backend as K - if len(K.tensorflow_backend._get_available_gpus()) > 0: - from keras.layers import CuDNNLSTM as LSTM - from keras.layers import CuDNNGRU as GRU -except: - pass - - -T = 8 -D = 2 -M = 3 - - -X = np.random.randn(1, T, D) - - -def lstm1(): - input_ = Input(shape=(T, D)) - rnn = LSTM(M, return_state=True) - x = rnn(input_) - - model = Model(inputs=input_, outputs=x) - o, h, c = model.predict(X) - print("o:", o) - print("h:", h) - print("c:", c) - - -def lstm2(): - input_ = Input(shape=(T, D)) - rnn = LSTM(M, return_state=True, return_sequences=True) - # rnn = GRU(M, return_state=True) - x = rnn(input_) - - model = Model(inputs=input_, outputs=x) - o, h, c = model.predict(X) - print("o:", o) - print("h:", h) - print("c:", c) - - -def gru1(): - input_ = Input(shape=(T, D)) - rnn = GRU(M, return_state=True) - x = rnn(input_) - - model = Model(inputs=input_, outputs=x) - o, h = model.predict(X) - print("o:", o) - print("h:", h) - - -def gru2(): - input_ = Input(shape=(T, D)) - rnn = GRU(M, return_state=True, return_sequences=True) - x = rnn(input_) - - model = Model(inputs=input_, outputs=x) - o, h = model.predict(X) - print("o:", o) - print("h:", h) - - - -print("lstm1:") -lstm1() -print("lstm2:") -lstm2() -print("gru1:") -gru1() -print("gru2:") -gru2() - - diff --git a/nlp_class3/wseq2seq.py b/nlp_class3/wseq2seq.py deleted file mode 100644 index 0f2e1c70..00000000 --- a/nlp_class3/wseq2seq.py +++ /dev/null @@ -1,386 +0,0 @@ -# # https://deeplearningcourses.com/c/deep-learning-advanced-nlp -# get the data at: http://www.manythings.org/anki/ -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import os, sys - -from keras.models import Model -from keras.layers import Input, LSTM, GRU, Dense, Embedding -from keras.preprocessing.text import Tokenizer -from keras.preprocessing.sequence import pad_sequences -from keras.utils import to_categorical -import numpy as np -import matplotlib.pyplot as plt - -try: - import keras.backend as K - if len(K.tensorflow_backend._get_available_gpus()) > 0: - from keras.layers import CuDNNLSTM as LSTM - from keras.layers import CuDNNGRU as GRU -except: - pass - - -# some config -BATCH_SIZE = 64 # Batch size for training. -EPOCHS = 40 # Number of epochs to train for. -LATENT_DIM = 256 # Latent dimensionality of the encoding space. -NUM_SAMPLES = 10000 # Number of samples to train on. -MAX_NUM_WORDS = 20000 -EMBEDDING_DIM = 100 - -# Where we will store the data -input_texts = [] # sentence in original language -target_texts = [] # sentence in target language -target_texts_inputs = [] # sentence in target language offset by 1 - - -# load in the data -# download the data at: http://www.manythings.org/anki/ -t = 0 -for line in open('../large_files/translation/spa.txt'): - # only keep a limited number of samples - t += 1 - if t > NUM_SAMPLES: - break - - # input and target are separated by tab - if '\t' not in line: - continue - - # split up the input and translation - input_text, translation, *rest = line.rstrip().split('\t') - - # make the target input and output - # recall we'll be using teacher forcing - target_text = translation + ' ' - target_text_input = ' ' + translation - - input_texts.append(input_text) - target_texts.append(target_text) - target_texts_inputs.append(target_text_input) -print("num samples:", len(input_texts)) - - -# tokenize the inputs -tokenizer_inputs = Tokenizer(num_words=MAX_NUM_WORDS) -tokenizer_inputs.fit_on_texts(input_texts) -input_sequences = tokenizer_inputs.texts_to_sequences(input_texts) - -# get the word to index mapping for input language -word2idx_inputs = tokenizer_inputs.word_index -print('Found %s unique input tokens.' % len(word2idx_inputs)) - -# determine maximum length input sequence -max_len_input = max(len(s) for s in input_sequences) - -# tokenize the outputs -# don't filter out special characters -# otherwise and won't appear -tokenizer_outputs = Tokenizer(num_words=MAX_NUM_WORDS, filters='') -tokenizer_outputs.fit_on_texts(target_texts + target_texts_inputs) # inefficient, oh well -target_sequences = tokenizer_outputs.texts_to_sequences(target_texts) -target_sequences_inputs = tokenizer_outputs.texts_to_sequences(target_texts_inputs) - -# get the word to index mapping for output language -word2idx_outputs = tokenizer_outputs.word_index -print('Found %s unique output tokens.' % len(word2idx_outputs)) - -# store number of output words for later -# remember to add 1 since indexing starts at 1 -num_words_output = len(word2idx_outputs) + 1 - -# determine maximum length output sequence -max_len_target = max(len(s) for s in target_sequences) - - -# pad the sequences -encoder_inputs = pad_sequences(input_sequences, maxlen=max_len_input) -print("encoder_inputs.shape:", encoder_inputs.shape) -print("encoder_inputs[0]:", encoder_inputs[0]) - -decoder_inputs = pad_sequences(target_sequences_inputs, maxlen=max_len_target, padding='post') -print("decoder_inputs[0]:", decoder_inputs[0]) -print("decoder_inputs.shape:", decoder_inputs.shape) - -decoder_targets = pad_sequences(target_sequences, maxlen=max_len_target, padding='post') - - - - - - - -# store all the pre-trained word vectors -print('Loading word vectors...') -word2vec = {} -with open(os.path.join('../large_files/glove.6B/glove.6B.%sd.txt' % EMBEDDING_DIM)) as f: - # is just a space-separated text file in the format: - # word vec[0] vec[1] vec[2] ... - for line in f: - values = line.split() - word = values[0] - vec = np.asarray(values[1:], dtype='float32') - word2vec[word] = vec -print('Found %s word vectors.' % len(word2vec)) - - - - -# prepare embedding matrix -print('Filling pre-trained embeddings...') -num_words = min(MAX_NUM_WORDS, len(word2idx_inputs) + 1) -embedding_matrix = np.zeros((num_words, EMBEDDING_DIM)) -for word, i in word2idx_inputs.items(): - if i < MAX_NUM_WORDS: - embedding_vector = word2vec.get(word) - if embedding_vector is not None: - # words not found in embedding index will be all zeros. - embedding_matrix[i] = embedding_vector - - - - -# create embedding layer -embedding_layer = Embedding( - num_words, - EMBEDDING_DIM, - weights=[embedding_matrix], - input_length=max_len_input, - # trainable=True -) - - -# create targets, since we cannot use sparse -# categorical cross entropy when we have sequences -decoder_targets_one_hot = np.zeros( - ( - len(input_texts), - max_len_target, - num_words_output - ), - dtype='float32' -) - -# assign the values -for i, d in enumerate(decoder_targets): - for t, word in enumerate(d): - if word != 0: - decoder_targets_one_hot[i, t, word] = 1 - - - - -##### build the model ##### -encoder_inputs_placeholder = Input(shape=(max_len_input,)) -x = embedding_layer(encoder_inputs_placeholder) -encoder = LSTM( - LATENT_DIM, - return_state=True, - # dropout=0.5 # dropout not available on gpu -) -encoder_outputs, h, c = encoder(x) -# encoder_outputs, h = encoder(x) #gru - -# keep only the states to pass into decoder -encoder_states = [h, c] -# encoder_states = [state_h] # gru - -# Set up the decoder, using [h, c] as initial state. -decoder_inputs_placeholder = Input(shape=(max_len_target,)) - -# this word embedding will not use pre-trained vectors -# although you could -decoder_embedding = Embedding(num_words_output, EMBEDDING_DIM) -decoder_inputs_x = decoder_embedding(decoder_inputs_placeholder) - -# since the decoder is a "to-many" model we want to have -# return_sequences=True -decoder_lstm = LSTM( - LATENT_DIM, - return_sequences=True, - return_state=True, - # dropout=0.5 # dropout not available on gpu -) -decoder_outputs, _, _ = decoder_lstm( - decoder_inputs_x, - initial_state=encoder_states -) - -# decoder_outputs, _ = decoder_gru( -# decoder_inputs_x, -# initial_state=encoder_states -# ) - -# final dense layer for predictions -decoder_dense = Dense(num_words_output, activation='softmax') -decoder_outputs = decoder_dense(decoder_outputs) - -# Create the model object -model = Model([encoder_inputs_placeholder, decoder_inputs_placeholder], decoder_outputs) - - -def custom_loss(y_true, y_pred): - # both are of shape N x T x K - mask = K.cast(y_true > 0, dtype='float32') - out = mask * y_true * K.log(y_pred) - return -K.sum(out) / K.sum(mask) - - -def acc(y_true, y_pred): - # both are of shape N x T x K - targ = K.argmax(y_true, axis=-1) - pred = K.argmax(y_pred, axis=-1) - correct = K.cast(K.equal(targ, pred), dtype='float32') - - # 0 is padding, don't include those - mask = K.cast(K.greater(targ, 0), dtype='float32') - n_correct = K.sum(mask * correct) - n_total = K.sum(mask) - return n_correct / n_total - -model.compile(optimizer='adam', loss=custom_loss, metrics=[acc]) - -# Compile the model and train it -# model.compile( -# optimizer='rmsprop', -# loss='categorical_crossentropy', -# metrics=['accuracy'] -# ) - - - -r = model.fit( - [encoder_inputs, decoder_inputs], decoder_targets_one_hot, - batch_size=BATCH_SIZE, - epochs=EPOCHS, - validation_split=0.2, -) - -# plot some data -plt.plot(r.history['loss'], label='loss') -plt.plot(r.history['val_loss'], label='val_loss') -plt.legend() -plt.show() - -# accuracies -plt.plot(r.history['accuracy'], label='acc') -plt.plot(r.history['val_accuracy'], label='val_acc') -plt.legend() -plt.show() - -# Save model -model.save('s2s.h5') - - - - -##### Make predictions ##### -# As with the poetry example, we need to create another model -# that can take in the RNN state and previous word as input -# and accept a T=1 sequence. - -# The encoder will be stand-alone -# From this we will get our initial decoder hidden state -encoder_model = Model(encoder_inputs_placeholder, encoder_states) - -decoder_state_input_h = Input(shape=(LATENT_DIM,)) -decoder_state_input_c = Input(shape=(LATENT_DIM,)) -decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c] -# decoder_states_inputs = [decoder_state_input_h] # gru - -decoder_inputs_single = Input(shape=(1,)) -decoder_inputs_single_x = decoder_embedding(decoder_inputs_single) - -# this time, we want to keep the states too, to be output -# by our sampling model -decoder_outputs, h, c = decoder_lstm( - decoder_inputs_single_x, - initial_state=decoder_states_inputs -) -# decoder_outputs, state_h = decoder_lstm( -# decoder_inputs_single_x, -# initial_state=decoder_states_inputs -# ) #gru -decoder_states = [h, c] -# decoder_states = [h] # gru -decoder_outputs = decoder_dense(decoder_outputs) - -# The sampling model -# inputs: y(t-1), h(t-1), c(t-1) -# outputs: y(t), h(t), c(t) -decoder_model = Model( - [decoder_inputs_single] + decoder_states_inputs, - [decoder_outputs] + decoder_states -) - -# map indexes back into real words -# so we can view the results -idx2word_eng = {v:k for k, v in word2idx_inputs.items()} -idx2word_trans = {v:k for k, v in word2idx_outputs.items()} - - -def decode_sequence(input_seq): - # Encode the input as state vectors. - states_value = encoder_model.predict(input_seq) - - # Generate empty target sequence of length 1. - target_seq = np.zeros((1, 1)) - - # Populate the first character of target sequence with the start character. - # NOTE: tokenizer lower-cases all words - target_seq[0, 0] = word2idx_outputs[''] - - # if we get this we break - eos = word2idx_outputs[''] - - # Create the translation - output_sentence = [] - for _ in range(max_len_target): - output_tokens, h, c = decoder_model.predict( - [target_seq] + states_value - ) - # output_tokens, h = decoder_model.predict( - # [target_seq] + states_value - # ) # gru - - # Get next word - idx = np.argmax(output_tokens[0, 0, :]) - - # End sentence of EOS - if eos == idx: - break - - word = '' - if idx > 0: - word = idx2word_trans[idx] - output_sentence.append(word) - - # Update the decoder input - # which is just the word just generated - target_seq[0, 0] = idx - - # Update states - states_value = [h, c] - # states_value = [h] # gru - - return ' '.join(output_sentence) - - - -while True: - # Do some test translations - i = np.random.choice(len(input_texts)) - input_seq = encoder_inputs[i:i+1] - translation = decode_sequence(input_seq) - print('-') - print('Input:', input_texts[i]) - print('Translation:', translation) - - ans = input("Continue? [Y/n]") - if ans and ans.lower().startswith('n'): - break - diff --git a/nlp_v2/WHERE ARE THE NOTEBOOKS.txt b/nlp_v2/WHERE ARE THE NOTEBOOKS.txt deleted file mode 100644 index 5446ce25..00000000 --- a/nlp_v2/WHERE ARE THE NOTEBOOKS.txt +++ /dev/null @@ -1,5 +0,0 @@ -As stated in the "where to get the code" / "where to get the notebooks" lecture, the notebooks are NOT on Github. - -If you missed this, please review the lecture for the actual location of the notebooks. - -If, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com. \ No newline at end of file diff --git a/nlp_v2/extra_reading.txt b/nlp_v2/extra_reading.txt deleted file mode 100644 index d7d382f9..00000000 --- a/nlp_v2/extra_reading.txt +++ /dev/null @@ -1,41 +0,0 @@ -An information-theoretic perspective of tf–idf measures -https://www.sciencedirect.com/science/article/abs/pii/S0306457302000213 - -A Mathematical Theory of Communication by Claude Shannon -https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf - -TextRank: Bringing Order into Texts -https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf - -Variations of the Similarity Function of TextRank for Automated Summarization -https://arxiv.org/abs/1602.03606 - -Generic Text Summarization Using Relevance Measure and Latent Semantic Analysis -https://www.cs.bham.ac.uk/~pxt/IDA/text_summary.pdf - -Using Latent Semantic Analysis in Text Summarization and Summary Evaluation -http://textmining.zcu.cz/publications/isim.pdf - -Spam Filtering with Naive Bayes – Which Naive Bayes? -http://www2.aueb.gr/users/ion/docs/ceas2006_paper.pdf - -Sentiment analysis using multinomial logistic regression -https://ieeexplore.ieee.org/document/8226700 - -Latent Dirichlet Allocation -https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf - -List of Hugging Face Pipelines for NLP -https://lazyprogrammer.me/list-of-hugging-face-pipelines-for-nlp/ - -Indexing by Latent Semantic Analysis (Latent Semantic Indexing) -http://lsa.colorado.edu/papers/JASIS.lsi.90.pdf - -Efficient Estimation of Word Representations in Vector Space (word2vec) -https://arxiv.org/abs/1301.3781 - -GloVe: Global Vectors for Word Representation (GloVe) -https://nlp.stanford.edu/pubs/glove.pdf - -Deep Learning with Tensorflow, a bit more in-depth -https://deeplearningcourses.com/c/deep-learning-tensorflow-2 \ No newline at end of file diff --git a/numpy_class/classification_example.py b/numpy_class/classification_example.py deleted file mode 100644 index bc65aeda..00000000 --- a/numpy_class/classification_example.py +++ /dev/null @@ -1,100 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python -# YouTube direct link: http://bit.ly/2LENC50 - -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -# just in case we need it -import numpy as np - - -# import the function that will get the data -# yes, sklearn comes with built-in datasets! -from sklearn.datasets import load_breast_cancer - -# load the data -data = load_breast_cancer() - -# check the type of 'data' -type(data) - -# note: it is a Bunch object -# this basically acts like a dictionary where you can treat the keys like attributes -data.keys() - -# 'data' (the attribute) means the input data -data.data.shape -# it has 569 samples, 30 features - -# 'targets' -data.target -# note how the targets are just 0s and 1s -# normally, when you have K targets, they are labeled 0..K-1 - -# their meaning is not lost -data.target_names - -# there are also 569 corresponding targets -data.target.shape - -# you can also determinw the meaning of each feature -data.feature_names - - -# normally we would put all of our imports at the top -# but this lets us tell a story -from sklearn.model_selection import train_test_split - - -# split the data into train and test sets -# this lets us simulate how our model will perform in the future -X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.33) - - -# instantiate a classifer and train it -from sklearn.ensemble import RandomForestClassifier - - -model = RandomForestClassifier() -model.fit(X_train, y_train) - - -# evaluate the model's performance -model.score(X_train, y_train) -model.score(X_test, y_test) - - -# how you can make predictions -predictions = model.predict(X_test) - -# what did we get? -predictions - -# manually check the accuracy of your predictions -N = len(y_test) -np.sum(predictions == y_test) / N # can also just call np.mean() - - - -# we can even use deep learning to solve the same problem! -from sklearn.neural_network import MLPClassifier - -# you'll learn why scaling is needed in a later course -from sklearn.preprocessing import StandardScaler - -scaler = StandardScaler() -X_train2 = scaler.fit_transform(X_train) -X_test2 = scaler.transform(X_test) - -model = MLPClassifier(max_iter=500) -model.fit(X_train2, y_train) - - -# evaluate the model's performance -model.score(X_train2, y_train) -model.score(X_test2, y_test) \ No newline at end of file diff --git a/numpy_class/dot_for.py b/numpy_class/dot_for.py deleted file mode 100644 index 2e093ff2..00000000 --- a/numpy_class/dot_for.py +++ /dev/null @@ -1,26 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python -import numpy as np -from datetime import datetime - -a = np.random.randn(100) -b = np.random.randn(100) -T = 100000 - -def slow_dot_product(a, b): - result = 0 - for e, f in zip(a, b): - result += e*f - return result - -t0 = datetime.now() -for t in xrange(T): - slow_dot_product(a, b) -dt1 = datetime.now() - t0 - -t0 = datetime.now() -for t in xrange(T): - a.dot(b) -dt2 = datetime.now() - t0 - -print "dt1 / dt2:", dt1.total_seconds() / dt2.total_seconds() \ No newline at end of file diff --git a/numpy_class/exercises/ex1.py b/numpy_class/exercises/ex1.py deleted file mode 100644 index c81d2c79..00000000 --- a/numpy_class/exercises/ex1.py +++ /dev/null @@ -1,29 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python - -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - -A = np.array([ - [0.3, 0.6, 0.1], - [0.5, 0.2, 0.3], - [0.4, 0.1, 0.5]]) - -v = np.ones(3) / 3 - -num_iters = 25 -distances = np.zeros(num_iters) -for i in range(num_iters): - v2 = v.dot(A) - d = np.linalg.norm(v2 - v) - distances[i] = d - v = v2 - -plt.plot(distances) -plt.show() \ No newline at end of file diff --git a/numpy_class/exercises/ex2.py b/numpy_class/exercises/ex2.py deleted file mode 100644 index cea7b7e2..00000000 --- a/numpy_class/exercises/ex2.py +++ /dev/null @@ -1,29 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python - -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - -def sampleY(n=1000): - # draw n samples from uniform dist. - X = np.random.random(n) - Y = X.sum() - return Y - - -# now draw N Y's -N = 1000 -Y_samples = np.zeros(N) -for i in range(N): - Y_samples[i] = sampleY() - - -# now plot the Y_samples -plt.hist(Y_samples, bins=20) -plt.show() \ No newline at end of file diff --git a/numpy_class/exercises/ex3.py b/numpy_class/exercises/ex3.py deleted file mode 100644 index eb46f424..00000000 --- a/numpy_class/exercises/ex3.py +++ /dev/null @@ -1,34 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python - -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - - -# load in the data -df = pd.read_csv('../../large_files/train.csv') -data = df.values -X = data[:, 1:] # images -Y = data[:, 0] # labels - -# loop through each label -for k in range(10): - Xk = X[Y == k] - - # mean image - Mk = Xk.mean(axis=0) - - # reshape into an image - im = Mk.reshape(28, 28) - - # plot the image - plt.imshow(im, cmap='gray') - plt.title("Label: %s" % k) - plt.show() diff --git a/numpy_class/exercises/ex4.py b/numpy_class/exercises/ex4.py deleted file mode 100644 index bdd0a90e..00000000 --- a/numpy_class/exercises/ex4.py +++ /dev/null @@ -1,54 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python - -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - - -# load in the data -df = pd.read_csv('../../large_files/train.csv') -data = df.values - -# shuffle the images -np.random.shuffle(data) - -X = data[:, 1:] # images -Y = data[:, 0] # labels - - -# define rotate functions -def rotate1(im): - return np.rot90(im, 3) - -def rotate2(im): - H, W = im.shape - im2 = np.zeros((W, H)) - for i in range(H): - for j in range(W): - im2[j,H - i - 1] = im[i,j] - return im2 - - -for i in range(X.shape[0]): - # get the image - im = X[i].reshape(28, 28) - - # flip the image - # im = rotate1(im) - im = rotate2(im) - - # plot the image - plt.imshow(im, cmap='gray') - plt.title("Label: %s" % Y[i]) - plt.show() - - ans = input("Continue? [Y/n]: ") - if ans and ans[0].lower() == 'n': - break diff --git a/numpy_class/exercises/ex5.py b/numpy_class/exercises/ex5.py deleted file mode 100644 index 8482d070..00000000 --- a/numpy_class/exercises/ex5.py +++ /dev/null @@ -1,60 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python - -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - - - -def is_symmetric1(A): - return np.all(A == A.T) - - -def is_symmetric2(A): - rows, cols = A.shape - if rows != cols: - return False - - for i in range(rows): - for j in range(cols): - if A[i,j] != A[j,i]: - return False - - return True - - -def check(A, b): - print("Testing:", A) - assert(is_symmetric1(A) == b) - assert(is_symmetric2(A) == b) - - -# test the functions -A = np.zeros((3, 3)) -check(A, True) - -A = np.eye(3) -check(A, True) - -A = np.random.randn(3, 2) -A = A.dot(A.T) -check(A, True) - -A = np.array([[1, 2, 3], [2, 4, 5], [3, 5, 6]]) -check(A, True) - -A = np.random.randn(3, 2) -check(A, False) - -A = np.random.randn(3, 3) -check(A, False) - -A = np.arange(9).reshape(3, 3) -check(A, False) - diff --git a/numpy_class/exercises/ex6.py b/numpy_class/exercises/ex6.py deleted file mode 100644 index b1c88288..00000000 --- a/numpy_class/exercises/ex6.py +++ /dev/null @@ -1,25 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python - -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - - -# generate unlabeled data -N = 2000 -X = np.random.random((N, 2))*2 - 1 - -# generate labels -Y = np.zeros(N) -Y[(X[:,0] < 0) & (X[:,1] > 0)] = 1 -Y[(X[:,0] > 0) & (X[:,1] < 0)] = 1 - -# plot it -plt.scatter(X[:,0], X[:,1], c=Y) -plt.show() \ No newline at end of file diff --git a/numpy_class/exercises/ex7.py b/numpy_class/exercises/ex7.py deleted file mode 100644 index 46f5e0fb..00000000 --- a/numpy_class/exercises/ex7.py +++ /dev/null @@ -1,34 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python - -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - -def get_donut(): - N = 2000 - R_inner = 5 - R_outer = 10 - - # distance from origin is radius + random normal - # angle theta is uniformly distributed between (0, 2pi) - R1 = np.random.randn(N//2) + R_inner - theta = 2*np.pi*np.random.random(N//2) - X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T - - R2 = np.random.randn(N//2) + R_outer - theta = 2*np.pi*np.random.random(N//2) - X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T - - X = np.concatenate([ X_inner, X_outer ]) - Y = np.array([0]*(N//2) + [1]*(N//2)) - return X, Y - -X, Y = get_donut() -plt.scatter(X[:,0], X[:,1], c=Y) -plt.show() \ No newline at end of file diff --git a/numpy_class/exercises/ex8.py b/numpy_class/exercises/ex8.py deleted file mode 100644 index 8d8926ba..00000000 --- a/numpy_class/exercises/ex8.py +++ /dev/null @@ -1,50 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python - -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - -def get_spiral(): - # Idea: radius -> low...high - # (don't start at 0, otherwise points will be "mushed" at origin) - # angle = low...high proportional to radius - # [0, 2pi/6, 4pi/6, ..., 10pi/6] --> [pi/2, pi/3 + pi/2, ..., ] - # x = rcos(theta), y = rsin(theta) as usual - - radius = np.linspace(1, 10, 100) - thetas = np.empty((6, 100)) - for i in range(6): - start_angle = np.pi*i / 3.0 - end_angle = start_angle + np.pi / 2 - points = np.linspace(start_angle, end_angle, 100) - thetas[i] = points - - # convert into cartesian coordinates - x1 = np.empty((6, 100)) - x2 = np.empty((6, 100)) - for i in range(6): - x1[i] = radius * np.cos(thetas[i]) - x2[i] = radius * np.sin(thetas[i]) - - # inputs - X = np.empty((600, 2)) - X[:,0] = x1.flatten() - X[:,1] = x2.flatten() - - # add noise - X += np.random.randn(600, 2)*0.5 - - # targets - Y = np.array([0]*100 + [1]*100 + [0]*100 + [1]*100 + [0]*100 + [1]*100) - return X, Y - - -X, Y = get_spiral() -plt.scatter(X[:,0], X[:,1], c=Y) -plt.show() \ No newline at end of file diff --git a/numpy_class/exercises/ex9.py b/numpy_class/exercises/ex9.py deleted file mode 100644 index c77cd812..00000000 --- a/numpy_class/exercises/ex9.py +++ /dev/null @@ -1,28 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python - -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - - -from ex8 import get_spiral - -# get the data -X, Y = get_spiral() - -# combine the data into one array -# data to be concatenated must have same # of dimensions -# e.g. N x D and N x 1 -# not N x D and N -data = np.concatenate((X, np.expand_dims(Y, 1)), axis=1) - -df = pd.DataFrame(data) -df.columns = ['x1', 'x2', 'y'] -df.to_csv('mydata.csv', index=False) \ No newline at end of file diff --git a/numpy_class/manual_data_loading.py b/numpy_class/manual_data_loading.py deleted file mode 100644 index e30e425f..00000000 --- a/numpy_class/manual_data_loading.py +++ /dev/null @@ -1,19 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python - -# NOTE: in class, we assumed the current working directory -# was linear_regression_class -# in this file, we assume you are running the script -# from the directory this file is in - -import numpy as np - -X = [] - -for line in open('../linear_regression_class/data_2d.csv'): - row = line.split(',') - sample = map(float, row) - X.append(sample) - -X = np.array(X) -print X \ No newline at end of file diff --git a/numpy_class/python3/dot_for.py b/numpy_class/python3/dot_for.py deleted file mode 100644 index 3a67b2bc..00000000 --- a/numpy_class/python3/dot_for.py +++ /dev/null @@ -1,33 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from datetime import datetime - -a = np.random.randn(100) -b = np.random.randn(100) -T = 100000 - -def slow_dot_product(a, b): - result = 0 - for e, f in zip(a, b): - result += e*f - return result - -t0 = datetime.now() -for t in range(T): - slow_dot_product(a, b) -dt1 = datetime.now() - t0 - -t0 = datetime.now() -for t in range(T): - a.dot(b) -dt2 = datetime.now() - t0 - -print("dt1 / dt2:", dt1.total_seconds() / dt2.total_seconds()) \ No newline at end of file diff --git a/numpy_class/python3/manual_data_loading.py b/numpy_class/python3/manual_data_loading.py deleted file mode 100644 index efeb2335..00000000 --- a/numpy_class/python3/manual_data_loading.py +++ /dev/null @@ -1,19 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python - -# NOTE: in class, we assumed the current working directory -# was linear_regression_class -# in this file, we assume you are running the script -# from the directory this file is in - -import numpy as np - -X = [] - -for line in open('../../linear_regression_class/data_2d.csv'): - row = line.split(',') - sample = list(map(float, row)) - X.append(sample) - -X = np.array(X) -print(X) \ No newline at end of file diff --git a/numpy_class/regression_example.py b/numpy_class/regression_example.py deleted file mode 100644 index aea9cc02..00000000 --- a/numpy_class/regression_example.py +++ /dev/null @@ -1,103 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python -# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python -# YouTube direct link: http://bit.ly/2LENC50 - -# Get the data from: -# https://archive.ics.uci.edu/ml/datasets/Airfoil+Self-Noise - -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -# just in case we need it -import numpy as np -import pandas as pd - - -# load the data -# important note: this is where we will usually put data files -df = pd.read_csv('../large_files/airfoil_self_noise.dat', sep='\t', header=None) - -# check the data -df.head() -df.info() - -# get the inputs -data = df[[0,1,2,3,4]].values - -# get the outputs -target = df[5].values - -# tiny update: pandas is moving from .as_matrix() to the equivalent .values - - -# normally we would put all of our imports at the top -# but this lets us tell a story -from sklearn.model_selection import train_test_split - - -# split the data into train and test sets -# this lets us simulate how our model will perform in the future -X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.33) - - -# instantiate a classifer and train it -from sklearn.linear_model import LinearRegression - - -model = LinearRegression() -model.fit(X_train, y_train) - - -# evaluate the model's performance -print(model.score(X_train, y_train)) -print(model.score(X_test, y_test)) - - -# how you can make predictions -predictions = model.predict(X_test) - -# what did we get? -predictions - - - -# we can even use random forest to solve the same problem! -from sklearn.ensemble import RandomForestRegressor - -model2 = RandomForestRegressor() -model2.fit(X_train, y_train) - - -# evaluate the model's performance -print(model2.score(X_train, y_train)) -print(model2.score(X_test, y_test)) - - - - -# we can even use deep learning to solve the same problem! -from sklearn.neural_network import MLPRegressor - -# you'll learn why scaling is needed in a later course -from sklearn.preprocessing import StandardScaler - -scaler = StandardScaler() -X_train2 = scaler.fit_transform(X_train) -X_test2 = scaler.transform(X_test) -scaler2 = StandardScaler() -y_train2 = scaler2.fit_transform(np.expand_dims(y_train, -1)).ravel() -y_test2 = scaler2.fit_transform(np.expand_dims(y_test, -1)).ravel() - -model = MLPRegressor(max_iter=500) -model.fit(X_train2, y_train2) - - -# evaluate the model's performance -print(model.score(X_train2, y_train2)) -print(model.score(X_test2, y_test2)) -# not as good as a random forest! -# but not as bad as linear regression diff --git a/numpy_class/table1.csv b/numpy_class/table1.csv deleted file mode 100644 index 7e58ec61..00000000 --- a/numpy_class/table1.csv +++ /dev/null @@ -1,4 +0,0 @@ -user_id,email,age -1,alice@gmail.com,20 -2,bob@gmail.com,25 -3,carol@gmail.com,30 \ No newline at end of file diff --git a/numpy_class/table2.csv b/numpy_class/table2.csv deleted file mode 100644 index 00248bb6..00000000 --- a/numpy_class/table2.csv +++ /dev/null @@ -1,12 +0,0 @@ -user_id,ad_id,click -1,1,1 -1,2,0 -1,5,0 -2,3,0 -2,4,1 -2,1,0 -3,2,0 -3,1,0 -3,3,0 -3,4,0 -3,5,1 \ No newline at end of file diff --git a/openai/extra_reading.txt b/openai/extra_reading.txt deleted file mode 100644 index 776b62c7..00000000 --- a/openai/extra_reading.txt +++ /dev/null @@ -1,21 +0,0 @@ -How to Set Environment Variables Permanently in Windows, Linux, and Mac -https://lazyprogrammer.me/how-to-set-environment-variables-permanently-in-windows-linux-and-mac/ - -How to make your completions outputs consistent with the new seed parameter -https://cookbook.openai.com/examples/reproducible_outputs_with_the_seed_parameter - -What is Temperature in NLP / LLMs? -https://medium.com/@lazyprogrammerofficial/what-is-temperature-in-nlp-llms-aa2a7212e687 - -Large Language Models are Zero-Shot Reasoners (CoT) -https://arxiv.org/abs/2205.11916 - -Chain-of-Thought Prompting Elicits Reasoning in Large Language Models -https://arxiv.org/abs/2201.11903 - -A much better example of "ELI5" -https://www.reddit.com/r/ChatGPT/comments/1c5s51g/my_mother_and_i_had_difficulty_understanding_my - -What is RAG? — Retrieval-Augmented Generation Explained -https://medium.com/@lazyprogrammerofficial/what-is-rag-retrieval-augmented-generation-explained-148c8bb9c00f -https://lazyprogrammer.me/what-is-rag-retrieval-augmented-generation-explained/ \ No newline at end of file diff --git a/openai/fight.mp4 b/openai/fight.mp4 deleted file mode 100644 index 24d83be4..00000000 Binary files a/openai/fight.mp4 and /dev/null differ diff --git a/openai/finance.png b/openai/finance.png deleted file mode 100644 index b274c4b9..00000000 Binary files a/openai/finance.png and /dev/null differ diff --git a/openai/handwriting.jpg b/openai/handwriting.jpg deleted file mode 100644 index 2e92adf4..00000000 Binary files a/openai/handwriting.jpg and /dev/null differ diff --git a/openai/physics_problem.jpeg b/openai/physics_problem.jpeg deleted file mode 100644 index 3f6d3200..00000000 Binary files a/openai/physics_problem.jpeg and /dev/null differ diff --git a/openai/replies.json b/openai/replies.json deleted file mode 100644 index 27d6761c..00000000 --- a/openai/replies.json +++ /dev/null @@ -1,206 +0,0 @@ -[ - { - "review": "(1) His answers are sometimes antagonistic but the guy wants us to think by ourselves. I feel he was guided by questions from students with very little background on the subject. (2) Links are not updated. I understand not updated them on the videos, but on the git repository and the scripts, it should have them updated. (3) Explanations are great, with a few inconsistencies when compared to Gemini.google.com understanding. (4) The course content in general is great.", - "response": "(1) I think all students should respect other students taking this course. All students deserve to have their questions answered, and no student can expect to have this course personalized to their own background.\n\n(2) This is incorrect, and you were already instructed on the Q&A to ensure you were looking at the correct repository with the most up-to-date files.\n\n(3) LLMs are known for hallucinating, and their output cannot be trusted, especially if you don't know what you're doing. Instead, you should be using the Q&A to rectify these issues, which is why it's the #1 rule in 'how to succeed in this course'." - }, - { - "review": "You should have explained in the introduction video that, you have not yet figured out Stock forecasting, so explaining your (thoughts or beliefs or work) in this course. But marketing in great way , nothing in content other than playing with data.", - "response": "Try paying attention and understanding the course. If you still believe there's some magic algorithm to perfectly predict stock prices and I \"just haven't figured it out yet\", you clearly weren't listening, know absolutely nothing about finance, and hence, spreading misinformation to readers." - }, - { - "review": "I'm really disappointed. The last update of your codes was 9 years ago. Nothing is running. I tried file after file - nothing works. I don't think you could even continue to sell these courses. Unfortunately, I can no longer cancel the current course, but I will cancel the next course. Your courses do not meet basic standards. Too bad.", - "response": "> The last update of your codes was 9 years ago.\n\nNOTE: Our friend here just doesn't know how to use Git properly. My ML Github repo was CREATED 9 years ago (long before I even started making courses)." - }, - { - "review": "nao é claro ainda como fazer o donlow de githum", - "response": "Thanks for the feedback! Please re-watch the lecture \"Where to get the code / notebooks\" carefully and follow the instructions. It clearly states 5 times (yes, that many times) that the notebooks are not on Github, but rather, are accessed via the code link." - }, - { - "review": "It is a good course about RL, as all his courses, but if you are here for the Trading agent, don't buy it... very basic code and does not really work.", - "response": "Thanks for your feedback! However, it seems you are basing your rating around your own misconceptions and naivete surrounding trading and finance, rather than the quality of the course itself." - }, - { - "review": "Need to explain more about the topic. CNN is more theoretical in the course than programming.", - "response": "Incorrect. There are equal parts theory and programming. Every \"theoretical\" concept is implemented in code. And obviously, you must understand \"what\" you are coding before you code it. Please pay attention to improve your understanding, thanks!" - }, - { - "review": "The content is ok but the links between videos and sections aren't always obvious. I like the fact that it goes in dept on my subjects but the quality of the audio isn't always good enough. I would still recommend to someone that really want to have a better understanding of AI/ML or specifically logistic regression but expect some extra reading if you really want to understand all the concepts well.", - "response": "You should probably revisit the \"introduction and outline\" of the course if you've forgotten the structure of the course. There's only \"extra reading\" if you do not sufficiently meet the prerequisites." - }, - { - "review": "I expected some coding and practice but most of the course till now is just theory", - "response": "That's incorrect, and this is even visible from simply looking at the lecture titles. There are equal parts theory and code. First we discuss \"what\" we will code, then we code it. Obviously, you can't write code without knowing what you're coding first... Please pay attention to improve your understanding, thanks!" - }, - { - "review": "It is all over the place ... not very structured and i have IT and python background , still finding difficult to follow ... i wonder how the Deep learning course will be", - "response": "Unclear why you're having trouble following the structure of such a simple short course. There are only 4 sections: Numpy, Matplotlib, Pandas, and Scipy. Please pay attention to improve your understanding, thanks!" - }, - { - "review": "Content is good so far, but lecturer seems hung up on the behaviors of the participants which is not helping the instruction time.", - "response": "It's important to remember that the course isn't customized for you individually. Therefore, it should be expected that common problems that afflict a non-trivial number of students will be addressed." - }, - { - "review": "Explanation is not clear as we are beginners. May be improve better to understand clearly.", - "response": "Thanks for the feedback! Please ensure you meet the prerequisites as listed twice in the course description and several more times in lectures such as \"how to succeed in this course\"." - }, - { - "review": "Much was great, some frequently explained by referring to other courses as well as skipping some code blocks or instrumental variables, which were readily explained in chat gpt by asking if to add copious explanatory comments. Most, but certainly not all AI ML course instructors are more concerned with taking the time to explain finer details of the code. Early parts on ANN and CNN were excellent in presentation although this was simpler material. In the course was more presenting and explaining than teaching how to code the models, both necessary individually and together sufficient for a solid learning experience. Perhaps sacrifice some optional topics for more time indepth to the course essentials - quantity versus quality.", - "response": "Unclear whether you wrote this comment for the wrong course, as there are no sections about ANNs or CNNs in this course... Furthermore, this course is not about in-depth theory - please check the course description and prerequisites, thanks!" - }, - { - "review": "Some more details about math equations could be added", - "response": "Thanks for the feedback! Please ensure you meet the prerequisites as listed twice in the course description and several more times in lectures such as \"how to succeed in this course\". Furthermore, you may want to read the course description that explains that this course is about Tensorflow 2, not the math behind deep learning. I already have courses on that, so check those out instead." - }, - { - "review": "Teaches only Syntax and not any ML or theory behind how any of the Neural Network architectures work.", - "response": "Incorrect. This course gives an overview of theory, and furthermore, I already have 15+ in-depth DL courses that go into the math behind DL. Luckily, this is all in the course description, which I'm sure you've diligently read. ;)" - }, - { - "review": "It was a good match for my current abilities. I could not access the live python notebooks or links used. I would have appreciated solutions to the end of section exercises. Overall though the instructor is very knowledgeable and the course is free so I can't complain.", - "response": "The key is to follow the instructions. All notebooks and exercise solutions are provided, if you can just follow the instructions on how to get them (clicking links). It really is very easy, it just requires paying attention. :)" - }, - { - "review": "The Neural Network part is not very clear", - "response": "This course doesn't talk about neural networks..." - }, - { - "review": "need more examples", - "response": "The course is full of examples, please use the Q&A if you have difficulties understanding them" - }, - { - "review": "Could use a bit more time spent on explaining certain concepts, like Box-Cox, giving more intuition and explanation of why that is useful", - "response": "Please use the Q&A to inquire about your misunderstandings, as stated in \"how to succeed in this course\"." - }, - { - "review": "Interesting course with lots of examples and lectures. Although some parts of the course become repetitive. There are lectures where he explains the code step by step and then goes on to repeat the same thing in the \"... in Python\" lectures. It would have been nice if he had proposed other exercises with a different dataset than the lectures, even though he does not provide the solution. It is fine to say that we should try to write the code ourselves first and then check the solution, but when this is reduced to copy and paste from the previous lecture it seems ridiculous to me.", - "response": "This is a practical course, meaning that you are shown the code and can do with it what you wish (there's no way to \"exercise\" writing library code without first being shown the syntax, which doesn't make any sense). Additionally, repetition is a research-backed learning technique: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8442015/ Furthermore, please remember that the course is not customized to you. Therefore, if you personally choose to avoid repetition, you are free to do so, nobody is stopping you." - }, - { - "review": "I enjoyed the author explanation on the differences between statistics regression and ML regression content. But I think the lecture on the differences was pedantic and unnecessary even. The content list and summary to this lecture is where the distinction could be made. Instead, it was spent ranting within the lecture, how does that provide any value and meaningful experience? Worry not about the review, if the content is good, the review would come.", - "response": "> But I think the lecture on the differences was pedantic and unnecessary even.\n\nPlease remember, the course is not customized for you personally, it's for all students and therefore addresses common student mistakes, irrespective of your personal needs. Obviously, I have to answer ALL student questions, whether or not you personally find the question/answer helpful." - }, - { - "review": "The instructor seems inadequately prepared for each segment of the course. There is a noticeable absence of demonstrations that are essential for a comprehensive understanding of the material. Furthermore, there is a disproportionate emphasis on paid websites over free alternatives, raising concerns about potential conflicts of interest due to affiliate marketing associations with these paid platforms.", - "response": "Everything is demonstrated in Colab notebooks. I challenge you to list specific examples instead of vague accusations like \"inadequately prepared\" and \"noticeable absence of demonstrations\".\n\n> disproportionate emphasis on paid websites over free alternatives\n\nThat is merely what you are paying attention to and triggered by. Most tools in the course are free or have free alternatives. Furthermore, I don't choose what's free, they are not my companies, so I am unsure why you think this is my fault...?" - }, - { - "review": "I have taken this course and have spent lot of hrs and can tell that, this 12 hrs course will take lot of time to understand and complete (150 hrs). This can be done if we have 100% free time for 4 weeks. Good thing is if you have little idea of Calculus and we flow what is being taught, this course will make us ready to understand ML. Having said that the instructor should again go back and try to minimize the un-necessary or repetitive content.", - "response": "There is no unnecessary or repetitive content, and if you believe there is, you should use the Q&A to clear up any misunderstandings as instructed in the \"how to succeed in this course\" video." - }, - { - "review": "Content is inconsistent in difficulty. Instructor is not good at explaining highly complicated topics that require a lot of Mathematics.", - "response": "Thanks for the feedback! Please ensure you meet the prerequisites as listed twice in the course description and several more times in lectures such as \"how to succeed in this course\". Please recall that each section is denoted as \"beginner\", \"intermediate\", or \"advanced\" (as stated in the intro). One must choose according to their own skill level. This obvious lack of attention explains why you are having trouble understanding the course (since clearly, even the basic info in the intro was beyond your understanding)." - }, - { - "review": "you are READING the slide nicely.", - "response": "Incorrect. The slides provide a summary of the spoken audio, like all courses on this site, all other video-based courses, and all presentations around the world. I'm amused that you would act \"surprised\" by such a normal occurrence... The slides are not being \"read\", and anyone taking the course can confirm this fact readily, so this comment is both inaccurate and misleading to potential students." - }, - { - "review": "There are some good topics explained, but lots of tutorials are just off topic (not about NLP), but various attempts from the instructor the explain to others why they're not able to understand stuff.", - "response": "You just have to remember that the course is not customized for you, it's for everyone. The \"FAQ\" section (meaning \"frequently asked questions\") answers questions other students are asking. It's unkind to suggest that I shouldn't answer questions from your fellow students." - }, - { - "review": "It doesn’t make sense to have to make an account outside of Udemy to then have to read an article that gives an analogy about \"burgers\" just to then inform you that you have to wait 31 days to view the material. Just upload the video like every other instructor and inform us that you have other material. Don’t force us to the material.", - "response": "Incorrect. You don't have to \"make an account outside of Udemy\" to take this course. That is for a different VERSION of the course for students who want to go above and beyond and learn more exciting material, in case they want to learn stuff OUTSIDE the course description [which I'm sure you've diligently read ;)]." - }, - { - "review": "I feel pretty lost, I feel like showing an example of what we're trying to achieve even thought it comes way later in the course would show how the individual parts of the course will play into it.", - "response": "This is what happens when you don't follow the instructions, like meeting the prerequisites or using the Q&A." - }, - { - "review": "Teacher is good at explaining concepts,albeit he has some language problems.", - "response": "English is my only language, but based on your comment alone, it is clear that the language problems may be on your end. I suggest improving in this area to better understand the course, thanks!" - }, - { - "review": "Honestly !! this is highly insufficient material. When we open the books of Machine Learning, we are lost in understanding the mathematical notations. However, this course is teaching Integration way below the levels of Class 12. My comment - It needs improvement", - "response": "Please make sure you read the course description so you understand the purpose of this course. For example, hard integration problems would not serve that purpose." - }, - { - "review": "The course has no depth where the instructor explains an intuition and runs off. The exercises given have no solutions and you have to do it either yourself or you suck and they can't help you! I wouldn't recommend it. Feels more like a refresher course than a course for someone to learn from scratch.", - "response": "> The course has no depth where the instructor explains an intuition and runs off.\n\nIncorrect. Every concept lecture is followed by Python code.\n\n> The exercises given have no solutions\n\nIncorrect. Exercise solutions are in the videos... please pay attention to improve your understanding, thanks." - }, - { - "review": "I feel helped by this course, but I am a bit confused about understanding the Markov model, but in other materials I can smoothly. Thank you for making this class, I hope you are always healthy.", - "response": "Please note that the sections are clearly marked beginner, intermediate, or advanced. This is so that you can stick to your level without being confused about material that is too advanced for you." - }, - { - "review": "Thank you for your prompt response. Let's be frank. I'm no novice to this topic, and I took your course hoping to get a fresh perspective. However, I was met with content that seemed hastily put together and felt more like a reference guide rather than a comprehensive educational course. I've previously enrolled in some of your courses, which were of higher quality. My feedback is based on a comparison with your own past materials. I hope you'll take this as an opportunity to review and enhance the course content for the benefit of future students.", - "response": "All algorithms are derived from scratch and based on the prerequisites, it is not a \"reference guide\". It seems strange that someone who is \"no novice to this topic\" would get those confused..." - }, - { - "review": "Too much talking, less content till now", - "response": "It's a video course, I'm not sure how one would avoid talking..." - }, - { - "review": "While this course has multiple sections on how LP believes you should be learning. That time could have been spent reinforcing some of the more difficult concepts with additional examples.", - "response": "This is a common misunderstanding of the appendix/FAQ. It's not \"That time could have been spent reinforcing some of the more difficult concepts with additional examples\". This content doesn't displace any other content." - }, - { - "review": "Therotical only.....No Example......just copy from book and paste it.......read it.....No Implementation...................", - "response": "Incorrect. Everything has been implemented from scratch. Please pay attention to improve your understanding, and please watch the FAQ lecture \"Beginner's Coding Tips\", thanks!" - }, - { - "review": "I haven't proceeded in the course yet but I wouldn't say I liked the instructor's stance on students asking dumb questions.", - "response": "That seems very weird. In the \"how to succeed\" lecture it clearly states that I encourage any and all questions. Why would you disagree with that?" - }, - { - "review": "If you hang out on YouTube probably you find the same information in the same time", - "response": "You can say that about any subject. The real question is, if it's so easy, then why haven't you done so? ;)" - }, - { - "review": "Generally useful but structure of content and direction of course is not always clear. We jump backwards and forwards between methods more than I would like.", - "response": "Each section is devoted to a different \"method\", there's no jumping \"back and forth\" between them..." - }, - { - "review": "I do not think its advanced stuff at all, nevertheless its good.", - "response": "Read the course description to learn what this course is about. In addition, please see the FAQ, which answers questions such as whether this course is for beginners or experts." - }, - { - "review": "There is constant talk about not having to understand the theory but it seems like the maths goes hand in hand with the models so not sure if it is feasible to just learn the code without understanding why you do certain things", - "response": "Because you're not implementing any of that math yourself, only using high level libraries. Please pay more attention to improve your understanding, thanks!" - }, - { - "review": "not get any technical knowledge yet", - "response": "Why not read the course description so that you understand what this course is about?" - }, - { - "review": "I came here to learn industry level but it does not meet my expectations. this course suits you well for beginners because you can learn all the math and coding from scratch.", - "response": "You simply have an incorrect understanding about what constitutes \"industry level\" (hint: you are not at this level)." - }, - { - "review": "there could be hands-on session rather than pre written code . This would help in understanding the logic better.", - "response": "I've instructed you to code by yourself, not to peek at my prewritten solutions. Therefore, you have simply not followed the instructions. You claim to want to be \"hands-on\", yet you haven't even done the hands-on work I've prescribed." - }, - { - "review": "If you are new to AI, don't take this course. Find something else to start with. Most of what I got from this course is exposure to possibilities with recommender systems. It is not the most organized course either", - "response": "Why should you be new to AI? You should meet the prerequisites, as instructed. Furthermore, it's not \"exposure to possibilities\", we are implementing many concrete algorithms. If you're having trouble understanding how the course is organized, it's one algorithm per section. I suggest simply paying more attention. Thanks!" - }, - { - "review": "sometimes a little superficial", - "response": "Thanks for your feedback. Please make sure to READ the course description so you understand what this course is about before taking it, thanks!" - }, - { - "review": "I am an industry data scientist with an academic background in Machine learning, I have done several deep learning projects in my school years, I am taking this as a refresher. But equations don't have an explanation of variables, and what they stand for, the instructor repetitively mentions that if you don't know any equations you are not ready for this course but no one knows an equation, how it is derived, and what all the greek symbols mean right off the bat especially if you are away from macadamia for a few years. If you add additional resources(eg: citation as you should!) we can read and understand your variables, also many textbooks use different notations, so you need to make your description clear. Also, I don't like the tone and rudeness of the instructor. He sounds like a mad professor, this is a recorded video, so take away the anger", - "response": "> But equations don't have an explanation of variables, and what they stand for\n\nThere is literally a lecture titled \"What do all these symbols and letters mean?\", which is actually a review of previously taught explanations (in other words, variables have been defined multiple times in many cases). Perhaps the problem is that you're simply not paying attention...\n\nAt the very least, thank you for making it obvious to the readers here that your claims are unfounded.\n\n> Also, I don't like the tone and rudeness of the instructor.\n\nYes, I know some students don't like being corrected (as above) and construe all corrections as rude because it's impossible for them to be wrong. But how can a teacher do his job if every correction is interpreted as rude?" - }, - { - "review": "They can't send me slide for this course.", - "response": "Incorrect. Slides are available upon request. Simply use the Q&A as instructed in the how to succeed lecture." - }, - { - "review": "The instructor is poor. Read their responses to other negative reviews. Really off-putting. Constantly stating how people are INCORRECT showing the instructor clearly has no ability to take constructive criticism. If only the instructor could A/B test their own responses. Honestly thought it was a good-ish course. The instructor earned a 1-star here.", - "response": "No, \"incorrect\" is used to denote factually wrong statements. It appears you are too emotional, focusing on your feelings (you're offended that I've corrected others) instead of the facts." - }, - { - "review": "Please respect yourself, you must be ashamed of yourself because of this. Peyser", - "response": "Thanks for your feedback! It'd be great if you could provide specifics regarding what you didn't like about the course..." - }, - { - "review": "Mentor is providing slides for video and he is also fooling student to stay 31 on Udemy but after 31 days I am not finding any slides and note from instructor side.", - "response": "Incorrect. Comment speaks for itself really." - }, - { - "review": "This course kind of breezes over the topics, there are colabs used in the videos that we do not have access to and Section 4 is irrelevant to the course, I would rather see my on the topic and learn something than how to install tools that I already know how to do. Section 4 should actually be on his youtube site. There should also be more links to the tools he speaks of or uses, you have to stop the video and actually go google the tools and search for them. I am still trying to find ffmpegexamples.ipynb that is used for a whole segment of the training but there is no access to. Good course but needs a lot of fine-tuning to be better. I hope to see more added to create better content.", - "response": "> there are colabs used in the videos that we do not have access to\n\nIncorrect. Lecture 4 is called \"COURSE RESOURCES\". Any guesses about what this is for?\n\n> Section 4 is irrelevant to the course\n\nInteresting, who should decide what's relevant to the course? Instructor (who understands the course content) or student (who does not)?\n\n> I would rather see my on the topic\n\nThis is not even a coherent sentence.\n\n> There should also be more links to the tools he speaks of or uses, you have to stop the video and actually go google the tools and search for them\n\nAgain, no you do not. This is what happens when you don't pay attention.\n\n> I am still trying to find ffmpegexamples.ipynb\n\nIf you paid attention, you would have already found it.\n\n> Good course but needs a lot of fine-tuning to be better. I hope to see more added to create better content.\n\nNo, you just need to follow the instructions and use the Q&A to fix your misunderstandings. I don't see how it could be any simpler." - } -] diff --git a/openai/robots_playing_soccer.jpeg b/openai/robots_playing_soccer.jpeg deleted file mode 100644 index 547761ae..00000000 Binary files a/openai/robots_playing_soccer.jpeg and /dev/null differ diff --git a/openai/webdesign.jpg b/openai/webdesign.jpg deleted file mode 100644 index ecce702e..00000000 Binary files a/openai/webdesign.jpg and /dev/null differ diff --git a/probability/WHERE ARE THE NOTEBOOKS.txt b/probability/WHERE ARE THE NOTEBOOKS.txt deleted file mode 100644 index 5446ce25..00000000 --- a/probability/WHERE ARE THE NOTEBOOKS.txt +++ /dev/null @@ -1,5 +0,0 @@ -As stated in the "where to get the code" / "where to get the notebooks" lecture, the notebooks are NOT on Github. - -If you missed this, please review the lecture for the actual location of the notebooks. - -If, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com. \ No newline at end of file diff --git a/probability/extra_reading.txt b/probability/extra_reading.txt deleted file mode 100644 index e2df1a0d..00000000 --- a/probability/extra_reading.txt +++ /dev/null @@ -1,2 +0,0 @@ -Multivariate Change of Variables -https://math.libretexts.org/Bookshelves/Calculus/Book%3A_Active_Calculus_(Boelkins_et_al.)/11%3A_Multiple_Integrals/11.09%3A_Change_of_Variables \ No newline at end of file diff --git a/prophet/extra_reading.txt b/prophet/extra_reading.txt deleted file mode 100644 index 1e2ea58c..00000000 --- a/prophet/extra_reading.txt +++ /dev/null @@ -1,2 +0,0 @@ -Forecasting at Scale (Facebook Prophet) -https://peerj.com/preprints/3190.pdf \ No newline at end of file diff --git a/pytorch/.gitignore b/pytorch/.gitignore deleted file mode 100644 index f9187508..00000000 --- a/pytorch/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -*rl_trader_models -*rl_trader_rewards -*.png diff --git a/pytorch/WHERE ARE THE NOTEBOOKS.txt b/pytorch/WHERE ARE THE NOTEBOOKS.txt deleted file mode 100644 index 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Y) -plt.show() - -# Build the model -model = nn.Sequential( - nn.Linear(2, 128), - nn.ReLU(), - nn.Linear(128, 1) -) - -# Loss and optimizer -criterion = nn.MSELoss() -optimizer = torch.optim.Adam(model.parameters(), lr=0.01) - -# Train the model -def full_gd(model, criterion, optimizer, X_train, y_train, epochs=1000): - # Stuff to store - train_losses = np.zeros(epochs) - - for it in range(epochs): - # zero the parameter gradients - optimizer.zero_grad() - - # Forward pass - outputs = model(X_train) - loss = criterion(outputs, y_train) - - # Backward and optimize - loss.backward() - optimizer.step() - - # Save losses - train_losses[it] = loss.item() - - if (it + 1) % 50 == 0: - print(f'Epoch {it+1}/{epochs}, Train Loss: {loss.item():.4f}') - - return train_losses - -X_train = torch.from_numpy(X.astype(np.float32)) -y_train = torch.from_numpy(Y.astype(np.float32).reshape(-1, 1)) -train_losses = full_gd(model, criterion, optimizer, X_train, y_train) - -plt.plot(train_losses) -plt.show() - - -# Plot the prediction surface -fig = plt.figure() -ax = fig.add_subplot(111, projection='3d') -ax.scatter(X[:,0], X[:,1], Y) - -# surface plot -with torch.no_grad(): - line = np.linspace(-3, 3, 50) - xx, yy = np.meshgrid(line, line) - Xgrid = np.vstack((xx.flatten(), yy.flatten())).T - Xgrid_torch = torch.from_numpy(Xgrid.astype(np.float32)) - Yhat = model(Xgrid_torch).numpy().flatten() - ax.plot_trisurf(Xgrid[:,0], Xgrid[:,1], Yhat, linewidth=0.2, antialiased=True) - plt.show() - -# Can it extrapolate? -# Plot the prediction surface -fig = plt.figure() -ax = fig.add_subplot(111, projection='3d') -ax.scatter(X[:,0], X[:,1], Y) - -# surface plot -with torch.no_grad(): - line = np.linspace(-5, 5, 50) - xx, yy = np.meshgrid(line, line) - Xgrid = np.vstack((xx.flatten(), yy.flatten())).T - Xgrid_torch = torch.from_numpy(Xgrid.astype(np.float32)) - Yhat = model(Xgrid_torch).numpy().flatten() - ax.plot_trisurf(Xgrid[:,0], Xgrid[:,1], Yhat, linewidth=0.2, antialiased=True) - plt.show() \ No newline at end of file diff --git a/pytorch/exercises.txt b/pytorch/exercises.txt deleted file mode 100644 index 6fdee299..00000000 --- a/pytorch/exercises.txt +++ /dev/null @@ -1,36 +0,0 @@ -Logistic Regression -https://www.kaggle.com/uciml/pima-indians-diabetes-database -https://lazyprogrammer.me/course_files/exercises/diabetes.csv - -Linear Regression -https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html -https://lazyprogrammer.me/course_files/exercises/boston.txt - -ANN -https://archive.ics.uci.edu/ml/datasets/ecoli (orig) -https://www.kaggle.com/elikplim/ecoli-data-set (alt) -https://lazyprogrammer.me/course_files/exercises/ecoli.csv - -CNN -https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge -https://archive.org/download/fer2013_202311/fer2013.csv - -RNN -Find your own stock price dataset! - -NLP -https://www.kaggle.com/crowdflower/twitter-airline-sentiment -https://lazyprogrammer.me/course_files/exercises/AirlineSentimentTweets.csv - -Recommender Systems -http://www2.informatik.uni-freiburg.de/~cziegler/BX/ -http://lazyprogrammer.me/course_files/exercises/BX-CSV-Dump.zip - -Transfer Learning -https://www.kaggle.com/c/dogs-vs-cats - -GAN -https://www.kaggle.com/c/dogs-vs-cats - -DeepRL -Find your own stock price dataset! \ No newline at end of file diff --git a/pytorch/extra_reading.txt b/pytorch/extra_reading.txt deleted file mode 100644 index 7fccf01f..00000000 --- a/pytorch/extra_reading.txt +++ /dev/null @@ -1,30 +0,0 @@ -Gradient Descent: Convergence Analysis -http://www.stat.cmu.edu/~ryantibs/convexopt-F13/scribes/lec6.pdf - -Deep learning improved by biological activation functions -https://arxiv.org/pdf/1804.11237.pdf - -Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift -Sergey Ioffe, Christian Szegedy -https://arxiv.org/abs/1502.03167 - -Dropout: A Simple Way to Prevent Neural Networks from Overfitting -https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf - -Implementing Dropout -https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow/ - -Convolution arithmetic tutorial -https://theano-pymc.readthedocs.io/en/latest/tutorial/conv_arithmetic.html - -On the Practical Computational Power of Finite Precision RNNs for Language Recognition -https://arxiv.org/abs/1805.04908 - -Massive Exploration of Neural Machine Translation Architectures -https://arxiv.org/abs/1703.03906 - -Practical Deep Reinforcement Learning Approach for Stock Trading -https://arxiv.org/abs/1811.07522 - -Inceptionism: Going Deeper into Neural Networks -https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html \ No newline at end of file diff --git a/pytorch/plot_rl_rewards.py b/pytorch/plot_rl_rewards.py deleted file mode 100644 index 3eb8e171..00000000 --- a/pytorch/plot_rl_rewards.py +++ /dev/null @@ -1,22 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import argparse - -parser = argparse.ArgumentParser() -parser.add_argument('-m', '--mode', type=str, required=True, - help='either "train" or "test"') -args = parser.parse_args() - -a = np.load(f'rl_trader_rewards/{args.mode}.npy') - -print(f"average reward: {a.mean():.2f}, min: {a.min():.2f}, max: {a.max():.2f}") - -if args.mode == 'train': - # show the training progress - plt.plot(a) -else: - # test - show a histogram of rewards - plt.hist(a, bins=20) - -plt.title(args.mode) -plt.show() \ No newline at end of file diff --git a/pytorch/rl_trader.py b/pytorch/rl_trader.py deleted file mode 100644 index 5738c9ac..00000000 --- a/pytorch/rl_trader.py +++ /dev/null @@ -1,441 +0,0 @@ -import numpy as np -import pandas as pd - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from datetime import datetime -import itertools -import argparse -import re -import os -import pickle - -from sklearn.preprocessing import StandardScaler - - -# Let's use AAPL (Apple), MSI (Motorola), SBUX (Starbucks) -def get_data(): - # returns a T x 3 list of stock prices - # each row is a different stock - # 0 = AAPL - # 1 = MSI - # 2 = SBUX - df = pd.read_csv('aapl_msi_sbux.csv') - return df.values - - - -### The experience replay memory ### -class ReplayBuffer: - def __init__(self, obs_dim, act_dim, size): - self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32) - self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32) - self.acts_buf = np.zeros(size, dtype=np.uint8) - self.rews_buf = np.zeros(size, dtype=np.float32) - self.done_buf = np.zeros(size, dtype=np.uint8) - self.ptr, self.size, self.max_size = 0, 0, size - - def store(self, obs, act, rew, next_obs, done): - self.obs1_buf[self.ptr] = obs - self.obs2_buf[self.ptr] = next_obs - self.acts_buf[self.ptr] = act - self.rews_buf[self.ptr] = rew - self.done_buf[self.ptr] = done - self.ptr = (self.ptr+1) % self.max_size - self.size = min(self.size+1, self.max_size) - - def sample_batch(self, batch_size=32): - idxs = np.random.randint(0, self.size, size=batch_size) - return dict(s=self.obs1_buf[idxs], - s2=self.obs2_buf[idxs], - a=self.acts_buf[idxs], - r=self.rews_buf[idxs], - d=self.done_buf[idxs]) - - - - - -def get_scaler(env): - # return scikit-learn scaler object to scale the states - # Note: you could also populate the replay buffer here - - states = [] - for _ in range(env.n_step): - action = np.random.choice(env.action_space) - state, reward, done, info = env.step(action) - states.append(state) - if done: - break - - scaler = StandardScaler() - scaler.fit(states) - return scaler - - - - -def maybe_make_dir(directory): - if not os.path.exists(directory): - os.makedirs(directory) - - - - -class MLP(nn.Module): - def __init__(self, n_inputs, n_action, n_hidden_layers=1, hidden_dim=32): - super(MLP, self).__init__() - - M = n_inputs - self.layers = [] - for _ in range(n_hidden_layers): - layer = nn.Linear(M, hidden_dim) - M = hidden_dim - self.layers.append(layer) - self.layers.append(nn.ReLU()) - - # final layer - self.layers.append(nn.Linear(M, n_action)) - self.layers = nn.Sequential(*self.layers) - - def forward(self, X): - return self.layers(X) - - def save_weights(self, path): - torch.save(self.state_dict(), path) - - def load_weights(self, path): - self.load_state_dict(torch.load(path)) - - - -def predict(model, np_states): - with torch.no_grad(): - inputs = torch.from_numpy(np_states.astype(np.float32)) - output = model(inputs) - # print("output:", output) - return output.numpy() - - - -def train_one_step(model, criterion, optimizer, inputs, targets): - # convert to tensors - inputs = torch.from_numpy(inputs.astype(np.float32)) - targets = torch.from_numpy(targets.astype(np.float32)) - - # zero the parameter gradients - optimizer.zero_grad() - - # Forward pass - outputs = model(inputs) - loss = criterion(outputs, targets) - - # Backward and optimize - loss.backward() - optimizer.step() - - - -class MultiStockEnv: - """ - A 3-stock trading environment. - State: vector of size 7 (n_stock * 2 + 1) - - # shares of stock 1 owned - - # shares of stock 2 owned - - # shares of stock 3 owned - - price of stock 1 (using daily close price) - - price of stock 2 - - price of stock 3 - - cash owned (can be used to purchase more stocks) - Action: categorical variable with 27 (3^3) possibilities - - for each stock, you can: - - 0 = sell - - 1 = hold - - 2 = buy - """ - def __init__(self, data, initial_investment=20000): - # data - self.stock_price_history = data - self.n_step, self.n_stock = self.stock_price_history.shape - - # instance attributes - self.initial_investment = initial_investment - self.cur_step = None - self.stock_owned = None - self.stock_price = None - self.cash_in_hand = None - - self.action_space = np.arange(3**self.n_stock) - - # action permutations - # returns a nested list with elements like: - # [0,0,0] - # [0,0,1] - # [0,0,2] - # [0,1,0] - # [0,1,1] - # etc. - # 0 = sell - # 1 = hold - # 2 = buy - self.action_list = list(map(list, itertools.product([0, 1, 2], repeat=self.n_stock))) - - # calculate size of state - self.state_dim = self.n_stock * 2 + 1 - - self.reset() - - - def reset(self): - self.cur_step = 0 - self.stock_owned = np.zeros(self.n_stock) - self.stock_price = self.stock_price_history[self.cur_step] - self.cash_in_hand = self.initial_investment - return self._get_obs() - - - def step(self, action): - assert action in self.action_space - - # get current value before performing the action - prev_val = self._get_val() - - # update price, i.e. go to the next day - self.cur_step += 1 - self.stock_price = self.stock_price_history[self.cur_step] - - # perform the trade - self._trade(action) - - # get the new value after taking the action - cur_val = self._get_val() - - # reward is the increase in porfolio value - reward = cur_val - prev_val - - # done if we have run out of data - done = self.cur_step == self.n_step - 1 - - # store the current value of the portfolio here - info = {'cur_val': cur_val} - - # conform to the Gym API - return self._get_obs(), reward, done, info - - - def _get_obs(self): - obs = np.empty(self.state_dim) - obs[:self.n_stock] = self.stock_owned - obs[self.n_stock:2*self.n_stock] = self.stock_price - obs[-1] = self.cash_in_hand - return obs - - - - def _get_val(self): - return self.stock_owned.dot(self.stock_price) + self.cash_in_hand - - - def _trade(self, action): - # index the action we want to perform - # 0 = sell - # 1 = hold - # 2 = buy - # e.g. [2,1,0] means: - # buy first stock - # hold second stock - # sell third stock - action_vec = self.action_list[action] - - # determine which stocks to buy or sell - sell_index = [] # stores index of stocks we want to sell - buy_index = [] # stores index of stocks we want to buy - for i, a in enumerate(action_vec): - if a == 0: - sell_index.append(i) - elif a == 2: - buy_index.append(i) - - # sell any stocks we want to sell - # then buy any stocks we want to buy - if sell_index: - # NOTE: to simplify the problem, when we sell, we will sell ALL shares of that stock - for i in sell_index: - self.cash_in_hand += self.stock_price[i] * self.stock_owned[i] - self.stock_owned[i] = 0 - if buy_index: - # NOTE: when buying, we will loop through each stock we want to buy, - # and buy one share at a time until we run out of cash - can_buy = True - while can_buy: - for i in buy_index: - if self.cash_in_hand > self.stock_price[i]: - self.stock_owned[i] += 1 # buy one share - self.cash_in_hand -= self.stock_price[i] - else: - can_buy = False - - - - - -class DQNAgent(object): - def __init__(self, state_size, action_size): - self.state_size = state_size - self.action_size = action_size - self.memory = ReplayBuffer(state_size, action_size, size=500) - self.gamma = 0.95 # discount rate - self.epsilon = 1.0 # exploration rate - self.epsilon_min = 0.01 - self.epsilon_decay = 0.995 - self.model = MLP(state_size, action_size) - - # Loss and optimizer - self.criterion = nn.MSELoss() - self.optimizer = torch.optim.Adam(self.model.parameters()) - - - def update_replay_memory(self, state, action, reward, next_state, done): - self.memory.store(state, action, reward, next_state, done) - - - def act(self, state): - if np.random.rand() <= self.epsilon: - return np.random.choice(self.action_size) - act_values = predict(self.model, state) - return np.argmax(act_values[0]) # returns action - - - def replay(self, batch_size=32): - # first check if replay buffer contains enough data - if self.memory.size < batch_size: - return - - # sample a batch of data from the replay memory - minibatch = self.memory.sample_batch(batch_size) - states = minibatch['s'] - actions = minibatch['a'] - rewards = minibatch['r'] - next_states = minibatch['s2'] - done = minibatch['d'] - - # Calculate the target: Q(s',a) - target = rewards + (1 - done) * self.gamma * np.amax(predict(self.model, next_states), axis=1) - - # With the PyTorch API, it is simplest to have the target be the - # same shape as the predictions. - # However, we only need to update the network for the actions - # which were actually taken. - # We can accomplish this by setting the target to be equal to - # the prediction for all values. - # Then, only change the targets for the actions taken. - # Q(s,a) - target_full = predict(self.model, states) - target_full[np.arange(batch_size), actions] = target - - # Run one training step - train_one_step(self.model, self.criterion, self.optimizer, states, target_full) - - if self.epsilon > self.epsilon_min: - self.epsilon *= self.epsilon_decay - - - def load(self, name): - self.model.load_weights(name) - - - def save(self, name): - self.model.save_weights(name) - - -def play_one_episode(agent, env, is_train): - # note: after transforming states are already 1xD - state = env.reset() - state = scaler.transform([state]) - done = False - - while not done: - action = agent.act(state) - next_state, reward, done, info = env.step(action) - next_state = scaler.transform([next_state]) - if is_train == 'train': - agent.update_replay_memory(state, action, reward, next_state, done) - agent.replay(batch_size) - state = next_state - - return info['cur_val'] - - - -if __name__ == '__main__': - - # config - models_folder = 'rl_trader_models' - rewards_folder = 'rl_trader_rewards' - num_episodes = 2000 - batch_size = 32 - initial_investment = 20000 - - - parser = argparse.ArgumentParser() - parser.add_argument('-m', '--mode', type=str, required=True, - help='either "train" or "test"') - args = parser.parse_args() - - maybe_make_dir(models_folder) - maybe_make_dir(rewards_folder) - - data = get_data() - n_timesteps, n_stocks = data.shape - - n_train = n_timesteps // 2 - - train_data = data[:n_train] - test_data = data[n_train:] - - env = MultiStockEnv(train_data, initial_investment) - state_size = env.state_dim - action_size = len(env.action_space) - agent = DQNAgent(state_size, action_size) - scaler = get_scaler(env) - - # store the final value of the portfolio (end of episode) - portfolio_value = [] - - if args.mode == 'test': - # then load the previous scaler - with open(f'{models_folder}/scaler.pkl', 'rb') as f: - scaler = pickle.load(f) - - # remake the env with test data - env = MultiStockEnv(test_data, initial_investment) - - # make sure epsilon is not 1! - # no need to run multiple episodes if epsilon = 0, it's deterministic - agent.epsilon = 0.01 - - # load trained weights - agent.load(f'{models_folder}/dqn.ckpt') - - # play the game num_episodes times - for e in range(num_episodes): - t0 = datetime.now() - val = play_one_episode(agent, env, args.mode) - dt = datetime.now() - t0 - print(f"episode: {e + 1}/{num_episodes}, episode end value: {val:.2f}, duration: {dt}") - portfolio_value.append(val) # append episode end portfolio value - - # save the weights when we are done - if args.mode == 'train': - # save the DQN - agent.save(f'{models_folder}/dqn.ckpt') - - # save the scaler - with open(f'{models_folder}/scaler.pkl', 'wb') as f: - pickle.dump(scaler, f) - - - # save portfolio value for each episode - np.save(f'{rewards_folder}/{args.mode}.npy', portfolio_value) diff --git a/recommenders/autorec.py b/recommenders/autorec.py deleted file mode 100644 index fa0bd415..00000000 --- a/recommenders/autorec.py +++ /dev/null @@ -1,126 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.utils import shuffle -from scipy.sparse import save_npz, load_npz - -import keras.backend as K -from keras.models import Model -from keras.layers import Input, Dropout, Dense -from keras.regularizers import l2 -from keras.optimizers import SGD - -# config -batch_size = 128 -epochs = 20 -reg = 0.0001 -# reg = 0 - -A = load_npz("Atrain.npz") -A_test = load_npz("Atest.npz") -mask = (A > 0) * 1.0 -mask_test = (A_test > 0) * 1.0 - -# make copies since we will shuffle -A_copy = A.copy() -mask_copy = mask.copy() -A_test_copy = A_test.copy() -mask_test_copy = mask_test.copy() - -N, M = A.shape -print("N:", N, "M:", M) -print("N // batch_size:", N // batch_size) - -# center the data -mu = A.sum() / mask.sum() -print("mu:", mu) - - - -# build the model - just a 1 hidden layer autoencoder -i = Input(shape=(M,)) -# bigger hidden layer size seems to help! -x = Dropout(0.7)(i) -x = Dense(700, activation='tanh', kernel_regularizer=l2(reg))(x) -# x = Dropout(0.5)(x) -x = Dense(M, kernel_regularizer=l2(reg))(x) - - - -def custom_loss(y_true, y_pred): - mask = K.cast(K.not_equal(y_true, 0), dtype='float32') - diff = y_pred - y_true - sqdiff = diff * diff * mask - sse = K.sum(K.sum(sqdiff)) - n = K.sum(K.sum(mask)) - return sse / n - - -def generator(A, M): - while True: - A, M = shuffle(A, M) - for i in range(A.shape[0] // batch_size + 1): - upper = min((i+1)*batch_size, A.shape[0]) - a = A[i*batch_size:upper].toarray() - m = M[i*batch_size:upper].toarray() - a = a - mu * m # must keep zeros at zero! - # m2 = (np.random.random(a.shape) > 0.5) - # noisy = a * m2 - noisy = a # no noise - yield noisy, a - - -def test_generator(A, M, A_test, M_test): - # assumes A and A_test are in corresponding order - # both of size N x M - while True: - for i in range(A.shape[0] // batch_size + 1): - upper = min((i+1)*batch_size, A.shape[0]) - a = A[i*batch_size:upper].toarray() - m = M[i*batch_size:upper].toarray() - at = A_test[i*batch_size:upper].toarray() - mt = M_test[i*batch_size:upper].toarray() - a = a - mu * m - at = at - mu * mt - yield a, at - - - -model = Model(i, x) -model.compile( - loss=custom_loss, - optimizer=SGD(lr=0.08, momentum=0.9), - # optimizer='adam', - metrics=[custom_loss], -) - - -r = model.fit( - generator(A, mask), - validation_data=test_generator(A_copy, mask_copy, A_test_copy, mask_test_copy), - epochs=epochs, - steps_per_epoch=A.shape[0] // batch_size + 1, - validation_steps=A_test.shape[0] // batch_size + 1, -) -print(r.history.keys()) - - - -# plot losses -plt.plot(r.history['loss'], label="train loss") -plt.plot(r.history['val_loss'], label="test loss") -plt.legend() -plt.show() - -# plot mse -plt.plot(r.history['custom_loss'], label="train mse") -plt.plot(r.history['val_custom_loss'], label="test mse") -plt.legend() -plt.show() diff --git a/recommenders/extra_reading.txt b/recommenders/extra_reading.txt deleted file mode 100644 index 510410cd..00000000 --- a/recommenders/extra_reading.txt +++ /dev/null @@ -1,62 +0,0 @@ -How Hacker News ranking really works: scoring, controversy, and penalties -http://www.righto.com/2013/11/how-hacker-news-ranking-really-works.html - -The Evolution Of Hacker News -https://techcrunch.com/2013/05/18/the-evolution-of-hacker-news/ - -Reddit sorting code -https://github.com/reddit-archive/reddit/blob/master/r2/r2/lib/db/_sorts.pyx - -Revealed: US spy operation that manipulates social media -https://www.theguardian.com/technology/2011/mar/17/us-spy-operation-social-networks - -5G Got me Fired -https://medium.com/@dvorak/5g-got-me-fired-ce407e584c4a - -Learning to rank -https://en.wikipedia.org/wiki/Learning_to_rank#Evaluation_measures - -How Not To Sort By Average Rating -https://www.evanmiller.org/how-not-to-sort-by-average-rating.html - -Wilson score interval -https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Wilson_score_interval - -reddit’s new comment sorting system -https://redditblog.com/2009/10/15/reddits-new-comment-sorting-system/ - -Markov Chains Explained Visually -http://setosa.io/ev/markov-chains/ - -An algorithmic framework for performing collaborative filtering -https://dl.acm.org/citation.cfm?id=312682 - -Item-based collaborative filtering recommendation algorithms -https://dl.acm.org/citation.cfm?id=372071 - -FunkSVD -http://sifter.org/~simon/journal/20061211.html - -Probabilistic Matrix Factorization -https://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf - -Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo -https://www.cs.toronto.edu/~amnih/papers/bpmf.pdf - -Algorithms for Non-negative Matrix Factorization -https://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf - -Learning the parts of objects by non-negative matrix factorization -http://www.columbia.edu/~jwp2128/Teaching/E4903/papers/nmf_nature.pdf - -Restricted Boltzmann Machines for Collaborative Filtering -https://www.cs.toronto.edu/~rsalakhu/papers/rbmcf.pdf - -AutoRec: Autoencoders Meet Collaborative Filtering -http://users.cecs.anu.edu.au/~u5098633/papers/www15.pdf - -Collaborative Filtering for Implicit Feedback Datasets -http://yifanhu.net/PUB/cf.pdf - -Neural Collaborative Filtering -https://arxiv.org/abs/1708.05031 \ No newline at end of file diff --git a/recommenders/itembased.py b/recommenders/itembased.py deleted file mode 100644 index f87f9481..00000000 --- a/recommenders/itembased.py +++ /dev/null @@ -1,171 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import pickle -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.utils import shuffle -from datetime import datetime -from sortedcontainers import SortedList - -# load in the data -import os -if not os.path.exists('user2movie.json') or \ - not os.path.exists('movie2user.json') or \ - not os.path.exists('usermovie2rating.json') or \ - not os.path.exists('usermovie2rating_test.json'): - import preprocess2dict - - -with open('user2movie.json', 'rb') as f: - user2movie = pickle.load(f) - -with open('movie2user.json', 'rb') as f: - movie2user = pickle.load(f) - -with open('usermovie2rating.json', 'rb') as f: - usermovie2rating = pickle.load(f) - -with open('usermovie2rating_test.json', 'rb') as f: - usermovie2rating_test = pickle.load(f) - - -N = np.max(list(user2movie.keys())) + 1 -# the test set may contain movies the train set doesn't have data on -m1 = np.max(list(movie2user.keys())) -m2 = np.max([m for (u, m), r in usermovie2rating_test.items()]) -M = max(m1, m2) + 1 -print("N:", N, "M:", M) - -if M > 2000: - print("N =", N, "are you sure you want to continue?") - print("Comment out these lines if so...") - exit() - - -# to find the user similarities, you have to do O(M^2 * N) calculations! -# in the "real-world" you'd want to parallelize this -# note: we really only have to do half the calculations, since w_ij is symmetric -K = 20 # number of neighbors we'd like to consider -limit = 5 # number of common movies users must have in common in order to consider -neighbors = [] # store neighbors in this list -averages = [] # each item's average rating for later use -deviations = [] # each item's deviation for later use - -for i in range(M): - # find the K closest items to item i - users_i = movie2user[i] - users_i_set = set(users_i) - - # calculate avg and deviation - ratings_i = { user:usermovie2rating[(user, i)] for user in users_i } - avg_i = np.mean(list(ratings_i.values())) - dev_i = { user:(rating - avg_i) for user, rating in ratings_i.items() } - dev_i_values = np.array(list(dev_i.values())) - sigma_i = np.sqrt(dev_i_values.dot(dev_i_values)) - - # save these for later use - averages.append(avg_i) - deviations.append(dev_i) - - sl = SortedList() - for j in range(M): - # don't include yourself - if j != i: - users_j = movie2user[j] - users_j_set = set(users_j) - common_users = (users_i_set & users_j_set) # intersection - if len(common_users) > limit: - # calculate avg and deviation - ratings_j = { user:usermovie2rating[(user, j)] for user in users_j } - avg_j = np.mean(list(ratings_j.values())) - dev_j = { user:(rating - avg_j) for user, rating in ratings_j.items() } - dev_j_values = np.array(list(dev_j.values())) - sigma_j = np.sqrt(dev_j_values.dot(dev_j_values)) - - # calculate correlation coefficient - numerator = sum(dev_i[m]*dev_j[m] for m in common_users) - w_ij = numerator / (sigma_i * sigma_j) - - # insert into sorted list and truncate - # negate weight, because list is sorted ascending - # maximum value (1) is "closest" - sl.add((-w_ij, j)) - if len(sl) > K: - del sl[-1] - - # store the neighbors - neighbors.append(sl) - - # print out useful things - if i % 1 == 0: - print(i) - - - -# using neighbors, calculate train and test MSE - -def predict(i, u): - # calculate the weighted sum of deviations - numerator = 0 - denominator = 0 - for neg_w, j in neighbors[i]: - # remember, the weight is stored as its negative - # so the negative of the negative weight is the positive weight - try: - numerator += -neg_w * deviations[j][u] - denominator += abs(neg_w) - except KeyError: - # neighbor may not have been rated by the same user - # don't want to do dictionary lookup twice - # so just throw exception - pass - - if denominator == 0: - prediction = averages[i] - else: - prediction = numerator / denominator + averages[i] - prediction = min(5, prediction) - prediction = max(0.5, prediction) # min rating is 0.5 - return prediction - - - -train_predictions = [] -train_targets = [] -for (u, m), target in usermovie2rating.items(): - # calculate the prediction for this movie - prediction = predict(m, u) - - # save the prediction and target - train_predictions.append(prediction) - train_targets.append(target) - -test_predictions = [] -test_targets = [] -# same thing for test set -for (u, m), target in usermovie2rating_test.items(): - # calculate the prediction for this movie - prediction = predict(m, u) - - # save the prediction and target - test_predictions.append(prediction) - test_targets.append(target) - - -# calculate accuracy -def mse(p, t): - p = np.array(p) - t = np.array(t) - return np.mean((p - t)**2) - -print('train mse:', mse(train_predictions, train_targets)) -print('test mse:', mse(test_predictions, test_targets)) - - - diff --git a/recommenders/mf.py b/recommenders/mf.py deleted file mode 100644 index 6507da83..00000000 --- a/recommenders/mf.py +++ /dev/null @@ -1,146 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import pickle -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.utils import shuffle -from datetime import datetime - -# load in the data -import os -if not os.path.exists('user2movie.json') or \ - not os.path.exists('movie2user.json') or \ - not os.path.exists('usermovie2rating.json') or \ - not os.path.exists('usermovie2rating_test.json'): - import preprocess2dict - - -with open('user2movie.json', 'rb') as f: - user2movie = pickle.load(f) - -with open('movie2user.json', 'rb') as f: - movie2user = pickle.load(f) - -with open('usermovie2rating.json', 'rb') as f: - usermovie2rating = pickle.load(f) - -with open('usermovie2rating_test.json', 'rb') as f: - usermovie2rating_test = pickle.load(f) - - -N = np.max(list(user2movie.keys())) + 1 -# the test set may contain movies the train set doesn't have data on -m1 = np.max(list(movie2user.keys())) -m2 = np.max([m for (u, m), r in usermovie2rating_test.items()]) -M = max(m1, m2) + 1 -print("N:", N, "M:", M) - - -# initialize variables -K = 10 # latent dimensionality -W = np.random.randn(N, K) -b = np.zeros(N) -U = np.random.randn(M, K) -c = np.zeros(M) -mu = np.mean(list(usermovie2rating.values())) - -# prediction[i,j] = W[i].dot(U[j]) + b[i] + c.T[j] + mu - -def get_loss(d): - # d: (user_id, movie_id) -> rating - N = float(len(d)) - sse = 0 - for k, r in d.items(): - i, j = k - p = W[i].dot(U[j]) + b[i] + c[j] + mu - sse += (p - r)*(p - r) - return sse / N - - -# train the parameters -epochs = 25 -reg =20. # regularization penalty -train_losses = [] -test_losses = [] -for epoch in range(epochs): - print("epoch:", epoch) - epoch_start = datetime.now() - # perform updates - - # update W and b - t0 = datetime.now() - for i in range(N): - # for W - matrix = np.eye(K) * reg - vector = np.zeros(K) - - # for b - bi = 0 - for j in user2movie[i]: - r = usermovie2rating[(i,j)] - matrix += np.outer(U[j], U[j]) - vector += (r - b[i] - c[j] - mu)*U[j] - bi += (r - W[i].dot(U[j]) - c[j] - mu) - - # set the updates - W[i] = np.linalg.solve(matrix, vector) - b[i] = bi / (len(user2movie[i]) + reg) - - if i % (N//10) == 0: - print("i:", i, "N:", N) - print("updated W and b:", datetime.now() - t0) - - # update U and c - t0 = datetime.now() - for j in range(M): - # for U - matrix = np.eye(K) * reg - vector = np.zeros(K) - - # for c - cj = 0 - try: - for i in movie2user[j]: - r = usermovie2rating[(i,j)] - matrix += np.outer(W[i], W[i]) - vector += (r - b[i] - c[j] - mu)*W[i] - cj += (r - W[i].dot(U[j]) - b[i] - mu) - - # set the updates - U[j] = np.linalg.solve(matrix, vector) - c[j] = cj / (len(movie2user[j]) + reg) - - if j % (M//10) == 0: - print("j:", j, "M:", M) - except KeyError: - # possible not to have any ratings for a movie - pass - print("updated U and c:", datetime.now() - t0) - print("epoch duration:", datetime.now() - epoch_start) - - - # store train loss - t0 = datetime.now() - train_losses.append(get_loss(usermovie2rating)) - - # store test loss - test_losses.append(get_loss(usermovie2rating_test)) - print("calculate cost:", datetime.now() - t0) - print("train loss:", train_losses[-1]) - print("test loss:", test_losses[-1]) - - -print("train losses:", train_losses) -print("test losses:", test_losses) - -# plot losses -plt.plot(train_losses, label="train loss") -plt.plot(test_losses, label="test loss") -plt.legend() -plt.show() diff --git a/recommenders/mf2.py b/recommenders/mf2.py deleted file mode 100644 index 62b599c6..00000000 --- a/recommenders/mf2.py +++ /dev/null @@ -1,159 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import pickle -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.utils import shuffle -from datetime import datetime -from copy import deepcopy - -# load in the data -import os -if not os.path.exists('user2movie.json') or \ - not os.path.exists('movie2user.json') or \ - not os.path.exists('usermovie2rating.json') or \ - not os.path.exists('usermovie2rating_test.json'): - import preprocess2dict - - -with open('user2movie.json', 'rb') as f: - user2movie = pickle.load(f) - -with open('movie2user.json', 'rb') as f: - movie2user = pickle.load(f) - -with open('usermovie2rating.json', 'rb') as f: - usermovie2rating = pickle.load(f) - -with open('usermovie2rating_test.json', 'rb') as f: - usermovie2rating_test = pickle.load(f) - - -N = np.max(list(user2movie.keys())) + 1 -# the test set may contain movies the train set doesn't have data on -m1 = np.max(list(movie2user.keys())) -m2 = np.max([m for (u, m), r in usermovie2rating_test.items()]) -M = max(m1, m2) + 1 -print("N:", N, "M:", M) - - -# convert user2movie and movie2user to include ratings -print("converting...") -user2movierating = {} -for i, movies in user2movie.items(): - r = np.array([usermovie2rating[(i,j)] for j in movies]) - user2movierating[i] = (movies, r) -movie2userrating = {} -for j, users in movie2user.items(): - r = np.array([usermovie2rating[(i,j)] for i in users]) - movie2userrating[j] = (users, r) - -# create a movie2user for test set, since we need it for loss -movie2userrating_test = {} -for (i, j), r in usermovie2rating_test.items(): - if j not in movie2userrating_test: - movie2userrating_test[j] = [[i], [r]] - else: - movie2userrating_test[j][0].append(i) - movie2userrating_test[j][1].append(r) -for j, (users, r) in movie2userrating_test.items(): - movie2userrating_test[j][1] = np.array(r) -print("conversion done") - -# initialize variables -K = 10 # latent dimensionality -W = np.random.randn(N, K) -b = np.zeros(N) -U = np.random.randn(M, K) -c = np.zeros(M) -mu = np.mean(list(usermovie2rating.values())) - - - -def get_loss(m2u): - # d: movie_id -> (user_ids, ratings) - N = 0. - sse = 0 - for j, (u_ids, r) in m2u.items(): - p = W[u_ids].dot(U[j]) + b[u_ids] + c[j] + mu - delta = p - r - sse += delta.dot(delta) - N += len(r) - return sse / N - - - -# train the parameters -epochs = 25 -reg = 20. # regularization penalty -train_losses = [] -test_losses = [] -for epoch in range(epochs): - print("epoch:", epoch) - epoch_start = datetime.now() - # perform updates - - # update W and b - t0 = datetime.now() - for i in range(N): - m_ids, r = user2movierating[i] - matrix = U[m_ids].T.dot(U[m_ids]) + np.eye(K) * reg - vector = (r - b[i] - c[m_ids] - mu).dot(U[m_ids]) - bi = (r - U[m_ids].dot(W[i]) - c[m_ids] - mu).sum() - - # set the updates - W[i] = np.linalg.solve(matrix, vector) - b[i] = bi / (len(user2movie[i]) + reg) - - if i % (N//10) == 0: - print("i:", i, "N:", N) - print("updated W and b:", datetime.now() - t0) - - - # update U and c - t0 = datetime.now() - for j in range(M): - try: - u_ids, r = movie2userrating[j] - matrix = W[u_ids].T.dot(W[u_ids]) + np.eye(K) * reg - vector = (r - b[u_ids] - c[j] - mu).dot(W[u_ids]) - cj = (r - W[u_ids].dot(U[j]) - b[u_ids] - mu).sum() - - # set the updates - U[j] = np.linalg.solve(matrix, vector) - c[j] = cj / (len(movie2user[j]) + reg) - - if j % (M//10) == 0: - print("j:", j, "M:", M) - except KeyError: - # possible not to have any ratings for a movie - pass - print("updated U and c:", datetime.now() - t0) - print("epoch duration:", datetime.now() - epoch_start) - - - # store train loss - t0 = datetime.now() - train_losses.append(get_loss(movie2userrating)) - - # store test loss - test_losses.append(get_loss(movie2userrating_test)) - print("calculate cost:", datetime.now() - t0) - print("train loss:", train_losses[-1]) - print("test loss:", test_losses[-1]) - - -print("train losses:", train_losses) -print("test losses:", test_losses) - -# plot losses -plt.plot(train_losses, label="train loss") -plt.plot(test_losses, label="test loss") -plt.legend() -plt.show() diff --git a/recommenders/mf_keras.py b/recommenders/mf_keras.py deleted file mode 100644 index efc3315b..00000000 --- a/recommenders/mf_keras.py +++ /dev/null @@ -1,100 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import pickle -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.utils import shuffle - -from keras.models import Model -from keras.layers import Input, Embedding, Dot, Add, Flatten -from keras.regularizers import l2 -from keras.optimizers import SGD, Adam - -# load in the data -df = pd.read_csv('../large_files/movielens-20m-dataset/edited_rating.csv') - -N = df.userId.max() + 1 # number of users -M = df.movie_idx.max() + 1 # number of movies - -# split into train and test -df = shuffle(df) -cutoff = int(0.8*len(df)) -df_train = df.iloc[:cutoff] -df_test = df.iloc[cutoff:] - -# initialize variables -K = 10 # latent dimensionality -mu = df_train.rating.mean() -epochs = 15 -reg = 0. # regularization penalty - - -# keras model -u = Input(shape=(1,)) -m = Input(shape=(1,)) -u_embedding = Embedding(N, K, embeddings_regularizer=l2(reg))(u) # (N, 1, K) -m_embedding = Embedding(M, K, embeddings_regularizer=l2(reg))(m) # (N, 1, K) - -# subsubmodel = Model([u, m], [u_embedding, m_embedding]) -# user_ids = df_train.userId.values[0:5] -# movie_ids = df_train.movie_idx.values[0:5] -# print("user_ids.shape", user_ids.shape) -# p = subsubmodel.predict([user_ids, movie_ids]) -# print("p[0].shape:", p[0].shape) -# print("p[1].shape:", p[1].shape) -# exit() - - -u_bias = Embedding(N, 1, embeddings_regularizer=l2(reg))(u) # (N, 1, 1) -m_bias = Embedding(M, 1, embeddings_regularizer=l2(reg))(m) # (N, 1, 1) -x = Dot(axes=2)([u_embedding, m_embedding]) # (N, 1, 1) - -# submodel = Model([u, m], x) -# user_ids = df_train.userId.values[0:5] -# movie_ids = df_train.movie_idx.values[0:5] -# p = submodel.predict([user_ids, movie_ids]) -# print("p.shape:", p.shape) -# exit() - - -x = Add()([x, u_bias, m_bias]) -x = Flatten()(x) # (N, 1) - -model = Model(inputs=[u, m], outputs=x) -model.compile( - loss='mse', - # optimizer='adam', - # optimizer=Adam(lr=0.01), - optimizer=SGD(lr=0.08, momentum=0.9), - metrics=['mse'], -) - -r = model.fit( - x=[df_train.userId.values, df_train.movie_idx.values], - y=df_train.rating.values - mu, - epochs=epochs, - batch_size=128, - validation_data=( - [df_test.userId.values, df_test.movie_idx.values], - df_test.rating.values - mu - ) -) - - -# plot losses -plt.plot(r.history['loss'], label="train loss") -plt.plot(r.history['val_loss'], label="test loss") -plt.legend() -plt.show() - -# plot mse -plt.plot(r.history['mean_squared_error'], label="train mse") -plt.plot(r.history['val_mean_squared_error'], label="test mse") -plt.legend() -plt.show() diff --git a/recommenders/mf_keras_deep.py b/recommenders/mf_keras_deep.py deleted file mode 100644 index f3888a7a..00000000 --- a/recommenders/mf_keras_deep.py +++ /dev/null @@ -1,89 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import pickle -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.utils import shuffle - -from keras.models import Model -from keras.layers import Input, Embedding, Flatten, Dense, Concatenate -from keras.layers import Dropout, BatchNormalization, Activation -from keras.regularizers import l2 -from keras.optimizers import SGD, Adam - -# load in the data -df = pd.read_csv('../large_files/movielens-20m-dataset/edited_rating.csv') - -N = df.userId.max() + 1 # number of users -M = df.movie_idx.max() + 1 # number of movies - -# split into train and test -df = shuffle(df) -cutoff = int(0.8*len(df)) -df_train = df.iloc[:cutoff] -df_test = df.iloc[cutoff:] - -# initialize variables -K = 10 # latent dimensionality -mu = df_train.rating.mean() -epochs = 15 -# reg = 0.0001 # regularization penalty - - -# keras model -u = Input(shape=(1,)) -m = Input(shape=(1,)) -u_embedding = Embedding(N, K)(u) # (N, 1, K) -m_embedding = Embedding(M, K)(m) # (N, 1, K) -u_embedding = Flatten()(u_embedding) # (N, K) -m_embedding = Flatten()(m_embedding) # (N, K) -x = Concatenate()([u_embedding, m_embedding]) # (N, 2K) - -# the neural network -x = Dense(400)(x) -# x = BatchNormalization()(x) -x = Activation('relu')(x) -# x = Dropout(0.5)(x) -# x = Dense(100)(x) -# x = BatchNormalization()(x) -# x = Activation('relu')(x) -x = Dense(1)(x) - -model = Model(inputs=[u, m], outputs=x) -model.compile( - loss='mse', - # optimizer='adam', - # optimizer=Adam(lr=0.01), - optimizer=SGD(lr=0.08, momentum=0.9), - metrics=['mse'], -) - -r = model.fit( - x=[df_train.userId.values, df_train.movie_idx.values], - y=df_train.rating.values - mu, - epochs=epochs, - batch_size=128, - validation_data=( - [df_test.userId.values, df_test.movie_idx.values], - df_test.rating.values - mu - ) -) - - -# plot losses -plt.plot(r.history['loss'], label="train loss") -plt.plot(r.history['val_loss'], label="test loss") -plt.legend() -plt.show() - -# plot mse -plt.plot(r.history['mean_squared_error'], label="train mse") -plt.plot(r.history['val_mean_squared_error'], label="test mse") -plt.legend() -plt.show() diff --git a/recommenders/mf_keras_res.py b/recommenders/mf_keras_res.py deleted file mode 100644 index b362eded..00000000 --- a/recommenders/mf_keras_res.py +++ /dev/null @@ -1,98 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import pickle -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.utils import shuffle - -from keras.models import Model -from keras.layers import Input, Embedding, Dot, Add, Flatten, Dense, Concatenate -from keras.layers import Dropout, BatchNormalization, Activation -from keras.regularizers import l2 -from keras.optimizers import SGD, Adam - -# load in the data -df = pd.read_csv('../large_files/movielens-20m-dataset/edited_rating.csv') - -N = df.userId.max() + 1 # number of users -M = df.movie_idx.max() + 1 # number of movies - -# split into train and test -df = shuffle(df) -cutoff = int(0.8*len(df)) -df_train = df.iloc[:cutoff] -df_test = df.iloc[cutoff:] - -# initialize variables -K = 10 # latent dimensionality -mu = df_train.rating.mean() -epochs = 15 -reg = 0. # regularization penalty - - -# keras model -u = Input(shape=(1,)) -m = Input(shape=(1,)) -u_embedding = Embedding(N, K)(u) # (N, 1, K) -m_embedding = Embedding(M, K)(m) # (N, 1, K) - - -##### main branch -u_bias = Embedding(N, 1)(u) # (N, 1, 1) -m_bias = Embedding(M, 1)(m) # (N, 1, 1) -x = Dot(axes=2)([u_embedding, m_embedding]) # (N, 1, 1) -x = Add()([x, u_bias, m_bias]) -x = Flatten()(x) # (N, 1) - - -##### side branch -u_embedding = Flatten()(u_embedding) # (N, K) -m_embedding = Flatten()(m_embedding) # (N, K) -y = Concatenate()([u_embedding, m_embedding]) # (N, 2K) -y = Dense(400)(y) -y = Activation('elu')(y) -# y = Dropout(0.5)(y) -y = Dense(1)(y) - - -##### merge -x = Add()([x, y]) - -model = Model(inputs=[u, m], outputs=x) -model.compile( - loss='mse', - # optimizer='adam', - # optimizer=Adam(lr=0.01), - optimizer=SGD(lr=0.08, momentum=0.9), - metrics=['mse'], -) - -r = model.fit( - x=[df_train.userId.values, df_train.movie_idx.values], - y=df_train.rating.values - mu, - epochs=epochs, - batch_size=128, - validation_data=( - [df_test.userId.values, df_test.movie_idx.values], - df_test.rating.values - mu - ) -) - - -# plot losses -plt.plot(r.history['loss'], label="train loss") -plt.plot(r.history['val_loss'], label="test loss") -plt.legend() -plt.show() - -# plot mse -plt.plot(r.history['mean_squared_error'], label="train mse") -plt.plot(r.history['val_mean_squared_error'], label="test mse") -plt.legend() -plt.show() \ No newline at end of file diff --git a/recommenders/preprocess.py b/recommenders/preprocess.py deleted file mode 100644 index 72585460..00000000 --- a/recommenders/preprocess.py +++ /dev/null @@ -1,41 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import pandas as pd - -# https://www.kaggle.com/grouplens/movielens-20m-dataset -df = pd.read_csv('../large_files/movielens-20m-dataset/rating.csv') - - - -# note: -# user ids are ordered sequentially from 1..138493 -# with no missing numbers -# movie ids are integers from 1..131262 -# NOT all movie ids appear -# there are only 26744 movie ids -# write code to check it yourself! - - -# make the user ids go from 0...N-1 -df.userId = df.userId - 1 - -# create a mapping for movie ids -unique_movie_ids = set(df.movieId.values) -movie2idx = {} -count = 0 -for movie_id in unique_movie_ids: - movie2idx[movie_id] = count - count += 1 - -# add them to the data frame -# takes awhile -df['movie_idx'] = df.apply(lambda row: movie2idx[row.movieId], axis=1) - -df = df.drop(columns=['timestamp']) - -df.to_csv('../large_files/movielens-20m-dataset/edited_rating.csv', index=False) \ No newline at end of file diff --git a/recommenders/preprocess2dict.py b/recommenders/preprocess2dict.py deleted file mode 100644 index 2ed5d8b7..00000000 --- a/recommenders/preprocess2dict.py +++ /dev/null @@ -1,82 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import pickle -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.utils import shuffle - -# load in the data -# https://www.kaggle.com/grouplens/movielens-20m-dataset -df = pd.read_csv('../large_files/movielens-20m-dataset/very_small_rating.csv') - -N = df.userId.max() + 1 # number of users -M = df.movie_idx.max() + 1 # number of movies - -# split into train and test -df = shuffle(df) -cutoff = int(0.8*len(df)) -df_train = df.iloc[:cutoff] -df_test = df.iloc[cutoff:] - -# a dictionary to tell us which users have rated which movies -user2movie = {} -# a dicationary to tell us which movies have been rated by which users -movie2user = {} -# a dictionary to look up ratings -usermovie2rating = {} -print("Calling: update_user2movie_and_movie2user") -count = 0 -def update_user2movie_and_movie2user(row): - global count - count += 1 - if count % 100000 == 0: - print("processed: %.3f" % (float(count)/cutoff)) - - i = int(row.userId) - j = int(row.movie_idx) - if i not in user2movie: - user2movie[i] = [j] - else: - user2movie[i].append(j) - - if j not in movie2user: - movie2user[j] = [i] - else: - movie2user[j].append(i) - - usermovie2rating[(i,j)] = row.rating -df_train.apply(update_user2movie_and_movie2user, axis=1) - -# test ratings dictionary -usermovie2rating_test = {} -print("Calling: update_usermovie2rating_test") -count = 0 -def update_usermovie2rating_test(row): - global count - count += 1 - if count % 100000 == 0: - print("processed: %.3f" % (float(count)/len(df_test))) - - i = int(row.userId) - j = int(row.movie_idx) - usermovie2rating_test[(i,j)] = row.rating -df_test.apply(update_usermovie2rating_test, axis=1) - -# note: these are not really JSONs -with open('user2movie.json', 'wb') as f: - pickle.dump(user2movie, f) - -with open('movie2user.json', 'wb') as f: - pickle.dump(movie2user, f) - -with open('usermovie2rating.json', 'wb') as f: - pickle.dump(usermovie2rating, f) - -with open('usermovie2rating_test.json', 'wb') as f: - pickle.dump(usermovie2rating_test, f) diff --git a/recommenders/preprocess2sparse.py b/recommenders/preprocess2sparse.py deleted file mode 100644 index 864de56d..00000000 --- a/recommenders/preprocess2sparse.py +++ /dev/null @@ -1,62 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.utils import shuffle -from scipy.sparse import lil_matrix, csr_matrix, save_npz, load_npz - -# load in the data -df = pd.read_csv('../large_files/movielens-20m-dataset/edited_rating.csv') -# df = pd.read_csv('../large_files/movielens-20m-dataset/small_rating.csv') - -N = df.userId.max() + 1 # number of users -M = df.movie_idx.max() + 1 # number of movies - -# split into train and test -df = shuffle(df) -cutoff = int(0.8*len(df)) -df_train = df.iloc[:cutoff] -df_test = df.iloc[cutoff:] - -A = lil_matrix((N, M)) -print("Calling: update_train") -count = 0 -def update_train(row): - global count - count += 1 - if count % 100000 == 0: - print("processed: %.3f" % (float(count)/cutoff)) - - i = int(row.userId) - j = int(row.movie_idx) - A[i,j] = row.rating -df_train.apply(update_train, axis=1) - -# mask, to tell us which entries exist and which do not -A = A.tocsr() -mask = (A > 0) -save_npz("Atrain.npz", A) - -# test ratings dictionary -A_test = lil_matrix((N, M)) -print("Calling: update_test") -count = 0 -def update_test(row): - global count - count += 1 - if count % 100000 == 0: - print("processed: %.3f" % (float(count)/len(df_test))) - - i = int(row.userId) - j = int(row.movie_idx) - A_test[i,j] = row.rating -df_test.apply(update_test, axis=1) -A_test = A_test.tocsr() -mask_test = (A_test > 0) -save_npz("Atest.npz", A_test) diff --git a/recommenders/preprocess_shrink.py b/recommenders/preprocess_shrink.py deleted file mode 100644 index 665a80e6..00000000 --- a/recommenders/preprocess_shrink.py +++ /dev/null @@ -1,58 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import pickle -import numpy as np -import pandas as pd -from collections import Counter - -# load in the data -# https://www.kaggle.com/grouplens/movielens-20m-dataset -df = pd.read_csv('../large_files/movielens-20m-dataset/edited_rating.csv') -print("original dataframe size:", len(df)) - -N = df.userId.max() + 1 # number of users -M = df.movie_idx.max() + 1 # number of movies - -user_ids_count = Counter(df.userId) -movie_ids_count = Counter(df.movie_idx) - -# number of users and movies we would like to keep -n = 10000 -m = 2000 - -user_ids = [u for u, c in user_ids_count.most_common(n)] -movie_ids = [m for m, c in movie_ids_count.most_common(m)] - -# make a copy, otherwise ids won't be overwritten -df_small = df[df.userId.isin(user_ids) & df.movie_idx.isin(movie_ids)].copy() - -# need to remake user ids and movie ids since they are no longer sequential -new_user_id_map = {} -i = 0 -for old in user_ids: - new_user_id_map[old] = i - i += 1 -print("i:", i) - -new_movie_id_map = {} -j = 0 -for old in movie_ids: - new_movie_id_map[old] = j - j += 1 -print("j:", j) - -print("Setting new ids") -df_small.loc[:, 'userId'] = df_small.apply(lambda row: new_user_id_map[row.userId], axis=1) -df_small.loc[:, 'movie_idx'] = df_small.apply(lambda row: new_movie_id_map[row.movie_idx], axis=1) -# df_small.drop(columns=['userId', 'movie_idx']) -# df_small.rename(index=str, columns={'new_userId': 'userId', 'new_movie_idx': 'movie_idx'}) -print("max user id:", df_small.userId.max()) -print("max movie id:", df_small.movie_idx.max()) - -print("small dataframe size:", len(df_small)) -df_small.to_csv('../large_files/movielens-20m-dataset/small_rating.csv', index=False) diff --git a/recommenders/rbm_tf_k.py b/recommenders/rbm_tf_k.py deleted file mode 100644 index 836a5595..00000000 --- a/recommenders/rbm_tf_k.py +++ /dev/null @@ -1,248 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt -from sklearn.utils import shuffle - -import pandas as pd -from scipy.sparse import lil_matrix, csr_matrix, save_npz, load_npz -from datetime import datetime - -if tf.__version__.startswith('2'): - tf.compat.v1.disable_eager_execution() - - -# is it possible to one-hot encode the data prior to feeding it -# into the neural network, so that we don't have to do it on the fly? -# yes, but: -# 1) scipy sparse doesn't support N-D matrices -# 2) you can use the 'sparse' standalone package, but it takes very long -# and you will run out of RAM - - -def one_hot_encode(X, K): - # input is N x D - # output is N x D x K - N, D = X.shape - Y = np.zeros((N, D, K)) - for n, d in zip(*X.nonzero()): - # 0.5...5 --> 1..10 --> 0..9 - k = int(X[n,d]*2 - 1) - Y[n,d,k] = 1 - return Y - -def one_hot_mask(X, K): - # input is N x D - # output is N x D x K - N, D = X.shape - Y = np.zeros((N, D, K)) - # if X[n,d] == 0, there's a missing rating - # so the mask should be all zeros - # else, it should be all ones - for n, d in zip(*X.nonzero()): - Y[n,d,:] = 1 - return Y - -one_to_ten = np.arange(10) + 1 # [1, 2, 3, ..., 10] -def convert_probs_to_ratings(probs): - # probs is N x D x K - # output is N x D matrix of predicted ratings - # N, D, K = probs.shape - # out = np.zeros((N, D)) - # each predicted rating is a weighted average using the probabilities - # for n in range(N): - # for d in range(D): - # out[n,d] = probs[n,d].dot(one_to_ten) / 2 - # return out - return probs.dot(one_to_ten) / 2 - - - -def dot1(V, W): - # V is N x D x K (batch of visible units) - # W is D x K x M (weights) - # returns N x M (hidden layer size) - return tf.tensordot(V, W, axes=[[1,2], [0,1]]) - -def dot2(H, W): - # H is N x M (batch of hiddens) - # W is D x K x M (weights transposed) - # returns N x D x K (visible) - return tf.tensordot(H, W, axes=[[1], [2]]) - - -class RBM(object): - def __init__(self, D, M, K): - self.D = D # input feature size - self.M = M # hidden size - self.K = K # number of ratings - self.build(D, M, K) - - - def build(self, D, M, K): - # params - self.W = tf.Variable(tf.random.normal(shape=(D, K, M)) * np.sqrt(2.0 / M)) - self.c = tf.Variable(np.zeros(M).astype(np.float32)) - self.b = tf.Variable(np.zeros((D, K)).astype(np.float32)) - - # data - self.X_in = tf.compat.v1.placeholder(tf.float32, shape=(None, D, K)) - self.mask = tf.compat.v1.placeholder(tf.float32, shape=(None, D, K)) - - # conditional probabilities - # NOTE: tf.contrib.distributions.Bernoulli API has changed in Tensorflow v1.2 - V = self.X_in - p_h_given_v = tf.nn.sigmoid(dot1(V, self.W) + self.c) - self.p_h_given_v = p_h_given_v # save for later - - # draw a sample from p(h | v) - r = tf.random.uniform(shape=tf.shape(input=p_h_given_v)) - H = tf.cast(r < p_h_given_v, dtype=tf.float32) - - # draw a sample from p(v | h) - # note: we don't have to actually do the softmax - logits = dot2(H, self.W) + self.b - cdist = tf.compat.v1.distributions.Categorical(logits=logits) - X_sample = cdist.sample() # shape is (N, D) - X_sample = tf.one_hot(X_sample, depth=K) # turn it into (N, D, K) - X_sample = X_sample * self.mask # missing ratings shouldn't contribute to objective - - - # build the objective - objective = tf.reduce_mean(input_tensor=self.free_energy(self.X_in)) - tf.reduce_mean(input_tensor=self.free_energy(X_sample)) - self.train_op = tf.compat.v1.train.AdamOptimizer(1e-2).minimize(objective) - # self.train_op = tf.train.GradientDescentOptimizer(1e-3).minimize(objective) - - # build the cost - # we won't use this to optimize the model parameters - # just to observe what happens during training - logits = self.forward_logits(self.X_in) - self.cost = tf.reduce_mean( - input_tensor=tf.nn.softmax_cross_entropy_with_logits( - labels=tf.stop_gradient(self.X_in), - logits=logits, - ) - ) - - # to get the output - self.output_visible = self.forward_output(self.X_in) - - initop = tf.compat.v1.global_variables_initializer() - self.session = tf.compat.v1.Session() - self.session.run(initop) - - def fit(self, X, mask, X_test, mask_test, epochs=10, batch_sz=256, show_fig=True): - N, D = X.shape - n_batches = N // batch_sz - - - costs = [] - test_costs = [] - for i in range(epochs): - t0 = datetime.now() - print("epoch:", i) - X, mask, X_test, mask_test = shuffle(X, mask, X_test, mask_test) # everything has to be shuffled accordingly - for j in range(n_batches): - x = X[j*batch_sz:(j*batch_sz + batch_sz)].toarray() - m = mask[j*batch_sz:(j*batch_sz + batch_sz)].toarray() - - # both visible units and mask have to be in one-hot form - # N x D --> N x D x K - batch_one_hot = one_hot_encode(x, self.K) - m = one_hot_mask(m, self.K) - - _, c = self.session.run( - (self.train_op, self.cost), - feed_dict={self.X_in: batch_one_hot, self.mask: m} - ) - - if j % 100 == 0: - print("j / n_batches:", j, "/", n_batches, "cost:", c) - print("duration:", datetime.now() - t0) - - # calculate the true train and test cost - t0 = datetime.now() - sse = 0 - test_sse = 0 - n = 0 - test_n = 0 - for j in range(n_batches): - x = X[j*batch_sz:(j*batch_sz + batch_sz)].toarray() - m = mask[j*batch_sz:(j*batch_sz + batch_sz)].toarray() - - # only visible input has to be in one-hot form - xoh = one_hot_encode(x, self.K) - - probs = self.get_visible(xoh) - xhat = convert_probs_to_ratings(probs) - sse += (m * (xhat - x)*(xhat - x)).sum() - n += m.sum() - - # the test PREDICTIONS come from the train data! - # X_test and mask_test are only used for targets - xt = X_test[j*batch_sz:(j*batch_sz + batch_sz)].toarray() - mt = mask_test[j*batch_sz:(j*batch_sz + batch_sz)].toarray() - - test_sse += (mt * (xhat - xt) * (xhat - xt)).sum() - test_n += mt.sum() - c = sse/n - ct = test_sse/test_n - print("train mse:", c) - print("test mse:", ct) - print("calculate cost duration:", datetime.now() - t0) - costs.append(c) - test_costs.append(ct) - if show_fig: - plt.plot(costs, label='train mse') - plt.plot(test_costs, label='test mse') - plt.legend() - plt.show() - - def free_energy(self, V): - first_term = -tf.reduce_sum(input_tensor=dot1(V, self.b)) - second_term = -tf.reduce_sum( - # tf.log(1 + tf.exp(tf.matmul(V, self.W) + self.c)), - input_tensor=tf.nn.softplus(dot1(V, self.W) + self.c), - axis=1 - ) - return first_term + second_term - - def forward_hidden(self, X): - return tf.nn.sigmoid(dot1(X, self.W) + self.c) - - def forward_logits(self, X): - Z = self.forward_hidden(X) - return dot2(Z, self.W) + self.b - - def forward_output(self, X): - return tf.nn.softmax(self.forward_logits(X)) - - def transform(self, X): - # accepts and returns a real numpy array - # unlike forward_hidden and forward_output - # which deal with tensorflow variables - return self.session.run(self.p_h_given_v, feed_dict={self.X_in: X}) - - def get_visible(self, X): - return self.session.run(self.output_visible, feed_dict={self.X_in: X}) - - -def main(): - A = load_npz("Atrain.npz") - A_test = load_npz("Atest.npz") - mask = (A > 0) * 1.0 - mask_test = (A_test > 0) * 1.0 - - N, M = A.shape - rbm = RBM(M, 50, 10) - rbm.fit(A, mask, A_test, mask_test) - - -if __name__ == '__main__': - main() diff --git a/recommenders/rbm_tf_k_faster.py b/recommenders/rbm_tf_k_faster.py deleted file mode 100644 index 9a1a242a..00000000 --- a/recommenders/rbm_tf_k_faster.py +++ /dev/null @@ -1,221 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt -from sklearn.utils import shuffle - -import pandas as pd -from scipy.sparse import lil_matrix, csr_matrix, save_npz, load_npz -from datetime import datetime - -if tf.__version__.startswith('2'): - tf.compat.v1.disable_eager_execution() - - -def dot1(V, W): - # V is N x D x K (batch of visible units) - # W is D x K x M (weights) - # returns N x M (hidden layer size) - return tf.tensordot(V, W, axes=[[1,2], [0,1]]) - -def dot2(H, W): - # H is N x M (batch of hiddens) - # W is D x K x M (weights transposed) - # returns N x D x K (visible) - return tf.tensordot(H, W, axes=[[1], [2]]) - - -class RBM(object): - def __init__(self, D, M, K): - self.D = D # input feature size - self.M = M # hidden size - self.K = K # number of ratings - self.build(D, M, K) - - - def build(self, D, M, K): - # params - self.W = tf.Variable(tf.random.normal(shape=(D, K, M)) * np.sqrt(2.0 / M)) - self.c = tf.Variable(np.zeros(M).astype(np.float32)) - self.b = tf.Variable(np.zeros((D, K)).astype(np.float32)) - - # data - self.X_in = tf.compat.v1.placeholder(tf.float32, shape=(None, D)) - - # one hot encode X - # first, make each rating an int - X = tf.cast(self.X_in * 2 - 1, tf.int32) - X = tf.one_hot(X, K) - - # conditional probabilities - # NOTE: tf.contrib.distributions.Bernoulli API has changed in Tensorflow v1.2 - V = X - p_h_given_v = tf.nn.sigmoid(dot1(V, self.W) + self.c) - self.p_h_given_v = p_h_given_v # save for later - - # draw a sample from p(h | v) - r = tf.random.uniform(shape=tf.shape(input=p_h_given_v)) - H = tf.cast(r < p_h_given_v, dtype=tf.float32) - - # draw a sample from p(v | h) - # note: we don't have to actually do the softmax - logits = dot2(H, self.W) + self.b - cdist = tf.compat.v1.distributions.Categorical(logits=logits) - X_sample = cdist.sample() # shape is (N, D) - X_sample = tf.one_hot(X_sample, depth=K) # turn it into (N, D, K) - - # mask X_sample to remove missing ratings - mask2d = tf.cast(self.X_in > 0, tf.float32) - mask3d = tf.stack([mask2d]*K, axis=-1) # repeat K times in last dimension - X_sample = X_sample * mask3d - - - # build the objective - objective = tf.reduce_mean(input_tensor=self.free_energy(X)) - tf.reduce_mean(input_tensor=self.free_energy(X_sample)) - self.train_op = tf.compat.v1.train.AdamOptimizer(1e-2).minimize(objective) - # self.train_op = tf.train.GradientDescentOptimizer(1e-3).minimize(objective) - - # build the cost - # we won't use this to optimize the model parameters - # just to observe what happens during training - logits = self.forward_logits(X) - self.cost = tf.reduce_mean( - input_tensor=tf.nn.softmax_cross_entropy_with_logits( - labels=tf.stop_gradient(X), - logits=logits, - ) - ) - - # to get the output - self.output_visible = self.forward_output(X) - - - # for calculating SSE - self.one_to_ten = tf.constant((np.arange(10) + 1).astype(np.float32) / 2) - self.pred = tf.tensordot(self.output_visible, self.one_to_ten, axes=[[2], [0]]) - mask = tf.cast(self.X_in > 0, tf.float32) - se = mask * (self.X_in - self.pred) * (self.X_in - self.pred) - self.sse = tf.reduce_sum(input_tensor=se) - - # test SSE - self.X_test = tf.compat.v1.placeholder(tf.float32, shape=(None, D)) - mask = tf.cast(self.X_test > 0, tf.float32) - tse = mask * (self.X_test - self.pred) * (self.X_test - self.pred) - self.tsse = tf.reduce_sum(input_tensor=tse) - - - initop = tf.compat.v1.global_variables_initializer() - self.session = tf.compat.v1.Session() - self.session.run(initop) - - def fit(self, X, X_test, epochs=10, batch_sz=256, show_fig=True): - N, D = X.shape - n_batches = N // batch_sz - - - costs = [] - test_costs = [] - for i in range(epochs): - t0 = datetime.now() - print("epoch:", i) - X, X_test = shuffle(X, X_test) # everything has to be shuffled accordingly - for j in range(n_batches): - x = X[j*batch_sz:(j*batch_sz + batch_sz)].toarray() - - _, c = self.session.run( - (self.train_op, self.cost), - feed_dict={self.X_in: x} - ) - - if j % 100 == 0: - print("j / n_batches:", j, "/", n_batches, "cost:", c) - print("duration:", datetime.now() - t0) - - # calculate the true train and test cost - t0 = datetime.now() - sse = 0 - test_sse = 0 - n = 0 - test_n = 0 - for j in range(n_batches): - x = X[j*batch_sz:(j*batch_sz + batch_sz)].toarray() - xt = X_test[j*batch_sz:(j*batch_sz + batch_sz)].toarray() - - # number of train ratings - n += np.count_nonzero(x) - - # number of test ratings - test_n += np.count_nonzero(xt) - - # use tensorflow to get SSEs - sse_j, tsse_j = self.get_sse(x, xt) - sse += sse_j - test_sse += tsse_j - c = sse/n - ct = test_sse/test_n - print("train mse:", c) - print("test mse:", ct) - print("calculate cost duration:", datetime.now() - t0) - costs.append(c) - test_costs.append(ct) - if show_fig: - plt.plot(costs, label='train mse') - plt.plot(test_costs, label='test mse') - plt.legend() - plt.show() - - def free_energy(self, V): - first_term = -tf.reduce_sum(input_tensor=dot1(V, self.b)) - second_term = -tf.reduce_sum( - # tf.log(1 + tf.exp(tf.matmul(V, self.W) + self.c)), - input_tensor=tf.nn.softplus(dot1(V, self.W) + self.c), - axis=1 - ) - return first_term + second_term - - def forward_hidden(self, X): - return tf.nn.sigmoid(dot1(X, self.W) + self.c) - - def forward_logits(self, X): - Z = self.forward_hidden(X) - return dot2(Z, self.W) + self.b - - def forward_output(self, X): - return tf.nn.softmax(self.forward_logits(X)) - - def transform(self, X): - # accepts and returns a real numpy array - # unlike forward_hidden and forward_output - # which deal with tensorflow variables - return self.session.run(self.p_h_given_v, feed_dict={self.X_in: X}) - - def get_visible(self, X): - return self.session.run(self.output_visible, feed_dict={self.X_in: X}) - - def get_sse(self, X, Xt): - return self.session.run( - (self.sse, self.tsse), - feed_dict={ - self.X_in: X, - self.X_test: Xt, - }) - - - -def main(): - A = load_npz("Atrain.npz") - A_test = load_npz("Atest.npz") - - N, M = A.shape - rbm = RBM(M, 50, 10) - rbm.fit(A, A_test) - - -if __name__ == '__main__': - main() diff --git a/recommenders/spark.py b/recommenders/spark.py deleted file mode 100644 index 23ea365a..00000000 --- a/recommenders/spark.py +++ /dev/null @@ -1,54 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems - -### meant to be pasted into console ### - -# notes: -# you may have trouble with full dataset on just your local machine -# if you want to know what's in an RDD, use .take(n), ex: -# tmp = p.take(5) -# print(tmp) - -from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating -import os - -# load in the data -data = sc.textFile("../large_files/movielens-20m-dataset/small_rating.csv") - -# filter out header -header = data.first() #extract header -data = data.filter(lambda row: row != header) - -# convert into a sequence of Rating objects -ratings = data.map( - lambda l: l.split(',') -).map( - lambda l: Rating(int(l[0]), int(l[1]), float(l[2])) -) - -# split into train and test -train, test = ratings.randomSplit([0.8, 0.2]) - -# train the model -K = 10 -epochs = 10 -model = ALS.train(train, K, epochs) - -# evaluate the model - -# train -x = train.map(lambda p: (p[0], p[1])) -p = model.predictAll(x).map(lambda r: ((r[0], r[1]), r[2])) -ratesAndPreds = train.map(lambda r: ((r[0], r[1]), r[2])).join(p) -# joins on first item: (user_id, movie_id) -# each row of result is: ((user_id, movie_id), (rating, prediction)) -mse = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean() -print("train mse: %s" % mse) - - -# test -x = test.map(lambda p: (p[0], p[1])) -p = model.predictAll(x).map(lambda r: ((r[0], r[1]), r[2])) -ratesAndPreds = test.map(lambda r: ((r[0], r[1]), r[2])).join(p) -mse = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean() -print("test mse: %s" % mse) \ No newline at end of file diff --git a/recommenders/spark2.py b/recommenders/spark2.py deleted file mode 100644 index 5879269d..00000000 --- a/recommenders/spark2.py +++ /dev/null @@ -1,60 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems - -# notes: -# you may have trouble with full dataset on just your local machine -# if you want to know what's in an RDD, use .take(n), ex: -# tmp = p.take(5) -# print(tmp) - -from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating -from pyspark import SparkContext - -# increase memory -# SparkContext.setSystemProperty('spark.driver.memory', '10g') -# SparkContext.setSystemProperty('spark.executor.memory', '10g') - -sc = SparkContext("local", "Your App Name Here") - - -# load in the data -# data = sc.textFile("../large_files/movielens-20m-dataset/small_rating.csv") -data = sc.textFile("../large_files/movielens-20m-dataset/rating.csv.gz") - -# filter out header -header = data.first() #extract header -data = data.filter(lambda row: row != header) - -# convert into a sequence of Rating objects -ratings = data.map( - lambda l: l.split(',') -).map( - lambda l: Rating(int(l[0]), int(l[1]), float(l[2])) -) - -# split into train and test -train, test = ratings.randomSplit([0.8, 0.2]) - -# train the model -K = 10 -epochs = 10 -model = ALS.train(train, K, epochs) - -# evaluate the model - -# train -x = train.map(lambda p: (p[0], p[1])) -p = model.predictAll(x).map(lambda r: ((r[0], r[1]), r[2])) -ratesAndPreds = train.map(lambda r: ((r[0], r[1]), r[2])).join(p) -# joins on first item: (user_id, movie_id) -# each row of result is: ((user_id, movie_id), (rating, prediction)) -mse = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean() -print("***** train mse: %s *****" % mse) - - -# test -x = test.map(lambda p: (p[0], p[1])) -p = model.predictAll(x).map(lambda r: ((r[0], r[1]), r[2])) -ratesAndPreds = test.map(lambda r: ((r[0], r[1]), r[2])).join(p) -mse = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean() -print("***** test mse: %s *****" % mse) \ No newline at end of file diff --git a/recommenders/tfidf.py b/recommenders/tfidf.py deleted file mode 100644 index a6078ec3..00000000 --- a/recommenders/tfidf.py +++ /dev/null @@ -1,72 +0,0 @@ -import pandas as pd -import json - -from sklearn.feature_extraction.text import TfidfVectorizer -from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances - - -# get the data from: https://www.kaggle.com/tmdb/tmdb-movie-metadata -# load in the data -df = pd.read_csv('../large_files/tmdb_5000_movies.csv') - - -# convert the relevant data for each movie into a single string -# to be ingested by TfidfVectorizer -def genres_and_keywords_to_string(row): - genres = json.loads(row['genres']) - genres = ' '.join(''.join(j['name'].split()) for j in genres) - - keywords = json.loads(row['keywords']) - keywords = ' '.join(''.join(j['name'].split()) for j in keywords) - return "%s %s" % (genres, keywords) - - -# create a new string representation of each movie -df['string'] = df.apply(genres_and_keywords_to_string, axis=1) - - -# create a tf-idf vectorizer object -# remove stopwords automatically -tfidf = TfidfVectorizer(max_features=2000) - -# create a data matrix from the overviews -X = tfidf.fit_transform(df['string']) - -# check the shape of X -print("X.shape:", X.shape) - -# generate a mapping from movie title -> index (in df) -movie2idx = pd.Series(df.index, index=df['title']) - -# create a function that generates recommendations -def recommend(title): - # get the row in the dataframe for this movie - idx = movie2idx[title] - if type(idx) == pd.Series: - idx = idx.iloc[0] - # print("idx:", idx) - - # calculate the pairwise similarities for this movie - query = X[idx] - scores = cosine_similarity(query, X) - - # currently the array is 1 x N, make it just a 1-D array - scores = scores.flatten() - - # get the indexes of the highest scoring movies - # get the first K recommendations - # don't return itself! - recommended_idx = (-scores).argsort()[1:6] - - # return the titles of the recommendations - return df['title'].iloc[recommended_idx] - - -print("\nRecommendations for 'Scream 3':") -print(recommend('Scream 3')) - -print("\nRecommendations for 'Mortal Kombat':") -print(recommend('Mortal Kombat')) - -print("\nRecommendations for 'Runaway Bride':") -print(recommend('Runaway Bride')) diff --git a/recommenders/userbased.py b/recommenders/userbased.py deleted file mode 100644 index b512a722..00000000 --- a/recommenders/userbased.py +++ /dev/null @@ -1,168 +0,0 @@ -# https://udemy.com/recommender-systems -# https://deeplearningcourses.com/recommender-systems -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import pickle -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.utils import shuffle -from datetime import datetime -from sortedcontainers import SortedList - -# load in the data -import os -if not os.path.exists('user2movie.json') or \ - not os.path.exists('movie2user.json') or \ - not os.path.exists('usermovie2rating.json') or \ - not os.path.exists('usermovie2rating_test.json'): - import preprocess2dict - - -with open('user2movie.json', 'rb') as f: - user2movie = pickle.load(f) - -with open('movie2user.json', 'rb') as f: - movie2user = pickle.load(f) - -with open('usermovie2rating.json', 'rb') as f: - usermovie2rating = pickle.load(f) - -with open('usermovie2rating_test.json', 'rb') as f: - usermovie2rating_test = pickle.load(f) - - -N = np.max(list(user2movie.keys())) + 1 -# the test set may contain movies the train set doesn't have data on -m1 = np.max(list(movie2user.keys())) -m2 = np.max([m for (u, m), r in usermovie2rating_test.items()]) -M = max(m1, m2) + 1 -print("N:", N, "M:", M) - -if N > 10000: - print("N =", N, "are you sure you want to continue?") - print("Comment out these lines if so...") - exit() - - -# to find the user similarities, you have to do O(N^2 * M) calculations! -# in the "real-world" you'd want to parallelize this -# note: we really only have to do half the calculations, since w_ij is symmetric -K = 25 # number of neighbors we'd like to consider -limit = 5 # number of common movies users must have in common in order to consider -neighbors = [] # store neighbors in this list -averages = [] # each user's average rating for later use -deviations = [] # each user's deviation for later use -for i in range(N): - # find the 25 closest users to user i - movies_i = user2movie[i] - movies_i_set = set(movies_i) - - # calculate avg and deviation - ratings_i = { movie:usermovie2rating[(i, movie)] for movie in movies_i } - avg_i = np.mean(list(ratings_i.values())) - dev_i = { movie:(rating - avg_i) for movie, rating in ratings_i.items() } - dev_i_values = np.array(list(dev_i.values())) - sigma_i = np.sqrt(dev_i_values.dot(dev_i_values)) - - # save these for later use - averages.append(avg_i) - deviations.append(dev_i) - - sl = SortedList() - for j in range(N): - # don't include yourself - if j != i: - movies_j = user2movie[j] - movies_j_set = set(movies_j) - common_movies = (movies_i_set & movies_j_set) # intersection - if len(common_movies) > limit: - # calculate avg and deviation - ratings_j = { movie:usermovie2rating[(j, movie)] for movie in movies_j } - avg_j = np.mean(list(ratings_j.values())) - dev_j = { movie:(rating - avg_j) for movie, rating in ratings_j.items() } - dev_j_values = np.array(list(dev_j.values())) - sigma_j = np.sqrt(dev_j_values.dot(dev_j_values)) - - # calculate correlation coefficient - numerator = sum(dev_i[m]*dev_j[m] for m in common_movies) - w_ij = numerator / (sigma_i * sigma_j) - - # insert into sorted list and truncate - # negate weight, because list is sorted ascending - # maximum value (1) is "closest" - sl.add((-w_ij, j)) - if len(sl) > K: - del sl[-1] - - # store the neighbors - neighbors.append(sl) - - # print out useful things - if i % 1 == 0: - print(i) - - -# using neighbors, calculate train and test MSE - -def predict(i, m): - # calculate the weighted sum of deviations - numerator = 0 - denominator = 0 - for neg_w, j in neighbors[i]: - # remember, the weight is stored as its negative - # so the negative of the negative weight is the positive weight - try: - numerator += -neg_w * deviations[j][m] - denominator += abs(neg_w) - except KeyError: - # neighbor may not have rated the same movie - # don't want to do dictionary lookup twice - # so just throw exception - pass - - if denominator == 0: - prediction = averages[i] - else: - prediction = numerator / denominator + averages[i] - prediction = min(5, prediction) - prediction = max(0.5, prediction) # min rating is 0.5 - return prediction - - -train_predictions = [] -train_targets = [] -for (i, m), target in usermovie2rating.items(): - # calculate the prediction for this movie - prediction = predict(i, m) - - # save the prediction and target - train_predictions.append(prediction) - train_targets.append(target) - -test_predictions = [] -test_targets = [] -# same thing for test set -for (i, m), target in usermovie2rating_test.items(): - # calculate the prediction for this movie - prediction = predict(i, m) - - # save the prediction and target - test_predictions.append(prediction) - test_targets.append(target) - - -# calculate accuracy -def mse(p, t): - p = np.array(p) - t = np.array(t) - return np.mean((p - t)**2) - -print('train mse:', mse(train_predictions, train_targets)) -print('test mse:', mse(test_predictions, test_targets)) - - - diff --git a/regressor.py b/regressor.py new file mode 100644 index 00000000..9af0925e --- /dev/null +++ b/regressor.py @@ -0,0 +1,16 @@ +import numpy as np + + +class Regressor(object): + def score(self, X, y): + SSres = y - self.predict(X) + SSres = np.dot(SSres.T, SSres) + SStot = y - np.mean(y) + SStot = np.dot(SStot.T, SStot) + return 1 - SSres/SStot + + def predict(self, X): + raise Exception("Not implemented: predict") + + def fit(self, X, y): + raise Exception("Not implemented: fit") diff --git a/rl/approx_control.py b/rl/approx_control.py deleted file mode 100644 index ba19e58f..00000000 --- a/rl/approx_control.py +++ /dev/null @@ -1,162 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from grid_world import standard_grid, negative_grid -from iterative_policy_evaluation import print_values, print_policy -from sklearn.kernel_approximation import Nystroem, RBFSampler - -GAMMA = 0.9 -ALPHA = 0.1 -ALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R') -ACTION2INT = {a: i for i, a in enumerate(ALL_POSSIBLE_ACTIONS)} -INT2ONEHOT = np.eye(len(ALL_POSSIBLE_ACTIONS)) - - -def epsilon_greedy(model, s, eps=0.1): - # we'll use epsilon-soft to ensure all states are visited - # what happens if you don't do this? i.e. eps=0 - p = np.random.random() - if p < (1 - eps): - values = model.predict_all_actions(s) - return ALL_POSSIBLE_ACTIONS[np.argmax(values)] - else: - return np.random.choice(ALL_POSSIBLE_ACTIONS) - - -def one_hot(k): - return INT2ONEHOT[k] - - -def merge_state_action(s, a): - ai = one_hot(ACTION2INT[a]) - return np.concatenate((s, ai)) - - -def gather_samples(grid, n_episodes=1000): - samples = [] - for _ in range(n_episodes): - s = grid.reset() - while not grid.game_over(): - a = np.random.choice(ALL_POSSIBLE_ACTIONS) - sa = merge_state_action(s, a) - samples.append(sa) - - r = grid.move(a) - s = grid.current_state() - return samples - - -class Model: - def __init__(self, grid): - # fit the featurizer to data - samples = gather_samples(grid) - # self.featurizer = Nystroem() - self.featurizer = RBFSampler() - self.featurizer.fit(samples) - dims = self.featurizer.n_components - - # initialize linear model weights - self.w = np.zeros(dims) - - def predict(self, s, a): - sa = merge_state_action(s, a) - x = self.featurizer.transform([sa])[0] - return x @ self.w - - def predict_all_actions(self, s): - return [self.predict(s, a) for a in ALL_POSSIBLE_ACTIONS] - - def grad(self, s, a): - sa = merge_state_action(s, a) - x = self.featurizer.transform([sa])[0] - return x - - -if __name__ == '__main__': - # use the standard grid again (0 for every step) so that we can compare - # to iterative policy evaluation - # grid = standard_grid() - grid = negative_grid(step_cost=-0.1) - - # print rewards - print("rewards:") - print_values(grid.rewards, grid) - - model = Model(grid) - reward_per_episode = [] - state_visit_count = {} - - # repeat until convergence - n_episodes = 20000 - for it in range(n_episodes): - if (it + 1) % 100 == 0: - print(it + 1) - - s = grid.reset() - state_visit_count[s] = state_visit_count.get(s, 0) + 1 - episode_reward = 0 - while not grid.game_over(): - a = epsilon_greedy(model, s) - r = grid.move(a) - s2 = grid.current_state() - state_visit_count[s2] = state_visit_count.get(s2, 0) + 1 - - # get the target - if grid.game_over(): - target = r - else: - values = model.predict_all_actions(s2) - target = r + GAMMA * np.max(values) - - # update the model - g = model.grad(s, a) - err = target - model.predict(s, a) - model.w += ALPHA * err * g - - # accumulate reward - episode_reward += r - - # update state - s = s2 - - reward_per_episode.append(episode_reward) - - plt.plot(reward_per_episode) - plt.title("Reward per episode") - plt.show() - - # obtain V* and pi* - V = {} - greedy_policy = {} - states = grid.all_states() - for s in states: - if s in grid.actions: - values = model.predict_all_actions(s) - V[s] = np.max(values) - greedy_policy[s] = ALL_POSSIBLE_ACTIONS[np.argmax(values)] - else: - # terminal state or state we can't otherwise get to - V[s] = 0 - - print("values:") - print_values(V, grid) - print("policy:") - print_policy(greedy_policy, grid) - - - print("state_visit_count:") - state_sample_count_arr = np.zeros((grid.rows, grid.cols)) - for i in range(grid.rows): - for j in range(grid.cols): - if (i, j) in state_visit_count: - state_sample_count_arr[i,j] = state_visit_count[(i, j)] - df = pd.DataFrame(state_sample_count_arr) - print(df) diff --git a/rl/approx_prediction.py b/rl/approx_prediction.py deleted file mode 100644 index 4e75d9fc..00000000 --- a/rl/approx_prediction.py +++ /dev/null @@ -1,144 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from grid_world import standard_grid, negative_grid -from iterative_policy_evaluation import print_values, print_policy -from sklearn.kernel_approximation import Nystroem, RBFSampler - -GAMMA = 0.9 -ALPHA = 0.01 -ALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R') - - -def epsilon_greedy(greedy, s, eps=0.1): - # we'll use epsilon-soft to ensure all states are visited - # what happens if you don't do this? i.e. eps=0 - p = np.random.random() - if p < (1 - eps): - return greedy[s] - else: - return np.random.choice(ALL_POSSIBLE_ACTIONS) - - -def gather_samples(grid, n_episodes=10000): - samples = [] - for _ in range(n_episodes): - s = grid.reset() - samples.append(s) - while not grid.game_over(): - a = np.random.choice(ALL_POSSIBLE_ACTIONS) - r = grid.move(a) - s = grid.current_state() - samples.append(s) - return samples - - -class Model: - def __init__(self, grid): - # fit the featurizer to data - samples = gather_samples(grid) - # self.featurizer = Nystroem() - self.featurizer = RBFSampler() - self.featurizer.fit(samples) - dims = self.featurizer.n_components - - # initialize linear model weights - self.w = np.zeros(dims) - - def predict(self, s): - x = self.featurizer.transform([s])[0] - return x @ self.w - - def grad(self, s): - x = self.featurizer.transform([s])[0] - return x - - -if __name__ == '__main__': - # use the standard grid again (0 for every step) so that we can compare - # to iterative policy evaluation - grid = standard_grid() - - # print rewards - print("rewards:") - print_values(grid.rewards, grid) - - # state -> action - greedy_policy = { - (2, 0): 'U', - (1, 0): 'U', - (0, 0): 'R', - (0, 1): 'R', - (0, 2): 'R', - (1, 2): 'R', - (2, 1): 'R', - (2, 2): 'R', - (2, 3): 'U', - } - - model = Model(grid) - mse_per_episode = [] - - # repeat until convergence - n_episodes = 10000 - for it in range(n_episodes): - if (it + 1) % 100 == 0: - print(it + 1) - - s = grid.reset() - Vs = model.predict(s) - n_steps = 0 - episode_err = 0 - while not grid.game_over(): - a = epsilon_greedy(greedy_policy, s) - r = grid.move(a) - s2 = grid.current_state() - - # get the target - if grid.is_terminal(s2): - target = r - else: - Vs2 = model.predict(s2) - target = r + GAMMA * Vs2 - - # update the model - g = model.grad(s) - err = target - Vs - model.w += ALPHA * err * g - - # accumulate error - n_steps += 1 - episode_err += err*err - - # update state - s = s2 - Vs = Vs2 - - mse = episode_err / n_steps - mse_per_episode.append(mse) - - plt.plot(mse_per_episode) - plt.title("MSE per episode") - plt.show() - - # obtain predicted values - V = {} - states = grid.all_states() - for s in states: - if s in grid.actions: - V[s] = model.predict(s) - else: - # terminal state or state we can't otherwise get to - V[s] = 0 - - print("values:") - print_values(V, grid) - print("policy:") - print_policy(greedy_policy, grid) diff --git a/rl/bayesian_bandit.py b/rl/bayesian_bandit.py deleted file mode 100644 index 61e8f812..00000000 --- a/rl/bayesian_bandit.py +++ /dev/null @@ -1,78 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import matplotlib.pyplot as plt -import numpy as np -from scipy.stats import beta - - -# np.random.seed(2) -NUM_TRIALS = 2000 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - self.p = p - self.a = 1 - self.b = 1 - self.N = 0 # for information only - - def pull(self): - return np.random.random() < self.p - - def sample(self): - return np.random.beta(self.a, self.b) - - def update(self, x): - self.a += x - self.b += 1 - x - self.N += 1 - - -def plot(bandits, trial): - x = np.linspace(0, 1, 200) - for b in bandits: - y = beta.pdf(x, b.a, b.b) - plt.plot(x, y, label=f"real p: {b.p:.4f}, win rate = {b.a - 1}/{b.N}") - plt.title(f"Bandit distributions after {trial} trials") - plt.legend() - plt.show() - - -def experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - - sample_points = [5,10,20,50,100,200,500,1000,1500,1999] - rewards = np.zeros(NUM_TRIALS) - for i in range(NUM_TRIALS): - # Thompson sampling - j = np.argmax([b.sample() for b in bandits]) - - # plot the posteriors - if i in sample_points: - plot(bandits, i) - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update rewards - rewards[i] = x - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - # print total reward - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num times selected each bandit:", [b.N for b in bandits]) - - -if __name__ == "__main__": - experiment() diff --git a/rl/bayesian_normal.py b/rl/bayesian_normal.py deleted file mode 100644 index 07083d1b..00000000 --- a/rl/bayesian_normal.py +++ /dev/null @@ -1,84 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt -from scipy.stats import norm - - -np.random.seed(1) -NUM_TRIALS = 2000 -BANDIT_MEANS = [1, 2, 3] - - -class Bandit: - def __init__(self, true_mean): - self.true_mean = true_mean - # parameters for mu - prior is N(0,1) - self.m = 0 - self.lambda_ = 1 - self.tau = 1 - self.N = 0 - - def pull(self): - return np.random.randn() / np.sqrt(self.tau) + self.true_mean - - def sample(self): - return np.random.randn() / np.sqrt(self.lambda_) + self.m - - def update(self, x): - self.m = (self.tau * x + self.lambda_ * self.m) / (self.tau + self.lambda_) - self.lambda_ += self.tau - self.N += 1 - - -def plot(bandits, trial): - x = np.linspace(-3, 6, 200) - for b in bandits: - y = norm.pdf(x, b.m, np.sqrt(1. / b.lambda_)) - plt.plot(x, y, label=f"real mean: {b.true_mean:.4f}, num plays: {b.N}") - plt.title(f"Bandit distributions after {trial} trials") - plt.legend() - plt.show() - - -def run_experiment(): - bandits = [Bandit(m) for m in BANDIT_MEANS] - - sample_points = [5,10,20,50,100,200,500,1000,1500,1999] - rewards = np.empty(NUM_TRIALS) - for i in range(NUM_TRIALS): - # Thompson sampling - j = np.argmax([b.sample() for b in bandits]) - - # plot the posteriors - if i in sample_points: - plot(bandits, i) - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - # update rewards - rewards[i] = x - - cumulative_average = np.cumsum(rewards) / (np.arange(NUM_TRIALS) + 1) - - # plot moving average ctr - plt.plot(cumulative_average) - for m in BANDIT_MEANS: - plt.plot(np.ones(NUM_TRIALS)*m) - plt.show() - - return cumulative_average - -if __name__ == '__main__': - run_experiment() - - diff --git a/rl/bayesian_starter.py b/rl/bayesian_starter.py deleted file mode 100644 index 68e12f75..00000000 --- a/rl/bayesian_starter.py +++ /dev/null @@ -1,78 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import matplotlib.pyplot as plt -import numpy as np -from scipy.stats import beta - - -# np.random.seed(2) -NUM_TRIALS = 2000 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - self.p = p - self.a = # TODO - self.b = # TODO - self.N = 0 # for information only - - def pull(self): - return np.random.random() < self.p - - def sample(self): - return # TODO - draw a sample from Beta(a, b) - - def update(self, x): - self.a = # TODO - self.b = # TODO - self.N += 1 - - -def plot(bandits, trial): - x = np.linspace(0, 1, 200) - for b in bandits: - y = beta.pdf(x, b.a, b.b) - plt.plot(x, y, label=f"real p: {b.p:.4f}, win rate = {b.a - 1}/{b.N}") - plt.title(f"Bandit distributions after {trial} trials") - plt.legend() - plt.show() - - -def experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - - sample_points = [5,10,20,50,100,200,500,1000,1500,1999] - rewards = np.zeros(NUM_TRIALS) - for i in range(NUM_TRIALS): - # Thompson sampling - j = # TODO - - # plot the posteriors - if i in sample_points: - plot(bandits, i) - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update rewards - rewards[i] = x - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - # print total reward - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num times selected each bandit:", [b.N for b in bandits]) - - -if __name__ == "__main__": - experiment() diff --git a/rl/cartpole.py b/rl/cartpole.py deleted file mode 100644 index abb1b617..00000000 --- a/rl/cartpole.py +++ /dev/null @@ -1,157 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import gym -import numpy as np -import matplotlib.pyplot as plt -from sklearn.kernel_approximation import RBFSampler - - -GAMMA = 0.99 -ALPHA = 0.1 - - -def epsilon_greedy(model, s, eps=0.1): - # we'll use epsilon-soft to ensure all states are visited - # what happens if you don't do this? i.e. eps=0 - p = np.random.random() - if p < (1 - eps): - values = model.predict_all_actions(s) - return np.argmax(values) - else: - return model.env.action_space.sample() - - -def gather_samples(env, n_episodes=10000): - samples = [] - for _ in range(n_episodes): - s, info = env.reset() - done = False - truncated = False - while not (done or truncated): - a = env.action_space.sample() - sa = np.concatenate((s, [a])) - samples.append(sa) - - s, r, done, truncated, info = env.step(a) - return samples - - -class Model: - def __init__(self, env): - # fit the featurizer to data - self.env = env - samples = gather_samples(env) - self.featurizer = RBFSampler() - self.featurizer.fit(samples) - dims = self.featurizer.n_components - - # initialize linear model weights - self.w = np.zeros(dims) - - def predict(self, s, a): - sa = np.concatenate((s, [a])) - x = self.featurizer.transform([sa])[0] - return x @ self.w - - def predict_all_actions(self, s): - return [self.predict(s, a) for a in range(self.env.action_space.n)] - - def grad(self, s, a): - sa = np.concatenate((s, [a])) - x = self.featurizer.transform([sa])[0] - return x - - -def test_agent(model, env, n_episodes=20): - reward_per_episode = np.zeros(n_episodes) - for it in range(n_episodes): - done = False - truncated = False - episode_reward = 0 - s, info = env.reset() - while not (done or truncated): - a = epsilon_greedy(model, s, eps=0) - s, r, done, truncated, info = env.step(a) - episode_reward += r - reward_per_episode[it] = episode_reward - return np.mean(reward_per_episode) - - -def watch_agent(model, env, eps): - done = False - truncated = False - episode_reward = 0 - s, info = env.reset() - while not (done or truncated): - a = epsilon_greedy(model, s, eps=eps) - s, r, done, truncated, info = env.step(a) - episode_reward += r - print("Episode reward:", episode_reward) - - -if __name__ == '__main__': - # instantiate environment - env = gym.make("CartPole-v1", render_mode="rgb_array") - - model = Model(env) - reward_per_episode = [] - - # watch untrained agent - watch_agent(model, env, eps=0) - - # repeat until convergence - n_episodes = 1500 - for it in range(n_episodes): - s, info = env.reset() - episode_reward = 0 - done = False - truncated = False - while not (done or truncated): - a = epsilon_greedy(model, s) - s2, r, done, truncated, info = env.step(a) - - # get the target - if done: - target = r - else: - values = model.predict_all_actions(s2) - target = r + GAMMA * np.max(values) - - # update the model - g = model.grad(s, a) - err = target - model.predict(s, a) - model.w += ALPHA * err * g - - # accumulate reward - episode_reward += r - - # update state - s = s2 - - if (it + 1) % 50 == 0: - print(f"Episode: {it + 1}, Reward: {episode_reward}") - - # early exit - if it > 20 and np.mean(reward_per_episode[-20:]) == 200: - print("Early exit") - break - - reward_per_episode.append(episode_reward) - - # test trained agent - test_reward = test_agent(model, env) - print(f"Average test reward: {test_reward}") - - plt.plot(reward_per_episode) - plt.title("Reward per episode") - plt.show() - - # watch trained agent - env = gym.make("CartPole-v1", render_mode="human") - watch_agent(model, env, eps=0) - diff --git a/rl/cartpole_gym0.19.py b/rl/cartpole_gym0.19.py deleted file mode 100644 index 2ef157b5..00000000 --- a/rl/cartpole_gym0.19.py +++ /dev/null @@ -1,153 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import gym -import numpy as np -import matplotlib.pyplot as plt -from sklearn.kernel_approximation import RBFSampler - - -GAMMA = 0.99 -ALPHA = 0.1 - - -def epsilon_greedy(model, s, eps=0.1): - # we'll use epsilon-soft to ensure all states are visited - # what happens if you don't do this? i.e. eps=0 - p = np.random.random() - if p < (1 - eps): - values = model.predict_all_actions(s) - return np.argmax(values) - else: - return model.env.action_space.sample() - - -def gather_samples(env, n_episodes=10000): - samples = [] - for _ in range(n_episodes): - s = env.reset() - done = False - while not done: - a = env.action_space.sample() - sa = np.concatenate((s, [a])) - samples.append(sa) - - s, r, done, info = env.step(a) - return samples - - -class Model: - def __init__(self, env): - # fit the featurizer to data - self.env = env - samples = gather_samples(env) - self.featurizer = RBFSampler() - self.featurizer.fit(samples) - dims = self.featurizer.n_components - - # initialize linear model weights - self.w = np.zeros(dims) - - def predict(self, s, a): - sa = np.concatenate((s, [a])) - x = self.featurizer.transform([sa])[0] - return x @ self.w - - def predict_all_actions(self, s): - return [self.predict(s, a) for a in range(self.env.action_space.n)] - - def grad(self, s, a): - sa = np.concatenate((s, [a])) - x = self.featurizer.transform([sa])[0] - return x - - -def test_agent(model, env, n_episodes=20): - reward_per_episode = np.zeros(n_episodes) - for it in range(n_episodes): - done = False - episode_reward = 0 - s = env.reset() - while not done: - a = epsilon_greedy(model, s, eps=0) - s, r, done, info = env.step(a) - episode_reward += r - reward_per_episode[it] = episode_reward - return np.mean(reward_per_episode) - - -def watch_agent(model, env, eps): - done = False - episode_reward = 0 - s = env.reset() - while not done: - a = epsilon_greedy(model, s, eps=eps) - s, r, done, info = env.step(a) - env.render() - episode_reward += r - print("Episode reward:", episode_reward) - - -if __name__ == '__main__': - # instantiate environment - env = gym.make("CartPole-v0") - - model = Model(env) - reward_per_episode = [] - - # watch untrained agent - watch_agent(model, env, eps=0) - - # repeat until convergence - n_episodes = 1500 - for it in range(n_episodes): - s = env.reset() - episode_reward = 0 - done = False - while not done: - a = epsilon_greedy(model, s) - s2, r, done, info = env.step(a) - - # get the target - if done: - target = r - else: - values = model.predict_all_actions(s2) - target = r + GAMMA * np.max(values) - - # update the model - g = model.grad(s, a) - err = target - model.predict(s, a) - model.w += ALPHA * err * g - - # accumulate reward - episode_reward += r - - # update state - s = s2 - - if (it + 1) % 50 == 0: - print(f"Episode: {it + 1}, Reward: {episode_reward}") - - # early exit - if it > 20 and np.mean(reward_per_episode[-20:]) == 200: - print("Early exit") - break - - reward_per_episode.append(episode_reward) - - # test trained agent - test_reward = test_agent(model, env) - print(f"Average test reward: {test_reward}") - - plt.plot(reward_per_episode) - plt.title("Reward per episode") - plt.show() - - # watch trained agent - watch_agent(model, env, eps=0) - diff --git a/rl/comparing_epsilons.py b/rl/comparing_epsilons.py deleted file mode 100644 index ca06b72f..00000000 --- a/rl/comparing_epsilons.py +++ /dev/null @@ -1,78 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - - -class Bandit: - def __init__(self, m): - self.m = m - self.mean = 0 - self.N = 0 - - def pull(self): - return np.random.randn() + self.m - - def update(self, x): - self.N += 1 - self.mean = (1 - 1.0/self.N)*self.mean + 1.0/self.N*x - - -def run_experiment(m1, m2, m3, eps, N): - bandits = [Bandit(m1), Bandit(m2), Bandit(m3)] - - data = np.empty(N) - - for i in range(N): - # epsilon greedy - p = np.random.random() - if p < eps: - j = np.random.choice(3) - else: - j = np.argmax([b.mean for b in bandits]) - x = bandits[j].pull() - bandits[j].update(x) - - # for the plot - data[i] = x - cumulative_average = np.cumsum(data) / (np.arange(N) + 1) - - # plot moving average ctr - plt.plot(cumulative_average) - plt.plot(np.ones(N)*m1) - plt.plot(np.ones(N)*m2) - plt.plot(np.ones(N)*m3) - plt.xscale('log') - plt.show() - - for b in bandits: - print(b.mean) - - return cumulative_average - -if __name__ == '__main__': - c_1 = run_experiment(1.0, 2.0, 3.0, 0.1, 100000) - c_05 = run_experiment(1.0, 2.0, 3.0, 0.05, 100000) - c_01 = run_experiment(1.0, 2.0, 3.0, 0.01, 100000) - - # log scale plot - plt.plot(c_1, label='eps = 0.1') - plt.plot(c_05, label='eps = 0.05') - plt.plot(c_01, label='eps = 0.01') - plt.legend() - plt.xscale('log') - plt.show() - - - # linear plot - plt.plot(c_1, label='eps = 0.1') - plt.plot(c_05, label='eps = 0.05') - plt.plot(c_01, label='eps = 0.01') - plt.legend() - plt.show() - diff --git a/rl/comparing_explore_exploit_methods.py b/rl/comparing_explore_exploit_methods.py deleted file mode 100644 index 0005b8a1..00000000 --- a/rl/comparing_explore_exploit_methods.py +++ /dev/null @@ -1,119 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt -from comparing_epsilons import Bandit -from optimistic_initial_values import run_experiment as run_experiment_oiv -from ucb1 import run_experiment as run_experiment_ucb - -class BayesianBandit: - def __init__(self, true_mean): - self.true_mean = true_mean - # parameters for mu - prior is N(0,1) - self.predicted_mean = 0 - self.lambda_ = 1 - self.sum_x = 0 # for convenience - self.tau = 1 - - def pull(self): - return np.random.randn() + self.true_mean - - def sample(self): - return np.random.randn() / np.sqrt(self.lambda_) + self.predicted_mean - - def update(self, x): - self.lambda_ += self.tau - self.sum_x += x - self.predicted_mean = self.tau*self.sum_x / self.lambda_ - - -def run_experiment_decaying_epsilon(m1, m2, m3, N): - bandits = [Bandit(m1), Bandit(m2), Bandit(m3)] - - data = np.empty(N) - - for i in range(N): - # epsilon greedy - p = np.random.random() - if p < 1.0/(i+1): - j = np.random.choice(3) - else: - j = np.argmax([b.mean for b in bandits]) - x = bandits[j].pull() - bandits[j].update(x) - - # for the plot - data[i] = x - cumulative_average = np.cumsum(data) / (np.arange(N) + 1) - - # plot moving average ctr - plt.plot(cumulative_average) - plt.plot(np.ones(N)*m1) - plt.plot(np.ones(N)*m2) - plt.plot(np.ones(N)*m3) - plt.xscale('log') - plt.show() - - for b in bandits: - print(b.mean) - - return cumulative_average - - -def run_experiment(m1, m2, m3, N): - bandits = [BayesianBandit(m1), BayesianBandit(m2), BayesianBandit(m3)] - - data = np.empty(N) - - for i in range(N): - # optimistic initial values - j = np.argmax([b.sample() for b in bandits]) - x = bandits[j].pull() - bandits[j].update(x) - - # for the plot - data[i] = x - cumulative_average = np.cumsum(data) / (np.arange(N) + 1) - - # plot moving average ctr - plt.plot(cumulative_average) - plt.plot(np.ones(N)*m1) - plt.plot(np.ones(N)*m2) - plt.plot(np.ones(N)*m3) - plt.xscale('log') - plt.show() - - return cumulative_average - -if __name__ == '__main__': - m1 = 1.0 - m2 = 2.0 - m3 = 3.0 - eps = run_experiment_decaying_epsilon(m1, m2, m3, 100000) - oiv = run_experiment_oiv(m1, m2, m3, 100000) - ucb = run_experiment_ucb(m1, m2, m3, 100000) - bayes = run_experiment(m1, m2, m3, 100000) - - # log scale plot - plt.plot(eps, label='decaying-epsilon-greedy') - plt.plot(oiv, label='optimistic') - plt.plot(ucb, label='ucb1') - plt.plot(bayes, label='bayesian') - plt.legend() - plt.xscale('log') - plt.show() - - - # linear plot - plt.plot(eps, label='decaying-epsilon-greedy') - plt.plot(oiv, label='optimistic') - plt.plot(ucb, label='ucb1') - plt.plot(bayes, label='bayesian') - plt.legend() - plt.show() - diff --git a/rl/epsilon_greedy.py b/rl/epsilon_greedy.py deleted file mode 100644 index b906de88..00000000 --- a/rl/epsilon_greedy.py +++ /dev/null @@ -1,88 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import matplotlib.pyplot as plt -import numpy as np - - -NUM_TRIALS = 10000 -EPS = 0.1 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - # p: the win rate - self.p = p - self.p_estimate = 0. - self.N = 0. # num samples collected so far - - def pull(self): - # draw a 1 with probability p - return np.random.random() < self.p - - def update(self, x): - self.N += 1. - self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N - - -def experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - - rewards = np.zeros(NUM_TRIALS) - num_times_explored = 0 - num_times_exploited = 0 - num_optimal = 0 - optimal_j = np.argmax([b.p for b in bandits]) - print("optimal j:", optimal_j) - - for i in range(NUM_TRIALS): - - # use epsilon-greedy to select the next bandit - if np.random.random() < EPS: - num_times_explored += 1 - j = np.random.randint(len(bandits)) - else: - num_times_exploited += 1 - j = np.argmax([b.p_estimate for b in bandits]) - - if j == optimal_j: - num_optimal += 1 - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update rewards log - rewards[i] = x - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - - - # print mean estimates for each bandit - for b in bandits: - print("mean estimate:", b.p_estimate) - - # print total reward - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num_times_explored:", num_times_explored) - print("num_times_exploited:", num_times_exploited) - print("num times selected optimal bandit:", num_optimal) - - # plot the results - cumulative_rewards = np.cumsum(rewards) - win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1) - plt.plot(win_rates) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.show() - -if __name__ == "__main__": - experiment() diff --git a/rl/epsilon_greedy_starter.py b/rl/epsilon_greedy_starter.py deleted file mode 100644 index 4bb9d278..00000000 --- a/rl/epsilon_greedy_starter.py +++ /dev/null @@ -1,88 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import matplotlib.pyplot as plt -import numpy as np - - -NUM_TRIALS = 10000 -EPS = 0.1 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - # p: the win rate - self.p = p - self.p_estimate = # TODO - self.N = # TODO - - def pull(self): - # draw a 1 with probability p - return np.random.random() < self.p - - def update(self, x): - self.N = # TODO - self.p_estimate = # TODO - - -def experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - - rewards = np.zeros(NUM_TRIALS) - num_times_explored = 0 - num_times_exploited = 0 - num_optimal = 0 - optimal_j = np.argmax([b.p for b in bandits]) - print("optimal j:", optimal_j) - - for i in range(NUM_TRIALS): - - # use epsilon-greedy to select the next bandit - if np.random.random() < EPS: - num_times_explored += 1 - j = # TODO - else: - num_times_exploited += 1 - j = # TODO - - if j == optimal_j: - num_optimal += 1 - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update rewards log - rewards[i] = x - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - - - # print mean estimates for each bandit - for b in bandits: - print("mean estimate:", b.p_estimate) - - # print total reward - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num_times_explored:", num_times_explored) - print("num_times_exploited:", num_times_exploited) - print("num times selected optimal bandit:", num_optimal) - - # plot the results - cumulative_rewards = np.cumsum(rewards) - win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1) - plt.plot(win_rates) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.show() - -if __name__ == "__main__": - experiment() diff --git a/rl/extra_reading.txt b/rl/extra_reading.txt deleted file mode 100644 index fac79d64..00000000 --- a/rl/extra_reading.txt +++ /dev/null @@ -1,53 +0,0 @@ -Finite-time Analysis of the Multiarmed Bandit Problem -https://homes.di.unimi.it/cesa-bianchi/Pubblicazioni/ml-02.pdf - -A Nice Lecture for Students Who Claim "RL Doesn't Use Math" -https://www.youtube.com/watch?v=dhEF5pfYmvc - -Hacking Google reCAPTCHA v3 using Reinforcement Learning -https://arxiv.org/pdf/1903.01003.pdf - -Practical Deep Reinforcement Learning Approach for Stock Trading -https://arxiv.org/abs/1811.07522 - -Reinforcement Learning: A Tutorial Survey and Recent Advances - Abhijit Gosavi -http://web.mst.edu/~gosavia/joc.pdf - -Algorithms for Reinforcement Learning - Csaba Szepesv´ari -http://old.sztaki.hu/~szcsaba/papers/RLAlgsInMDPs-lecture.pdf - -Markov Decision Processes in Artificial Intelligence -https://zodml.org/sites/default/files/Markov_Decision_Processes_and_Artificial_Intelligence.pdf - -MDP Preliminaries -http://nanjiang.cs.illinois.edu/files/cs598/note1.pdf - -Concentration Inequalities and Multi-Armed Bandits -http://nanjiang.cs.illinois.edu/files/cs598/note_bandit.pdf - -Notes on Tabular Methods -http://nanjiang.cs.illinois.edu/files/cs598/note3.pdf - -Notes on State Abstractions -http://nanjiang.cs.illinois.edu/files/cs598/note4.pdf - -Notes on Fitted Q-iteration -http://nanjiang.cs.illinois.edu/files/cs598/note5.pdf - -Convergence of Stochastic Iterative Dynamic Programming Algorithms -https://papers.nips.cc/paper/764-convergence-of-stochastic-iterative-dynamic-programming-algorithms.pdf - -Sutton & Barto -http://incompleteideas.net/sutton/book/the-book-2nd.html - -Finite-Sample Analysis of Proximal Gradient TD Algorithms -https://marek.petrik.us/pub/Liu2015.pdf - -Finite Sample Analyses for TD(0) with Function Approximation -https://arxiv.org/pdf/1704.01161.pdf - -Mastering the game of Go with deep neural networks and tree search - Silver, D. et al. -https://storage.googleapis.com/deepmind-media/alphago/AlphaGoNaturePaper.pdf - -Learning Rates for Q-learning -http://www.jmlr.org/papers/volume5/evendar03a/evendar03a.pdf \ No newline at end of file diff --git a/rl/grid_world.py b/rl/grid_world.py deleted file mode 100644 index ff3c68af..00000000 --- a/rl/grid_world.py +++ /dev/null @@ -1,370 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np - - -ACTION_SPACE = ('U', 'D', 'L', 'R') - - -class Grid: # Environment - def __init__(self, rows, cols, start): - self.rows = rows - self.cols = cols - self.i = start[0] - self.j = start[1] - - def set(self, rewards, actions): - # rewards should be a dict of: (i, j): r (row, col): reward - # actions should be a dict of: (i, j): A (row, col): list of possible actions - self.rewards = rewards - self.actions = actions - - def set_state(self, s): - self.i = s[0] - self.j = s[1] - - def current_state(self): - return (self.i, self.j) - - def is_terminal(self, s): - return s not in self.actions - - def reset(self): - # put agent back in start position - self.i = 2 - self.j = 0 - return (self.i, self.j) - - def get_next_state(self, s, a): - # this answers: where would I end up if I perform action 'a' in state 's'? - i, j = s[0], s[1] - - # if this action moves you somewhere else, then it will be in this dictionary - if a in self.actions[(i, j)]: - if a == 'U': - i -= 1 - elif a == 'D': - i += 1 - elif a == 'R': - j += 1 - elif a == 'L': - j -= 1 - return i, j - - def move(self, action): - # check if legal move first - if action in self.actions[(self.i, self.j)]: - if action == 'U': - self.i -= 1 - elif action == 'D': - self.i += 1 - elif action == 'R': - self.j += 1 - elif action == 'L': - self.j -= 1 - # return a reward (if any) - return self.rewards.get((self.i, self.j), 0) - - def undo_move(self, action): - # these are the opposite of what U/D/L/R should normally do - if action == 'U': - self.i += 1 - elif action == 'D': - self.i -= 1 - elif action == 'R': - self.j -= 1 - elif action == 'L': - self.j += 1 - # raise an exception if we arrive somewhere we shouldn't be - # should never happen - assert(self.current_state() in self.all_states()) - - def game_over(self): - # returns true if game is over, else false - # true if we are in a state where no actions are possible - return (self.i, self.j) not in self.actions - - def all_states(self): - # possibly buggy but simple way to get all states - # either a position that has possible next actions - # or a position that yields a reward - return set(self.actions.keys()) | set(self.rewards.keys()) - - -def standard_grid(): - # define a grid that describes the reward for arriving at each state - # and possible actions at each state - # the grid looks like this - # x means you can't go there - # s means start position - # number means reward at that state - # . . . 1 - # . x . -1 - # s . . . - g = Grid(3, 4, (2, 0)) - rewards = {(0, 3): 1, (1, 3): -1} - actions = { - (0, 0): ('D', 'R'), - (0, 1): ('L', 'R'), - (0, 2): ('L', 'D', 'R'), - (1, 0): ('U', 'D'), - (1, 2): ('U', 'D', 'R'), - (2, 0): ('U', 'R'), - (2, 1): ('L', 'R'), - (2, 2): ('L', 'R', 'U'), - (2, 3): ('L', 'U'), - } - g.set(rewards, actions) - return g - - -def negative_grid(step_cost=-0.1): - # in this game we want to try to minimize the number of moves - # so we will penalize every move - g = standard_grid() - g.rewards.update({ - (0, 0): step_cost, - (0, 1): step_cost, - (0, 2): step_cost, - (1, 0): step_cost, - (1, 2): step_cost, - (2, 0): step_cost, - (2, 1): step_cost, - (2, 2): step_cost, - (2, 3): step_cost, - }) - return g - - - - - -class WindyGrid: - def __init__(self, rows, cols, start): - self.rows = rows - self.cols = cols - self.i = start[0] - self.j = start[1] - - def set(self, rewards, actions, probs): - # rewards should be a dict of: (i, j): r (row, col): reward - # actions should be a dict of: (i, j): A (row, col): list of possible actions - self.rewards = rewards - self.actions = actions - self.probs = probs - - def set_state(self, s): - self.i = s[0] - self.j = s[1] - - def current_state(self): - return (self.i, self.j) - - def is_terminal(self, s): - return s not in self.actions - - def move(self, action): - s = (self.i, self.j) - a = action - - next_state_probs = self.probs[(s, a)] - next_states = list(next_state_probs.keys()) - next_probs = list(next_state_probs.values()) - next_state_idx = np.random.choice(len(next_states), p=next_probs) - s2 = next_states[next_state_idx] - - # update the current state - self.i, self.j = s2 - - # return a reward (if any) - return self.rewards.get(s2, 0) - - def game_over(self): - # returns true if game is over, else false - # true if we are in a state where no actions are possible - return (self.i, self.j) not in self.actions - - def all_states(self): - # possibly buggy but simple way to get all states - # either a position that has possible next actions - # or a position that yields a reward - return set(self.actions.keys()) | set(self.rewards.keys()) - - -def windy_grid(): - g = WindyGrid(3, 4, (2, 0)) - rewards = {(0, 3): 1, (1, 3): -1} - actions = { - (0, 0): ('D', 'R'), - (0, 1): ('L', 'R'), - (0, 2): ('L', 'D', 'R'), - (1, 0): ('U', 'D'), - (1, 2): ('U', 'D', 'R'), - (2, 0): ('U', 'R'), - (2, 1): ('L', 'R'), - (2, 2): ('L', 'R', 'U'), - (2, 3): ('L', 'U'), - } - - # p(s' | s, a) represented as: - # KEY: (s, a) --> VALUE: {s': p(s' | s, a)} - probs = { - ((2, 0), 'U'): {(1, 0): 1.0}, - ((2, 0), 'D'): {(2, 0): 1.0}, - ((2, 0), 'L'): {(2, 0): 1.0}, - ((2, 0), 'R'): {(2, 1): 1.0}, - ((1, 0), 'U'): {(0, 0): 1.0}, - ((1, 0), 'D'): {(2, 0): 1.0}, - ((1, 0), 'L'): {(1, 0): 1.0}, - ((1, 0), 'R'): {(1, 0): 1.0}, - ((0, 0), 'U'): {(0, 0): 1.0}, - ((0, 0), 'D'): {(1, 0): 1.0}, - ((0, 0), 'L'): {(0, 0): 1.0}, - ((0, 0), 'R'): {(0, 1): 1.0}, - ((0, 1), 'U'): {(0, 1): 1.0}, - ((0, 1), 'D'): {(0, 1): 1.0}, - ((0, 1), 'L'): {(0, 0): 1.0}, - ((0, 1), 'R'): {(0, 2): 1.0}, - ((0, 2), 'U'): {(0, 2): 1.0}, - ((0, 2), 'D'): {(1, 2): 1.0}, - ((0, 2), 'L'): {(0, 1): 1.0}, - ((0, 2), 'R'): {(0, 3): 1.0}, - ((2, 1), 'U'): {(2, 1): 1.0}, - ((2, 1), 'D'): {(2, 1): 1.0}, - ((2, 1), 'L'): {(2, 0): 1.0}, - ((2, 1), 'R'): {(2, 2): 1.0}, - ((2, 2), 'U'): {(1, 2): 1.0}, - ((2, 2), 'D'): {(2, 2): 1.0}, - ((2, 2), 'L'): {(2, 1): 1.0}, - ((2, 2), 'R'): {(2, 3): 1.0}, - ((2, 3), 'U'): {(1, 3): 1.0}, - ((2, 3), 'D'): {(2, 3): 1.0}, - ((2, 3), 'L'): {(2, 2): 1.0}, - ((2, 3), 'R'): {(2, 3): 1.0}, - ((1, 2), 'U'): {(0, 2): 0.5, (1, 3): 0.5}, - ((1, 2), 'D'): {(2, 2): 1.0}, - ((1, 2), 'L'): {(1, 2): 1.0}, - ((1, 2), 'R'): {(1, 3): 1.0}, - } - g.set(rewards, actions, probs) - return g - - -def windy_grid_no_wind(): - g = windy_grid() - g.probs[((1, 2), 'U')] = {(0, 2): 1.0} - return g - - - -def windy_grid_penalized(step_cost=-0.1): - g = WindyGrid(3, 4, (2, 0)) - rewards = { - (0, 0): step_cost, - (0, 1): step_cost, - (0, 2): step_cost, - (1, 0): step_cost, - (1, 2): step_cost, - (2, 0): step_cost, - (2, 1): step_cost, - (2, 2): step_cost, - (2, 3): step_cost, - (0, 3): 1, - (1, 3): -1 - } - actions = { - (0, 0): ('D', 'R'), - (0, 1): ('L', 'R'), - (0, 2): ('L', 'D', 'R'), - (1, 0): ('U', 'D'), - (1, 2): ('U', 'D', 'R'), - (2, 0): ('U', 'R'), - (2, 1): ('L', 'R'), - (2, 2): ('L', 'R', 'U'), - (2, 3): ('L', 'U'), - } - - # p(s' | s, a) represented as: - # KEY: (s, a) --> VALUE: {s': p(s' | s, a)} - probs = { - ((2, 0), 'U'): {(1, 0): 1.0}, - ((2, 0), 'D'): {(2, 0): 1.0}, - ((2, 0), 'L'): {(2, 0): 1.0}, - ((2, 0), 'R'): {(2, 1): 1.0}, - ((1, 0), 'U'): {(0, 0): 1.0}, - ((1, 0), 'D'): {(2, 0): 1.0}, - ((1, 0), 'L'): {(1, 0): 1.0}, - ((1, 0), 'R'): {(1, 0): 1.0}, - ((0, 0), 'U'): {(0, 0): 1.0}, - ((0, 0), 'D'): {(1, 0): 1.0}, - ((0, 0), 'L'): {(0, 0): 1.0}, - ((0, 0), 'R'): {(0, 1): 1.0}, - ((0, 1), 'U'): {(0, 1): 1.0}, - ((0, 1), 'D'): {(0, 1): 1.0}, - ((0, 1), 'L'): {(0, 0): 1.0}, - ((0, 1), 'R'): {(0, 2): 1.0}, - ((0, 2), 'U'): {(0, 2): 1.0}, - ((0, 2), 'D'): {(1, 2): 1.0}, - ((0, 2), 'L'): {(0, 1): 1.0}, - ((0, 2), 'R'): {(0, 3): 1.0}, - ((2, 1), 'U'): {(2, 1): 1.0}, - ((2, 1), 'D'): {(2, 1): 1.0}, - ((2, 1), 'L'): {(2, 0): 1.0}, - ((2, 1), 'R'): {(2, 2): 1.0}, - ((2, 2), 'U'): {(1, 2): 1.0}, - ((2, 2), 'D'): {(2, 2): 1.0}, - ((2, 2), 'L'): {(2, 1): 1.0}, - ((2, 2), 'R'): {(2, 3): 1.0}, - ((2, 3), 'U'): {(1, 3): 1.0}, - ((2, 3), 'D'): {(2, 3): 1.0}, - ((2, 3), 'L'): {(2, 2): 1.0}, - ((2, 3), 'R'): {(2, 3): 1.0}, - ((1, 2), 'U'): {(0, 2): 0.5, (1, 3): 0.5}, - ((1, 2), 'D'): {(2, 2): 1.0}, - ((1, 2), 'L'): {(1, 2): 1.0}, - ((1, 2), 'R'): {(1, 3): 1.0}, - } - g.set(rewards, actions, probs) - return g - - - -def grid_5x5(step_cost=-0.1): - g = Grid(5, 5, (4, 0)) - rewards = {(0, 4): 1, (1, 4): -1} - actions = { - (0, 0): ('D', 'R'), - (0, 1): ('L', 'R'), - (0, 2): ('L', 'R'), - (0, 3): ('L', 'D', 'R'), - (1, 0): ('U', 'D', 'R'), - (1, 1): ('U', 'D', 'L'), - (1, 3): ('U', 'D', 'R'), - (2, 0): ('U', 'D', 'R'), - (2, 1): ('U', 'L', 'R'), - (2, 2): ('L', 'R', 'D'), - (2, 3): ('L', 'R', 'U'), - (2, 4): ('L', 'U', 'D'), - (3, 0): ('U', 'D'), - (3, 2): ('U', 'D'), - (3, 4): ('U', 'D'), - (4, 0): ('U', 'R'), - (4, 1): ('L', 'R'), - (4, 2): ('L', 'R', 'U'), - (4, 3): ('L', 'R'), - (4, 4): ('L', 'U'), - } - g.set(rewards, actions) - - # non-terminal states - visitable_states = actions.keys() - for s in visitable_states: - g.rewards[s] = step_cost - - return g - diff --git a/rl/iterative_policy_evaluation_deterministic.py b/rl/iterative_policy_evaluation_deterministic.py deleted file mode 100644 index 06ddc479..00000000 --- a/rl/iterative_policy_evaluation_deterministic.py +++ /dev/null @@ -1,111 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from grid_world import standard_grid, ACTION_SPACE - -SMALL_ENOUGH = 1e-3 # threshold for convergence - - -def print_values(V, g): - for i in range(g.rows): - print("---------------------------") - for j in range(g.cols): - v = V.get((i,j), 0) - if v >= 0: - print(" %.2f|" % v, end="") - else: - print("%.2f|" % v, end="") # -ve sign takes up an extra space - print("") - - -def print_policy(P, g): - for i in range(g.rows): - print("---------------------------") - for j in range(g.cols): - a = P.get((i,j), ' ') - print(" %s |" % a, end="") - print("") - - - -if __name__ == '__main__': - - ### define transition probabilities and grid ### - # the key is (s, a, s'), the value is the probability - # that is, transition_probs[(s, a, s')] = p(s' | s, a) - # any key NOT present will considered to be impossible (i.e. probability 0) - transition_probs = {} - - # to reduce the dimensionality of the dictionary, we'll use deterministic - # rewards, r(s, a, s') - # note: you could make it simpler by using r(s') since the reward doesn't - # actually depend on (s, a) - rewards = {} - - grid = standard_grid() - for i in range(grid.rows): - for j in range(grid.cols): - s = (i, j) - if not grid.is_terminal(s): - for a in ACTION_SPACE: - s2 = grid.get_next_state(s, a) - transition_probs[(s, a, s2)] = 1 - if s2 in grid.rewards: - rewards[(s, a, s2)] = grid.rewards[s2] - - ### fixed policy ### - policy = { - (2, 0): 'U', - (1, 0): 'U', - (0, 0): 'R', - (0, 1): 'R', - (0, 2): 'R', - (1, 2): 'U', - (2, 1): 'R', - (2, 2): 'U', - (2, 3): 'L', - } - print_policy(policy, grid) - - # initialize V(s) = 0 - V = {} - for s in grid.all_states(): - V[s] = 0 - - gamma = 0.9 # discount factor - - # repeat until convergence - it = 0 - while True: - biggest_change = 0 - for s in grid.all_states(): - if not grid.is_terminal(s): - old_v = V[s] - new_v = 0 # we will accumulate the answer - for a in ACTION_SPACE: - for s2 in grid.all_states(): - - # action probability is deterministic - action_prob = 1 if policy.get(s) == a else 0 - - # reward is a function of (s, a, s'), 0 if not specified - r = rewards.get((s, a, s2), 0) - new_v += action_prob * transition_probs.get((s, a, s2), 0) * (r + gamma * V[s2]) - - # after done getting the new value, update the value table - V[s] = new_v - biggest_change = max(biggest_change, np.abs(old_v - V[s])) - - print("iter:", it, "biggest_change:", biggest_change) - print_values(V, grid) - it += 1 - - if biggest_change < SMALL_ENOUGH: - break - print("\n\n") diff --git a/rl/iterative_policy_evaluation_probabilistic.py b/rl/iterative_policy_evaluation_probabilistic.py deleted file mode 100644 index 07d019c0..00000000 --- a/rl/iterative_policy_evaluation_probabilistic.py +++ /dev/null @@ -1,112 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from grid_world import windy_grid, ACTION_SPACE - -SMALL_ENOUGH = 1e-3 # threshold for convergence - - -def print_values(V, g): - for i in range(g.rows): - print("---------------------------") - for j in range(g.cols): - v = V.get((i,j), 0) - if v >= 0: - print(" %.2f|" % v, end="") - else: - print("%.2f|" % v, end="") # -ve sign takes up an extra space - print("") - - -def print_policy(P, g): - for i in range(g.rows): - print("---------------------------") - for j in range(g.cols): - a = P.get((i,j), ' ') - print(" %s |" % a, end="") - print("") - - - -if __name__ == '__main__': - - ### define transition probabilities and grid ### - # the key is (s, a, s'), the value is the probability - # that is, transition_probs[(s, a, s')] = p(s' | s, a) - # any key NOT present will considered to be impossible (i.e. probability 0) - # we can take this from the grid object and convert it to the format we want - transition_probs = {} - - # to reduce the dimensionality of the dictionary, we'll use deterministic - # rewards, r(s, a, s') - # note: you could make it simpler by using r(s') since the reward doesn't - # actually depend on (s, a) - rewards = {} - - grid = windy_grid() - for (s, a), v in grid.probs.items(): - for s2, p in v.items(): - transition_probs[(s, a, s2)] = p - rewards[(s, a, s2)] = grid.rewards.get(s2, 0) - - ### probabilistic policy ### - policy = { - (2, 0): {'U': 0.5, 'R': 0.5}, - (1, 0): {'U': 1.0}, - (0, 0): {'R': 1.0}, - (0, 1): {'R': 1.0}, - (0, 2): {'R': 1.0}, - (1, 2): {'U': 1.0}, - (2, 1): {'R': 1.0}, - (2, 2): {'U': 1.0}, - (2, 3): {'L': 1.0}, - } - print_policy(policy, grid) - - # initialize V(s) = 0 - V = {} - for s in grid.all_states(): - V[s] = 0 - - gamma = 0.9 # discount factor - - # repeat until convergence - it = 0 - while True: - biggest_change = 0 - for s in grid.all_states(): - if not grid.is_terminal(s): - old_v = V[s] - new_v = 0 # we will accumulate the answer - for a in ACTION_SPACE: - for s2 in grid.all_states(): - - # action probability is deterministic - action_prob = policy[s].get(a, 0) - - # reward is a function of (s, a, s'), 0 if not specified - r = rewards.get((s, a, s2), 0) - new_v += action_prob * transition_probs.get((s, a, s2), 0) * (r + gamma * V[s2]) - - # after done getting the new value, update the value table - V[s] = new_v - biggest_change = max(biggest_change, np.abs(old_v - V[s])) - - print("iter:", it, "biggest_change:", biggest_change) - print_values(V, grid) - it += 1 - - if biggest_change < SMALL_ENOUGH: - break - print("V:", V) - print("\n\n") - - # sanity check - # at state (1, 2), value is 0.5 * 0.9 * 1 + 0.5 * (-1) = -0.05 - diff --git a/rl/linear_rl_trader.py b/rl/linear_rl_trader.py deleted file mode 100644 index 9bda27e7..00000000 --- a/rl/linear_rl_trader.py +++ /dev/null @@ -1,385 +0,0 @@ -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from datetime import datetime -import itertools -import argparse -import re -import os -import pickle - -from sklearn.preprocessing import StandardScaler - - -# Let's use AAPL (Apple), MSI (Motorola), SBUX (Starbucks) -def get_data(): - # returns a T x 3 list of stock prices - # each row is a different stock - # 0 = AAPL - # 1 = MSI - # 2 = SBUX - df = pd.read_csv('../tf2.0/aapl_msi_sbux.csv') - return df.values - - - - - -def get_scaler(env): - # return scikit-learn scaler object to scale the states - # Note: you could also populate the replay buffer here - - states = [] - for _ in range(env.n_step): - action = np.random.choice(env.action_space) - state, reward, done, info = env.step(action) - states.append(state) - if done: - break - - scaler = StandardScaler() - scaler.fit(states) - return scaler - - - - -def maybe_make_dir(directory): - if not os.path.exists(directory): - os.makedirs(directory) - - - -class LinearModel: - """ A linear regression model """ - def __init__(self, input_dim, n_action): - self.W = np.random.randn(input_dim, n_action) / np.sqrt(input_dim) - self.b = np.zeros(n_action) - - # momentum terms - self.vW = 0 - self.vb = 0 - - self.losses = [] - - def predict(self, X): - # make sure X is N x D - assert(len(X.shape) == 2) - return X.dot(self.W) + self.b - - def sgd(self, X, Y, learning_rate=0.01, momentum=0.9): - # make sure X is N x D - assert(len(X.shape) == 2) - - # the loss values are 2-D - # normally we would divide by N only - # but now we divide by N x K - num_values = np.prod(Y.shape) - - # do one step of gradient descent - # we multiply by 2 to get the exact gradient - # (not adjusting the learning rate) - # i.e. d/dx (x^2) --> 2x - Yhat = self.predict(X) - gW = 2 * X.T.dot(Yhat - Y) / num_values - gb = 2 * (Yhat - Y).sum(axis=0) / num_values - - # update momentum terms - self.vW = momentum * self.vW - learning_rate * gW - self.vb = momentum * self.vb - learning_rate * gb - - # update params - self.W += self.vW - self.b += self.vb - - mse = np.mean((Yhat - Y)**2) - self.losses.append(mse) - - def load_weights(self, filepath): - npz = np.load(filepath) - self.W = npz['W'] - self.b = npz['b'] - - def save_weights(self, filepath): - np.savez(filepath, W=self.W, b=self.b) - - - - -class MultiStockEnv: - """ - A 3-stock trading environment. - State: vector of size 7 (n_stock * 2 + 1) - - # shares of stock 1 owned - - # shares of stock 2 owned - - # shares of stock 3 owned - - price of stock 1 (using daily close price) - - price of stock 2 - - price of stock 3 - - cash owned (can be used to purchase more stocks) - Action: categorical variable with 27 (3^3) possibilities - - for each stock, you can: - - 0 = sell - - 1 = hold - - 2 = buy - """ - def __init__(self, data, initial_investment=20000): - # data - self.stock_price_history = data - self.n_step, self.n_stock = self.stock_price_history.shape - - # instance attributes - self.initial_investment = initial_investment - self.cur_step = None - self.stock_owned = None - self.stock_price = None - self.cash_in_hand = None - - self.action_space = np.arange(3**self.n_stock) - - # action permutations - # returns a nested list with elements like: - # [0,0,0] - # [0,0,1] - # [0,0,2] - # [0,1,0] - # [0,1,1] - # etc. - # 0 = sell - # 1 = hold - # 2 = buy - self.action_list = list(map(list, itertools.product([0, 1, 2], repeat=self.n_stock))) - - # calculate size of state - self.state_dim = self.n_stock * 2 + 1 - - self.reset() - - - def reset(self): - self.cur_step = 0 - self.stock_owned = np.zeros(self.n_stock) - self.stock_price = self.stock_price_history[self.cur_step] - self.cash_in_hand = self.initial_investment - return self._get_obs() - - - def step(self, action): - assert action in self.action_space - - # get current value before performing the action - prev_val = self._get_val() - - # update price, i.e. go to the next day - self.cur_step += 1 - self.stock_price = self.stock_price_history[self.cur_step] - - # perform the trade - self._trade(action) - - # get the new value after taking the action - cur_val = self._get_val() - - # reward is the increase in porfolio value - reward = cur_val - prev_val - - # done if we have run out of data - done = self.cur_step == self.n_step - 1 - - # store the current value of the portfolio here - info = {'cur_val': cur_val} - - # conform to the Gym API - return self._get_obs(), reward, done, info - - - def _get_obs(self): - obs = np.empty(self.state_dim) - obs[:self.n_stock] = self.stock_owned - obs[self.n_stock:2*self.n_stock] = self.stock_price - obs[-1] = self.cash_in_hand - return obs - - - - def _get_val(self): - return self.stock_owned.dot(self.stock_price) + self.cash_in_hand - - - def _trade(self, action): - # index the action we want to perform - # 0 = sell - # 1 = hold - # 2 = buy - # e.g. [2,1,0] means: - # buy first stock - # hold second stock - # sell third stock - action_vec = self.action_list[action] - - # determine which stocks to buy or sell - sell_index = [] # stores index of stocks we want to sell - buy_index = [] # stores index of stocks we want to buy - for i, a in enumerate(action_vec): - if a == 0: - sell_index.append(i) - elif a == 2: - buy_index.append(i) - - # sell any stocks we want to sell - # then buy any stocks we want to buy - if sell_index: - # NOTE: to simplify the problem, when we sell, we will sell ALL shares of that stock - for i in sell_index: - self.cash_in_hand += self.stock_price[i] * self.stock_owned[i] - self.stock_owned[i] = 0 - if buy_index: - # NOTE: when buying, we will loop through each stock we want to buy, - # and buy one share at a time until we run out of cash - can_buy = True - while can_buy: - for i in buy_index: - if self.cash_in_hand > self.stock_price[i]: - self.stock_owned[i] += 1 # buy one share - self.cash_in_hand -= self.stock_price[i] - else: - can_buy = False - - - - - -class DQNAgent(object): - def __init__(self, state_size, action_size): - self.state_size = state_size - self.action_size = action_size - self.gamma = 0.95 # discount rate - self.epsilon = 1.0 # exploration rate - self.epsilon_min = 0.01 - self.epsilon_decay = 0.995 - self.model = LinearModel(state_size, action_size) - - def act(self, state): - if np.random.rand() <= self.epsilon: - return np.random.choice(self.action_size) - act_values = self.model.predict(state) - return np.argmax(act_values[0]) # returns action - - - def train(self, state, action, reward, next_state, done): - if done: - target = reward - else: - target = reward + self.gamma * np.amax(self.model.predict(next_state), axis=1) - - target_full = self.model.predict(state) - target_full[0, action] = target - - # Run one training step - self.model.sgd(state, target_full) - - if self.epsilon > self.epsilon_min: - self.epsilon *= self.epsilon_decay - - - def load(self, name): - self.model.load_weights(name) - - - def save(self, name): - self.model.save_weights(name) - - -def play_one_episode(agent, env, is_train): - # note: after transforming states are already 1xD - state = env.reset() - state = scaler.transform([state]) - done = False - - while not done: - action = agent.act(state) - next_state, reward, done, info = env.step(action) - next_state = scaler.transform([next_state]) - if is_train == 'train': - agent.train(state, action, reward, next_state, done) - state = next_state - - return info['cur_val'] - - - -if __name__ == '__main__': - - # config - models_folder = 'linear_rl_trader_models' - rewards_folder = 'linear_rl_trader_rewards' - num_episodes = 2000 - batch_size = 32 - initial_investment = 20000 - - - parser = argparse.ArgumentParser() - parser.add_argument('-m', '--mode', type=str, required=True, - help='either "train" or "test"') - args = parser.parse_args() - - maybe_make_dir(models_folder) - maybe_make_dir(rewards_folder) - - data = get_data() - n_timesteps, n_stocks = data.shape - - n_train = n_timesteps // 2 - - train_data = data[:n_train] - test_data = data[n_train:] - - env = MultiStockEnv(train_data, initial_investment) - state_size = env.state_dim - action_size = len(env.action_space) - agent = DQNAgent(state_size, action_size) - scaler = get_scaler(env) - - # store the final value of the portfolio (end of episode) - portfolio_value = [] - - if args.mode == 'test': - # then load the previous scaler - with open(f'{models_folder}/scaler.pkl', 'rb') as f: - scaler = pickle.load(f) - - # remake the env with test data - env = MultiStockEnv(test_data, initial_investment) - - # make sure epsilon is not 1! - # no need to run multiple episodes if epsilon = 0, it's deterministic - agent.epsilon = 0.01 - - # load trained weights - agent.load(f'{models_folder}/linear.npz') - - # play the game num_episodes times - for e in range(num_episodes): - t0 = datetime.now() - val = play_one_episode(agent, env, args.mode) - dt = datetime.now() - t0 - print(f"episode: {e + 1}/{num_episodes}, episode end value: {val:.2f}, duration: {dt}") - portfolio_value.append(val) # append episode end portfolio value - - # save the weights when we are done - if args.mode == 'train': - # save the DQN - agent.save(f'{models_folder}/linear.npz') - - # save the scaler - with open(f'{models_folder}/scaler.pkl', 'wb') as f: - pickle.dump(scaler, f) - - # plot losses - plt.plot(agent.model.losses) - plt.show() - - - # save portfolio value for each episode - np.save(f'{rewards_folder}/{args.mode}.npy', portfolio_value) diff --git a/rl/monte_carlo.py b/rl/monte_carlo.py deleted file mode 100644 index aedf786f..00000000 --- a/rl/monte_carlo.py +++ /dev/null @@ -1,110 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from grid_world import standard_grid, negative_grid -from iterative_policy_evaluation import print_values, print_policy - -GAMMA = 0.9 - -# NOTE: this is only policy evaluation, not optimization - -def play_game(grid, policy, max_steps=20): - # returns a list of states and corresponding returns - - # reset game to start at a random position - # we need to do this, because given our current deterministic policy - # we would never end up at certain states, but we still want to measure their value - start_states = list(grid.actions.keys()) - start_idx = np.random.choice(len(start_states)) - grid.set_state(start_states[start_idx]) - - s = grid.current_state() - - # keep track of all states and rewards encountered - states = [s] - rewards = [0] - - steps = 0 - while not grid.game_over(): - a = policy[s] - r = grid.move(a) - next_s = grid.current_state() - - # update states and rewards lists - states.append(next_s) - rewards.append(r) - - steps += 1 - if steps >= max_steps: - break - - # update state - # note: there is no need to store the final terminal state - s = next_s - - # we want to return: - # states = [s(0), s(1), ..., S(T)] - # rewards = [R(0), R(1), ..., R(T)] - - return states, rewards - - -if __name__ == '__main__': - # use the standard grid again (0 for every step) so that we can compare - # to iterative policy evaluation - grid = standard_grid() - - # print rewards - print("rewards:") - print_values(grid.rewards, grid) - - # state -> action - policy = { - (2, 0): 'U', - (1, 0): 'U', - (0, 0): 'R', - (0, 1): 'R', - (0, 2): 'R', - (1, 2): 'R', - (2, 1): 'R', - (2, 2): 'R', - (2, 3): 'U', - } - - # initialize V(s) and returns - V = {} - returns = {} # dictionary of state -> list of returns we've received - states = grid.all_states() - for s in states: - if s in grid.actions: - returns[s] = [] - else: - # terminal state or state we can't otherwise get to - V[s] = 0 - - # repeat - for _ in range(100): - # generate an episode using pi - states, rewards = play_game(grid, policy) - G = 0 - T = len(states) - for t in range(T - 2, -1, -1): - s = states[t] - r = rewards[t+1] - G = r + GAMMA * G # update return - - # we'll use first-visit Monte Carlo - if s not in states[:t]: - returns[s].append(G) - V[s] = np.mean(returns[s]) - - print("values:") - print_values(V, grid) - print("policy:") - print_policy(policy, grid) diff --git a/rl/monte_carlo_es.py b/rl/monte_carlo_es.py deleted file mode 100644 index 8f5f8573..00000000 --- a/rl/monte_carlo_es.py +++ /dev/null @@ -1,159 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from grid_world import standard_grid, negative_grid -from iterative_policy_evaluation import print_values, print_policy - -GAMMA = 0.9 -ALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R') - -# NOTE: this script implements the Monte Carlo Exploring-Starts method -# for finding the optimal policy - - -def play_game(grid, policy, max_steps=20): - # reset game to start at a random position - # we need to do this if we have a deterministic policy - # we would never end up at certain states, but we still want to measure their value - # this is called the "exploring starts" method - start_states = list(grid.actions.keys()) - start_idx = np.random.choice(len(start_states)) - grid.set_state(start_states[start_idx]) - - s = grid.current_state() - a = np.random.choice(ALL_POSSIBLE_ACTIONS) # first action is uniformly random - - states = [s] - actions = [a] - rewards = [0] - - for _ in range(max_steps): - r = grid.move(a) - s = grid.current_state() - - rewards.append(r) - states.append(s) - - if grid.game_over(): - break - else: - a = policy[s] - actions.append(a) - - # we want to return: - # states = [s(0), s(1), ..., s(T-1), s(T)] - # actions = [a(0), a(1), ..., a(T-1), ] - # rewards = [ 0, R(1), ..., R(T-1), R(T)] - - return states, actions, rewards - - -def max_dict(d): - # returns the argmax (key) and max (value) from a dictionary - # put this into a function since we are using it so often - - # find max val - max_val = max(d.values()) - - # find keys corresponding to max val - max_keys = [key for key, val in d.items() if val == max_val] - - ### slow version - # max_keys = [] - # for key, val in d.items(): - # if val == max_val: - # max_keys.append(key) - - return np.random.choice(max_keys), max_val - - -if __name__ == '__main__': - # use the standard grid again (0 for every step) so that we can compare - # to iterative policy evaluation - grid = standard_grid() - # try the negative grid too, to see if agent will learn to go past the "bad spot" - # in order to minimize number of steps - # grid = negative_grid(step_cost=-0.1) - - # print rewards - print("rewards:") - print_values(grid.rewards, grid) - - # state -> action - # initialize a random policy - policy = {} - for s in grid.actions.keys(): - policy[s] = np.random.choice(ALL_POSSIBLE_ACTIONS) - - # initialize Q(s,a) and returns - Q = {} - sample_counts = {} - states = grid.all_states() - for s in states: - if s in grid.actions: # not a terminal state - Q[s] = {} - sample_counts[s] = {} - for a in ALL_POSSIBLE_ACTIONS: - Q[s][a] = 0 - sample_counts[s][a] = 0 - else: - # terminal state or state we can't otherwise get to - pass - - # repeat until convergence - deltas = [] - for it in range(10000): - if it % 1000 == 0: - print(it) - - # generate an episode using pi - biggest_change = 0 - states, actions, rewards = play_game(grid, policy) - - # create a list of only state-action pairs for lookup - states_actions = list(zip(states, actions)) - - T = len(states) - G = 0 - for t in range(T - 2, -1, -1): - # retrieve current s, a, r tuple - s = states[t] - a = actions[t] - - # update G - G = rewards[t+1] + GAMMA * G - - # check if we have already seen (s, a) ("first-visit") - if (s, a) not in states_actions[:t]: - old_q = Q[s][a] - sample_counts[s][a] += 1 - lr = 1 / sample_counts[s][a] - Q[s][a] = old_q + lr * (G - old_q) - - # update policy - policy[s] = max_dict(Q[s])[0] - - # update delta - biggest_change = max(biggest_change, np.abs(old_q - Q[s][a])) - deltas.append(biggest_change) - - plt.plot(deltas) - plt.show() - - print("final policy:") - print_policy(policy, grid) - - # find V - V = {} - for s, Qs in Q.items(): - V[s] = max_dict(Q[s])[1] - - print("final values:") - print_values(V, grid) diff --git a/rl/monte_carlo_no_es.py b/rl/monte_carlo_no_es.py deleted file mode 100644 index 245ccb2f..00000000 --- a/rl/monte_carlo_no_es.py +++ /dev/null @@ -1,176 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from grid_world import standard_grid, negative_grid -from iterative_policy_evaluation import print_values, print_policy - -GAMMA = 0.9 -ALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R') - - - -def epsilon_greedy(policy, s, eps=0.1): - p = np.random.random() - if p < (1 - eps): - return policy[s] - else: - return np.random.choice(ALL_POSSIBLE_ACTIONS) - - -def play_game(grid, policy, max_steps=20): - - # start state - s = grid.reset() - - # choose action - a = epsilon_greedy(policy, s) - - states = [s] - actions = [a] - rewards = [0] - - for _ in range(max_steps): - r = grid.move(a) - s = grid.current_state() - - rewards.append(r) - states.append(s) - - if grid.game_over(): - break - else: - a = epsilon_greedy(policy, s) - actions.append(a) - - # we want to return: - # states = [s(0), s(1), ..., s(T-1), s(T)] - # actions = [a(0), a(1), ..., a(T-1), ] - # rewards = [ 0, R(1), ..., R(T-1), R(T)] - - return states, actions, rewards - - -def max_dict(d): - # returns the argmax (key) and max (value) from a dictionary - # put this into a function since we are using it so often - - # find max val - max_val = max(d.values()) - - # find keys corresponding to max val - max_keys = [key for key, val in d.items() if val == max_val] - - ### slow version - # max_keys = [] - # for key, val in d.items(): - # if val == max_val: - # max_keys.append(key) - - return np.random.choice(max_keys), max_val - - -if __name__ == '__main__': - # use the standard grid again (0 for every step) so that we can compare - # to iterative policy evaluation - grid = standard_grid() - # try the negative grid too, to see if agent will learn to go past the "bad spot" - # in order to minimize number of steps - # grid = negative_grid(step_cost=-0.1) - - # print rewards - print("rewards:") - print_values(grid.rewards, grid) - - # state -> action - # initialize a random policy - policy = {} - for s in grid.actions.keys(): - policy[s] = np.random.choice(ALL_POSSIBLE_ACTIONS) - - # initialize Q(s,a) and returns - Q = {} - sample_counts = {} - state_sample_count = {} - states = grid.all_states() - for s in states: - if s in grid.actions: # not a terminal state - Q[s] = {} - sample_counts[s] = {} - state_sample_count[s] = 0 - for a in ALL_POSSIBLE_ACTIONS: - Q[s][a] = 0 - sample_counts[s][a] = 0 - else: - # terminal state or state we can't otherwise get to - pass - - # repeat until convergence - deltas = [] - for it in range(10000): - if it % 1000 == 0: - print(it) - - # generate an episode using pi - biggest_change = 0 - states, actions, rewards = play_game(grid, policy) - - # create a list of only state-action pairs for lookup - states_actions = list(zip(states, actions)) - - T = len(states) - G = 0 - for t in range(T - 2, -1, -1): - # retrieve current s, a, r tuple - s = states[t] - a = actions[t] - - # update G - G = rewards[t+1] + GAMMA * G - - # check if we have already seen (s, a) ("first-visit") - if (s, a) not in states_actions[:t]: - old_q = Q[s][a] - sample_counts[s][a] += 1 - lr = 1 / sample_counts[s][a] - Q[s][a] = old_q + lr * (G - old_q) - - # update policy - policy[s] = max_dict(Q[s])[0] - - # update state sample count - state_sample_count[s] += 1 - - # update delta - biggest_change = max(biggest_change, np.abs(old_q - Q[s][a])) - deltas.append(biggest_change) - - plt.plot(deltas) - plt.show() - - print("final policy:") - print_policy(policy, grid) - - # find V - V = {} - for s, Qs in Q.items(): - V[s] = max_dict(Q[s])[1] - - print("final values:") - print_values(V, grid) - - print("state_sample_count:") - state_sample_count_arr = np.zeros((grid.rows, grid.cols)) - for i in range(grid.rows): - for j in range(grid.cols): - if (i, j) in state_sample_count: - state_sample_count_arr[i,j] = state_sample_count[(i, j)] - df = pd.DataFrame(state_sample_count_arr) - print(df) diff --git a/rl/optimistic.py b/rl/optimistic.py deleted file mode 100644 index 1d024fef..00000000 --- a/rl/optimistic.py +++ /dev/null @@ -1,71 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import matplotlib.pyplot as plt -import numpy as np - - -NUM_TRIALS = 10000 -EPS = 0.1 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - # p: the win rate - self.p = p - self.p_estimate = 5. - self.N = 1. # num samples collected so far - - def pull(self): - # draw a 1 with probability p - return np.random.random() < self.p - - def update(self, x): - self.N += 1. - self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N - - -def experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - - rewards = np.zeros(NUM_TRIALS) - for i in range(NUM_TRIALS): - # use optimistic initial values to select the next bandit - j = np.argmax([b.p_estimate for b in bandits]) - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update rewards log - rewards[i] = x - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - - # print mean estimates for each bandit - for b in bandits: - print("mean estimate:", b.p_estimate) - - # print total reward - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num times selected each bandit:", [b.N for b in bandits]) - - # plot the results - cumulative_rewards = np.cumsum(rewards) - win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1) - plt.ylim([0, 1]) - plt.plot(win_rates) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.show() - -if __name__ == "__main__": - experiment() diff --git a/rl/optimistic_initial_values.py b/rl/optimistic_initial_values.py deleted file mode 100644 index c2447887..00000000 --- a/rl/optimistic_initial_values.py +++ /dev/null @@ -1,72 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt -from comparing_epsilons import run_experiment as run_experiment_eps - - -class Bandit: - def __init__(self, m, upper_limit): - self.m = m - self.mean = upper_limit - self.N = 1 - - def pull(self): - return np.random.randn() + self.m - - def update(self, x): - self.N += 1 - self.mean = (1 - 1.0/self.N)*self.mean + 1.0/self.N*x - - -def run_experiment(m1, m2, m3, N, upper_limit=10): - bandits = [Bandit(m1, upper_limit), Bandit(m2, upper_limit), Bandit(m3, upper_limit)] - - data = np.empty(N) - - for i in range(N): - # optimistic initial values - j = np.argmax([b.mean for b in bandits]) - x = bandits[j].pull() - bandits[j].update(x) - - # for the plot - data[i] = x - cumulative_average = np.cumsum(data) / (np.arange(N) + 1) - - # plot moving average ctr - plt.plot(cumulative_average) - plt.plot(np.ones(N)*m1) - plt.plot(np.ones(N)*m2) - plt.plot(np.ones(N)*m3) - plt.xscale('log') - plt.show() - - for b in bandits: - print(b.mean) - - return cumulative_average - -if __name__ == '__main__': - c_1 = run_experiment_eps(1.0, 2.0, 3.0, 0.1, 100000) - oiv = run_experiment(1.0, 2.0, 3.0, 100000) - - # log scale plot - plt.plot(c_1, label='eps = 0.1') - plt.plot(oiv, label='optimistic') - plt.legend() - plt.xscale('log') - plt.show() - - - # linear plot - plt.plot(c_1, label='eps = 0.1') - plt.plot(oiv, label='optimistic') - plt.legend() - plt.show() - diff --git a/rl/optimistic_starter.py b/rl/optimistic_starter.py deleted file mode 100644 index 56b4e5c9..00000000 --- a/rl/optimistic_starter.py +++ /dev/null @@ -1,71 +0,0 @@ -# From the course: Bayesin Machine Learning in Python: A/B Testing -# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing -# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import matplotlib.pyplot as plt -import numpy as np - - -NUM_TRIALS = 10000 -EPS = 0.1 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - # p: the win rate - self.p = p - self.p_estimate = # TODO - self.N = # TODO - - def pull(self): - # draw a 1 with probability p - return np.random.random() < self.p - - def update(self, x): - # TODO - self.p_estimate = # TODO - - -def experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - - rewards = np.zeros(NUM_TRIALS) - for i in range(NUM_TRIALS): - # use optimistic initial values to select the next bandit - j = # TODO - - # pull the arm for the bandit with the largest sample - x = bandits[j].pull() - - # update rewards log - rewards[i] = x - - # update the distribution for the bandit whose arm we just pulled - bandits[j].update(x) - - - # print mean estimates for each bandit - for b in bandits: - print("mean estimate:", b.p_estimate) - - # print total reward - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num times selected each bandit:", [b.N for b in bandits]) - - # plot the results - cumulative_rewards = np.cumsum(rewards) - win_rates = cumulative_rewards / (np.arange(NUM_TRIALS) + 1) - plt.ylim([0, 1]) - plt.plot(win_rates) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.show() - -if __name__ == "__main__": - experiment() diff --git a/rl/plot_rl_rewards.py b/rl/plot_rl_rewards.py deleted file mode 100644 index ba182c9f..00000000 --- a/rl/plot_rl_rewards.py +++ /dev/null @@ -1,22 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import argparse - -parser = argparse.ArgumentParser() -parser.add_argument('-m', '--mode', type=str, required=True, - help='either "train" or "test"') -args = parser.parse_args() - -a = np.load(f'linear_rl_trader_rewards/{args.mode}.npy') - -print(f"average reward: {a.mean():.2f}, min: {a.min():.2f}, max: {a.max():.2f}") - -if args.mode == 'train': - # show the training progress - plt.plot(a) -else: - # test - show a histogram of rewards - plt.hist(a, bins=20) - -plt.title(args.mode) -plt.show() \ No newline at end of file diff --git a/rl/policy_iteration_deterministic.py b/rl/policy_iteration_deterministic.py deleted file mode 100644 index e18e75bf..00000000 --- a/rl/policy_iteration_deterministic.py +++ /dev/null @@ -1,140 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from grid_world import standard_grid, ACTION_SPACE -from iterative_policy_evaluation_deterministic import print_values, print_policy - -SMALL_ENOUGH = 1e-3 -GAMMA = 0.9 - - -# copied from iterative_policy_evaluation -def get_transition_probs_and_rewards(grid): - ### define transition probabilities and grid ### - # the key is (s, a, s'), the value is the probability - # that is, transition_probs[(s, a, s')] = p(s' | s, a) - # any key NOT present will considered to be impossible (i.e. probability 0) - transition_probs = {} - - # to reduce the dimensionality of the dictionary, we'll use deterministic - # rewards, r(s, a, s') - # note: you could make it simpler by using r(s') since the reward doesn't - # actually depend on (s, a) - rewards = {} - - for i in range(grid.rows): - for j in range(grid.cols): - s = (i, j) - if not grid.is_terminal(s): - for a in ACTION_SPACE: - s2 = grid.get_next_state(s, a) - transition_probs[(s, a, s2)] = 1 - if s2 in grid.rewards: - rewards[(s, a, s2)] = grid.rewards[s2] - - return transition_probs, rewards - - -def evaluate_deterministic_policy(grid, policy, initV=None): - # initialize V(s) = 0 - if initV is None: - V = {} - for s in grid.all_states(): - V[s] = 0 - else: - # it's faster to use the existing V(s) since the value won't change - # that much from one policy to the next - V = initV - - # repeat until convergence - it = 0 - while True: - biggest_change = 0 - for s in grid.all_states(): - if not grid.is_terminal(s): - old_v = V[s] - new_v = 0 # we will accumulate the answer - for a in ACTION_SPACE: - for s2 in grid.all_states(): - - # action probability is deterministic - action_prob = 1 if policy.get(s) == a else 0 - - # reward is a function of (s, a, s'), 0 if not specified - r = rewards.get((s, a, s2), 0) - new_v += action_prob * transition_probs.get((s, a, s2), 0) * (r + GAMMA * V[s2]) - - # after done getting the new value, update the value table - V[s] = new_v - biggest_change = max(biggest_change, np.abs(old_v - V[s])) - it += 1 - - if biggest_change < SMALL_ENOUGH: - break - return V - - -if __name__ == '__main__': - - grid = standard_grid() - transition_probs, rewards = get_transition_probs_and_rewards(grid) - - # print rewards - print("rewards:") - print_values(grid.rewards, grid) - - # state -> action - # we'll randomly choose an action and update as we learn - policy = {} - for s in grid.actions.keys(): - policy[s] = np.random.choice(ACTION_SPACE) - - # initial policy - print("initial policy:") - print_policy(policy, grid) - - # repeat until convergence - will break out when policy does not change - V = None - while True: - - # policy evaluation step - we already know how to do this! - V = evaluate_deterministic_policy(grid, policy, initV=V) - - # policy improvement step - is_policy_converged = True - for s in grid.actions.keys(): - old_a = policy[s] - new_a = None - best_value = float('-inf') - - # loop through all possible actions to find the best current action - for a in ACTION_SPACE: - v = 0 - for s2 in grid.all_states(): - # reward is a function of (s, a, s'), 0 if not specified - r = rewards.get((s, a, s2), 0) - v += transition_probs.get((s, a, s2), 0) * (r + GAMMA * V[s2]) - - if v > best_value: - best_value = v - new_a = a - - # new_a now represents the best action in this state - policy[s] = new_a - if new_a != old_a: - is_policy_converged = False - - if is_policy_converged: - break - - # once we're done, print the final policy and values - print("values:") - print_values(V, grid) - print("policy:") - print_policy(policy, grid) diff --git a/rl/policy_iteration_probabilistic.py b/rl/policy_iteration_probabilistic.py deleted file mode 100644 index 0468886c..00000000 --- a/rl/policy_iteration_probabilistic.py +++ /dev/null @@ -1,136 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from grid_world import windy_grid, windy_grid_penalized, ACTION_SPACE -from iterative_policy_evaluation import print_values, print_policy - -SMALL_ENOUGH = 1e-3 -GAMMA = 0.9 - - -# copied from iterative_policy_evaluation -def get_transition_probs_and_rewards(grid): - ### define transition probabilities and grid ### - # the key is (s, a, s'), the value is the probability - # that is, transition_probs[(s, a, s')] = p(s' | s, a) - # any key NOT present will considered to be impossible (i.e. probability 0) - transition_probs = {} - - # to reduce the dimensionality of the dictionary, we'll use deterministic - # rewards, r(s, a, s') - # note: you could make it simpler by using r(s') since the reward doesn't - # actually depend on (s, a) - rewards = {} - - for (s, a), v in grid.probs.items(): - for s2, p in v.items(): - transition_probs[(s, a, s2)] = p - rewards[(s, a, s2)] = grid.rewards.get(s2, 0) - - return transition_probs, rewards - - -def evaluate_deterministic_policy(grid, policy, initV=None): - # initialize V(s) = 0 - if initV is None: - V = {} - for s in grid.all_states(): - V[s] = 0 - else: - # it's faster to use the existing V(s) since the value won't change - # that much from one policy to the next - V = initV - - # repeat until convergence - it = 0 - while True: - biggest_change = 0 - for s in grid.all_states(): - if not grid.is_terminal(s): - old_v = V[s] - new_v = 0 # we will accumulate the answer - for a in ACTION_SPACE: - for s2 in grid.all_states(): - - # action probability is deterministic - action_prob = 1 if policy.get(s) == a else 0 - - # reward is a function of (s, a, s'), 0 if not specified - r = rewards.get((s, a, s2), 0) - new_v += action_prob * transition_probs.get((s, a, s2), 0) * (r + GAMMA * V[s2]) - - # after done getting the new value, update the value table - V[s] = new_v - biggest_change = max(biggest_change, np.abs(old_v - V[s])) - it += 1 - - if biggest_change < SMALL_ENOUGH: - break - return V - - -if __name__ == '__main__': - - grid = windy_grid_penalized(-0.1) - # grid = windy_grid() - transition_probs, rewards = get_transition_probs_and_rewards(grid) - - # print rewards - print("rewards:") - print_values(grid.rewards, grid) - - # state -> action - # we'll randomly choose an action and update as we learn - policy = {} - for s in grid.actions.keys(): - policy[s] = np.random.choice(ACTION_SPACE) - - # initial policy - print("initial policy:") - print_policy(policy, grid) - - # repeat until convergence - will break out when policy does not change - V = None - while True: - - # policy evaluation step - we already know how to do this! - V = evaluate_deterministic_policy(grid, policy, initV=V) - - # policy improvement step - is_policy_converged = True - for s in grid.actions.keys(): - old_a = policy[s] - new_a = None - best_value = float('-inf') - - # loop through all possible actions to find the best current action - for a in ACTION_SPACE: - v = 0 - for s2 in grid.all_states(): - # reward is a function of (s, a, s'), 0 if not specified - r = rewards.get((s, a, s2), 0) - v += transition_probs.get((s, a, s2), 0) * (r + GAMMA * V[s2]) - - if v > best_value: - best_value = v - new_a = a - - # new_a now represents the best action in this state - policy[s] = new_a - if new_a != old_a: - is_policy_converged = False - - if is_policy_converged: - break - - # once we're done, print the final policy and values - print("values:") - print_values(V, grid) - print("policy:") - print_policy(policy, grid) diff --git a/rl/q_learning.py b/rl/q_learning.py deleted file mode 100644 index d4c3c145..00000000 --- a/rl/q_learning.py +++ /dev/null @@ -1,102 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from grid_world import standard_grid, negative_grid -from iterative_policy_evaluation import print_values, print_policy -from monte_carlo_es import max_dict - -GAMMA = 0.9 -ALPHA = 0.1 -ALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R') - - -def epsilon_greedy(Q, s, eps=0.1): - if np.random.random() < eps: - return np.random.choice(ALL_POSSIBLE_ACTIONS) - else: - a_opt = max_dict(Q[s])[0] - return a_opt - - -if __name__ == '__main__': - # grid = standard_grid() - grid = negative_grid(step_cost=-0.1) - - # print rewards - print("rewards:") - print_values(grid.rewards, grid) - - # initialize Q(s,a) - Q = {} - states = grid.all_states() - for s in states: - Q[s] = {} - for a in ALL_POSSIBLE_ACTIONS: - Q[s][a] = 0 - - # let's also keep track of how many times Q[s] has been updated - update_counts = {} - - # repeat until convergence - reward_per_episode = [] - for it in range(10000): - if it % 2000 == 0: - print("it:", it) - - # begin a new episode - s = grid.reset() - episode_reward = 0 - while not grid.game_over(): - # perform action and get next state + reward - a = epsilon_greedy(Q, s, eps=0.1) - r = grid.move(a) - s2 = grid.current_state() - - # update reward - episode_reward += r - - # update Q(s,a) - maxQ = max_dict(Q[s2])[1] - Q[s][a] = Q[s][a] + ALPHA*(r + GAMMA*maxQ - Q[s][a]) - - # we would like to know how often Q(s) has been updated too - update_counts[s] = update_counts.get(s,0) + 1 - - # next state becomes current state - s = s2 - - # log the reward for this episode - reward_per_episode.append(episode_reward) - - plt.plot(reward_per_episode) - plt.title("reward_per_episode") - plt.show() - - # determine the policy from Q* - # find V* from Q* - policy = {} - V = {} - for s in grid.actions.keys(): - a, max_q = max_dict(Q[s]) - policy[s] = a - V[s] = max_q - - # what's the proportion of time we spend updating each part of Q? - print("update counts:") - total = np.sum(list(update_counts.values())) - for k, v in update_counts.items(): - update_counts[k] = float(v) / total - print_values(update_counts, grid) - - print("values:") - print_values(V, grid) - print("policy:") - print_policy(policy, grid) - diff --git a/rl/sarsa.py b/rl/sarsa.py deleted file mode 100644 index 5e1b9d5a..00000000 --- a/rl/sarsa.py +++ /dev/null @@ -1,105 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from grid_world import standard_grid, negative_grid -from iterative_policy_evaluation import print_values, print_policy -from monte_carlo_es import max_dict - -GAMMA = 0.9 -ALPHA = 0.1 -ALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R') - - -def epsilon_greedy(Q, s, eps=0.1): - if np.random.random() < eps: - return np.random.choice(ALL_POSSIBLE_ACTIONS) - else: - a_opt = max_dict(Q[s])[0] - return a_opt - - -if __name__ == '__main__': - # grid = standard_grid() - grid = negative_grid(step_cost=-0.1) - - # print rewards - print("rewards:") - print_values(grid.rewards, grid) - - # initialize Q(s,a) - Q = {} - states = grid.all_states() - for s in states: - Q[s] = {} - for a in ALL_POSSIBLE_ACTIONS: - Q[s][a] = 0 - - # let's also keep track of how many times Q[s] has been updated - update_counts = {} - - # repeat until convergence - reward_per_episode = [] - for it in range(10000): - if it % 2000 == 0: - print("it:", it) - - # begin a new episode - s = grid.reset() - a = epsilon_greedy(Q, s, eps=0.1) - episode_reward = 0 - while not grid.game_over(): - # perform action and get next state + reward - r = grid.move(a) - s2 = grid.current_state() - - # update reward - episode_reward += r - - # get next action - a2 = epsilon_greedy(Q, s2, eps=0.1) - - # update Q(s,a) - Q[s][a] = Q[s][a] + ALPHA*(r + GAMMA*Q[s2][a2] - Q[s][a]) - - # we would like to know how often Q(s) has been updated too - update_counts[s] = update_counts.get(s,0) + 1 - - # next state becomes current state - s = s2 - a = a2 - - # log the reward for this episode - reward_per_episode.append(episode_reward) - - plt.plot(reward_per_episode) - plt.title("reward_per_episode") - plt.show() - - # determine the policy from Q* - # find V* from Q* - policy = {} - V = {} - for s in grid.actions.keys(): - a, max_q = max_dict(Q[s]) - policy[s] = a - V[s] = max_q - - # what's the proportion of time we spend updating each part of Q? - print("update counts:") - total = np.sum(list(update_counts.values())) - for k, v in update_counts.items(): - update_counts[k] = float(v) / total - print_values(update_counts, grid) - - print("values:") - print_values(V, grid) - print("policy:") - print_policy(policy, grid) - diff --git a/rl/td0_prediction.py b/rl/td0_prediction.py deleted file mode 100755 index 98101eff..00000000 --- a/rl/td0_prediction.py +++ /dev/null @@ -1,91 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from grid_world import standard_grid, negative_grid -from iterative_policy_evaluation import print_values, print_policy - -SMALL_ENOUGH = 1e-3 -GAMMA = 0.9 -ALPHA = 0.1 -ALL_POSSIBLE_ACTIONS = ('U', 'D', 'L', 'R') - - -def epsilon_greedy(policy, s, eps=0.1): - # we'll use epsilon-soft to ensure all states are visited - # what happens if you don't do this? i.e. eps=0 - p = np.random.random() - if p < (1 - eps): - return policy[s] - else: - return np.random.choice(ALL_POSSIBLE_ACTIONS) - - -if __name__ == '__main__': - # use the standard grid again (0 for every step) so that we can compare - # to iterative policy evaluation - grid = standard_grid() - - # print rewards - print("rewards:") - print_values(grid.rewards, grid) - - # state -> action - policy = { - (2, 0): 'U', - (1, 0): 'U', - (0, 0): 'R', - (0, 1): 'R', - (0, 2): 'R', - (1, 2): 'R', - (2, 1): 'R', - (2, 2): 'R', - (2, 3): 'U', - } - - # initialize V(s) and returns - V = {} - states = grid.all_states() - for s in states: - V[s] = 0 - - # store max change in V(s) per episode - deltas = [] - - # repeat until convergence - n_episodes = 10000 - for it in range(n_episodes): - # begin a new episode - s = grid.reset() - - delta = 0 - while not grid.game_over(): - a = epsilon_greedy(policy, s) - - r = grid.move(a) - s_next = grid.current_state() - - # update V(s) - v_old = V[s] - V[s] = V[s] + ALPHA*(r + GAMMA*V[s_next] - V[s]) - delta = max(delta, np.abs(V[s] - v_old)) - - # next state becomes current state - s = s_next - - # store delta - deltas.append(delta) - - plt.plot(deltas) - plt.show() - - print("values:") - print_values(V, grid) - print("policy:") - print_policy(policy, grid) diff --git a/rl/tic_tac_toe.py b/rl/tic_tac_toe.py deleted file mode 100644 index e3af285d..00000000 --- a/rl/tic_tac_toe.py +++ /dev/null @@ -1,446 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -# Simple reinforcement learning algorithm for learning tic-tac-toe -# Use the update rule: V(s) = V(s) + alpha*(V(s') - V(s)) -# Use the epsilon-greedy policy: -# action|s = argmax[over all actions possible from state s]{ V(s) } if rand > epsilon -# action|s = select random action from possible actions from state s if rand < epsilon -# -# -# INTERESTING THINGS TO TRY: -# -# Currently, both agents use the same learning strategy while they play against each other. -# What if they have different learning rates? -# What if they have different epsilons? (probability of exploring) -# Who will converge faster? -# What if one agent doesn't learn at all? -# Poses an interesting philosophical question: If there's no one around to challenge you, -# can you reach your maximum potential? -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt - -LENGTH = 3 - - -class Agent: - def __init__(self, eps=0.1, alpha=0.5): - self.eps = eps # probability of choosing random action instead of greedy - self.alpha = alpha # learning rate - self.verbose = False - self.state_history = [] - - def setV(self, V): - self.V = V - - def set_symbol(self, sym): - self.sym = sym - - def set_verbose(self, v): - # if true, will print values for each position on the board - self.verbose = v - - def reset_history(self): - self.state_history = [] - - def take_action(self, env): - # choose an action based on epsilon-greedy strategy - r = np.random.rand() - best_state = None - if r < self.eps: - # take a random action - if self.verbose: - print("Taking a random action") - - possible_moves = [] - for i in range(LENGTH): - for j in range(LENGTH): - if env.is_empty(i, j): - possible_moves.append((i, j)) - idx = np.random.choice(len(possible_moves)) - next_move = possible_moves[idx] - else: - # choose the best action based on current values of states - # loop through all possible moves, get their values - # keep track of the best value - pos2value = {} # for debugging - next_move = None - best_value = -1 - for i in range(LENGTH): - for j in range(LENGTH): - if env.is_empty(i, j): - # what is the state if we made this move? - env.board[i,j] = self.sym - state = env.get_state() - env.board[i,j] = 0 # don't forget to change it back! - pos2value[(i,j)] = self.V[state] - if self.V[state] > best_value: - best_value = self.V[state] - best_state = state - next_move = (i, j) - - # if verbose, draw the board w/ the values - if self.verbose: - print("Taking a greedy action") - for i in range(LENGTH): - print("------------------") - for j in range(LENGTH): - if env.is_empty(i, j): - # print the value - print(" %.2f|" % pos2value[(i,j)], end="") - else: - print(" ", end="") - if env.board[i,j] == env.x: - print("x |", end="") - elif env.board[i,j] == env.o: - print("o |", end="") - else: - print(" |", end="") - print("") - print("------------------") - - # make the move - env.board[next_move[0], next_move[1]] = self.sym - - def update_state_history(self, s): - # cannot put this in take_action, because take_action only happens - # once every other iteration for each player - # state history needs to be updated every iteration - # s = env.get_state() # don't want to do this twice so pass it in - self.state_history.append(s) - - def update(self, env): - # we want to BACKTRACK over the states, so that: - # V(prev_state) = V(prev_state) + alpha*(V(next_state) - V(prev_state)) - # where V(next_state) = reward if it's the most current state - # - # NOTE: we ONLY do this at the end of an episode - # not so for all the algorithms we will study - reward = env.reward(self.sym) - target = reward - for prev in reversed(self.state_history): - value = self.V[prev] + self.alpha*(target - self.V[prev]) - self.V[prev] = value - target = value - self.reset_history() - - -# this class represents a tic-tac-toe game -# is a CS101-type of project -class Environment: - def __init__(self): - self.board = np.zeros((LENGTH, LENGTH)) - self.x = -1 # represents an x on the board, player 1 - self.o = 1 # represents an o on the board, player 2 - self.winner = None - self.ended = False - self.num_states = 3**(LENGTH*LENGTH) - - def is_empty(self, i, j): - return self.board[i,j] == 0 - - def reward(self, sym): - # no reward until game is over - if not self.game_over(): - return 0 - - # if we get here, game is over - # sym will be self.x or self.o - return 1 if self.winner == sym else 0 - - def get_state(self): - # returns the current state, represented as an int - # from 0...|S|-1, where S = set of all possible states - # |S| = 3^(BOARD SIZE), since each cell can have 3 possible values - empty, x, o - # some states are not possible, e.g. all cells are x, but we ignore that detail - # this is like finding the integer represented by a base-3 number - k = 0 - h = 0 - for i in range(LENGTH): - for j in range(LENGTH): - if self.board[i,j] == 0: - v = 0 - elif self.board[i,j] == self.x: - v = 1 - elif self.board[i,j] == self.o: - v = 2 - h += (3**k) * v - k += 1 - return h - - def game_over(self, force_recalculate=False): - # returns true if game over (a player has won or it's a draw) - # otherwise returns false - # also sets 'winner' instance variable and 'ended' instance variable - if not force_recalculate and self.ended: - return self.ended - - # check rows - for i in range(LENGTH): - for player in (self.x, self.o): - if self.board[i].sum() == player*LENGTH: - self.winner = player - self.ended = True - return True - - # check columns - for j in range(LENGTH): - for player in (self.x, self.o): - if self.board[:,j].sum() == player*LENGTH: - self.winner = player - self.ended = True - return True - - # check diagonals - for player in (self.x, self.o): - # top-left -> bottom-right diagonal - if self.board.trace() == player*LENGTH: - self.winner = player - self.ended = True - return True - # top-right -> bottom-left diagonal - if np.fliplr(self.board).trace() == player*LENGTH: - self.winner = player - self.ended = True - return True - - # check if draw - if np.all((self.board == 0) == False): - # winner stays None - self.winner = None - self.ended = True - return True - - # game is not over - self.winner = None - return False - - def is_draw(self): - return self.ended and self.winner is None - - # Example board - # ------------- - # | x | | | - # ------------- - # | | | | - # ------------- - # | | | o | - # ------------- - def draw_board(self): - for i in range(LENGTH): - print("-------------") - for j in range(LENGTH): - print(" ", end="") - if self.board[i,j] == self.x: - print("x ", end="") - elif self.board[i,j] == self.o: - print("o ", end="") - else: - print(" ", end="") - print("") - print("-------------") - - - -class Human: - def __init__(self): - pass - - def set_symbol(self, sym): - self.sym = sym - - def take_action(self, env): - while True: - # break if we make a legal move - move = input("Enter coordinates i,j for your next move (i,j=0..2): ") - i, j = move.split(',') - i = int(i) - j = int(j) - if env.is_empty(i, j): - env.board[i,j] = self.sym - break - - def update(self, env): - pass - - def update_state_history(self, s): - pass - - -# recursive function that will return all -# possible states (as ints) and who the corresponding winner is for those states (if any) -# (i, j) refers to the next cell on the board to permute (we need to try -1, 0, 1) -# impossible games are ignored, i.e. 3x's and 3o's in a row simultaneously -# since that will never happen in a real game -def get_state_hash_and_winner(env, i=0, j=0): - results = [] - - for v in (0, env.x, env.o): - env.board[i,j] = v # if empty board it should already be 0 - if j == 2: - # j goes back to 0, increase i, unless i = 2, then we are done - if i == 2: - # the board is full, collect results and return - state = env.get_state() - ended = env.game_over(force_recalculate=True) - winner = env.winner - results.append((state, winner, ended)) - else: - results += get_state_hash_and_winner(env, i + 1, 0) - else: - # increment j, i stays the same - results += get_state_hash_and_winner(env, i, j + 1) - - return results - -# play all possible games -# need to also store if game is over or not -# because we are going to initialize those values to 0.5 -# NOTE: THIS IS SLOW because MANY possible games lead to the same outcome / state -# def get_state_hash_and_winner(env, turn='x'): -# results = [] - -# state = env.get_state() -# # board_before = env.board.copy() -# ended = env.game_over(force_recalculate=True) -# winner = env.winner -# results.append((state, winner, ended)) - -# # DEBUG -# # if ended: -# # if winner is not None and env.win_type.startswith('col'): -# # env.draw_board() -# # print "Winner:", 'x' if winner == -1 else 'o', env.win_type -# # print "\n\n" -# # assert(np.all(board_before == env.board)) - -# if not ended: -# if turn == 'x': -# sym = env.x -# next_sym = 'o' -# else: -# sym = env.o -# next_sym = 'x' - -# for i in xrange(LENGTH): -# for j in xrange(LENGTH): -# if env.is_empty(i, j): -# env.board[i,j] = sym -# results += get_state_hash_and_winner(env, next_sym) -# env.board[i,j] = 0 # reset it -# return results - - -def initialV_x(env, state_winner_triples): - # initialize state values as follows - # if x wins, V(s) = 1 - # if x loses or draw, V(s) = 0 - # otherwise, V(s) = 0.5 - V = np.zeros(env.num_states) - for state, winner, ended in state_winner_triples: - if ended: - if winner == env.x: - v = 1 - else: - v = 0 - else: - v = 0.5 - V[state] = v - return V - - -def initialV_o(env, state_winner_triples): - # this is (almost) the opposite of initial V for player x - # since everywhere where x wins (1), o loses (0) - # but a draw is still 0 for o - V = np.zeros(env.num_states) - for state, winner, ended in state_winner_triples: - if ended: - if winner == env.o: - v = 1 - else: - v = 0 - else: - v = 0.5 - V[state] = v - return V - - -def play_game(p1, p2, env, draw=False): - # loops until the game is over - current_player = None - while not env.game_over(): - # alternate between players - # p1 always starts first - if current_player == p1: - current_player = p2 - else: - current_player = p1 - - # draw the board before the user who wants to see it makes a move - if draw: - if draw == 1 and current_player == p1: - env.draw_board() - if draw == 2 and current_player == p2: - env.draw_board() - - # current player makes a move - current_player.take_action(env) - - # update state histories - state = env.get_state() - p1.update_state_history(state) - p2.update_state_history(state) - - if draw: - env.draw_board() - - # do the value function update - p1.update(env) - p2.update(env) - - -if __name__ == '__main__': - # train the agent - p1 = Agent() - p2 = Agent() - - # set initial V for p1 and p2 - env = Environment() - state_winner_triples = get_state_hash_and_winner(env) - - - Vx = initialV_x(env, state_winner_triples) - p1.setV(Vx) - Vo = initialV_o(env, state_winner_triples) - p2.setV(Vo) - - # give each player their symbol - p1.set_symbol(env.x) - p2.set_symbol(env.o) - - T = 10000 - for t in range(T): - if t % 200 == 0: - print(t) - play_game(p1, p2, Environment()) - - # play human vs. agent - # do you think the agent learned to play the game well? - human = Human() - human.set_symbol(env.o) - while True: - p1.set_verbose(True) - play_game(p1, human, Environment(), draw=2) - # I made the agent player 1 because I wanted to see if it would - # select the center as its starting move. If you want the agent - # to go second you can switch the human and AI. - answer = input("Play again? [Y/n]: ") - if answer and answer.lower()[0] == 'n': - break - diff --git a/rl/ucb1.py b/rl/ucb1.py deleted file mode 100644 index 5779b654..00000000 --- a/rl/ucb1.py +++ /dev/null @@ -1,81 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -# https://books.google.ca/books?id=_ATpBwAAQBAJ&lpg=PA201&ots=rinZM8jQ6s&dq=hoeffding%20bound%20gives%20probability%20%22greater%20than%201%22&pg=PA201#v=onepage&q&f=false -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - - -NUM_TRIALS = 100000 -EPS = 0.1 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - # p: the win rate - self.p = p - self.p_estimate = 0. - self.N = 0. # num samples collected so far - - def pull(self): - # draw a 1 with probability p - return np.random.random() < self.p - - def update(self, x): - self.N += 1. - self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N - - -def ucb(mean, n, nj): - return mean + np.sqrt(2*np.log(n) / nj) - - -def run_experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - rewards = np.empty(NUM_TRIALS) - total_plays = 0 - - # initialization: play each bandit once - for j in range(len(bandits)): - x = bandits[j].pull() - total_plays += 1 - bandits[j].update(x) - - for i in range(NUM_TRIALS): - j = np.argmax([ucb(b.p_estimate, total_plays, b.N) for b in bandits]) - x = bandits[j].pull() - total_plays += 1 - bandits[j].update(x) - - # for the plot - rewards[i] = x - cumulative_average = np.cumsum(rewards) / (np.arange(NUM_TRIALS) + 1) - - # plot moving average ctr - plt.plot(cumulative_average) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.xscale('log') - plt.show() - - # plot moving average ctr linear - plt.plot(cumulative_average) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.show() - - for b in bandits: - print(b.p_estimate) - - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num times selected each bandit:", [b.N for b in bandits]) - - return cumulative_average - -if __name__ == '__main__': - run_experiment() - diff --git a/rl/ucb1_starter.py b/rl/ucb1_starter.py deleted file mode 100644 index 9e9c3106..00000000 --- a/rl/ucb1_starter.py +++ /dev/null @@ -1,81 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -# https://books.google.ca/books?id=_ATpBwAAQBAJ&lpg=PA201&ots=rinZM8jQ6s&dq=hoeffding%20bound%20gives%20probability%20%22greater%20than%201%22&pg=PA201#v=onepage&q&f=false -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - - -NUM_TRIALS = 100000 -EPS = 0.1 -BANDIT_PROBABILITIES = [0.2, 0.5, 0.75] - - -class Bandit: - def __init__(self, p): - # p: the win rate - self.p = p - self.p_estimate = 0. - self.N = 0. # num samples collected so far - - def pull(self): - # draw a 1 with probability p - return np.random.random() < self.p - - def update(self, x): - self.N += 1. - self.p_estimate = ((self.N - 1)*self.p_estimate + x) / self.N - - -def ucb(mean, n, nj): - return # TODO - - -def run_experiment(): - bandits = [Bandit(p) for p in BANDIT_PROBABILITIES] - rewards = np.empty(NUM_TRIALS) - total_plays = 0 - - # initialization: play each bandit once - for j in range(len(bandits)): - x = bandits[j].pull() - total_plays += 1 - bandits[j].update(x) - - for i in range(NUM_TRIALS): - j = # TODO - x = bandits[j].pull() - total_plays += 1 - bandits[j].update(x) - - # for the plot - rewards[i] = x - cumulative_average = np.cumsum(rewards) / (np.arange(NUM_TRIALS) + 1) - - # plot moving average ctr - plt.plot(cumulative_average) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.xscale('log') - plt.show() - - # plot moving average ctr linear - plt.plot(cumulative_average) - plt.plot(np.ones(NUM_TRIALS)*np.max(BANDIT_PROBABILITIES)) - plt.show() - - for b in bandits: - print(b.p_estimate) - - print("total reward earned:", rewards.sum()) - print("overall win rate:", rewards.sum() / NUM_TRIALS) - print("num times selected each bandit:", [b.N for b in bandits]) - - return cumulative_average - -if __name__ == '__main__': - run_experiment() - diff --git a/rl/value_iteration.py b/rl/value_iteration.py deleted file mode 100644 index 8f6738d2..00000000 --- a/rl/value_iteration.py +++ /dev/null @@ -1,102 +0,0 @@ -# https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python -# https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from grid_world import windy_grid, ACTION_SPACE -from iterative_policy_evaluation import print_values, print_policy - -SMALL_ENOUGH = 1e-3 -GAMMA = 0.9 - -# copied from iterative_policy_evaluation -def get_transition_probs_and_rewards(grid): - ### define transition probabilities and grid ### - # the key is (s, a, s'), the value is the probability - # that is, transition_probs[(s, a, s')] = p(s' | s, a) - # any key NOT present will considered to be impossible (i.e. probability 0) - transition_probs = {} - - # to reduce the dimensionality of the dictionary, we'll use deterministic - # rewards, r(s, a, s') - # note: you could make it simpler by using r(s') since the reward doesn't - # actually depend on (s, a) - rewards = {} - - for (s, a), v in grid.probs.items(): - for s2, p in v.items(): - transition_probs[(s, a, s2)] = p - rewards[(s, a, s2)] = grid.rewards.get(s2, 0) - - return transition_probs, rewards - -if __name__ == '__main__': - grid = windy_grid() - transition_probs, rewards = get_transition_probs_and_rewards(grid) - - # print rewards - print("rewards:") - print_values(grid.rewards, grid) - - # initialize V(s) - V = {} - states = grid.all_states() - for s in states: - V[s] = 0 - - # repeat until convergence - # V[s] = max[a]{ sum[s',r] { p(s',r|s,a)[r + gamma*V[s']] } } - it = 0 - while True: - biggest_change = 0 - for s in grid.all_states(): - if not grid.is_terminal(s): - old_v = V[s] - new_v = float('-inf') - - for a in ACTION_SPACE: - v = 0 - for s2 in grid.all_states(): - # reward is a function of (s, a, s'), 0 if not specified - r = rewards.get((s, a, s2), 0) - v += transition_probs.get((s, a, s2), 0) * (r + GAMMA * V[s2]) - - # keep v if it's better - if v > new_v: - new_v = v - - V[s] = new_v - biggest_change = max(biggest_change, np.abs(old_v - V[s])) - - it += 1 - if biggest_change < SMALL_ENOUGH: - break - - # find a policy that leads to optimal value function - policy = {} - for s in grid.actions.keys(): - best_a = None - best_value = float('-inf') - # loop through all possible actions to find the best current action - for a in ACTION_SPACE: - v = 0 - for s2 in grid.all_states(): - # reward is a function of (s, a, s'), 0 if not specified - r = rewards.get((s, a, s2), 0) - v += transition_probs.get((s, a, s2), 0) * (r + GAMMA * V[s2]) - - # best_a is the action associated with best_value - if v > best_value: - best_value = v - best_a = a - policy[s] = best_a - - # our goal here is to verify that we get the same answer as with policy iteration - print("values:") - print_values(V, grid) - print("policy:") - print_policy(policy, grid) diff --git a/rl2/a3c/main.py b/rl2/a3c/main.py deleted file mode 100644 index a902ff03..00000000 --- a/rl2/a3c/main.py +++ /dev/null @@ -1,106 +0,0 @@ -import gym -import sys -import os -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt -import itertools -import shutil -import threading -import multiprocessing - -from nets import create_networks -from worker import Worker - - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - -if tf.__version__.startswith('2'): - exit("Please install Tensorflow 1.x") - - -ENV_NAME = "Breakout-v0" -MAX_GLOBAL_STEPS = 5e6 -STEPS_PER_UPDATE = 5 - - -def Env(): - return gym.envs.make(ENV_NAME) - -# Depending on the game we may have a limited action space -if ENV_NAME == "Pong-v0" or ENV_NAME == "Breakout-v0": - NUM_ACTIONS = 4 # env.action_space.n returns a bigger number -else: - env = Env() - NUM_ACTIONS = env.action_space.n - env.close() - - -def smooth(x): - # last 100 - n = len(x) - y = np.zeros(n) - for i in range(n): - start = max(0, i - 99) - y[i] = float(x[start:(i+1)].sum()) / (i - start + 1) - return y - - -# Set the number of workers -NUM_WORKERS = multiprocessing.cpu_count() - -with tf.device("/cpu:0"): - - # Keeps track of the number of updates we've performed - # https://www.tensorflow.org/api_docs/python/tf/train/global_step - global_step = tf.Variable(0, name="global_step", trainable=False) - - # Global policy and value nets - with tf.variable_scope("global") as vs: - policy_net, value_net = create_networks(NUM_ACTIONS) - - # Global step iterator - global_counter = itertools.count() - - # Save returns - returns_list = [] - - # Create workers - workers = [] - for worker_id in range(NUM_WORKERS): - worker = Worker( - name="worker_{}".format(worker_id), - env=Env(), - policy_net=policy_net, - value_net=value_net, - global_counter=global_counter, - returns_list=returns_list, - discount_factor = 0.99, - max_global_steps=MAX_GLOBAL_STEPS) - workers.append(worker) - -with tf.Session() as sess: - sess.run(tf.global_variables_initializer()) - coord = tf.train.Coordinator() - - # Start worker threads - worker_threads = [] - for worker in workers: - worker_fn = lambda: worker.run(sess, coord, STEPS_PER_UPDATE) - t = threading.Thread(target=worker_fn) - t.start() - worker_threads.append(t) - - # Wait for all workers to finish - coord.join(worker_threads, stop_grace_period_secs=300) - - # Plot the smoothed returns - x = np.array(returns_list) - y = smooth(x) - plt.plot(x, label='orig') - plt.plot(y, label='smoothed') - plt.legend() - plt.show() - diff --git a/rl2/a3c/nets.py b/rl2/a3c/nets.py deleted file mode 100644 index c10dfd5a..00000000 --- a/rl2/a3c/nets.py +++ /dev/null @@ -1,122 +0,0 @@ -import tensorflow as tf - - -def build_feature_extractor(input_): - # We only want to create the weights once - # In all future calls we should set reuse = True - - # scale the inputs from 0..255 to 0..1 - input_ = tf.to_float(input_) / 255.0 - - # conv layers - conv1 = tf.contrib.layers.conv2d( - input_, - 16, # num output feature maps - 8, # kernel size - 4, # stride - activation_fn=tf.nn.relu, - scope="conv1") - conv2 = tf.contrib.layers.conv2d( - conv1, - 32, # num output feature maps - 4, # kernel size - 2, # stride - activation_fn=tf.nn.relu, - scope="conv2") - - # image -> feature vector - flat = tf.contrib.layers.flatten(conv2) - - # dense layer - fc1 = tf.contrib.layers.fully_connected( - inputs=flat, - num_outputs=256, - scope="fc1") - - return fc1 - -class PolicyNetwork: - def __init__(self, num_outputs, reg=0.01): - self.num_outputs = num_outputs - - # Graph inputs - # After resizing we have 4 consecutive frames of size 84 x 84 - self.states = tf.placeholder(shape=[None, 84, 84, 4], dtype=tf.uint8, name="X") - # Advantage = G - V(s) - self.advantage = tf.placeholder(shape=[None], dtype=tf.float32, name="y") - # Selected actions - self.actions = tf.placeholder(shape=[None], dtype=tf.int32, name="actions") - - # Since we set reuse=False here, that means we MUST - # create the PolicyNetwork before creating the ValueNetwork - # ValueNetwork will use reuse=True - with tf.variable_scope("shared", reuse=False): - fc1 = build_feature_extractor(self.states) - - # Use a separate scope for output and loss - with tf.variable_scope("policy_network"): - self.logits = tf.contrib.layers.fully_connected(fc1, num_outputs, activation_fn=None) - self.probs = tf.nn.softmax(self.logits) - - # Sample an action - cdist = tf.distributions.Categorical(logits=self.logits) - self.sample_action = cdist.sample() - - # Add regularization to increase exploration - self.entropy = -tf.reduce_sum(self.probs * tf.log(self.probs), axis=1) - - # Get the predictions for the chosen actions only - batch_size = tf.shape(self.states)[0] - gather_indices = tf.range(batch_size) * tf.shape(self.probs)[1] + self.actions - self.selected_action_probs = tf.gather(tf.reshape(self.probs, [-1]), gather_indices) - - self.loss = tf.log(self.selected_action_probs) * self.advantage + reg * self.entropy - self.loss = -tf.reduce_sum(self.loss, name="loss") - - # training - self.optimizer = tf.train.RMSPropOptimizer(0.00025, 0.99, 0.0, 1e-6) - - # we'll need these later for running gradient descent steps - self.grads_and_vars = self.optimizer.compute_gradients(self.loss) - self.grads_and_vars = [[grad, var] for grad, var in self.grads_and_vars if grad is not None] - - -class ValueNetwork: - def __init__(self): - # Placeholders for our input - # After resizing we have 4 consecutive frames of size 84 x 84 - self.states = tf.placeholder(shape=[None, 84, 84, 4], dtype=tf.uint8, name="X") - # The TD target value - self.targets = tf.placeholder(shape=[None], dtype=tf.float32, name="y") - - # Since we set reuse=True here, that means we MUST - # create the PolicyNetwork before creating the ValueNetwork - # PolictyNetwork will use reuse=False - with tf.variable_scope("shared", reuse=True): - fc1 = build_feature_extractor(self.states) - - # Use a separate scope for output and loss - with tf.variable_scope("value_network"): - self.vhat = tf.contrib.layers.fully_connected( - inputs=fc1, - num_outputs=1, - activation_fn=None) - self.vhat = tf.squeeze(self.vhat, squeeze_dims=[1], name="vhat") - - self.loss = tf.squared_difference(self.vhat, self.targets) - self.loss = tf.reduce_sum(self.loss, name="loss") - - # training - self.optimizer = tf.train.RMSPropOptimizer(0.00025, 0.99, 0.0, 1e-6) - - # we'll need these later for running gradient descent steps - self.grads_and_vars = self.optimizer.compute_gradients(self.loss) - self.grads_and_vars = [[grad, var] for grad, var in self.grads_and_vars if grad is not None] - - -# Should use this to create networks -# to ensure they're created in the correct order -def create_networks(num_outputs): - policy_network = PolicyNetwork(num_outputs=num_outputs) - value_network = ValueNetwork() - return policy_network, value_network diff --git a/rl2/a3c/thread_example.py b/rl2/a3c/thread_example.py deleted file mode 100644 index 0d5fa1a4..00000000 --- a/rl2/a3c/thread_example.py +++ /dev/null @@ -1,45 +0,0 @@ -import itertools -import threading -import time -import multiprocessing -import numpy as np - - -class Worker: - def __init__(self, id_, global_counter): - self.id = id_ - self.global_counter = global_counter - self.local_counter = itertools.count() - - def run(self): - while True: - time.sleep(np.random.rand()*2) - global_step = next(self.global_counter) - local_step = next(self.local_counter) - print("Worker({}): {}".format(self.id, local_step)) - if global_step >= 20: - break - -global_counter = itertools.count() -NUM_WORKERS = multiprocessing.cpu_count() - -# create the workers -workers = [] -for worker_id in range(NUM_WORKERS): - worker = Worker(worker_id, global_counter) - workers.append(worker) - -# start the threads -worker_threads = [] -for worker in workers: - worker_fn = lambda: worker.run() - t = threading.Thread(target=worker_fn) - t.start() - worker_threads.append(t) - - -# join the threads -for t in worker_threads: - t.join() - -print("DONE!") \ No newline at end of file diff --git a/rl2/a3c/worker.py b/rl2/a3c/worker.py deleted file mode 100644 index bb7d82a8..00000000 --- a/rl2/a3c/worker.py +++ /dev/null @@ -1,269 +0,0 @@ -import gym -import sys -import os -import numpy as np -import tensorflow as tf - -from nets import create_networks - - -class Step: - def __init__(self, state, action, reward, next_state, done): - self.state = state - self.action = action - self.reward = reward - self.next_state = next_state - self.done = done - - -# Transform raw images for input into neural network -# 1) Convert to grayscale -# 2) Resize -# 3) Crop -class ImageTransformer: - def __init__(self): - with tf.variable_scope("image_transformer"): - self.input_state = tf.placeholder(shape=[210, 160, 3], dtype=tf.uint8) - self.output = tf.image.rgb_to_grayscale(self.input_state) - self.output = tf.image.crop_to_bounding_box(self.output, 34, 0, 160, 160) - self.output = tf.image.resize_images( - self.output, - [84, 84], - method=tf.image.ResizeMethod.NEAREST_NEIGHBOR) - self.output = tf.squeeze(self.output) - - def transform(self, state, sess=None): - sess = sess or tf.get_default_session() - return sess.run(self.output, { self.input_state: state }) - - -# Create initial state by repeating the same frame 4 times -def repeat_frame(frame): - return np.stack([frame] * 4, axis=2) - - -# Create next state by shifting each frame by 1 -# Throw out the oldest frame -# And concatenate the newest frame -def shift_frames(state, next_frame): - return np.append(state[:,:,1:], np.expand_dims(next_frame, 2), axis=2) - - -# Make a Tensorflow op to copy weights from one scope to another -def get_copy_params_op(src_vars, dst_vars): - src_vars = list(sorted(src_vars, key=lambda v: v.name)) - dst_vars = list(sorted(dst_vars, key=lambda v: v.name)) - - ops = [] - for s, d in zip(src_vars, dst_vars): - op = d.assign(s) - ops.append(op) - - return ops - - -def make_train_op(local_net, global_net): - """ - Use gradients from local network to update the global network - """ - - # Idea: - # We want a list of gradients and corresponding variables - # e.g. [[g1, g2, g3], [v1, v2, v3]] - # Since that's what the optimizer expects. - # But we would like the gradients to come from the local network - # And the variables to come from the global network - # So we want to make a list like this: - # [[local_g1, local_g2, local_g3], [global_v1, global_v2, global_v3]] - - # First get only the gradients - local_grads, _ = zip(*local_net.grads_and_vars) - - # Clip gradients to avoid large values - local_grads, _ = tf.clip_by_global_norm(local_grads, 5.0) - - # Get global vars - _, global_vars = zip(*global_net.grads_and_vars) - - # Combine local grads and global vars - local_grads_global_vars = list(zip(local_grads, global_vars)) - - # Run a gradient descent step, e.g. - # var = var - learning_rate * grad - return global_net.optimizer.apply_gradients( - local_grads_global_vars, - global_step=tf.train.get_global_step()) - - -# Worker object to be run in a thread -# name (String) should be unique for each thread -# env (OpenAI Gym Environment) should be unique for each thread -# policy_net (PolicyNetwork) should be a global passed to every worker -# value_net (ValueNetwork) should be a global passed to every worker -# returns_list (List) should be a global passed to every worker -class Worker: - def __init__( - self, - name, - env, - policy_net, - value_net, - global_counter, - returns_list, - discount_factor=0.99, - max_global_steps=None): - - self.name = name - self.env = env - self.global_policy_net = policy_net - self.global_value_net = value_net - self.global_counter = global_counter - self.discount_factor = discount_factor - self.max_global_steps = max_global_steps - self.global_step = tf.train.get_global_step() - self.img_transformer = ImageTransformer() - - # Create local policy and value networks that belong only to this worker - with tf.variable_scope(name): - # self.policy_net = PolicyNetwork(num_outputs=policy_net.num_outputs) - # self.value_net = ValueNetwork() - self.policy_net, self.value_net = create_networks(policy_net.num_outputs) - - # We will use this op to copy the global network weights - # back to the local policy and value networks - self.copy_params_op = get_copy_params_op( - tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="global"), - tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name+'/')) - - # These will take the gradients from the local networks - # and use those gradients to update the global network - self.vnet_train_op = make_train_op(self.value_net, self.global_value_net) - self.pnet_train_op = make_train_op(self.policy_net, self.global_policy_net) - - self.state = None # Keep track of the current state - self.total_reward = 0. # After each episode print the total (sum of) reward - self.returns_list = returns_list # Global returns list to plot later - - def run(self, sess, coord, t_max): - with sess.as_default(), sess.graph.as_default(): - # Assign the initial state - self.state = repeat_frame(self.img_transformer.transform(self.env.reset())) - - try: - while not coord.should_stop(): - # Copy weights from global networks to local networks - sess.run(self.copy_params_op) - - # Collect some experience - steps, global_step = self.run_n_steps(t_max, sess) - - # Stop once the max number of global steps has been reached - if self.max_global_steps is not None and global_step >= self.max_global_steps: - coord.request_stop() - return - - # Update the global networks using local gradients - self.update(steps, sess) - - except tf.errors.CancelledError: - return - - def sample_action(self, state, sess): - # Make input N x D (N = 1) - feed_dict = { self.policy_net.states: [state] } - actions = sess.run(self.policy_net.sample_action, feed_dict) - # Prediction is a 1-D array of length N, just want the first value - return actions[0] - - def get_value_prediction(self, state, sess): - # Make input N x D (N = 1) - feed_dict = { self.value_net.states: [state] } - vhat = sess.run(self.value_net.vhat, feed_dict) - # Prediction is a 1-D array of length N, just want the first value - return vhat[0] - - def run_n_steps(self, n, sess): - steps = [] - for _ in range(n): - # Take a step - action = self.sample_action(self.state, sess) - next_frame, reward, done, _ = self.env.step(action) - - # Shift the state to include the latest frame - next_state = shift_frames(self.state, self.img_transformer.transform(next_frame)) - - # Save total return - if done: - print("Total reward:", self.total_reward, "Worker:", self.name) - self.returns_list.append(self.total_reward) - if len(self.returns_list) > 0 and len(self.returns_list) % 100 == 0: - print("*** Total average reward (last 100):", np.mean(self.returns_list[-100:]), "Collected so far:", len(self.returns_list)) - self.total_reward = 0. - else: - self.total_reward += reward - - # Save step - step = Step(self.state, action, reward, next_state, done) - steps.append(step) - - # Increase local and global counters - global_step = next(self.global_counter) - - if done: - self.state = repeat_frame(self.img_transformer.transform(self.env.reset())) - break - else: - self.state = next_state - return steps, global_step - - def update(self, steps, sess): - """ - Updates global policy and value networks using the local networks' gradients - """ - - # In order to accumulate the total return - # We will use V_hat(s') to predict the future returns - # But we will use the actual rewards if we have them - # Ex. if we have s1, s2, s3 with rewards r1, r2, r3 - # Then G(s3) = r3 + V(s4) - # G(s2) = r2 + r3 + V(s4) - # G(s1) = r1 + r2 + r3 + V(s4) - reward = 0.0 - if not steps[-1].done: - reward = self.get_value_prediction(steps[-1].next_state, sess) - - # Accumulate minibatch samples - states = [] - advantages = [] - value_targets = [] - actions = [] - - # loop through steps in reverse order - for step in reversed(steps): - reward = step.reward + self.discount_factor * reward - advantage = reward - self.get_value_prediction(step.state, sess) - # Accumulate updates - states.append(step.state) - actions.append(step.action) - advantages.append(advantage) - value_targets.append(reward) - - feed_dict = { - self.policy_net.states: np.array(states), - self.policy_net.advantage: advantages, - self.policy_net.actions: actions, - self.value_net.states: np.array(states), - self.value_net.targets: value_targets, - } - - # Train the global estimators using local gradients - global_step, pnet_loss, vnet_loss, _, _ = sess.run([ - self.global_step, - self.policy_net.loss, - self.value_net.loss, - self.pnet_train_op, - self.vnet_train_op, - ], feed_dict) - - # Theoretically could plot these later - return pnet_loss, vnet_loss diff --git a/rl2/atari/dqn_tf.py b/rl2/atari/dqn_tf.py deleted file mode 100755 index e37394d1..00000000 --- a/rl2/atari/dqn_tf.py +++ /dev/null @@ -1,458 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import copy -import gym -import os -import sys -import random -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime -from scipy.misc import imresize - - - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - -if tf.__version__.startswith('2'): - exit("Please install Tensorflow 1.x") - -##### testing only -# MAX_EXPERIENCES = 10000 -# MIN_EXPERIENCES = 1000 - - -MAX_EXPERIENCES = 500000 -MIN_EXPERIENCES = 50000 -TARGET_UPDATE_PERIOD = 10000 -IM_SIZE = 84 -K = 4 #env.action_space.n - - -# Transform raw images for input into neural network -# 1) Convert to grayscale -# 2) Resize -# 3) Crop -class ImageTransformer: - def __init__(self): - with tf.variable_scope("image_transformer"): - self.input_state = tf.placeholder(shape=[210, 160, 3], dtype=tf.uint8) - self.output = tf.image.rgb_to_grayscale(self.input_state) - self.output = tf.image.crop_to_bounding_box(self.output, 34, 0, 160, 160) - self.output = tf.image.resize_images( - self.output, - [IM_SIZE, IM_SIZE], - method=tf.image.ResizeMethod.NEAREST_NEIGHBOR) - self.output = tf.squeeze(self.output) - - def transform(self, state, sess=None): - sess = sess or tf.get_default_session() - return sess.run(self.output, { self.input_state: state }) - - -def update_state(state, obs_small): - return np.append(state[:,:,1:], np.expand_dims(obs_small, 2), axis=2) - - - -class ReplayMemory: - def __init__(self, size=MAX_EXPERIENCES, frame_height=IM_SIZE, frame_width=IM_SIZE, - agent_history_length=4, batch_size=32): - """ - Args: - size: Integer, Number of stored transitions - frame_height: Integer, Height of a frame of an Atari game - frame_width: Integer, Width of a frame of an Atari game - agent_history_length: Integer, Number of frames stacked together to create a state - batch_size: Integer, Number of transitions returned in a minibatch - """ - self.size = size - self.frame_height = frame_height - self.frame_width = frame_width - self.agent_history_length = agent_history_length - self.batch_size = batch_size - self.count = 0 - self.current = 0 - - # Pre-allocate memory - self.actions = np.empty(self.size, dtype=np.int32) - self.rewards = np.empty(self.size, dtype=np.float32) - self.frames = np.empty((self.size, self.frame_height, self.frame_width), dtype=np.uint8) - self.terminal_flags = np.empty(self.size, dtype=np.bool) - - # Pre-allocate memory for the states and new_states in a minibatch - self.states = np.empty((self.batch_size, self.agent_history_length, - self.frame_height, self.frame_width), dtype=np.uint8) - self.new_states = np.empty((self.batch_size, self.agent_history_length, - self.frame_height, self.frame_width), dtype=np.uint8) - self.indices = np.empty(self.batch_size, dtype=np.int32) - - def add_experience(self, action, frame, reward, terminal): - """ - Args: - action: An integer-encoded action - frame: One grayscale frame of the game - reward: reward the agend received for performing an action - terminal: A bool stating whether the episode terminated - """ - if frame.shape != (self.frame_height, self.frame_width): - raise ValueError('Dimension of frame is wrong!') - self.actions[self.current] = action - self.frames[self.current, ...] = frame - self.rewards[self.current] = reward - self.terminal_flags[self.current] = terminal - self.count = max(self.count, self.current+1) - self.current = (self.current + 1) % self.size - - def _get_state(self, index): - if self.count is 0: - raise ValueError("The replay memory is empty!") - if index < self.agent_history_length - 1: - raise ValueError("Index must be min 3") - return self.frames[index-self.agent_history_length+1:index+1, ...] - - def _get_valid_indices(self): - for i in range(self.batch_size): - while True: - index = random.randint(self.agent_history_length, self.count - 1) - if index < self.agent_history_length: - continue - if index >= self.current and index - self.agent_history_length <= self.current: - continue - if self.terminal_flags[index - self.agent_history_length:index].any(): - continue - break - self.indices[i] = index - - def get_minibatch(self): - """ - Returns a minibatch of self.batch_size transitions - """ - if self.count < self.agent_history_length: - raise ValueError('Not enough memories to get a minibatch') - - self._get_valid_indices() - - for i, idx in enumerate(self.indices): - self.states[i] = self._get_state(idx - 1) - self.new_states[i] = self._get_state(idx) - - return np.transpose(self.states, axes=(0, 2, 3, 1)), \ - self.actions[self.indices], \ - self.rewards[self.indices], \ - np.transpose(self.new_states, axes=(0, 2, 3, 1)), \ - self.terminal_flags[self.indices] - - -class DQN: - def __init__(self, K, conv_layer_sizes, hidden_layer_sizes, scope): - - self.K = K - self.scope = scope - - with tf.variable_scope(scope): - - # inputs and targets - self.X = tf.placeholder(tf.float32, shape=(None, IM_SIZE, IM_SIZE, 4), name='X') - - # tensorflow convolution needs the order to be: - # (num_samples, height, width, "color") - - self.G = tf.placeholder(tf.float32, shape=(None,), name='G') - self.actions = tf.placeholder(tf.int32, shape=(None,), name='actions') - - # calculate output and cost - # convolutional layers - # these built-in layers are faster and don't require us to - # calculate the size of the output of the final conv layer! - Z = self.X / 255.0 - for num_output_filters, filtersz, poolsz in conv_layer_sizes: - Z = tf.contrib.layers.conv2d( - Z, - num_output_filters, - filtersz, - poolsz, - activation_fn=tf.nn.relu - ) - - # fully connected layers - Z = tf.contrib.layers.flatten(Z) - for M in hidden_layer_sizes: - Z = tf.contrib.layers.fully_connected(Z, M) - - # final output layer - self.predict_op = tf.contrib.layers.fully_connected(Z, K) - - selected_action_values = tf.reduce_sum( - self.predict_op * tf.one_hot(self.actions, K), - reduction_indices=[1] - ) - - # cost = tf.reduce_mean(tf.square(self.G - selected_action_values)) - cost = tf.reduce_mean(tf.losses.huber_loss(self.G, selected_action_values)) - self.train_op = tf.train.AdamOptimizer(1e-5).minimize(cost) - # self.train_op = tf.train.AdagradOptimizer(1e-2).minimize(cost) - # self.train_op = tf.train.RMSPropOptimizer(2.5e-4, decay=0.99, epsilon=1e-3).minimize(cost) - # self.train_op = tf.train.RMSPropOptimizer(0.00025, 0.99, 0.0, 1e-6).minimize(cost) - # self.train_op = tf.train.MomentumOptimizer(1e-3, momentum=0.9).minimize(cost) - # self.train_op = tf.train.GradientDescentOptimizer(1e-4).minimize(cost) - - self.cost = cost - - - def copy_from(self, other): - mine = [t for t in tf.trainable_variables() if t.name.startswith(self.scope)] - mine = sorted(mine, key=lambda v: v.name) - theirs = [t for t in tf.trainable_variables() if t.name.startswith(other.scope)] - theirs = sorted(theirs, key=lambda v: v.name) - - ops = [] - for p, q in zip(mine, theirs): - op = p.assign(q) - ops.append(op) - self.session.run(ops) - - - def save(self): - params = [t for t in tf.trainable_variables() if t.name.startswith(self.scope)] - params = self.session.run(params) - np.savez('tf_dqn_weights.npz', *params) - - - def load(self): - params = [t for t in tf.trainable_variables() if t.name.startswith(self.scope)] - npz = np.load('tf_dqn_weights.npz') - ops = [] - for p, (_, v) in zip(params, npz.iteritems()): - ops.append(p.assign(v)) - self.session.run(ops) - - - def set_session(self, session): - self.session = session - - def predict(self, states): - return self.session.run(self.predict_op, feed_dict={self.X: states}) - - def update(self, states, actions, targets): - c, _ = self.session.run( - [self.cost, self.train_op], - feed_dict={ - self.X: states, - self.G: targets, - self.actions: actions - } - ) - return c - - def sample_action(self, x, eps): - if np.random.random() < eps: - return np.random.choice(self.K) - else: - return np.argmax(self.predict([x])[0]) - - -def learn(model, target_model, experience_replay_buffer, gamma, batch_size): - # Sample experiences - states, actions, rewards, next_states, dones = experience_replay_buffer.get_minibatch() - - # Calculate targets - next_Qs = target_model.predict(next_states) - next_Q = np.amax(next_Qs, axis=1) - targets = rewards + np.invert(dones).astype(np.float32) * gamma * next_Q - - # Update model - loss = model.update(states, actions, targets) - return loss - - -def play_one( - env, - sess, - total_t, - experience_replay_buffer, - model, - target_model, - image_transformer, - gamma, - batch_size, - epsilon, - epsilon_change, - epsilon_min): - - t0 = datetime.now() - - # Reset the environment - obs = env.reset() - obs_small = image_transformer.transform(obs, sess) - state = np.stack([obs_small] * 4, axis=2) - loss = None - - - total_time_training = 0 - num_steps_in_episode = 0 - episode_reward = 0 - - done = False - while not done: - - # Update target network - if total_t % TARGET_UPDATE_PERIOD == 0: - target_model.copy_from(model) - print("Copied model parameters to target network. total_t = %s, period = %s" % (total_t, TARGET_UPDATE_PERIOD)) - - - # Take action - action = model.sample_action(state, epsilon) - obs, reward, done, _ = env.step(action) - obs_small = image_transformer.transform(obs, sess) - next_state = update_state(state, obs_small) - - # Compute total reward - episode_reward += reward - - # Save the latest experience - experience_replay_buffer.add_experience(action, obs_small, reward, done) - - # Train the model, keep track of time - t0_2 = datetime.now() - loss = learn(model, target_model, experience_replay_buffer, gamma, batch_size) - dt = datetime.now() - t0_2 - - # More debugging info - total_time_training += dt.total_seconds() - num_steps_in_episode += 1 - - - state = next_state - total_t += 1 - - epsilon = max(epsilon - epsilon_change, epsilon_min) - - return total_t, episode_reward, (datetime.now() - t0), num_steps_in_episode, total_time_training/num_steps_in_episode, epsilon - - -def smooth(x): - # last 100 - n = len(x) - y = np.zeros(n) - for i in range(n): - start = max(0, i - 99) - y[i] = float(x[start:(i+1)].sum()) / (i - start + 1) - return y - - -if __name__ == '__main__': - - # hyperparams and initialize stuff - conv_layer_sizes = [(32, 8, 4), (64, 4, 2), (64, 3, 1)] - hidden_layer_sizes = [512] - gamma = 0.99 - batch_sz = 32 - num_episodes = 3500 - total_t = 0 - experience_replay_buffer = ReplayMemory() - episode_rewards = np.zeros(num_episodes) - - - - # epsilon - # decays linearly until 0.1 - epsilon = 1.0 - epsilon_min = 0.1 - epsilon_change = (epsilon - epsilon_min) / 500000 - - - - # Create environment - env = gym.envs.make("Breakout-v0") - - - - # Create models - model = DQN( - K=K, - conv_layer_sizes=conv_layer_sizes, - hidden_layer_sizes=hidden_layer_sizes, - scope="model") - target_model = DQN( - K=K, - conv_layer_sizes=conv_layer_sizes, - hidden_layer_sizes=hidden_layer_sizes, - scope="target_model" - ) - image_transformer = ImageTransformer() - - - - with tf.Session() as sess: - model.set_session(sess) - target_model.set_session(sess) - sess.run(tf.global_variables_initializer()) - - - print("Populating experience replay buffer...") - obs = env.reset() - - for i in range(MIN_EXPERIENCES): - - action = np.random.choice(K) - obs, reward, done, _ = env.step(action) - obs_small = image_transformer.transform(obs, sess) # not used anymore - experience_replay_buffer.add_experience(action, obs_small, reward, done) - - if done: - obs = env.reset() - - - # Play a number of episodes and learn! - t0 = datetime.now() - for i in range(num_episodes): - - total_t, episode_reward, duration, num_steps_in_episode, time_per_step, epsilon = play_one( - env, - sess, - total_t, - experience_replay_buffer, - model, - target_model, - image_transformer, - gamma, - batch_sz, - epsilon, - epsilon_change, - epsilon_min, - ) - episode_rewards[i] = episode_reward - - last_100_avg = episode_rewards[max(0, i - 100):i + 1].mean() - print("Episode:", i, - "Duration:", duration, - "Num steps:", num_steps_in_episode, - "Reward:", episode_reward, - "Training time per step:", "%.3f" % time_per_step, - "Avg Reward (Last 100):", "%.3f" % last_100_avg, - "Epsilon:", "%.3f" % epsilon - ) - sys.stdout.flush() - print("Total duration:", datetime.now() - t0) - - model.save() - - # Plot the smoothed returns - y = smooth(episode_rewards) - plt.plot(episode_rewards, label='orig') - plt.plot(y, label='smoothed') - plt.legend() - plt.show() - - diff --git a/rl2/atari/dqn_theano.py b/rl2/atari/dqn_theano.py deleted file mode 100755 index 0ad3b36a..00000000 --- a/rl2/atari/dqn_theano.py +++ /dev/null @@ -1,504 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import copy -import gym -import os -import sys -import random -import numpy as np -import theano -import theano.tensor as T -from theano.tensor.nnet import conv2d -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime -from scipy.misc import imresize - - - - -##### testing only -# MAX_EXPERIENCES = 10000 -# MIN_EXPERIENCES = 1000 - - -MAX_EXPERIENCES = 500000 -MIN_EXPERIENCES = 50000 -TARGET_UPDATE_PERIOD = 10000 -IM_SIZE = 84 -K = 4 #env.action_space.n - - -def rgb2gray(rgb): - r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2] - gray = 0.2989 * r + 0.5870 * g + 0.1140 * b - return gray.astype(np.uint8) - - -# TODO: can this be converted into a Theano function? -def downsample_image(A): - B = A[34:194] # select the important parts of the image - B = rgb2gray(B) # convert to grayscale - - # downsample image - # changing aspect ratio doesn't significantly distort the image - # nearest neighbor interpolation produces a much sharper image - # than default bilinear - B = imresize(B, size=(IM_SIZE, IM_SIZE), interp='nearest') - return B - - -def update_state(state, obs): - obs_small = downsample_image(obs) - return np.append(state[1:], np.expand_dims(obs_small, 0), axis=0) - - -class ReplayMemory: - def __init__(self, size=MAX_EXPERIENCES, frame_height=IM_SIZE, frame_width=IM_SIZE, - agent_history_length=4, batch_size=32): - """ - Args: - size: Integer, Number of stored transitions - frame_height: Integer, Height of a frame of an Atari game - frame_width: Integer, Width of a frame of an Atari game - agent_history_length: Integer, Number of frames stacked together to create a state - batch_size: Integer, Number of transitions returned in a minibatch - """ - self.size = size - self.frame_height = frame_height - self.frame_width = frame_width - self.agent_history_length = agent_history_length - self.batch_size = batch_size - self.count = 0 - self.current = 0 - - # Pre-allocate memory - self.actions = np.empty(self.size, dtype=np.int32) - self.rewards = np.empty(self.size, dtype=np.float32) - self.frames = np.empty((self.size, self.frame_height, self.frame_width), dtype=np.uint8) - self.terminal_flags = np.empty(self.size, dtype=np.bool) - - # Pre-allocate memory for the states and new_states in a minibatch - self.states = np.empty((self.batch_size, self.agent_history_length, - self.frame_height, self.frame_width), dtype=np.uint8) - self.new_states = np.empty((self.batch_size, self.agent_history_length, - self.frame_height, self.frame_width), dtype=np.uint8) - self.indices = np.empty(self.batch_size, dtype=np.int32) - - def add_experience(self, action, frame, reward, terminal): - """ - Args: - action: An integer-encoded action - frame: One grayscale frame of the game - reward: reward the agend received for performing an action - terminal: A bool stating whether the episode terminated - """ - if frame.shape != (self.frame_height, self.frame_width): - raise ValueError('Dimension of frame is wrong!') - self.actions[self.current] = action - self.frames[self.current, ...] = frame - self.rewards[self.current] = reward - self.terminal_flags[self.current] = terminal - self.count = max(self.count, self.current+1) - self.current = (self.current + 1) % self.size - - def _get_state(self, index): - if self.count is 0: - raise ValueError("The replay memory is empty!") - if index < self.agent_history_length - 1: - raise ValueError("Index must be min 3") - return self.frames[index-self.agent_history_length+1:index+1, ...] - - def _get_valid_indices(self): - for i in range(self.batch_size): - while True: - index = random.randint(self.agent_history_length, self.count - 1) - if index < self.agent_history_length: - continue - if index >= self.current and index - self.agent_history_length <= self.current: - continue - if self.terminal_flags[index - self.agent_history_length:index].any(): - continue - break - self.indices[i] = index - - def get_minibatch(self): - """ - Returns a minibatch of self.batch_size transitions - """ - if self.count < self.agent_history_length: - raise ValueError('Not enough memories to get a minibatch') - - self._get_valid_indices() - - for i, idx in enumerate(self.indices): - self.states[i] = self._get_state(idx - 1) - self.new_states[i] = self._get_state(idx) - - return self.states, \ - self.actions[self.indices], \ - self.rewards[self.indices], \ - self.new_states, \ - self.terminal_flags[self.indices] - - -def init_filter(shape): - w = np.random.randn(*shape) * np.sqrt(2.0 / np.prod(shape[1:])) - return w.astype(np.float32) - - -def adam(cost, params, lr0=1e-5, beta1=0.9, beta2=0.999, eps=1e-8): - # cast - lr0 = np.float32(lr0) - beta1 = np.float32(beta1) - beta2 = np.float32(beta2) - eps = np.float32(eps) - one = np.float32(1) - zero = np.float32(0) - - grads = T.grad(cost, params) - updates = [] - time = theano.shared(zero) - new_time = time + one - updates.append((time, new_time)) - lr = lr0*T.sqrt(one - beta2**new_time) / (one - beta1**new_time) - for p, g in zip(params, grads): - m = theano.shared(p.get_value() * zero) - v = theano.shared(p.get_value() * zero) - new_m = beta1*m + (one - beta1)*g - new_v = beta2*v + (one - beta2)*g*g - new_p = p - lr*new_m / (T.sqrt(new_v) + eps) - updates.append((m, new_m)) - updates.append((v, new_v)) - updates.append((p, new_p)) - return updates - - -class ConvLayer(object): - def __init__(self, mi, mo, filtsz=5, stride=2, f=T.nnet.relu): - # mi = input feature map size - # mo = output feature map size - sz = (mo, mi, filtsz, filtsz) - W0 = init_filter(sz) - self.W = theano.shared(W0) - b0 = np.zeros(mo, dtype=np.float32) - self.b = theano.shared(b0) - self.stride = (stride, stride) - self.params = [self.W, self.b] - self.f = f - # self.cut = cut - - def forward(self, X): - conv_out = conv2d( - input=X, - filters=self.W, - subsample=self.stride, - # border_mode='half', - border_mode='valid', - ) - # cut off 1 pixel from each edge - # to make the output the same size as input - # like tensorflow - # if self.cut: - # conv_out = conv_out[:, : ,:self.cut ,:self.cut] - return self.f(conv_out + self.b.dimshuffle('x', 0, 'x', 'x')) - -class HiddenLayer: - def __init__(self, M1, M2, f=T.nnet.relu): - W = np.random.randn(M1, M2) * np.sqrt(2 / M1) - self.W = theano.shared(W.astype(np.float32)) - self.b = theano.shared(np.zeros(M2).astype(np.float32)) - self.params = [self.W, self.b] - self.f = f - - def forward(self, X): - a = X.dot(self.W) + self.b - return self.f(a) - -class DQN: - def __init__(self, K, conv_layer_sizes, hidden_layer_sizes): - self.K = K - - # inputs and targets - X = T.ftensor4('X') - G = T.fvector('G') - actions = T.ivector('actions') - - # create the graph - self.conv_layers = [] - num_input_filters = 4 # number of filters / color channels - current_size = IM_SIZE - for num_output_filters, filtersz, stride in conv_layer_sizes: - ### not using this currently, it didn't make a difference ### - # cut = None - # if filtersz % 2 == 0: # if even - # cut = (current_size + stride - 1) // stride - layer = ConvLayer(num_input_filters, num_output_filters, filtersz, stride) - current_size = (current_size + stride - 1) // stride - # print("current_size:", current_size) - self.conv_layers.append(layer) - num_input_filters = num_output_filters - - # get conv output size - Z = X / 255.0 - for layer in self.conv_layers: - Z = layer.forward(Z) - conv_out = Z.flatten(ndim=2) - conv_out_op = theano.function(inputs=[X], outputs=conv_out, allow_input_downcast=True) - test = conv_out_op(np.random.randn(1, 4, IM_SIZE, IM_SIZE)) - flattened_ouput_size = test.shape[1] - - - # build fully connected layers - self.layers = [] - M1 = flattened_ouput_size - print("flattened_ouput_size:", flattened_ouput_size) - for M2 in hidden_layer_sizes: - layer = HiddenLayer(M1, M2) - self.layers.append(layer) - M1 = M2 - - # final layer - layer = HiddenLayer(M1, K, lambda x: x) - self.layers.append(layer) - - # collect params for copy - self.params = [] - for layer in (self.conv_layers + self.layers): - self.params += layer.params - - - # calculate final output and cost - Z = conv_out - for layer in self.layers: - Z = layer.forward(Z) - Y_hat = Z - - selected_action_values = Y_hat[T.arange(actions.shape[0]), actions] - cost = T.mean((G - selected_action_values)**2) - - # create train function - updates = adam(cost, self.params) - - # compile functions - self.train_op = theano.function( - inputs=[X, G, actions], - outputs=cost, - updates=updates, - allow_input_downcast=True - ) - - self.predict_op = theano.function( - inputs=[X], - outputs=Y_hat, - allow_input_downcast=True - ) - - def copy_from(self, other): - my_params = self.params - other_params = other.params - for p, q in zip(my_params, other_params): - actual = q.get_value() - p.set_value(actual) - - def predict(self, X): - return self.predict_op(X) - - def update(self, states, actions, targets): - return self.train_op(states, targets, actions) - - def sample_action(self, x, eps): - if np.random.random() < eps: - return np.random.choice(self.K) - else: - return np.argmax(self.predict([x])[0]) - - - - -def learn(model, target_model, experience_replay_buffer, gamma, batch_size): - # Sample experiences - states, actions, rewards, next_states, dones = experience_replay_buffer.get_minibatch() - - # Calculate targets - next_Qs = target_model.predict(next_states) - next_Q = np.amax(next_Qs, axis=1) - targets = rewards + np.invert(dones).astype(np.float32) * gamma * next_Q - - # Update model - loss = model.update(states, actions, targets) - return loss - - -def play_one( - env, - total_t, - experience_replay_buffer, - model, - target_model, - gamma, - batch_size, - epsilon, - epsilon_change, - epsilon_min): - - t0 = datetime.now() - - # Reset the environment - obs = env.reset() - obs_small = downsample_image(obs) - state = np.stack([obs_small] * 4, axis=0) - loss = None - - - total_time_training = 0 - num_steps_in_episode = 0 - episode_reward = 0 - - done = False - while not done: - - # Update target network - if total_t % TARGET_UPDATE_PERIOD == 0: - target_model.copy_from(model) - print("Copied model parameters to target network. total_t = %s, period = %s" % (total_t, TARGET_UPDATE_PERIOD)) - - # Take action - action = model.sample_action(state, epsilon) - obs, reward, done, _ = env.step(action) - obs_small = downsample_image(obs) - next_state = np.append(state[1:], np.expand_dims(obs_small, 0), axis=0) - - episode_reward += reward - - # Save the latest experience - experience_replay_buffer.add_experience(action, obs_small, reward, done) - - # Train the model, keep track of time - t0_2 = datetime.now() - loss = learn(model, target_model, experience_replay_buffer, gamma, batch_size) - dt = datetime.now() - t0_2 - - total_time_training += dt.total_seconds() - num_steps_in_episode += 1 - - - state = next_state - total_t += 1 - - epsilon = max(epsilon - epsilon_change, epsilon_min) - - return total_t, episode_reward, (datetime.now() - t0), num_steps_in_episode, total_time_training/num_steps_in_episode, epsilon - - -def smooth(x): - # last 100 - n = len(x) - y = np.zeros(n) - for i in range(n): - start = max(0, i - 99) - y[i] = float(x[start:(i+1)].sum()) / (i - start + 1) - return y - - -if __name__ == '__main__': - - # hyperparams and initialize stuff - conv_layer_sizes = [(32, 8, 4), (64, 4, 2), (64, 3, 1)] - hidden_layer_sizes = [512] - gamma = 0.99 - batch_sz = 32 - num_episodes = 5000 - total_t = 0 - experience_replay_buffer = ReplayMemory() - episode_rewards = np.zeros(num_episodes) - step_counts = np.zeros(num_episodes) - - - - # epsilon - # decays linearly until 0.1 - epsilon = 1.0 - epsilon_min = 0.1 - epsilon_change = (epsilon - epsilon_min) / 500000 - - - - # Create environment - env = gym.envs.make("Breakout-v0") - - - - # Create models - model = DQN( - K=K, - conv_layer_sizes=conv_layer_sizes, - hidden_layer_sizes=hidden_layer_sizes, - ) - target_model = DQN( - K=K, - conv_layer_sizes=conv_layer_sizes, - hidden_layer_sizes=hidden_layer_sizes, - ) - - - print("Populating experience replay buffer...") - obs = env.reset() - obs_small = downsample_image(obs) - for i in range(MIN_EXPERIENCES): - - action = np.random.choice(K) - obs, reward, done, _ = env.step(action) - obs_small = downsample_image(obs) - experience_replay_buffer.add_experience(action, obs_small, reward, done) - - if done: - obs = env.reset() - - - # Play a number of episodes and learn! - t0 = datetime.now() - for i in range(num_episodes): - - total_t, episode_reward, duration, num_steps_in_episode, time_per_step, epsilon = play_one( - env, - total_t, - experience_replay_buffer, - model, - target_model, - gamma, - batch_sz, - epsilon, - epsilon_change, - epsilon_min, - ) - episode_rewards[i] = episode_reward - step_counts[i] = num_steps_in_episode - - last_100_avg = episode_rewards[max(0, i - 100):i + 1].mean() - last_100_avg_steps = step_counts[max(0, i - 100):i + 1].mean() - print("Episode:", i, - "Duration:", duration, - "Num steps:", num_steps_in_episode, - "Reward:", episode_reward, - "Training time per step:", "%.3f" % time_per_step, - "Avg Reward (Last 100):", "%.3f" % last_100_avg, - "Avg Steps (Last 100):", "%.1f" % last_100_avg_steps, - "Epsilon:", "%.3f" % epsilon - ) - sys.stdout.flush() - print("Total duration:", datetime.now() - t0) - - # Plot the smoothed returns - y = smooth(episode_rewards) - plt.plot(episode_rewards, label='orig') - plt.plot(y, label='smoothed') - plt.legend() - plt.show() - - diff --git a/rl2/cartpole/dqn_tf.py b/rl2/cartpole/dqn_tf.py deleted file mode 100644 index 133772df..00000000 --- a/rl2/cartpole/dqn_tf.py +++ /dev/null @@ -1,243 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import gym -import os -import sys -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime -from q_learning_bins import plot_running_avg - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - -if tf.__version__.startswith('2'): - exit("Please install Tensorflow 1.x") - - -# global counter -global_iters = 0 - - -# a version of HiddenLayer that keeps track of params -class HiddenLayer: - def __init__(self, M1, M2, f=tf.nn.tanh, use_bias=True): - self.W = tf.Variable(tf.random_normal(shape=(M1, M2))) - self.params = [self.W] - self.use_bias = use_bias - if use_bias: - self.b = tf.Variable(np.zeros(M2).astype(np.float32)) - self.params.append(self.b) - self.f = f - - def forward(self, X): - if self.use_bias: - a = tf.matmul(X, self.W) + self.b - else: - a = tf.matmul(X, self.W) - return self.f(a) - - -class DQN: - def __init__(self, D, K, hidden_layer_sizes, gamma, max_experiences=10000, min_experiences=100, batch_sz=32): - self.K = K - - # create the graph - self.layers = [] - M1 = D - for M2 in hidden_layer_sizes: - layer = HiddenLayer(M1, M2) - self.layers.append(layer) - M1 = M2 - - # final layer - layer = HiddenLayer(M1, K, lambda x: x) - self.layers.append(layer) - - # collect params for copy - self.params = [] - for layer in self.layers: - self.params += layer.params - - # inputs and targets - self.X = tf.placeholder(tf.float32, shape=(None, D), name='X') - self.G = tf.placeholder(tf.float32, shape=(None,), name='G') - self.actions = tf.placeholder(tf.int32, shape=(None,), name='actions') - - # calculate output and cost - Z = self.X - for layer in self.layers: - Z = layer.forward(Z) - Y_hat = Z - self.predict_op = Y_hat - - selected_action_values = tf.reduce_sum( - Y_hat * tf.one_hot(self.actions, K), - reduction_indices=[1] - ) - - cost = tf.reduce_sum(tf.square(self.G - selected_action_values)) - self.train_op = tf.train.AdamOptimizer(1e-2).minimize(cost) - # self.train_op = tf.train.AdagradOptimizer(1e-2).minimize(cost) - # self.train_op = tf.train.MomentumOptimizer(1e-3, momentum=0.9).minimize(cost) - # self.train_op = tf.train.GradientDescentOptimizer(1e-4).minimize(cost) - - # create replay memory - self.experience = {'s': [], 'a': [], 'r': [], 's2': [], 'done': []} - self.max_experiences = max_experiences - self.min_experiences = min_experiences - self.batch_sz = batch_sz - self.gamma = gamma - - def set_session(self, session): - self.session = session - - def copy_from(self, other): - # collect all the ops - ops = [] - my_params = self.params - other_params = other.params - for p, q in zip(my_params, other_params): - actual = self.session.run(q) - op = p.assign(actual) - ops.append(op) - # now run them all - self.session.run(ops) - - def predict(self, X): - X = np.atleast_2d(X) - return self.session.run(self.predict_op, feed_dict={self.X: X}) - - def train(self, target_network): - # sample a random batch from buffer, do an iteration of GD - if len(self.experience['s']) < self.min_experiences: - # don't do anything if we don't have enough experience - return - - # randomly select a batch - idx = np.random.choice(len(self.experience['s']), size=self.batch_sz, replace=False) - # print("idx:", idx) - states = [self.experience['s'][i] for i in idx] - actions = [self.experience['a'][i] for i in idx] - rewards = [self.experience['r'][i] for i in idx] - next_states = [self.experience['s2'][i] for i in idx] - dones = [self.experience['done'][i] for i in idx] - next_Q = np.max(target_network.predict(next_states), axis=1) - targets = [r + self.gamma*next_q if not done else r for r, next_q, done in zip(rewards, next_Q, dones)] - - # call optimizer - self.session.run( - self.train_op, - feed_dict={ - self.X: states, - self.G: targets, - self.actions: actions - } - ) - - def add_experience(self, s, a, r, s2, done): - if len(self.experience['s']) >= self.max_experiences: - self.experience['s'].pop(0) - self.experience['a'].pop(0) - self.experience['r'].pop(0) - self.experience['s2'].pop(0) - self.experience['done'].pop(0) - self.experience['s'].append(s) - self.experience['a'].append(a) - self.experience['r'].append(r) - self.experience['s2'].append(s2) - self.experience['done'].append(done) - - def sample_action(self, x, eps): - if np.random.random() < eps: - return np.random.choice(self.K) - else: - X = np.atleast_2d(x) - return np.argmax(self.predict(X)[0]) - - -def play_one(env, model, tmodel, eps, gamma, copy_period): - global global_iters - observation = env.reset() - done = False - totalreward = 0 - iters = 0 - while not done and iters < 2000: - # if we reach 2000, just quit, don't want this going forever - # the 200 limit seems a bit early - action = model.sample_action(observation, eps) - prev_observation = observation - observation, reward, done, info = env.step(action) - - totalreward += reward - if done: - reward = -200 - - # update the model - model.add_experience(prev_observation, action, reward, observation, done) - model.train(tmodel) - - iters += 1 - global_iters += 1 - - if global_iters % copy_period == 0: - tmodel.copy_from(model) - - return totalreward - - -def main(): - env = gym.make('CartPole-v0') - gamma = 0.99 - copy_period = 50 - - D = len(env.observation_space.sample()) - K = env.action_space.n - sizes = [200,200] - model = DQN(D, K, sizes, gamma) - tmodel = DQN(D, K, sizes, gamma) - init = tf.global_variables_initializer() - session = tf.InteractiveSession() - session.run(init) - model.set_session(session) - tmodel.set_session(session) - - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - - N = 500 - totalrewards = np.empty(N) - costs = np.empty(N) - for n in range(N): - eps = 1.0/np.sqrt(n+1) - totalreward = play_one(env, model, tmodel, eps, gamma, copy_period) - totalrewards[n] = totalreward - if n % 100 == 0: - print("episode:", n, "total reward:", totalreward, "eps:", eps, "avg reward (last 100):", totalrewards[max(0, n-100):(n+1)].mean()) - - print("avg reward for last 100 episodes:", totalrewards[-100:].mean()) - print("total steps:", totalrewards.sum()) - - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - plot_running_avg(totalrewards) - - -if __name__ == '__main__': - main() - - diff --git a/rl2/cartpole/dqn_theano.py b/rl2/cartpole/dqn_theano.py deleted file mode 100644 index 18e6844c..00000000 --- a/rl2/cartpole/dqn_theano.py +++ /dev/null @@ -1,249 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import gym -import os -import sys -import numpy as np -import theano -import theano.tensor as T -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime -from q_learning_bins import plot_running_avg - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - - -# global counter -global_iters = 0 - - -# helper for adam optimizer -# use tensorflow defaults -def adam(cost, params, lr0=1e-2, beta1=0.9, beta2=0.999, eps=1e-8): - grads = T.grad(cost, params) - updates = [] - time = theano.shared(0) - new_time = time + 1 - updates.append((time, new_time)) - lr = lr0*T.sqrt(1 - beta2**new_time) / (1 - beta1**new_time) - for p, g in zip(params, grads): - m = theano.shared(p.get_value() * 0.) - v = theano.shared(p.get_value() * 0.) - new_m = beta1*m + (1 - beta1)*g - new_v = beta2*v + (1 - beta2)*g*g - new_p = p - lr*new_m / (T.sqrt(new_v) + eps) - updates.append((m, new_m)) - updates.append((v, new_v)) - updates.append((p, new_p)) - return updates - - -# a version of HiddenLayer that keeps track of params -class HiddenLayer: - def __init__(self, M1, M2, f=T.tanh, use_bias=True): - self.W = theano.shared(np.random.randn(M1, M2) * np.sqrt(2 / M1)) - self.params = [self.W] - self.use_bias = use_bias - if use_bias: - self.b = theano.shared(np.zeros(M2)) - self.params += [self.b] - self.f = f - - def forward(self, X): - if self.use_bias: - a = X.dot(self.W) + self.b - else: - a = X.dot(self.W) - return self.f(a) - - -class DQN: - def __init__(self, D, K, hidden_layer_sizes, gamma, max_experiences=10000, min_experiences=100, batch_sz=32): - self.K = K - lr = 1e-2 - mu = 0. - decay = 0.99 - - # create the graph - self.layers = [] - M1 = D - for M2 in hidden_layer_sizes: - layer = HiddenLayer(M1, M2) - self.layers.append(layer) - M1 = M2 - - # final layer - layer = HiddenLayer(M1, K, lambda x: x) - self.layers.append(layer) - - # collect params for copy - self.params = [] - for layer in self.layers: - self.params += layer.params - - # inputs and targets - X = T.matrix('X') - G = T.vector('G') - actions = T.ivector('actions') - - # calculate output and cost - Z = X - for layer in self.layers: - Z = layer.forward(Z) - Y_hat = Z - - selected_action_values = Y_hat[T.arange(actions.shape[0]), actions] - cost = T.sum((G - selected_action_values)**2) - - # create train function - updates = adam(cost, self.params) - - # compile functions - self.train_op = theano.function( - inputs=[X, G, actions], - updates=updates, - allow_input_downcast=True - ) - self.predict_op = theano.function( - inputs=[X], - outputs=Y_hat, - allow_input_downcast=True - ) - - # create replay memory - self.experience = {'s': [], 'a': [], 'r': [], 's2': [], 'done': []} - self.max_experiences = max_experiences - self.min_experiences = min_experiences - self.batch_sz = batch_sz - self.gamma = gamma - - def copy_from(self, other): - my_params = self.params - other_params = other.params - for p, q in zip(my_params, other_params): - actual = q.get_value() - p.set_value(actual) - - def predict(self, X): - X = np.atleast_2d(X) - return self.predict_op(X) - - def train(self, target_network): - # sample a random batch from buffer, do an iteration of GD - if len(self.experience['s']) < self.min_experiences: - # don't do anything if we don't have enough experience - return - - # randomly select a batch - idx = np.random.choice(len(self.experience['s']), size=self.batch_sz, replace=False) - # print("idx:", idx) - states = [self.experience['s'][i] for i in idx] - actions = [self.experience['a'][i] for i in idx] - rewards = [self.experience['r'][i] for i in idx] - next_states = [self.experience['s2'][i] for i in idx] - dones = [self.experience['done'][i] for i in idx] - next_Q = np.max(target_network.predict(next_states), axis=1) - targets = [r + self.gamma*next_q if not done else r for r, next_q, done in zip(rewards, next_Q, dones)] - - # call optimizer - self.train_op(states, targets, actions) - - def add_experience(self, s, a, r, s2, done): - if len(self.experience['s']) >= self.max_experiences: - self.experience['s'].pop(0) - self.experience['a'].pop(0) - self.experience['r'].pop(0) - self.experience['s2'].pop(0) - self.experience['done'].pop(0) - self.experience['s'].append(s) - self.experience['a'].append(a) - self.experience['r'].append(r) - self.experience['s2'].append(s2) - self.experience['done'].append(done) - - def sample_action(self, x, eps): - if np.random.random() < eps: - return np.random.choice(self.K) - else: - X = np.atleast_2d(x) - return np.argmax(self.predict(X)[0]) - - -def play_one(env, model, tmodel, eps, gamma, copy_period): - global global_iters - observation = env.reset() - done = False - totalreward = 0 - iters = 0 - while not done and iters < 2000: - # if we reach 2000, just quit, don't want this going forever - # the 200 limit seems a bit early - action = model.sample_action(observation, eps) - prev_observation = observation - observation, reward, done, info = env.step(action) - - totalreward += reward - if done: - reward = -200 - - # update the model - model.add_experience(prev_observation, action, reward, observation, done) - model.train(tmodel) - - iters += 1 - global_iters += 1 - - if global_iters % copy_period == 0: - tmodel.copy_from(model) - - return totalreward - - -def main(): - env = gym.make('CartPole-v0') - gamma = 0.99 - copy_period = 50 - - D = len(env.observation_space.sample()) - K = env.action_space.n - sizes = [200,200] - model = DQN(D, K, sizes, gamma) - tmodel = DQN(D, K, sizes, gamma) - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - N = 500 - totalrewards = np.empty(N) - costs = np.empty(N) - for n in range(N): - eps = 1.0/np.sqrt(n+1) - totalreward = play_one(env, model, tmodel, eps, gamma, copy_period) - totalrewards[n] = totalreward - if n % 100 == 0: - print("episode:", n, "total reward:", totalreward, "eps:", eps, "avg reward (last 100):", totalrewards[max(0, n-100):(n+1)].mean()) - - print("avg reward for last 100 episodes:", totalrewards[-100:].mean()) - print("total steps:", totalrewards.sum()) - - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - plot_running_avg(totalrewards) - - -if __name__ == '__main__': - main() - - diff --git a/rl2/cartpole/pg_tf.py b/rl2/cartpole/pg_tf.py deleted file mode 100644 index 40122df0..00000000 --- a/rl2/cartpole/pg_tf.py +++ /dev/null @@ -1,285 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future -# Inspired by https://github.com/dennybritz/reinforcement-learning - -import gym -import os -import sys -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime -from q_learning_bins import plot_running_avg - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - -if tf.__version__.startswith('2'): - exit("Please install Tensorflow 1.x") - - -# so you can test different architectures -class HiddenLayer: - def __init__(self, M1, M2, f=tf.nn.tanh, use_bias=True): - self.W = tf.Variable(tf.random_normal(shape=(M1, M2))) - self.use_bias = use_bias - if use_bias: - self.b = tf.Variable(np.zeros(M2).astype(np.float32)) - self.f = f - - def forward(self, X): - if self.use_bias: - a = tf.matmul(X, self.W) + self.b - else: - a = tf.matmul(X, self.W) - return self.f(a) - - -# approximates pi(a | s) -class PolicyModel: - def __init__(self, D, K, hidden_layer_sizes): - # create the graph - # K = number of actions - self.layers = [] - M1 = D - for M2 in hidden_layer_sizes: - layer = HiddenLayer(M1, M2) - self.layers.append(layer) - M1 = M2 - - # final layer - # layer = HiddenLayer(M1, K, lambda x: x, use_bias=False) - layer = HiddenLayer(M1, K, tf.nn.softmax, use_bias=False) - self.layers.append(layer) - - # inputs and targets - self.X = tf.placeholder(tf.float32, shape=(None, D), name='X') - self.actions = tf.placeholder(tf.int32, shape=(None,), name='actions') - self.advantages = tf.placeholder(tf.float32, shape=(None,), name='advantages') - - # calculate output and cost - Z = self.X - for layer in self.layers: - Z = layer.forward(Z) - p_a_given_s = Z - # action_scores = Z - # p_a_given_s = tf.nn.softmax(action_scores) - # self.action_scores = action_scores - self.predict_op = p_a_given_s - - # self.one_hot_actions = tf.one_hot(self.actions, K) - - selected_probs = tf.log( - tf.reduce_sum( - p_a_given_s * tf.one_hot(self.actions, K), - reduction_indices=[1] - ) - ) - - # self.selected_probs = selected_probs - cost = -tf.reduce_sum(self.advantages * selected_probs) - # self.cost = cost - # self.train_op = tf.train.AdamOptimizer(1e-1).minimize(cost) - self.train_op = tf.train.AdagradOptimizer(1e-1).minimize(cost) - # self.train_op = tf.train.MomentumOptimizer(1e-4, momentum=0.9).minimize(cost) - # self.train_op = tf.train.GradientDescentOptimizer(1e-4).minimize(cost) - - def set_session(self, session): - self.session = session - - def partial_fit(self, X, actions, advantages): - X = np.atleast_2d(X) - actions = np.atleast_1d(actions) - advantages = np.atleast_1d(advantages) - self.session.run( - self.train_op, - feed_dict={ - self.X: X, - self.actions: actions, - self.advantages: advantages, - } - ) - - def predict(self, X): - X = np.atleast_2d(X) - return self.session.run(self.predict_op, feed_dict={self.X: X}) - - def sample_action(self, X): - p = self.predict(X)[0] - return np.random.choice(len(p), p=p) - - -# approximates V(s) -class ValueModel: - def __init__(self, D, hidden_layer_sizes): - # create the graph - self.layers = [] - M1 = D - for M2 in hidden_layer_sizes: - layer = HiddenLayer(M1, M2) - self.layers.append(layer) - M1 = M2 - - # final layer - layer = HiddenLayer(M1, 1, lambda x: x) - self.layers.append(layer) - - # inputs and targets - self.X = tf.placeholder(tf.float32, shape=(None, D), name='X') - self.Y = tf.placeholder(tf.float32, shape=(None,), name='Y') - - # calculate output and cost - Z = self.X - for layer in self.layers: - Z = layer.forward(Z) - Y_hat = tf.reshape(Z, [-1]) # the output - self.predict_op = Y_hat - - cost = tf.reduce_sum(tf.square(self.Y - Y_hat)) - # self.train_op = tf.train.AdamOptimizer(1e-2).minimize(cost) - # self.train_op = tf.train.MomentumOptimizer(1e-2, momentum=0.9).minimize(cost) - self.train_op = tf.train.GradientDescentOptimizer(1e-4).minimize(cost) - - def set_session(self, session): - self.session = session - - def partial_fit(self, X, Y): - X = np.atleast_2d(X) - Y = np.atleast_1d(Y) - self.session.run(self.train_op, feed_dict={self.X: X, self.Y: Y}) - - def predict(self, X): - X = np.atleast_2d(X) - return self.session.run(self.predict_op, feed_dict={self.X: X}) - - -def play_one_td(env, pmodel, vmodel, gamma): - observation = env.reset() - done = False - totalreward = 0 - iters = 0 - - while not done and iters < 2000: - # if we reach 2000, just quit, don't want this going forever - # the 200 limit seems a bit early - action = pmodel.sample_action(observation) - prev_observation = observation - observation, reward, done, info = env.step(action) - - # if done: - # reward = -200 - - # update the models - V_next = vmodel.predict(observation)[0] - G = reward + gamma*V_next - advantage = G - vmodel.predict(prev_observation) - pmodel.partial_fit(prev_observation, action, advantage) - vmodel.partial_fit(prev_observation, G) - - if reward == 1: # if we changed the reward to -200 - totalreward += reward - iters += 1 - - return totalreward - - - -def play_one_mc(env, pmodel, vmodel, gamma): - observation = env.reset() - done = False - totalreward = 0 - iters = 0 - - states = [] - actions = [] - rewards = [] - - reward = 0 - while not done and iters < 2000: - # if we reach 2000, just quit, don't want this going forever - # the 200 limit seems a bit early - action = pmodel.sample_action(observation) - - states.append(observation) - actions.append(action) - rewards.append(reward) - - prev_observation = observation - observation, reward, done, info = env.step(action) - - if done: - reward = -200 - - if reward == 1: # if we changed the reward to -200 - totalreward += reward - iters += 1 - - # save the final (s,a,r) tuple - action = pmodel.sample_action(observation) - states.append(observation) - actions.append(action) - rewards.append(reward) - - returns = [] - advantages = [] - G = 0 - for s, r in zip(reversed(states), reversed(rewards)): - returns.append(G) - advantages.append(G - vmodel.predict(s)[0]) - G = r + gamma*G - returns.reverse() - advantages.reverse() - - # update the models - pmodel.partial_fit(states, actions, advantages) - vmodel.partial_fit(states, returns) - - return totalreward - - -def main(): - env = gym.make('CartPole-v0') - D = env.observation_space.shape[0] - K = env.action_space.n - pmodel = PolicyModel(D, K, []) - vmodel = ValueModel(D, [10]) - init = tf.global_variables_initializer() - session = tf.InteractiveSession() - session.run(init) - pmodel.set_session(session) - vmodel.set_session(session) - gamma = 0.99 - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - N = 1000 - totalrewards = np.empty(N) - costs = np.empty(N) - for n in range(N): - totalreward = play_one_mc(env, pmodel, vmodel, gamma) - totalrewards[n] = totalreward - if n % 100 == 0: - print("episode:", n, "total reward:", totalreward, "avg reward (last 100):", totalrewards[max(0, n-100):(n+1)].mean()) - - print("avg reward for last 100 episodes:", totalrewards[-100:].mean()) - print("total steps:", totalrewards.sum()) - - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - plot_running_avg(totalrewards) - - -if __name__ == '__main__': - main() - diff --git a/rl2/cartpole/pg_theano.py b/rl2/cartpole/pg_theano.py deleted file mode 100644 index 16979d5f..00000000 --- a/rl2/cartpole/pg_theano.py +++ /dev/null @@ -1,293 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future -# Inspired by https://github.com/dennybritz/reinforcement-learning - -import gym -import os -import sys -import numpy as np -import theano -import theano.tensor as T -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime -from q_learning_bins import plot_running_avg - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - - -# so you can test different architectures -class HiddenLayer: - def __init__(self, M1, M2, f=T.tanh, use_bias=True): - self.W = theano.shared(np.random.randn(M1, M2) * np.sqrt(2 / M1)) - self.params = [self.W] - self.use_bias = use_bias - if use_bias: - self.b = theano.shared(np.zeros(M2)) - self.params += [self.b] - self.f = f - - def forward(self, X): - if self.use_bias: - a = X.dot(self.W) + self.b - else: - a = X.dot(self.W) - return self.f(a) - - -# approximates pi(a | s) -class PolicyModel: - def __init__(self, D, K, hidden_layer_sizes): - # learning rate and other hyperparams - lr = 1e-4 - - # create the graph - # K = number of actions - self.layers = [] - M1 = D - for M2 in hidden_layer_sizes: - layer = HiddenLayer(M1, M2) - self.layers.append(layer) - M1 = M2 - - # final layer - layer = HiddenLayer(M1, K, lambda x: x, use_bias=False) - self.layers.append(layer) - - # get all params for gradient later - params = [] - for layer in self.layers: - params += layer.params - - # inputs and targets - X = T.matrix('X') - actions = T.ivector('actions') - advantages = T.vector('advantages') - - # calculate output and cost - Z = X - for layer in self.layers: - Z = layer.forward(Z) - action_scores = Z - p_a_given_s = T.nnet.softmax(action_scores) - - selected_probs = T.log(p_a_given_s[T.arange(actions.shape[0]), actions]) - cost = -T.sum(advantages * selected_probs) - - # specify update rule - grads = T.grad(cost, params) - updates = [(p, p - lr*g) for p, g in zip(params, grads)] - - # compile functions - self.train_op = theano.function( - inputs=[X, actions, advantages], - updates=updates, - allow_input_downcast=True - ) - self.predict_op = theano.function( - inputs=[X], - outputs=p_a_given_s, - allow_input_downcast=True - ) - - def partial_fit(self, X, actions, advantages): - X = np.atleast_2d(X) - actions = np.atleast_1d(actions) - advantages = np.atleast_1d(advantages) - self.train_op(X, actions, advantages) - - def predict(self, X): - X = np.atleast_2d(X) - return self.predict_op(X) - - def sample_action(self, X): - p = self.predict(X)[0] - nonans = np.all(~np.isnan(p)) - assert(nonans) - return np.random.choice(len(p), p=p) - - -# approximates V(s) -class ValueModel: - def __init__(self, D, hidden_layer_sizes): - # constant learning rate is fine - lr = 1e-4 - - # create the graph - self.layers = [] - M1 = D - for M2 in hidden_layer_sizes: - layer = HiddenLayer(M1, M2) - self.layers.append(layer) - M1 = M2 - - # final layer - layer = HiddenLayer(M1, 1, lambda x: x) - self.layers.append(layer) - - # get all params for gradient later - params = [] - for layer in self.layers: - params += layer.params - - # inputs and targets - X = T.matrix('X') - Y = T.vector('Y') - - # calculate output and cost - Z = X - for layer in self.layers: - Z = layer.forward(Z) - Y_hat = T.flatten(Z) - cost = T.sum((Y - Y_hat)**2) - - # specify update rule - grads = T.grad(cost, params) - updates = [(p, p - lr*g) for p, g in zip(params, grads)] - - # compile functions - self.train_op = theano.function( - inputs=[X, Y], - updates=updates, - allow_input_downcast=True - ) - self.predict_op = theano.function( - inputs=[X], - outputs=Y_hat, - allow_input_downcast=True - ) - - def partial_fit(self, X, Y): - X = np.atleast_2d(X) - Y = np.atleast_1d(Y) - self.train_op(X, Y) - - def predict(self, X): - X = np.atleast_2d(X) - return self.predict_op(X) - - -def play_one_td(env, pmodel, vmodel, gamma): - observation = env.reset() - done = False - totalreward = 0 - iters = 0 - - while not done and iters < 2000: - # if we reach 2000, just quit, don't want this going forever - # the 200 limit seems a bit early - action = pmodel.sample_action(observation) - prev_observation = observation - observation, reward, done, info = env.step(action) - - if done: - reward = -200 - - # update the models - V_next = vmodel.predict(observation) - G = reward + gamma*np.max(V_next) - advantage = G - vmodel.predict(prev_observation) - pmodel.partial_fit(prev_observation, action, advantage) - vmodel.partial_fit(prev_observation, G) - - if reward == 1: # if we changed the reward to -200 - totalreward += reward - iters += 1 - - return totalreward - - -def play_one_mc(env, pmodel, vmodel, gamma): - observation = env.reset() - done = False - totalreward = 0 - iters = 0 - - states = [] - actions = [] - rewards = [] - - reward = 0 - while not done and iters < 2000: - # if we reach 2000, just quit, don't want this going forever - # the 200 limit seems a bit early - action = pmodel.sample_action(observation) - - states.append(observation) - actions.append(action) - rewards.append(reward) - - prev_observation = observation - observation, reward, done, info = env.step(action) - - if done: - reward = -200 - - if reward == 1: # if we changed the reward to -200 - totalreward += reward - iters += 1 - - # save the final (s,a,r) tuple - action = pmodel.sample_action(observation) - states.append(observation) - actions.append(action) - rewards.append(reward) - - returns = [] - advantages = [] - G = 0 - for s, r in zip(reversed(states), reversed(rewards)): - returns.append(G) - advantages.append(G - vmodel.predict(s)[0]) - G = r + gamma*G - returns.reverse() - advantages.reverse() - - # update the models - pmodel.partial_fit(states[1:], actions[1:], advantages[1:]) - vmodel.partial_fit(states, returns) - - return totalreward - - -def main(): - env = gym.make('CartPole-v0') - D = env.observation_space.shape[0] - K = env.action_space.n - pmodel = PolicyModel(D, K, []) - vmodel = ValueModel(D, [10]) - gamma = 0.99 - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - N = 1000 - totalrewards = np.empty(N) - costs = np.empty(N) - for n in range(N): - totalreward = play_one_mc(env, pmodel, vmodel, gamma) - totalrewards[n] = totalreward - if n % 100 == 0: - print("episode:", n, "total reward:", totalreward, "avg reward (last 100):", totalrewards[max(0, n-100):(n+1)].mean()) - - print("avg reward for last 100 episodes:", totalrewards[-100:].mean()) - print("total steps:", totalrewards.sum()) - - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - plot_running_avg(totalrewards) - - -if __name__ == '__main__': - main() - diff --git a/rl2/cartpole/q_learning.py b/rl2/cartpole/q_learning.py deleted file mode 100644 index 3e7cc4a5..00000000 --- a/rl2/cartpole/q_learning.py +++ /dev/null @@ -1,157 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future -# Inspired by https://github.com/dennybritz/reinforcement-learning - -# Works best w/ multiply RBF kernels at var=0.05, 0.1, 0.5, 1.0 - -import gym -import os -import sys -import numpy as np -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime -from sklearn.pipeline import FeatureUnion -from sklearn.preprocessing import StandardScaler -from sklearn.kernel_approximation import RBFSampler -from q_learning_bins import plot_running_avg - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - - -class SGDRegressor: - def __init__(self, D): - self.w = np.random.randn(D) / np.sqrt(D) - self.lr = 0.1 - - def partial_fit(self, X, Y): - self.w += self.lr*(Y - X.dot(self.w)).dot(X) - - def predict(self, X): - return X.dot(self.w) - - - -class FeatureTransformer: - def __init__(self, env): - # observation_examples = np.array([env.observation_space.sample() for x in range(10000)]) - # NOTE!! state samples are poor, b/c you get velocities --> infinity - observation_examples = np.random.random((20000, 4))*2 - 1 - scaler = StandardScaler() - scaler.fit(observation_examples) - - # Used to converte a state to a featurizes represenation. - # We use RBF kernels with different variances to cover different parts of the space - featurizer = FeatureUnion([ - ("rbf1", RBFSampler(gamma=0.05, n_components=1000)), - ("rbf2", RBFSampler(gamma=1.0, n_components=1000)), - ("rbf3", RBFSampler(gamma=0.5, n_components=1000)), - ("rbf4", RBFSampler(gamma=0.1, n_components=1000)) - ]) - feature_examples = featurizer.fit_transform(scaler.transform(observation_examples)) - - self.dimensions = feature_examples.shape[1] - self.scaler = scaler - self.featurizer = featurizer - - def transform(self, observations): - scaled = self.scaler.transform(observations) - return self.featurizer.transform(scaled) - - -# Holds one SGDRegressor for each action -class Model: - def __init__(self, env, feature_transformer): - self.env = env - self.models = [] - self.feature_transformer = feature_transformer - for i in range(env.action_space.n): - model = SGDRegressor(feature_transformer.dimensions) - self.models.append(model) - - def predict(self, s): - X = self.feature_transformer.transform(np.atleast_2d(s)) - result = np.stack([m.predict(X) for m in self.models]).T - return result - - def update(self, s, a, G): - X = self.feature_transformer.transform(np.atleast_2d(s)) - self.models[a].partial_fit(X, [G]) - - def sample_action(self, s, eps): - if np.random.random() < eps: - return self.env.action_space.sample() - else: - return np.argmax(self.predict(s)) - - -def play_one(env, model, eps, gamma): - observation = env.reset() - done = False - totalreward = 0 - iters = 0 - while not done and iters < 2000: - # if we reach 2000, just quit, don't want this going forever - # the 200 limit seems a bit early - action = model.sample_action(observation, eps) - prev_observation = observation - observation, reward, done, info = env.step(action) - - if done: - reward = -200 - - # update the model - next = model.predict(observation) - # print(next.shape) - assert(next.shape == (1, env.action_space.n)) - G = reward + gamma*np.max(next) - model.update(prev_observation, action, G) - - if reward == 1: # if we changed the reward to -200 - totalreward += reward - iters += 1 - - return totalreward - - -def main(): - env = gym.make('CartPole-v0') - ft = FeatureTransformer(env) - model = Model(env, ft) - gamma = 0.99 - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - - N = 500 - totalrewards = np.empty(N) - costs = np.empty(N) - for n in range(N): - eps = 1.0/np.sqrt(n+1) - totalreward = play_one(env, model, eps, gamma) - totalrewards[n] = totalreward - if n % 100 == 0: - print("episode:", n, "total reward:", totalreward, "eps:", eps, "avg reward (last 100):", totalrewards[max(0, n-100):(n+1)].mean()) - - print("avg reward for last 100 episodes:", totalrewards[-100:].mean()) - print("total steps:", totalrewards.sum()) - - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - plot_running_avg(totalrewards) - - -if __name__ == '__main__': - main() - diff --git a/rl2/cartpole/q_learning_bins.py b/rl2/cartpole/q_learning_bins.py deleted file mode 100644 index 198ceb2a..00000000 --- a/rl2/cartpole/q_learning_bins.py +++ /dev/null @@ -1,140 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# if builtins is not defined -# sudo pip install -U future - -import gym -import os -import sys -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - - -# turns list of integers into an int -# Ex. -# build_state([1,2,3,4,5]) -> 12345 -def build_state(features): - return int("".join(map(lambda feature: str(int(feature)), features))) - -def to_bin(value, bins): - return np.digitize(x=[value], bins=bins)[0] - - -class FeatureTransformer: - def __init__(self): - # Note: to make this better you could look at how often each bin was - # actually used while running the script. - # It's not clear from the high/low values nor sample() what values - # we really expect to get. - self.cart_position_bins = np.linspace(-2.4, 2.4, 9) - self.cart_velocity_bins = np.linspace(-2, 2, 9) # (-inf, inf) (I did not check that these were good values) - self.pole_angle_bins = np.linspace(-0.4, 0.4, 9) - self.pole_velocity_bins = np.linspace(-3.5, 3.5, 9) # (-inf, inf) (I did not check that these were good values) - - def transform(self, observation): - # returns an int - cart_pos, cart_vel, pole_angle, pole_vel = observation - return build_state([ - to_bin(cart_pos, self.cart_position_bins), - to_bin(cart_vel, self.cart_velocity_bins), - to_bin(pole_angle, self.pole_angle_bins), - to_bin(pole_vel, self.pole_velocity_bins), - ]) - - -class Model: - def __init__(self, env, feature_transformer): - self.env = env - self.feature_transformer = feature_transformer - - num_states = 10**env.observation_space.shape[0] - num_actions = env.action_space.n - self.Q = np.random.uniform(low=-1, high=1, size=(num_states, num_actions)) - - def predict(self, s): - x = self.feature_transformer.transform(s) - return self.Q[x] - - def update(self, s, a, G): - x = self.feature_transformer.transform(s) - self.Q[x,a] += 1e-2*(G - self.Q[x,a]) - - def sample_action(self, s, eps): - if np.random.random() < eps: - return self.env.action_space.sample() - else: - p = self.predict(s) - return np.argmax(p) - - -def play_one(model, eps, gamma): - observation = env.reset() - done = False - totalreward = 0 - iters = 0 - while not done and iters < 10000: - action = model.sample_action(observation, eps) - prev_observation = observation - observation, reward, done, info = env.step(action) - - totalreward += reward - - if done and iters < 199: - reward = -300 - - # update the model - G = reward + gamma*np.max(model.predict(observation)) - model.update(prev_observation, action, G) - - iters += 1 - - return totalreward - - -def plot_running_avg(totalrewards): - N = len(totalrewards) - running_avg = np.empty(N) - for t in range(N): - running_avg[t] = totalrewards[max(0, t-100):(t+1)].mean() - plt.plot(running_avg) - plt.title("Running Average") - plt.show() - - -if __name__ == '__main__': - env = gym.make('CartPole-v0') - ft = FeatureTransformer() - model = Model(env, ft) - gamma = 0.9 - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - N = 10000 - totalrewards = np.empty(N) - for n in range(N): - eps = 1.0/np.sqrt(n+1) - totalreward = play_one(model, eps, gamma) - totalrewards[n] = totalreward - if n % 100 == 0: - print("episode:", n, "total reward:", totalreward, "eps:", eps) - print("avg reward for last 100 episodes:", totalrewards[-100:].mean()) - print("total steps:", totalrewards.sum()) - - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - plot_running_avg(totalrewards) diff --git a/rl2/cartpole/random_search.py b/rl2/cartpole/random_search.py deleted file mode 100644 index 985bcfda..00000000 --- a/rl2/cartpole/random_search.py +++ /dev/null @@ -1,71 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import gym -import numpy as np -import matplotlib.pyplot as plt - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - - -def get_action(s, w): - return 1 if s.dot(w) > 0 else 0 - - -def play_one_episode(env, params): - observation = env.reset() - done = False - t = 0 - - while not done and t < 10000: - # env.render() - t += 1 - action = get_action(observation, params) - observation, reward, done, info = env.step(action) - if done: - break - - return t - - -def play_multiple_episodes(env, T, params): - episode_lengths = np.empty(T) - - for i in range(T): - episode_lengths[i] = play_one_episode(env, params) - - avg_length = episode_lengths.mean() - print("avg length:", avg_length) - return avg_length - - -def random_search(env): - episode_lengths = [] - best = 0 - params = None - for t in range(100): - new_params = np.random.random(4)*2 - 1 - avg_length = play_multiple_episodes(env, 100, new_params) - episode_lengths.append(avg_length) - - if avg_length > best: - params = new_params - best = avg_length - return episode_lengths, params - - -if __name__ == '__main__': - env = gym.make('CartPole-v0') - episode_lengths, params = random_search(env) - plt.plot(episode_lengths) - plt.show() - - # play a final set of episodes - print("***Final run with final weights***") - play_multiple_episodes(env, 100, params) diff --git a/rl2/cartpole/save_a_video.py b/rl2/cartpole/save_a_video.py deleted file mode 100644 index e7128fc9..00000000 --- a/rl2/cartpole/save_a_video.py +++ /dev/null @@ -1,72 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import gym -from gym import wrappers -import numpy as np -import matplotlib.pyplot as plt - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - - - -def get_action(s, w): - return 1 if s.dot(w) > 0 else 0 - - -def play_one_episode(env, params): - observation = env.reset() - done = False - t = 0 - - while not done and t < 10000: - t += 1 - action = get_action(observation, params) - observation, reward, done, info = env.step(action) - if done: - break - - return t - - -def play_multiple_episodes(env, T, params): - episode_lengths = np.empty(T) - - for i in range(T): - episode_lengths[i] = play_one_episode(env, params) - - avg_length = episode_lengths.mean() - print("avg length:", avg_length) - return avg_length - - -def random_search(env): - episode_lengths = [] - best = 0 - params = None - for t in range(100): - new_params = np.random.random(4)*2 - 1 - avg_length = play_multiple_episodes(env, 100, new_params) - episode_lengths.append(avg_length) - - if avg_length > best: - params = new_params - best = avg_length - return episode_lengths, params - - -if __name__ == '__main__': - env = gym.make('CartPole-v0') - episode_lengths, params = random_search(env) - plt.plot(episode_lengths) - plt.show() - - # play a final set of episodes - env = wrappers.RecordVideo(env, 'my_awesome_dir') - print("***Final run with final weights***:", play_one_episode(env, params)) diff --git a/rl2/cartpole/td_lambda.py b/rl2/cartpole/td_lambda.py deleted file mode 100644 index ba9883bc..00000000 --- a/rl2/cartpole/td_lambda.py +++ /dev/null @@ -1,155 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import gym -import os -import sys -import numpy as np -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime -from q_learning import FeatureTransformer -from q_learning_bins import plot_running_avg - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - - - -class SGDRegressor: - def __init__(self, D): - self.w = np.random.randn(D) / np.sqrt(D) - - def partial_fit(self, x, y, e, lr=1e-1): - self.w += lr*(y - x.dot(self.w))*e - - def predict(self, X): - X = np.array(X) - return X.dot(self.w) - - -# Holds one SGDRegressor for each action -class Model: - def __init__(self, env, feature_transformer): - self.env = env - self.models = [] - self.feature_transformer = feature_transformer - - sample_feature = feature_transformer.transform( [env.reset()] ) - D = sample_feature.shape[1] - - for i in range(env.action_space.n): - # model = SGDRegressor(learning_rate="constant") - # model.partial_fit(feature_transformer.transform( [env.reset()] ), [0]) - model = SGDRegressor(D) - self.models.append(model) - - self.eligibilities = np.zeros((env.action_space.n, D)) - - def reset(self): - self.eligibilities = np.zeros_like(self.eligibilities) - - def predict(self, s): - X = self.feature_transformer.transform([s]) - result = np.stack([m.predict(X) for m in self.models]).T - return result - - def update(self, s, a, G, gamma, lambda_): - X = self.feature_transformer.transform([s]) - # assert(len(X.shape) == 2) - - # slower - # for action in range(self.env.action_space.n): - # if action != a: - # self.eligibilities[action] *= gamma*lambda_ - # else: - # self.eligibilities[a] = grad + gamma*lambda_*self.eligibilities[a] - - self.eligibilities *= gamma*lambda_ - self.eligibilities[a] += X[0] - self.models[a].partial_fit(X[0], G, self.eligibilities[a]) - - def sample_action(self, s, eps): - if np.random.random() < eps: - return self.env.action_space.sample() - else: - return np.argmax(self.predict(s)) - - -# returns a list of states_and_rewards, and the total reward -def play_one(model, env, eps, gamma, lambda_): - observation = env.reset() - done = False - totalreward = 0 - states_actions_rewards = [] - iters = 0 - model.reset() - while not done and iters < 1000000: - action = model.sample_action(observation, eps) - prev_observation = observation - observation, reward, done, info = env.step(action) - - if done: - reward = -300 - - # update the model - next = model.predict(observation) - assert(next.shape == (1, env.action_space.n)) - G = reward + gamma*np.max(next[0]) - model.update(prev_observation, action, G, gamma, lambda_) - - states_actions_rewards.append((prev_observation, action, reward)) - - if reward == 1: # if we changed the reward to -200 - totalreward += reward - - iters += 1 - - # if iters > 0 and iters % 1000 == 0: - # print(iters) - # if done: - # print "finished in < 1000 steps!" - - return states_actions_rewards, totalreward - - -if __name__ == '__main__': - env = gym.make('CartPole-v0') - ft = FeatureTransformer(env) - model = Model(env, ft) - gamma = 0.999 - lambda_ = 0.7 - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - - N = 500 - totalrewards = np.empty(N) - # costs = np.empty(N) - for n in range(N): - # eps = 1.0/(0.1*n+1) - # eps = 0.1*(0.97**n) - eps = 1.0/np.sqrt(n+1) - # eps = 0.1 - states_actions_rewards, totalreward = play_one(model, env, eps, gamma, lambda_) - totalrewards[n] = totalreward - if n % 100 == 0: - print("episode:", n, "total reward:", totalreward, "eps:", eps, "avg reward (last 100):", totalrewards[max(0, n-100):(n+1)].mean()) - print("avg reward for last 100 episodes:", totalrewards[-100:].mean()) - print("total steps:", totalrewards.sum()) - - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - plot_running_avg(totalrewards) - - diff --git a/rl2/cartpole/tf_warmup.py b/rl2/cartpole/tf_warmup.py deleted file mode 100644 index 1cc2efee..00000000 --- a/rl2/cartpole/tf_warmup.py +++ /dev/null @@ -1,49 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range - -import numpy as np -import tensorflow as tf -import q_learning - -if tf.__version__.startswith('2'): - exit("Please install Tensorflow 1.x") - - -class SGDRegressor: - def __init__(self, D): - print("Hello TensorFlow!") - lr = 0.1 - - # create inputs, targets, params - # matmul doesn't like when w is 1-D - # so we make it 2-D and then flatten the prediction - self.w = tf.Variable(tf.random_normal(shape=(D, 1)), name='w') - self.X = tf.placeholder(tf.float32, shape=(None, D), name='X') - self.Y = tf.placeholder(tf.float32, shape=(None,), name='Y') - - # make prediction and cost - Y_hat = tf.reshape( tf.matmul(self.X, self.w), [-1] ) - delta = self.Y - Y_hat - cost = tf.reduce_sum(delta * delta) - - # ops we want to call later - self.train_op = tf.train.GradientDescentOptimizer(lr).minimize(cost) - self.predict_op = Y_hat - - # start the session and initialize params - init = tf.global_variables_initializer() - self.session = tf.InteractiveSession() - self.session.run(init) - - def partial_fit(self, X, Y): - self.session.run(self.train_op, feed_dict={self.X: X, self.Y: Y}) - - def predict(self, X): - return self.session.run(self.predict_op, feed_dict={self.X: X}) - - -if __name__ == '__main__': - q_learning.SGDRegressor = SGDRegressor - q_learning.main() diff --git a/rl2/cartpole/theano_warmup.py b/rl2/cartpole/theano_warmup.py deleted file mode 100644 index afc7825b..00000000 --- a/rl2/cartpole/theano_warmup.py +++ /dev/null @@ -1,45 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range - -import numpy as np -import theano -import theano.tensor as T -import q_learning - - -class SGDRegressor: - def __init__(self, D): - print("Hello Theano!") - w = np.random.randn(D) / np.sqrt(D) - self.w = theano.shared(w) - self.lr = 0.1 - - X = T.matrix('X') - Y = T.vector('Y') - Y_hat = X.dot(self.w) - delta = Y - Y_hat - cost = delta.dot(delta) - grad = T.grad(cost, self.w) - updates = [(self.w, self.w - self.lr*grad)] - - self.train_op = theano.function( - inputs=[X, Y], - updates=updates, - ) - self.predict_op = theano.function( - inputs=[X], - outputs=Y_hat, - ) - - def partial_fit(self, X, Y): - self.train_op(X, Y) - - def predict(self, X): - return self.predict_op(X) - - -if __name__ == '__main__': - q_learning.SGDRegressor = SGDRegressor - q_learning.main() diff --git a/rl2/extra_reading.txt b/rl2/extra_reading.txt deleted file mode 100644 index a4c20829..00000000 --- a/rl2/extra_reading.txt +++ /dev/null @@ -1,26 +0,0 @@ -Random Features for Large-Scale Kernel Machines -http://www.robots.ox.ac.uk/~vgg/rg/papers/randomfeatures.pdf - -Reflections on Random Kitchen Sinks -http://www.argmin.net/2017/12/05/kitchen-sinks/ - -Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning -https://papers.nips.cc/paper/3495-weighted-sums-of-random-kitchen-sinks-replacing-minimization-with-randomization-in-learning - -This guy generated some nice plots and code to demonstrate that RBFSampler works like a real RBF Kernel -https://www.kaggle.com/sy2002/rbfsampler-actually-is-not-using-any-rbfs - -Sutton & Barto -http://incompleteideas.net/sutton/book/the-book-2nd.html - -Implementation Details of the TD(λ) Procedure for the Case of Vector Predictions and Backpropagation -http://incompleteideas.net/papers/sutton-89.pdf - -Policy Gradient Methods for Reinforcement Learning with Function Approximation -https://homes.cs.washington.edu/~todorov/courses/amath579/reading/PolicyGradient.pdf - -Playing Atari with Deep Reinforcement Learning -https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf - -Asynchronous Methods for Deep Reinforcement Learning -https://arxiv.org/pdf/1602.01783.pdf \ No newline at end of file diff --git a/rl2/gym_tutorial.py b/rl2/gym_tutorial.py deleted file mode 100644 index ace01452..00000000 --- a/rl2/gym_tutorial.py +++ /dev/null @@ -1,60 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -import gym -# Wiki: -# https://github.com/openai/gym/wiki/CartPole-v0 -# Environment page: -# https://gym.openai.com/envs/CartPole-v0 - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - - -# get the environment -env = gym.make('CartPole-v0') - -# put yourself in the start state -# it also returns the state -env.reset() -# Out[50]: array([-0.04533731, -0.03231478, -0.01469216, 0.04151 ]) - -# what do the state variables mean? -# Num Observation Min Max -# 0 Cart Position -2.4 2.4 -# 1 Cart Velocity -Inf Inf -# 2 Pole Angle ~ -41.8° ~ 41.8° -# 3 Pole Velocity At Tip -Inf Inf - -box = env.observation_space - -# In [53]: box -# Out[53]: Box(4,) - -# In [54]: box. -# box.contains box.high box.sample box.to_jsonable -# box.from_jsonable box.low box.shape - -env.action_space - -# In [71]: env.action_space -# Out[71]: Discrete(2) - -# In [72]: env.action_space. -# env.action_space.contains env.action_space.n env.action_space.to_jsonable -# env.action_space.from_jsonable env.action_space.sample - -# pick an action -action = env.action_space.sample() - -# do an action -observation, reward, done, info = env.step(action) - - -# run through an episode -done = False -while not done: - observation, reward, done, _ = env.step(env.action_space.sample()) - - - diff --git a/rl2/mountaincar/n_step.py b/rl2/mountaincar/n_step.py deleted file mode 100644 index 628fdbcf..00000000 --- a/rl2/mountaincar/n_step.py +++ /dev/null @@ -1,167 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future -# -# Note: gym changed from version 0.7.3 to 0.8.0 -# MountainCar episode length is capped at 200 in later versions. -# This means your agent can't learn as much in the earlier episodes -# since they are no longer as long. -# -# Adapt Q-Learning script to use N-step method instead - -import gym -import os -import sys -import numpy as np -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime - -# code we already wrote -import q_learning -from q_learning import plot_cost_to_go, FeatureTransformer, Model, plot_running_avg - - -class SGDRegressor: - def __init__(self, **kwargs): - self.w = None - self.lr = 1e-2 - - def partial_fit(self, X, Y): - if self.w is None: - D = X.shape[1] - self.w = np.random.randn(D) / np.sqrt(D) - self.w += self.lr*(Y - X.dot(self.w)).dot(X) - - def predict(self, X): - return X.dot(self.w) - -# replace SKLearn Regressor -q_learning.SGDRegressor = SGDRegressor - -# calculate everything up to max[Q(s,a)] -# Ex. -# R(t) + gamma*R(t+1) + ... + (gamma^(n-1))*R(t+n-1) + (gamma^n)*max[Q(s(t+n), a(t+n))] -# def calculate_return_before_prediction(rewards, gamma): -# ret = 0 -# for r in reversed(rewards[1:]): -# ret += r + gamma*ret -# ret += rewards[0] -# return ret - -# returns a list of states_and_rewards, and the total reward -def play_one(model, eps, gamma, n=5): - observation = env.reset()[0] - done = False - totalreward = 0 - rewards = [] - states = [] - actions = [] - iters = 0 - # array of [gamma^0, gamma^1, ..., gamma^(n-1)] - multiplier = np.array([gamma]*n)**np.arange(n) - # while not done and iters < 200: - while not done and iters < 10000: - # in earlier versions of gym, episode doesn't automatically - # end when you hit 200 steps - action = model.sample_action(observation, eps) - - states.append(observation) - actions.append(action) - - prev_observation = observation - observation, reward, done, truncated, info = env.step(action) - - rewards.append(reward) - - # update the model - if len(rewards) >= n: - # return_up_to_prediction = calculate_return_before_prediction(rewards, gamma) - return_up_to_prediction = multiplier.dot(rewards[-n:]) - action_values = model.predict(observation)[0] - # print("action_values.shape:", action_values.shape) - G = return_up_to_prediction + (gamma**n)*np.max(action_values) - # print("G:", G) - model.update(states[-n], actions[-n], G) - - # if len(rewards) > n: - # rewards.pop(0) - # states.pop(0) - # actions.pop(0) - # assert(len(rewards) <= n) - - totalreward += reward - iters += 1 - - # empty the cache - if n == 1: - rewards = [] - states = [] - actions = [] - else: - rewards = rewards[-n+1:] - states = states[-n+1:] - actions = actions[-n+1:] - # unfortunately, new version of gym cuts you off at 200 steps - # even if you haven't reached the goal. - # it's not good to do this UNLESS you've reached the goal. - # we are "really done" if position >= 0.5 - if observation[0] >= 0.5: - # we actually made it to the goal - # print("made it!") - while len(rewards) > 0: - G = multiplier[:len(rewards)].dot(rewards) - model.update(states[0], actions[0], G) - rewards.pop(0) - states.pop(0) - actions.pop(0) - else: - # we did not make it to the goal - # print("didn't make it...") - while len(rewards) > 0: - guess_rewards = rewards + [-1]*(n - len(rewards)) - G = multiplier.dot(guess_rewards) - model.update(states[0], actions[0], G) - rewards.pop(0) - states.pop(0) - actions.pop(0) - - return totalreward - - -if __name__ == '__main__': - env = gym.make('MountainCar-v0') - ft = FeatureTransformer(env) - model = Model(env, ft, "constant") - gamma = 0.99 - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - - N = 300 - totalrewards = np.empty(N) - costs = np.empty(N) - for n in range(N): - # eps = 1.0/(0.1*n+1) - eps = 0.1*(0.97**n) - totalreward = play_one(model, eps, gamma) - totalrewards[n] = totalreward - print("episode:", n, "total reward:", totalreward) - print("avg reward for last 100 episodes:", totalrewards[-100:].mean()) - print("total steps:", -totalrewards.sum()) - - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - plot_running_avg(totalrewards) - - # plot the optimal state-value function - plot_cost_to_go(env, model) - diff --git a/rl2/mountaincar/pg_tf.py b/rl2/mountaincar/pg_tf.py deleted file mode 100755 index fe04b416..00000000 --- a/rl2/mountaincar/pg_tf.py +++ /dev/null @@ -1,241 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import gym -import os -import sys -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime -from q_learning import plot_running_avg, FeatureTransformer, plot_cost_to_go - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - -if tf.__version__.startswith('2'): - exit("Please install Tensorflow 1.x") - - -# so you can test different architectures -class HiddenLayer: - def __init__(self, M1, M2, f=tf.nn.tanh, use_bias=True, zeros=False): - if zeros: - W = np.zeros((M1, M2), dtype=np.float32) - else: - W = tf.random_normal(shape=(M1, M2)) * np.sqrt(2. / M1, dtype=np.float32) - self.W = tf.Variable(W) - - self.use_bias = use_bias - if use_bias: - self.b = tf.Variable(np.zeros(M2).astype(np.float32)) - - self.f = f - - def forward(self, X): - if self.use_bias: - a = tf.matmul(X, self.W) + self.b - else: - a = tf.matmul(X, self.W) - return self.f(a) - - -# approximates pi(a | s) -class PolicyModel: - def __init__(self, D, ft, hidden_layer_sizes=[]): - self.ft = ft - - ##### hidden layers ##### - M1 = D - self.hidden_layers = [] - for M2 in hidden_layer_sizes: - layer = HiddenLayer(M1, M2) - self.hidden_layers.append(layer) - M1 = M2 - - # final layer mean - self.mean_layer = HiddenLayer(M1, 1, lambda x: x, use_bias=False, zeros=True) - - # final layer variance - self.stdv_layer = HiddenLayer(M1, 1, tf.nn.softplus, use_bias=False, zeros=False) - - # inputs and targets - self.X = tf.placeholder(tf.float32, shape=(None, D), name='X') - self.actions = tf.placeholder(tf.float32, shape=(None,), name='actions') - self.advantages = tf.placeholder(tf.float32, shape=(None,), name='advantages') - - # get final hidden layer - Z = self.X - for layer in self.hidden_layers: - Z = layer.forward(Z) - - # calculate output and cost - mean = self.mean_layer.forward(Z) - stdv = self.stdv_layer.forward(Z) + 1e-5 # smoothing - - # make them 1-D - mean = tf.reshape(mean, [-1]) - stdv = tf.reshape(stdv, [-1]) - - norm = tf.contrib.distributions.Normal(mean, stdv) - self.predict_op = tf.clip_by_value(norm.sample(), -1, 1) - - log_probs = norm.log_prob(self.actions) - cost = -tf.reduce_sum(self.advantages * log_probs + 0.1*norm.entropy()) - self.train_op = tf.train.AdamOptimizer(1e-3).minimize(cost) - - def set_session(self, session): - self.session = session - - def partial_fit(self, X, actions, advantages): - X = np.atleast_2d(X) - X = self.ft.transform(X) - - actions = np.atleast_1d(actions) - advantages = np.atleast_1d(advantages) - self.session.run( - self.train_op, - feed_dict={ - self.X: X, - self.actions: actions, - self.advantages: advantages, - } - ) - - def predict(self, X): - X = np.atleast_2d(X) - X = self.ft.transform(X) - return self.session.run(self.predict_op, feed_dict={self.X: X}) - - def sample_action(self, X): - p = self.predict(X)[0] - return p - - -# approximates V(s) -class ValueModel: - def __init__(self, D, ft, hidden_layer_sizes=[]): - self.ft = ft - self.costs = [] - - # create the graph - self.layers = [] - M1 = D - for M2 in hidden_layer_sizes: - layer = HiddenLayer(M1, M2) - self.layers.append(layer) - M1 = M2 - - # final layer - layer = HiddenLayer(M1, 1, lambda x: x) - self.layers.append(layer) - - # inputs and targets - self.X = tf.placeholder(tf.float32, shape=(None, D), name='X') - self.Y = tf.placeholder(tf.float32, shape=(None,), name='Y') - - # calculate output and cost - Z = self.X - for layer in self.layers: - Z = layer.forward(Z) - Y_hat = tf.reshape(Z, [-1]) # the output - self.predict_op = Y_hat - - cost = tf.reduce_sum(tf.square(self.Y - Y_hat)) - self.cost = cost - self.train_op = tf.train.AdamOptimizer(1e-1).minimize(cost) - - def set_session(self, session): - self.session = session - - def partial_fit(self, X, Y): - X = np.atleast_2d(X) - X = self.ft.transform(X) - Y = np.atleast_1d(Y) - self.session.run(self.train_op, feed_dict={self.X: X, self.Y: Y}) - cost = self.session.run(self.cost, feed_dict={self.X: X, self.Y: Y}) - self.costs.append(cost) - - def predict(self, X): - X = np.atleast_2d(X) - X = self.ft.transform(X) - return self.session.run(self.predict_op, feed_dict={self.X: X}) - - -def play_one_td(env, pmodel, vmodel, gamma): - observation = env.reset() - done = False - totalreward = 0 - iters = 0 - - while not done and iters < 2000: - # if we reach 2000, just quit, don't want this going forever - # the 200 limit seems a bit early - action = pmodel.sample_action(observation) - prev_observation = observation - observation, reward, done, info = env.step([action]) - - totalreward += reward - - # update the models - if done: - G = reward - else: - V_next = vmodel.predict(observation) - G = reward + gamma*V_next - - advantage = G - vmodel.predict(prev_observation) - pmodel.partial_fit(prev_observation, action, advantage) - vmodel.partial_fit(prev_observation, G) - - iters += 1 - - return totalreward, iters - - -def main(): - env = gym.make('MountainCarContinuous-v0') - ft = FeatureTransformer(env, n_components=100) - D = ft.dimensions - pmodel = PolicyModel(D, ft, []) - vmodel = ValueModel(D, ft, []) - init = tf.global_variables_initializer() - session = tf.InteractiveSession() - session.run(init) - pmodel.set_session(session) - vmodel.set_session(session) - gamma = 0.95 - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - N = 50 - totalrewards = np.empty(N) - costs = np.empty(N) - for n in range(N): - totalreward, num_steps = play_one_td(env, pmodel, vmodel, gamma) - totalrewards[n] = totalreward - if n % 1 == 0: - print("episode:", n, "total reward: %.1f" % totalreward, "num steps: %d" % num_steps, "avg reward (last 100): %.1f" % totalrewards[max(0, n-100):(n+1)].mean()) - - print("avg reward for last 100 episodes:", totalrewards[-100:].mean()) - - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - plot_running_avg(totalrewards) - plot_cost_to_go(env, vmodel) - - -if __name__ == '__main__': - main() - diff --git a/rl2/mountaincar/pg_tf_random.py b/rl2/mountaincar/pg_tf_random.py deleted file mode 100644 index e46b7b25..00000000 --- a/rl2/mountaincar/pg_tf_random.py +++ /dev/null @@ -1,266 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import gym -import os -import sys -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime -from q_learning import plot_running_avg, FeatureTransformer - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - -if tf.__version__.startswith('2'): - exit("Please install Tensorflow 1.x") - - -# so you can test different architectures -class HiddenLayer: - def __init__(self, M1, M2, f=tf.nn.tanh, use_bias=True, zeros=False): - if zeros: - W = np.zeros((M1, M2)).astype(np.float32) - self.W = tf.Variable(W) - else: - self.W = tf.Variable(tf.random_normal(shape=(M1, M2))) - - self.params = [self.W] - - self.use_bias = use_bias - if use_bias: - self.b = tf.Variable(np.zeros(M2).astype(np.float32)) - self.params.append(self.b) - self.f = f - - def forward(self, X): - if self.use_bias: - a = tf.matmul(X, self.W) + self.b - else: - a = tf.matmul(X, self.W) - return self.f(a) - - -# approximates pi(a | s) -class PolicyModel: - def __init__(self, ft, D, hidden_layer_sizes_mean=[], hidden_layer_sizes_var=[]): - - # save inputs for copy - self.ft = ft - self.D = D - self.hidden_layer_sizes_mean = hidden_layer_sizes_mean - self.hidden_layer_sizes_var = hidden_layer_sizes_var - - ##### model the mean ##### - self.mean_layers = [] - M1 = D - for M2 in hidden_layer_sizes_mean: - layer = HiddenLayer(M1, M2) - self.mean_layers.append(layer) - M1 = M2 - - # final layer - layer = HiddenLayer(M1, 1, lambda x: x, use_bias=False, zeros=True) - self.mean_layers.append(layer) - - - ##### model the variance ##### - self.var_layers = [] - M1 = D - for M2 in hidden_layer_sizes_var: - layer = HiddenLayer(M1, M2) - self.var_layers.append(layer) - M1 = M2 - - # final layer - layer = HiddenLayer(M1, 1, tf.nn.softplus, use_bias=False, zeros=False) - self.var_layers.append(layer) - - # gather params - self.params = [] - for layer in (self.mean_layers + self.var_layers): - self.params += layer.params - - # inputs and targets - self.X = tf.placeholder(tf.float32, shape=(None, D), name='X') - self.actions = tf.placeholder(tf.float32, shape=(None,), name='actions') - self.advantages = tf.placeholder(tf.float32, shape=(None,), name='advantages') - - def get_output(layers): - Z = self.X - for layer in layers: - Z = layer.forward(Z) - return tf.reshape(Z, [-1]) - - # calculate output and cost - mean = get_output(self.mean_layers) - std = get_output(self.var_layers) + 1e-4 # smoothing - - # note: the 'variance' is actually standard deviation - norm = tf.contrib.distributions.Normal(mean, std) - self.predict_op = tf.clip_by_value(norm.sample(), -1, 1) - - - def set_session(self, session): - self.session = session - - def init_vars(self): - init_op = tf.variables_initializer(self.params) - self.session.run(init_op) - - # def partial_fit(self, X, actions, advantages): - # X = np.atleast_2d(X) - # X = self.ft.transform(X) - - # actions = np.atleast_1d(actions) - # advantages = np.atleast_1d(advantages) - # self.session.run( - # self.train_op, - # feed_dict={ - # self.X: X, - # self.actions: actions, - # self.advantages: advantages, - # } - # ) - - def predict(self, X): - X = np.atleast_2d(X) - X = self.ft.transform(X) - - return self.session.run(self.predict_op, feed_dict={self.X: X}) - - def sample_action(self, X): - p = self.predict(X)[0] - # print("action:", p) - return p - - def copy(self): - clone = PolicyModel(self.ft, self.D, self.hidden_layer_sizes_mean, self.hidden_layer_sizes_mean) - clone.set_session(self.session) - clone.init_vars() # tf will complain if we don't do this - clone.copy_from(self) - return clone - - def copy_from(self, other): - # collect all the ops - ops = [] - my_params = self.params - other_params = other.params - for p, q in zip(my_params, other_params): - actual = self.session.run(q) - op = p.assign(actual) - ops.append(op) - # now run them all - self.session.run(ops) - - def perturb_params(self): - ops = [] - for p in self.params: - v = self.session.run(p) - noise = np.random.randn(*v.shape) / np.sqrt(v.shape[0]) * 5.0 - if np.random.random() < 0.1: - # with probability 0.1 start completely from scratch - op = p.assign(noise) - else: - op = p.assign(v + noise) - ops.append(op) - self.session.run(ops) - - - -def play_one(env, pmodel, gamma): - observation = env.reset() - done = False - totalreward = 0 - iters = 0 - - while not done and iters < 2000: - # if we reach 2000, just quit, don't want this going forever - # the 200 limit seems a bit early - action = pmodel.sample_action(observation) - # oddly, the mountain car environment requires the action to be in - # an object where the actual action is stored in object[0] - observation, reward, done, info = env.step([action]) - - totalreward += reward - iters += 1 - return totalreward - - -def play_multiple_episodes(env, T, pmodel, gamma, print_iters=False): - totalrewards = np.empty(T) - - for i in range(T): - totalrewards[i] = play_one(env, pmodel, gamma) - - if print_iters: - print(i, "avg so far:", totalrewards[:(i+1)].mean()) - - avg_totalrewards = totalrewards.mean() - print("avg totalrewards:", avg_totalrewards) - return avg_totalrewards - - -def random_search(env, pmodel, gamma): - totalrewards = [] - best_avg_totalreward = float('-inf') - best_pmodel = pmodel - num_episodes_per_param_test = 3 - for t in range(100): - tmp_pmodel = best_pmodel.copy() - - tmp_pmodel.perturb_params() - - avg_totalrewards = play_multiple_episodes( - env, - num_episodes_per_param_test, - tmp_pmodel, - gamma - ) - totalrewards.append(avg_totalrewards) - - if avg_totalrewards > best_avg_totalreward: - best_pmodel = tmp_pmodel - best_avg_totalreward = avg_totalrewards - return totalrewards, best_pmodel - - -def main(): - env = gym.make('MountainCarContinuous-v0') - ft = FeatureTransformer(env, n_components=100) - D = ft.dimensions - pmodel = PolicyModel(ft, D, [], []) - # init = tf.global_variables_initializer() - session = tf.InteractiveSession() - # session.run(init) - pmodel.set_session(session) - pmodel.init_vars() - gamma = 0.99 - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - totalrewards, pmodel = random_search(env, pmodel, gamma) - print("max reward:", np.max(totalrewards)) - - # play 100 episodes and check the average - avg_totalrewards = play_multiple_episodes(env, 100, pmodel, gamma, print_iters=True) - print("avg reward over 100 episodes with best models:", avg_totalrewards) - - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - -if __name__ == '__main__': - main() - diff --git a/rl2/mountaincar/pg_theano.py b/rl2/mountaincar/pg_theano.py deleted file mode 100755 index 669fc416..00000000 --- a/rl2/mountaincar/pg_theano.py +++ /dev/null @@ -1,276 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import gym -import os -import sys -import numpy as np -import theano -import theano.tensor as T -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime -from q_learning import plot_running_avg, FeatureTransformer, plot_cost_to_go - - - -# helper for adam optimizer -# use tensorflow defaults -def adam(cost, params, lr0=1e-3, beta1=0.9, beta2=0.999, eps=1e-8): - grads = T.grad(cost, params) - updates = [] - time = theano.shared(0) - new_time = time + 1 - updates.append((time, new_time)) - lr = lr0*T.sqrt(1 - beta2**new_time) / (1 - beta1**new_time) - for p, g in zip(params, grads): - m = theano.shared(p.get_value() * 0.) - v = theano.shared(p.get_value() * 0.) - new_m = beta1*m + (1 - beta1)*g - new_v = beta2*v + (1 - beta2)*g*g - new_p = p - lr*new_m / (T.sqrt(new_v) + eps) - updates.append((m, new_m)) - updates.append((v, new_v)) - updates.append((p, new_p)) - return updates - - -# so you can test different architectures -class HiddenLayer: - def __init__(self, M1, M2, f=T.nnet.relu, use_bias=True, zeros=False): - if zeros: - W = np.zeros((M1, M2)) - else: - W = np.random.randn(M1, M2) * np.sqrt(2. / M1) - self.W = theano.shared(W) - self.params = [self.W] - self.use_bias = use_bias - if use_bias: - self.b = theano.shared(np.zeros(M2)) - self.params += [self.b] - self.f = f - - def forward(self, X): - if self.use_bias: - a = X.dot(self.W) + self.b - else: - a = X.dot(self.W) - return self.f(a) - - -# approximates pi(a | s) -class PolicyModel: - def __init__(self, D, ft, hidden_layer_sizes=[]): - self.ft = ft - - ##### hidden layers ##### - M1 = D - self.hidden_layers = [] - for M2 in hidden_layer_sizes: - layer = HiddenLayer(M1, M2) - self.hidden_layers.append(layer) - M1 = M2 - - # final layer mean - self.mean_layer = HiddenLayer(M1, 1, lambda x: x, use_bias=False, zeros=True) - - # final layer variance - self.var_layer = HiddenLayer(M1, 1, T.nnet.softplus, use_bias=False, zeros=False) - - # get all params for gradient later - params = self.mean_layer.params + self.var_layer.params - for layer in self.hidden_layers: - params += layer.params - - # inputs and targets - X = T.matrix('X') - actions = T.vector('actions') - advantages = T.vector('advantages') - target_value = T.vector('target_value') - - # get final hidden layer - Z = X - for layer in self.hidden_layers: - Z = layer.forward(Z) - - mean = self.mean_layer.forward(Z).flatten() - var = self.var_layer.forward(Z).flatten() + 1e-5 # smoothing - - # can't find Theano log pdf, we will make it - def log_pdf(actions, mean, var): - k1 = T.log(2*np.pi*var) - k2 = (actions - mean)**2 / var - return -0.5*(k1 + k2) - - def entropy(var): - return 0.5*T.log(2*np.pi*np.e*var) - - log_probs = log_pdf(actions, mean, var) - cost = -T.sum(advantages * log_probs + 0.1*entropy(var)) - updates = adam(cost, params) - - # compile functions - self.train_op = theano.function( - inputs=[X, actions, advantages], - updates=updates, - allow_input_downcast=True - ) - - # alternatively, we could create a RandomStream and sample from - # the Gaussian using Theano code - self.predict_op = theano.function( - inputs=[X], - outputs=[mean, var], - allow_input_downcast=True - ) - - def partial_fit(self, X, actions, advantages): - X = np.atleast_2d(X) - X = self.ft.transform(X) - actions = np.atleast_1d(actions) - advantages = np.atleast_1d(advantages) - self.train_op(X, actions, advantages) - - def predict(self, X): - X = np.atleast_2d(X) - X = self.ft.transform(X) - return self.predict_op(X) - - def sample_action(self, X): - pred = self.predict(X) - mu = pred[0][0] - v = pred[1][0] - a = np.random.randn()*np.sqrt(v) + mu - return min(max(a, -1), 1) - - -# approximates V(s) -class ValueModel: - def __init__(self, D, ft, hidden_layer_sizes=[]): - self.ft = ft - - # create the graph - self.layers = [] - M1 = D - for M2 in hidden_layer_sizes: - layer = HiddenLayer(M1, M2) - self.layers.append(layer) - M1 = M2 - - # final layer - layer = HiddenLayer(M1, 1, lambda x: x) - self.layers.append(layer) - - # get all params for gradient later - params = [] - for layer in self.layers: - params += layer.params - - # inputs and targets - X = T.matrix('X') - Y = T.vector('Y') - - # calculate output and cost - Z = X - for layer in self.layers: - Z = layer.forward(Z) - Y_hat = T.flatten(Z) - cost = T.sum((Y - Y_hat)**2) - - # specify update rule - updates = adam(cost, params, lr0=1e-1) - - # compile functions - self.train_op = theano.function( - inputs=[X, Y], - updates=updates, - allow_input_downcast=True - ) - self.predict_op = theano.function( - inputs=[X], - outputs=Y_hat, - allow_input_downcast=True - ) - - def partial_fit(self, X, Y): - X = np.atleast_2d(X) - X = self.ft.transform(X) - Y = np.atleast_1d(Y) - self.train_op(X, Y) - - def predict(self, X): - X = np.atleast_2d(X) - X = self.ft.transform(X) - return self.predict_op(X) - - -def play_one_td(env, pmodel, vmodel, gamma): - observation = env.reset() - done = False - totalreward = 0 - iters = 0 - - while not done and iters < 2000: - # if we reach 2000, just quit, don't want this going forever - # the 200 limit seems a bit early - action = pmodel.sample_action(observation) - prev_observation = observation - observation, reward, done, info = env.step([action]) - - totalreward += reward - - # update the models - if done: - G = reward - else: - V_next = vmodel.predict(observation) - G = reward + gamma*V_next - - advantage = G - vmodel.predict(prev_observation) - pmodel.partial_fit(prev_observation, action, advantage) - vmodel.partial_fit(prev_observation, G) - - iters += 1 - - return totalreward - - -def main(): - env = gym.make('MountainCarContinuous-v0') - ft = FeatureTransformer(env, n_components=100) - D = ft.dimensions - pmodel = PolicyModel(D, ft) - vmodel = ValueModel(D, ft) - gamma = 0.99 - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - N = 50 - totalrewards = np.empty(N) - costs = np.empty(N) - for n in range(N): - totalreward = play_one_td(env, pmodel, vmodel, gamma) - totalrewards[n] = totalreward - if n % 1 == 0: - print("episode:", n, "total reward: %.1f" % totalreward, "avg reward (last 100): %.1f" % totalrewards[max(0, n-100):(n+1)].mean()) - - print("avg reward for last 100 episodes:", totalrewards[-100:].mean()) - - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - plot_running_avg(totalrewards) - plot_cost_to_go(env, vmodel) - - -if __name__ == '__main__': - main() - diff --git a/rl2/mountaincar/pg_theano_random.py b/rl2/mountaincar/pg_theano_random.py deleted file mode 100644 index c95c5971..00000000 --- a/rl2/mountaincar/pg_theano_random.py +++ /dev/null @@ -1,230 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import gym -import os -import sys -import numpy as np -import theano -import theano.tensor as T -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime -from q_learning import plot_running_avg, FeatureTransformer - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - - - -# so you can test different architectures -class HiddenLayer: - def __init__(self, M1, M2, f=T.nnet.relu, use_bias=True, zeros=False): - if zeros: - W = np.zeros((M1, M2)) - else: - W = np.random.randn(M1, M2) * np.sqrt(2 / M1) - self.W = theano.shared(W) - self.params = [self.W] - self.use_bias = use_bias - if use_bias: - self.b = theano.shared(np.zeros(M2)) - self.params += [self.b] - self.f = f - - def forward(self, X): - if self.use_bias: - a = X.dot(self.W) + self.b - else: - a = X.dot(self.W) - return self.f(a) - - -# approximates pi(a | s) -class PolicyModel: - def __init__(self, ft, D, hidden_layer_sizes_mean=[], hidden_layer_sizes_var=[]): - # save inputs for copy - self.ft = ft - self.D = D - self.hidden_layer_sizes_mean = hidden_layer_sizes_mean - self.hidden_layer_sizes_var = hidden_layer_sizes_var - - ##### model the mean ##### - self.mean_layers = [] - M1 = D - for M2 in hidden_layer_sizes_mean: - layer = HiddenLayer(M1, M2) - self.mean_layers.append(layer) - M1 = M2 - - # final layer - layer = HiddenLayer(M1, 1, lambda x: x, use_bias=False, zeros=True) - self.mean_layers.append(layer) - - - ##### model the variance ##### - self.var_layers = [] - M1 = D - for M2 in hidden_layer_sizes_var: - layer = HiddenLayer(M1, M2) - self.var_layers.append(layer) - M1 = M2 - - # final layer - layer = HiddenLayer(M1, 1, T.nnet.softplus, use_bias=False, zeros=False) - self.var_layers.append(layer) - - # get all params for gradient later - params = [] - for layer in (self.mean_layers + self.var_layers): - params += layer.params - self.params = params - - # inputs and targets - X = T.matrix('X') - actions = T.vector('actions') - advantages = T.vector('advantages') - - # calculate output and cost - def get_output(layers): - Z = X - for layer in layers: - Z = layer.forward(Z) - return Z.flatten() - - mean = get_output(self.mean_layers) - var = get_output(self.var_layers) + 1e-4 # smoothing - - # alternatively, we could create a RandomStream and sample from - # the Gaussian using Theano code - self.predict_op = theano.function( - inputs=[X], - outputs=[mean, var], - allow_input_downcast=True - ) - - def predict(self, X): - X = np.atleast_2d(X) - X = self.ft.transform(X) - return self.predict_op(X) - - def sample_action(self, X): - pred = self.predict(X) - mu = pred[0][0] - v = pred[1][0] - a = np.random.randn()*np.sqrt(v) + mu - return min(max(a, -1), 1) - - def copy(self): - clone = PolicyModel(self.ft, self.D, self.hidden_layer_sizes_mean, self.hidden_layer_sizes_mean) - clone.copy_from(self) - return clone - - def copy_from(self, other): - # self is being copied from other - for p, q in zip(self.params, other.params): - v = q.get_value() - p.set_value(v) - - def perturb_params(self): - for p in self.params: - v = p.get_value() - noise = np.random.randn(*v.shape) / np.sqrt(v.shape[0]) * 5.0 - if np.random.random() < 0.1: - # with probability 0.1 start completely from scratch - p.set_value(noise) - else: - p.set_value(v + noise) - - -def play_one(env, pmodel, gamma): - observation = env.reset() - done = False - totalreward = 0 - iters = 0 - - while not done and iters < 2000: - # if we reach 2000, just quit, don't want this going forever - # the 200 limit seems a bit early - action = pmodel.sample_action(observation) - # oddly, the mountain car environment requires the action to be in - # an object where the actual action is stored in object[0] - observation, reward, done, info = env.step([action]) - - totalreward += reward - iters += 1 - return totalreward - - -def play_multiple_episodes(env, T, pmodel, gamma, print_iters=False): - totalrewards = np.empty(T) - - for i in range(T): - totalrewards[i] = play_one(env, pmodel, gamma) - - if print_iters: - print(i, "avg so far:", totalrewards[:(i+1)].mean()) - - avg_totalrewards = totalrewards.mean() - print("avg totalrewards:", avg_totalrewards) - return avg_totalrewards - - -def random_search(env, pmodel, gamma): - totalrewards = [] - best_avg_totalreward = float('-inf') - best_pmodel = pmodel - num_episodes_per_param_test = 3 - for t in range(100): - tmp_pmodel = best_pmodel.copy() - - tmp_pmodel.perturb_params() - - avg_totalrewards = play_multiple_episodes( - env, - num_episodes_per_param_test, - tmp_pmodel, - gamma - ) - totalrewards.append(avg_totalrewards) - - if avg_totalrewards > best_avg_totalreward: - best_pmodel = tmp_pmodel - best_avg_totalreward = avg_totalrewards - return totalrewards, best_pmodel - - -def main(): - env = gym.make('MountainCarContinuous-v0') - ft = FeatureTransformer(env, n_components=100) - D = ft.dimensions - pmodel = PolicyModel(ft, D, [], []) - gamma = 0.99 - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - - totalrewards, pmodel = random_search(env, pmodel, gamma) - - print("max reward:", np.max(totalrewards)) - - # play 100 episodes and check the average - avg_totalrewards = play_multiple_episodes(env, 100, pmodel, gamma, print_iters=True) - print("avg reward over 100 episodes with best models:", avg_totalrewards) - - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - -if __name__ == '__main__': - main() - diff --git a/rl2/mountaincar/q_learning.py b/rl2/mountaincar/q_learning.py deleted file mode 100755 index 129d67e0..00000000 --- a/rl2/mountaincar/q_learning.py +++ /dev/null @@ -1,198 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future -# -# This takes 4min 30s to run in Python 2.7 -# But only 1min 30s to run in Python 3.5! -# -# Note: gym changed from version 0.7.3 to 0.8.0 -# MountainCar episode length is capped at 200 in later versions. -# This means your agent can't learn as much in the earlier episodes -# since they are no longer as long. - -import gym -import os -import sys -import numpy as np -import matplotlib -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D -from gym import wrappers -from datetime import datetime -from sklearn.pipeline import FeatureUnion -from sklearn.preprocessing import StandardScaler -from sklearn.kernel_approximation import RBFSampler -from sklearn.linear_model import SGDRegressor - - -# SGDRegressor defaults: -# loss='squared_loss', penalty='l2', alpha=0.0001, -# l1_ratio=0.15, fit_intercept=True, n_iter=5, shuffle=True, -# verbose=0, epsilon=0.1, random_state=None, learning_rate='invscaling', -# eta0=0.01, power_t=0.25, warm_start=False, average=False - -# Inspired by https://github.com/dennybritz/reinforcement-learning -class FeatureTransformer: - def __init__(self, env, n_components=500): - observation_examples = np.array([env.observation_space.sample() for x in range(10000)]) - scaler = StandardScaler() - scaler.fit(observation_examples) - - # Used to converte a state to a featurizes represenation. - # We use RBF kernels with different variances to cover different parts of the space - featurizer = FeatureUnion([ - ("rbf1", RBFSampler(gamma=5.0, n_components=n_components)), - ("rbf2", RBFSampler(gamma=2.0, n_components=n_components)), - ("rbf3", RBFSampler(gamma=1.0, n_components=n_components)), - ("rbf4", RBFSampler(gamma=0.5, n_components=n_components)) - ]) - example_features = featurizer.fit_transform(scaler.transform(observation_examples)) - - self.dimensions = example_features.shape[1] - self.scaler = scaler - self.featurizer = featurizer - - def transform(self, observations): - # print "observations:", observations - scaled = self.scaler.transform(observations) - # assert(len(scaled.shape) == 2) - return self.featurizer.transform(scaled) - - -# Holds one SGDRegressor for each action -class Model: - def __init__(self, env, feature_transformer, learning_rate): - self.env = env - self.models = [] - self.feature_transformer = feature_transformer - for i in range(env.action_space.n): - model = SGDRegressor(learning_rate=learning_rate) - model.partial_fit(feature_transformer.transform( [env.reset()[0]] ), [0]) - self.models.append(model) - - def predict(self, s): - X = self.feature_transformer.transform([s]) - result = np.stack([m.predict(X) for m in self.models]).T - assert(len(result.shape) == 2) - return result - - def update(self, s, a, G): - X = self.feature_transformer.transform([s]) - assert(len(X.shape) == 2) - self.models[a].partial_fit(X, [G]) - - def sample_action(self, s, eps): - # eps = 0 - # Technically, we don't need to do epsilon-greedy - # because SGDRegressor predicts 0 for all states - # until they are updated. This works as the - # "Optimistic Initial Values" method, since all - # the rewards for Mountain Car are -1. - if np.random.random() < eps: - return self.env.action_space.sample() - else: - return np.argmax(self.predict(s)) - - -# returns a list of states_and_rewards, and the total reward -def play_one(model, env, eps, gamma): - observation = env.reset()[0] - done = False - totalreward = 0 - iters = 0 - while not done and iters < 10000: - action = model.sample_action(observation, eps) - prev_observation = observation - observation, reward, done, truncated, info = env.step(action) - - # update the model - if done: - G = reward - else: - Qnext = model.predict(observation) - # assert(next.shape == (1, env.action_space.n)) - G = reward + gamma*np.max(Qnext[0]) - - model.update(prev_observation, action, G) - - totalreward += reward - iters += 1 - - return totalreward - - -def plot_cost_to_go(env, estimator, num_tiles=20): - x = np.linspace(env.observation_space.low[0], env.observation_space.high[0], num=num_tiles) - y = np.linspace(env.observation_space.low[1], env.observation_space.high[1], num=num_tiles) - X, Y = np.meshgrid(x, y) - # both X and Y will be of shape (num_tiles, num_tiles) - Z = np.apply_along_axis(lambda _: -np.max(estimator.predict(_)), 2, np.dstack([X, Y])) - # Z will also be of shape (num_tiles, num_tiles) - - fig = plt.figure(figsize=(10, 5)) - ax = fig.add_subplot(111, projection='3d') - surf = ax.plot_surface(X, Y, Z, - rstride=1, cstride=1, cmap=matplotlib.cm.coolwarm, vmin=-1.0, vmax=1.0) - ax.set_xlabel('Position') - ax.set_ylabel('Velocity') - ax.set_zlabel('Cost-To-Go == -V(s)') - ax.set_title("Cost-To-Go Function") - fig.colorbar(surf) - plt.show() - - -def plot_running_avg(totalrewards): - N = len(totalrewards) - running_avg = np.empty(N) - for t in range(N): - running_avg[t] = totalrewards[max(0, t-100):(t+1)].mean() - plt.plot(running_avg) - plt.title("Running Average") - plt.show() - - -def main(show_plots=True): - env = gym.make('MountainCar-v0') - ft = FeatureTransformer(env) - model = Model(env, ft, "constant") - gamma = 0.99 - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - - N = 300 - totalrewards = np.empty(N) - for n in range(N): - eps = 1.0/(0.1*n+1) - # eps = 0.1*(0.97**n) - if n == 199: - print("eps:", eps) - # eps = 1.0/np.sqrt(n+1) - totalreward = play_one(model, env, eps, gamma) - totalrewards[n] = totalreward - if (n + 1) % 10 == 0: - print("episode:", n, "total reward:", totalreward) - print("avg reward for last 100 episodes:", totalrewards[-100:].mean()) - print("total steps:", -totalrewards.sum()) - - if show_plots: - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - plot_running_avg(totalrewards) - - # plot the optimal state-value function - plot_cost_to_go(env, model) - - -if __name__ == '__main__': - # for i in range(10): - # main(show_plots=False) - main() \ No newline at end of file diff --git a/rl2/mountaincar/td_lambda.py b/rl2/mountaincar/td_lambda.py deleted file mode 100755 index 3d7dd8ac..00000000 --- a/rl2/mountaincar/td_lambda.py +++ /dev/null @@ -1,135 +0,0 @@ -# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python -# https://www.udemy.com/deep-reinforcement-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future -# -# Note: gym changed from version 0.7.3 to 0.8.0 -# MountainCar episode length is capped at 200 in later versions. -# This means your agent can't learn as much in the earlier episodes -# since they are no longer as long. -# -# Adapt Q-Learning script to use TD(lambda) method instead - -import gym -import os -import sys -import numpy as np -import matplotlib.pyplot as plt -from gym import wrappers -from datetime import datetime - -# code we already wrote -from q_learning import plot_cost_to_go, FeatureTransformer, plot_running_avg - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - - -class BaseModel: - def __init__(self, D): - self.w = np.random.randn(D) / np.sqrt(D) - - def partial_fit(self, input_, target, eligibility, lr=1e-2): - self.w += lr*(target - input_.dot(self.w))*eligibility - - def predict(self, X): - X = np.array(X) - return X.dot(self.w) - - -# Holds one BaseModel for each action -class Model: - def __init__(self, env, feature_transformer): - self.env = env - self.models = [] - self.feature_transformer = feature_transformer - - D = feature_transformer.dimensions - self.eligibilities = np.zeros((env.action_space.n, D)) - for i in range(env.action_space.n): - model = BaseModel(D) - self.models.append(model) - - def predict(self, s): - X = self.feature_transformer.transform([s]) - assert(len(X.shape) == 2) - result = np.stack([m.predict(X) for m in self.models]).T - assert(len(result.shape) == 2) - return result - - def update(self, s, a, G, gamma, lambda_): - X = self.feature_transformer.transform([s]) - assert(len(X.shape) == 2) - self.eligibilities *= gamma*lambda_ - self.eligibilities[a] += X[0] - self.models[a].partial_fit(X[0], G, self.eligibilities[a]) - - def sample_action(self, s, eps): - if np.random.random() < eps: - return self.env.action_space.sample() - else: - return np.argmax(self.predict(s)) - - -# returns a list of states_and_rewards, and the total reward -def play_one(model, env, eps, gamma, lambda_): - observation = env.reset() - done = False - totalreward = 0 - iters = 0 - # while not done and iters < 200: - while not done and iters < 10000: - action = model.sample_action(observation, eps) - prev_observation = observation - observation, reward, done, info = env.step(action) - - # update the model - Qnext = model.predict(observation) - assert(Qnext.shape == (1, env.action_space.n)) - G = reward + gamma*np.max(Qnext[0]) - model.update(prev_observation, action, G, gamma, lambda_) - - totalreward += reward - iters += 1 - - return totalreward - - -if __name__ == '__main__': - env = gym.make('MountainCar-v0') - ft = FeatureTransformer(env) - model = Model(env, ft) - gamma = 0.9999 - lambda_ = 0.7 - - if 'monitor' in sys.argv: - filename = os.path.basename(__file__).split('.')[0] - monitor_dir = './' + filename + '_' + str(datetime.now()) - env = wrappers.Monitor(env, monitor_dir) - - - N = 300 - totalrewards = np.empty(N) - costs = np.empty(N) - for n in range(N): - # eps = 1.0/(0.1*n+1) - eps = 0.1*(0.97**n) - # eps = 0.5/np.sqrt(n+1) - totalreward = play_one(model, env, eps, gamma, lambda_) - totalrewards[n] = totalreward - print("episode:", n, "total reward:", totalreward) - print("avg reward for last 100 episodes:", totalrewards[-100:].mean()) - print("total steps:", -totalrewards.sum()) - - plt.plot(totalrewards) - plt.title("Rewards") - plt.show() - - plot_running_avg(totalrewards) - - # plot the optimal state-value function - plot_cost_to_go(env, model) - diff --git a/rl2v2/extra_reading.txt b/rl2v2/extra_reading.txt deleted file mode 100644 index b1b113f2..00000000 --- a/rl2v2/extra_reading.txt +++ /dev/null @@ -1,8 +0,0 @@ -Gymnasium Library -https://gymnasium.farama.org/ - -Stable Baselines 3 -https://github.com/DLR-RM/stable-baselines3 - -Reinforcement Learning Prerequisites -https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python \ No newline at end of file diff --git a/rl3/a2c/a2c.py b/rl3/a2c/a2c.py deleted file mode 100644 index ce1667b1..00000000 --- a/rl3/a2c/a2c.py +++ /dev/null @@ -1,217 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import time -import joblib -import numpy as np -import tensorflow as tf -import os - -if tf.__version__.startswith('2'): - exit("Please install Tensorflow 1.x") - - -def set_global_seeds(i): - tf.set_random_seed(i) - np.random.seed(i) - - -def cat_entropy(logits): - a0 = logits - tf.reduce_max(logits, 1, keepdims=True) - ea0 = tf.exp(a0) - z0 = tf.reduce_sum(ea0, 1, keepdims=True) - p0 = ea0 / z0 - return tf.reduce_sum(p0 * (tf.log(z0) - a0), 1) - - -def find_trainable_variables(key): - with tf.variable_scope(key): - return tf.trainable_variables() - - -def discount_with_dones(rewards, dones, gamma): - discounted = [] - r = 0 - for reward, done in zip(rewards[::-1], dones[::-1]): - r = reward + gamma * r * (1. - done) # fixed off by one bug - discounted.append(r) - return discounted[::-1] - - - -class Agent: - def __init__(self, Network, ob_space, ac_space, nenvs, nsteps, nstack, - ent_coef=0.01, vf_coef=0.5, max_grad_norm=0.5, lr=7e-4, - alpha=0.99, epsilon=1e-5, total_timesteps=int(80e6)): - config = tf.ConfigProto(intra_op_parallelism_threads=nenvs, - inter_op_parallelism_threads=nenvs) - config.gpu_options.allow_growth = True - sess = tf.Session(config=config) - nbatch = nenvs * nsteps - - A = tf.placeholder(tf.int32, [nbatch]) - ADV = tf.placeholder(tf.float32, [nbatch]) - R = tf.placeholder(tf.float32, [nbatch]) - LR = tf.placeholder(tf.float32, []) - - step_model = Network(sess, ob_space, ac_space, nenvs, 1, nstack, reuse=False) - train_model = Network(sess, ob_space, ac_space, nenvs, nsteps, nstack, reuse=True) - - neglogpac = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=train_model.pi, labels=A) - pg_loss = tf.reduce_mean(ADV * neglogpac) - vf_loss = tf.reduce_mean(tf.squared_difference(tf.squeeze(train_model.vf), R) / 2.0) - entropy = tf.reduce_mean(cat_entropy(train_model.pi)) - loss = pg_loss - entropy * ent_coef + vf_loss * vf_coef - - params = find_trainable_variables("model") - grads = tf.gradients(loss, params) - if max_grad_norm is not None: - grads, grad_norm = tf.clip_by_global_norm(grads, max_grad_norm) - grads_and_params = list(zip(grads, params)) - trainer = tf.train.RMSPropOptimizer(learning_rate=LR, decay=alpha, epsilon=epsilon) - _train = trainer.apply_gradients(grads_and_params) - - def train(states, rewards, actions, values): - advs = rewards - values - feed_dict = {train_model.X: states, A: actions, ADV: advs, R: rewards, LR: lr} - policy_loss, value_loss, policy_entropy, _ = sess.run( - [pg_loss, vf_loss, entropy, _train], - feed_dict - ) - return policy_loss, value_loss, policy_entropy - - def save(save_path): - ps = sess.run(params) - joblib.dump(ps, save_path) - - def load(load_path): - loaded_params = joblib.load(load_path) - restores = [] - for p, loaded_p in zip(params, loaded_params): - restores.append(p.assign(loaded_p)) - ps = sess.run(restores) - - self.train = train - self.train_model = train_model - self.step_model = step_model - self.step = step_model.step - self.value = step_model.value - self.save = save - self.load = load - tf.global_variables_initializer().run(session=sess) - - -class Runner: - def __init__(self, env, agent, nsteps=5, nstack=4, gamma=0.99): - self.env = env - self.agent = agent - nh, nw, nc = env.observation_space.shape - nenv = env.num_envs - self.batch_ob_shape = (nenv * nsteps, nh, nw, nc * nstack) - self.state = np.zeros((nenv, nh, nw, nc * nstack), dtype=np.uint8) - self.nc = nc - obs = env.reset() - self.update_state(obs) - self.gamma = gamma - self.nsteps = nsteps - self.dones = [False for _ in range(nenv)] - self.total_rewards = [] # store all workers' total rewards - self.real_total_rewards = [] - - def update_state(self, obs): - # Do frame-stacking here instead of the FrameStack wrapper to reduce IPC overhead - self.state = np.roll(self.state, shift=-self.nc, axis=3) - self.state[:, :, :, -self.nc:] = obs - - def run(self): - mb_states, mb_rewards, mb_actions, mb_values, mb_dones = [], [], [], [], [] - for n in range(self.nsteps): - actions, values = self.agent.step(self.state) - mb_states.append(np.copy(self.state)) - mb_actions.append(actions) - mb_values.append(values) - mb_dones.append(self.dones) - obs, rewards, dones, infos = self.env.step(actions) - for done, info in zip(dones, infos): - if done: - self.total_rewards.append(info['reward']) - if info['total_reward'] != -1: - self.real_total_rewards.append(info['total_reward']) - self.dones = dones - for n, done in enumerate(dones): - if done: - self.state[n] = self.state[n] * 0 - self.update_state(obs) - mb_rewards.append(rewards) - mb_dones.append(self.dones) - # batch of steps to batch of rollouts - mb_states = np.asarray(mb_states, dtype=np.uint8).swapaxes(1, 0).reshape(self.batch_ob_shape) - mb_rewards = np.asarray(mb_rewards, dtype=np.float32).swapaxes(1, 0) - mb_actions = np.asarray(mb_actions, dtype=np.int32).swapaxes(1, 0) - mb_values = np.asarray(mb_values, dtype=np.float32).swapaxes(1, 0) - mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0) - mb_dones = mb_dones[:, 1:] - last_values = self.agent.value(self.state).tolist() - # discount/bootstrap off value fn - for n, (rewards, dones, value) in enumerate(zip(mb_rewards, mb_dones, last_values)): - rewards = rewards.tolist() - dones = dones.tolist() - if dones[-1] == 0: - rewards = discount_with_dones(rewards + [value], dones + [0], self.gamma)[:-1] - else: - rewards = discount_with_dones(rewards, dones, self.gamma) - mb_rewards[n] = rewards - mb_rewards = mb_rewards.flatten() - mb_actions = mb_actions.flatten() - mb_values = mb_values.flatten() - return mb_states, mb_rewards, mb_actions, mb_values - - -def learn(network, env, seed, new_session=True, nsteps=5, nstack=4, total_timesteps=int(80e6), - vf_coef=0.5, ent_coef=0.01, max_grad_norm=0.5, lr=7e-4, - epsilon=1e-5, alpha=0.99, gamma=0.99, log_interval=1000): - tf.reset_default_graph() - set_global_seeds(seed) - - nenvs = env.num_envs - env_id = env.env_id - save_name = os.path.join('models', env_id + '.save') - ob_space = env.observation_space - ac_space = env.action_space - agent = Agent(Network=network, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, - nsteps=nsteps, nstack=nstack, - ent_coef=ent_coef, vf_coef=vf_coef, - max_grad_norm=max_grad_norm, - lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps) - if os.path.exists(save_name): - agent.load(save_name) - - runner = Runner(env, agent, nsteps=nsteps, nstack=nstack, gamma=gamma) - - nbatch = nenvs * nsteps - tstart = time.time() - for update in range(1, total_timesteps // nbatch + 1): - states, rewards, actions, values = runner.run() - policy_loss, value_loss, policy_entropy = agent.train( - states, rewards, actions, values) - nseconds = time.time() - tstart - fps = int((update * nbatch) / nseconds) - if update % log_interval == 0 or update == 1: - print(' - - - - - - - ') - print("nupdates", update) - print("total_timesteps", update * nbatch) - print("fps", fps) - print("policy_entropy", float(policy_entropy)) - print("value_loss", float(value_loss)) - - # total reward - r = runner.total_rewards[-100:] # get last 100 - tr = runner.real_total_rewards[-100:] - if len(r) == 100: - print("avg reward (last 100):", np.mean(r)) - if len(tr) == 100: - print("avg total reward (last 100):", np.mean(tr)) - print("max (last 100):", np.max(tr)) - - agent.save(save_name) - - env.close() - agent.save(save_name) diff --git a/rl3/a2c/atari_wrappers.py b/rl3/a2c/atari_wrappers.py deleted file mode 100644 index d0b6531a..00000000 --- a/rl3/a2c/atari_wrappers.py +++ /dev/null @@ -1,289 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import numpy as np -from collections import deque -import gym -from gym import spaces -import cv2 # opencv-python - - -class NoopResetEnv(gym.Wrapper): - def __init__(self, env, noop_max=30): - """Sample initial states by taking random number of no-ops on reset. - No-op is assumed to be action 0. - """ - gym.Wrapper.__init__(self, env) - self.noop_max = noop_max - self.override_num_noops = None - self.noop_action = 0 - assert env.unwrapped.get_action_meanings()[0] == 'NOOP' - - def reset(self, **kwargs): - """ Do no-op action for a number of steps in [1, noop_max].""" - self.env.reset(**kwargs) - if self.override_num_noops is not None: - noops = self.override_num_noops - else: - noops = self.unwrapped.np_random.randint(1, self.noop_max + 1) # pylint: disable=E1101 - assert noops > 0 - obs = None - for _ in range(noops): - obs, _, done, _ = self.env.step(self.noop_action) - if done: - obs = self.env.reset(**kwargs) - return obs - - def step(self, ac): - return self.env.step(ac) - - -class FireResetEnv(gym.Wrapper): - def __init__(self, env): - """Take action on reset for environments that are fixed until firing.""" - gym.Wrapper.__init__(self, env) - assert env.unwrapped.get_action_meanings()[1] == 'FIRE' - assert len(env.unwrapped.get_action_meanings()) >= 3 - - def reset(self, **kwargs): - self.env.reset(**kwargs) - obs, _, done, _ = self.env.step(1) - if done: - self.env.reset(**kwargs) - obs, _, done, _ = self.env.step(2) - if done: - self.env.reset(**kwargs) - return obs - - def step(self, ac): - return self.env.step(ac) - - -class EpisodicLifeEnv(gym.Wrapper): - def __init__(self, env): - """Make end-of-life == end-of-episode, but only reset on true game over. - Done by DeepMind for the DQN and co. since it helps value estimation. - """ - gym.Wrapper.__init__(self, env) - self.lives = 0 - self.was_real_done = True - - def step(self, action): - obs, reward, done, info = self.env.step(action) - self.was_real_done = done - # check current lives, make loss of life terminal, - # then update lives to handle bonus lives - lives = self.env.unwrapped.ale.lives() - if lives < self.lives and lives > 0: - # for Qbert sometimes we stay in lives == 0 condtion for a few frames - # so its important to keep lives > 0, so that we only reset once - # the environment advertises done. - done = True - self.lives = lives - return obs, reward, done, info - - def reset(self, **kwargs): - """Reset only when lives are exhausted. - This way all states are still reachable even though lives are episodic, - and the learner need not know about any of this behind-the-scenes. - """ - if self.was_real_done: - obs = self.env.reset(**kwargs) - else: - # no-op step to advance from terminal/lost life state - obs, _, _, _ = self.env.step(0) - self.lives = self.env.unwrapped.ale.lives() - return obs - - -class MaxAndSkipEnv(gym.Wrapper): - def __init__(self, env, skip=4): - """Return only every `skip`-th frame""" - gym.Wrapper.__init__(self, env) - # most recent raw observations (for max pooling across time steps) - self._obs_buffer = np.zeros((2,) + env.observation_space.shape, dtype='uint8') - self._skip = skip - - def step(self, action): - """Repeat action, sum reward, and max over last observations.""" - total_reward = 0.0 - done = None - for i in range(self._skip): - obs, reward, done, info = self.env.step(action) - if i == self._skip - 2: - self._obs_buffer[0] = obs - if i == self._skip - 1: - self._obs_buffer[1] = obs - total_reward += reward - if done: - break - # Note that the observation on the done=True frame - # doesn't matter - max_frame = self._obs_buffer.max(axis=0) - - return max_frame, total_reward, done, info - - def reset(self, **kwargs): - return self.env.reset(**kwargs) - - -class ClipRewardEnv(gym.RewardWrapper): - def reward(self, reward): - """Bin reward to {+1, 0, -1} by its sign.""" - return np.sign(reward) - - -# class WarpFrame(gym.ObservationWrapper): -# def __init__(self, env): -# """Warp frames to 84x84 as done in the Nature paper and later work.""" -# gym.ObservationWrapper.__init__(self, env) -# self.width = 84 -# self.height = 84 -# self.observation_space = spaces.Box(low=0, high=255, shape=(self.height, self.width, 1)) - -# def _observation(self, frame): -# frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) -# frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA) -# return frame[:, :, None] -class WarpFrame(gym.ObservationWrapper): - def __init__(self, env, width=84, height=84, grayscale=True): - """Warp frames to 84x84 as done in the Nature paper and later work.""" - gym.ObservationWrapper.__init__(self, env) - self.width = width - self.height = height - self.grayscale = grayscale - if self.grayscale: - self.observation_space = spaces.Box(low=0, high=255, - shape=(self.height, self.width, 1), dtype=np.uint8) - else: - self.observation_space = spaces.Box(low=0, high=255, - shape=(self.height, self.width, 3), dtype=np.uint8) - - def observation(self, frame): - if self.grayscale: - frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) - frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA) - if self.grayscale: - frame = np.expand_dims(frame, -1) - return frame - - -class FrameStack(gym.Wrapper): - def __init__(self, env, k): - """Stack k last frames. - - Returns lazy array, which is much more memory efficient. - - See Also - -------- - baselines.common.atari_wrappers.LazyFrames - """ - gym.Wrapper.__init__(self, env) - self.k = k - self.frames = deque([], maxlen=k) - shp = env.observation_space.shape - self.observation_space = spaces.Box(low=0, high=255, shape=(shp[0], shp[1], shp[2] * k)) - - def reset(self): - ob = self.env.reset() - for _ in range(self.k): - self.frames.append(ob) - return self._get_ob() - - def step(self, action): - ob, reward, done, info = self.env.step(action) - self.frames.append(ob) - return self._get_ob(), reward, done, info - - def _get_ob(self): - assert len(self.frames) == self.k - return LazyFrames(list(self.frames)) - - -class LazyFrames: - def __init__(self, frames): - """This object ensures that common frames between the observations are only stored once. - It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay - buffers. - - This object should only be converted to numpy array before being passed to the model. - - You'd not believe how complex the previous solution was.""" - self._frames = frames - - def __array__(self, dtype=None): - out = np.concatenate(self._frames, axis=2) - if dtype is not None: - out = out.astype(dtype) - return out - - -def make_atari(env_id): - env = gym.make(env_id) - assert 'NoFrameskip' in env.spec.id - env = NoopResetEnv(env, noop_max=30) - env = MaxAndSkipEnv(env, skip=4) - return env - - -def wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False): - """Configure environment for DeepMind-style Atari. - """ - if episode_life: - env = EpisodicLifeEnv(env) - - if 'FIRE' in env.unwrapped.get_action_meanings(): - env = FireResetEnv(env) - env = WarpFrame(env) - - if clip_rewards: - env = ClipRewardEnv(env) - - if frame_stack: - env = FrameStack(env, 4) - - return env - - -class Monitor(gym.Wrapper): - def __init__(self, env, rank=0): - gym.Wrapper.__init__(self, env=env) - self.rank = rank - self.rewards = [] - self.total_reward = [] - self.summaries_dict = {'reward': 0, 'episode_length': 0, 'total_reward': 0, 'total_episode_length': 0} - env = self.env - while True: - if hasattr(env, 'was_real_done'): - self.episodic_env = env - if not hasattr(env, 'env'): - break - env = env.env - - def reset(self): - self.summaries_dict['reward'] = -1 - self.summaries_dict['episode_length'] = -1 - self.summaries_dict['total_reward'] = -1 - self.summaries_dict['total_episode_length'] = -1 - self.rewards = [] - env = self.env - if self.episodic_env.was_real_done: - self.summaries_dict['total_reward'] = -1 - self.summaries_dict['total_episode_length'] = -1 - self.total_reward = [] - return self.env.reset() - - def step(self, action): - observation, reward, done, info = self.env.step(action) - self.rewards.append(reward) - self.total_reward.append(reward) - if done: - # print("Done! R = %s, N = %s" % (sum(self.rewards), len(self.rewards))) - self.summaries_dict['reward'] = sum(self.rewards) - self.summaries_dict['episode_length'] = len(self.rewards) - - if self.episodic_env.was_real_done: - self.summaries_dict['total_reward'] = sum(self.total_reward) - self.summaries_dict['total_episode_length'] = len(self.total_reward) - info = self.summaries_dict.copy() # otherwise it will be overwritten - # if done: - # print("info:", info) - return observation, reward, done, info diff --git a/rl3/a2c/main.py b/rl3/a2c/main.py deleted file mode 100644 index b42c86d9..00000000 --- a/rl3/a2c/main.py +++ /dev/null @@ -1,59 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -from subproc_vec_env import SubprocVecEnv -from atari_wrappers import make_atari, wrap_deepmind, Monitor - -from neural_network import CNN -from a2c import learn - -import os - -import gym -import argparse -import logging - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Mute missing instructions errors - -MODEL_PATH = 'models' -SEED = 0 - - -def get_args(): - # Get some basic command line arguements - parser = argparse.ArgumentParser() - parser.add_argument('-e', '--env', help='environment ID', default='BreakoutNoFrameskip-v4') - parser.add_argument('-s', '--steps', help='training steps', type=int, default=int(80e6)) - parser.add_argument('--nenv', help='No. of environments', type=int, default=16) - return parser.parse_args() - - -def train(env_id, num_timesteps, num_cpu): - def make_env(rank): - def _thunk(): - env = make_atari(env_id) - env.seed(SEED + rank) - gym.logger.setLevel(logging.WARN) - env = wrap_deepmind(env) - - # wrap the env one more time for getting total reward - env = Monitor(env, rank) - return env - return _thunk - - env = SubprocVecEnv([make_env(i) for i in range(num_cpu)]) - learn(CNN, env, SEED, total_timesteps=int(num_timesteps * 1.1)) - env.close() - pass - - -def main(): - args = get_args() - os.makedirs(MODEL_PATH, exist_ok=True) - train(args.env, args.steps, num_cpu=args.nenv) - - -if __name__ == "__main__": - main() diff --git a/rl3/a2c/neural_network.py b/rl3/a2c/neural_network.py deleted file mode 100644 index f23db4eb..00000000 --- a/rl3/a2c/neural_network.py +++ /dev/null @@ -1,55 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import numpy as np -import tensorflow as tf - - -def sample(logits): - noise = tf.random_uniform(tf.shape(logits)) - return tf.argmax(logits - tf.log(-tf.log(noise)), 1) - - -def conv(inputs, nf, ks, strides, gain=1.0): - return tf.layers.conv2d(inputs=inputs, filters=nf, kernel_size=ks, - strides=(strides, strides), activation=tf.nn.relu, - kernel_initializer=tf.orthogonal_initializer(gain=gain)) - - -def dense(inputs, n, act=tf.nn.relu, gain=1.0): - return tf.layers.dense(inputs=inputs, units=n, activation=act, - kernel_initializer=tf.orthogonal_initializer(gain)) - - -class CNN: - - def __init__(self, sess, ob_space, ac_space, nenv, nsteps, nstack, reuse=False): - gain = np.sqrt(2) - nbatch = nenv * nsteps - nh, nw, nc = ob_space.shape - ob_shape = (nbatch, nh, nw, nc * nstack) - X = tf.placeholder(tf.uint8, ob_shape) # obs - X_normal = tf.cast(X, tf.float32) / 255.0 - with tf.variable_scope("model", reuse=reuse): - h1 = conv(X_normal, 32, 8, 4, gain) - h2 = conv(h1, 64, 4, 2, gain) - h3 = conv(h2, 64, 3, 1, gain) - h3 = tf.layers.flatten(h3) - h4 = dense(h3, 512, gain=gain) - pi = dense(h4, ac_space.n, act=None) - vf = dense(h4, 1, act=None) - - v0 = vf[:, 0] - a0 = sample(pi) - # self.initial_state = [] # State reserved for LSTM - - def step(ob): - a, v = sess.run([a0, v0], {X: ob}) - return a, v#, [] # dummy state - - def value(ob): - return sess.run(v0, {X: ob}) - - self.X = X - self.pi = pi - self.vf = vf - self.step = step - self.value = value diff --git a/rl3/a2c/play.py b/rl3/a2c/play.py deleted file mode 100644 index c35ae6bb..00000000 --- a/rl3/a2c/play.py +++ /dev/null @@ -1,56 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import argparse -import os -import numpy as np -from atari_wrappers import make_atari, wrap_deepmind, Monitor -from a2c import Agent -from neural_network import CNN -import imageio -import time - - -def get_args(): - # Get some basic command line arguements - parser = argparse.ArgumentParser() - parser.add_argument('-e', '--env', help='environment ID', default='BreakoutNoFrameskip-v4') - return parser.parse_args() - - -def get_agent(env, nsteps=5, nstack=1, total_timesteps=int(80e6), - vf_coef=0.5, ent_coef=0.01, max_grad_norm=0.5, lr=7e-4, - epsilon=1e-5, alpha=0.99): - # Note: nstack=1 since frame_stack=True, during training frame_stack=False - agent = Agent(Network=CNN, ob_space=env.observation_space, - ac_space=env.action_space, nenvs=1, nsteps=nsteps, nstack=nstack, - ent_coef=ent_coef, vf_coef=vf_coef, max_grad_norm=max_grad_norm, - lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps) - return agent - - -def main(): - env_id = get_args().env - env = make_atari(env_id) - env = wrap_deepmind(env, frame_stack=True, clip_rewards=False, episode_life=True) - env = Monitor(env) - # rewards will appear higher than during training since rewards are not clipped - - agent = get_agent(env) - - # check for save path - save_path = os.path.join('models', env_id + '.save') - agent.load(save_path) - - obs = env.reset() - renders = [] - while True: - obs = np.expand_dims(obs.__array__(), axis=0) - a, v = agent.step(obs) - obs, reward, done, info = env.step(a) - env.render() - if done: - print(info) - env.reset() - - -if __name__ == '__main__': - main() diff --git a/rl3/a2c/subproc_vec_env.py b/rl3/a2c/subproc_vec_env.py deleted file mode 100644 index 2c0a0808..00000000 --- a/rl3/a2c/subproc_vec_env.py +++ /dev/null @@ -1,106 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import numpy as np -from multiprocessing import Process, Pipe - - -def worker(remote, parent_remote, env_fn_wrapper): - parent_remote.close() - env = env_fn_wrapper.x() - while True: - cmd, data = remote.recv() - if cmd == 'step': - ob, reward, done, info = env.step(data) - if done: - ob = env.reset() - remote.send((ob, reward, done, info)) - elif cmd == 'reset': - ob = env.reset() - remote.send(ob) - elif cmd == 'reset_task': - ob = env.reset_task() - remote.send(ob) - elif cmd == 'close': - remote.close() - break - elif cmd == 'get_spaces': - remote.send((env.action_space, env.observation_space)) - elif cmd == 'get_id': - remote.send(env.spec.id) - else: - raise NotImplementedError - - -class CloudpickleWrapper(): - """ - Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle) - """ - - def __init__(self, x): - self.x = x - - def __getstate__(self): - import cloudpickle - return cloudpickle.dumps(self.x) - - def __setstate__(self, ob): - import pickle - self.x = pickle.loads(ob) - - -class SubprocVecEnv(): - def __init__(self, env_fns): - """ - envs: list of gym environments to run in subprocesses - """ - self.closed = False - nenvs = len(env_fns) - self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)]) - self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn))) - for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)] - for p in self.ps: - p.daemon = True # if the main process crashes, we should not cause things to hang - p.start() - for remote in self.work_remotes: - remote.close() - - self.remotes[0].send(('get_spaces', None)) - self.action_space, self.observation_space = self.remotes[0].recv() - - self.remotes[0].send(('get_id', None)) - self.env_id = self.remotes[0].recv() - - def step(self, actions): - for remote, action in zip(self.remotes, actions): - remote.send(('step', action)) - results = [remote.recv() for remote in self.remotes] - obs, rews, dones, infos = zip(*results) - # print("Infos:", infos) - # for done, info in zip(dones, infos): - # if done: - # # print("Total reward:", info['reward'], "Num steps:", info['episode_length']) - # print("Returned info:", info, "Done:", done) - return np.stack(obs), np.stack(rews), np.stack(dones), infos - - def reset(self): - for remote in self.remotes: - remote.send(('reset', None)) - return np.stack([remote.recv() for remote in self.remotes]) - - def reset_task(self): - for remote in self.remotes: - remote.send(('reset_task', None)) - return np.stack([remote.recv() for remote in self.remotes]) - - def close(self): - if self.closed: - return - - for remote in self.remotes: - remote.send(('close', None)) - for p in self.ps: - p.join() - self.closed = True - - @property - def num_envs(self): - return len(self.remotes) diff --git a/rl3/ddpg.py b/rl3/ddpg.py deleted file mode 100644 index 3913cedd..00000000 --- a/rl3/ddpg.py +++ /dev/null @@ -1,330 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import numpy as np -import tensorflow as tf -import gym -import matplotlib.pyplot as plt -from datetime import datetime - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - -if tf.__version__.startswith('2'): - exit("Please install Tensorflow 1.x") - - -### avoid crashing on Mac -# doesn't seem to work -from sys import platform as sys_pf -if sys_pf == 'darwin': - import matplotlib - matplotlib.use("TkAgg") - - -# simple feedforward neural net -def ANN(x, layer_sizes, hidden_activation=tf.nn.relu, output_activation=None): - for h in layer_sizes[:-1]: - x = tf.layers.dense(x, units=h, activation=hidden_activation) - return tf.layers.dense(x, units=layer_sizes[-1], activation=output_activation) - - -# get all variables within a scope -def get_vars(scope): - return [x for x in tf.global_variables() if scope in x.name] - - -### Create both the actor and critic networks at once ### -### Q(s, mu(s)) returns the maximum Q for a given state s ### -def CreateNetworks( - s, a, - num_actions, - action_max, - hidden_sizes=(300,), - hidden_activation=tf.nn.relu, - output_activation=tf.tanh): - - with tf.variable_scope('mu'): - mu = action_max * ANN(s, list(hidden_sizes)+[num_actions], hidden_activation, output_activation) - with tf.variable_scope('q'): - input_ = tf.concat([s, a], axis=-1) # (state, action) - q = tf.squeeze(ANN(input_, list(hidden_sizes)+[1], hidden_activation, None), axis=1) - with tf.variable_scope('q', reuse=True): - # reuse is True, so it reuses the weights from the previously defined Q network - input_ = tf.concat([s, mu], axis=-1) # (state, mu(state)) - q_mu = tf.squeeze(ANN(input_, list(hidden_sizes)+[1], hidden_activation, None), axis=1) - return mu, q, q_mu - - -### The experience replay memory ### -class ReplayBuffer: - def __init__(self, obs_dim, act_dim, size): - self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32) - self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32) - self.acts_buf = np.zeros([size, act_dim], dtype=np.float32) - self.rews_buf = np.zeros(size, dtype=np.float32) - self.done_buf = np.zeros(size, dtype=np.float32) - self.ptr, self.size, self.max_size = 0, 0, size - - def store(self, obs, act, rew, next_obs, done): - self.obs1_buf[self.ptr] = obs - self.obs2_buf[self.ptr] = next_obs - self.acts_buf[self.ptr] = act - self.rews_buf[self.ptr] = rew - self.done_buf[self.ptr] = done - self.ptr = (self.ptr+1) % self.max_size - self.size = min(self.size+1, self.max_size) - - def sample_batch(self, batch_size=32): - idxs = np.random.randint(0, self.size, size=batch_size) - return dict(s=self.obs1_buf[idxs], - s2=self.obs2_buf[idxs], - a=self.acts_buf[idxs], - r=self.rews_buf[idxs], - d=self.done_buf[idxs]) - - -### Implement the DDPG algorithm ### -def ddpg( - env_fn, - ac_kwargs=dict(), - seed=0, - save_folder=None, - num_train_episodes=100, - test_agent_every=25, - replay_size=int(1e6), - gamma=0.99, - decay=0.995, - mu_lr=1e-3, - q_lr=1e-3, - batch_size=100, - start_steps=10000, - action_noise=0.1, - max_episode_length=1000): - - tf.set_random_seed(seed) - np.random.seed(seed) - - env, test_env = env_fn(), env_fn() - - # comment out this line if you don't want to record a video of the agent - if save_folder is not None: - test_env = gym.wrappers.Monitor(test_env, save_folder) - - # get size of state space and action space - num_states = env.observation_space.shape[0] - num_actions = env.action_space.shape[0] - - # Maximum value of action - # Assumes both low and high values are the same - # Assumes all actions have the same bounds - # May NOT be the case for all environments - action_max = env.action_space.high[0] - - # Create Tensorflow placeholders (neural network inputs) - X = tf.placeholder(dtype=tf.float32, shape=(None, num_states)) # state - A = tf.placeholder(dtype=tf.float32, shape=(None, num_actions)) # action - X2 = tf.placeholder(dtype=tf.float32, shape=(None, num_states)) # next state - R = tf.placeholder(dtype=tf.float32, shape=(None,)) # reward - D = tf.placeholder(dtype=tf.float32, shape=(None,)) # done - - # Main network outputs - with tf.variable_scope('main'): - mu, q, q_mu = CreateNetworks(X, A, num_actions, action_max, **ac_kwargs) - - # Target networks - with tf.variable_scope('target'): - # We don't need the Q network output with arbitrary input action A - # because that's not actually used in our loss functions - # NOTE 1: The state input is X2, NOT X - # We only care about max_a{ Q(s', a) } - # Where this is equal to Q(s', mu(s')) - # This is because it's used in the target calculation: r + gamma * max_a{ Q(s',a) } - # Where s' = X2 - # NOTE 2: We ignore the first 2 networks for the same reason - _, _, q_mu_targ = CreateNetworks(X2, A, num_actions, action_max, **ac_kwargs) - - # Experience replay memory - replay_buffer = ReplayBuffer(obs_dim=num_states, act_dim=num_actions, size=replay_size) - - - # Target value for the Q-network loss - # We use stop_gradient to tell Tensorflow not to differentiate - # q_mu_targ wrt any params - # i.e. consider q_mu_targ values constant - q_target = tf.stop_gradient(R + gamma * (1 - D) * q_mu_targ) - - # DDPG losses - mu_loss = -tf.reduce_mean(q_mu) - q_loss = tf.reduce_mean((q - q_target)**2) - - # Train each network separately - mu_optimizer = tf.train.AdamOptimizer(learning_rate=mu_lr) - q_optimizer = tf.train.AdamOptimizer(learning_rate=q_lr) - mu_train_op = mu_optimizer.minimize(mu_loss, var_list=get_vars('main/mu')) - q_train_op = q_optimizer.minimize(q_loss, var_list=get_vars('main/q')) - - # Use soft updates to update the target networks - target_update = tf.group( - [tf.assign(v_targ, decay*v_targ + (1 - decay)*v_main) - for v_main, v_targ in zip(get_vars('main'), get_vars('target')) - ] - ) - - # Copy main network params to target networks - target_init = tf.group( - [tf.assign(v_targ, v_main) - for v_main, v_targ in zip(get_vars('main'), get_vars('target')) - ] - ) - - # boilerplate (and copy to the target networks!) - sess = tf.Session() - sess.run(tf.global_variables_initializer()) - sess.run(target_init) - - def get_action(s, noise_scale): - a = sess.run(mu, feed_dict={X: s.reshape(1,-1)})[0] - a += noise_scale * np.random.randn(num_actions) - return np.clip(a, -action_max, action_max) - - test_returns = [] - def test_agent(num_episodes=5): - t0 = datetime.now() - n_steps = 0 - for j in range(num_episodes): - s, episode_return, episode_length, d = test_env.reset(), 0, 0, False - while not (d or (episode_length == max_episode_length)): - # Take deterministic actions at test time (noise_scale=0) - test_env.render() - s, r, d, _ = test_env.step(get_action(s, 0)) - episode_return += r - episode_length += 1 - n_steps += 1 - print('test return:', episode_return, 'episode_length:', episode_length) - test_returns.append(episode_return) - # print("test steps per sec:", n_steps / (datetime.now() - t0).total_seconds()) - - - # Main loop: play episode and train - returns = [] - q_losses = [] - mu_losses = [] - num_steps = 0 - for i_episode in range(num_train_episodes): - - # reset env - s, episode_return, episode_length, d = env.reset(), 0, 0, False - - while not (d or (episode_length == max_episode_length)): - # For the first `start_steps` steps, use randomly sampled actions - # in order to encourage exploration. - if num_steps > start_steps: - a = get_action(s, action_noise) - else: - a = env.action_space.sample() - - # Keep track of the number of steps done - num_steps += 1 - if num_steps == start_steps: - print("USING AGENT ACTIONS NOW") - - # Step the env - s2, r, d, _ = env.step(a) - episode_return += r - episode_length += 1 - - # Ignore the "done" signal if it comes from hitting the time - # horizon (that is, when it's an artificial terminal signal - # that isn't based on the agent's state) - d_store = False if episode_length == max_episode_length else d - - # Store experience to replay buffer - replay_buffer.store(s, a, r, s2, d_store) - - # Assign next state to be the current state on the next round - s = s2 - - # Perform the updates - for _ in range(episode_length): - batch = replay_buffer.sample_batch(batch_size) - feed_dict = { - X: batch['s'], - X2: batch['s2'], - A: batch['a'], - R: batch['r'], - D: batch['d'] - } - - # Q network update - # Note: plot the Q loss if you want - ql, _, _ = sess.run([q_loss, q, q_train_op], feed_dict) - q_losses.append(ql) - - # Policy update - # (And target networks update) - # Note: plot the mu loss if you want - mul, _, _ = sess.run([mu_loss, mu_train_op, target_update], feed_dict) - mu_losses.append(mul) - - print("Episode:", i_episode + 1, "Return:", episode_return, 'episode_length:', episode_length) - returns.append(episode_return) - - # Test the agent - if i_episode > 0 and i_episode % test_agent_every == 0: - test_agent() - - # on Mac, plotting results in an error, so just save the results for later - # if you're not on Mac, feel free to uncomment the below lines - np.savez('ddpg_results.npz', train=returns, test=test_returns, q_losses=q_losses, mu_losses=mu_losses) - - # plt.plot(returns) - # plt.plot(smooth(np.array(returns))) - # plt.title("Train returns") - # plt.show() - - # plt.plot(test_returns) - # plt.plot(smooth(np.array(test_returns))) - # plt.title("Test returns") - # plt.show() - - # plt.plot(q_losses) - # plt.title('q_losses') - # plt.show() - - # plt.plot(mu_losses) - # plt.title('mu_losses') - # plt.show() - - -def smooth(x): - # last 100 - n = len(x) - y = np.zeros(n) - for i in range(n): - start = max(0, i - 99) - y[i] = float(x[start:(i+1)].sum()) / (i - start + 1) - return y - - -if __name__ == '__main__': - import argparse - parser = argparse.ArgumentParser() - # parser.add_argument('--env', type=str, default='HalfCheetah-v2') - parser.add_argument('--env', type=str, default='Pendulum-v0') - parser.add_argument('--hidden_layer_sizes', type=int, default=300) - parser.add_argument('--num_layers', type=int, default=1) - parser.add_argument('--gamma', type=float, default=0.99) - parser.add_argument('--seed', type=int, default=0) - parser.add_argument('--num_train_episodes', type=int, default=200) - parser.add_argument('--save_folder', type=str, default='ddpg_monitor') - args = parser.parse_args() - - - ddpg( - lambda : gym.make(args.env), - ac_kwargs=dict(hidden_sizes=[args.hidden_layer_sizes]*args.num_layers), - gamma=args.gamma, - seed=args.seed, - save_folder=args.save_folder, - num_train_episodes=args.num_train_episodes, - ) diff --git a/rl3/es_flappy.py b/rl3/es_flappy.py deleted file mode 100644 index 6002ef78..00000000 --- a/rl3/es_flappy.py +++ /dev/null @@ -1,252 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import numpy as np -import matplotlib.pyplot as plt - -from datetime import datetime - -# import multiprocessing -# from multiprocessing.dummy import Pool - -# INSTRUCTIONS FOR INSTALLING PLE: -# https://pygame-learning-environment.readthedocs.io/en/latest/user/home.html -from ple import PLE -from ple.games.flappybird import FlappyBird - -import sys - - -# thread pool for parallelization -# pool = Pool(4) - - -HISTORY_LENGTH = 1 - - -class Env: - def __init__(self): - self.game = FlappyBird(pipe_gap=125) - self.env = PLE(self.game, fps=30, display_screen=False) - self.env.init() - self.env.getGameState = self.game.getGameState # maybe not necessary - - # by convention we want to use (0,1) - # but the game uses (None, 119) - self.action_map = self.env.getActionSet() #[None, 119] - - def step(self, action): - action = self.action_map[action] - reward = self.env.act(action) - done = self.env.game_over() - obs = self.get_observation() - # don't bother returning an info dictionary like gym - return obs, reward, done - - def reset(self): - self.env.reset_game() - return self.get_observation() - - def get_observation(self): - # game state returns a dictionary which describes - # the meaning of each value - # we only want the values - obs = self.env.getGameState() - return np.array(list(obs.values())) - - def set_display(self, boolean_value): - self.env.display_screen = boolean_value - - -# make a global environment to be used throughout the script -env = Env() - - -### neural network - -# hyperparameters -D = len(env.reset())*HISTORY_LENGTH -M = 50 -K = 2 - -def softmax(a): - c = np.max(a, axis=1, keepdims=True) - e = np.exp(a - c) - return e / e.sum(axis=-1, keepdims=True) - -def relu(x): - return x * (x > 0) - -class ANN: - def __init__(self, D, M, K, f=relu): - self.D = D - self.M = M - self.K = K - self.f = f - - def init(self): - D, M, K = self.D, self.M, self.K - self.W1 = np.random.randn(D, M) / np.sqrt(D) - # self.W1 = np.zeros((D, M)) - self.b1 = np.zeros(M) - self.W2 = np.random.randn(M, K) / np.sqrt(M) - # self.W2 = np.zeros((M, K)) - self.b2 = np.zeros(K) - - def forward(self, X): - Z = self.f(X.dot(self.W1) + self.b1) - return softmax(Z.dot(self.W2) + self.b2) - - def sample_action(self, x): - # assume input is a single state of size (D,) - # first make it (N, D) to fit ML conventions - X = np.atleast_2d(x) - P = self.forward(X) - p = P[0] # the first row - # return np.random.choice(len(p), p=p) - return np.argmax(p) - - def get_params(self): - # return a flat array of parameters - return np.concatenate([self.W1.flatten(), self.b1, self.W2.flatten(), self.b2]) - - def get_params_dict(self): - return { - 'W1': self.W1, - 'b1': self.b1, - 'W2': self.W2, - 'b2': self.b2, - } - - def set_params(self, params): - # params is a flat list - # unflatten into individual weights - D, M, K = self.D, self.M, self.K - self.W1 = params[:D * M].reshape(D, M) - self.b1 = params[D * M:D * M + M] - self.W2 = params[D * M + M:D * M + M + M * K].reshape(M, K) - self.b2 = params[-K:] - - -def evolution_strategy( - f, - population_size, - sigma, - lr, - initial_params, - num_iters): - - # assume initial params is a 1-D array - num_params = len(initial_params) - reward_per_iteration = np.zeros(num_iters) - - params = initial_params - for t in range(num_iters): - t0 = datetime.now() - N = np.random.randn(population_size, num_params) - - ### slow way - R = np.zeros(population_size) # stores the reward - - # loop through each "offspring" - for j in range(population_size): - params_try = params + sigma*N[j] - R[j] = f(params_try) - - ### fast way - # R = pool.map(f, [params + sigma*N[j] for j in range(population_size)]) - # R = np.array(R) - - m = R.mean() - s = R.std() - if s == 0: - # we can't apply the following equation - print("Skipping") - continue - - A = (R - m) / s - reward_per_iteration[t] = m - params = params + lr/(population_size*sigma) * np.dot(N.T, A) - - # update the learning rate - lr *= 0.992354 - # sigma *= 0.99 - - print("Iter:", t, "Avg Reward: %.3f" % m, "Max:", R.max(), "Duration:", (datetime.now() - t0)) - - return params, reward_per_iteration - - -def reward_function(params): - model = ANN(D, M, K) - model.set_params(params) - - # play one episode and return the total reward - episode_reward = 0 - episode_length = 0 # not sure if it will be used - done = False - obs = env.reset() - obs_dim = len(obs) - if HISTORY_LENGTH > 1: - state = np.zeros(HISTORY_LENGTH*obs_dim) # current state - state[-obs_dim:] = obs - else: - state = obs - while not done: - # get the action - action = model.sample_action(state) - - # perform the action - obs, reward, done = env.step(action) - - # update total reward - episode_reward += reward - episode_length += 1 - - # update state - if HISTORY_LENGTH > 1: - state = np.roll(state, -obs_dim) - state[-obs_dim:] = obs - else: - state = obs - return episode_reward - - -if __name__ == '__main__': - model = ANN(D, M, K) - - if len(sys.argv) > 1 and sys.argv[1] == 'play': - # play with a saved model - j = np.load('es_flappy_results.npz') - best_params = np.concatenate([j['W1'].flatten(), j['b1'], j['W2'].flatten(), j['b2']]) - - # in case initial shapes are not correct - D, M = j['W1'].shape - K = len(j['b2']) - model.D, model.M, model.K = D, M, K - else: - # train and save - model.init() - params = model.get_params() - best_params, rewards = evolution_strategy( - f=reward_function, - population_size=30, - sigma=0.1, - lr=0.03, - initial_params=params, - num_iters=300, - ) - - # plot the rewards per iteration - # plt.plot(rewards) - # plt.show() - model.set_params(best_params) - np.savez( - 'es_flappy_results.npz', - train=rewards, - **model.get_params_dict(), - ) - - # play 5 test episodes - env.set_display(True) - for _ in range(5): - print("Test:", reward_function(best_params)) - diff --git a/rl3/es_mnist.py b/rl3/es_mnist.py deleted file mode 100644 index 5c1a5ec5..00000000 --- a/rl3/es_mnist.py +++ /dev/null @@ -1,159 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from datetime import datetime - -import multiprocessing -from multiprocessing.dummy import Pool - - -# thread pool for parallelization -pool = Pool(4) - -# get the data from: https://www.kaggle.com/c/digit-recognizer -# although you can feel free to use any dataset -df = pd.read_csv('../large_files/train.csv') - -# convert to numpy -data = df.values.astype(np.float32) - -# randomize and split the data -np.random.shuffle(data) - -X = data[:, 1:] / 255. -Y = data[:, 0].astype(np.int32) - -Xtrain = X[:-1000] -Ytrain = Y[:-1000] -Xtest = X[-1000:] -Ytest = Y[-1000:] -print("Finished loading in and splitting data") - -# layer sizes -D = Xtrain.shape[1] -M = 100 -K = len(set(Y)) - - -def softmax(a): - c = np.max(a, axis=1, keepdims=True) - e = np.exp(a - c) - return e / e.sum(axis=-1, keepdims=True) - - -def relu(x): - return x * (x > 0) - - -def log_likelihood(Y, P): - # assume Y is not one-hot encoded - N = len(Y) - return np.log(P[np.arange(N), Y]).mean() - - -class ANN: - def __init__(self, D, M, K): - self.D = D - self.M = M - self.K = K - - def init(self): - D, M, K = self.D, self.M, self.K - self.W1 = np.random.randn(D, M) / np.sqrt(D) - self.b1 = np.zeros(M) - self.W2 = np.random.randn(M, K) / np.sqrt(M) - self.b2 = np.zeros(K) - - def forward(self, X): - Z = np.tanh(X.dot(self.W1) + self.b1) - return softmax(Z.dot(self.W2) + self.b2) - - def score(self, X, Y): - P = np.argmax(self.forward(X), axis=1) - return np.mean(Y == P) - - def get_params(self): - # return a flat array of parameters - return np.concatenate([self.W1.flatten(), self.b1, self.W2.flatten(), self.b2]) - - def set_params(self, params): - # params is a flat list - # unflatten into individual weights - D, M, K = self.D, self.M, self.K - self.W1 = params[:D * M].reshape(D, M) - self.b1 = params[D * M:D * M + M] - self.W2 = params[D * M + M:D * M + M + M * K].reshape(M, K) - self.b2 = params[-K:] - - -def evolution_strategy( - f, - population_size, - sigma, - lr, - initial_params, - num_iters): - - # assume initial params is a 1-D array - num_params = len(initial_params) - reward_per_iteration = np.zeros(num_iters) - - params = initial_params - for t in range(num_iters): - t0 = datetime.now() - N = np.random.randn(population_size, num_params) - - # ### slow way - # R = np.zeros(population_size) # stores the reward - - # # loop through each "offspring" - # for j in range(population_size): - # params_try = params + sigma*N[j] - # R[j] = f(params_try) - - ### fast way - R = pool.map(f, [params + sigma*N[j] for j in range(population_size)]) - R = np.array(R) - - m = R.mean() - A = (R - m) / R.std() - reward_per_iteration[t] = m - params = params + lr/(population_size*sigma) * np.dot(N.T, A) - print("Iter:", t, "Avg Reward:", m, "Duration:", (datetime.now() - t0)) - - return params, reward_per_iteration - - -def reward_function(params): - model = ANN(D, M, K) - model.set_params(params) - # Ptrain = model.forward(Xtrain) - # return log_likelihood(Ytrain, Ptrain) - return model.score(Xtrain, Ytrain) - - - -if __name__ == '__main__': - model = ANN(D, M, K) - model.init() - params = model.get_params() - best_params, rewards = evolution_strategy( - f=reward_function, - population_size=50, - sigma=0.1, - lr=0.2, - initial_params=params, - num_iters=600, - ) - - # plot the rewards per iteration - plt.plot(rewards) - plt.show() - - # final train and test accuracy - model.set_params(best_params) - print("Train score:", model.score(Xtrain, Ytrain)) - print("Test score:", model.score(Xtest, Ytest)) - diff --git a/rl3/es_mujoco.py b/rl3/es_mujoco.py deleted file mode 100644 index 3ef4ffd9..00000000 --- a/rl3/es_mujoco.py +++ /dev/null @@ -1,209 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import numpy as np -import matplotlib.pyplot as plt - -from datetime import datetime - -import multiprocessing -from multiprocessing.dummy import Pool - -import gym -import sys - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - - - -# environment -ENV_NAME = 'HalfCheetah-v2' - - -# thread pool for parallelization -pool = Pool(4) - - -### neural network - -# hyperparameters -env = gym.make(ENV_NAME) -D = len(env.reset()) -M = 300 -K = env.action_space.shape[0] -action_max = env.action_space.high[0] - - -def relu(x): - return x * (x > 0) - -# def output_activation(x): -# return action_max * np.tanh(x) - -class ANN: - def __init__(self, D, M, K, f=relu): - self.D = D - self.M = M - self.K = K - self.f = f - - def init(self): - D, M, K = self.D, self.M, self.K - self.W1 = np.random.randn(D, M) / np.sqrt(D) - # self.W1 = np.zeros((D, M)) - self.b1 = np.zeros(M) - self.W2 = np.random.randn(M, K) / np.sqrt(M) - # self.W2 = np.zeros((M, K)) - self.b2 = np.zeros(K) - - def forward(self, X): - Z = self.f(X.dot(self.W1) + self.b1) - return np.tanh(Z.dot(self.W2) + self.b2) * action_max - - def sample_action(self, x): - # assume input is a single state of size (D,) - # first make it (N, D) to fit ML conventions - X = np.atleast_2d(x) - Y = self.forward(X) - return Y[0] # the first row - - def get_params(self): - # return a flat array of parameters - return np.concatenate([self.W1.flatten(), self.b1, self.W2.flatten(), self.b2]) - - def get_params_dict(self): - return { - 'W1': self.W1, - 'b1': self.b1, - 'W2': self.W2, - 'b2': self.b2, - } - - def set_params(self, params): - # params is a flat list - # unflatten into individual weights - D, M, K = self.D, self.M, self.K - self.W1 = params[:D * M].reshape(D, M) - self.b1 = params[D * M:D * M + M] - self.W2 = params[D * M + M:D * M + M + M * K].reshape(M, K) - self.b2 = params[-K:] - - -def evolution_strategy( - f, - population_size, - sigma, - lr, - initial_params, - num_iters): - - # assume initial params is a 1-D array - num_params = len(initial_params) - reward_per_iteration = np.zeros(num_iters) - - params = initial_params - for t in range(num_iters): - t0 = datetime.now() - N = np.random.randn(population_size, num_params) - - # ### slow way - # R = np.zeros(population_size) # stores the reward - - # # loop through each "offspring" - # for j in range(population_size): - # params_try = params + sigma*N[j] - # R[j] = f(params_try) - - ### fast way - R = pool.map(f, [params + sigma*N[j] for j in range(population_size)]) - R = np.array(R) - - m = R.mean() - s = R.std() - if s == 0: - # we can't apply the following equation - print("Skipping") - continue - - A = (R - m) / s - reward_per_iteration[t] = m - params = params + lr/(population_size*sigma) * np.dot(N.T, A) - - # update the learning rate - # lr *= 0.992354 - # sigma *= 0.99 - - print("Iter:", t, "Avg Reward: %.3f" % m, "Max:", R.max(), "Duration:", (datetime.now() - t0)) - - return params, reward_per_iteration - - -def reward_function(params, display=False): - model = ANN(D, M, K) - model.set_params(params) - - env = gym.make(ENV_NAME) - if display: - env = gym.wrappers.Monitor(env, 'es_monitor') - - # play one episode and return the total reward - episode_reward = 0 - episode_length = 0 # not sure if it will be used - done = False - state = env.reset() - while not done: - # display the env - if display: - env.render() - - # get the action - action = model.sample_action(state) - - # perform the action - state, reward, done, _ = env.step(action) - - # update total reward - episode_reward += reward - episode_length += 1 - - return episode_reward - - -if __name__ == '__main__': - model = ANN(D, M, K) - - if len(sys.argv) > 1 and sys.argv[1] == 'play': - # play with a saved model - j = np.load('es_mujoco_results.npz') - best_params = np.concatenate([j['W1'].flatten(), j['b1'], j['W2'].flatten(), j['b2']]) - - # in case initial shapes are not correct - D, M = j['W1'].shape - K = len(j['b2']) - model.D, model.M, model.K = D, M, K - else: - # train and save - model.init() - params = model.get_params() - best_params, rewards = evolution_strategy( - f=reward_function, - population_size=30, - sigma=0.1, - lr=0.03, - initial_params=params, - num_iters=300, - ) - - # plot the rewards per iteration - # plt.plot(rewards) - # plt.show() - model.set_params(best_params) - np.savez( - 'es_mujoco_results.npz', - train=rewards, - **model.get_params_dict(), - ) - - # play test episode - print("Test:", reward_function(best_params, display=True)) - diff --git a/rl3/es_simple.py b/rl3/es_simple.py deleted file mode 100644 index 0e9739d9..00000000 --- a/rl3/es_simple.py +++ /dev/null @@ -1,59 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import numpy as np -import matplotlib.pyplot as plt - - -def evolution_strategy( - f, - population_size, - sigma, - lr, - initial_params, - num_iters): - - # assume initial params is a 1-D array - num_params = len(initial_params) - reward_per_iteration = np.zeros(num_iters) - - params = initial_params - for t in range(num_iters): - N = np.random.randn(population_size, num_params) - R = np.zeros(population_size) # stores the reward - - # loop through each "offspring" - for j in range(population_size): - params_try = params + sigma*N[j] - R[j] = f(params_try) - - m = R.mean() - A = (R - m) / R.std() - reward_per_iteration[t] = m - params = params + lr/(population_size*sigma) * np.dot(N.T, A) - - return params, reward_per_iteration - - -def reward_function(params): - x0 = params[0] - x1 = params[1] - x2 = params[2] - return -(x0**2 + 0.1*(x1 - 1)**2 + 0.5*(x2 + 2)**2) - - -if __name__ == '__main__': - best_params, rewards = evolution_strategy( - f=reward_function, - population_size=50, - sigma=0.1, - lr=1e-3, - initial_params=np.random.randn(3), - num_iters=500, - ) - - # plot the rewards per iteration - plt.plot(rewards) - plt.show() - - # final params - print("Final params:", best_params) - diff --git a/rl3/extra_reading.txt b/rl3/extra_reading.txt deleted file mode 100644 index b190c6ac..00000000 --- a/rl3/extra_reading.txt +++ /dev/null @@ -1,22 +0,0 @@ -How do I sample from a discrete (categorical) distribution in log space? -https://stats.stackexchange.com/questions/64081/how-do-i-sample-from-a-discrete-categorical-distribution-in-log-space - -A2C (Advantage Actor-Critic) -https://openai.com/blog/baselines-acktr-a2c/ - -DDPG (Deep Deterministic Policy Gradient) -"Continuous control with deep reinforcement learning" -https://arxiv.org/abs/1509.02971 - -Deterministic Policy Gradient Algorithms -http://proceedings.mlr.press/v32/silver14.pdf - -ES (Evolution Strategies) -"Evolution Strategies as a Scalable Alternative to Reinforcement Learning" -https://arxiv.org/abs/1703.03864 - -Trust Region Evolution Strategies -https://www.microsoft.com/en-us/research/uploads/prod/2018/11/trust-region-evolution-strategies.pdf - -Addressing Function Approximation Error in Actor-Critic Methods -https://arxiv.org/abs/1802.09477 \ No newline at end of file diff --git a/rl3/flappy2envs.py b/rl3/flappy2envs.py deleted file mode 100644 index 2ce2bf5d..00000000 --- a/rl3/flappy2envs.py +++ /dev/null @@ -1,184 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import numpy as np -import matplotlib.pyplot as plt - -from datetime import datetime - -from ple import PLE -from ple.games.flappybird import FlappyBird - -import sys - -from threading import Thread - - - -HISTORY_LENGTH = 1 - - -class Env: - def __init__(self): - self.game = FlappyBird(pipe_gap=125) - self.env = PLE(self.game, fps=30, display_screen=True) - self.env.init() - self.env.getGameState = self.game.getGameState # maybe not necessary - - # by convention we want to use (0,1) - # but the game uses (None, 119) - self.action_map = self.env.getActionSet() #[None, 119] - - def step(self, action): - action = self.action_map[action] - reward = self.env.act(action) - done = self.env.game_over() - obs = self.get_observation() - # don't bother returning an info dictionary like gym - return obs, reward, done - - def reset(self): - self.env.reset_game() - return self.get_observation() - - def get_observation(self): - # game state returns a dictionary which describes - # the meaning of each value - # we only want the values - obs = self.env.getGameState() - return np.array(list(obs.values())) - - def set_display(self, boolean_value): - self.env.display_screen = boolean_value - - -# make a global environment to be used throughout the script -env = Env() - - -### neural network - -# hyperparameters -D = len(env.reset())*HISTORY_LENGTH -M = 50 -K = 2 - -def softmax(a): - c = np.max(a, axis=1, keepdims=True) - e = np.exp(a - c) - return e / e.sum(axis=-1, keepdims=True) - -def relu(x): - return x * (x > 0) - -class ANN: - def __init__(self, D, M, K, f=relu): - self.D = D - self.M = M - self.K = K - self.f = f - - def init(self): - D, M, K = self.D, self.M, self.K - self.W1 = np.random.randn(D, M) / np.sqrt(D) - # self.W1 = np.zeros((D, M)) - self.b1 = np.zeros(M) - self.W2 = np.random.randn(M, K) / np.sqrt(M) - # self.W2 = np.zeros((M, K)) - self.b2 = np.zeros(K) - - def forward(self, X): - Z = self.f(X.dot(self.W1) + self.b1) - return softmax(Z.dot(self.W2) + self.b2) - - def sample_action(self, x): - # assume input is a single state of size (D,) - # first make it (N, D) to fit ML conventions - X = np.atleast_2d(x) - P = self.forward(X) - p = P[0] # the first row - # return np.random.choice(len(p), p=p) - return np.argmax(p) - - def score(self, X, Y): - P = np.argmax(self.forward(X), axis=1) - return np.mean(Y == P) - - def get_params(self): - # return a flat array of parameters - return np.concatenate([self.W1.flatten(), self.b1, self.W2.flatten(), self.b2]) - - def get_params_dict(self): - return { - 'W1': self.W1, - 'b1': self.b1, - 'W2': self.W2, - 'b2': self.b2, - } - - def set_params(self, params): - # params is a flat list - # unflatten into individual weights - D, M, K = self.D, self.M, self.K - self.W1 = params[:D * M].reshape(D, M) - self.b1 = params[D * M:D * M + M] - self.W2 = params[D * M + M:D * M + M + M * K].reshape(M, K) - self.b2 = params[-K:] - - - -env1, env2 = Env(), Env() - - - - -def reward_function(params, env): - model = ANN(D, M, K) - model.set_params(params) - - # play one episode and return the total reward - episode_reward = 0 - episode_length = 0 # not sure if it will be used - done = False - obs = env.reset() - obs_dim = len(obs) - if HISTORY_LENGTH > 1: - state = np.zeros(HISTORY_LENGTH*obs_dim) # current state - state[obs_dim:] = obs - else: - state = obs - while not done: - # get the action - action = model.sample_action(state) - - # perform the action - obs, reward, done = env.step(action) - - # update total reward - episode_reward += reward - episode_length += 1 - - # update state - if HISTORY_LENGTH > 1: - state = np.roll(state, -obs_dim) - state[-obs_dim:] = obs - else: - state = obs - print("Reward:", episode_reward) - - -if __name__ == '__main__': - - j = np.load('es_flappy_results.npz') - best_params = np.concatenate([j['W1'].flatten(), j['b1'], j['W2'].flatten(), j['b2']]) - - # in case D isn't correct - D, M = j['W1'].shape - K = len(j['b2']) - - t1 = Thread(target=reward_function, args=(best_params, env1)) - t2 = Thread(target=reward_function, args=(best_params, env2)) - t1.start() - t2.start() - t1.join() - t2.join() - - diff --git a/rl3/gym_review.py b/rl3/gym_review.py deleted file mode 100644 index 3be2ac98..00000000 --- a/rl3/gym_review.py +++ /dev/null @@ -1,65 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import gym -import numpy as np -import matplotlib.pyplot as plt - -gym_minor_version = int(gym.__version__.split('.')[1]) -if gym_minor_version >= 19: - exit("Please install OpenAI Gym 0.19.0 or earlier") - - - -def get_action(s, w): - return 1 if s.dot(w) > 0 else 0 - - -def play_one_episode(env, params): - observation = env.reset() - done = False - t = 0 - r = 0 - - while not done and t < 10000: - t += 1 - action = get_action(observation, params) - observation, reward, done, info = env.step(action) - r += reward - - return r - - -def play_multiple_episodes(env, T, params): - episode_rewards = np.empty(T) - - for i in range(T): - episode_rewards[i] = play_one_episode(env, params) - - avg_reward = episode_rewards.mean() - print("avg reward:", avg_reward) - return avg_reward - - -def random_search(env): - episode_rewards = [] - best = 0 - params = None - for t in range(100): - new_params = np.random.random(4)*2 - 1 - avg_reward = play_multiple_episodes(env, 100, new_params) - episode_rewards.append(avg_reward) - - if avg_reward > best: - params = new_params - best = avg_reward - return episode_rewards, params - - -if __name__ == '__main__': - env = gym.make('CartPole-v0') - episode_rewards, params = random_search(env) - plt.plot(episode_rewards) - plt.show() - - # play a final set of episodes - print("***Final run with final weights***") - play_multiple_episodes(env, 100, params) diff --git a/rl3/plot_ddpg_result.py b/rl3/plot_ddpg_result.py deleted file mode 100644 index 33e549db..00000000 --- a/rl3/plot_ddpg_result.py +++ /dev/null @@ -1,38 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import json -import matplotlib.pyplot as plt -import numpy as np - -def smooth(x): - # last 100 - n = len(x) - y = np.zeros(n) - for i in range(n): - start = max(0, i - 99) - y[i] = float(x[start:(i+1)].sum()) / (i - start + 1) - return y - -j = np.load('ddpg_results.npz') - -returns = j['train'] -test_returns = j['test'] -q_losses = j['q_losses'] -mu_losses = j['mu_losses'] - -plt.plot(returns) -plt.plot(smooth(np.array(returns))) -plt.title("Train returns") -plt.show() - -plt.plot(test_returns) -plt.plot(smooth(np.array(test_returns))) -plt.title("Test returns") -plt.show() - -plt.plot(q_losses) -plt.title('q_losses') -plt.show() - -plt.plot(mu_losses) -plt.title('mu_losses') -plt.show() \ No newline at end of file diff --git a/rl3/plot_es_flappy_results.py b/rl3/plot_es_flappy_results.py deleted file mode 100644 index b0b725af..00000000 --- a/rl3/plot_es_flappy_results.py +++ /dev/null @@ -1,21 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import matplotlib.pyplot as plt -import numpy as np - -def smooth(x): - # last 100 - n = len(x) - y = np.zeros(n) - for i in range(n): - start = max(0, i - 99) - y[i] = float(x[start:(i+1)].sum()) / (i - start + 1) - return y - -j = np.load('es_flappy_results.npz') - -returns = j['train'] - -plt.plot(returns) -plt.plot(smooth(np.array(returns))) -plt.title("Train returns") -plt.show() \ No newline at end of file diff --git a/rl3/plot_es_mujoco_results.py b/rl3/plot_es_mujoco_results.py deleted file mode 100644 index 10a10617..00000000 --- a/rl3/plot_es_mujoco_results.py +++ /dev/null @@ -1,21 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import matplotlib.pyplot as plt -import numpy as np - -def smooth(x): - # last 100 - n = len(x) - y = np.zeros(n) - for i in range(n): - start = max(0, i - 99) - y[i] = float(x[start:(i+1)].sum()) / (i - start + 1) - return y - -j = np.load('es_mujoco_results.npz') - -returns = j['train'] - -plt.plot(returns) -plt.plot(smooth(np.array(returns))) -plt.title("Train returns") -plt.show() \ No newline at end of file diff --git a/rl3/sample_test.py b/rl3/sample_test.py deleted file mode 100644 index 48d82f1d..00000000 --- a/rl3/sample_test.py +++ /dev/null @@ -1,16 +0,0 @@ -# https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence -import numpy as np -import matplotlib.pyplot as plt - -logits = np.log([0.1, 0.2, 0.3, 0.4]) - -samples = [] - -for _ in range(10000): - noise = np.random.random(len(logits)) - sample = np.argmax(logits - np.log(-np.log(noise))) - samples.append(sample) - - -plt.hist(samples) -plt.show() \ No newline at end of file diff --git a/rl3v2/extra_reading.txt b/rl3v2/extra_reading.txt deleted file mode 100644 index cdff9892..00000000 --- a/rl3v2/extra_reading.txt +++ /dev/null @@ -1,23 +0,0 @@ -=== PART 1 === - -ES (Evolution Strategies) -"Evolution Strategies as a Scalable Alternative to Reinforcement Learning" -https://arxiv.org/abs/1703.03864 - -Trust Region Evolution Strategies -https://www.microsoft.com/en-us/research/uploads/prod/2018/11/trust-region-evolution-strategies.pdf - -The CMA Evolution Strategy: A Tutorial -https://arxiv.org/pdf/1604.00772 - -Simple random search provides a competitive approach to reinforcement learning (Augmented Random Search) -https://arxiv.org/abs/1803.07055 - -=== PART 2 === - -DDPG (Deep Deterministic Policy Gradient) -"Continuous control with deep reinforcement learning" -https://arxiv.org/abs/1509.02971 - -Deterministic Policy Gradient Algorithms -http://proceedings.mlr.press/v32/silver14.pdf \ No newline at end of file diff --git a/rl3v2/visualize_es.py b/rl3v2/visualize_es.py deleted file mode 100644 index e518c388..00000000 --- a/rl3v2/visualize_es.py +++ /dev/null @@ -1,59 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - -# Objective function to minimize (you can change this) -def f(x, y): - # return np.sin(x) + np.cos(y) - return -((x - 1)**2 + y**2) - -# Evolution Strategies optimizer (simple version) -def evolution_strategies( - f, bounds, pop_size=50, sigma=0.3, alpha=0.03, iterations=100 -): - dim = 2 - mu = np.random.uniform(bounds[0], bounds[1], size=dim) - - history = [] - - for gen in range(iterations): - # Sample noise - noise = np.random.randn(pop_size, dim) - population = mu + sigma * noise - fitness = np.array([f(x[0], x[1]) for x in population]) - - history.append((population.copy(), mu.copy())) - - # Normalize fitness for weighting - fitness_norm = (fitness - np.mean(fitness)) / (np.std(fitness) + 1e-8) - mu += alpha / (pop_size * sigma) * np.dot(noise.T, fitness_norm) - - return history - -# Visualization function -def visualize_es(history, bounds, f, resolution=100): - x = np.linspace(bounds[0], bounds[1], resolution) - y = np.linspace(bounds[0], bounds[1], resolution) - X, Y = np.meshgrid(x, y) - Z = f(X, Y) - - plt.figure(figsize=(8, 6)) - for i, (pop, mu) in enumerate(history): - plt.clf() - plt.contourf(X, Y, Z, levels=50, cmap='viridis') - plt.colorbar(label="f(x, y)") - plt.scatter(pop[:, 0], pop[:, 1], c='white', s=20, label='Population') - plt.scatter(mu[0], mu[1], c='red', s=80, label='Mean', edgecolors='black') - plt.title(f"Evolution Strategies - Step {i+1}") - plt.xlim(bounds[0], bounds[1]) - plt.ylim(bounds[0], bounds[1]) - plt.xlabel('x') - plt.ylabel('y') - plt.legend() - # plt.pause(0.1) - plt.waitforbuttonpress() - plt.show() - -# Run -bounds = (-5, 5) -history = evolution_strategies(f, bounds, iterations=80) -visualize_es(history, bounds, f) diff --git a/rl3v2/visualize_hill_climbing.py b/rl3v2/visualize_hill_climbing.py deleted file mode 100644 index d640a20e..00000000 --- a/rl3v2/visualize_hill_climbing.py +++ /dev/null @@ -1,61 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - -# Objective function to minimize (you can change this) -def f(x, y): - # return np.sin(x) + np.cos(y) - return -((x - 1)**2 + y**2) - -# Evolution Strategies optimizer (simple version) -def hill_climb( - f, bounds, pop_size=1, sigma=0.3, alpha=0.3, iterations=100 -): - dim = 2 - mu = np.random.uniform(bounds[0], bounds[1], size=dim) - - history = [] - best_f = f(mu) - - for gen in range(iterations): - # Sample noise - noise = np.random.randn(pop_size, dim) - population = mu + sigma * noise - fitness = np.array([f(x[0], x[1]) for x in population]) - - history.append((population.copy(), mu.copy())) - - # Update point if it's better - if fitness[0] > best_f: - best_f = fitness[0] - mu = population.flatten() - - return history - -# Visualization function -def visualize_es(history, bounds, f, resolution=100): - x = np.linspace(bounds[0], bounds[1], resolution) - y = np.linspace(bounds[0], bounds[1], resolution) - X, Y = np.meshgrid(x, y) - Z = f(X, Y) - - plt.figure(figsize=(8, 6)) - for i, (pop, mu) in enumerate(history): - plt.clf() - plt.contourf(X, Y, Z, levels=50, cmap='viridis') - plt.colorbar(label="f(x, y)") - plt.scatter(pop[:, 0], pop[:, 1], c='white', s=20, label='Population') - plt.scatter(mu[0], mu[1], c='red', s=80, label='Mean', edgecolors='black') - plt.title(f"Hill Climbing - Step {i+1}") - plt.xlim(bounds[0], bounds[1]) - plt.ylim(bounds[0], bounds[1]) - plt.xlabel('x') - plt.ylabel('y') - plt.legend() - # plt.pause(0.1) - plt.waitforbuttonpress() - plt.show() - -# Run -bounds = (-5, 5) -history = hill_climb(f, bounds, iterations=80) -visualize_es(history, bounds, f) diff --git a/rnn_class/WHERE ARE THE NOTEBOOKS.txt b/rnn_class/WHERE ARE THE NOTEBOOKS.txt deleted file mode 100644 index 5446ce25..00000000 --- a/rnn_class/WHERE ARE THE NOTEBOOKS.txt +++ /dev/null @@ -1,5 +0,0 @@ -As stated in the "where to get the code" / "where to get the notebooks" lecture, the notebooks are NOT on Github. - -If you missed this, please review the lecture for the actual location of the notebooks. - -If, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com. \ No newline at end of file diff --git a/rnn_class/__init__.py b/rnn_class/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/rnn_class/batch_gru.py b/rnn_class/batch_gru.py deleted file mode 100644 index ce9f9a03..00000000 --- a/rnn_class/batch_gru.py +++ /dev/null @@ -1,76 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python -# https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import theano -import theano.tensor as T - -from util import init_weight - - -class GRU: - def __init__(self, Mi, Mo, activation): - self.Mi = Mi - self.Mo = Mo - self.f = activation - - # numpy init - Wxr = init_weight(Mi, Mo) - Whr = init_weight(Mo, Mo) - br = np.zeros(Mo) - Wxz = init_weight(Mi, Mo) - Whz = init_weight(Mo, Mo) - bz = np.zeros(Mo) - Wxh = init_weight(Mi, Mo) - Whh = init_weight(Mo, Mo) - bh = np.zeros(Mo) - h0 = np.zeros(Mo) - - # theano vars - self.Wxr = theano.shared(Wxr) - self.Whr = theano.shared(Whr) - self.br = theano.shared(br) - self.Wxz = theano.shared(Wxz) - self.Whz = theano.shared(Whz) - self.bz = theano.shared(bz) - self.Wxh = theano.shared(Wxh) - self.Whh = theano.shared(Whh) - self.bh = theano.shared(bh) - self.h0 = theano.shared(h0) - self.params = [self.Wxr, self.Whr, self.br, self.Wxz, self.Whz, self.bz, self.Wxh, self.Whh, self.bh, self.h0] - - def get_ht(self, xWxr_t, xWxz_t, xWxh_t, h_t1): - r = T.nnet.sigmoid(xWxr_t + h_t1.dot(self.Whr) + self.br) - z = T.nnet.sigmoid(xWxz_t + h_t1.dot(self.Whz) + self.bz) - hhat = self.f(xWxh_t + (r * h_t1).dot(self.Whh) + self.bh) - h = (1 - z) * h_t1 + z * hhat - return h - - def recurrence(self, xWxr_t, xWxz_t, xWxh_t, is_start, h_t1, h0): - h_t = T.switch( - T.eq(is_start, 1), - self.get_ht(xWxr_t, xWxz_t, xWxh_t, h0), - self.get_ht(xWxr_t, xWxz_t, xWxh_t, h_t1) - ) - return h_t - - def output(self, Xflat, startPoints): - # Xflat should be (NT, D) - # calculate X after multiplying input weights - XWxr = Xflat.dot(self.Wxr) - XWxz = Xflat.dot(self.Wxz) - XWxh = Xflat.dot(self.Wxh) - - h, _ = theano.scan( - fn=self.recurrence, - sequences=[XWxr, XWxz, XWxh, startPoints], - outputs_info=[self.h0], - non_sequences=[self.h0], - n_steps=Xflat.shape[0], - ) - return h diff --git a/rnn_class/batch_parity.py b/rnn_class/batch_parity.py deleted file mode 100644 index 11ee6997..00000000 --- a/rnn_class/batch_parity.py +++ /dev/null @@ -1,171 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python -# https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import theano -import theano.tensor as T -import numpy as np -import matplotlib.pyplot as plt - -from sklearn.utils import shuffle -from util import init_weight, all_parity_pairs_with_sequence_labels - - -class SimpleRNN: - def __init__(self, M): - self.M = M # hidden layer size - - def fit(self, X, Y, batch_sz=20, learning_rate=1.0, mu=0.99, reg=1.0, activation=T.tanh, epochs=100, show_fig=False): - D = X[0].shape[1] # X is of size N x T(n) x D - K = len(set(Y.flatten())) - N = len(Y) - M = self.M - self.f = activation - - # initial weights - Wx = init_weight(D, M) - Wh = init_weight(M, M) - bh = np.zeros(M) - h0 = np.zeros(M) - Wo = init_weight(M, K) - bo = np.zeros(K) - - # make them theano shared - self.Wx = theano.shared(Wx) - self.Wh = theano.shared(Wh) - self.bh = theano.shared(bh) - self.h0 = theano.shared(h0) - self.Wo = theano.shared(Wo) - self.bo = theano.shared(bo) - self.params = [self.Wx, self.Wh, self.bh, self.h0, self.Wo, self.bo] - - thX = T.fmatrix('X') # will represent multiple batches concatenated - thY = T.ivector('Y') - thStartPoints = T.ivector('start_points') - - XW = thX.dot(self.Wx) - - # startPoints will contain 1 where a sequence starts and 0 otherwise - # Ex. if I have 3 sequences: [[1,2,3], [4,5], [6,7,8]] - # Then I will concatenate these into one X: [1,2,3,4,5,6,7,8] - # And startPoints will be [1,0,0,1,0,1,0,0] - - # One possible solution: loop through index - # def recurrence(t, h_t1, XW, h0, startPoints): - # # returns h(t) - - # # if at a boundary, state should be h0 - # h_t = T.switch( - # T.eq(startPoints[t], 1), - # self.f(XW[t] + h0.dot(self.Wh) + self.bh), - # self.f(XW[t] + h_t1.dot(self.Wh) + self.bh) - # ) - # return h_t - - # h, _ = theano.scan( - # fn=recurrence, - # outputs_info=[self.h0], - # sequences=T.arange(XW.shape[0]), - # non_sequences=[XW, self.h0, thStartPoints], - # n_steps=XW.shape[0], - # ) - - # other solution - loop through all sequences simultaneously - def recurrence(xw_t, is_start, h_t1, h0): - # if at a boundary, state should be h0 - h_t = T.switch( - T.eq(is_start, 1), - self.f(xw_t + h0.dot(self.Wh) + self.bh), - self.f(xw_t + h_t1.dot(self.Wh) + self.bh) - ) - return h_t - - h, _ = theano.scan( - fn=recurrence, - outputs_info=[self.h0], - sequences=[XW, thStartPoints], - non_sequences=[self.h0], - n_steps=XW.shape[0], - ) - - # h is of shape (T*batch_sz, M) - py_x = T.nnet.softmax(h.dot(self.Wo) + self.bo) - prediction = T.argmax(py_x, axis=1) - - cost = -T.mean(T.log(py_x[T.arange(thY.shape[0]), thY])) - grads = T.grad(cost, self.params) - dparams = [theano.shared(p.get_value()*0) for p in self.params] - - updates = [ - (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads) - ] + [ - (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads) - ] - - # self.predict_op = theano.function(inputs=[thX, thStartPoints], outputs=prediction) - self.train_op = theano.function( - inputs=[thX, thY, thStartPoints], - outputs=[cost, prediction, py_x], - updates=updates - ) - - costs = [] - n_batches = N // batch_sz - sequenceLength = X.shape[1] - - # if each sequence was of variable length, we would need to - # initialize this inside the loop for every new batch - startPoints = np.zeros(sequenceLength*batch_sz, dtype=np.int32) - for b in range(batch_sz): - startPoints[b*sequenceLength] = 1 - for i in range(epochs): - X, Y = shuffle(X, Y) - n_correct = 0 - cost = 0 - for j in range(n_batches): - Xbatch = X[j*batch_sz:(j+1)*batch_sz].reshape(sequenceLength*batch_sz, D) - Ybatch = Y[j*batch_sz:(j+1)*batch_sz].reshape(sequenceLength*batch_sz).astype(np.int32) - c, p, rout = self.train_op(Xbatch, Ybatch, startPoints) - # print "p:", p - cost += c - # P = p.reshape(batch_sz, sequenceLength) - for b in range(batch_sz): - idx = sequenceLength*(b + 1) - 1 - if p[idx] == Ybatch[idx]: - n_correct += 1 - # else: - # print "pred:", p[idx], "actual:", Ybatch[idx] - if i % 10 == 0: - print("shape y:", rout.shape) - print("i:", i, "cost:", cost, "classification rate:", (float(n_correct)/N)) - if n_correct == N: - print("i:", i, "cost:", cost, "classification rate:", (float(n_correct)/N)) - break - costs.append(cost) - - if show_fig: - plt.plot(costs) - plt.show() - - - -def parity(B=12, learning_rate=1e-3, epochs=3000): - X, Y = all_parity_pairs_with_sequence_labels(B) - - rnn = SimpleRNN(4) - rnn.fit(X, Y, - batch_sz=10, - learning_rate=learning_rate, - epochs=epochs, - activation=T.nnet.sigmoid, - show_fig=False - ) - - -if __name__ == '__main__': - parity() - diff --git a/rnn_class/batch_units.py b/rnn_class/batch_units.py deleted file mode 100644 index 21e60417..00000000 --- a/rnn_class/batch_units.py +++ /dev/null @@ -1,220 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python -# https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import theano -import theano.tensor as T - -def init_weight(Mi, Mo): - return np.random.randn(Mi, Mo) * np.sqrt(2.0 / Mi) - - -class SimpleRecurrentLayer: - def __init__(self, Mi, Mo, activation): - self.Mi = Mi - self.Mo = Mo - self.f = activation - - # numpy init - Wxh = init_weight(Mi, Mo) - Whh = init_weight(Mo, Mo) - b = np.zeros(Mo) - h0 = np.zeros(Mo) - - # theano vars - self.Wxh = theano.shared(Wxh) - self.Whh = theano.shared(Whh) - self.b = theano.shared(b) - self.h0 = theano.shared(h0) - self.params = [self.Wxh, self.Whh, self.b, self.h0] - - def get_ht(self, xWxh_t, h_t1): - return self.f(xWxh_t + h_t1.dot(self.Whh) + self.b) - - def recurrence(self, xWxh_t, is_start, h_t1, h0): - h_t = T.switch( - T.eq(is_start, 1), - self.get_ht(xWxh_t, h0), - self.get_ht(xWxh_t, h_t1) - ) - return h_t - - def output(self, Xflat, startPoints): - # Xflat should be (NT, D) - # calculate X after multiplying input weights - XWxh = Xflat.dot(self.Wxh) - - h, _ = theano.scan( - fn=self.recurrence, - sequences=[XWxh, startPoints], - outputs_info=[self.h0], - non_sequences=[self.h0], - n_steps=Xflat.shape[0], - ) - return h - - -class GRU: - def __init__(self, Mi, Mo, activation): - self.Mi = Mi - self.Mo = Mo - self.f = activation - - # numpy init - Wxr = init_weight(Mi, Mo) - Whr = init_weight(Mo, Mo) - br = np.zeros(Mo) - Wxz = init_weight(Mi, Mo) - Whz = init_weight(Mo, Mo) - bz = np.zeros(Mo) - Wxh = init_weight(Mi, Mo) - Whh = init_weight(Mo, Mo) - bh = np.zeros(Mo) - h0 = np.zeros(Mo) - - # theano vars - self.Wxr = theano.shared(Wxr) - self.Whr = theano.shared(Whr) - self.br = theano.shared(br) - self.Wxz = theano.shared(Wxz) - self.Whz = theano.shared(Whz) - self.bz = theano.shared(bz) - self.Wxh = theano.shared(Wxh) - self.Whh = theano.shared(Whh) - self.bh = theano.shared(bh) - self.h0 = theano.shared(h0) - self.params = [self.Wxr, self.Whr, self.br, self.Wxz, self.Whz, self.bz, self.Wxh, self.Whh, self.bh, self.h0] - - def get_ht(self, xWxr_t, xWxz_t, xWxh_t, h_t1): - r = T.nnet.sigmoid(xWxr_t + h_t1.dot(self.Whr) + self.br) - z = T.nnet.sigmoid(xWxz_t + h_t1.dot(self.Whz) + self.bz) - hhat = self.f(xWxh_t + (r * h_t1).dot(self.Whh) + self.bh) - h = (1 - z) * h_t1 + z * hhat - return h - - def recurrence(self, xWxr_t, xWxz_t, xWxh_t, is_start, h_t1, h0): - h_t = T.switch( - T.eq(is_start, 1), - self.get_ht(xWxr_t, xWxz_t, xWxh_t, h0), - self.get_ht(xWxr_t, xWxz_t, xWxh_t, h_t1) - ) - return h_t - - def output(self, Xflat, startPoints): - # Xflat should be (NT, D) - # calculate X after multiplying input weights - XWxr = Xflat.dot(self.Wxr) - XWxz = Xflat.dot(self.Wxz) - XWxh = Xflat.dot(self.Wxh) - - h, _ = theano.scan( - fn=self.recurrence, - sequences=[XWxr, XWxz, XWxh, startPoints], - outputs_info=[self.h0], - non_sequences=[self.h0], - n_steps=Xflat.shape[0], - ) - return h - - - -class LSTM: - def __init__(self, Mi, Mo, activation): - self.Mi = Mi - self.Mo = Mo - self.f = activation - - # numpy init - Wxi = init_weight(Mi, Mo) - Whi = init_weight(Mo, Mo) - Wci = init_weight(Mo, Mo) - bi = np.zeros(Mo) - Wxf = init_weight(Mi, Mo) - Whf = init_weight(Mo, Mo) - Wcf = init_weight(Mo, Mo) - bf = np.zeros(Mo) - Wxc = init_weight(Mi, Mo) - Whc = init_weight(Mo, Mo) - bc = np.zeros(Mo) - Wxo = init_weight(Mi, Mo) - Who = init_weight(Mo, Mo) - Wco = init_weight(Mo, Mo) - bo = np.zeros(Mo) - c0 = np.zeros(Mo) - h0 = np.zeros(Mo) - - # theano vars - self.Wxi = theano.shared(Wxi) - self.Whi = theano.shared(Whi) - self.Wci = theano.shared(Wci) - self.bi = theano.shared(bi) - self.Wxf = theano.shared(Wxf) - self.Whf = theano.shared(Whf) - self.Wcf = theano.shared(Wcf) - self.bf = theano.shared(bf) - self.Wxc = theano.shared(Wxc) - self.Whc = theano.shared(Whc) - self.bc = theano.shared(bc) - self.Wxo = theano.shared(Wxo) - self.Who = theano.shared(Who) - self.Wco = theano.shared(Wco) - self.bo = theano.shared(bo) - self.c0 = theano.shared(c0) - self.h0 = theano.shared(h0) - self.params = [ - self.Wxi, - self.Whi, - self.Wci, - self.bi, - self.Wxf, - self.Whf, - self.Wcf, - self.bf, - self.Wxc, - self.Whc, - self.bc, - self.Wxo, - self.Who, - self.Wco, - self.bo, - self.c0, - self.h0, - ] - - def get_ht_ct(self, xWxi_t, xWxf_t, xWxc_t, xWxo_t, h_t1, c_t1): - i_t = T.nnet.sigmoid(xWxi_t + h_t1.dot(self.Whi) + c_t1.dot(self.Wci) + self.bi) - f_t = T.nnet.sigmoid(xWxf_t + h_t1.dot(self.Whf) + c_t1.dot(self.Wcf) + self.bf) - c_t = f_t * c_t1 + i_t * T.tanh(xWxc_t + h_t1.dot(self.Whc) + self.bc) - o_t = T.nnet.sigmoid(xWxo_t + h_t1.dot(self.Who) + c_t.dot(self.Wco) + self.bo) - h_t = o_t * T.tanh(c_t) - return h_t, c_t - - def recurrence(self, xWxi_t, xWxf_t, xWxc_t, xWxo_t, is_start, h_t1, c_t1, h0, c0): - h_t_c_t = T.switch( - T.eq(is_start, 1), - self.get_ht_ct(xWxi_t, xWxf_t, xWxc_t, xWxo_t, h0, c0), - self.get_ht_ct(xWxi_t, xWxf_t, xWxc_t, xWxo_t, h_t1, c_t1) - ) - return h_t_c_t[0], h_t_c_t[1] - - def output(self, Xflat, startPoints): - # Xflat should be (NT, D) - # calculate X after multiplying input weights - XWxi = Xflat.dot(self.Wxi) - XWxf = Xflat.dot(self.Wxf) - XWxc = Xflat.dot(self.Wxc) - XWxo = Xflat.dot(self.Wxo) - - [h, c], _ = theano.scan( - fn=self.recurrence, - sequences=[XWxi, XWxf, XWxc, XWxo, startPoints], - outputs_info=[self.h0, self.c0], - non_sequences=[self.h0, self.c0], - n_steps=Xflat.shape[0], - ) - return h diff --git a/rnn_class/batch_wiki.py b/rnn_class/batch_wiki.py deleted file mode 100644 index a96b0b3b..00000000 --- a/rnn_class/batch_wiki.py +++ /dev/null @@ -1,190 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python -# https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import os -import sys -import theano -import theano.tensor as T -import numpy as np -import matplotlib.pyplot as plt -import json - -from datetime import datetime -from sklearn.utils import shuffle -from batch_units import GRU, LSTM -from util import init_weight, get_wikipedia_data -from brown import get_sentences_with_word2idx_limit_vocab - - -class RNN: - def __init__(self, D, hidden_layer_sizes, V): - self.hidden_layer_sizes = hidden_layer_sizes - self.D = D - self.V = V - - def fit(self, X, learning_rate=1e-4, mu=0.99, epochs=10, batch_sz=100, show_fig=True, activation=T.nnet.relu, RecurrentUnit=LSTM): - D = self.D - V = self.V - N = len(X) - - We = init_weight(V, D) - self.hidden_layers = [] - Mi = D - for Mo in self.hidden_layer_sizes: - ru = RecurrentUnit(Mi, Mo, activation) - self.hidden_layers.append(ru) - Mi = Mo - - Wo = init_weight(Mi, V) - bo = np.zeros(V) - - self.We = theano.shared(We) - self.Wo = theano.shared(Wo) - self.bo = theano.shared(bo) - self.params = [self.We, self.Wo, self.bo] - for ru in self.hidden_layers: - self.params += ru.params - - thX = T.ivector('X') # will represent multiple batches concatenated - thY = T.ivector('Y') # represents next word - thStartPoints = T.ivector('start_points') - - Z = self.We[thX] - for ru in self.hidden_layers: - Z = ru.output(Z, thStartPoints) - py_x = T.nnet.softmax(Z.dot(self.Wo) + self.bo) - prediction = T.argmax(py_x, axis=1) - - cost = -T.mean(T.log(py_x[T.arange(thY.shape[0]), thY])) - grads = T.grad(cost, self.params) - dparams = [theano.shared(p.get_value()*0) for p in self.params] - - updates = [ - (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads) - ] + [ - (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads) - ] - - # self.predict_op = theano.function(inputs=[thX, thStartPoints], outputs=prediction) - self.train_op = theano.function( - inputs=[thX, thY, thStartPoints], - outputs=[cost, prediction], - updates=updates - ) - - costs = [] - n_batches = N // batch_sz - for i in range(epochs): - t0 = datetime.now() - X = shuffle(X) - n_correct = 0 - n_total = 0 - cost = 0 - - for j in range(n_batches): - # construct input sequence and output sequence as - # concatenatation of multiple input sequences and output sequences - # input X should be a list of 2-D arrays or one 3-D array - # N x T(n) x D - batch size x sequence length x num features - # sequence length can be variable - sequenceLengths = [] - input_sequence = [] - output_sequence = [] - for k in range(j*batch_sz, (j+1)*batch_sz): - # don't always add the end token - if np.random.random() < 0.01 or len(X[k]) <= 1: - input_sequence += [0] + X[k] - output_sequence += X[k] + [1] - sequenceLengths.append(len(X[k]) + 1) - else: - input_sequence += [0] + X[k][:-1] - output_sequence += X[k] - sequenceLengths.append(len(X[k])) - n_total += len(output_sequence) - - startPoints = np.zeros(len(output_sequence), dtype=np.int32) - last = 0 - for length in sequenceLengths: - startPoints[last] = 1 - last += length - - c, p = self.train_op(input_sequence, output_sequence, startPoints) - cost += c - for pj, xj in zip(p, output_sequence): - if pj == xj: - n_correct += 1 - if j % 1 == 0: - sys.stdout.write("j/n_batches: %d/%d correct rate so far: %f\r" % (j, n_batches, float(n_correct)/n_total)) - sys.stdout.flush() - print("i:", i, "cost:", cost, "correct rate:", (float(n_correct)/n_total), "time for epoch:", (datetime.now() - t0)) - costs.append(cost) - - if show_fig: - plt.plot(costs) - plt.show() - - - -def train_wikipedia(we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json', RecurrentUnit=GRU): - # there are 32 files - ### note: you can pick between Wikipedia data and Brown corpus - ### just comment one out, and uncomment the other! - # sentences, word2idx = get_wikipedia_data(n_files=100, n_vocab=2000) - sentences, word2idx = get_sentences_with_word2idx_limit_vocab() - print("finished retrieving data") - print("vocab size:", len(word2idx), "number of sentences:", len(sentences)) - rnn = RNN(30, [30], len(word2idx)) - rnn.fit(sentences, learning_rate=2*1e-4, epochs=10, show_fig=True, activation=T.nnet.relu) - - np.save(we_file, rnn.We.get_value()) - with open(w2i_file, 'w') as f: - json.dump(word2idx, f) - - - -def find_analogies(w1, w2, w3, we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json'): - We = np.load(we_file) - with open(w2i_file) as f: - word2idx = json.load(f) - - king = We[word2idx[w1]] - man = We[word2idx[w2]] - woman = We[word2idx[w3]] - v0 = king - man + woman - - def dist1(a, b): - return np.linalg.norm(a - b) - def dist2(a, b): - return 1 - a.dot(b) / (np.linalg.norm(a) * np.linalg.norm(b)) - - for dist, name in [(dist1, 'Euclidean'), (dist2, 'cosine')]: - min_dist = float('inf') - best_word = '' - for word, idx in iteritems(word2idx): - if word not in (w1, w2, w3): - v1 = We[idx] - d = dist(v0, v1) - if d < min_dist: - min_dist = d - best_word = word - print("closest match by", name, "distance:", best_word) - print(w1, "-", w2, "=", best_word, "-", w3) - - - -if __name__ == '__main__': - if not os.path.exists('working_files'): - os.mkdir('working_files') - we = 'working_files/batch_gru_word_embeddings.npy' - w2i = 'working_files/batch_wikipedia_word2idx.json' - train_wikipedia(we, w2i, RecurrentUnit=LSTM) - find_analogies('king', 'man', 'woman', we, w2i) - find_analogies('france', 'paris', 'london', we, w2i) - find_analogies('france', 'paris', 'rome', we, w2i) - find_analogies('paris', 'france', 'italy', we, w2i) - diff --git a/rnn_class/brown.py b/rnn_class/brown.py deleted file mode 100644 index 178a1ae0..00000000 --- a/rnn_class/brown.py +++ /dev/null @@ -1,114 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python -# https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -from nltk.corpus import brown -import operator - -KEEP_WORDS = set([ - 'king', 'man', 'queen', 'woman', - 'italy', 'rome', 'france', 'paris', - 'london', 'britain', 'england', -]) - - -def get_sentences(): - # returns 57340 of the Brown corpus - # each sentence is represented as a list of individual string tokens - return brown.sents() - - -def get_sentences_with_word2idx(): - sentences = get_sentences() - indexed_sentences = [] - - i = 2 - word2idx = {'START': 0, 'END': 1} - for sentence in sentences: - indexed_sentence = [] - for token in sentence: - token = token.lower() - if token not in word2idx: - word2idx[token] = i - i += 1 - - indexed_sentence.append(word2idx[token]) - indexed_sentences.append(indexed_sentence) - - print("Vocab size:", i) - return indexed_sentences, word2idx - - -def get_sentences_with_word2idx_limit_vocab(n_vocab=2000, keep_words=KEEP_WORDS): - sentences = get_sentences() - indexed_sentences = [] - - i = 2 - word2idx = {'START': 0, 'END': 1} - idx2word = ['START', 'END'] - - word_idx_count = { - 0: float('inf'), - 1: float('inf'), - } - - for sentence in sentences: - indexed_sentence = [] - for token in sentence: - token = token.lower() - if token not in word2idx: - idx2word.append(token) - word2idx[token] = i - i += 1 - - # keep track of counts for later sorting - idx = word2idx[token] - word_idx_count[idx] = word_idx_count.get(idx, 0) + 1 - - indexed_sentence.append(idx) - indexed_sentences.append(indexed_sentence) - - - - # restrict vocab size - - # set all the words I want to keep to infinity - # so that they are included when I pick the most - # common words - for word in keep_words: - word_idx_count[word2idx[word]] = float('inf') - - sorted_word_idx_count = sorted(word_idx_count.items(), key=operator.itemgetter(1), reverse=True) - word2idx_small = {} - new_idx = 0 - idx_new_idx_map = {} - for idx, count in sorted_word_idx_count[:n_vocab]: - word = idx2word[idx] - print(word, count) - word2idx_small[word] = new_idx - idx_new_idx_map[idx] = new_idx - new_idx += 1 - # let 'unknown' be the last token - word2idx_small['UNKNOWN'] = new_idx - unknown = new_idx - - assert('START' in word2idx_small) - assert('END' in word2idx_small) - for word in keep_words: - assert(word in word2idx_small) - - # map old idx to new idx - sentences_small = [] - for sentence in indexed_sentences: - if len(sentence) > 1: - new_sentence = [idx_new_idx_map[idx] if idx in idx_new_idx_map else unknown for idx in sentence] - sentences_small.append(new_sentence) - - return sentences_small, word2idx_small - - - diff --git a/rnn_class/exercises.txt b/rnn_class/exercises.txt deleted file mode 100644 index 612eea3e..00000000 --- a/rnn_class/exercises.txt +++ /dev/null @@ -1,19 +0,0 @@ -Logistic Regression -https://www.kaggle.com/uciml/pima-indians-diabetes-database -https://lazyprogrammer.me/course_files/exercises/diabetes.csv - -Linear Regression -https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html -https://lazyprogrammer.me/course_files/exercises/boston.txt - -ANN -https://archive.ics.uci.edu/ml/datasets/ecoli (orig) -https://www.kaggle.com/elikplim/ecoli-data-set (alt) -https://lazyprogrammer.me/course_files/exercises/ecoli.csv - -RNN -Find your own stock price dataset! - -NLP -https://www.kaggle.com/crowdflower/twitter-airline-sentiment -https://lazyprogrammer.me/course_files/exercises/AirlineSentimentTweets.csv \ No newline at end of file diff --git a/rnn_class/extra_reading.txt b/rnn_class/extra_reading.txt deleted file mode 100644 index 89052713..00000000 --- a/rnn_class/extra_reading.txt +++ /dev/null @@ -1,2 +0,0 @@ -Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling, Chung 2014 -https://arxiv.org/pdf/1412.3555v1.pdf \ No newline at end of file diff --git a/rnn_class/gru.py b/rnn_class/gru.py index 7c7ecf6a..198f4378 100644 --- a/rnn_class/gru.py +++ b/rnn_class/gru.py @@ -1,11 +1,4 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python # https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import theano import theano.tensor as T diff --git a/rnn_class/lstm.py b/rnn_class/lstm.py index 90525da2..a5861e84 100644 --- a/rnn_class/lstm.py +++ b/rnn_class/lstm.py @@ -1,10 +1,4 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python -# https://udemy.com/deep-learning-recurrent-neural-networks-in-pythonfrom __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - +# https://udemy.com/deep-learning-recurrent-neural-networks-in-python import numpy as np import theano import theano.tensor as T diff --git a/rnn_class/lstm_wiki.py b/rnn_class/lstm_wiki.py new file mode 100644 index 00000000..946993dc --- /dev/null +++ b/rnn_class/lstm_wiki.py @@ -0,0 +1,113 @@ +# OLD - use wiki.py instead +import json +import numpy as np +import theano +import theano.tensor as T + +from util import init_weight, get_wikipedia_data +from gru_wiki import RNN, find_analogies + +class LSTM: + def __init__(self, Mi, Mo, activation): + self.Mi = Mi + self.Mo = Mo + self.f = activation + + # numpy init + Wxi = init_weight(Mi, Mo) + Whi = init_weight(Mo, Mo) + Wci = init_weight(Mo, Mo) + bi = np.zeros(Mo) + Wxf = init_weight(Mi, Mo) + Whf = init_weight(Mo, Mo) + Wcf = init_weight(Mo, Mo) + bf = np.zeros(Mo) + Wxc = init_weight(Mi, Mo) + Whc = init_weight(Mo, Mo) + bc = np.zeros(Mo) + Wxo = init_weight(Mi, Mo) + Who = init_weight(Mo, Mo) + Wco = init_weight(Mo, Mo) + bo = np.zeros(Mo) + c0 = np.zeros(Mo) + h0 = np.zeros(Mo) + + # theano vars + self.Wxi = theano.shared(Wxi) + self.Whi = theano.shared(Whi) + self.Wci = theano.shared(Wci) + self.bi = theano.shared(bi) + self.Wxf = theano.shared(Wxf) + self.Whf = theano.shared(Whf) + self.Wcf = theano.shared(Wcf) + self.bf = theano.shared(bf) + self.Wxc = theano.shared(Wxc) + self.Whc = theano.shared(Whc) + self.bc = theano.shared(bc) + self.Wxo = theano.shared(Wxo) + self.Who = theano.shared(Who) + self.Wco = theano.shared(Wco) + self.bo = theano.shared(bo) + self.c0 = theano.shared(c0) + self.h0 = theano.shared(h0) + self.params = [ + self.Wxi, + self.Whi, + self.Wci, + self.bi, + self.Wxf, + self.Whf, + self.Wcf, + self.bf, + self.Wxc, + self.Whc, + self.bc, + self.Wxo, + self.Who, + self.Wco, + self.bo, + self.c0, + self.h0, + ] + + def recurrence(self, x_t, h_t1, c_t1): + i_t = T.nnet.sigmoid(x_t.dot(self.Wxi) + h_t1.dot(self.Whi) + c_t1.dot(self.Wci) + self.bi) + f_t = T.nnet.sigmoid(x_t.dot(self.Wxf) + h_t1.dot(self.Whf) + c_t1.dot(self.Wcf) + self.bf) + c_t = f_t * c_t1 + i_t * T.tanh(x_t.dot(self.Wxc) + h_t1.dot(self.Whc) + self.bc) + o_t = T.nnet.sigmoid(x_t.dot(self.Wxo) + h_t1.dot(self.Who) + c_t.dot(self.Wco) + self.bo) + h_t = o_t * T.tanh(c_t) + return h_t, c_t + + def output(self, x): + # input X should be a matrix (2-D) + # rows index time + [h, c], _ = theano.scan( + fn=self.recurrence, + sequences=x, + outputs_info=[self.h0, self.c0], + n_steps=x.shape[0], + ) + return h + + +def train_wikipedia(we_file='lstm_word_embeddings.npy', w2i_file='lstm_wikipedia_word2idx.json'): + # there are 32 files + sentences, word2idx = get_wikipedia_data(n_files=100, n_vocab=2000) + print "finished retrieving data" + print "vocab size:", len(word2idx), "number of sentences:", len(sentences) + rnn = RNN(50, [50], len(word2idx)) + # todo: next try increas LR + rnn.fit(sentences, learning_rate=10e-6, epochs=10, show_fig=True, activation=T.nnet.relu, RecurrentUnit=LSTM, normalize=False) + + np.save(we_file, rnn.We.get_value()) + with open(w2i_file, 'w') as f: + json.dump(word2idx, f) + + +if __name__ == '__main__': + train_wikipedia() + find_analogies('king', 'man', 'woman', 'lstm_word_embeddings.npy', 'lstm_wikipedia_word2idx.json') + find_analogies('france', 'paris', 'london', 'lstm_word_embeddings.npy', 'lstm_wikipedia_word2idx.json') + find_analogies('france', 'paris', 'rome', 'lstm_word_embeddings.npy', 'lstm_wikipedia_word2idx.json') + find_analogies('paris', 'france', 'italy', 'lstm_word_embeddings.npy', 'lstm_wikipedia_word2idx.json') + diff --git a/rnn_class/mlp_parity.py b/rnn_class/mlp_parity.py index 1f4d6748..fe796c01 100644 --- a/rnn_class/mlp_parity.py +++ b/rnn_class/mlp_parity.py @@ -1,11 +1,4 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python # https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import theano import theano.tensor as T @@ -34,7 +27,7 @@ class ANN(object): def __init__(self, hidden_layer_sizes): self.hidden_layer_sizes = hidden_layer_sizes - def fit(self, X, Y, learning_rate=1e-2, mu=0.99, reg=1e-12, epochs=400, batch_sz=20, print_period=1, show_fig=False): + def fit(self, X, Y, learning_rate=10e-3, mu=0.99, reg=10e-12, eps=10e-10, epochs=400, batch_sz=20, print_period=1, show_fig=False): # X = X.astype(np.float32) Y = Y.astype(np.int32) @@ -89,11 +82,13 @@ def fit(self, X, Y, learning_rate=1e-2, mu=0.99, reg=1e-12, epochs=400, batch_sz updates=updates, ) - n_batches = N // batch_sz + n_batches = N / batch_sz + # print "N:", N, "batch_sz:", batch_sz + # print "n_batches:", n_batches costs = [] - for i in range(epochs): + for i in xrange(epochs): X, Y = shuffle(X, Y) - for j in range(n_batches): + for j in xrange(n_batches): Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)] Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)] @@ -102,7 +97,7 @@ def fit(self, X, Y, learning_rate=1e-2, mu=0.99, reg=1e-12, epochs=400, batch_sz if j % print_period == 0: costs.append(c) e = np.mean(Ybatch != p) - print("i:", i, "j:", j, "nb:", n_batches, "cost:", c, "error rate:", e) + print "i:", i, "j:", j, "nb:", n_batches, "cost:", c, "error rate:", e if show_fig: plt.plot(costs) @@ -122,13 +117,13 @@ def predict(self, X): def wide(): X, Y = all_parity_pairs(12) model = ANN([2048]) - model.fit(X, Y, learning_rate=1e-4, print_period=10, epochs=300, show_fig=True) + model.fit(X, Y, learning_rate=10e-5, print_period=10, epochs=300, show_fig=True) def deep(): # Challenge - find a deeper, slimmer network to solve the problem X, Y = all_parity_pairs(12) model = ANN([1024]*2) - model.fit(X, Y, learning_rate=1e-3, print_period=10, epochs=100, show_fig=True) + model.fit(X, Y, learning_rate=10e-4, print_period=10, epochs=100, show_fig=True) if __name__ == '__main__': wide() diff --git a/rnn_class/poetry_classifier.py b/rnn_class/poetry_classifier.py index 81fbf77c..e9a4e6db 100644 --- a/rnn_class/poetry_classifier.py +++ b/rnn_class/poetry_classifier.py @@ -1,11 +1,4 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python # https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import theano import theano.tensor as T import numpy as np @@ -20,11 +13,11 @@ def __init__(self, M, V): self.M = M # hidden layer size self.V = V # vocabulary size - def fit(self, X, Y, learning_rate=1.0, mu=0.99, reg=1.0, activation=T.tanh, epochs=500, show_fig=False): + def fit(self, X, Y, learning_rate=10e-1, mu=0.99, reg=1.0, activation=T.tanh, epochs=500, show_fig=False): M = self.M V = self.V K = len(set(Y)) - print("V:", V) + print "V:", V X, Y = shuffle(X, Y) Nvalid = 10 @@ -61,11 +54,11 @@ def fit(self, X, Y, learning_rate=1.0, mu=0.99, reg=1.0, activation=T.tanh, epoc ) costs = [] - for i in range(epochs): + for i in xrange(epochs): X, Y = shuffle(X, Y) n_correct = 0 cost = 0 - for j in range(N): + for j in xrange(N): # we set 0 to start and 1 to end # print "X[%d]:" % j, X[j], "len:", len(X[j]) c, p = self.train_op(X[j], Y[j], learning_rate) @@ -78,12 +71,12 @@ def fit(self, X, Y, learning_rate=1.0, mu=0.99, reg=1.0, activation=T.tanh, epoc # calculate validation accuracy n_correct_valid = 0 - for j in range(Nvalid): + for j in xrange(Nvalid): p = self.predict_op(Xvalid[j]) if p == Yvalid[j]: n_correct_valid += 1 - print("i:", i, "cost:", cost, "correct rate:", (float(n_correct)/N), end=" ") - print("validation correct rate:", (float(n_correct_valid)/Nvalid)) + print "i:", i, "cost:", cost, "correct rate:", (float(n_correct)/N), + print "validation correct rate:", (float(n_correct_valid)/Nvalid) costs.append(cost) if show_fig: @@ -149,7 +142,7 @@ def recurrence(x_t, h_t1): def train_poetry(): X, Y, V = get_poetry_classifier_data(samples_per_class=500) rnn = SimpleRNN(30, V) - rnn.fit(X, Y, learning_rate=1e-6, show_fig=True, activation=T.nnet.relu, epochs=1000) + rnn.fit(X, Y, learning_rate=10e-7, show_fig=True, activation=T.nnet.relu, epochs=1000) if __name__ == '__main__': train_poetry() diff --git a/rnn_class/renet.py b/rnn_class/renet.py new file mode 100644 index 00000000..9424cfa3 --- /dev/null +++ b/rnn_class/renet.py @@ -0,0 +1,558 @@ +# https://udemy.com/deep-learning-recurrent-neural-networks-in-python +import os +import gzip +import cPickle +import urllib2 +import numpy as np +import pandas as pd +import theano +import theano.tensor as T +import matplotlib.pyplot as plt + +from theano.tensor.nnet import conv2d +from theano.tensor.signal import downsample + +from sklearn.utils import shuffle + +from datetime import datetime + +# TODO: add LSTM +# NOTE: batch training is slow to compile, do it better with slicing +# TODO: use only Ytest in cost instead of indicator +# TODO: multiply X with W replicated so that X.dot(W) is not inside the scan loop + +def error_rate(p, t): + return np.mean(p != t) + + +def relu(a): + return a * (a > 0) + + +def y2indicator(y): + N = len(y) + ind = np.zeros((N, 10), dtype='int32') + for i in xrange(N): + ind[i, y[i]] = 1 + return ind + + +def init_filter(shape): + w = np.random.randn(*shape) / np.sqrt(sum(shape)) + return w.astype(np.float32) + + +def rearrange(X): + N = len(X) + out = np.zeros((N, 1, 28, 28), dtype=np.float32) + for i in xrange(N): + out[i, 0, :, :] = X[i].reshape(28, 28) + return out / 255 + + +class GRU(object): + def __init__(self, name, wp, hp, C, M): + Wx_shape = (M, C*wp*hp) + + Wxr_init = init_filter(Wx_shape) + Whr_init = init_filter((M, M)) + br_init = np.zeros((M,), dtype=np.float32) + + Wxz_init = init_filter(Wx_shape) + Whz_init = init_filter((M, M)) + bz_init = np.zeros((M,), dtype=np.float32) + + Wxh_init = init_filter(Wx_shape) + Whh_init = init_filter((M, M) ) + bh_init = np.zeros((M,), dtype=np.float32) + + H0_init = init_filter((M,)) + # --- + self.Wxr = theano.shared(Wxr_init, 'Wxr_%s' % name) + self.Whr = theano.shared(Whr_init, 'Whr_%s' % name) + self.br = theano.shared(br_init, 'br_%s' % name) + + self.Wxz = theano.shared(Wxz_init, 'Wxz_%s' % name) + self.Whz = theano.shared(Whz_init, 'Whz_%s' % name) + self.bz = theano.shared(bz_init, 'bz_%s' % name) + + self.Wxh = theano.shared(Wxh_init, 'Wxh_%s' % name) + self.Whh = theano.shared(Whh_init, 'Whh_%s' % name) + self.bh = theano.shared(bh_init, 'bh_%s' % name) + + self.H0 = theano.shared(H0_init, 'H0_%s' % name) + # --- + self.params = [self.Wxr, self.Whr, self.br, self.Wxz, self.Whz, self.bz, self.Wxh, self.Whh, self.bh, self.H0] + + def recurrence(self, x_t, h_tm1): + r_t = T.nnet.sigmoid( T.dot(self.Wxr, x_t) + T.dot(h_tm1, self.Whr) + self.br) + z_t = T.nnet.sigmoid( T.dot(self.Wxz, x_t) + T.dot(h_tm1, self.Whz) + self.bz) + hht = relu( T.dot(self.Wxh, x_t) + T.dot( r_t*h_tm1, self.Whh ) + self.bh) + h_t = (1 - z_t)*h_tm1 + z_t*hht + return h_t + + def output(self, x, go_backwards=False): + # input X should be a matrix (2-D) + # rows index time + h, _ = theano.scan( + fn=self.recurrence, + sequences=x, + outputs_info=[self.H0], + n_steps=x.shape[0], + go_backwards=go_backwards + ) + return h + + +class RNNUnit(object): + def __init__(self, name, wp, hp, C, M): + # C = num input feature maps + # M = num output feature maps + # print "NAME:", name + Wx_shape = (M, C*wp*hp) + Wx_init = init_filter(Wx_shape) + Wh_init = init_filter( (M, M) ) + bh_init = np.zeros((M,), dtype=np.float32) + H0_init = init_filter( (M,) ) + + self.Wx = theano.shared(Wx_init, 'Wx_%s' % name) + self.Wh = theano.shared(Wh_init, 'Wh_%s' % name) + self.bh = theano.shared(bh_init, 'bh_%s' % name) + self.H0 = theano.shared(H0_init, 'H0_%s' % name) + self.params = [self.Wx, self.Wh, self.bh, self.H0] + + def recurrence(self, x_t, h_tm1): + dot = T.dot(self.Wx, x_t) + h_t = relu(dot + T.dot(h_tm1, self.Wh) + self.bh) + return h_t + + def output(self, x, go_backwards=False): + # input X should be a matrix (2-D) + # rows index time + h, _ = theano.scan( + fn=self.recurrence, + sequences=x, + outputs_info=[self.H0], + n_steps=x.shape[0], + go_backwards=go_backwards, + # non_sequences=self.params, + # strict=True, + ) + return h + + +def renet_layer_lr_noscan(X, rnn1, rnn2, w, h, wp, hp): + list_of_images = [] + for i in xrange(h/hp): + # x = X[:,i*hp:(i*hp + hp),:].dimshuffle((2, 0, 1)).flatten().reshape((w/wp, X.shape[0]*wp*hp)) + h_tm1 = rnn1.H0 + hr_tm1 = rnn2.H0 + h1 = [] + h2 = [] + for j in xrange(w/wp): + x = X[:,i*hp:(i*hp + hp),j*wp:(j*wp + wp)].flatten() + h_t = rnn1.recurrence(x, h_tm1) + h1.append(h_t) + h_tm1 = h_t + + jr = w/wp - j - 1 + xr = X[:,i*hp:(i*hp + hp),jr*wp:(jr*wp + wp)].flatten() + hr_t = rnn2.recurrence(x, hr_tm1) + h2.append(hr_t) + hr_tm1 = hr_t + img = T.concatenate([h1, h2]) + list_of_images.append(img) + return T.stacklists(list_of_images).dimshuffle((1, 0, 2)) + + +def renet_layer_lr_allscan(X, rnn1, rnn2, w, h, wp, hp): + # list_of_images = [] + C = X.shape[0] + X = X.dimshuffle((1, 0, 2)).reshape((h/hp, hp*C*w)) # split the rows for the first scan + def rnn_pass(x): + x = x.reshape((hp, C, w)).dimshuffle((2, 1, 0)).reshape((w/wp, C*wp*hp)) + h1 = rnn1.output(x) + h2 = rnn2.output(x, go_backwards=True) + img = T.concatenate([h1.T, h2.T]) + # list_of_images.append(img) + return img + + results, _ = theano.scan( + fn=rnn_pass, + sequences=X, + outputs_info=None, + n_steps=h/hp, + ) + return results.dimshuffle((1, 0, 2)) + # return T.stacklists(list_of_images).dimshuffle((1, 0, 2)) + + +def renet_layer_ud_allscan(X, rnn1, rnn2, w, h, wp, hp): + return renet_layer_lr_allscan(X.dimshuffle((0, 2, 1)), rnn1, rnn2, w, h, wp, hp) + + +# expect the input image to be K x width x height +# def renet_layer_lr(X, Wx1, Wh1, Bh1, H01, Wx2, Wh2, Bh2, H02, w, h, wp, hp): +def renet_layer_lr(X, rnn1, rnn2, w, h, wp, hp): + # def recurrence1(x_t, h_tm1): + # dot = T.dot(Wx1, x_t) + # h_t = relu(dot + T.dot(h_tm1, Wh1) + Bh1) + # return h_t + + # def recurrence2(x_t, h_tm1): + # dot = T.dot(Wx2, x_t) + # h_t = relu(dot + T.dot(h_tm1, Wh2) + Bh2) + # return h_t + + list_of_images = [] + # lefts = [] + # rights = [] + for i in xrange(h/hp): + x = X[:,i*hp:(i*hp + hp),:].dimshuffle((2, 0, 1)).flatten().reshape((w/wp, X.shape[0]*wp*hp)) + # reshape the row into a 2-D matrix to be fed into scan + # h1, _ = theano.scan( + # fn=recurrence1, + # sequences=x, + # outputs_info=[H01], + # n_steps=x.shape[0] + # ) + # h2, _ = theano.scan( + # fn=recurrence2, + # sequences=x, + # outputs_info=[H02], + # n_steps=x.shape[0], + # go_backwards=True + # ) + h1 = rnn1.output(x) + h2 = rnn2.output(x, go_backwards=True) + + # combine the last values of s1 and s2 into an image + img = T.concatenate([h1.T, h2.T]) + list_of_images.append(img) + # lefts.append(s1.T) + # rights.append(s2.T) + + return T.stacklists(list_of_images).dimshuffle((1, 0, 2)) + + +def renet_layer_ud(X, rnn1, rnn2, w, h, wp, hp): + # def recurrence1(x_t, h_tm1): + # dot = T.dot(Wx1, x_t) + # h_t = relu(dot + T.dot(h_tm1, Wh1) + Bh1) + # return h_t + # def recurrence2(x_t, h_tm1): + # dot = T.dot(Wx2, x_t) + # h_t = relu(dot + T.dot(h_tm1, Wh2) + Bh2) + # return h_t + + list_of_images = [] + for j in xrange(w/wp): + # x = X[:,:,j*wp:(j*wp + wp)].dimshuffle((2, 0, 1)).flatten(ndim=2) + # reshape the row into a 2-D matrix to be fed into scan + x = X[:,:,j*wp:(j*wp + wp)].dimshuffle((2, 0, 1)).flatten().reshape((h/hp, X.shape[0]*wp*hp)) + # h1, _ = theano.scan( + # fn=recurrence1, + # sequences=x, + # outputs_info=[H01], + # n_steps=x.shape[0] + # ) + # h2, _ = theano.scan( + # fn=recurrence2, + # sequences=x, + # outputs_info=[H02], + # n_steps=x.shape[0], + # go_backwards=True + # ) + h1 = rnn1.output(x) + h2 = rnn2.output(x, go_backwards=True) + # combine the last values of s1 and s2 into an image + img = T.concatenate([h1.T, h2.T]) + list_of_images.append(img) + + return T.stacklists(list_of_images).dimshuffle((1, 0, 2)) + + +def getKaggleMNIST(): + # MNIST data: + # column 0 is labels + # column 1-785 is data, with values 0 .. 255 + # total size of CSV: (42000, 1, 28, 28) + train = pd.read_csv('../large_files/train.csv').as_matrix() + train = shuffle(train) + + Xtrain = rearrange( train[:-1000,1:] ) + Ytrain = train[:-1000,0] + Ytrain_ind = y2indicator(Ytrain) + + Xtest = rearrange( train[-1000:,1:] ) + Ytest = train[-1000:,0] + Ytest_ind = y2indicator(Ytest) + return Xtrain, Ytrain, Ytrain_ind, Xtest, Ytest, Ytest_ind + + +def getMNIST(): + # data shape: train (50000, 784), test (10000, 784) + # already scaled from 0..1 and converted to float32 + datadir = '../large_files/' + if not os.path.exists(datadir): + datadir = '' + + input_file = "%smnist.pkl.gz" % datadir + if not os.path.exists(input_file): + url = 'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz' + with open(input_file, "wb") as out: + f = urllib2.urlopen(url) + out.write(f.read()) + out.flush() + + with gzip.open(input_file) as f: + train, valid, test = cPickle.load(f) + + Xtrain, Ytrain = train + Xvalid, Yvalid = valid + Xtest, Ytest = test + + Ytrain_ind = y2indicator(Ytrain) + Ytest_ind = y2indicator(Ytest) + + Xtrain, Ytrain = shuffle(Xtrain, Ytrain) + Xtest, Ytest = shuffle(Xtest, Ytest) + + # try to take a smaller sample + Xtrain = Xtrain[0:30000] + Ytrain = Ytrain[0:30000] + Xtest = Xtest[0:1000] + Ytest = Ytest[0:1000] + + return Xtrain.reshape(len(Xtrain), 1, 28, 28), Ytrain, Ytrain_ind, Xtest.reshape(len(Xtest), 1, 28, 28), Ytest, Ytest_ind + + +def main(ReUnit=RNNUnit, getData=getMNIST): + t0 = datetime.now() + print "Start time:", t0 + + Xtrain, Ytrain, Ytrain_ind, Xtest, Ytest, Ytest_ind = getData() + + max_iter = 8 + print_period = 200 + + lr = np.float32(0.05) + reg = np.float32(0.0001) + mu = np.float32(0.99) + + N = Xtrain.shape[0] + C = Xtrain.shape[1] + M = 300 + K = 10 + + batch_sz = 1 + n_batches = N / batch_sz + + M1 = 256 # num feature maps + rnn1 = ReUnit('1', 2, 2, C, M1) + rnn2 = ReUnit('2', 2, 2, C, M1) + + M2 = 256 # num feature maps + rnn3 = ReUnit('3', 1, 1, 2*M1, M2) + rnn4 = ReUnit('4', 1, 1, 2*M1, M2) + + M3 = 64 + rnn5 = ReUnit('5', 2, 2, 2*M2, M3) + rnn6 = ReUnit('6', 2, 2, 2*M2, M3) + + M4 = 64 + rnn7 = ReUnit('7', 1, 1, 2*M3, M4) + rnn8 = ReUnit('8', 1, 1, 2*M3, M4) + + print "Finished creating rnn objects, elapsed time:", (datetime.now() - t0) + + + # vanilla ANN weights + W9_init = np.random.randn(2*M4*7*7, M) / np.sqrt(2*M4*7*7 + M) + b9_init = np.zeros(M, dtype=np.float32) + W10_init = np.random.randn(M, K) / np.sqrt(M + K) + b10_init = np.zeros(K, dtype=np.float32) + + + # step 2: define theano variables and expressions + X = T.tensor4('X', dtype='float32') + x = T.tensor3('x', dtype='float32') + Y = T.matrix('T') + + W9 = theano.shared(W9_init.astype(np.float32), 'W9') + b9 = theano.shared(b9_init, 'b9') + W10 = theano.shared(W10_init.astype(np.float32), 'W10') + b10 = theano.shared(b10_init, 'b10') + params = [W9, b9, W10, b10] + for rnn in (rnn1, rnn2, rnn3, rnn4, rnn5, rnn6, rnn7, rnn8): + params += rnn.params + + + print "Finished creating all shared vars, elapsed time:", (datetime.now() - t0) + # momentum changes + # dW1 = theano.shared(np.zeros(W1_init.shape, dtype=np.float32), 'dW1') + # db1 = theano.shared(np.zeros(b1_init.shape, dtype=np.float32), 'db1') + # dW2 = theano.shared(np.zeros(W2_init.shape, dtype=np.float32), 'dW2') + # db2 = theano.shared(np.zeros(b2_init.shape, dtype=np.float32), 'db2') + # dW3 = theano.shared(np.zeros(W3_init.shape, dtype=np.float32), 'dW3') + # db3 = theano.shared(np.zeros(b3_init.shape, dtype=np.float32), 'db3') + # dW4 = theano.shared(np.zeros(W4_init.shape, dtype=np.float32), 'dW4') + # db4 = theano.shared(np.zeros(b4_init.shape, dtype=np.float32), 'db4') + + # Z_tmp = renet_layer_lr_allscan(x, rnn1, rnn2, 28, 28, 2, 2) + # # Z_tmp = renet_layer_lr_noscan(x, rnn1, rnn2, 28, 28, 2, 2) + # tmp_op = theano.function( + # inputs=[x], + # outputs=Z_tmp, + # ) + # print "Xtrain[0].shape:", Xtrain[0].shape + # out = tmp_op(Xtrain[0]) + # print "Z_tmp.shape:", out.shape + # exit() + + def forward(x): + # x = args[0] + # forward pass + Z1 = renet_layer_lr_allscan(x, rnn1, rnn2, 28, 28, 2, 2) + Z2 = renet_layer_ud_allscan(Z1, rnn3, rnn4, 14, 14, 1, 1) + Z3 = renet_layer_lr_allscan(Z2, rnn5, rnn6, 14, 14, 2, 2) + Z4 = renet_layer_ud_allscan(Z3, rnn7, rnn8, 7, 7, 1, 1) + Z5 = relu(Z4.flatten().dot(W9) + b9) + pY = T.nnet.softmax( Z5.dot(W10) + b10) + return pY + + if True: #batch_sz > 1: + batch_forward_out3, _ = theano.scan( + fn=forward, + sequences=X, + # outputs_info=[self.H0], + n_steps=X.shape[0], + # non_sequences=params, + # strict=True, + ) + else: + batch_forward_out3 = forward(X[0]) + + print "Finished creating output scan, elapsed time:", (datetime.now() - t0) + batch_forward_out = batch_forward_out3.flatten(ndim=2) # the output will be (N, 1, 10) + + print "Finished reshaping output, elapsed time:", (datetime.now() - t0) + + ## TMP: just test the first/second layer ## + # tmp_op = theano.function( + # inputs=[X], + # outputs=Z1, + # ) + # print "Xtrain[0].shape:", Xtrain[0].shape + # out = tmp_op(Xtrain[0]) + # print "Z1.shape:", out.shape + # exit() + + + + # tmp_op2 = theano.function( + # inputs=[X], + # outputs=Z2, + # ) + # out = tmp_op2(Xtrain[0]) + # print "Z2.shape:", out.shape + # exit() + + + + # tmp_op3 = theano.function( + # inputs=[X], + # outputs=Z3, + # ) + # out = tmp_op3(Xtrain[0]) + # print "Z3.shape:", out.shape + # exit() + + + + # tmp_op4 = theano.function( + # inputs=[X], + # outputs=Z4, + # ) + # out = tmp_op4(Xtrain[0]) + # print "Z4.shape:", out.shape + # exit() + + # tmp_op_out = theano.function(inputs=[X], outputs=batch_forward_out) + # out = tmp_op_out(Xtest[0:50,]) + # print "out.shape:", out.shape + # exit() + + # define the cost function and prediction + # params = (W1, b1, W2, b2, W3, b3, W4, b4) + reg_cost = reg*np.sum((param*param).sum() for param in params) + cost = -(Y * T.log(batch_forward_out)).sum() + reg_cost + prediction = T.argmax(batch_forward_out, axis=1) + + # step 3: training expressions and functions + updates = [(param, param - lr*T.grad(cost, param)) for param in params] + + print "Finished creating update expressions, elapsed time:", (datetime.now() - t0) + + # update weight changes + # update_dW1 = mu*dW1 - lr*T.grad(cost, W1) + # update_db1 = mu*db1 - lr*T.grad(cost, b1) + # update_dW2 = mu*dW2 - lr*T.grad(cost, W2) + # update_db2 = mu*db2 - lr*T.grad(cost, b2) + # update_dW3 = mu*dW3 - lr*T.grad(cost, W3) + # update_db3 = mu*db3 - lr*T.grad(cost, b3) + # update_dW4 = mu*dW4 - lr*T.grad(cost, W4) + # update_db4 = mu*db4 - lr*T.grad(cost, b4) + + train = theano.function( + inputs=[X, Y], + updates=updates, + allow_input_downcast=True, + ) + + # create another function for this because we want it over the whole dataset + get_prediction = theano.function( + inputs=[X, Y], + outputs=[cost, prediction], + allow_input_downcast=True, + ) + + print "Setup elapsed time:", (datetime.now() - t0) + + # test it + # print get_prediction(Xtest, Ytest_ind) + # exit() + + t0 = datetime.now() + LL = [] + t1 = t0 + for i in xrange(max_iter): + print "i:", i + for j in xrange(n_batches): + # print "j:", j + Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),:] + Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),:] + + train(Xbatch, Ybatch) + if j % print_period == 0: + cost_val, prediction_val = get_prediction(Xtest, Ytest_ind) + # cost_val = 0 + # prediction_val = np.zeros(len(Ytest)) + # for k in xrange(len(Ytest)): + # c, p = get_prediction(Xtest[k], Ytest_ind[k:k+1,:]) + # cost_val += c + # prediction_val[k] = p[0] + # # print "pred:", p[0], type(p[0]), "target:", Ytest[k], type(Ytest[k]) + err = error_rate(prediction_val, Ytest) + print "Cost / err at iteration i=%d, j=%d: %.3f / %.2f" % (i, j, cost_val / len(Ytest), err) + t2 = datetime.now() + print "Time since last print:", (t2 - t1) + t1 = t2 + LL.append(cost_val) + print "Elapsed time:", (datetime.now() - t0) + plt.plot(LL) + plt.show() + + +if __name__ == '__main__': + main() diff --git a/rnn_class/renet_mnist.py b/rnn_class/renet_mnist.py new file mode 100644 index 00000000..3fe3489e --- /dev/null +++ b/rnn_class/renet_mnist.py @@ -0,0 +1,350 @@ +# https://udemy.com/deep-learning-recurrent-neural-networks-in-python +import numpy as np +import pandas as pd +import theano +import theano.tensor as T +import matplotlib.pyplot as plt + +from theano.tensor.nnet import conv2d +from theano.tensor.signal import downsample + +from sklearn.utils import shuffle + +from datetime import datetime + + +def error_rate(p, t): + return np.mean(p != t) + + +def relu(a): + return a * (a > 0) + + +def y2indicator(y): + N = len(y) + ind = np.zeros((N, 10)) + for i in xrange(N): + ind[i, y[i]] = 1 + return ind + + +def init_filter(shape): + w = np.random.randn(*shape) / np.sqrt(sum(shape)) + return w.astype(np.float32) + + +def rearrange(X): + N = len(X) + out = np.zeros((N, 1, 28, 28), dtype=np.float32) + for i in xrange(N): + out[i, 0, :, :] = X[i].reshape(28, 28) + return out / 255 + + +# expect the input image to be K x width x height +def renet_layer_lr(X, Wx, Wh, Wo, Bh, Bo, H0, w, h, wp, hp): + def recurrence(x_t, h_tm1): + dot = T.dot(Wx, x_t) + h_t = T.tanh(dot + T.dot(h_tm1, Wh) + Bh) + s_t = T.tanh(T.dot(h_t, Wo) + Bo) + return [h_t, s_t] + + list_of_images = [] + # lefts = [] + # rights = [] + for i in xrange(h/hp): + x = X[:,i*hp:(i*hp + hp),:].dimshuffle((1, 0, 2)).flatten().reshape((w/wp, X.shape[0]*wp*hp)) + # reshape the row into a 2-D matrix to be fed into scan + [h1, s1], _ = theano.scan( + fn=recurrence, + sequences=x, + outputs_info=[H0, None], + n_steps=x.shape[0] + ) + [h2, s2], _ = theano.scan( + fn=recurrence, + sequences=x, + outputs_info=[H0, None], + n_steps=x.shape[0], + go_backwards=True + ) + # combine the last values of s1 and s2 into an image + img = T.concatenate([s1.T, s2.T]) + list_of_images.append(img) + # lefts.append(s1.T) + # rights.append(s2.T) + + return T.stacklists(list_of_images).dimshuffle((1, 0, 2)) + + +def renet_layer_ud(X, Wx, Wh, Wo, Bh, Bo, H0, w, h, wp, hp): + def recurrence(x_t, h_tm1): + dot = T.dot(Wx, x_t) + h_t = T.tanh(dot + T.dot(h_tm1, Wh) + Bh) + s_t = T.tanh(T.dot(h_t, Wo) + Bo) + return [h_t, s_t] + + list_of_images = [] + for j in xrange(w/wp): + # x = X[:,:,j*wp:(j*wp + wp)].dimshuffle((2, 0, 1)).flatten(ndim=2) + # reshape the row into a 2-D matrix to be fed into scan + x = X[:,:,j*wp:(j*wp + wp)].dimshuffle((2, 0, 1)).flatten().reshape((h/hp, X.shape[0]*wp*hp)) + [h1, s1], _ = theano.scan( + fn=recurrence, + sequences=x, + outputs_info=[H0, None], + n_steps=x.shape[0] + ) + [h2, s2], _ = theano.scan( + fn=recurrence, + sequences=x, + outputs_info=[H0, None], + n_steps=x.shape[0], + go_backwards=True + ) + # combine the last values of s1 and s2 into an image + img = T.concatenate([s1.T, s2.T]) + list_of_images.append(img) + + return T.stacklists(list_of_images).dimshuffle((1, 0, 2)) + + + +def main(): + t0 = datetime.now() + # MNIST data: + # column 0 is labels + # column 1-785 is data, with values 0 .. 255 + train = pd.read_csv('../large_files/train.csv').as_matrix() + train = shuffle(train) + + Xtrain = rearrange( train[:-100,1:] ) + Ytrain = train[:-100,0] + Ytrain_ind = y2indicator(Ytrain) + + Xtest = rearrange( train[-100:,1:] ) + Ytest = train[-100:,0] + Ytest_ind = y2indicator(Ytest) + + + max_iter = 8 + print_period = 200 + + lr = np.float32(0.01) + reg = np.float32(0.01) + mu = np.float32(0.99) + + N = Xtrain.shape[0] + + M = 4096 + K = 10 + + # New + wp, hp = 2, 2 + + M1 = 64 # hidden layer size + M2 = 256 # num feature maps + W1x_shape = (M1, 1*wp*hp) + W1x_init = init_filter(W1x_shape) + W1h_init = init_filter( (M1,M1) ) + b1h_init = np.zeros((M1,), dtype=np.float32) + W1o_init = init_filter( (M1, M2) ) + b1o_init = np.zeros((M2,), dtype=np.float32) + H01_init = init_filter( (M1,) ) + + M3 = 64 # hidden layer size + M4 = 256 # num feature maps + W2x_shape = (M3, 2*M2*1*1) # TODO: revert + W2x_init = init_filter(W2x_shape) + W2h_init = init_filter( (M3,M3) ) + b2h_init = np.zeros((M3,), dtype=np.float32) + W2o_init = init_filter( (M3, M4) ) + b2o_init = np.zeros((M4,), dtype=np.float32) + H02_init = init_filter( (M3,) ) + + M5 = 64 + M6 = 256 + W3x_shape = (M5, 2*M4*wp*hp) + W3x_init = init_filter(W3x_shape) + W3h_init = init_filter( (M5,M5) ) + b3h_init = np.zeros((M5,), dtype=np.float32) + W3o_init = init_filter( (M5, M6) ) + b3o_init = np.zeros((M6,), dtype=np.float32) + H03_init = init_filter( (M5,) ) + + M7 = 64 + M8 = 256 + W4x_shape = (M7, 2*M6*1*1) + W4x_init = init_filter(W4x_shape) + W4h_init = init_filter( (M7,M7) ) + b4h_init = np.zeros((M7,), dtype=np.float32) + W4o_init = init_filter( (M7, M8) ) + b4o_init = np.zeros((M8,), dtype=np.float32) + H04_init = init_filter( (M7,) ) + + + # vanilla ANN weights + W5_init = np.random.randn(2*M8*7*7, M) / np.sqrt(2*M8*7*7 + M) + b5_init = np.zeros(M, dtype=np.float32) + W6_init = np.random.randn(M, K) / np.sqrt(M + K) + b6_init = np.zeros(K, dtype=np.float32) + + + # step 2: define theano variables and expressions + X = T.tensor3('X', dtype='float32') + Y = T.matrix('T') + W1x = theano.shared(W1x_init, 'W1x') + W1h = theano.shared(W1h_init, 'W1h') + b1h = theano.shared(b1h_init, 'b1h') + W1o = theano.shared(W1o_init, 'W1o') + b1o = theano.shared(b1o_init, 'b1o') + H01 = theano.shared(H01_init, 'H01') + W2x = theano.shared(W2x_init, 'W2x') + W2h = theano.shared(W2h_init, 'W2h') + b2h = theano.shared(b2h_init, 'b2h') + W2o = theano.shared(W2o_init, 'W2o') + b2o = theano.shared(b2o_init, 'b2o') + H02 = theano.shared(H02_init, 'H02') + W3x = theano.shared(W3x_init, 'W3x') + W3h = theano.shared(W3h_init, 'W3h') + b3h = theano.shared(b3h_init, 'b3h') + W3o = theano.shared(W3o_init, 'W3o') + b3o = theano.shared(b3o_init, 'b3o') + H03 = theano.shared(H03_init, 'H03') + W4x = theano.shared(W4x_init, 'W4x') + W4h = theano.shared(W4h_init, 'W4h') + b4h = theano.shared(b4h_init, 'b4h') + W4o = theano.shared(W4o_init, 'W4o') + b4o = theano.shared(b4o_init, 'b4o') + H04 = theano.shared(H04_init, 'H04') + W5 = theano.shared(W5_init.astype(np.float32), 'W5') + b5 = theano.shared(b5_init, 'b5') + W6 = theano.shared(W6_init.astype(np.float32), 'W6') + b6 = theano.shared(b6_init, 'b6') + params = [W1x, W1h, b1h, W1o, b1o, H01, W2x, W2h, b2h, W2o, b2o, H02, W3x, W3h, b3h, W3o, b3o, H03, W4x, W4h, b4h, W4o, b4o, H04, W5, b5, W6, b6] + + # momentum changes + # dW1 = theano.shared(np.zeros(W1_init.shape, dtype=np.float32), 'dW1') + # db1 = theano.shared(np.zeros(b1_init.shape, dtype=np.float32), 'db1') + # dW2 = theano.shared(np.zeros(W2_init.shape, dtype=np.float32), 'dW2') + # db2 = theano.shared(np.zeros(b2_init.shape, dtype=np.float32), 'db2') + # dW3 = theano.shared(np.zeros(W3_init.shape, dtype=np.float32), 'dW3') + # db3 = theano.shared(np.zeros(b3_init.shape, dtype=np.float32), 'db3') + # dW4 = theano.shared(np.zeros(W4_init.shape, dtype=np.float32), 'dW4') + # db4 = theano.shared(np.zeros(b4_init.shape, dtype=np.float32), 'db4') + + # forward pass + Z1 = renet_layer_lr(X, W1x, W1h, W1o, b1h, b1o, H01, 28, 28, wp, hp) + + ## TMP: just test the first/second layer ## + # tmp_op = theano.function( + # inputs=[X], + # outputs=Z1, + # ) + # print "Xtrain[0].shape:", Xtrain[0].shape + # out = tmp_op(Xtrain[0]) + # print "Z1.shape:", out.shape + + Z2 = renet_layer_ud(Z1, W2x, W2h, W2o, b2h, b2o, H02, 14, 14, 1, 1) + + # tmp_op2 = theano.function( + # inputs=[X], + # outputs=Z2, + # ) + # out = tmp_op2(Xtrain[0]) + # print "Z2.shape:", out.shape + # exit() + + + Z3 = renet_layer_lr(Z2, W3x, W3h, W3o, b3h, b3o, H03, 14, 14, wp, hp) + Z4 = renet_layer_ud(Z3, W4x, W4h, W4o, b4h, b4o, H04, 7, 7, 1, 1) + Z5 = relu(Z4.flatten().dot(W5) + b5) + pY = T.nnet.softmax( Z5.dot(W6) + b6) + + + # tmp_op3 = theano.function( + # inputs=[X], + # outputs=Z3, + # ) + # out = tmp_op3(Xtrain[0]) + # print "Z3.shape:", out.shape + + # tmp_op4 = theano.function( + # inputs=[X], + # outputs=Z4, + # ) + # out = tmp_op4(Xtrain[0]) + # print "Z4.shape:", out.shape + # exit() + + # define the cost function and prediction + # params = (W1, b1, W2, b2, W3, b3, W4, b4) + reg_cost = reg*np.sum((param*param).sum() for param in params) + cost = -(Y * T.log(pY)).sum() + reg_cost + prediction = T.argmax(pY, axis=1) + + # step 3: training expressions and functions + # update_W1 = W1 + mu*dW1 - lr*T.grad(cost, W1) + # update_b1 = b1 + mu*db1 - lr*T.grad(cost, b1) + # update_W2 = W2 + mu*dW2 - lr*T.grad(cost, W2) + # update_b2 = b2 + mu*db2 - lr*T.grad(cost, b2) + # update_W3 = W3 + mu*dW3 - lr*T.grad(cost, W3) + # update_b3 = b3 + mu*db3 - lr*T.grad(cost, b3) + # update_W4 = W4 + mu*dW4 - lr*T.grad(cost, W4) + # update_b4 = b4 + mu*db4 - lr*T.grad(cost, b4) + updates = [(param, param - lr*T.grad(cost, param)) for param in params] + + # update weight changes + # update_dW1 = mu*dW1 - lr*T.grad(cost, W1) + # update_db1 = mu*db1 - lr*T.grad(cost, b1) + # update_dW2 = mu*dW2 - lr*T.grad(cost, W2) + # update_db2 = mu*db2 - lr*T.grad(cost, b2) + # update_dW3 = mu*dW3 - lr*T.grad(cost, W3) + # update_db3 = mu*db3 - lr*T.grad(cost, b3) + # update_dW4 = mu*dW4 - lr*T.grad(cost, W4) + # update_db4 = mu*db4 - lr*T.grad(cost, b4) + + train = theano.function( + inputs=[X, Y], + updates=updates, + ) + + # create another function for this because we want it over the whole dataset + get_prediction = theano.function( + inputs=[X, Y], + outputs=[cost, prediction], + ) + + print "Setup elapsed time:", (datetime.now() - t0) + t0 = datetime.now() + LL = [] + t1 = t0 + for i in xrange(max_iter): + print "i:", i + for j in xrange(N): + # print "j:", j + Xbatch = Xtrain[j,:] + Ybatch = Ytrain_ind[j:j+1,:] + + train(Xbatch, Ybatch) + if j % print_period == 0: + cost_val = 0 + prediction_val = np.zeros(100) + for k in xrange(100): + c, p = get_prediction(Xtest[k], Ytest_ind[k:k+1,:]) + cost_val += c + prediction_val[k] = p + err = error_rate(prediction_val, Ytest) + print "Cost / err at iteration i=%d, j=%d: %.3f / %.2f" % (i, j, cost_val / len(Ytest), err) + t2 = datetime.now() + print "Time since last print:", (t2 - t1) + t1 = t2 + LL.append(cost_val) + print "Elapsed time:", (datetime.now() - t0) + plt.plot(LL) + plt.show() + + +if __name__ == '__main__': + main() diff --git a/rnn_class/rrnn_language.py b/rnn_class/rrnn_language.py index 969ec3ee..c6247b25 100644 --- a/rnn_class/rrnn_language.py +++ b/rnn_class/rrnn_language.py @@ -1,12 +1,4 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python # https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import theano import theano.tensor as T import numpy as np @@ -22,7 +14,7 @@ def __init__(self, D, M, V): self.M = M # hidden layer size self.V = V # vocabulary size - def fit(self, X, learning_rate=10., mu=0.9, reg=0., activation=T.tanh, epochs=500, show_fig=False): + def fit(self, X, learning_rate=10e-1, mu=0.99, reg=1.0, activation=T.tanh, epochs=500, show_fig=False): N = len(X) D = self.D M = self.M @@ -49,13 +41,11 @@ def fit(self, X, learning_rate=10., mu=0.9, reg=0., activation=T.tanh, epochs=50 grads = T.grad(cost, self.params) dparams = [theano.shared(p.get_value()*0) for p in self.params] - updates = [] - for p, dp, g in zip(self.params, dparams, grads): - new_dp = mu*dp - lr*g - updates.append((dp, new_dp)) - - new_p = p + new_dp - updates.append((p, new_p)) + updates = [ + (p, p + mu*dp - lr*g) for p, dp, g in zip(self.params, dparams, grads) + ] + [ + (dp, mu*dp - lr*g) for dp, g in zip(dparams, grads) + ] self.predict_op = theano.function(inputs=[thX], outputs=prediction) self.train_op = theano.function( @@ -65,12 +55,12 @@ def fit(self, X, learning_rate=10., mu=0.9, reg=0., activation=T.tanh, epochs=50 ) costs = [] - for i in range(epochs): + for i in xrange(epochs): X = shuffle(X) n_correct = 0 n_total = 0 cost = 0 - for j in range(N): + for j in xrange(N): if np.random.random() < 0.1: input_sequence = [0] + X[j] output_sequence = X[j] + [1] @@ -87,7 +77,7 @@ def fit(self, X, learning_rate=10., mu=0.9, reg=0., activation=T.tanh, epochs=50 for pj, xj in zip(p, output_sequence): if pj == xj: n_correct += 1 - print("i:", i, "cost:", cost, "correct rate:", (float(n_correct)/n_total)) + print "i:", i, "cost:", cost, "correct rate:", (float(n_correct)/n_total) if (i + 1) % 500 == 0: learning_rate /= 2 costs.append(cost) @@ -166,7 +156,7 @@ def recurrence(x_t, h_t1): def generate(self, word2idx): # convert word2idx -> idx2word - idx2word = {v:k for k,v in iteritems(word2idx)} + idx2word = {v:k for k,v in word2idx.iteritems()} V = len(word2idx) # generate 4 lines at a time @@ -185,19 +175,19 @@ def generate(self, word2idx): if P > 1: # it's a real word, not start/end token word = idx2word[P] - print(word, end=" ") + print word, elif P == 1: # end token n_lines += 1 X = [0] - print('') + print '' def train_poetry(): # students: tanh didn't work but you should try it sentences, word2idx = get_robert_frost() rnn = SimpleRNN(50, 50, len(word2idx)) - rnn.fit(sentences, learning_rate=1e-4, show_fig=True, activation=T.nnet.relu, epochs=2000) + rnn.fit(sentences, learning_rate=10e-5, show_fig=True, activation=T.nnet.relu, epochs=2000) rnn.save('RRNN_D50_M50_epochs2000_relu.npz') def generate_poetry(): @@ -207,6 +197,6 @@ def generate_poetry(): if __name__ == '__main__': - train_poetry() + # train_poetry() generate_poetry() diff --git a/rnn_class/srn_language.py b/rnn_class/srn_language.py index b02a7f0c..c64a5d63 100644 --- a/rnn_class/srn_language.py +++ b/rnn_class/srn_language.py @@ -1,12 +1,4 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python # https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import theano import theano.tensor as T import numpy as np @@ -22,7 +14,7 @@ def __init__(self, D, M, V): self.M = M # hidden layer size self.V = V # vocabulary size - def fit(self, X, learning_rate=1., mu=0.99, reg=1.0, activation=T.tanh, epochs=500, show_fig=False): + def fit(self, X, learning_rate=10e-1, mu=0.99, reg=1.0, activation=T.tanh, epochs=500, show_fig=False): N = len(X) D = self.D M = self.M @@ -77,13 +69,11 @@ def recurrence(x_t, h_t1): grads = T.grad(cost, self.params) dparams = [theano.shared(p.get_value()*0) for p in self.params] - updates = [] - for p, dp, g in zip(self.params, dparams, grads): - new_dp = mu*dp - learning_rate*g - updates.append((dp, new_dp)) - - new_p = p + new_dp - updates.append((p, new_p)) + updates = [ + (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads) + ] + [ + (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads) + ] self.predict_op = theano.function(inputs=[thX], outputs=prediction) self.train_op = theano.function( @@ -94,11 +84,11 @@ def recurrence(x_t, h_t1): costs = [] n_total = sum((len(sentence)+1) for sentence in X) - for i in range(epochs): + for i in xrange(epochs): X = shuffle(X) n_correct = 0 cost = 0 - for j in range(N): + for j in xrange(N): # problem! many words --> END token are overrepresented # result: generated lines will be very short # we will try to fix in a later iteration @@ -114,7 +104,7 @@ def recurrence(x_t, h_t1): for pj, xj in zip(p, output_sequence): if pj == xj: n_correct += 1 - print("i:", i, "cost:", cost, "correct rate:", (float(n_correct)/n_total)) + print "i:", i, "cost:", cost, "correct rate:", (float(n_correct)/n_total) costs.append(cost) if show_fig: @@ -181,7 +171,7 @@ def recurrence(x_t, h_t1): def generate(self, pi, word2idx): # convert word2idx -> idx2word - idx2word = {v:k for k,v in iteritems(word2idx)} + idx2word = {v:k for k,v in word2idx.iteritems()} V = len(pi) # generate 4 lines at a time @@ -189,7 +179,7 @@ def generate(self, pi, word2idx): # why? because using the START symbol will always yield the same first word! X = [ np.random.choice(V, p=pi) ] - print(idx2word[X[0]], end=" ") + print idx2word[X[0]], while n_lines < 4: # print "X:", X @@ -198,20 +188,21 @@ def generate(self, pi, word2idx): if P > 1: # it's a real word, not start/end token word = idx2word[P] - print(word, end=" ") + print word, elif P == 1: # end token n_lines += 1 - print('') + print '' if n_lines < 4: X = [ np.random.choice(V, p=pi) ] # reset to start of line - print(idx2word[X[0]], end=" ") + print idx2word[X[0]], def train_poetry(): + # students: tanh didn't work but you should try it sentences, word2idx = get_robert_frost() rnn = SimpleRNN(30, 30, len(word2idx)) - rnn.fit(sentences, learning_rate=1e-4, show_fig=True, activation=T.nnet.relu, epochs=2000) + rnn.fit(sentences, learning_rate=10e-5, show_fig=True, activation=T.nnet.relu, epochs=2000) rnn.save('RNN_D30_M30_epochs2000_relu.npz') def generate_poetry(): @@ -229,13 +220,13 @@ def generate_poetry(): def wikipedia(): sentences, word2idx = get_wikipedia_data() - print("finished retrieving data") - print("vocab size:", len(word2idx), "number of sentences:", len(sentences)) + print "finished retrieving data" + print "vocab size:", len(word2idx), "number of sentences:", len(sentences) rnn = SimpleRNN(20, 15, len(word2idx)) - rnn.fit(sentences, learning_rate=1e-4, show_fig=True, activation=T.nnet.relu) + rnn.fit(sentences, learning_rate=10e-5, show_fig=True, activation=T.nnet.relu) if __name__ == '__main__': - train_poetry() + # train_poetry() generate_poetry() # wikipedia() diff --git a/rnn_class/srn_language_tf.py b/rnn_class/srn_language_tf.py deleted file mode 100644 index ff619a83..00000000 --- a/rnn_class/srn_language_tf.py +++ /dev/null @@ -1,246 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python -# https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt - -from sklearn.utils import shuffle -from util import init_weight, get_robert_frost, get_wikipedia_data - - -class SimpleRNN: - def __init__(self, D, M, V, f, session): - self.D = D # dimensionality of word embedding - self.M = M # hidden layer size - self.V = V # vocabulary size - self.f = f - self.session = session - - def set_session(self, session): - self.session = session - - def build(self, We, Wx, Wh, bh, h0, Wo, bo): - # make them tf Variables - self.We = tf.Variable(We) - self.Wx = tf.Variable(Wx) - self.Wh = tf.Variable(Wh) - self.bh = tf.Variable(bh) - self.h0 = tf.Variable(h0) - self.Wo = tf.Variable(Wo) - self.bo = tf.Variable(bo) - self.params = [self.We, self.Wx, self.Wh, self.bh, self.h0, self.Wo, self.bo] - - # for easy access - V = self.V - D = self.D - M = self.M - - # placeholders - self.tfX = tf.placeholder(tf.int32, shape=(None,), name='X') - self.tfY = tf.placeholder(tf.int32, shape=(None,), name='Y') - - # convert word indexes to word vectors - # this would be equivalent to doing - # We[tfX] in Numpy / Theano - # or: - # X_one_hot = one_hot_encode(X) - # X_one_hot.dot(We) - XW = tf.nn.embedding_lookup(We, self.tfX) - - # multiply it by input->hidden so we don't have to do - # it inside recurrence - XW_Wx = tf.matmul(XW, self.Wx) - - - def recurrence(h_t1, XW_Wx_t): - # returns h(t), y(t) - h_t1 = tf.reshape(h_t1, (1, M)) - h_t = self.f(XW_Wx_t + tf.matmul(h_t1, self.Wh) + self.bh) - h_t = tf.reshape(h_t, (M,)) - return h_t - - h = tf.scan( - fn=recurrence, - elems=XW_Wx, - initializer=self.h0, - ) - - # output - logits = tf.matmul(h, self.Wo) + self.bo - prediction = tf.argmax(logits, 1) - self.output_probs = tf.nn.softmax(logits) - - nce_weights = tf.transpose(self.Wo, [1,0]) # needs to be VxD, not DxV - nce_biases = self.bo - - h = tf.reshape(h, (-1, M)) - labels = tf.reshape(self.tfY, (-1, 1)) - - self.cost = tf.reduce_mean( - tf.nn.sampled_softmax_loss( - weights=nce_weights, - biases=nce_biases, - labels=labels, - inputs=h, - num_sampled=50, # number of negative samples - num_classes=V - ) - ) - - self.predict_op = prediction - self.train_op = tf.train.AdamOptimizer(1e-2).minimize(self.cost) - # self.train_op = tf.train.MomentumOptimizer(1e-3, 0.9).minimize(self.cost) - - # init all variables - init = tf.global_variables_initializer() - self.session.run(init) - - - def fit(self, X, epochs=500, show_fig=False): - N = len(X) - D = self.D - M = self.M - V = self.V - - # initial weights - We = init_weight(V, D).astype(np.float32) - Wx = init_weight(D, M).astype(np.float32) - Wh = init_weight(M, M).astype(np.float32) - bh = np.zeros(M).astype(np.float32) - h0 = np.zeros(M).astype(np.float32) - Wo = init_weight(M, V).astype(np.float32) - bo = np.zeros(V).astype(np.float32) - - # build tensorflow functions - self.build(We, Wx, Wh, bh, h0, Wo, bo) - - # sentence input: - # [START, w1, w2, ..., wn] - # sentence target: - # [w1, w2, w3, ..., END] - - costs = [] - n_total = sum((len(sentence)+1) for sentence in X) - for i in range(epochs): - X = shuffle(X) - n_correct = 0 - cost = 0 - for j in range(N): - # problem! many words --> END token are overrepresented - # result: generated lines will be very short - # we will try to fix in a later iteration - # BAD! magic numbers 0 and 1... - input_sequence = [0] + X[j] - output_sequence = X[j] + [1] - - # we set 0 to start and 1 to end - _, c, p = self.session.run( - (self.train_op, self.cost, self.predict_op), - feed_dict={self.tfX: input_sequence, self.tfY: output_sequence} - ) - # print "p:", p - cost += c - # print "j:", j, "c:", c/len(X[j]+1) - for pj, xj in zip(p, output_sequence): - if pj == xj: - n_correct += 1 - print("i:", i, "cost:", cost, "correct rate:", (float(n_correct)/n_total)) - costs.append(cost) - - if show_fig: - plt.plot(costs) - plt.show() - - def predict(self, prev_words): - # don't use argmax, so that we can sample - # from this probability distribution - return self.session.run( - self.output_probs, - feed_dict={self.tfX: prev_words} - ) - - def save(self, filename): - actual_params = self.session.run(self.params) - np.savez(filename, *[p for p in actual_params]) - - @staticmethod - def load(filename, activation, session): - # TODO: would prefer to save activation to file too - npz = np.load(filename) - We = npz['arr_0'] - Wx = npz['arr_1'] - Wh = npz['arr_2'] - bh = npz['arr_3'] - h0 = npz['arr_4'] - Wo = npz['arr_5'] - bo = npz['arr_6'] - V, D = We.shape - _, M = Wx.shape - rnn = SimpleRNN(D, M, V, activation, session) - rnn.build(We, Wx, Wh, bh, h0, Wo, bo) - return rnn - - def generate(self, pi, word2idx): - # convert word2idx -> idx2word - idx2word = {v:k for k,v in iteritems(word2idx)} - V = len(pi) - - # generate 4 lines at a time - n_lines = 0 - - # why? because using the START symbol will always yield the same first word! - X = [ np.random.choice(V, p=pi) ] - print(idx2word[X[0]], end=" ") - - while n_lines < 4: - probs = self.predict(X)[-1] - word_idx = np.random.choice(V, p=probs) - X.append(word_idx) - if word_idx > 1: - # it's a real word, not start/end token - word = idx2word[word_idx] - print(word, end=" ") - elif word_idx == 1: - # end token - n_lines += 1 - print('') - if n_lines < 4: - X = [ np.random.choice(V, p=pi) ] # reset to start of line - print(idx2word[X[0]], end=" ") - - -def train_poetry(session, dims, savefile): - sentences, word2idx = get_robert_frost() - rnn = SimpleRNN(dims, dims, len(word2idx), tf.nn.relu, session) - rnn.fit(sentences, epochs=17, show_fig=True) - rnn.save(savefile) - - -def generate_poetry(session, savefile): - sentences, word2idx = get_robert_frost() - rnn = SimpleRNN.load(savefile, tf.nn.relu, session) - - # determine initial state distribution for starting sentences - V = len(word2idx) - pi = np.zeros(V) - for sentence in sentences: - pi[sentence[0]] += 1 - pi /= pi.sum() - - rnn.generate(pi, word2idx) - - -if __name__ == '__main__': - dims = 50 - savefile = 'RNN_D50_M50_tf.npz' - session = tf.InteractiveSession() - train_poetry(session, dims, savefile) - generate_poetry(session, savefile) - diff --git a/rnn_class/srn_parity.py b/rnn_class/srn_parity.py index 266ebfb3..09d8323d 100644 --- a/rnn_class/srn_parity.py +++ b/rnn_class/srn_parity.py @@ -1,25 +1,18 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python # https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import theano import theano.tensor as T import numpy as np import matplotlib.pyplot as plt from sklearn.utils import shuffle -from util import init_weight, all_parity_pairs_with_sequence_labels +from util import init_weight, all_parity_pairs class SimpleRNN: def __init__(self, M): self.M = M # hidden layer size - def fit(self, X, Y, learning_rate=0.1, mu=0.99, reg=1.0, activation=T.tanh, epochs=100, show_fig=False): + def fit(self, X, Y, learning_rate=10e-1, mu=0.99, reg=1.0, activation=T.tanh, epochs=100, show_fig=False): D = X[0].shape[1] # X is of size N x T(n) x D K = len(set(Y.flatten())) N = len(Y) @@ -80,21 +73,19 @@ def recurrence(x_t, h_t1): ) costs = [] - for i in range(epochs): + for i in xrange(epochs): X, Y = shuffle(X, Y) n_correct = 0 cost = 0 - for j in range(N): + for j in xrange(N): c, p, rout = self.train_op(X[j], Y[j]) # print "p:", p cost += c if p[-1] == Y[j,-1]: n_correct += 1 - print("shape y:", rout.shape) - print("i:", i, "cost:", cost, "classification rate:", (float(n_correct)/N)) + print "shape y:", rout.shape + print "i:", i, "cost:", cost, "classification rate:", (float(n_correct)/N) costs.append(cost) - if n_correct == N: - break if show_fig: plt.plot(costs) @@ -102,11 +93,26 @@ def recurrence(x_t, h_t1): -def parity(B=12, learning_rate=1e-4, epochs=200): - X, Y = all_parity_pairs_with_sequence_labels(B) +def parity(B=12, learning_rate=10e-5, epochs=200): + X, Y = all_parity_pairs(B) + N, t = X.shape + + # we want every time step to have a label + Y_t = np.zeros(X.shape, dtype=np.int32) + for n in xrange(N): + ones_count = 0 + for i in xrange(t): + if X[n,i] == 1: + ones_count += 1 + if ones_count % 2 == 1: + Y_t[n,i] = 1 + + # for x, y in zip(X, Y_t): + # print "x:", x, "y:", y + X = X.reshape(N, t, 1).astype(np.float32) - rnn = SimpleRNN(20) - rnn.fit(X, Y, learning_rate=learning_rate, epochs=epochs, activation=T.nnet.relu, show_fig=False) + rnn = SimpleRNN(4) + rnn.fit(X, Y_t, learning_rate=learning_rate, epochs=epochs, activation=T.nnet.sigmoid, show_fig=True) if __name__ == '__main__': diff --git a/rnn_class/srn_parity_tf.py b/rnn_class/srn_parity_tf.py deleted file mode 100644 index 8c7b5665..00000000 --- a/rnn_class/srn_parity_tf.py +++ /dev/null @@ -1,109 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python -# https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt - -from sklearn.utils import shuffle -from util import init_weight, all_parity_pairs_with_sequence_labels - - -class SimpleRNN: - def __init__(self, M): - self.M = M # hidden layer size - - def fit(self, X, Y, learning_rate=1.0, mu=0.99, reg=1.0, activation=tf.tanh, epochs=100, show_fig=False): - N, T, D = X.shape - K = len(set(Y.flatten())) - M = self.M - self.f = activation - - # initial weights - Wx = init_weight(D, M).astype(np.float32) - Wh = init_weight(M, M).astype(np.float32) - bh = np.zeros(M, dtype=np.float32) - h0 = np.zeros(M, dtype=np.float32) - Wo = init_weight(M, K).astype(np.float32) - bo = np.zeros(K, dtype=np.float32) - - # make them theano shared - self.Wx = tf.Variable(Wx) - self.Wh = tf.Variable(Wh) - self.bh = tf.Variable(bh) - self.h0 = tf.Variable(h0) - self.Wo = tf.Variable(Wo) - self.bo = tf.Variable(bo) - - tfX = tf.placeholder(tf.float32, shape=(T, D), name='X') - tfY = tf.placeholder(tf.int32, shape=(T,), name='Y') - - XWx = tf.matmul(tfX, self.Wx) - - def recurrence(h_t1, xw_t): - # matmul() only works with 2-D objects - # we want to return a 1-D object of size M - # so that the final result is T x M - # not T x 1 x M - h_t = self.f(xw_t + tf.matmul(tf.reshape(h_t1, (1, M)), self.Wh) + self.bh) - return tf.reshape(h_t, (M,)) - - h = tf.scan( - fn=recurrence, - elems=XWx, - initializer=self.h0, - ) - - logits = tf.matmul(h, self.Wo) + self.bo - - cost = tf.reduce_mean( - tf.nn.sparse_softmax_cross_entropy_with_logits( - labels=tfY, - logits=logits, - ) - ) - - predict_op = tf.argmax(logits, 1) - train_op = tf.train.AdamOptimizer(1e-2).minimize(cost) - - init = tf.global_variables_initializer() - with tf.Session() as session: - session.run(init) - - costs = [] - for i in range(epochs): - X, Y = shuffle(X, Y) - n_correct = 0 - batch_cost = 0 - for j in range(N): - _, c, p = session.run([train_op, cost, predict_op], feed_dict={tfX: X[j].reshape(T, D), tfY: Y[j]}) - batch_cost += c - if p[-1] == Y[j,-1]: - n_correct += 1 - print("i:", i, "cost:", batch_cost, "classification rate:", (float(n_correct)/N)) - costs.append(batch_cost) - if n_correct == N: - break - - if show_fig: - plt.plot(costs) - plt.show() - - - -def parity(B=12, learning_rate=1e-4, epochs=200): - X, Y = all_parity_pairs_with_sequence_labels(B) - X = X.astype(np.float32) - - rnn = SimpleRNN(20) - rnn.fit(X, Y, learning_rate=learning_rate, epochs=epochs, activation=tf.nn.relu, show_fig=False) - - -if __name__ == '__main__': - parity() - diff --git a/rnn_class/test_theano.py b/rnn_class/test_theano.py new file mode 100644 index 00000000..1061c148 --- /dev/null +++ b/rnn_class/test_theano.py @@ -0,0 +1,25 @@ +# copied from http://deeplearning.net/software/theano/tutorial/using_gpu.html +# for convenience + +from theano import function, config, shared, sandbox +import theano.tensor as T +import numpy +import time + +vlen = 10 * 30 * 768 # 10 x #cores x # threads per core +iters = 1000 + +rng = numpy.random.RandomState(22) +x = shared(numpy.asarray(rng.rand(vlen), config.floatX)) +f = function([], T.exp(x)) +print(f.maker.fgraph.toposort()) +t0 = time.time() +for i in range(iters): + r = f() +t1 = time.time() +print("Looping %d times took %f seconds" % (iters, t1 - t0)) +print("Result is %s" % (r,)) +if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]): + print('Used the cpu') +else: + print('Used the gpu') \ No newline at end of file diff --git a/rnn_class/tf_parity.py b/rnn_class/tf_parity.py deleted file mode 100644 index 41c31499..00000000 --- a/rnn_class/tf_parity.py +++ /dev/null @@ -1,140 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python -# https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - -import tensorflow as tf -import numpy as np -import matplotlib.pyplot as plt - -# from tensorflow.python.ops import rnn as rnn_module - -######## This only works for pre-1.0 versions ########## -# from tensorflow.python.ops.rnn import rnn as get_rnn_output -# from tensorflow.python.ops.rnn_cell import BasicRNNCell, GRUCell -######################################################## - -########## This works for TensorFlow v1.0 ############## -from tensorflow.contrib.rnn import static_rnn as get_rnn_output -from tensorflow.contrib.rnn import BasicRNNCell, GRUCell -######################################################## - -from sklearn.utils import shuffle -from util import init_weight, all_parity_pairs_with_sequence_labels, all_parity_pairs - - -def x2sequence(x, T, D, batch_sz): - # Permuting batch_size and n_steps - x = tf.transpose(x, (1, 0, 2)) - # Reshaping to (n_steps*batch_size, n_input) - x = tf.reshape(x, (T*batch_sz, D)) - # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) - # x = tf.split(0, T, x) # v0.1 - x = tf.split(x, T) # v1.0 - # print "type(x):", type(x) - return x - -class SimpleRNN: - def __init__(self, M): - self.M = M # hidden layer size - - - def fit(self, X, Y, batch_sz=20, learning_rate=0.1, mu=0.9, activation=tf.nn.sigmoid, epochs=100, show_fig=False): - N, T, D = X.shape # X is of size N x T(n) x D - K = len(set(Y.flatten())) - M = self.M - self.f = activation - - # initial weights - # note: Wx, Wh, bh are all part of the RNN unit and will be created - # by BasicRNNCell - Wo = init_weight(M, K).astype(np.float32) - bo = np.zeros(K, dtype=np.float32) - - # make them tf variables - self.Wo = tf.Variable(Wo) - self.bo = tf.Variable(bo) - - # tf Graph input - tfX = tf.placeholder(tf.float32, shape=(batch_sz, T, D), name='inputs') - tfY = tf.placeholder(tf.int64, shape=(batch_sz, T), name='targets') - - # turn tfX into a sequence, e.g. T tensors all of size (batch_sz, D) - sequenceX = x2sequence(tfX, T, D, batch_sz) - - # create the simple rnn unit - rnn_unit = BasicRNNCell(num_units=self.M, activation=self.f) - - # Get rnn cell output - # outputs, states = rnn_module.rnn(rnn_unit, sequenceX, dtype=tf.float32) - outputs, states = get_rnn_output(rnn_unit, sequenceX, dtype=tf.float32) - - # outputs are now of size (T, batch_sz, M) - # so make it (batch_sz, T, M) - outputs = tf.transpose(outputs, (1, 0, 2)) - outputs = tf.reshape(outputs, (T*batch_sz, M)) - - # Linear activation, using rnn inner loop last output - logits = tf.matmul(outputs, self.Wo) + self.bo - predict_op = tf.argmax(logits, 1) - targets = tf.reshape(tfY, (T*batch_sz,)) - - cost_op = tf.reduce_mean( - tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=logits, - labels=targets - ) - ) - train_op = tf.train.MomentumOptimizer(learning_rate, momentum=mu).minimize(cost_op) - - costs = [] - n_batches = N // batch_sz - - init = tf.global_variables_initializer() - with tf.Session() as session: - session.run(init) - for i in range(epochs): - X, Y = shuffle(X, Y) - n_correct = 0 - cost = 0 - for j in range(n_batches): - Xbatch = X[j*batch_sz:(j+1)*batch_sz] - Ybatch = Y[j*batch_sz:(j+1)*batch_sz] - - _, c, p = session.run([train_op, cost_op, predict_op], feed_dict={tfX: Xbatch, tfY: Ybatch}) - cost += c - for b in range(batch_sz): - idx = (b + 1)*T - 1 - n_correct += (p[idx] == Ybatch[b][-1]) - if i % 10 == 0: - print("i:", i, "cost:", cost, "classification rate:", (float(n_correct)/N)) - if n_correct == N: - print("i:", i, "cost:", cost, "classification rate:", (float(n_correct)/N)) - break - costs.append(cost) - - if show_fig: - plt.plot(costs) - plt.show() - - - -def parity(B=12, learning_rate=1., epochs=1000): - X, Y = all_parity_pairs_with_sequence_labels(B) - - rnn = SimpleRNN(4) - rnn.fit(X, Y, - batch_sz=len(Y), - learning_rate=learning_rate, - epochs=epochs, - activation=tf.nn.sigmoid, - show_fig=False - ) - - -if __name__ == '__main__': - parity() - diff --git a/rnn_class/util.py b/rnn_class/util.py index 54801efa..3ccf5df3 100644 --- a/rnn_class/util.py +++ b/rnn_class/util.py @@ -1,15 +1,7 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python # https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import string import os -import sys import operator from nltk import pos_tag, word_tokenize from datetime import datetime @@ -25,46 +17,19 @@ def all_parity_pairs(nbit): Ntotal = N + remainder X = np.zeros((Ntotal, nbit)) Y = np.zeros(Ntotal) - for ii in range(Ntotal): + for ii in xrange(Ntotal): i = ii % N # now generate the ith sample - for j in range(nbit): + for j in xrange(nbit): if i % (2**(j+1)) != 0: i -= 2**j X[ii,j] = 1 Y[ii] = X[ii].sum() % 2 return X, Y -def all_parity_pairs_with_sequence_labels(nbit): - X, Y = all_parity_pairs(nbit) - N, t = X.shape - - # we want every time step to have a label - Y_t = np.zeros(X.shape, dtype=np.int32) - for n in range(N): - ones_count = 0 - for i in range(t): - if X[n,i] == 1: - ones_count += 1 - if ones_count % 2 == 1: - Y_t[n,i] = 1 - - X = X.reshape(N, t, 1).astype(np.float32) - return X, Y_t - -# unfortunately Python 2 and 3 translates work differently -def remove_punctuation_2(s): +def remove_punctuation(s): return s.translate(None, string.punctuation) -def remove_punctuation_3(s): - return s.translate(str.maketrans('','',string.punctuation)) - -if sys.version.startswith('2'): - remove_punctuation = remove_punctuation_2 -else: - remove_punctuation = remove_punctuation_3 - - def get_robert_frost(): word2idx = {'START': 0, 'END': 1} current_idx = 2 @@ -88,24 +53,10 @@ def my_tokenizer(s): s = s.lower() # downcase return s.split() -def get_wikipedia_data(n_files, n_vocab, by_paragraph=False): +def get_wikipedia_data(n_files, n_vocab): prefix = '../large_files/' - - if not os.path.exists(prefix): - print("Are you sure you've downloaded, converted, and placed the Wikipedia data into the proper folder?") - print("I'm looking for a folder called large_files, adjacent to the class folder, but it does not exist.") - print("Please download the data from https://dumps.wikimedia.org/") - print("Quitting...") - exit() - input_files = [f for f in os.listdir(prefix) if f.startswith('enwiki') and f.endswith('txt')] - if len(input_files) == 0: - print("Looks like you don't have any data files, or they're in the wrong location.") - print("Please download the data from https://dumps.wikimedia.org/") - print("Quitting...") - exit() - # return variables sentences = [] word2idx = {'START': 0, 'END': 1} @@ -117,15 +68,12 @@ def get_wikipedia_data(n_files, n_vocab, by_paragraph=False): input_files = input_files[:n_files] for f in input_files: - print("reading:", f) + print "reading:", f for line in open(prefix + f): line = line.strip() # don't count headers, structured data, lists, etc... if line and line[0] not in ('[', '*', '-', '|', '=', '{', '}'): - if by_paragraph: - sentence_lines = [line] - else: - sentence_lines = line.split('. ') + sentence_lines = line.split('. ') for sentence in sentence_lines: tokens = my_tokenizer(sentence) for t in tokens: @@ -145,7 +93,7 @@ def get_wikipedia_data(n_files, n_vocab, by_paragraph=False): idx_new_idx_map = {} for idx, count in sorted_word_idx_count[:n_vocab]: word = idx2word[idx] - print(word, count) + print word, count word2idx_small[word] = new_idx idx_new_idx_map[idx] = new_idx new_idx += 1 @@ -191,7 +139,7 @@ def get_poetry_classifier_data(samples_per_class, load_cached=True, save_cached= for line in open(fn): line = line.rstrip() if line: - print(line) + print line # tokens = remove_punctuation(line.lower()).split() tokens = get_tags(line) if len(tokens) > 1: @@ -204,7 +152,7 @@ def get_poetry_classifier_data(samples_per_class, load_cached=True, save_cached= X.append(sequence) Y.append(label) count += 1 - print(count) + print count # quit early because the tokenizer is very slow if count >= samples_per_class: break @@ -236,7 +184,7 @@ def get_stock_data(): if date > max_min_date: max_min_date = date - print("max min date:", max_min_date) + print "max min date:", max_min_date # now collect the data up to min date all_binary_targets = [] diff --git a/rnn_class/visualize_embeddings.py b/rnn_class/visualize_embeddings.py index 83f546d7..71a4c2aa 100644 --- a/rnn_class/visualize_embeddings.py +++ b/rnn_class/visualize_embeddings.py @@ -1,12 +1,4 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python # https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import json import numpy as np import matplotlib.pyplot as plt @@ -18,12 +10,12 @@ def main(we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json', Mode V, D = We.shape with open(w2i_file) as f: word2idx = json.load(f) - idx2word = {v:k for k,v in iteritems(word2idx)} + idx2word = {v:k for k,v in word2idx.iteritems()} model = Model() Z = model.fit_transform(We) plt.scatter(Z[:,0], Z[:,1]) - for i in range(V): + for i in xrange(V): plt.annotate(s=idx2word[i], xy=(Z[i,0], Z[i,1])) plt.show() @@ -32,5 +24,4 @@ def main(we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json', Mode # main(Model=TSNE) # D=80, M=80 - # main(we_file='gru_nonorm_part1_word_embeddings.npy', w2i_file='gru_nonorm_part1_wikipedia_word2idx.json', Model=TSNE) - main(we_file='working_files/batch_gru_word_embeddings.npy', w2i_file='working_files/batch_wikipedia_word2idx.json', Model=TSNE) + main(we_file='gru_nonorm_part1_word_embeddings.npy', w2i_file='gru_nonorm_part1_wikipedia_word2idx.json', Model=TSNE) diff --git a/rnn_class/wiki.py b/rnn_class/wiki.py index f6d30993..7ba5b51f 100644 --- a/rnn_class/wiki.py +++ b/rnn_class/wiki.py @@ -1,12 +1,4 @@ -# https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-python # https://udemy.com/deep-learning-recurrent-neural-networks-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - - import sys import theano import theano.tensor as T @@ -19,7 +11,6 @@ from gru import GRU from lstm import LSTM from util import init_weight, get_wikipedia_data -from brown import get_sentences_with_word2idx_limit_vocab class RNN: @@ -28,7 +19,7 @@ def __init__(self, D, hidden_layer_sizes, V): self.D = D self.V = V - def fit(self, X, learning_rate=1e-5, mu=0.99, epochs=10, show_fig=True, activation=T.nnet.relu, RecurrentUnit=GRU, normalize=True): + def fit(self, X, learning_rate=10e-5, mu=0.99, epochs=10, show_fig=True, activation=T.nnet.relu, RecurrentUnit=GRU, normalize=True): D = self.D V = self.V N = len(X) @@ -93,13 +84,13 @@ def fit(self, X, learning_rate=1e-5, mu=0.99, epochs=10, show_fig=True, activati ) costs = [] - for i in range(epochs): + for i in xrange(epochs): t0 = datetime.now() X = shuffle(X) n_correct = 0 n_total = 0 cost = 0 - for j in range(N): + for j in xrange(N): if np.random.random() < 0.01 or len(X[j]) <= 1: input_sequence = [0] + X[j] output_sequence = X[j] + [1] @@ -115,9 +106,9 @@ def fit(self, X, learning_rate=1e-5, mu=0.99, epochs=10, show_fig=True, activati c, p = self.train_op(input_sequence, output_sequence) except Exception as e: PYX, pred = self.predict_op(input_sequence) - print("input_sequence len:", len(input_sequence)) - print("PYX.shape:",PYX.shape) - print("pred.shape:", pred.shape) + print "input_sequence len:", len(input_sequence) + print "PYX.shape:",PYX.shape + print "pred.shape:", pred.shape raise e # print "p:", p cost += c @@ -128,7 +119,7 @@ def fit(self, X, learning_rate=1e-5, mu=0.99, epochs=10, show_fig=True, activati if j % 200 == 0: sys.stdout.write("j/N: %d/%d correct rate so far: %f\r" % (j, N, float(n_correct)/n_total)) sys.stdout.flush() - print("i:", i, "cost:", cost, "correct rate:", (float(n_correct)/n_total), "time for epoch:", (datetime.now() - t0)) + print "i:", i, "cost:", cost, "correct rate:", (float(n_correct)/n_total), "time for epoch:", (datetime.now() - t0) costs.append(cost) if show_fig: @@ -138,20 +129,19 @@ def fit(self, X, learning_rate=1e-5, mu=0.99, epochs=10, show_fig=True, activati def train_wikipedia(we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json', RecurrentUnit=GRU): # there are 32 files - ### note: you can pick between Wikipedia data and Brown corpus - ### just comment one out, and uncomment the other! - # sentences, word2idx = get_wikipedia_data(n_files=100, n_vocab=2000) - sentences, word2idx = get_sentences_with_word2idx_limit_vocab() - - print("finished retrieving data") - print("vocab size:", len(word2idx), "number of sentences:", len(sentences)) + sentences, word2idx = get_wikipedia_data(n_files=1, n_vocab=2000) + print "finished retrieving data" + print "vocab size:", len(word2idx), "number of sentences:", len(sentences) rnn = RNN(30, [30], len(word2idx)) - rnn.fit(sentences, learning_rate=1e-5, epochs=10, show_fig=True, activation=T.nnet.relu) + rnn.fit(sentences, learning_rate=10e-6, epochs=10, show_fig=True, activation=T.nnet.relu) np.save(we_file, rnn.We.get_value()) with open(w2i_file, 'w') as f: json.dump(word2idx, f) +def generate_wikipedia(): + pass + def find_analogies(w1, w2, w3, we_file='word_embeddings.npy', w2i_file='wikipedia_word2idx.json'): We = np.load(we_file) with open(w2i_file) as f: @@ -170,20 +160,21 @@ def dist2(a, b): for dist, name in [(dist1, 'Euclidean'), (dist2, 'cosine')]: min_dist = float('inf') best_word = '' - for word, idx in iteritems(word2idx): + for word, idx in word2idx.iteritems(): if word not in (w1, w2, w3): v1 = We[idx] d = dist(v0, v1) if d < min_dist: min_dist = d best_word = word - print("closest match by", name, "distance:", best_word) - print(w1, "-", w2, "=", best_word, "-", w3) + print "closest match by", name, "distance:", best_word + print w1, "-", w2, "=", best_word, "-", w3 if __name__ == '__main__': + # train_wikipedia() # GRU we = 'lstm_word_embeddings2.npy' w2i = 'lstm_wikipedia_word2idx2.json' - train_wikipedia(we, w2i, RecurrentUnit=GRU) + train_wikipedia(we, w2i, RecurrentUnit=LSTM) find_analogies('king', 'man', 'woman', we, w2i) find_analogies('france', 'paris', 'london', we, w2i) find_analogies('france', 'paris', 'rome', we, w2i) diff --git a/stats/extra_reading.txt b/stats/extra_reading.txt deleted file mode 100644 index 9dc9b858..00000000 --- a/stats/extra_reading.txt +++ /dev/null @@ -1,2 +0,0 @@ -The Unbiased Estimate of the Covariance Matrix -https://lazyprogrammer.me/covariance-matrix-divide-by-n-or-n-1/ \ No newline at end of file diff --git a/supervised_class/app.py b/supervised_class/app.py deleted file mode 100644 index d1ccc4f1..00000000 --- a/supervised_class/app.py +++ /dev/null @@ -1,46 +0,0 @@ -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -import pickle -import numpy as np -import os -import json - -import tornado.ioloop -import tornado.web - -if not os.path.exists('mymodel.pkl'): - exit("Can't run without the model!") - -with open('mymodel.pkl', 'rb') as f: - model = pickle.load(f) - -class MainHandler(tornado.web.RequestHandler): - def get(self): - self.write("Hello, Tornado!") - -class PredictionHandler(tornado.web.RequestHandler): - # predict one sample at a time - def post(self): - # print "body:", self.request.body - # print "arguments:", self.request.arguments - # will look like this: - # body: three=four&one=two - # arguments: {'three': ['four'], 'one': ['two']} - params = self.request.arguments - x = np.array(list(map(float, params['input']))) - y = model.predict([x])[0] - self.write(json.dumps({'prediction': y.item()})) - self.finish() - -if __name__ == "__main__": - application = tornado.web.Application([ - (r"/", MainHandler), - (r"/predict", PredictionHandler), - ]) - application.listen(8888) - tornado.ioloop.IOLoop.current().start() diff --git a/supervised_class/app_caller.py b/supervised_class/app_caller.py deleted file mode 100644 index 832f3f84..00000000 --- a/supervised_class/app_caller.py +++ /dev/null @@ -1,33 +0,0 @@ -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -import requests -import numpy as np -import matplotlib.pyplot as plt -from util import get_data - -# make a prediction from our own server! -# in reality this could be coming from any client - -X, Y = get_data() -N = len(Y) -while True: - i = np.random.choice(N) - r = requests.post("http://localhost:8888/predict", data={'input': X[i]}) - print("RESPONSE:") - print(r.content) - j = r.json() - print(j) - print("target:", Y[i]) - - plt.imshow(X[i].reshape(28, 28), cmap='gray') - plt.title("Target: %d, Prediction: %d" % (Y[i], j['prediction'])) - plt.show() - - response = input("Continue? (Y/n)\n") - if response in ('n', 'N'): - break diff --git a/supervised_class/app_trainer.py b/supervised_class/app_trainer.py deleted file mode 100644 index 6a5dd306..00000000 --- a/supervised_class/app_trainer.py +++ /dev/null @@ -1,26 +0,0 @@ -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -import pickle -import numpy as np -from util import get_data -from sklearn.ensemble import RandomForestClassifier - -if __name__ == '__main__': - X, Y = get_data() - Ntrain = len(Y) // 4 - Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] - - model = RandomForestClassifier() - model.fit(Xtrain, Ytrain) - - # just in case you're curious - Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - print("test accuracy:", model.score(Xtest, Ytest)) - - with open('mymodel.pkl', 'wb') as f: - pickle.dump(model, f) diff --git a/supervised_class/bayes.py b/supervised_class/bayes.py deleted file mode 100644 index ab3979cf..00000000 --- a/supervised_class/bayes.py +++ /dev/null @@ -1,69 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -# This is an example of a Bayes classifier on MNIST data. -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from util import get_data -from datetime import datetime -from scipy.stats import norm -from scipy.stats import multivariate_normal as mvn - -class Bayes(object): - def fit(self, X, Y, smoothing=1e-2): - N, D = X.shape - self.gaussians = dict() - self.priors = dict() - labels = set(Y) - for c in labels: - current_x = X[Y == c] - self.gaussians[c] = { - 'mean': current_x.mean(axis=0), - 'cov': np.cov(current_x.T) + np.eye(D)*smoothing, - } - self.priors[c] = float(len(Y[Y == c])) / len(Y) - - def score(self, X, Y): - P = self.predict(X) - return np.mean(P == Y) - - def predict(self, X): - N, D = X.shape - K = len(self.gaussians) - P = np.zeros((N, K)) - for c, g in iteritems(self.gaussians): - mean, cov = g['mean'], g['cov'] - P[:,c] = mvn.logpdf(X, mean=mean, cov=cov) + np.log(self.priors[c]) - return np.argmax(P, axis=1) - - -if __name__ == '__main__': - X, Y = get_data(10000) - Ntrain = len(Y) // 2 - Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] - Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - - model = Bayes() - t0 = datetime.now() - model.fit(Xtrain, Ytrain) - print("Training time:", (datetime.now() - t0)) - - t0 = datetime.now() - print("Train accuracy:", model.score(Xtrain, Ytrain)) - print("Time to compute train accuracy:", (datetime.now() - t0), "Train size:", len(Ytrain)) - - t0 = datetime.now() - print("Test accuracy:", model.score(Xtest, Ytest)) - print("Time to compute test accuracy:", (datetime.now() - t0), "Test size:", len(Ytest)) - - # plot the mean of each class - for c, g in iteritems(model.gaussians): - plt.imshow(g['mean'].reshape(28, 28)) - plt.title(c) - plt.show() diff --git a/supervised_class/dt.py b/supervised_class/dt.py deleted file mode 100644 index b31933ff..00000000 --- a/supervised_class/dt.py +++ /dev/null @@ -1,214 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -# Decision Tree for continuous-vector input, binary output -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from util import get_data, get_xor, get_donut -from datetime import datetime - - -def entropy(y): - # assume y is binary - 0 or 1 - N = len(y) - s1 = (y == 1).sum() - if 0 == s1 or N == s1: - return 0 - p1 = float(s1) / N - p0 = 1 - p1 - return -p0*np.log2(p0) - p1*np.log2(p1) - - -class TreeNode: - def __init__(self, depth=1, max_depth=None): - print('depth:', depth) - self.depth = depth - self.max_depth = max_depth - if self.max_depth is not None and self.max_depth < self.depth: - raise Exception("depth > max_depth") - - def fit(self, X, Y): - if len(Y) == 1 or len(set(Y)) == 1: - # base case, only 1 sample - # another base case - # this node only receives examples from 1 class - # we can't make a split - self.col = None - self.split = None - self.left = None - self.right = None - self.prediction = Y[0] - - else: - D = X.shape[1] - cols = range(D) - - max_ig = 0 - best_col = None - best_split = None - for col in cols: - ig, split = self.find_split(X, Y, col) - # print "ig:", ig - if ig > max_ig: - max_ig = ig - best_col = col - best_split = split - - if max_ig == 0: - # nothing we can do - # no further splits - self.col = None - self.split = None - self.left = None - self.right = None - self.prediction = np.round(Y.mean()) - else: - self.col = best_col - self.split = best_split - - if self.depth == self.max_depth: - self.left = None - self.right = None - self.prediction = [ - np.round(Y[X[:,best_col] < self.split].mean()), - np.round(Y[X[:,best_col] >= self.split].mean()), - ] - else: - # print "best split:", best_split - left_idx = (X[:,best_col] < best_split) - # print "left_idx.shape:", left_idx.shape, "len(X):", len(X) - Xleft = X[left_idx] - Yleft = Y[left_idx] - self.left = TreeNode(self.depth + 1, self.max_depth) - self.left.fit(Xleft, Yleft) - - right_idx = (X[:,best_col] >= best_split) - Xright = X[right_idx] - Yright = Y[right_idx] - self.right = TreeNode(self.depth + 1, self.max_depth) - self.right.fit(Xright, Yright) - - def find_split(self, X, Y, col): - # print "finding split for col:", col - x_values = X[:, col] - sort_idx = np.argsort(x_values) - x_values = x_values[sort_idx] - y_values = Y[sort_idx] - - # Note: optimal split is the midpoint between 2 points - # Note: optimal split is only on the boundaries between 2 classes - - # if boundaries[i] is true - # then y_values[i] != y_values[i+1] - # nonzero() gives us indices where arg is true - # but for some reason it returns a tuple of size 1 - boundaries = np.nonzero(y_values[:-1] != y_values[1:])[0] - best_split = None - max_ig = 0 - for b in boundaries: - split = (x_values[b] + x_values[b+1]) / 2 - ig = self.information_gain(x_values, y_values, split) - if ig > max_ig: - max_ig = ig - best_split = split - return max_ig, best_split - - def information_gain(self, x, y, split): - # assume classes are 0 and 1 - # print "split:", split - y0 = y[x < split] - y1 = y[x >= split] - N = len(y) - y0len = len(y0) - if y0len == 0 or y0len == N: - return 0 - p0 = float(len(y0)) / N - p1 = 1 - p0 #float(len(y1)) / N - # print "entropy(y):", entropy(y) - # print "p0:", p0 - # print "entropy(y0):", entropy(y0) - # print "p1:", p1 - # print "entropy(y1):", entropy(y1) - return entropy(y) - p0*entropy(y0) - p1*entropy(y1) - - def predict_one(self, x): - # use "is not None" because 0 means False - if self.col is not None and self.split is not None: - feature = x[self.col] - if feature < self.split: - if self.left: - p = self.left.predict_one(x) - else: - p = self.prediction[0] - else: - if self.right: - p = self.right.predict_one(x) - else: - p = self.prediction[1] - else: - # corresponds to having only 1 prediction - p = self.prediction - return p - - def predict(self, X): - N = len(X) - P = np.zeros(N) - for i in range(N): - P[i] = self.predict_one(X[i]) - return P - - -# This class is kind of redundant -class DecisionTree: - def __init__(self, max_depth=None): - self.max_depth = max_depth - - def fit(self, X, Y): - self.root = TreeNode(max_depth=self.max_depth) - self.root.fit(X, Y) - - def predict(self, X): - return self.root.predict(X) - - def score(self, X, Y): - P = self.predict(X) - return np.mean(P == Y) - - -if __name__ == '__main__': - X, Y = get_data() - - # try donut and xor - # from sklearn.utils import shuffle - # X, Y = get_xor() - # # X, Y = get_donut() - # X, Y = shuffle(X, Y) - - # only take 0s and 1s since we're doing binary classification - idx = np.logical_or(Y == 0, Y == 1) - X = X[idx] - Y = Y[idx] - - # split the data - Ntrain = len(Y) // 2 - Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] - Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - - model = DecisionTree() - # model = DecisionTree(max_depth=7) - t0 = datetime.now() - model.fit(Xtrain, Ytrain) - print("Training time:", (datetime.now() - t0)) - - t0 = datetime.now() - print("Train accuracy:", model.score(Xtrain, Ytrain)) - print("Time to compute train accuracy:", (datetime.now() - t0)) - - t0 = datetime.now() - print("Test accuracy:", model.score(Xtest, Ytest)) - print("Time to compute test accuracy:", (datetime.now() - t0)) diff --git a/supervised_class/dt_without_recursion.py b/supervised_class/dt_without_recursion.py deleted file mode 100644 index 662bba1e..00000000 --- a/supervised_class/dt_without_recursion.py +++ /dev/null @@ -1,301 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -# Decision Tree for continuous-vector input, binary output -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from util import get_data, get_xor, get_donut -from datetime import datetime - - -def entropy(y): - # assume y is binary - 0 or 1 - N = len(y) - s1 = (y == 1).sum() - if 0 == s1 or N == s1: - return 0 - p1 = float(s1) / N - p0 = 1 - p1 - # return -p0*np.log2(p0) - p1*np.log2(p1) - return 1 - p0*p0 - p1*p1 - - -class DecisionTree: - def __init__(self, depth=0, max_depth=None): - # print 'depth:', depth - # self.depth = depth - self.max_depth = max_depth - self.root = {} # is a tree node - # each node will have the attributes (k-v pairs): - # - col - # - split - # - left - # - right - # - prediction - - def fit(self, X, Y): - - current_node = self.root - depth = 0 - queue = [] - # origX = X - # origY = Y - while True: - - if len(Y) == 1 or len(set(Y)) == 1: - # base case, only 1 sample - # another base case - # this node only receives examples from 1 class - # we can't make a split - # self.col = None - # self.split = None - # self.left = None - # self.right = None - # self.prediction = Y[0] - current_node['col'] = None - current_node['split'] = None - current_node['left'] = None - current_node['right'] = None - current_node['prediction'] = Y[0] - - else: - D = X.shape[1] - cols = range(D) - - max_ig = 0 - best_col = None - best_split = None - for col in cols: - ig, split = self.find_split(X, Y, col) - # print "ig:", ig - if ig > max_ig: - max_ig = ig - best_col = col - best_split = split - - if max_ig == 0: - # nothing we can do - # no further splits - # self.col = None - # self.split = None - # self.left = None - # self.right = None - # self.prediction = np.round(Y.mean()) - current_node['col'] = None - current_node['split'] = None - current_node['left'] = None - current_node['right'] = None - current_node['prediction'] = np.round(Y.mean()) - else: - # self.col = best_col - # self.split = best_split - current_node['col'] = best_col - current_node['split'] = best_split - - # if self.depth == self.max_depth: - if depth == self.max_depth: - # self.left = None - # self.right = None - # self.prediction = [ - # np.round(Y[X[:,best_col] < self.split].mean()), - # np.round(Y[X[:,best_col] >= self.split].mean()), - # ] - current_node['left'] = None - current_node['right'] = None - current_node['prediction'] = [ - np.round(Y[X[:,best_col] < self.split].mean()), - np.round(Y[X[:,best_col] >= self.split].mean()), - ] - else: - # print "best split:", best_split - left_idx = (X[:,best_col] < best_split) - # print "left_idx.shape:", left_idx.shape, "len(X):", len(X) - # TODO: bad but I can't figure out a better way atm - Xleft = X[left_idx] - Yleft = Y[left_idx] - # self.left = TreeNode(self.depth + 1, self.max_depth) - # self.left.fit(Xleft, Yleft) - new_node = {} - current_node['left'] = new_node - left_data = { - 'node': new_node, - 'X': Xleft, - 'Y': Yleft, - } - queue.insert(0, left_data) - - right_idx = (X[:,best_col] >= best_split) - Xright = X[right_idx] - Yright = Y[right_idx] - # self.right = TreeNode(self.depth + 1, self.max_depth) - # self.right.fit(Xright, Yright) - new_node = {} - current_node['right'] = new_node - right_data = { - 'node': new_node, - 'X': Xright, - 'Y': Yright, - } - queue.insert(0, right_data) - - # setup for the next iteration of the loop - # idea is, queue stores list of work to be done - if len(queue) == 0: - break - - next_data = queue.pop() - current_node = next_data['node'] - X = next_data['X'] - Y = next_data['Y'] - - def find_split(self, X, Y, col): - # print "finding split for col:", col - x_values = X[:, col] - sort_idx = np.argsort(x_values) - x_values = x_values[sort_idx] - y_values = Y[sort_idx] - - # Note: optimal split is the midpoint between 2 points - # Note: optimal split is only on the boundaries between 2 classes - - # if boundaries[i] is true - # then y_values[i] != y_values[i+1] - # nonzero() gives us indices where arg is true - # but for some reason it returns a tuple of size 1 - boundaries = np.nonzero(y_values[:-1] != y_values[1:])[0] - best_split = None - max_ig = 0 - last_ig = 0 - for b in boundaries: - split = (x_values[b] + x_values[b+1]) / 2 - ig = self.information_gain(x_values, y_values, split) - if ig < last_ig: - break - last_ig = ig - if ig > max_ig: - max_ig = ig - best_split = split - return max_ig, best_split - - def information_gain(self, x, y, split): - # assume classes are 0 and 1 - # print "split:", split - y0 = y[x < split] - y1 = y[x >= split] - N = len(y) - y0len = len(y0) - if y0len == 0 or y0len == N: - return 0 - p0 = float(len(y0)) / N - p1 = 1 - p0 #float(len(y1)) / N - # print "entropy(y):", entropy(y) - # print "p0:", p0 - # print "entropy(y0):", entropy(y0) - # print "p1:", p1 - # print "entropy(y1):", entropy(y1) - return entropy(y) - p0*entropy(y0) - p1*entropy(y1) - - def predict_one(self, x): - # use "is not None" because 0 means False - # if self.col is not None and self.split is not None: - # feature = x[self.col] - # if feature < self.split: - # if self.left: - # p = self.left.predict_one(x) - # else: - # p = self.prediction[0] - # else: - # if self.right: - # p = self.right.predict_one(x) - # else: - # p = self.prediction[1] - # else: - # # corresponds to having only 1 prediction - # p = self.prediction - p = None - current_node = self.root - while True: - if current_node['col'] is not None and current_node['split'] is not None: - feature = x[current_node['col']] - if feature < current_node['split']: - if current_node['left']: - current_node = current_node['left'] - else: - p = current_node['prediction'][0] - break - else: - if current_node['right']: - current_node = current_node['right'] - else: - p = current_node['prediction'][1] - break - else: - # corresponds to having only 1 prediction - p = current_node['prediction'] - break - return p - - def predict(self, X): - N = len(X) - P = np.zeros(N) - for i in range(N): - P[i] = self.predict_one(X[i]) - return P - - def score(self, X, Y): - P = self.predict(X) - return np.mean(P == Y) - - -if __name__ == '__main__': - X, Y = get_data() - - # try donut and xor - # from sklearn.utils import shuffle - # X, Y = get_xor() - # # X, Y = get_donut() - # X, Y = shuffle(X, Y) - - # only take 0s and 1s since we're doing binary classification - idx = np.logical_or(Y == 0, Y == 1) - X = X[idx] - Y = Y[idx] - - # split the data - Ntrain = len(Y) // 2 - Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] - Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - - model = DecisionTree() - # model = DecisionTree(max_depth=7) - t0 = datetime.now() - model.fit(Xtrain, Ytrain) - print("Training time:", (datetime.now() - t0)) - - t0 = datetime.now() - print("Train accuracy:", model.score(Xtrain, Ytrain)) - print("Time to compute train accuracy:", (datetime.now() - t0)) - - t0 = datetime.now() - print("Test accuracy:", model.score(Xtest, Ytest)) - print("Time to compute test accuracy:", (datetime.now() - t0)) - - # test SKLearn - from sklearn.tree import DecisionTreeClassifier - model = DecisionTreeClassifier() - t0 = datetime.now() - model.fit(Xtrain, Ytrain) - print("SK: Training time:", (datetime.now() - t0)) - - t0 = datetime.now() - print("Train accuracy:", model.score(Xtrain, Ytrain)) - print("SK: Time to compute train accuracy:", (datetime.now() - t0)) - - t0 = datetime.now() - print("Test accuracy:", model.score(Xtest, Ytest)) - print("SK: Time to compute test accuracy:", (datetime.now() - t0)) diff --git a/supervised_class/knn.py b/supervised_class/knn.py deleted file mode 100644 index 015bffef..00000000 --- a/supervised_class/knn.py +++ /dev/null @@ -1,97 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -# This is an example of a K-Nearest Neighbors classifier on MNIST data. -# We try k=1...5 to show how we might choose the best k. -# sudo pip install sortedcontainers (if you don't have it) -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from sortedcontainers import SortedList -# Note: You can't use SortedDict because the key is distance -# if 2 close points are the same distance away, one will be overwritten -from util import get_data -from datetime import datetime - - -class KNN(object): - def __init__(self, k): - self.k = k - - def fit(self, X, y): - self.X = X - self.y = y - - def predict(self, X): - y = np.zeros(len(X)) - for i,x in enumerate(X): # test points - sl = SortedList() # stores (distance, class) tuples - for j,xt in enumerate(self.X): # training points - diff = x - xt - d = diff.dot(diff) - if len(sl) < self.k: - # don't need to check, just add - sl.add( (d, self.y[j]) ) - else: - if d < sl[-1][0]: - del sl[-1] - sl.add( (d, self.y[j]) ) - # print "input:", x - # print "sl:", sl - - # vote - votes = {} - for _, v in sl: - # print "v:", v - votes[v] = votes.get(v,0) + 1 - # print "votes:", votes, "true:", Ytest[i] - max_votes = 0 - max_votes_class = -1 - for v,count in iteritems(votes): - if count > max_votes: - max_votes = count - max_votes_class = v - y[i] = max_votes_class - return y - - def score(self, X, Y): - P = self.predict(X) - return np.mean(P == Y) - - -if __name__ == '__main__': - X, Y = get_data(2000) - Ntrain = 1000 - Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] - Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - train_scores = [] - test_scores = [] - ks = (1,2,3,4,5) - for k in ks: - print("\nk =", k) - knn = KNN(k) - t0 = datetime.now() - knn.fit(Xtrain, Ytrain) - print("Training time:", (datetime.now() - t0)) - - t0 = datetime.now() - train_score = knn.score(Xtrain, Ytrain) - train_scores.append(train_score) - print("Train accuracy:", train_score) - print("Time to compute train accuracy:", (datetime.now() - t0), "Train size:", len(Ytrain)) - - t0 = datetime.now() - test_score = knn.score(Xtest, Ytest) - print("Test accuracy:", test_score) - test_scores.append(test_score) - print("Time to compute test accuracy:", (datetime.now() - t0), "Test size:", len(Ytest)) - - plt.plot(ks, train_scores, label='train scores') - plt.plot(ks, test_scores, label='test scores') - plt.legend() - plt.show() \ No newline at end of file diff --git a/supervised_class/knn_donut.py b/supervised_class/knn_donut.py deleted file mode 100644 index 90dae14f..00000000 --- a/supervised_class/knn_donut.py +++ /dev/null @@ -1,23 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -from knn import KNN -from util import get_donut -import matplotlib.pyplot as plt - -if __name__ == '__main__': - X, Y = get_donut() - - # display the data - plt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5) - plt.show() - - # get the accuracy - model = KNN(3) - model.fit(X, Y) - print("Accuracy:", model.score(X, Y)) \ No newline at end of file diff --git a/supervised_class/knn_fail.py b/supervised_class/knn_fail.py deleted file mode 100644 index a40c7dcc..00000000 --- a/supervised_class/knn_fail.py +++ /dev/null @@ -1,43 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from knn import KNN - -def get_data(): - width = 8 - height = 8 - N = width * height - X = np.zeros((N, 2)) - Y = np.zeros(N) - n = 0 - start_t = 0 - for i in range(width): - t = start_t - for j in range(height): - X[n] = [i, j] - Y[n] = t - n += 1 - t = (t + 1) % 2 # alternate between 0 and 1 - start_t = (start_t + 1) % 2 - return X, Y - - -if __name__ == '__main__': - X, Y = get_data() - - # display the data - plt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5) - plt.show() - - # get the accuracy - model = KNN(3) - model.fit(X, Y) - print("Train accuracy:", model.score(X, Y)) diff --git a/supervised_class/knn_vectorized.py b/supervised_class/knn_vectorized.py deleted file mode 100644 index 8a2488d2..00000000 --- a/supervised_class/knn_vectorized.py +++ /dev/null @@ -1,94 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -# This is an example of a K-Nearest Neighbors classifier on MNIST data. -# We try k=1...5 to show how we might choose the best k. -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from util import get_data -from datetime import datetime -from sklearn.metrics.pairwise import pairwise_distances - - -class KNN(object): - def __init__(self, k): - self.k = k - - def fit(self, X, y): - self.X = X - self.y = y - - def predict(self, X): - N = len(X) - y = np.zeros(N) - - # returns distances in a matrix - # of shape (N_test, N_train) - distances = pairwise_distances(X, self.X) - - - # now get the minimum k elements' indexes - # https://stackoverflow.com/questions/16817948/i-have-need-the-n-minimum-index-values-in-a-numpy-array - idx = distances.argsort(axis=1)[:, :self.k] - - # now determine the winning votes - # each row of idx contains indexes from 0..Ntrain - # corresponding to the indexes of the closest samples - # from the training set - # NOTE: if you don't "believe" this works, test it - # in your console with simpler arrays - votes = self.y[idx] - - # now y contains the classes in each row - # e.g. - # sample 0 --> [class0, class1, class1, class0, ...] - # unfortunately there's no good way to vectorize this - # https://stackoverflow.com/questions/19201972/can-numpy-bincount-work-with-2d-arrays - for i in range(N): - y[i] = np.bincount(votes[i]).argmax() - - return y - - def score(self, X, Y): - P = self.predict(X) - return np.mean(P == Y) - - -if __name__ == '__main__': - X, Y = get_data(2000) - Ntrain = 1000 - Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] - Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - train_scores = [] - test_scores = [] - ks = (1,2,3,4,5) - for k in ks: - print("\nk =", k) - knn = KNN(k) - t0 = datetime.now() - knn.fit(Xtrain, Ytrain) - print("Training time:", (datetime.now() - t0)) - - t0 = datetime.now() - train_score = knn.score(Xtrain, Ytrain) - train_scores.append(train_score) - print("Train accuracy:", train_score) - print("Time to compute train accuracy:", (datetime.now() - t0), "Train size:", len(Ytrain)) - - t0 = datetime.now() - test_score = knn.score(Xtest, Ytest) - print("Test accuracy:", test_score) - test_scores.append(test_score) - print("Time to compute test accuracy:", (datetime.now() - t0), "Test size:", len(Ytest)) - - plt.plot(ks, train_scores, label='train scores') - plt.plot(ks, test_scores, label='test scores') - plt.legend() - plt.show() - diff --git a/supervised_class/knn_xor.py b/supervised_class/knn_xor.py deleted file mode 100644 index 8e4b513e..00000000 --- a/supervised_class/knn_xor.py +++ /dev/null @@ -1,23 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -from knn import KNN -from util import get_xor -import matplotlib.pyplot as plt - -if __name__ == '__main__': - X, Y = get_xor() - - # display the data - plt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5) - plt.show() - - # get the accuracy - model = KNN(3) - model.fit(X, Y) - print("Accuracy:", model.score(X, Y)) \ No newline at end of file diff --git a/supervised_class/multinomialnb.py b/supervised_class/multinomialnb.py deleted file mode 100644 index a24155e6..00000000 --- a/supervised_class/multinomialnb.py +++ /dev/null @@ -1,58 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -# This is an example of a Naive Bayes classifier on MNIST data. -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from util import get_data -from datetime import datetime - -class MultinomialNB(object): - def fit(self, X, Y, smoothing=1.0): - # one-hot encode Y - K = len(set(Y)) # number of classes - N = len(Y) # number of samples - labels = Y - Y = np.zeros((N, K)) - Y[np.arange(N), labels] = 1 - - # D x K matrix of feature counts - # feature_counts[d,k] = count of feature d in class k - feature_counts = X.T.dot(Y) + smoothing - class_counts = Y.sum(axis=0) - - self.weights = np.log(feature_counts) - np.log(feature_counts.sum(axis=0)) - self.priors = np.log(class_counts) - np.log(class_counts.sum()) - - def score(self, X, Y): - P = self.predict(X) - return np.mean(P == Y) - - def predict(self, X): - P = X.dot(self.weights) + self.priors - return np.argmax(P, axis=1) - - -if __name__ == '__main__': - X, Y = get_data(10000) - Ntrain = len(Y) // 2 - Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] - Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - - model = MultinomialNB() - t0 = datetime.now() - model.fit(Xtrain, Ytrain) - print("Training time:", (datetime.now() - t0)) - - t0 = datetime.now() - print("Train accuracy:", model.score(Xtrain, Ytrain)) - print("Time to compute train accuracy:", (datetime.now() - t0), "Train size:", len(Ytrain)) - - t0 = datetime.now() - print("Test accuracy:", model.score(Xtest, Ytest)) - print("Time to compute test accuracy:", (datetime.now() - t0), "Test size:", len(Ytest)) diff --git a/supervised_class/nb.py b/supervised_class/nb.py deleted file mode 100644 index 7301b67a..00000000 --- a/supervised_class/nb.py +++ /dev/null @@ -1,61 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -# This is an example of a Naive Bayes classifier on MNIST data. -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from util import get_data -from datetime import datetime -from scipy.stats import norm -from scipy.stats import multivariate_normal as mvn - -class NaiveBayes(object): - def fit(self, X, Y, smoothing=1e-2): - self.gaussians = dict() - self.priors = dict() - labels = set(Y) - for c in labels: - current_x = X[Y == c] - self.gaussians[c] = { - 'mean': current_x.mean(axis=0), - 'var': current_x.var(axis=0) + smoothing, - } - self.priors[c] = float(len(Y[Y == c])) / len(Y) - - def score(self, X, Y): - P = self.predict(X) - return np.mean(P == Y) - - def predict(self, X): - N, D = X.shape - K = len(self.gaussians) - P = np.zeros((N, K)) - for c, g in iteritems(self.gaussians): - mean, var = g['mean'], g['var'] - P[:,c] = mvn.logpdf(X, mean=mean, cov=var) + np.log(self.priors[c]) - return np.argmax(P, axis=1) - - -if __name__ == '__main__': - X, Y = get_data(10000) - Ntrain = len(Y) // 2 - Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] - Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - - model = NaiveBayes() - t0 = datetime.now() - model.fit(Xtrain, Ytrain) - print("Training time:", (datetime.now() - t0)) - - t0 = datetime.now() - print("Train accuracy:", model.score(Xtrain, Ytrain)) - print("Time to compute train accuracy:", (datetime.now() - t0), "Train size:", len(Ytrain)) - - t0 = datetime.now() - print("Test accuracy:", model.score(Xtest, Ytest)) - print("Time to compute test accuracy:", (datetime.now() - t0), "Test size:", len(Ytest)) diff --git a/supervised_class/perceptron.py b/supervised_class/perceptron.py deleted file mode 100644 index eeaff09b..00000000 --- a/supervised_class/perceptron.py +++ /dev/null @@ -1,113 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from util import get_data as get_mnist -from datetime import datetime - - -def get_data(): - w = np.array([-0.5, 0.5]) - b = 0.1 - X = np.random.random((300, 2))*2 - 1 - Y = np.sign(X.dot(w) + b) - return X, Y - - -def get_simple_xor(): - X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]]) - Y = np.array([0, 1, 1, 0]) - return X, Y - - -class Perceptron: - def fit(self, X, Y, learning_rate=1.0, epochs=1000): - # solution - # self.w = np.array([-0.5, 0.5]) - # self.b = 0.1 - - # initialize random weights - D = X.shape[1] - self.w = np.random.randn(D) - self.b = 0 - - N = len(Y) - costs = [] - for epoch in range(epochs): - # determine which samples are misclassified, if any - Yhat = self.predict(X) - incorrect = np.nonzero(Y != Yhat)[0] - if len(incorrect) == 0: - # we are done! - break - - # choose a random incorrect sample - i = np.random.choice(incorrect) - self.w += learning_rate*Y[i]*X[i] - self.b += learning_rate*Y[i] - - # cost is incorrect rate - c = len(incorrect) / float(N) - costs.append(c) - print("final w:", self.w, "final b:", self.b, "epochs:", (epoch+1), "/", epochs) - plt.plot(costs) - plt.show() - - def predict(self, X): - return np.sign(X.dot(self.w) + self.b) - - def score(self, X, Y): - P = self.predict(X) - return np.mean(P == Y) - - -if __name__ == '__main__': - # linearly separable data - X, Y = get_data() - plt.scatter(X[:,0], X[:,1], c=Y, s=100, alpha=0.5) - plt.show() - Ntrain = len(Y) // 2 - Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] - Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - - model = Perceptron() - t0 = datetime.now() - model.fit(Xtrain, Ytrain) - print("Training time:", (datetime.now() - t0)) - - t0 = datetime.now() - print("Train accuracy:", model.score(Xtrain, Ytrain)) - print("Time to compute train accuracy:", (datetime.now() - t0), "Train size:", len(Ytrain)) - - t0 = datetime.now() - print("Test accuracy:", model.score(Xtest, Ytest)) - print("Time to compute test accuracy:", (datetime.now() - t0), "Test size:", len(Ytest)) - - - # mnist - X, Y = get_mnist() - idx = np.logical_or(Y == 0, Y == 1) - X = X[idx] - Y = Y[idx] - Y[Y == 0] = -1 - model = Perceptron() - t0 = datetime.now() - model.fit(X, Y, learning_rate=1e-2) - print("MNIST train accuracy:", model.score(X, Y)) - - - # xor data - print("") - print("XOR results:") - X, Y = get_simple_xor() - Y[Y == 0] = -1 - model.fit(X, Y) - print("XOR accuracy:", model.score(X, Y)) - diff --git a/supervised_class/regression.py b/supervised_class/regression.py deleted file mode 100644 index 8f81d43f..00000000 --- a/supervised_class/regression.py +++ /dev/null @@ -1,35 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -# Works with Python 2 and 3 - - -import numpy as np -import matplotlib.pyplot as plt -from sklearn.neighbors import KNeighborsRegressor -from sklearn.tree import DecisionTreeRegressor - -N = 200 -X = np.linspace(0, 10, N).reshape(N, 1) -Y = np.sin(X) - -Ntrain = 20 -idx = np.random.choice(N, Ntrain) -Xtrain = X[idx] -Ytrain = Y[idx] - -knn = KNeighborsRegressor(n_neighbors=2, weights='distance') -knn.fit(Xtrain, Ytrain) -Yknn = knn.predict(X) - -dt = DecisionTreeRegressor() -dt.fit(Xtrain, Ytrain) -Ydt = dt.predict(X) - -plt.scatter(Xtrain, Ytrain) # show the training points -plt.plot(X, Y) # show the original data -plt.plot(X, Yknn, label='KNN') -plt.plot(X, Ydt, label='Decision Tree') -plt.legend() -plt.show() - - diff --git a/supervised_class/util.py b/supervised_class/util.py deleted file mode 100644 index d733eaad..00000000 --- a/supervised_class/util.py +++ /dev/null @@ -1,49 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-supervised-machine-learning-in-python -# https://www.udemy.com/data-science-supervised-machine-learning-in-python -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import pandas as pd - -def get_data(limit=None): - print("Reading in and transforming data...") - df = pd.read_csv('../large_files/train.csv') - data = df.values - np.random.shuffle(data) - X = data[:, 1:] / 255.0 # data is from 0..255 - Y = data[:, 0] - if limit is not None: - X, Y = X[:limit], Y[:limit] - return X, Y - -def get_xor(): - X = np.zeros((200, 2)) - X[:50] = np.random.random((50, 2)) / 2 + 0.5 # (0.5-1, 0.5-1) - X[50:100] = np.random.random((50, 2)) / 2 # (0-0.5, 0-0.5) - X[100:150] = np.random.random((50, 2)) / 2 + np.array([[0, 0.5]]) # (0-0.5, 0.5-1) - X[150:] = np.random.random((50, 2)) / 2 + np.array([[0.5, 0]]) # (0.5-1, 0-0.5) - Y = np.array([0]*100 + [1]*100) - return X, Y - -def get_donut(): - N = 200 - R_inner = 5 - R_outer = 10 - - # distance from origin is radius + random normal - # angle theta is uniformly distributed between (0, 2pi) - R1 = np.random.randn(N//2) + R_inner - theta = 2*np.pi*np.random.random(N//2) - X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T - - R2 = np.random.randn(N//2) + R_outer - theta = 2*np.pi*np.random.random(N//2) - X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T - - X = np.concatenate([ X_inner, X_outer ]) - Y = np.array([0]*(N//2) + [1]*(N//2)) - return X, Y diff --git a/supervised_class2/adaboost.py b/supervised_class2/adaboost.py deleted file mode 100644 index 457d4216..00000000 --- a/supervised_class2/adaboost.py +++ /dev/null @@ -1,98 +0,0 @@ -# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost -# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from sklearn.tree import DecisionTreeClassifier -from rf_classification import get_data - - -class AdaBoost: - def __init__(self, M): - self.M = M - - def fit(self, X, Y): - self.models = [] - self.alphas = [] - - N, _ = X.shape - W = np.ones(N) / N - - for m in range(self.M): - tree = DecisionTreeClassifier(max_depth=1) - tree.fit(X, Y, sample_weight=W) - P = tree.predict(X) - - err = W.dot(P != Y) - alpha = 0.5*(np.log(1 - err) - np.log(err)) - - W = W*np.exp(-alpha*Y*P) # vectorized form - W = W / W.sum() # normalize so it sums to 1 - - self.models.append(tree) - self.alphas.append(alpha) - - def predict(self, X): - # NOT like SKLearn API - # we want accuracy and exponential loss for plotting purposes - N, _ = X.shape - FX = np.zeros(N) - for alpha, tree in zip(self.alphas, self.models): - FX += alpha*tree.predict(X) - return np.sign(FX), FX - - def score(self, X, Y): - # NOT like SKLearn API - # we want accuracy and exponential loss for plotting purposes - P, FX = self.predict(X) - L = np.exp(-Y*FX).mean() - return np.mean(P == Y), L - - -if __name__ == '__main__': - - X, Y = get_data() - Y[Y == 0] = -1 # make the targets -1,+1 - Ntrain = int(0.8*len(X)) - Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] - Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - - T = 200 - train_errors = np.empty(T) - test_losses = np.empty(T) - test_errors = np.empty(T) - for num_trees in range(T): - if num_trees == 0: - train_errors[num_trees] = None - test_errors[num_trees] = None - test_losses[num_trees] = None - continue - if num_trees % 20 == 0: - print(num_trees) - - model = AdaBoost(num_trees) - model.fit(Xtrain, Ytrain) - acc, loss = model.score(Xtest, Ytest) - acc_train, _ = model.score(Xtrain, Ytrain) - train_errors[num_trees] = 1 - acc_train - test_errors[num_trees] = 1 - acc - test_losses[num_trees] = loss - - if num_trees == T - 1: - print("final train error:", 1 - acc_train) - print("final test error:", 1 - acc) - - plt.plot(test_errors, label='test errors') - plt.plot(test_losses, label='test losses') - plt.legend() - plt.show() - - plt.plot(train_errors, label='train errors') - plt.plot(test_errors, label='test errors') - plt.legend() - plt.show() diff --git a/supervised_class2/bagging_classification.py b/supervised_class2/bagging_classification.py deleted file mode 100644 index b7ad4575..00000000 --- a/supervised_class2/bagging_classification.py +++ /dev/null @@ -1,91 +0,0 @@ -# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost -# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from sklearn.tree import DecisionTreeClassifier -from sklearn.utils import shuffle -from util import plot_decision_boundary - -np.random.seed(10) - -# create the data -N = 500 -D = 2 -X = np.random.randn(N, D) - -# 2 gaussians -# sep = 1.5 -# X[:N/2] += np.array([sep, sep]) -# X[N/2:] += np.array([-sep, -sep]) -# Y = np.array([0]*(N/2) + [1]*(N/2)) - -# noisy XOR -sep = 2 -X[:125] += np.array([sep, sep]) -X[125:250] += np.array([sep, -sep]) -X[250:375] += np.array([-sep, -sep]) -X[375:] += np.array([-sep, sep]) -Y = np.array([0]*125 + [1]*125 + [0]*125 + [1]*125) - -# plot the data -plt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5) -plt.show() - -# lone decision tree -model = DecisionTreeClassifier() -model.fit(X, Y) -print("score for 1 tree:", model.score(X, Y)) - -# plot data with boundary -plt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5) -plot_decision_boundary(X, model) -plt.show() - - -# create the bagged model -class BaggedTreeClassifier: - def __init__(self, B): - self.B = B - - def fit(self, X, Y): - N = len(X) - self.models = [] - for b in range(self.B): - idx = np.random.choice(N, size=N, replace=True) - Xb = X[idx] - Yb = Y[idx] - - model = DecisionTreeClassifier(max_depth=2) - model.fit(Xb, Yb) - self.models.append(model) - - def predict(self, X): - # no need to keep a dictionary since we are doing binary classification - predictions = np.zeros(len(X)) - for model in self.models: - predictions += model.predict(X) - return np.round(predictions / self.B) - - def score(self, X, Y): - P = self.predict(X) - return np.mean(Y == P) - - -model = BaggedTreeClassifier(200) -model.fit(X, Y) - -print("score for bagged model:", model.score(X, Y)) - -# plot data with boundary -plt.scatter(X[:,0], X[:,1], s=100, c=Y, alpha=0.5) -plot_decision_boundary(X, model) -plt.show() - - - diff --git a/supervised_class2/bagging_regression.py b/supervised_class2/bagging_regression.py deleted file mode 100644 index 723299a8..00000000 --- a/supervised_class2/bagging_regression.py +++ /dev/null @@ -1,74 +0,0 @@ -# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost -# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from sklearn.tree import DecisionTreeRegressor -from sklearn.utils import shuffle - - -# create the data -T = 100 -x_axis = np.linspace(0, 2*np.pi, T) -y_axis = np.sin(x_axis) - -# get the training data -N = 30 -idx = np.random.choice(T, size=N, replace=False) -Xtrain = x_axis[idx].reshape(N, 1) -Ytrain = y_axis[idx] - -# try a lone decision tree -model = DecisionTreeRegressor() -model.fit(Xtrain, Ytrain) -prediction = model.predict(x_axis.reshape(T, 1)) -print("score for 1 tree:", model.score(x_axis.reshape(T, 1), y_axis)) - -# plot the lone decision tree's predictions -plt.plot(x_axis, prediction) -plt.plot(x_axis, y_axis) -plt.show() - -# now try bagging -class BaggedTreeRegressor: - def __init__(self, B): - self.B = B - - def fit(self, X, Y): - N = len(X) - self.models = [] - for b in range(self.B): - idx = np.random.choice(N, size=N, replace=True) - Xb = X[idx] - Yb = Y[idx] - - model = DecisionTreeRegressor() - model.fit(Xb, Yb) - self.models.append(model) - - def predict(self, X): - predictions = np.zeros(len(X)) - for model in self.models: - predictions += model.predict(X) - return predictions / self.B - - def score(self, X, Y): - d1 = Y - self.predict(X) - d2 = Y - Y.mean() - return 1 - d1.dot(d1) / d2.dot(d2) - - -model = BaggedTreeRegressor(200) -model.fit(Xtrain, Ytrain) -print("score for bagged tree:", model.score(x_axis.reshape(T, 1), y_axis)) -prediction = model.predict(x_axis.reshape(T, 1)) - -# plot the bagged regressor's predictions -plt.plot(x_axis, prediction) -plt.plot(x_axis, y_axis) -plt.show() diff --git a/supervised_class2/bias_variance_demo.py b/supervised_class2/bias_variance_demo.py deleted file mode 100644 index 4f4d2859..00000000 --- a/supervised_class2/bias_variance_demo.py +++ /dev/null @@ -1,136 +0,0 @@ -# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost -# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from sklearn.linear_model import LinearRegression -from sklearn.metrics import mean_squared_error as mse - -NUM_DATASETS = 50 -NOISE_VARIANCE = 0.5 -MAX_POLY = 12 -N = 25 -Ntrain = int(0.9*N) - -np.random.seed(2) - -# make a dataset with x^D, x^(D-1), ..., x^0 -def make_poly(x, D): - N = len(x) - X = np.empty((N, D+1)) - for d in range(D+1): - X[:,d] = x**d - if d > 1: - X[:,d] = (X[:,d] - X[:,d].mean()) / X[:,d].std() - return X - -def f(X): - return np.sin(X) - - -x_axis = np.linspace(-np.pi, np.pi, 100) -y_axis = f(x_axis) - -# plot the data -# plt.plot(x_axis, y_axis) -# plt.show() - -# f(x) = sin(x) from x = [-pi, +pi] -X = np.linspace(-np.pi, np.pi, N) -np.random.shuffle(X) -f_X = f(X) - -# just need to do this once -Xpoly = make_poly(X, MAX_POLY) - -# array to store all the scores -train_scores = np.zeros((NUM_DATASETS, MAX_POLY)) -test_scores = np.zeros((NUM_DATASETS, MAX_POLY)) -# squared_biases = np.zeros((NUM_DATASETS, MAX_POLY)) -# test_predictions = np.zeros((N - Ntrain, NUM_DATASETS, MAX_POLY)) -train_predictions = np.zeros((Ntrain, NUM_DATASETS, MAX_POLY)) -prediction_curves = np.zeros((100, NUM_DATASETS, MAX_POLY)) - -# create the model -model = LinearRegression() - -for k in range(NUM_DATASETS): - Y = f_X + np.random.randn(N)*NOISE_VARIANCE - - Xtrain = Xpoly[:Ntrain] - Ytrain = Y[:Ntrain] - - Xtest = Xpoly[Ntrain:] - Ytest = Y[Ntrain:] - - for d in range(MAX_POLY): - model.fit(Xtrain[:,:d+2], Ytrain) - predictions = model.predict(Xpoly[:,:d+2]) - - # debug - x_axis_poly = make_poly(x_axis, d+1) - prediction_axis = model.predict(x_axis_poly) - # plt.plot(x_axis, prediction_axis) - # plt.show() - - prediction_curves[:,k,d] = prediction_axis - - train_prediction = predictions[:Ntrain] - test_prediction = predictions[Ntrain:] - - train_predictions[:,k,d] = train_prediction # use this to calculate bias/variance later - - train_score = mse(train_prediction, Ytrain) - test_score = mse(test_prediction, Ytest) - - train_scores[k,d] = train_score - test_scores[k,d] = test_score - -# show all prediction curves for each polynomial degree -# along with the mean curve -for d in range(MAX_POLY): - for k in range(NUM_DATASETS): - plt.plot(x_axis, prediction_curves[:,k,d], color='green', alpha=0.5) - plt.plot(x_axis, prediction_curves[:,:,d].mean(axis=1), color='blue', linewidth=2.0) - plt.title("All curves for degree = %d" % (d+1)) - plt.show() - -# calculate the squared bias -avg_train_prediction = np.zeros((Ntrain, MAX_POLY)) -squared_bias = np.zeros(MAX_POLY) -f_Xtrain = f_X[:Ntrain] -for d in range(MAX_POLY): - for i in range(Ntrain): - avg_train_prediction[i,d] = train_predictions[i,:,d].mean() - squared_bias[d] = ((avg_train_prediction[:,d] - f_Xtrain)**2).mean() - -# calculate the variance -variances = np.zeros((Ntrain, MAX_POLY)) -for d in range(MAX_POLY): - for i in range(Ntrain): - delta = train_predictions[i,:,d] - avg_train_prediction[i,d] - variances[i,d] = delta.dot(delta) / len(delta) -variance = variances.mean(axis=0) - -# make bias-variance plots -degrees = np.arange(MAX_POLY) + 1 -best_degree = np.argmin(test_scores.mean(axis=0)) + 1 -plt.plot(degrees, squared_bias, label='squared bias') -plt.plot(degrees, variance, label='variance') -plt.plot(degrees, test_scores.mean(axis=0), label='test scores') -plt.plot(degrees, squared_bias + variance, label='squared bias + variance') -plt.axvline(x=best_degree, linestyle='--', label='best complexity') -plt.legend() -plt.show() - -# train score vs test score -plt.plot(degrees, train_scores.mean(axis=0), label='train scores') -plt.plot(degrees, test_scores.mean(axis=0), label='test scores') -plt.axvline(x=best_degree, linestyle='--', label='best complexity') -plt.legend() -plt.show() diff --git a/supervised_class2/bootstrap.py b/supervised_class2/bootstrap.py deleted file mode 100644 index d245d59b..00000000 --- a/supervised_class2/bootstrap.py +++ /dev/null @@ -1,42 +0,0 @@ -# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost -# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from scipy.stats import norm, t - -B = 200 -N = 20 -X = np.random.randn(N) - -print("sample mean of X:", X.mean()) - -individual_estimates = np.empty(B) -for b in range(B): - sample = np.random.choice(X, size=N) - individual_estimates[b] = sample.mean() - - -bmean = individual_estimates.mean() -bstd = individual_estimates.std() -lower = bmean + norm.ppf(0.025)*bstd # norm.ppf(0.025) == -1.96 -upper = bmean + norm.ppf(0.975)*bstd # norm.ppf(0.975) == +1.96 - -# traditional way of calculating CI -lower2 = X.mean() + norm.ppf(0.025)*X.std()/np.sqrt(N) -upper2 = X.mean() + norm.ppf(0.975)*X.std()/np.sqrt(N) - -print("bootstrap mean of X:", bmean) - -plt.hist(individual_estimates, bins=20) -plt.axvline(x=lower, linestyle='--', color='g', label="lower bound for 95%% CI (bootstrap)") -plt.axvline(x=upper, linestyle='--', color='g', label="upper bound for 95%% CI (bootstrap)") -plt.axvline(x=lower2, linestyle='--', color='r', label="lower bound for 95%% CI") -plt.axvline(x=upper2, linestyle='--', color='r', label="upper bound for 95%% CI") -plt.legend() -plt.show() diff --git a/supervised_class2/extra_reading.txt b/supervised_class2/extra_reading.txt deleted file mode 100644 index 79711307..00000000 --- a/supervised_class2/extra_reading.txt +++ /dev/null @@ -1,14 +0,0 @@ -RANDOM FORESTS -https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf - -A Short Introduction to Boosting -https://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf - -Explaining AdaBoost -http://rob.schapire.net/papers/explaining-adaboost.pdf - -Improved Boosting Algorithms Using Confidence-rated Predictions -https://sci2s.ugr.es/keel/pdf/algorithm/articulo/1999-ML-Improved%20boosting%20algorithms%20using%20confidence-rated%20predictions%20(Schapire%20y%20Singer).pdf - -Why does the bootstrap work? -http://www.stat.cmu.edu/~larry/=sml/Boot.pdf \ No newline at end of file diff --git a/supervised_class2/knn_dt_demo.py b/supervised_class2/knn_dt_demo.py deleted file mode 100644 index 713be546..00000000 --- a/supervised_class2/knn_dt_demo.py +++ /dev/null @@ -1,152 +0,0 @@ -# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost -# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier -from sklearn.neighbors import KNeighborsRegressor, KNeighborsClassifier -from sklearn.utils import shuffle - -N = 20 -Ntrain = 12 - -# create the data -X = np.linspace(0, 2*np.pi, N).reshape(N, 1) -Y = np.sin(3*X) -X, Y = shuffle(X, Y) - -Xtrain = X[:Ntrain] -Ytrain = Y[:Ntrain] - -# decision tree - low bias, high variance -model = DecisionTreeRegressor() # default max_depth=None -model.fit(Xtrain, Ytrain) - -T = 50 -Xaxis = np.linspace(0, 2*np.pi, T) -Yaxis = np.sin(3*Xaxis) - -plt.scatter(Xtrain, Ytrain, s=50, alpha=0.7, c='blue') -plt.scatter(Xtrain, model.predict(Xtrain.reshape(Ntrain, 1)), s=50, alpha=0.7, c='green') -plt.title("decision tree - low bias, high variance") -# plt.show() - -# plt.scatter(X, Y) -# plt.scatter(X, model.predict(X.reshape(N, 1))) -# plt.show() - -plt.plot(Xaxis, Yaxis) -plt.plot(Xaxis, model.predict(Xaxis.reshape(T, 1))) -plt.show() - - -# decision tree - high bias, low variance -model = DecisionTreeRegressor(max_depth=1) -model.fit(Xtrain, Ytrain) - -plt.scatter(Xtrain, Ytrain, s=50, alpha=0.7, c='blue') -plt.scatter(Xtrain, model.predict(Xtrain.reshape(Ntrain, 1)), s=50, alpha=0.7, c='green') -plt.plot(Xaxis, Yaxis) -plt.plot(Xaxis, model.predict(Xaxis.reshape(T, 1))) -plt.title("decision tree - high bias, low variance") -plt.show() - - -# knn - low bias, high variance -model = KNeighborsRegressor(n_neighbors=1) -model.fit(Xtrain, Ytrain) - -plt.scatter(Xtrain, Ytrain, s=50, alpha=0.7, c='blue') -plt.scatter(Xtrain, model.predict(Xtrain.reshape(Ntrain, 1)), s=50, alpha=0.7, c='green') -plt.plot(Xaxis, Yaxis) -plt.plot(Xaxis, model.predict(Xaxis.reshape(T, 1))) -plt.title("knn - low bias, high variance") -plt.show() - -# knn - high bias, low variance -model = KNeighborsRegressor(n_neighbors=10) -model.fit(Xtrain, Ytrain) - -plt.scatter(Xtrain, Ytrain, s=50, alpha=0.7, c='blue') -plt.scatter(Xtrain, model.predict(Xtrain.reshape(Ntrain, 1)), s=50, alpha=0.7, c='green') -plt.plot(Xaxis, Yaxis) -plt.plot(Xaxis, model.predict(Xaxis.reshape(T, 1))) -plt.title("knn - high bias, low variance") -plt.show() - - -# classification - -# generate the data -N = 100 -D = 2 -X = np.random.randn(N, D) -X[:N//2] += np.array([1, 1]) # center it at (1,1) -X[N//2:] += np.array([-1, -1]) # center it at (-1, -1) - -Y = np.array([0]*(N//2) + [1]*(N//2)) - - -def plot_decision_boundary(X, model): - h = .02 # step size in the mesh - # create a mesh to plot in - x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 - y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 - xx, yy = np.meshgrid(np.arange(x_min, x_max, h), - np.arange(y_min, y_max, h)) - - - # Plot the decision boundary. For that, we will assign a color to each - # point in the mesh [x_min, m_max]x[y_min, y_max]. - Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) - - # Put the result into a color plot - Z = Z.reshape(xx.shape) - plt.contour(xx, yy, Z, cmap=plt.cm.Paired) - - -# look at the data -plt.scatter(X[:,0], X[:,1], s=50, c=Y, alpha=0.7) -plt.show() - -# dt - low bias, high variance -model = DecisionTreeClassifier() -model.fit(X, Y) - -plt.scatter(X[:,0], X[:,1], s=50, c=Y, alpha=0.7) -plot_decision_boundary(X, model) -plt.title("dt - low bias, high variance") -plt.show() - -# dt - high bias, low variance -model = DecisionTreeClassifier(max_depth=2) -model.fit(X, Y) - -plt.scatter(X[:,0], X[:,1], s=50, c=Y, alpha=0.7) -plot_decision_boundary(X, model) -plt.title("dt - high bias, low variance") -plt.show() - - -# knn - low bias, high variance -model = KNeighborsClassifier(n_neighbors=1) -model.fit(X, Y) - -plt.scatter(X[:,0], X[:,1], s=50, c=Y, alpha=0.7) -plot_decision_boundary(X, model) -plt.title("knn - low bias, high variance") -plt.show() - -# knn - high bias, low variance -model = KNeighborsClassifier(n_neighbors=20) -model.fit(X, Y) - -plt.scatter(X[:,0], X[:,1], s=50, c=Y, alpha=0.7) -plot_decision_boundary(X, model) -plt.title("knn - high bias, low variance") -plt.show() diff --git a/supervised_class2/rf_classification.py b/supervised_class2/rf_classification.py deleted file mode 100644 index 6466f1b4..00000000 --- a/supervised_class2/rf_classification.py +++ /dev/null @@ -1,117 +0,0 @@ -# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost -# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost -# mushroom data from: -# https://archive.ics.uci.edu/ml/datasets/Mushroom -# put all files in the folder ../large_files/ -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.preprocessing import LabelEncoder, StandardScaler -from sklearn.tree import DecisionTreeClassifier -from sklearn.ensemble import RandomForestClassifier -from sklearn.linear_model import LogisticRegression -from sklearn.model_selection import cross_val_score - -NUMERICAL_COLS = () -CATEGORICAL_COLS = np.arange(22) + 1 # 1..22 inclusive - -# transforms data from dataframe to numerical matrix -# one-hot encodes categories and normalizes numerical columns -# we want to use the scales found in training when transforming the test set -# so only call fit() once -# call transform() for any subsequent data -class DataTransformer: - def fit(self, df): - self.labelEncoders = {} - self.scalers = {} - for col in NUMERICAL_COLS: - scaler = StandardScaler() - scaler.fit(df[col].reshape(-1, 1)) - self.scalers[col] = scaler - - for col in CATEGORICAL_COLS: - encoder = LabelEncoder() - # in case the train set does not have 'missing' value but test set does - values = df[col].tolist() - values.append('missing') - encoder.fit(values) - self.labelEncoders[col] = encoder - - # find dimensionality - self.D = len(NUMERICAL_COLS) - for col, encoder in iteritems(self.labelEncoders): - self.D += len(encoder.classes_) - print("dimensionality:", self.D) - - def transform(self, df): - N, _ = df.shape - X = np.zeros((N, self.D)) - i = 0 - for col, scaler in iteritems(self.scalers): - X[:,i] = scaler.transform(df[col].values.reshape(-1, 1)).flatten() - i += 1 - - for col, encoder in iteritems(self.labelEncoders): - # print "transforming col:", col - K = len(encoder.classes_) - X[np.arange(N), encoder.transform(df[col]) + i] = 1 - i += K - return X - - def fit_transform(self, df): - self.fit(df) - return self.transform(df) - - -def replace_missing(df): - # standard method of replacement for numerical columns is median - for col in NUMERICAL_COLS: - if np.any(df[col].isnull()): - med = np.median(df[ col ][ df[col].notnull() ]) - df.loc[ df[col].isnull(), col ] = med - - # set a special value = 'missing' - for col in CATEGORICAL_COLS: - if np.any(df[col].isnull()): - print(col) - df.loc[ df[col].isnull(), col ] = 'missing' - - -def get_data(): - df = pd.read_csv('../large_files/mushroom.data', header=None) - - # replace label column: e/p --> 0/1 - # e = edible = 0, p = poisonous = 1 - df[0] = df.apply(lambda row: 0 if row[0] == 'e' else 1, axis=1) - - # check if there is missing data - replace_missing(df) - - # transform the data - transformer = DataTransformer() - - X = transformer.fit_transform(df) - Y = df[0].values - return X, Y - - -if __name__ == '__main__': - X, Y = get_data() - - # do a quick baseline test - baseline = LogisticRegression() - print("CV baseline:", cross_val_score(baseline, X, Y, cv=8).mean()) - - # single tree - tree = DecisionTreeClassifier() - print("CV one tree:", cross_val_score(tree, X, Y, cv=8).mean()) - - model = RandomForestClassifier(n_estimators=20) # try 10, 20, 50, 100, 200 - print("CV forest:", cross_val_score(model, X, Y, cv=8).mean()) diff --git a/supervised_class2/rf_regression.py b/supervised_class2/rf_regression.py deleted file mode 100644 index ae31cef4..00000000 --- a/supervised_class2/rf_regression.py +++ /dev/null @@ -1,141 +0,0 @@ -# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost -# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost -# uses house dataset from https://archive.ics.uci.edu/ml/machine-learning-databases/housing/ -# Alternate data source: https://archive.org/download/housing_202405/housing.data -# put all files in the folder ../large_files -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.preprocessing import LabelEncoder, StandardScaler -from sklearn.ensemble import RandomForestRegressor -from sklearn.linear_model import LinearRegression -from sklearn.tree import DecisionTreeRegressor -from sklearn.model_selection import cross_val_score - -NUMERICAL_COLS = [ - 'crim', # numerical - 'zn', # numerical - 'nonretail', # numerical - 'nox', # numerical - 'rooms', # numerical - 'age', # numerical - 'dis', # numerical - 'rad', # numerical - 'tax', # numerical - 'ptratio', # numerical - 'b', # numerical - 'lstat', # numerical -] - -NO_TRANSFORM = ['river'] - -# transforms data from dataframe to numerical matrix -# we want to use the scales found in training when transforming the test set -# so only call fit() once -# call transform() for any subsequent data -class DataTransformer: - def fit(self, df): - self.scalers = {} - for col in NUMERICAL_COLS: - scaler = StandardScaler() - scaler.fit(df[col].values.reshape(-1, 1)) - self.scalers[col] = scaler - - def transform(self, df): - N, _ = df.shape - D = len(NUMERICAL_COLS) + len(NO_TRANSFORM) - X = np.zeros((N, D)) - i = 0 - for col, scaler in iteritems(self.scalers): - X[:,i] = scaler.transform(df[col].values.reshape(-1, 1)).flatten() - i += 1 - for col in NO_TRANSFORM: - X[:,i] = df[col] - i += 1 - return X - - def fit_transform(self, df): - self.fit(df) - return self.transform(df) - - -def get_data(): - df = pd.read_csv('housing.data', header=None, delim_whitespace=True) - df.columns = [ - 'crim', # numerical - 'zn', # numerical - 'nonretail', # numerical - 'river', # binary - 'nox', # numerical - 'rooms', # numerical - 'age', # numerical - 'dis', # numerical - 'rad', # numerical - 'tax', # numerical - 'ptratio', # numerical - 'b', # numerical - 'lstat', # numerical - 'medv', # numerical -- this is the target - ] - - # transform the data - transformer = DataTransformer() - - # shuffle the data - N = len(df) - train_idx = np.random.choice(N, size=int(0.7*N), replace=False) - test_idx = [i for i in range(N) if i not in train_idx] - df_train = df.loc[train_idx] - df_test = df.loc[test_idx] - - Xtrain = transformer.fit_transform(df_train) - Ytrain = np.log(df_train['medv'].values) - Xtest = transformer.transform(df_test) - Ytest = np.log(df_test['medv'].values) - return Xtrain, Ytrain, Xtest, Ytest - - -if __name__ == '__main__': - Xtrain, Ytrain, Xtest, Ytest = get_data() - - model = RandomForestRegressor(n_estimators=100) # try 10, 20, 50, 100, 200 - model.fit(Xtrain, Ytrain) - predictions = model.predict(Xtest) - - # plot predictions vs targets - plt.scatter(Ytest, predictions) - plt.xlabel("target") - plt.ylabel("prediction") - ymin = np.round( min( min(Ytest), min(predictions) ) ) - ymax = np.ceil( max( max(Ytest), max(predictions) ) ) - print("ymin:", ymin, "ymax:", ymax) - r = range(int(ymin), int(ymax) + 1) - plt.plot(r, r) - plt.show() - - plt.plot(Ytest, label='targets') - plt.plot(predictions, label='predictions') - plt.legend() - plt.show() - - # do a quick baseline test - baseline = LinearRegression() - single_tree = DecisionTreeRegressor() - print("CV single tree:", cross_val_score(single_tree, Xtrain, Ytrain, cv=5).mean()) - print("CV baseline:", cross_val_score(baseline, Xtrain, Ytrain, cv=5).mean()) - print("CV forest:", cross_val_score(model, Xtrain, Ytrain, cv=5).mean()) - - # test score - single_tree.fit(Xtrain, Ytrain) - baseline.fit(Xtrain, Ytrain) - print("test score single tree:", single_tree.score(Xtest, Ytest)) - print("test score baseline:", baseline.score(Xtest, Ytest)) - print("test score forest:", model.score(Xtest, Ytest)) - diff --git a/supervised_class2/rf_vs_bag.py b/supervised_class2/rf_vs_bag.py deleted file mode 100644 index 43bb8757..00000000 --- a/supervised_class2/rf_vs_bag.py +++ /dev/null @@ -1,59 +0,0 @@ -# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost -# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.ensemble import RandomForestRegressor, BaggingRegressor, RandomForestClassifier, BaggingClassifier -from util import BaggedTreeRegressor, BaggedTreeClassifier - -# make simple regression data -N = 15 -D = 100 -X = (np.random.random((N, D)) - 0.5)*10 -Y = X.sum(axis=1)**2 + 0.5*np.random.randn(N) -Ntrain = N//2 -Xtrain = X[:Ntrain] -Ytrain = Y[:Ntrain] -Xtest = X[Ntrain:] -Ytest = Y[Ntrain:] - -# from rf_classification import get_data -# X, Y = get_data() -# Ntrain = int(0.8*len(X)) -# Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] -# Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - -# from rf_regression import get_data -# Xtrain, Ytrain, Xtest, Ytest = get_data() - -T = 300 -test_error_rf = np.empty(T) -test_error_bag = np.empty(T) -for num_trees in range(T): - if num_trees == 0: - test_error_rf[num_trees] = None - test_error_bag[num_trees] = None - else: - rf = RandomForestRegressor(n_estimators=num_trees) - # rf = RandomForestClassifier(n_estimators=num_trees) - rf.fit(Xtrain, Ytrain) - test_error_rf[num_trees] = rf.score(Xtest, Ytest) - - bg = BaggedTreeRegressor(n_estimators=num_trees) - # bg = BaggedTreeClassifier(n_estimators=num_trees) - bg.fit(Xtrain, Ytrain) - test_error_bag[num_trees] = bg.score(Xtest, Ytest) - - if num_trees % 10 == 0: - print("num_trees:", num_trees) - -plt.plot(test_error_rf, label='rf') -plt.plot(test_error_bag, label='bag') -plt.legend() -plt.show() diff --git a/supervised_class2/rf_vs_bag2.py b/supervised_class2/rf_vs_bag2.py deleted file mode 100644 index 37ac95b3..00000000 --- a/supervised_class2/rf_vs_bag2.py +++ /dev/null @@ -1,103 +0,0 @@ -# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost -# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.tree import DecisionTreeClassifier -from sklearn.ensemble import RandomForestRegressor, BaggingRegressor, RandomForestClassifier, BaggingClassifier -from util import BaggedTreeRegressor, BaggedTreeClassifier - -# make simple regression data -# N = 15 -# D = 100 -# X = (np.random.random((N, D)) - 0.5)*10 -# Y = X.sum(axis=1)**2 + 0.5*np.random.randn(N) -# Ntrain = N/2 -# Xtrain = X[:Ntrain] -# Ytrain = Y[:Ntrain] -# Xtest = X[Ntrain:] -# Ytest = Y[Ntrain:] - -from rf_classification import get_data -X, Y = get_data() -Ntrain = int(0.8*len(X)) -Xtrain, Ytrain = X[:Ntrain], Y[:Ntrain] -Xtest, Ytest = X[Ntrain:], Y[Ntrain:] - -# from rf_regression import get_data -# Xtrain, Ytrain, Xtest, Ytest = get_data() - -class NotAsRandomForest: - def __init__(self, n_estimators): - self.B = n_estimators - - def fit(self, X, Y, M=None): - N, D = X.shape - if M is None: - M = int(np.sqrt(D)) - - self.models = [] - self.features = [] - for b in range(self.B): - tree = DecisionTreeClassifier() - - # sample features - features = np.random.choice(D, size=M, replace=False) - - # sample training samples - idx = np.random.choice(N, size=N, replace=True) - Xb = X[idx] - Yb = Y[idx] - - tree.fit(Xb[:, features], Yb) - self.features.append(features) - self.models.append(tree) - - def predict(self, X): - N = len(X) - P = np.zeros(N) - for features, tree in zip(self.features, self.models): - P += tree.predict(X[:, features]) - return np.round(P / self.B) - - def score(self, X, Y): - P = self.predict(X) - return np.mean(P == Y) - - -T = 500 -test_error_prf = np.empty(T) -test_error_rf = np.empty(T) -test_error_bag = np.empty(T) -for num_trees in range(T): - if num_trees == 0: - test_error_prf[num_trees] = None - test_error_rf[num_trees] = None - test_error_bag[num_trees] = None - else: - rf = RandomForestClassifier(n_estimators=num_trees) - rf.fit(Xtrain, Ytrain) - test_error_rf[num_trees] = rf.score(Xtest, Ytest) - - bg = BaggedTreeClassifier(n_estimators=num_trees) - bg.fit(Xtrain, Ytrain) - test_error_bag[num_trees] = bg.score(Xtest, Ytest) - - prf = NotAsRandomForest(n_estimators=num_trees) - prf.fit(Xtrain, Ytrain) - test_error_prf[num_trees] = prf.score(Xtest, Ytest) - - if num_trees % 10 == 0: - print("num_trees:", num_trees) - -plt.plot(test_error_rf, label='rf') -plt.plot(test_error_prf, label='pseudo rf') -plt.plot(test_error_bag, label='bag') -plt.legend() -plt.show() diff --git a/supervised_class2/util.py b/supervised_class2/util.py deleted file mode 100644 index b4172d87..00000000 --- a/supervised_class2/util.py +++ /dev/null @@ -1,86 +0,0 @@ -# https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost -# https://www.udemy.com/machine-learning-in-python-random-forest-adaboost -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor - -def plot_decision_boundary(X, model): - h = .02 # step size in the mesh - # create a mesh to plot in - x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 - y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 - xx, yy = np.meshgrid(np.arange(x_min, x_max, h), - np.arange(y_min, y_max, h)) - - - # Plot the decision boundary. For that, we will assign a color to each - # point in the mesh [x_min, m_max]x[y_min, y_max]. - Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) - - # Put the result into a color plot - Z = Z.reshape(xx.shape) - plt.contour(xx, yy, Z, cmap=plt.cm.Paired) - - -class BaggedTreeRegressor: - def __init__(self, n_estimators, max_depth=None): - self.B = n_estimators - self.max_depth = max_depth - - def fit(self, X, Y): - N = len(X) - self.models = [] - for b in range(self.B): - idx = np.random.choice(N, size=N, replace=True) - Xb = X[idx] - Yb = Y[idx] - - model = DecisionTreeRegressor(max_depth=self.max_depth) - model.fit(Xb, Yb) - self.models.append(model) - - def predict(self, X): - predictions = np.zeros(len(X)) - for model in self.models: - predictions += model.predict(X) - return predictions / self.B - - def score(self, X, Y): - d1 = Y - self.predict(X) - d2 = Y - Y.mean() - return 1 - d1.dot(d1) / d2.dot(d2) - - -class BaggedTreeClassifier: - def __init__(self, n_estimators, max_depth=None): - self.B = n_estimators - self.max_depth = max_depth - - def fit(self, X, Y): - N = len(X) - self.models = [] - for b in range(self.B): - idx = np.random.choice(N, size=N, replace=True) - Xb = X[idx] - Yb = Y[idx] - - model = DecisionTreeClassifier(max_depth=self.max_depth) - model.fit(Xb, Yb) - self.models.append(model) - - def predict(self, X): - # no need to keep a dictionary since we are doing binary classification - predictions = np.zeros(len(X)) - for model in self.models: - predictions += model.predict(X) - return np.round(predictions / self.B) - - def score(self, X, Y): - P = self.predict(X) - return np.mean(Y == P) diff --git a/svm_class/crossval.py b/svm_class/crossval.py deleted file mode 100644 index b897083a..00000000 --- a/svm_class/crossval.py +++ /dev/null @@ -1,25 +0,0 @@ -# https://deeplearningcourses.com/c/support-vector-machines-in-python -# https://www.udemy.com/support-vector-machines-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd - -from datetime import datetime -from sklearn.model_selection import cross_val_score -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler -from sklearn.datasets import load_breast_cancer -from sklearn.svm import SVC - -# load the data -data = load_breast_cancer() - -for C in (0.5, 1.0, 5.0, 10.0): - pipeline = Pipeline([('scaler', StandardScaler()), ('svm', SVC(C=C))]) - scores = cross_val_score(pipeline, data.data, data.target, cv=5) - print("C:", C, "mean:", scores.mean(), "std:", scores.std()) diff --git a/svm_class/extra_reading.txt b/svm_class/extra_reading.txt deleted file mode 100644 index 6f5c8ab9..00000000 --- a/svm_class/extra_reading.txt +++ /dev/null @@ -1,65 +0,0 @@ -Pattern Recognition and Machine Learning -https://amzn.to/2DeexU0 - -Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond -https://amzn.to/2FdyP2s - -Convex Optimization -http://stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf - -A Tutorial on Support Vector Machines for Pattern Recognition -https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/svmtutorial.pdf - -Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines -https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf - -Fast Training of Support Vector Machines using Sequential Minimal Optimization -https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/smo-book.pdf - -Convex Analysis -https://press.princeton.edu/titles/1815.html - -Generalized Lagrange multiplier method for solving problems of optimum allocation of resources -https://web.archive.org/web/20110724151508/http://or.journal.informs.org/cgi/reprint/11/3/399 - -Duality in Linear Programming -http://www.civilized.com/files/duality.pdf - -Linear programming -https://en.wikipedia.org/wiki/Linear_programming#Duality - -Karush–Kuhn–Tucker conditions -https://en.wikipedia.org/wiki/Karush–Kuhn–Tucker_conditions - -A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods -https://www.researchgate.net/publication/2478380_A_Study_on_Sigmoid_Kernels_for_SVM_and_the_Training_of_non-PSD_Kernels_by_SMO-type_Methods - -Text Classification using String Kernels -http://www.jmlr.org/papers/volume2/lodhi02a/lodhi02a.pdf - -A Comparison of Methods for Multi-class Support Vector Machines -https://www.csie.ntu.edu.tw/~cjlin/papers/multisvm.pdf - -A Tutorial on Support Vector Regression -https://alex.smola.org/papers/2003/SmoSch03b.pdf - -LIBSVM -- A Library for Support Vector Machines -https://www.csie.ntu.edu.tw/~cjlin/libsvm/ - -Random Features for Large-Scale Kernel Machines -http://www.robots.ox.ac.uk/~vgg/rg/papers/randomfeatures.pdf - -Reflections on Random Kitchen Sinks -http://www.argmin.net/2017/12/05/kitchen-sinks/ - -Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning -https://papers.nips.cc/paper/3495-weighted-sums-of-random-kitchen-sinks-replacing-minimization-with-randomization-in-learning - -Using the Nyström Method to Speed Up Kernel Machines -https://papers.nips.cc/paper/1866-using-the-nystrom-method-to-speed-up-kernel-machines - -Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison -https://papers.nips.cc/paper/4588-nystrom-method-vs-random-fourier-features-a-theoretical-and-empirical-comparison - -This guy generated some nice plots and code to demonstrate that RBFSampler works like a real RBF Kernel -https://www.kaggle.com/sy2002/rbfsampler-actually-is-not-using-any-rbfs \ No newline at end of file diff --git a/svm_class/fake_neural_net.py b/svm_class/fake_neural_net.py deleted file mode 100644 index 591e302f..00000000 --- a/svm_class/fake_neural_net.py +++ /dev/null @@ -1,128 +0,0 @@ -# https://deeplearningcourses.com/c/support-vector-machines-in-python -# https://www.udemy.com/support-vector-machines-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np - -from sklearn.svm import SVC -from util import getKaggleMNIST -from datetime import datetime -from sklearn.pipeline import Pipeline -from sklearn.linear_model import SGDClassifier -from sklearn.svm import LinearSVC -from sklearn.preprocessing import StandardScaler -from sklearn.cluster import KMeans -from sklearn.mixture import GaussianMixture -from sklearn.model_selection import cross_val_score -from sklearn.utils import shuffle -from scipy import stats -from sklearn.linear_model import LogisticRegression - - -class SigmoidFeaturizer: - def __init__(self, gamma=1.0, n_components=100, method='random'): - self.M = n_components - self.gamma = gamma - assert(method in ('normal', 'random', 'kmeans', 'gmm')) - self.method = method - - def _subsample_data(self, X, Y, n=10000): - if Y is not None: - X, Y = shuffle(X, Y) - return X[:n], Y[:n] - else: - X = shuffle(X) - return X[:n] - - def fit(self, X, Y=None): - if self.method == 'random': - N = len(X) - idx = np.random.randint(N, size=self.M) - self.samples = X[idx] - elif self.method == 'normal': - # just sample from N(0,1) - D = X.shape[1] - self.samples = np.random.randn(self.M, D) / np.sqrt(D) - elif self.method == 'kmeans': - X, Y = self._subsample_data(X, Y) - - print("Fitting kmeans...") - t0 = datetime.now() - kmeans = KMeans(n_clusters=len(set(Y))) - kmeans.fit(X) - print("Finished fitting kmeans, duration:", datetime.now() - t0) - - # calculate the most ambiguous points - # we will do this by finding the distance between each point - # and all cluster centers - # and return which points have the smallest variance - dists = kmeans.transform(X) # returns an N x K matrix - variances = dists.var(axis=1) - idx = np.argsort(variances) # smallest to largest - idx = idx[:self.M] - self.samples = X[idx] - elif self.method == 'gmm': - X, Y = self._subsample_data(X, Y) - - print("Fitting GMM") - t0 = datetime.now() - gmm = GaussianMixture( - n_components=len(set(Y)), - covariance_type='spherical', - reg_covar=1e-6) - gmm.fit(X) - print("Finished fitting GMM, duration:", datetime.now() - t0) - - # calculate the most ambiguous points - probs = gmm.predict_proba(X) - ent = stats.entropy(probs.T) # N-length vector of entropies - idx = np.argsort(-ent) # negate since we want biggest first - idx = idx[:self.M] - self.samples = X[idx] - return self - - def transform(self, X): - Z = X.dot(self.samples.T) # (Ntest x D) x (D x Nsamples) -> (Ntest x Nsamples) - return np.tanh(self.gamma * Z) - # return self.gamma * Z * (Z > 0) - - def fit_transform(self, X, Y=None): - return self.fit(X, Y).transform(X) - - -# get the data: https://www.kaggle.com/c/digit-recognizer -Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - -# with SGD -pipeline = Pipeline([ - ('scaler', StandardScaler()), - ('sigmoid', SigmoidFeaturizer(gamma=0.05, n_components=2000, method='normal')), - # ('linear', SGDClassifier(max_iter=1e6, tol=1e-5)) - ('linear', LogisticRegression()) # takes longer -]) - -# with Linear SVC -# n_components = 3000 -# pipeline = Pipeline([ -# ('scaler', StandardScaler()), -# ('sigmoid', SigmoidFeaturizer(n_components=n_components)), -# ('linear', LinearSVC()) -# ]) - -# let's do some cross-validation instead, why not -X = np.vstack((Xtrain, Xtest)) -Y = np.concatenate((Ytrain, Ytest)) -scores = cross_val_score(pipeline, X, Y, cv=5) -print(scores) -print("avg:", np.mean(scores)) - -# t0 = datetime.now() -# pipeline.fit(Xtrain, Ytrain) -# print("train duration:", datetime.now() - t0) -# t0 = datetime.now() -# print("train score:", pipeline.score(Xtrain, Ytrain), "duration:", datetime.now() - t0) -# t0 = datetime.now() -# print("test score:", pipeline.score(Xtest, Ytest), "duration:", datetime.now() - t0) diff --git a/svm_class/kernel_svm_gradient_primal.py b/svm_class/kernel_svm_gradient_primal.py deleted file mode 100644 index 1be107e1..00000000 --- a/svm_class/kernel_svm_gradient_primal.py +++ /dev/null @@ -1,195 +0,0 @@ -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -from sklearn.model_selection import train_test_split -from sklearn.datasets import load_breast_cancer -from sklearn.preprocessing import StandardScaler -from datetime import datetime -from util import get_spiral, get_xor, get_donut, get_clouds - -import numpy as np -import matplotlib.pyplot as plt - - -# kernels -def linear(X1, X2, c=0): - return X1.dot(X2.T) + c - -def rbf(X1, X2, gamma=None): - if gamma is None: - gamma = 1.0 / X1.shape[-1] # 1 / D - # gamma = 0.05 - # gamma = 5. # for donut and spiral - if np.ndim(X1) == 1 and np.ndim(X2) == 1: - result = np.exp(-gamma * np.linalg.norm(X1 - X2)**2) - elif (np.ndim(X1) > 1 and np.ndim(X2) == 1) or (np.ndim(X1) == 1 and np.ndim(X2) > 1): - result = np.exp(-gamma * np.linalg.norm(X1 - X2, axis=1)**2) - elif np.ndim(X1) > 1 and np.ndim(X2) > 1: - result = np.exp(-gamma * np.linalg.norm(X1[:, np.newaxis] - X2[np.newaxis, :], axis=2)**2) - return result - -def sigmoid(X1, X2, gamma=0.05, c=1): - return np.tanh(gamma * X1.dot(X2.T) + c) - - -class KernelSVM: - def __init__(self, kernel=linear, C=1.0): - self.C = C - self.kernel = kernel - - def _objective(self, margins): - return 0.5 * self.u.dot(self.K.dot(self.u)) + \ - self.C * np.maximum(0, 1 - margins).sum() - - def fit(self, X, Y, lr=1e-5, n_iters=400): - N, D = X.shape - self.N = N - self.u = np.random.randn(N) - self.b = 0 - - # setup kernel matrix - self.X = X - self.Y = Y - self.K = self.kernel(X, X) - - # gradient descent - losses = [] - for _ in range(n_iters): - margins = Y * (self.u.dot(self.K) + self.b) - loss = self._objective(margins) - losses.append(loss) - - idx = np.where(margins < 1)[0] - grad_u = self.K.dot(self.u) - self.C * Y[idx].dot(self.K[idx]) - self.u -= lr * grad_u - grad_b = -self.C * Y[idx].sum() - self.b -= lr * grad_b - - self.support_ = np.where((Y * (self.u.dot(self.K) + self.b)) <= 1)[0] - print("num SVs:", len(self.support_)) - - # print("w:", self.w) - # print("b:", self.b) - - # hist of margins - m = Y * (self.u.dot(self.K) + self.b) - plt.hist(m, bins=20) - plt.show() - - plt.plot(losses) - plt.title("loss per iteration") - plt.show() - - def _decision_function(self, X): - return self.u.dot(self.kernel(self.X, X)) + self.b - - def predict(self, X): - return np.sign(self._decision_function(X)) - - def score(self, X, Y): - P = self.predict(X) - return np.mean(Y == P) - - -def plot_decision_boundary(model, X, Y, resolution=100, colors=('b', 'k', 'r')): - np.warnings.filterwarnings('ignore') - fig, ax = plt.subplots() - - # Generate coordinate grid of shape [resolution x resolution] - # and evaluate the model over the entire space - x_range = np.linspace(X[:,0].min(), X[:,0].max(), resolution) - y_range = np.linspace(X[:,1].min(), X[:,1].max(), resolution) - grid = [[model._decision_function(np.array([[xr, yr]])) for yr in y_range] for xr in x_range] - grid = np.array(grid).reshape(len(x_range), len(y_range)) - - # Plot decision contours using grid and - # make a scatter plot of training data - ax.contour(x_range, y_range, grid.T, (-1, 0, 1), linewidths=(1, 1, 1), - linestyles=('--', '-', '--'), colors=colors) - ax.scatter(X[:,0], X[:,1], - c=Y, lw=0, alpha=0.3, cmap='seismic') - - # Plot support vectors (non-zero alphas) - # as circled points (linewidth > 0) - mask = model.support_ - ax.scatter(X[:,0][mask], X[:,1][mask], - c=Y[mask], cmap='seismic') - - # debug - ax.scatter([0], [0], c='black', marker='x') - - # debug - # x_axis = np.linspace(X[:,0].min(), X[:,0].max(), 100) - # w = model.w - # b = model.b - # # w[0]*x + w[1]*y + b = 0 - # y_axis = -(w[0]*x_axis + b)/w[1] - # plt.plot(x_axis, y_axis, color='purple') - # margin_p = (1 - w[0]*x_axis - b)/w[1] - # plt.plot(x_axis, margin_p, color='orange') - # margin_n = -(1 + w[0]*x_axis + b)/w[1] - # plt.plot(x_axis, margin_n, color='orange') - - plt.show() - - -def clouds(): - X, Y = get_clouds() - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - return Xtrain, Xtest, Ytrain, Ytest, linear, 1e-5, 500 - - -def medical(): - data = load_breast_cancer() - X, Y = data.data, data.target - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - return Xtrain, Xtest, Ytrain, Ytest, linear, 1e-3, 200 - -def xor(): - X, Y = get_xor() - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - kernel = lambda X1, X2: rbf(X1, X2, gamma=3.) - return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-3, 500 - -def donut(): - X, Y = get_donut() - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - kernel = lambda X1, X2: rbf(X1, X2, gamma=1.) - return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-3, 300 - -def spiral(): - X, Y = get_spiral() - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - kernel = lambda X1, X2: rbf(X1, X2, gamma=5.) - return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-3, 500 - - -if __name__ == '__main__': - Xtrain, Xtest, Ytrain, Ytest, kernel, lr, n_iters = donut() - print("Possible labels:", set(Ytrain)) - - # make sure the targets are (-1, +1) - Ytrain[Ytrain == 0] = -1 - Ytest[Ytest == 0] = -1 - - # scale the data - scaler = StandardScaler() - Xtrain = scaler.fit_transform(Xtrain) - Xtest = scaler.transform(Xtest) - - # now we'll use our custom implementation - model = KernelSVM(kernel=kernel, C=1.0) - - t0 = datetime.now() - model.fit(Xtrain, Ytrain, lr=lr, n_iters=n_iters) - print("train duration:", datetime.now() - t0) - t0 = datetime.now() - print("train score:", model.score(Xtrain, Ytrain), "duration:", datetime.now() - t0) - t0 = datetime.now() - print("test score:", model.score(Xtest, Ytest), "duration:", datetime.now() - t0) - - if Xtrain.shape[1] == 2: - plot_decision_boundary(model, Xtrain, Ytrain) diff --git a/svm_class/linear_svm_gradient.py b/svm_class/linear_svm_gradient.py deleted file mode 100644 index a5b4a927..00000000 --- a/svm_class/linear_svm_gradient.py +++ /dev/null @@ -1,151 +0,0 @@ -# https://deeplearningcourses.com/c/support-vector-machines-in-python -# https://www.udemy.com/support-vector-machines-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -from sklearn.model_selection import train_test_split -from sklearn.datasets import load_breast_cancer -from sklearn.preprocessing import StandardScaler -from datetime import datetime -from util import get_clouds - -import numpy as np -import matplotlib.pyplot as plt - - -class LinearSVM: - def __init__(self, C=1.0): - self.C = C - - def _objective(self, margins): - return 0.5 * self.w.dot(self.w) + self.C * np.maximum(0, 1 - margins).sum() - - def fit(self, X, Y, lr=1e-5, n_iters=400): - N, D = X.shape - self.N = N - self.w = np.random.randn(D) - self.b = 0 - - # gradient descent - losses = [] - for _ in range(n_iters): - margins = Y * self._decision_function(X) - loss = self._objective(margins) - losses.append(loss) - - idx = np.where(margins < 1)[0] - grad_w = self.w - self.C * Y[idx].dot(X[idx]) - self.w -= lr * grad_w - grad_b = -self.C * Y[idx].sum() - self.b -= lr * grad_b - - self.support_ = np.where((Y * self._decision_function(X)) <= 1)[0] - print("num SVs:", len(self.support_)) - - print("w:", self.w) - print("b:", self.b) - - # hist of margins - # m = Y * self._decision_function(X) - # plt.hist(m, bins=20) - # plt.show() - - plt.plot(losses) - plt.title("loss per iteration") - plt.show() - - def _decision_function(self, X): - return X.dot(self.w) + self.b - - def predict(self, X): - return np.sign(self._decision_function(X)) - - def score(self, X, Y): - P = self.predict(X) - return np.mean(Y == P) - - -def plot_decision_boundary(model, X, Y, resolution=100, colors=('b', 'k', 'r')): - np.warnings.filterwarnings('ignore') - fig, ax = plt.subplots() - - # Generate coordinate grid of shape [resolution x resolution] - # and evaluate the model over the entire space - x_range = np.linspace(X[:,0].min(), X[:,0].max(), resolution) - y_range = np.linspace(X[:,1].min(), X[:,1].max(), resolution) - grid = [[model._decision_function(np.array([[xr, yr]])) for yr in y_range] for xr in x_range] - grid = np.array(grid).reshape(len(x_range), len(y_range)) - - # Plot decision contours using grid and - # make a scatter plot of training data - ax.contour(x_range, y_range, grid.T, (-1, 0, 1), linewidths=(1, 1, 1), - linestyles=('--', '-', '--'), colors=colors) - ax.scatter(X[:,0], X[:,1], - c=Y, lw=0, alpha=0.3, cmap='seismic') - - # Plot support vectors (non-zero alphas) - # as circled points (linewidth > 0) - mask = model.support_ - ax.scatter(X[:,0][mask], X[:,1][mask], - c=Y[mask], cmap='seismic') - - # debug - ax.scatter([0], [0], c='black', marker='x') - - # debug - # x_axis = np.linspace(X[:,0].min(), X[:,0].max(), 100) - # w = model.w - # b = model.b - # # w[0]*x + w[1]*y + b = 0 - # y_axis = -(w[0]*x_axis + b)/w[1] - # plt.plot(x_axis, y_axis, color='purple') - # margin_p = (1 - w[0]*x_axis - b)/w[1] - # plt.plot(x_axis, margin_p, color='orange') - # margin_n = -(1 + w[0]*x_axis + b)/w[1] - # plt.plot(x_axis, margin_n, color='orange') - - plt.show() - - -def clouds(): - X, Y = get_clouds() - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - return Xtrain, Xtest, Ytrain, Ytest, 1e-3, 200 - - -def medical(): - data = load_breast_cancer() - X, Y = data.data, data.target - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - return Xtrain, Xtest, Ytrain, Ytest, 1e-3, 200 - - -if __name__ == '__main__': - Xtrain, Xtest, Ytrain, Ytest, lr, n_iters = clouds() - print("Possible labels:", set(Ytrain)) - - # make sure the targets are (-1, +1) - Ytrain[Ytrain == 0] = -1 - Ytest[Ytest == 0] = -1 - - # scale the data - scaler = StandardScaler() - Xtrain = scaler.fit_transform(Xtrain) - Xtest = scaler.transform(Xtest) - - # now we'll use our custom implementation - model = LinearSVM(C=1.0) - - t0 = datetime.now() - model.fit(Xtrain, Ytrain, lr=lr, n_iters=n_iters) - print("train duration:", datetime.now() - t0) - t0 = datetime.now() - print("train score:", model.score(Xtrain, Ytrain), "duration:", datetime.now() - t0) - t0 = datetime.now() - print("test score:", model.score(Xtest, Ytest), "duration:", datetime.now() - t0) - - if Xtrain.shape[1] == 2: - plot_decision_boundary(model, Xtrain, Ytrain) diff --git a/svm_class/rbfnetwork.py b/svm_class/rbfnetwork.py deleted file mode 100644 index 1b7c5bd7..00000000 --- a/svm_class/rbfnetwork.py +++ /dev/null @@ -1,64 +0,0 @@ -# https://deeplearningcourses.com/c/support-vector-machines-in-python -# https://www.udemy.com/support-vector-machines-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -from sklearn.svm import SVC -from util import getKaggleMNIST -from datetime import datetime -from sklearn.pipeline import Pipeline -from sklearn.kernel_approximation import RBFSampler -from sklearn.linear_model import SGDClassifier -from sklearn.svm import LinearSVC -from sklearn.pipeline import FeatureUnion -from sklearn.preprocessing import StandardScaler -from sklearn.kernel_approximation import Nystroem - -# get the data: https://www.kaggle.com/c/digit-recognizer -Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - -# linear SGD classifier -# pipeline = Pipeline([('linear', SGDClassifier(max_iter=1e6, tol=1e-5))]) - -# linear SVC - a bit faster than SVC with linear kernel -# pipeline = Pipeline([('linear', LinearSVC())]) - -# one RBFSampler with linear SGD classifier -# pipeline = Pipeline([ -# ('rbf', RBFSampler(gamma=0.01, n_components=1000)), -# ('linear', SGDClassifier(max_iter=1e6, tol=1e-5))]) - -# multiple RBFSamplers -# n_components = 2000 -# featurizer = FeatureUnion([ -# ("rbf1", RBFSampler(gamma=0.01, n_components=n_components)), -# ("rbf2", RBFSampler(gamma=0.005, n_components=n_components)), -# ("rbf3", RBFSampler(gamma=0.001, n_components=n_components)), -# ]) -# pipeline = Pipeline([('rbf', featurizer), ('linear', SGDClassifier(max_iter=1e6, tol=1e-5))]) - -# Nystroem approximation -# pipeline = Pipeline([ -# ('rbf', Nystroem(gamma=0.05, n_components=1000)), -# ('linear', SGDClassifier(max_iter=1e6, tol=1e-5))]) - -# multiple Nystroem -n_components = 1000 -featurizer = FeatureUnion([ - ("rbf0", Nystroem(gamma=0.05, n_components=n_components)), - ("rbf1", Nystroem(gamma=0.01, n_components=n_components)), - ("rbf2", Nystroem(gamma=0.005, n_components=n_components)), - ("rbf3", Nystroem(gamma=0.001, n_components=n_components)), - ]) -pipeline = Pipeline([('rbf', featurizer), ('linear', SGDClassifier(max_iter=1e6, tol=1e-5))]) - - -t0 = datetime.now() -pipeline.fit(Xtrain, Ytrain) -print("train duration:", datetime.now() - t0) -t0 = datetime.now() -print("train score:", pipeline.score(Xtrain, Ytrain), "duration:", datetime.now() - t0) -t0 = datetime.now() -print("test score:", pipeline.score(Xtest, Ytest), "duration:", datetime.now() - t0) diff --git a/svm_class/real_neural_net.py b/svm_class/real_neural_net.py deleted file mode 100644 index 215f9d4b..00000000 --- a/svm_class/real_neural_net.py +++ /dev/null @@ -1,35 +0,0 @@ -# https://deeplearningcourses.com/c/support-vector-machines-in-python -# https://www.udemy.com/support-vector-machines-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np - -from util import getKaggleMNIST -from datetime import datetime -from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler -from sklearn.neural_network import MLPClassifier -from sklearn.linear_model import LogisticRegression - -# get the data: https://www.kaggle.com/c/digit-recognizer -Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - -# scale first -pipeline = Pipeline([ - # ('scaler', StandardScaler()), - ('mlp', MLPClassifier(hidden_layer_sizes=(500,), activation='tanh')), - # ('lr', LogisticRegression()), -]) - - - -t0 = datetime.now() -pipeline.fit(Xtrain, Ytrain) -print("train duration:", datetime.now() - t0) -t0 = datetime.now() -print("train score:", pipeline.score(Xtrain, Ytrain), "duration:", datetime.now() - t0) -t0 = datetime.now() -print("test score:", pipeline.score(Xtest, Ytest), "duration:", datetime.now() - t0) diff --git a/svm_class/regression.py b/svm_class/regression.py deleted file mode 100644 index 4e1770f3..00000000 --- a/svm_class/regression.py +++ /dev/null @@ -1,40 +0,0 @@ -# https://deeplearningcourses.com/c/support-vector-machines-in-python -# https://www.udemy.com/support-vector-machines-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd - -from datetime import datetime -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from sklearn.svm import SVR - -# get the data: https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength -df = pd.read_excel('../large_files/Concrete_Data.xls') -df.columns = list(range(df.shape[1])) - -X = df[[0,1,2,3,4,5,6,7]].values -Y = df[8].values - -# split the data into train and test sets -# this lets us simulate how our model will perform in the future -Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - -# scale the data -scaler = StandardScaler() -Xtrain = scaler.fit_transform(Xtrain) -Xtest = scaler.transform(Xtest) - -target_scaler = StandardScaler() -Ytrain = target_scaler.fit_transform(Ytrain.reshape(-1, 1)).flatten() -Ytest = target_scaler.transform(Ytest.reshape(-1, 1)).flatten() - -model = SVR(kernel='rbf') -model.fit(Xtrain, Ytrain) -print("train score:", model.score(Xtrain, Ytrain)) -print("test score:", model.score(Xtest, Ytest)) diff --git a/svm_class/svm_gradient.py b/svm_class/svm_gradient.py deleted file mode 100644 index 1a978363..00000000 --- a/svm_class/svm_gradient.py +++ /dev/null @@ -1,154 +0,0 @@ -# https://deeplearningcourses.com/c/support-vector-machines-in-python -# https://www.udemy.com/support-vector-machines-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -from sklearn.model_selection import train_test_split -from sklearn.datasets import load_breast_cancer -from sklearn.preprocessing import StandardScaler -from datetime import datetime -from util import get_spiral, get_xor, get_donut, get_clouds, plot_decision_boundary - -import numpy as np -import matplotlib.pyplot as plt - - -# kernels -def linear(X1, X2, c=0): - return X1.dot(X2.T) + c - -def rbf(X1, X2, gamma=None): - if gamma is None: - gamma = 1.0 / X1.shape[-1] # 1 / D - # gamma = 0.05 - # gamma = 5. # for donut and spiral - if np.ndim(X1) == 1 and np.ndim(X2) == 1: - result = np.exp(-gamma * np.linalg.norm(X1 - X2)**2) - elif (np.ndim(X1) > 1 and np.ndim(X2) == 1) or (np.ndim(X1) == 1 and np.ndim(X2) > 1): - result = np.exp(-gamma * np.linalg.norm(X1 - X2, axis=1)**2) - elif np.ndim(X1) > 1 and np.ndim(X2) > 1: - result = np.exp(-gamma * np.linalg.norm(X1[:, np.newaxis] - X2[np.newaxis, :], axis=2)**2) - return result - -def sigmoid(X1, X2, gamma=0.05, c=1): - return np.tanh(gamma * X1.dot(X2.T) + c) - - -class SVM: - def __init__(self, kernel, C=1.0): - self.kernel = kernel - self.C = C - - def _train_objective(self): - return np.sum(self.alphas) - 0.5 * np.sum(self.YYK * np.outer(self.alphas, self.alphas)) - - def fit(self, X, Y, lr=1e-5, n_iters=400): - # we need these to make future predictions - self.Xtrain = X - self.Ytrain = Y - self.N = X.shape[0] - self.alphas = np.random.random(self.N) - self.b = 0 - - # kernel matrix - self.K = self.kernel(X, X) - self.YY = np.outer(Y, Y) - self.YYK = self.K * self.YY - - # gradient ascent - losses = [] - for _ in range(n_iters): - loss = self._train_objective() - losses.append(loss) - grad = np.ones(self.N) - self.YYK.dot(self.alphas) - self.alphas += lr * grad - - # clip - self.alphas[self.alphas < 0] = 0 - self.alphas[self.alphas > self.C] = self.C - - # distrbution of bs - idx = np.where((self.alphas) > 0 & (self.alphas < self.C))[0] - bs = Y[idx] - (self.alphas * Y).dot(self.kernel(X, X[idx])) - self.b = np.mean(bs) - - plt.plot(losses) - plt.title("loss per iteration") - plt.show() - - def _decision_function(self, X): - return (self.alphas * self.Ytrain).dot(self.kernel(self.Xtrain, X)) + self.b - - def predict(self, X): - return np.sign(self._decision_function(X)) - - def score(self, X, Y): - P = self.predict(X) - return np.mean(Y == P) - - -def medical(): - data = load_breast_cancer() - X, Y = data.data, data.target - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - return Xtrain, Xtest, Ytrain, Ytest, rbf, 1e-3, 200 - -def medical_sigmoid(): - data = load_breast_cancer() - X, Y = data.data, data.target - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - return Xtrain, Xtest, Ytrain, Ytest, sigmoid, 1e-3, 200 - -def xor(): - X, Y = get_xor() - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - kernel = lambda X1, X2: rbf(X1, X2, gamma=5.) - return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-2, 300 - -def donut(): - X, Y = get_donut() - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - kernel = lambda X1, X2: rbf(X1, X2, gamma=5.) - return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-2, 300 - -def spiral(): - X, Y = get_spiral() - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - kernel = lambda X1, X2: rbf(X1, X2, gamma=5.) - return Xtrain, Xtest, Ytrain, Ytest, kernel, 1e-2, 300 - -def clouds(): - X, Y = get_clouds() - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - return Xtrain, Xtest, Ytrain, Ytest, linear, 1e-5, 400 - - -if __name__ == '__main__': - Xtrain, Xtest, Ytrain, Ytest, kernel, lr, n_iters = spiral() - print("Possible labels:", set(Ytrain)) - - # make sure the targets are (-1, +1) - Ytrain[Ytrain == 0] = -1 - Ytest[Ytest == 0] = -1 - - # scale the data - scaler = StandardScaler() - Xtrain = scaler.fit_transform(Xtrain) - Xtest = scaler.transform(Xtest) - - # now we'll use our custom implementation - model = SVM(kernel=kernel, C=1.0) - - t0 = datetime.now() - model.fit(Xtrain, Ytrain, lr=lr, n_iters=n_iters) - print("train duration:", datetime.now() - t0) - t0 = datetime.now() - print("train score:", model.score(Xtrain, Ytrain), "duration:", datetime.now() - t0) - t0 = datetime.now() - print("test score:", model.score(Xtest, Ytest), "duration:", datetime.now() - t0) - - if Xtrain.shape[1] == 2: - plot_decision_boundary(model) diff --git a/svm_class/svm_medical.py b/svm_class/svm_medical.py deleted file mode 100644 index f9342760..00000000 --- a/svm_class/svm_medical.py +++ /dev/null @@ -1,30 +0,0 @@ -# https://deeplearningcourses.com/c/support-vector-machines-in-python -# https://www.udemy.com/support-vector-machines-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -from sklearn.svm import SVC -from sklearn.model_selection import train_test_split -from sklearn.datasets import load_breast_cancer -from sklearn.preprocessing import StandardScaler - -# load the data -data = load_breast_cancer() - -# split the data into train and test sets -# this lets us simulate how our model will perform in the future -Xtrain, Xtest, Ytrain, Ytest = train_test_split(data.data, data.target, test_size=0.33) - -# scale the data -scaler = StandardScaler() -Xtrain = scaler.fit_transform(Xtrain) -Xtest = scaler.transform(Xtest) - -model = SVC(kernel='rbf') -# model = SVC() -model.fit(Xtrain, Ytrain) -print("train score:", model.score(Xtrain, Ytrain)) -print("test score:", model.score(Xtest, Ytest)) diff --git a/svm_class/svm_mnist.py b/svm_class/svm_mnist.py deleted file mode 100644 index 9a71a650..00000000 --- a/svm_class/svm_mnist.py +++ /dev/null @@ -1,24 +0,0 @@ -# https://deeplearningcourses.com/c/support-vector-machines-in-python -# https://www.udemy.com/support-vector-machines-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -from sklearn.svm import SVC -from util import getKaggleMNIST -from datetime import datetime - -# get the data: https://www.kaggle.com/c/digit-recognizer -Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - -# model = SVC() -model = SVC(C=5., gamma=.05) - -t0 = datetime.now() -model.fit(Xtrain, Ytrain) -print("train duration:", datetime.now() - t0) -t0 = datetime.now() -print("train score:", model.score(Xtrain, Ytrain), "duration:", datetime.now() - t0) -t0 = datetime.now() -print("test score:", model.score(Xtest, Ytest), "duration:", datetime.now() - t0) diff --git a/svm_class/svm_smo.py b/svm_class/svm_smo.py deleted file mode 100644 index 7b1a8873..00000000 --- a/svm_class/svm_smo.py +++ /dev/null @@ -1,303 +0,0 @@ -# https://deeplearningcourses.com/c/support-vector-machines-in-python -# https://www.udemy.com/support-vector-machines-in-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -from sklearn.model_selection import train_test_split -from sklearn.datasets import load_breast_cancer -from sklearn.preprocessing import StandardScaler -from datetime import datetime -from util import get_spiral, get_xor, get_donut, get_clouds, plot_decision_boundary - -import numpy as np -import matplotlib.pyplot as plt - - -# kernels -def linear(X1, X2): - return X1.dot(X2.T) - -def rbf(X1, X2): - # gamma = 1.0 / X1.shape[-1] # 1 / D - gamma = 5. # for donut and spiral - if np.ndim(X1) == 1 and np.ndim(X2) == 1: - result = np.exp(-gamma * np.linalg.norm(X1 - X2)**2) - elif (np.ndim(X1) > 1 and np.ndim(X2) == 1) or (np.ndim(X1) == 1 and np.ndim(X2) > 1): - result = np.exp(-gamma * np.linalg.norm(X1 - X2, axis=1)**2) - elif np.ndim(X1) > 1 and np.ndim(X2) > 1: - result = np.exp(-gamma * np.linalg.norm(X1[:, np.newaxis] - X2[np.newaxis, :], axis=2)**2) - return result - -def sigmoid(X1, X2, gamma=0.05, c=1): - return np.tanh(gamma * X1.dot(X2.T) + c) - - -class SVM: - def __init__(self, kernel, C=1.0): - self.kernel = kernel - self.C = C - - def _loss(self, X, Y): - # return -np.sum(self.alphas) + \ - # 0.5 * np.sum(np.outer(Y, Y) * self.kernel(X, X) * np.outer(self.alphas, self.alphas)) - return -np.sum(self.alphas) + \ - 0.5 * np.sum(self.YYK * np.outer(self.alphas, self.alphas)) - - def _take_step(self, i1, i2): - # returns True if model params changed, False otherwise - - # Skip if chosen alphas are the same - if i1 == i2: - return False - - alph1 = self.alphas[i1] - alph2 = self.alphas[i2] - y1 = self.Ytrain[i1] - y2 = self.Ytrain[i2] - E1 = self.errors[i1] - E2 = self.errors[i2] - s = y1 * y2 - - # Compute L & H, the bounds on new possible alpha values - if (y1 != y2): - L = max(0, alph2 - alph1) - H = min(self.C, self.C + alph2 - alph1) - elif (y1 == y2): - L = max(0, alph1 + alph2 - self.C) - H = min(self.C, alph1 + alph2) - if (L == H): - return False - - # Compute kernel & 2nd derivative eta - k11 = self.kernel(self.Xtrain[i1], self.Xtrain[i1]) - k12 = self.kernel(self.Xtrain[i1], self.Xtrain[i2]) - k22 = self.kernel(self.Xtrain[i2], self.Xtrain[i2]) - eta = k11 + k22 - 2 * k12 - - # Usual case - eta is non-negative - if eta > 0: - a2 = alph2 + y2 * (E1 - E2) / eta - # Clip a2 based on bounds L & H - if (a2 < L): - a2 = L - elif (a2 > H): - a2 = H - # else a2 remains unchanged - - # Unusual case - eta is negative - # alpha2 should be set to whichever extreme (L or H) that yields the lowest - # value of the objective - else: - print("***** eta < 0 *****") - # keep it to assign it back later - alphas_i2 = self.alphas[i2] - # alphas_adj = self.alphas.copy() - # alphas_adj[i2] = L - self.alphas[i2] = L - # objective function output with a2 = L - Lobj = self._loss(self.Xtrain, self.Ytrain) - # alphas_adj[i2] = H - self.alphas[i2] = H - # objective function output with a2 = H - Hobj = self._loss(self.Xtrain, self.Ytrain) - if Lobj < Hobj - self.eps: - a2 = L - elif Lobj > Hobj + self.eps: - a2 = H - else: - a2 = alph2 - - # now assign it back - self.alphas[i2] = alphas_i2 - - # Push a2 to 0 or C if very close - if a2 < 1e-8: - a2 = 0.0 - elif a2 > (self.C - 1e-8): - a2 = self.C - - # If examples can't be optimized within epsilon (eps), skip this pair - if (np.abs(a2 - alph2) < self.eps * (a2 + alph2 + self.eps)): - return False - - # Calculate new alpha 1 (a1) - a1 = alph1 + s * (alph2 - a2) - - # Update threshold b to reflect newly calculated alphas - # Calculate both possible thresholds - b1 = E1 + y1 * (a1 - alph1) * k11 + y2 * (a2 - alph2) * k12 + self.b - b2 = E2 + y1 * (a1 - alph1) * k12 + y2 * (a2 - alph2) * k22 + self.b - - # Set new threshold based on if a1 or a2 is bound by L and/or H - if 0 < a1 and a1 < self.C: - b_new = b1 - elif 0 < a2 and a2 < self.C: - b_new = b2 - # Average thresholds if both are bound - else: - b_new = (b1 + b2) * 0.5 - - # Update model object with new alphas & threshold - self.alphas[i1] = a1 - self.alphas[i2] = a2 - - # Update error cache - # Error cache for optimized alphas is set to 0 if they're unbound - for index, alph in zip([i1, i2], [a1, a2]): - if 0.0 < alph < self.C: - self.errors[index] = 0.0 - - # Set non-optimized errors based on equation 12.11 in SMO book - # non_opt = [n for n in range(self.N) if (n != i1 and n != i2 and self.alphas[n] < self.C and self.alphas[n] > 0)] #new - non_opt = [n for n in range(self.N) if (n != i1 and n != i2)] # old - self.errors[non_opt] = self.errors[non_opt] + \ - y1*(a1 - alph1)*self.kernel(self.Xtrain[i1], self.Xtrain[non_opt]) + \ - y2*(a2 - alph2)*self.kernel(self.Xtrain[i2], self.Xtrain[non_opt]) + self.b - b_new - - # Update model threshold - self.b = b_new - - return True - - def _examine_example(self, i2): - # returns True (1) if alphas changed, False (0) otherwise - y2 = self.Ytrain[i2] - alph2 = self.alphas[i2] - E2 = self.errors[i2] - r2 = E2 * y2 - - # Proceed if error is within specified tolerance (tol) - if ((r2 < -self.tol and alph2 < self.C) or (r2 > self.tol and alph2 > 0)): - - if len(self.alphas[(self.alphas != 0) & (self.alphas != self.C)]) > 1: - # Use 2nd choice heuristic is choose max difference in error - if self.errors[i2] > 0: - i1 = np.argmin(self.errors) - elif self.errors[i2] <= 0: - i1 = np.argmax(self.errors) - if self._take_step(i1, i2): - return 1 - - # Loop through non-zero and non-C alphas, starting at a random point - # e.g. [1,2,3,4,5] -> [4,5,1,2,3] - for i1 in np.roll(np.where((self.alphas != 0) & (self.alphas != self.C))[0], - np.random.choice(np.arange(self.N))): - if self._take_step(i1, i2): - return 1 - - # loop through all alphas, starting at a random point - for i1 in np.roll(np.arange(self.N), np.random.choice(np.arange(self.N))): - if self._take_step(i1, i2): - return 1 - - return 0 - - def fit(self, X, Y, tol=0.00001, eps=0.01): - # we need these to make future predictions - self.tol = tol - self.eps = eps - self.Xtrain = X - self.Ytrain = Y - self.N = X.shape[0] - self.alphas = np.zeros(self.N) - self.b = 0. - self.errors = self._decision_function(self.Xtrain) - self.Ytrain - - # kernel matrix - self.K = self.kernel(X, X) - self.YY = np.outer(Y, Y) - self.YYK = self.K * self.YY - - iter_ = 0 - numChanged = 0 - examineAll = 1 - losses = [] - - while numChanged > 0 or examineAll: - print("iter:", iter_) - iter_ += 1 - numChanged = 0 - if examineAll: - # loop over all training examples - for i in range(self.alphas.shape[0]): - examine_result = self._examine_example(i) - numChanged += examine_result - if examine_result: - loss = self._loss(self.Xtrain, self.Ytrain) - losses.append(loss) - else: - # loop over examples where alphas are not already at their limits - for i in np.where((self.alphas != 0) & (self.alphas != self.C))[0]: - examine_result = self._examine_example(i) - numChanged += examine_result - if examine_result: - loss = self._loss(self.Xtrain, self.Ytrain) - losses.append(loss) - if examineAll == 1: - examineAll = 0 - elif numChanged == 0: - examineAll = 1 - - plt.plot(losses) - plt.title("loss per iteration") - plt.show() - - def _decision_function(self, X): - return (self.alphas * self.Ytrain).dot(self.kernel(self.Xtrain, X)) - self.b - - - def predict(self, X): - return np.sign(self._decision_function(X)) - - def score(self, X, Y): - P = self.predict(X) - return np.mean(Y == P) - - -def get_data(): - ### medical data - # load the data - # data = load_breast_cancer() - # X, Y = data.data, data.target - - # X, Y = get_xor() - # X, Y = get_donut() - # X, Y = get_spiral() - X, Y = get_clouds() - - # split the data into train and test sets - # this lets us simulate how our model will perform in the future - Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - return Xtrain, Xtest, Ytrain, Ytest - - -if __name__ == '__main__': - # np.random.seed(3) - Xtrain, Xtest, Ytrain, Ytest = get_data() - print("Possible labels:", set(Ytrain)) - - # make sure the targets are (-1, +1) - Ytrain[Ytrain == 0] = -1 - Ytest[Ytest == 0] = -1 - - # scale the data - scaler = StandardScaler() - Xtrain = scaler.fit_transform(Xtrain) - Xtest = scaler.transform(Xtest) - - # now we'll use our custom implementation - model = SVM(kernel=linear) - - t0 = datetime.now() - model.fit(Xtrain, Ytrain) - print("train duration:", datetime.now() - t0) - t0 = datetime.now() - print("train score:", model.score(Xtrain, Ytrain), "duration:", datetime.now() - t0) - t0 = datetime.now() - print("test score:", model.score(Xtest, Ytest), "duration:", datetime.now() - t0) - - if Xtrain.shape[1] == 2: - plot_decision_boundary(model) diff --git a/svm_class/svm_spam.py b/svm_class/svm_spam.py deleted file mode 100644 index 78d3f106..00000000 --- a/svm_class/svm_spam.py +++ /dev/null @@ -1,85 +0,0 @@ -# https://deeplearningcourses.com/c/support-vector-machines-in-python -# https://www.udemy.com/support-vector-machines-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -from sklearn.svm import SVC -from datetime import datetime -import pandas as pd -import matplotlib.pyplot as plt -from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer -from sklearn.model_selection import train_test_split -from wordcloud import WordCloud - -# data from: -# https://www.kaggle.com/uciml/sms-spam-collection-dataset -# file contains some invalid chars -# depending on which version of pandas you have -# an error may be thrown -df = pd.read_csv('../large_files/spam.csv', encoding='ISO-8859-1') - -# drop unnecessary columns -df = df.drop(["Unnamed: 2", "Unnamed: 3", "Unnamed: 4"], axis=1) - -# rename columns to something better -df.columns = ['labels', 'data'] - -# create binary labels -df['b_labels'] = df['labels'].map({'ham': 0, 'spam': 1}) -Y = df['b_labels'].values - -# try multiple ways of calculating features -tfidf = TfidfVectorizer(decode_error='ignore') -X = tfidf.fit_transform(df['data']) - -# count_vectorizer = CountVectorizer(decode_error='ignore') -# X = count_vectorizer.fit_transform(df['data']) - -# split the data -Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.33) - -model = SVC(kernel='linear', C=2.) - -t0 = datetime.now() -model.fit(Xtrain, Ytrain) -print("train duration:", datetime.now() - t0) -t0 = datetime.now() -print("train score:", model.score(Xtrain, Ytrain), "duration:", datetime.now() - t0) -t0 = datetime.now() -print("test score:", model.score(Xtest, Ytest), "duration:", datetime.now() - t0) - - - -# visualize the data -def visualize(label): - words = '' - for msg in df[df['labels'] == label]['data']: - msg = msg.lower() - words += msg + ' ' - wordcloud = WordCloud(width=600, height=400).generate(words) - plt.imshow(wordcloud) - plt.axis('off') - plt.title(label) - plt.show() - -visualize('spam') -visualize('ham') - - -# see what we're getting wrong -df['predictions'] = model.predict(X) - -# things that should be spam -print("*** things that should be spam ***") -sneaky_spam = df[(df['predictions'] == 0) & (df['b_labels'] == 1)]['data'] -for msg in sneaky_spam: - print(msg) - -# things that should not be spam -print("*** things that should not be spam ***") -not_actually_spam = df[(df['predictions'] == 1) & (df['b_labels'] == 0)]['data'] -for msg in not_actually_spam: - print(msg) - diff --git a/svm_class/util.py b/svm_class/util.py deleted file mode 100644 index 04e8003b..00000000 --- a/svm_class/util.py +++ /dev/null @@ -1,145 +0,0 @@ -# https://deeplearningcourses.com/c/support-vector-machines-in-python -# https://www.udemy.com/support-vector-machines-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from sklearn.utils import shuffle -from sklearn.preprocessing import StandardScaler - - -def getKaggleMNIST(): - # MNIST data: - # column 0 is labels - # column 1-785 is data, with values 0 .. 255 - # total size of CSV: (42000, 784) - train = pd.read_csv('../large_files/train.csv').values.astype(np.float32) - train = shuffle(train) - - Xtrain = train[:-1000,1:] - Ytrain = train[:-1000,0].astype(np.int32) - - Xtest = train[-1000:,1:] - Ytest = train[-1000:,0].astype(np.int32) - - # scale the data - Xtrain /= 255. - Xtest /= 255. - # scaler = StandardScaler() - # Xtrain = scaler.fit_transform(Xtrain) - # Xtest = scaler.transform(Xtest) - - return Xtrain, Ytrain, Xtest, Ytest - - -def get_spiral(): - # Idea: radius -> low...high - # (don't start at 0, otherwise points will be "mushed" at origin) - # angle = low...high proportional to radius - # [0, 2pi/6, 4pi/6, ..., 10pi/6] --> [pi/2, pi/3 + pi/2, ..., ] - # x = rcos(theta), y = rsin(theta) as usual - - radius = np.linspace(1, 10, 100) - thetas = np.empty((6, 100)) - for i in range(6): - start_angle = np.pi*i / 3.0 - end_angle = start_angle + np.pi / 2 - points = np.linspace(start_angle, end_angle, 100) - thetas[i] = points - - # convert into cartesian coordinates - x1 = np.empty((6, 100)) - x2 = np.empty((6, 100)) - for i in range(6): - x1[i] = radius * np.cos(thetas[i]) - x2[i] = radius * np.sin(thetas[i]) - - # inputs - X = np.empty((600, 2)) - X[:,0] = x1.flatten() - X[:,1] = x2.flatten() - - # add noise - X += np.random.randn(600, 2)*0.5 - - # targets - Y = np.array([0]*100 + [1]*100 + [0]*100 + [1]*100 + [0]*100 + [1]*100) - return X, Y - - -def get_xor(): - X = np.zeros((200, 2)) - X[:50] = np.random.random((50, 2)) / 2 + 0.5 # (0.5-1, 0.5-1) - X[50:100] = np.random.random((50, 2)) / 2 # (0-0.5, 0-0.5) - X[100:150] = np.random.random((50, 2)) / 2 + np.array([[0, 0.5]]) # (0-0.5, 0.5-1) - X[150:] = np.random.random((50, 2)) / 2 + np.array([[0.5, 0]]) # (0.5-1, 0-0.5) - Y = np.array([0]*100 + [1]*100) - return X, Y - - -def get_donut(): - N = 200 - R_inner = 5 - R_outer = 10 - - # distance from origin is radius + random normal - # angle theta is uniformly distributed between (0, 2pi) - R1 = np.random.randn(N//2) + R_inner - theta = 2*np.pi*np.random.random(N//2) - X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T - - R2 = np.random.randn(N//2) + R_outer - theta = 2*np.pi*np.random.random(N//2) - X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T - - X = np.concatenate([ X_inner, X_outer ]) - Y = np.array([0]*(N//2) + [1]*(N//2)) - return X, Y - - -def get_clouds(): - N = 1000 - c1 = np.array([2, 2]) - c2 = np.array([-2, -2]) - # c1 = np.array([0, 3]) - # c2 = np.array([0, 0]) - X1 = np.random.randn(N, 2) + c1 - X2 = np.random.randn(N, 2) + c2 - X = np.vstack((X1, X2)) - Y = np.array([-1]*N + [1]*N) - return X, Y - - -def plot_decision_boundary(model, resolution=100, colors=('b', 'k', 'r')): - np.warnings.filterwarnings('ignore') - fig, ax = plt.subplots() - - # Generate coordinate grid of shape [resolution x resolution] - # and evaluate the model over the entire space - x_range = np.linspace(model.Xtrain[:,0].min(), model.Xtrain[:,0].max(), resolution) - y_range = np.linspace(model.Xtrain[:,1].min(), model.Xtrain[:,1].max(), resolution) - grid = [[model._decision_function(np.array([[xr, yr]])) for yr in y_range] for xr in x_range] - grid = np.array(grid).reshape(len(x_range), len(y_range)) - - # Plot decision contours using grid and - # make a scatter plot of training data - ax.contour(x_range, y_range, grid.T, (-1, 0, 1), linewidths=(1, 1, 1), - linestyles=('--', '-', '--'), colors=colors) - ax.scatter(model.Xtrain[:,0], model.Xtrain[:,1], - c=model.Ytrain, lw=0, alpha=0.3, cmap='seismic') - - # Plot support vectors (non-zero alphas) - # as circled points (linewidth > 0) - mask = model.alphas > 0. - ax.scatter(model.Xtrain[:,0][mask], model.Xtrain[:,1][mask], - c=model.Ytrain[mask], cmap='seismic') - - # debug - ax.scatter([0], [0], c='black', marker='x') - - plt.show() diff --git a/tf2.0/.gitignore b/tf2.0/.gitignore deleted file mode 100644 index 5d414047..00000000 --- a/tf2.0/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -rl_trader_working*.py -*rl_trader_models -*rl_trader_rewards diff --git a/tf2.0/WHERE ARE THE NOTEBOOKS.txt b/tf2.0/WHERE ARE THE NOTEBOOKS.txt deleted file mode 100644 index 5446ce25..00000000 --- a/tf2.0/WHERE ARE THE NOTEBOOKS.txt +++ /dev/null @@ -1,5 +0,0 @@ -As stated in the "where to get the code" / "where to get the notebooks" lecture, the notebooks are NOT on Github. - -If you missed this, please review the lecture for the actual location of the notebooks. - -If, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com. \ No newline at end of file diff --git a/tf2.0/aapl_msi_sbux.csv 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brougham" -13.0 8 400.0 150.0 4464. 12.0 73 1 "chevrolet caprice classic" -13.0 8 351.0 158.0 4363. 13.0 73 1 "ford ltd" -14.0 8 318.0 150.0 4237. 14.5 73 1 "plymouth fury gran sedan" -13.0 8 440.0 215.0 4735. 11.0 73 1 "chrysler new yorker brougham" -12.0 8 455.0 225.0 4951. 11.0 73 1 "buick electra 225 custom" -13.0 8 360.0 175.0 3821. 11.0 73 1 "amc ambassador brougham" -18.0 6 225.0 105.0 3121. 16.5 73 1 "plymouth valiant" -16.0 6 250.0 100.0 3278. 18.0 73 1 "chevrolet nova custom" -18.0 6 232.0 100.0 2945. 16.0 73 1 "amc hornet" -18.0 6 250.0 88.00 3021. 16.5 73 1 "ford maverick" -23.0 6 198.0 95.00 2904. 16.0 73 1 "plymouth duster" -26.0 4 97.00 46.00 1950. 21.0 73 2 "volkswagen super beetle" -11.0 8 400.0 150.0 4997. 14.0 73 1 "chevrolet impala" -12.0 8 400.0 167.0 4906. 12.5 73 1 "ford country" -13.0 8 360.0 170.0 4654. 13.0 73 1 "plymouth custom suburb" -12.0 8 350.0 180.0 4499. 12.5 73 1 "oldsmobile vista cruiser" -18.0 6 232.0 100.0 2789. 15.0 73 1 "amc gremlin" -20.0 4 97.00 88.00 2279. 19.0 73 3 "toyota carina" -21.0 4 140.0 72.00 2401. 19.5 73 1 "chevrolet vega" -22.0 4 108.0 94.00 2379. 16.5 73 3 "datsun 610" -18.0 3 70.00 90.00 2124. 13.5 73 3 "maxda rx3" -19.0 4 122.0 85.00 2310. 18.5 73 1 "ford pinto" -21.0 6 155.0 107.0 2472. 14.0 73 1 "mercury capri v6" -26.0 4 98.00 90.00 2265. 15.5 73 2 "fiat 124 sport coupe" -15.0 8 350.0 145.0 4082. 13.0 73 1 "chevrolet monte carlo s" -16.0 8 400.0 230.0 4278. 9.50 73 1 "pontiac grand prix" -29.0 4 68.00 49.00 1867. 19.5 73 2 "fiat 128" -24.0 4 116.0 75.00 2158. 15.5 73 2 "opel manta" -20.0 4 114.0 91.00 2582. 14.0 73 2 "audi 100ls" -19.0 4 121.0 112.0 2868. 15.5 73 2 "volvo 144ea" -15.0 8 318.0 150.0 3399. 11.0 73 1 "dodge dart custom" -24.0 4 121.0 110.0 2660. 14.0 73 2 "saab 99le" -20.0 6 156.0 122.0 2807. 13.5 73 3 "toyota mark ii" -11.0 8 350.0 180.0 3664. 11.0 73 1 "oldsmobile omega" -20.0 6 198.0 95.00 3102. 16.5 74 1 "plymouth duster" -21.0 6 200.0 ? 2875. 17.0 74 1 "ford maverick" -19.0 6 232.0 100.0 2901. 16.0 74 1 "amc hornet" -15.0 6 250.0 100.0 3336. 17.0 74 1 "chevrolet nova" -31.0 4 79.00 67.00 1950. 19.0 74 3 "datsun b210" -26.0 4 122.0 80.00 2451. 16.5 74 1 "ford pinto" -32.0 4 71.00 65.00 1836. 21.0 74 3 "toyota corolla 1200" -25.0 4 140.0 75.00 2542. 17.0 74 1 "chevrolet vega" -16.0 6 250.0 100.0 3781. 17.0 74 1 "chevrolet chevelle malibu classic" -16.0 6 258.0 110.0 3632. 18.0 74 1 "amc matador" -18.0 6 225.0 105.0 3613. 16.5 74 1 "plymouth satellite sebring" -16.0 8 302.0 140.0 4141. 14.0 74 1 "ford gran torino" -13.0 8 350.0 150.0 4699. 14.5 74 1 "buick century luxus (sw)" -14.0 8 318.0 150.0 4457. 13.5 74 1 "dodge coronet custom (sw)" -14.0 8 302.0 140.0 4638. 16.0 74 1 "ford gran torino (sw)" -14.0 8 304.0 150.0 4257. 15.5 74 1 "amc matador (sw)" -29.0 4 98.00 83.00 2219. 16.5 74 2 "audi fox" -26.0 4 79.00 67.00 1963. 15.5 74 2 "volkswagen dasher" -26.0 4 97.00 78.00 2300. 14.5 74 2 "opel manta" -31.0 4 76.00 52.00 1649. 16.5 74 3 "toyota corona" -32.0 4 83.00 61.00 2003. 19.0 74 3 "datsun 710" -28.0 4 90.00 75.00 2125. 14.5 74 1 "dodge colt" -24.0 4 90.00 75.00 2108. 15.5 74 2 "fiat 128" -26.0 4 116.0 75.00 2246. 14.0 74 2 "fiat 124 tc" -24.0 4 120.0 97.00 2489. 15.0 74 3 "honda civic" -26.0 4 108.0 93.00 2391. 15.5 74 3 "subaru" -31.0 4 79.00 67.00 2000. 16.0 74 2 "fiat x1.9" -19.0 6 225.0 95.00 3264. 16.0 75 1 "plymouth valiant custom" -18.0 6 250.0 105.0 3459. 16.0 75 1 "chevrolet nova" -15.0 6 250.0 72.00 3432. 21.0 75 1 "mercury monarch" -15.0 6 250.0 72.00 3158. 19.5 75 1 "ford maverick" -16.0 8 400.0 170.0 4668. 11.5 75 1 "pontiac catalina" -15.0 8 350.0 145.0 4440. 14.0 75 1 "chevrolet bel air" -16.0 8 318.0 150.0 4498. 14.5 75 1 "plymouth grand fury" -14.0 8 351.0 148.0 4657. 13.5 75 1 "ford ltd" -17.0 6 231.0 110.0 3907. 21.0 75 1 "buick century" -16.0 6 250.0 105.0 3897. 18.5 75 1 "chevroelt chevelle malibu" -15.0 6 258.0 110.0 3730. 19.0 75 1 "amc matador" -18.0 6 225.0 95.00 3785. 19.0 75 1 "plymouth fury" -21.0 6 231.0 110.0 3039. 15.0 75 1 "buick skyhawk" -20.0 8 262.0 110.0 3221. 13.5 75 1 "chevrolet monza 2+2" -13.0 8 302.0 129.0 3169. 12.0 75 1 "ford mustang ii" -29.0 4 97.00 75.00 2171. 16.0 75 3 "toyota corolla" -23.0 4 140.0 83.00 2639. 17.0 75 1 "ford pinto" -20.0 6 232.0 100.0 2914. 16.0 75 1 "amc gremlin" -23.0 4 140.0 78.00 2592. 18.5 75 1 "pontiac astro" -24.0 4 134.0 96.00 2702. 13.5 75 3 "toyota corona" -25.0 4 90.00 71.00 2223. 16.5 75 2 "volkswagen dasher" -24.0 4 119.0 97.00 2545. 17.0 75 3 "datsun 710" -18.0 6 171.0 97.00 2984. 14.5 75 1 "ford pinto" -29.0 4 90.00 70.00 1937. 14.0 75 2 "volkswagen rabbit" -19.0 6 232.0 90.00 3211. 17.0 75 1 "amc pacer" -23.0 4 115.0 95.00 2694. 15.0 75 2 "audi 100ls" -23.0 4 120.0 88.00 2957. 17.0 75 2 "peugeot 504" -22.0 4 121.0 98.00 2945. 14.5 75 2 "volvo 244dl" -25.0 4 121.0 115.0 2671. 13.5 75 2 "saab 99le" -33.0 4 91.00 53.00 1795. 17.5 75 3 "honda civic cvcc" -28.0 4 107.0 86.00 2464. 15.5 76 2 "fiat 131" -25.0 4 116.0 81.00 2220. 16.9 76 2 "opel 1900" -25.0 4 140.0 92.00 2572. 14.9 76 1 "capri ii" -26.0 4 98.00 79.00 2255. 17.7 76 1 "dodge colt" -27.0 4 101.0 83.00 2202. 15.3 76 2 "renault 12tl" -17.5 8 305.0 140.0 4215. 13.0 76 1 "chevrolet chevelle malibu classic" -16.0 8 318.0 150.0 4190. 13.0 76 1 "dodge coronet brougham" -15.5 8 304.0 120.0 3962. 13.9 76 1 "amc matador" -14.5 8 351.0 152.0 4215. 12.8 76 1 "ford gran torino" -22.0 6 225.0 100.0 3233. 15.4 76 1 "plymouth valiant" -22.0 6 250.0 105.0 3353. 14.5 76 1 "chevrolet nova" -24.0 6 200.0 81.00 3012. 17.6 76 1 "ford maverick" -22.5 6 232.0 90.00 3085. 17.6 76 1 "amc hornet" -29.0 4 85.00 52.00 2035. 22.2 76 1 "chevrolet chevette" -24.5 4 98.00 60.00 2164. 22.1 76 1 "chevrolet woody" -29.0 4 90.00 70.00 1937. 14.2 76 2 "vw rabbit" -33.0 4 91.00 53.00 1795. 17.4 76 3 "honda civic" -20.0 6 225.0 100.0 3651. 17.7 76 1 "dodge aspen se" -18.0 6 250.0 78.00 3574. 21.0 76 1 "ford granada ghia" -18.5 6 250.0 110.0 3645. 16.2 76 1 "pontiac ventura sj" -17.5 6 258.0 95.00 3193. 17.8 76 1 "amc pacer d/l" -29.5 4 97.00 71.00 1825. 12.2 76 2 "volkswagen rabbit" -32.0 4 85.00 70.00 1990. 17.0 76 3 "datsun b-210" -28.0 4 97.00 75.00 2155. 16.4 76 3 "toyota corolla" -26.5 4 140.0 72.00 2565. 13.6 76 1 "ford pinto" -20.0 4 130.0 102.0 3150. 15.7 76 2 "volvo 245" -13.0 8 318.0 150.0 3940. 13.2 76 1 "plymouth volare premier v8" -19.0 4 120.0 88.00 3270. 21.9 76 2 "peugeot 504" -19.0 6 156.0 108.0 2930. 15.5 76 3 "toyota mark ii" -16.5 6 168.0 120.0 3820. 16.7 76 2 "mercedes-benz 280s" -16.5 8 350.0 180.0 4380. 12.1 76 1 "cadillac seville" -13.0 8 350.0 145.0 4055. 12.0 76 1 "chevy c10" -13.0 8 302.0 130.0 3870. 15.0 76 1 "ford f108" -13.0 8 318.0 150.0 3755. 14.0 76 1 "dodge d100" -31.5 4 98.00 68.00 2045. 18.5 77 3 "honda accord cvcc" -30.0 4 111.0 80.00 2155. 14.8 77 1 "buick opel isuzu deluxe" -36.0 4 79.00 58.00 1825. 18.6 77 2 "renault 5 gtl" -25.5 4 122.0 96.00 2300. 15.5 77 1 "plymouth arrow gs" -33.5 4 85.00 70.00 1945. 16.8 77 3 "datsun f-10 hatchback" -17.5 8 305.0 145.0 3880. 12.5 77 1 "chevrolet caprice classic" -17.0 8 260.0 110.0 4060. 19.0 77 1 "oldsmobile cutlass supreme" -15.5 8 318.0 145.0 4140. 13.7 77 1 "dodge monaco brougham" -15.0 8 302.0 130.0 4295. 14.9 77 1 "mercury cougar brougham" -17.5 6 250.0 110.0 3520. 16.4 77 1 "chevrolet concours" -20.5 6 231.0 105.0 3425. 16.9 77 1 "buick skylark" -19.0 6 225.0 100.0 3630. 17.7 77 1 "plymouth volare custom" -18.5 6 250.0 98.00 3525. 19.0 77 1 "ford granada" -16.0 8 400.0 180.0 4220. 11.1 77 1 "pontiac grand prix lj" -15.5 8 350.0 170.0 4165. 11.4 77 1 "chevrolet monte carlo landau" -15.5 8 400.0 190.0 4325. 12.2 77 1 "chrysler cordoba" -16.0 8 351.0 149.0 4335. 14.5 77 1 "ford thunderbird" -29.0 4 97.00 78.00 1940. 14.5 77 2 "volkswagen rabbit custom" -24.5 4 151.0 88.00 2740. 16.0 77 1 "pontiac sunbird coupe" -26.0 4 97.00 75.00 2265. 18.2 77 3 "toyota corolla liftback" -25.5 4 140.0 89.00 2755. 15.8 77 1 "ford mustang ii 2+2" -30.5 4 98.00 63.00 2051. 17.0 77 1 "chevrolet chevette" -33.5 4 98.00 83.00 2075. 15.9 77 1 "dodge colt m/m" -30.0 4 97.00 67.00 1985. 16.4 77 3 "subaru dl" -30.5 4 97.00 78.00 2190. 14.1 77 2 "volkswagen dasher" -22.0 6 146.0 97.00 2815. 14.5 77 3 "datsun 810" -21.5 4 121.0 110.0 2600. 12.8 77 2 "bmw 320i" -21.5 3 80.00 110.0 2720. 13.5 77 3 "mazda rx-4" -43.1 4 90.00 48.00 1985. 21.5 78 2 "volkswagen rabbit custom diesel" -36.1 4 98.00 66.00 1800. 14.4 78 1 "ford fiesta" -32.8 4 78.00 52.00 1985. 19.4 78 3 "mazda glc deluxe" -39.4 4 85.00 70.00 2070. 18.6 78 3 "datsun b210 gx" -36.1 4 91.00 60.00 1800. 16.4 78 3 "honda civic cvcc" -19.9 8 260.0 110.0 3365. 15.5 78 1 "oldsmobile cutlass salon brougham" -19.4 8 318.0 140.0 3735. 13.2 78 1 "dodge diplomat" -20.2 8 302.0 139.0 3570. 12.8 78 1 "mercury monarch ghia" -19.2 6 231.0 105.0 3535. 19.2 78 1 "pontiac phoenix lj" -20.5 6 200.0 95.00 3155. 18.2 78 1 "chevrolet malibu" -20.2 6 200.0 85.00 2965. 15.8 78 1 "ford fairmont (auto)" -25.1 4 140.0 88.00 2720. 15.4 78 1 "ford fairmont (man)" -20.5 6 225.0 100.0 3430. 17.2 78 1 "plymouth volare" -19.4 6 232.0 90.00 3210. 17.2 78 1 "amc concord" -20.6 6 231.0 105.0 3380. 15.8 78 1 "buick century special" -20.8 6 200.0 85.00 3070. 16.7 78 1 "mercury zephyr" -18.6 6 225.0 110.0 3620. 18.7 78 1 "dodge aspen" -18.1 6 258.0 120.0 3410. 15.1 78 1 "amc concord d/l" -19.2 8 305.0 145.0 3425. 13.2 78 1 "chevrolet monte carlo landau" -17.7 6 231.0 165.0 3445. 13.4 78 1 "buick regal sport coupe (turbo)" -18.1 8 302.0 139.0 3205. 11.2 78 1 "ford futura" -17.5 8 318.0 140.0 4080. 13.7 78 1 "dodge magnum xe" -30.0 4 98.00 68.00 2155. 16.5 78 1 "chevrolet chevette" -27.5 4 134.0 95.00 2560. 14.2 78 3 "toyota corona" -27.2 4 119.0 97.00 2300. 14.7 78 3 "datsun 510" -30.9 4 105.0 75.00 2230. 14.5 78 1 "dodge omni" -21.1 4 134.0 95.00 2515. 14.8 78 3 "toyota celica gt liftback" -23.2 4 156.0 105.0 2745. 16.7 78 1 "plymouth sapporo" -23.8 4 151.0 85.00 2855. 17.6 78 1 "oldsmobile starfire sx" -23.9 4 119.0 97.00 2405. 14.9 78 3 "datsun 200-sx" -20.3 5 131.0 103.0 2830. 15.9 78 2 "audi 5000" -17.0 6 163.0 125.0 3140. 13.6 78 2 "volvo 264gl" -21.6 4 121.0 115.0 2795. 15.7 78 2 "saab 99gle" -16.2 6 163.0 133.0 3410. 15.8 78 2 "peugeot 604sl" -31.5 4 89.00 71.00 1990. 14.9 78 2 "volkswagen scirocco" -29.5 4 98.00 68.00 2135. 16.6 78 3 "honda accord lx" -21.5 6 231.0 115.0 3245. 15.4 79 1 "pontiac lemans v6" -19.8 6 200.0 85.00 2990. 18.2 79 1 "mercury zephyr 6" -22.3 4 140.0 88.00 2890. 17.3 79 1 "ford fairmont 4" -20.2 6 232.0 90.00 3265. 18.2 79 1 "amc concord dl 6" -20.6 6 225.0 110.0 3360. 16.6 79 1 "dodge aspen 6" -17.0 8 305.0 130.0 3840. 15.4 79 1 "chevrolet caprice classic" -17.6 8 302.0 129.0 3725. 13.4 79 1 "ford ltd landau" -16.5 8 351.0 138.0 3955. 13.2 79 1 "mercury grand marquis" -18.2 8 318.0 135.0 3830. 15.2 79 1 "dodge st. regis" -16.9 8 350.0 155.0 4360. 14.9 79 1 "buick estate wagon (sw)" -15.5 8 351.0 142.0 4054. 14.3 79 1 "ford country squire (sw)" -19.2 8 267.0 125.0 3605. 15.0 79 1 "chevrolet malibu classic (sw)" -18.5 8 360.0 150.0 3940. 13.0 79 1 "chrysler lebaron town @ country (sw)" -31.9 4 89.00 71.00 1925. 14.0 79 2 "vw rabbit custom" -34.1 4 86.00 65.00 1975. 15.2 79 3 "maxda glc deluxe" -35.7 4 98.00 80.00 1915. 14.4 79 1 "dodge colt hatchback custom" -27.4 4 121.0 80.00 2670. 15.0 79 1 "amc spirit dl" -25.4 5 183.0 77.00 3530. 20.1 79 2 "mercedes benz 300d" -23.0 8 350.0 125.0 3900. 17.4 79 1 "cadillac eldorado" -27.2 4 141.0 71.00 3190. 24.8 79 2 "peugeot 504" -23.9 8 260.0 90.00 3420. 22.2 79 1 "oldsmobile cutlass salon brougham" -34.2 4 105.0 70.00 2200. 13.2 79 1 "plymouth horizon" -34.5 4 105.0 70.00 2150. 14.9 79 1 "plymouth horizon tc3" -31.8 4 85.00 65.00 2020. 19.2 79 3 "datsun 210" -37.3 4 91.00 69.00 2130. 14.7 79 2 "fiat strada custom" -28.4 4 151.0 90.00 2670. 16.0 79 1 "buick skylark limited" -28.8 6 173.0 115.0 2595. 11.3 79 1 "chevrolet citation" -26.8 6 173.0 115.0 2700. 12.9 79 1 "oldsmobile omega brougham" -33.5 4 151.0 90.00 2556. 13.2 79 1 "pontiac phoenix" -41.5 4 98.00 76.00 2144. 14.7 80 2 "vw rabbit" -38.1 4 89.00 60.00 1968. 18.8 80 3 "toyota corolla tercel" -32.1 4 98.00 70.00 2120. 15.5 80 1 "chevrolet chevette" -37.2 4 86.00 65.00 2019. 16.4 80 3 "datsun 310" -28.0 4 151.0 90.00 2678. 16.5 80 1 "chevrolet citation" -26.4 4 140.0 88.00 2870. 18.1 80 1 "ford fairmont" -24.3 4 151.0 90.00 3003. 20.1 80 1 "amc concord" -19.1 6 225.0 90.00 3381. 18.7 80 1 "dodge aspen" -34.3 4 97.00 78.00 2188. 15.8 80 2 "audi 4000" -29.8 4 134.0 90.00 2711. 15.5 80 3 "toyota corona liftback" -31.3 4 120.0 75.00 2542. 17.5 80 3 "mazda 626" -37.0 4 119.0 92.00 2434. 15.0 80 3 "datsun 510 hatchback" -32.2 4 108.0 75.00 2265. 15.2 80 3 "toyota corolla" -46.6 4 86.00 65.00 2110. 17.9 80 3 "mazda glc" -27.9 4 156.0 105.0 2800. 14.4 80 1 "dodge colt" -40.8 4 85.00 65.00 2110. 19.2 80 3 "datsun 210" -44.3 4 90.00 48.00 2085. 21.7 80 2 "vw rabbit c (diesel)" -43.4 4 90.00 48.00 2335. 23.7 80 2 "vw dasher (diesel)" -36.4 5 121.0 67.00 2950. 19.9 80 2 "audi 5000s (diesel)" -30.0 4 146.0 67.00 3250. 21.8 80 2 "mercedes-benz 240d" -44.6 4 91.00 67.00 1850. 13.8 80 3 "honda civic 1500 gl" -40.9 4 85.00 ? 1835. 17.3 80 2 "renault lecar deluxe" -33.8 4 97.00 67.00 2145. 18.0 80 3 "subaru dl" -29.8 4 89.00 62.00 1845. 15.3 80 2 "vokswagen rabbit" -32.7 6 168.0 132.0 2910. 11.4 80 3 "datsun 280-zx" -23.7 3 70.00 100.0 2420. 12.5 80 3 "mazda rx-7 gs" -35.0 4 122.0 88.00 2500. 15.1 80 2 "triumph tr7 coupe" -23.6 4 140.0 ? 2905. 14.3 80 1 "ford mustang cobra" -32.4 4 107.0 72.00 2290. 17.0 80 3 "honda accord" -27.2 4 135.0 84.00 2490. 15.7 81 1 "plymouth reliant" -26.6 4 151.0 84.00 2635. 16.4 81 1 "buick skylark" -25.8 4 156.0 92.00 2620. 14.4 81 1 "dodge aries wagon (sw)" -23.5 6 173.0 110.0 2725. 12.6 81 1 "chevrolet citation" -30.0 4 135.0 84.00 2385. 12.9 81 1 "plymouth reliant" -39.1 4 79.00 58.00 1755. 16.9 81 3 "toyota starlet" -39.0 4 86.00 64.00 1875. 16.4 81 1 "plymouth champ" -35.1 4 81.00 60.00 1760. 16.1 81 3 "honda civic 1300" -32.3 4 97.00 67.00 2065. 17.8 81 3 "subaru" -37.0 4 85.00 65.00 1975. 19.4 81 3 "datsun 210 mpg" -37.7 4 89.00 62.00 2050. 17.3 81 3 "toyota tercel" -34.1 4 91.00 68.00 1985. 16.0 81 3 "mazda glc 4" -34.7 4 105.0 63.00 2215. 14.9 81 1 "plymouth horizon 4" -34.4 4 98.00 65.00 2045. 16.2 81 1 "ford escort 4w" -29.9 4 98.00 65.00 2380. 20.7 81 1 "ford escort 2h" -33.0 4 105.0 74.00 2190. 14.2 81 2 "volkswagen jetta" -34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i" -33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude" -32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla" -32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx" -31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626" -28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel" -30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel" -25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida" -24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima" -22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century" -26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls" -20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl" -17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon" -28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier" -27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon" -34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door" -31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback" -29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se" -27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix" -24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura" -23.0 4 151.0 ? 3035. 20.5 82 1 "amc concord dl" -36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l" -37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l" -31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom" -38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser" -36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l" -36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe" -36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord" -34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla" -38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic" -32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)" -38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx" -25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited" -38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)" -26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion" -22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l" -32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt" -36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2" -27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro" -27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl" -44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup" -32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage" -28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger" -31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10" diff --git a/tf2.0/daily-minimum-temperatures-in-me.csv b/tf2.0/daily-minimum-temperatures-in-me.csv deleted file mode 100644 index be45e5a8..00000000 --- a/tf2.0/daily-minimum-temperatures-in-me.csv +++ /dev/null @@ -1,3654 +0,0 @@ -"Date","Daily minimum temperatures in Melbourne, Australia, 1981-1990" -"1981-01-01",20.7 -"1981-01-02",17.9 -"1981-01-03",18.8 -"1981-01-04",14.6 -"1981-01-05",15.8 -"1981-01-06",15.8 -"1981-01-07",15.8 -"1981-01-08",17.4 -"1981-01-09",21.8 -"1981-01-10",20.0 -"1981-01-11",16.2 -"1981-01-12",13.3 -"1981-01-13",16.7 -"1981-01-14",21.5 -"1981-01-15",25.0 -"1981-01-16",20.7 -"1981-01-17",20.6 -"1981-01-18",24.8 -"1981-01-19",17.7 -"1981-01-20",15.5 -"1981-01-21",18.2 -"1981-01-22",12.1 -"1981-01-23",14.4 -"1981-01-24",16.0 -"1981-01-25",16.5 -"1981-01-26",18.7 -"1981-01-27",19.4 -"1981-01-28",17.2 -"1981-01-29",15.5 -"1981-01-30",15.1 -"1981-01-31",15.4 -"1981-02-01",15.3 -"1981-02-02",18.8 -"1981-02-03",21.9 -"1981-02-04",19.9 -"1981-02-05",16.6 -"1981-02-06",16.8 -"1981-02-07",14.6 -"1981-02-08",17.1 -"1981-02-09",25.0 -"1981-02-10",15.0 -"1981-02-11",13.7 -"1981-02-12",13.9 -"1981-02-13",18.3 -"1981-02-14",22.0 -"1981-02-15",22.1 -"1981-02-16",21.2 -"1981-02-17",18.4 -"1981-02-18",16.6 -"1981-02-19",16.1 -"1981-02-20",15.7 -"1981-02-21",16.6 -"1981-02-22",16.5 -"1981-02-23",14.4 -"1981-02-24",14.4 -"1981-02-25",18.5 -"1981-02-26",16.9 -"1981-02-27",17.5 -"1981-02-28",21.2 -"1981-03-01",17.8 -"1981-03-02",18.6 -"1981-03-03",17.0 -"1981-03-04",16.0 -"1981-03-05",13.3 -"1981-03-06",14.3 -"1981-03-07",11.4 -"1981-03-08",16.3 -"1981-03-09",16.1 -"1981-03-10",11.8 -"1981-03-11",12.2 -"1981-03-12",14.7 -"1981-03-13",11.8 -"1981-03-14",11.3 -"1981-03-15",10.6 -"1981-03-16",11.7 -"1981-03-17",14.2 -"1981-03-18",11.2 -"1981-03-19",16.9 -"1981-03-20",16.7 -"1981-03-21",8.1 -"1981-03-22",8.0 -"1981-03-23",8.8 -"1981-03-24",13.4 -"1981-03-25",10.9 -"1981-03-26",13.4 -"1981-03-27",11.0 -"1981-03-28",15.0 -"1981-03-29",15.7 -"1981-03-30",14.5 -"1981-03-31",15.8 -"1981-04-01",16.7 -"1981-04-02",16.8 -"1981-04-03",17.5 -"1981-04-04",17.1 -"1981-04-05",18.1 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-"1989-04-15",8.5 -"1989-04-16",11.4 -"1989-04-17",10.9 -"1989-04-18",12.0 -"1989-04-19",8.6 -"1989-04-20",9.0 -"1989-04-21",9.6 -"1989-04-22",10.2 -"1989-04-23",9.8 -"1989-04-24",8.3 -"1989-04-25",11.0 -"1989-04-26",11.9 -"1989-04-27",14.0 -"1989-04-28",15.8 -"1989-04-29",14.5 -"1989-04-30",13.2 -"1989-05-01",14.2 -"1989-05-02",14.6 -"1989-05-03",11.8 -"1989-05-04",14.4 -"1989-05-05",10.4 -"1989-05-06",10.3 -"1989-05-07",10.8 -"1989-05-08",10.5 -"1989-05-09",9.5 -"1989-05-10",12.5 -"1989-05-11",13.7 -"1989-05-12",12.7 -"1989-05-13",11.9 -"1989-05-14",11.4 -"1989-05-15",9.7 -"1989-05-16",8.3 -"1989-05-17",8.1 -"1989-05-18",11.7 -"1989-05-19",11.6 -"1989-05-20",7.4 -"1989-05-21",5.2 -"1989-05-22",11.0 -"1989-05-23",9.5 -"1989-05-24",9.2 -"1989-05-25",10.7 -"1989-05-26",9.0 -"1989-05-27",10.2 -"1989-05-28",10.3 -"1989-05-29",12.1 -"1989-05-30",13.2 -"1989-05-31",6.6 -"1989-06-01",2.3 -"1989-06-02",1.4 -"1989-06-03",2.1 -"1989-06-04",6.6 -"1989-06-05",8.9 -"1989-06-06",7.8 -"1989-06-07",9.0 -"1989-06-08",10.3 -"1989-06-09",7.9 -"1989-06-10",7.2 -"1989-06-11",8.6 -"1989-06-12",8.8 -"1989-06-13",6.2 -"1989-06-14",9.5 -"1989-06-15",10.2 -"1989-06-16",9.7 -"1989-06-17",11.2 -"1989-06-18",10.2 -"1989-06-19",10.1 -"1989-06-20",8.1 -"1989-06-21",6.6 -"1989-06-22",5.0 -"1989-06-23",4.7 -"1989-06-24",5.3 -"1989-06-25",4.5 -"1989-06-26",2.3 -"1989-06-27",1.4 -"1989-06-28",0.5 -"1989-06-29",2.4 -"1989-06-30",8.0 -"1989-07-01",6.0 -"1989-07-02",7.1 -"1989-07-03",9.7 -"1989-07-04",6.9 -"1989-07-05",5.3 -"1989-07-06",7.0 -"1989-07-07",6.2 -"1989-07-08",7.0 -"1989-07-09",9.7 -"1989-07-10",8.0 -"1989-07-11",8.5 -"1989-07-12",7.1 -"1989-07-13",7.5 -"1989-07-14",3.3 -"1989-07-15",1.8 -"1989-07-16",2.6 -"1989-07-17",5.3 -"1989-07-18",5.8 -"1989-07-19",5.8 -"1989-07-20",7.2 -"1989-07-21",5.3 -"1989-07-22",1.6 -"1989-07-23",3.1 -"1989-07-24",5.3 -"1989-07-25",7.7 -"1989-07-26",4.2 -"1989-07-27",5.5 -"1989-07-28",9.0 -"1989-07-29",11.2 -"1989-07-30",8.0 -"1989-07-31",7.6 -"1989-08-01",3.7 -"1989-08-02",7.5 -"1989-08-03",8.1 -"1989-08-04",8.4 -"1989-08-05",7.1 -"1989-08-06",7.6 -"1989-08-07",7.6 -"1989-08-08",5.6 -"1989-08-09",7.0 -"1989-08-10",10.5 -"1989-08-11",7.3 -"1989-08-12",7.8 -"1989-08-13",5.8 -"1989-08-14",3.8 -"1989-08-15",5.8 -"1989-08-16",6.7 -"1989-08-17",6.6 -"1989-08-18",6.6 -"1989-08-19",9.0 -"1989-08-20",8.1 -"1989-08-21",5.1 -"1989-08-22",8.6 -"1989-08-23",7.0 -"1989-08-24",5.5 -"1989-08-25",7.4 -"1989-08-26",6.2 -"1989-08-27",4.2 -"1989-08-28",6.3 -"1989-08-29",7.0 -"1989-08-30",4.0 -"1989-08-31",8.0 -"1989-09-01",8.8 -"1989-09-02",8.8 -"1989-09-03",6.1 -"1989-09-04",8.6 -"1989-09-05",8.9 -"1989-09-06",7.8 -"1989-09-07",5.0 -"1989-09-08",7.0 -"1989-09-09",13.3 -"1989-09-10",7.9 -"1989-09-11",7.5 -"1989-09-12",8.3 -"1989-09-13",7.2 -"1989-09-14",6.5 -"1989-09-15",8.9 -"1989-09-16",7.4 -"1989-09-17",9.9 -"1989-09-18",9.3 -"1989-09-19",10.6 -"1989-09-20",8.6 -"1989-09-21",7.2 -"1989-09-22",12.6 -"1989-09-23",7.8 -"1989-09-24",6.3 -"1989-09-25",9.2 -"1989-09-26",5.8 -"1989-09-27",9.0 -"1989-09-28",5.0 -"1989-09-29",11.9 -"1989-09-30",13.4 -"1989-10-01",10.5 -"1989-10-02",6.2 -"1989-10-03",5.1 -"1989-10-04",9.5 -"1989-10-05",11.7 -"1989-10-06",9.2 -"1989-10-07",7.3 -"1989-10-08",9.7 -"1989-10-09",9.4 -"1989-10-10",10.0 -"1989-10-11",10.9 -"1989-10-12",11.0 -"1989-10-13",10.9 -"1989-10-14",8.0 -"1989-10-15",11.2 -"1989-10-16",7.5 -"1989-10-17",7.2 -"1989-10-18",13.2 -"1989-10-19",12.9 -"1989-10-20",9.4 -"1989-10-21",10.2 -"1989-10-22",9.5 -"1989-10-23",12.4 -"1989-10-24",10.2 -"1989-10-25",13.4 -"1989-10-26",11.6 -"1989-10-27",8.0 -"1989-10-28",9.0 -"1989-10-29",9.3 -"1989-10-30",13.5 -"1989-10-31",8.0 -"1989-11-01",8.1 -"1989-11-02",10.0 -"1989-11-03",8.5 -"1989-11-04",12.5 -"1989-11-05",15.0 -"1989-11-06",13.3 -"1989-11-07",11.0 -"1989-11-08",11.9 -"1989-11-09",8.3 -"1989-11-10",9.7 -"1989-11-11",11.3 -"1989-11-12",12.5 -"1989-11-13",9.4 -"1989-11-14",11.4 -"1989-11-15",13.2 -"1989-11-16",13.8 -"1989-11-17",16.0 -"1989-11-18",10.9 -"1989-11-19",11.9 -"1989-11-20",12.4 -"1989-11-21",13.2 -"1989-11-22",15.5 -"1989-11-23",21.6 -"1989-11-24",14.9 -"1989-11-25",14.4 -"1989-11-26",12.9 -"1989-11-27",13.1 -"1989-11-28",14.0 -"1989-11-29",17.9 -"1989-11-30",17.7 -"1989-12-01",16.3 -"1989-12-02",18.3 -"1989-12-03",13.7 -"1989-12-04",13.3 -"1989-12-05",10.6 -"1989-12-06",14.1 -"1989-12-07",16.0 -"1989-12-08",16.5 -"1989-12-09",14.1 -"1989-12-10",18.7 -"1989-12-11",16.2 -"1989-12-12",14.8 -"1989-12-13",12.6 -"1989-12-14",10.4 -"1989-12-15",12.2 -"1989-12-16",12.6 -"1989-12-17",12.1 -"1989-12-18",17.3 -"1989-12-19",16.4 -"1989-12-20",12.6 -"1989-12-21",12.3 -"1989-12-22",11.8 -"1989-12-23",12.0 -"1989-12-24",12.7 -"1989-12-25",16.4 -"1989-12-26",16.0 -"1989-12-27",13.3 -"1989-12-28",11.7 -"1989-12-29",10.4 -"1989-12-30",14.4 -"1989-12-31",12.7 -"1990-01-01",14.8 -"1990-01-02",13.3 -"1990-01-03",15.6 -"1990-01-04",14.5 -"1990-01-05",14.3 -"1990-01-06",15.3 -"1990-01-07",16.4 -"1990-01-08",14.8 -"1990-01-09",17.4 -"1990-01-10",18.8 -"1990-01-11",22.1 -"1990-01-12",19.0 -"1990-01-13",15.5 -"1990-01-14",15.8 -"1990-01-15",14.7 -"1990-01-16",10.7 -"1990-01-17",11.5 -"1990-01-18",15.0 -"1990-01-19",14.5 -"1990-01-20",14.5 -"1990-01-21",13.3 -"1990-01-22",14.3 -"1990-01-23",14.3 -"1990-01-24",20.5 -"1990-01-25",15.0 -"1990-01-26",17.1 -"1990-01-27",16.9 -"1990-01-28",16.9 -"1990-01-29",13.6 -"1990-01-30",16.4 -"1990-01-31",16.1 -"1990-02-01",12.0 -"1990-02-02",12.2 -"1990-02-03",14.8 -"1990-02-04",14.8 -"1990-02-05",14.4 -"1990-02-06",12.9 -"1990-02-07",13.4 -"1990-02-08",15.9 -"1990-02-09",16.1 -"1990-02-10",17.6 -"1990-02-11",15.6 -"1990-02-12",15.0 -"1990-02-13",13.0 -"1990-02-14",14.1 -"1990-02-15",17.3 -"1990-02-16",15.7 -"1990-02-17",18.6 -"1990-02-18",12.7 -"1990-02-19",14.0 -"1990-02-20",13.7 -"1990-02-21",16.3 -"1990-02-22",20.0 -"1990-02-23",17.0 -"1990-02-24",15.2 -"1990-02-25",16.5 -"1990-02-26",16.5 -"1990-02-27",17.3 -"1990-02-28",19.1 -"1990-03-01",19.3 -"1990-03-02",17.3 -"1990-03-03",19.0 -"1990-03-04",19.8 -"1990-03-05",19.3 -"1990-03-06",17.2 -"1990-03-07",14.2 -"1990-03-08",10.3 -"1990-03-09",13.0 -"1990-03-10",15.3 -"1990-03-11",15.0 -"1990-03-12",12.1 -"1990-03-13",9.2 -"1990-03-14",11.0 -"1990-03-15",15.0 -"1990-03-16",11.6 -"1990-03-17",11.6 -"1990-03-18",15.1 -"1990-03-19",15.0 -"1990-03-20",13.6 -"1990-03-21",12.5 -"1990-03-22",14.3 -"1990-03-23",16.0 -"1990-03-24",17.4 -"1990-03-25",16.9 -"1990-03-26",18.0 -"1990-03-27",20.6 -"1990-03-28",14.2 -"1990-03-29",10.9 -"1990-03-30",11.9 -"1990-03-31",13.3 -"1990-04-01",15.3 -"1990-04-02",14.7 -"1990-04-03",11.0 -"1990-04-04",12.2 -"1990-04-05",14.2 -"1990-04-06",17.0 -"1990-04-07",15.8 -"1990-04-08",15.2 -"1990-04-09",15.1 -"1990-04-10",14.7 -"1990-04-11",18.5 -"1990-04-12",16.4 -"1990-04-13",18.4 -"1990-04-14",15.1 -"1990-04-15",9.9 -"1990-04-16",10.2 -"1990-04-17",12.6 -"1990-04-18",13.2 -"1990-04-19",11.5 -"1990-04-20",13.8 -"1990-04-21",14.5 -"1990-04-22",14.7 -"1990-04-23",11.2 -"1990-04-24",12.7 -"1990-04-25",13.7 -"1990-04-26",11.5 -"1990-04-27",10.4 -"1990-04-28",8.9 -"1990-04-29",11.1 -"1990-04-30",9.5 -"1990-05-01",13.0 -"1990-05-02",13.9 -"1990-05-03",12.6 -"1990-05-04",14.3 -"1990-05-05",16.0 -"1990-05-06",13.3 -"1990-05-07",7.0 -"1990-05-08",4.9 -"1990-05-09",6.9 -"1990-05-10",13.7 -"1990-05-11",10.6 -"1990-05-12",12.3 -"1990-05-13",11.1 -"1990-05-14",10.2 -"1990-05-15",9.5 -"1990-05-16",8.9 -"1990-05-17",13.4 -"1990-05-18",9.1 -"1990-05-19",9.4 -"1990-05-20",8.7 -"1990-05-21",5.8 -"1990-05-22",4.5 -"1990-05-23",7.2 -"1990-05-24",10.0 -"1990-05-25",10.5 -"1990-05-26",10.7 -"1990-05-27",8.2 -"1990-05-28",6.1 -"1990-05-29",4.5 -"1990-05-30",6.1 -"1990-05-31",9.8 -"1990-06-01",9.7 -"1990-06-02",8.2 -"1990-06-03",8.4 -"1990-06-04",8.5 -"1990-06-05",10.4 -"1990-06-06",6.8 -"1990-06-07",6.0 -"1990-06-08",6.6 -"1990-06-09",7.8 -"1990-06-10",10.3 -"1990-06-11",7.2 -"1990-06-12",7.4 -"1990-06-13",11.4 -"1990-06-14",5.4 -"1990-06-15",4.4 -"1990-06-16",6.4 -"1990-06-17",9.3 -"1990-06-18",7.7 -"1990-06-19",8.1 -"1990-06-20",8.3 -"1990-06-21",9.1 -"1990-06-22",7.7 -"1990-06-23",10.6 -"1990-06-24",8.2 -"1990-06-25",7.9 -"1990-06-26",5.2 -"1990-06-27",5.9 -"1990-06-28",3.7 -"1990-06-29",5.6 -"1990-06-30",9.4 -"1990-07-01",7.4 -"1990-07-02",7.3 -"1990-07-03",7.7 -"1990-07-04",7.7 -"1990-07-05",9.3 -"1990-07-06",4.4 -"1990-07-07",5.7 -"1990-07-08",10.2 -"1990-07-09",10.2 -"1990-07-10",9.3 -"1990-07-11",5.4 -"1990-07-12",5.0 -"1990-07-13",7.6 -"1990-07-14",9.6 -"1990-07-15",10.4 -"1990-07-16",11.2 -"1990-07-17",9.1 -"1990-07-18",11.2 -"1990-07-19",6.8 -"1990-07-20",8.3 -"1990-07-21",9.7 -"1990-07-22",9.6 -"1990-07-23",9.8 -"1990-07-24",10.8 -"1990-07-25",9.2 -"1990-07-26",6.5 -"1990-07-27",8.1 -"1990-07-28",7.3 -"1990-07-29",7.9 -"1990-07-30",6.0 -"1990-07-31",5.0 -"1990-08-01",6.8 -"1990-08-02",9.8 -"1990-08-03",5.7 -"1990-08-04",8.6 -"1990-08-05",10.6 -"1990-08-06",7.8 -"1990-08-07",7.7 -"1990-08-08",8.6 -"1990-08-09",6.5 -"1990-08-10",6.9 -"1990-08-11",6.4 -"1990-08-12",8.5 -"1990-08-13",7.8 -"1990-08-14",9.3 -"1990-08-15",8.4 -"1990-08-16",7.8 -"1990-08-17",7.4 -"1990-08-18",7.7 -"1990-08-19",8.9 -"1990-08-20",9.7 -"1990-08-21",9.9 -"1990-08-22",6.1 -"1990-08-23",6.6 -"1990-08-24",7.6 -"1990-08-25",7.4 -"1990-08-26",8.0 -"1990-08-27",2.1 -"1990-08-28",5.9 -"1990-08-29",11.6 -"1990-08-30",8.6 -"1990-08-31",7.9 -"1990-09-01",6.0 -"1990-09-02",9.5 -"1990-09-03",8.6 -"1990-09-04",7.6 -"1990-09-05",10.4 -"1990-09-06",10.3 -"1990-09-07",7.5 -"1990-09-08",3.0 -"1990-09-09",5.3 -"1990-09-10",10.5 -"1990-09-11",14.6 -"1990-09-12",12.6 -"1990-09-13",9.8 -"1990-09-14",7.2 -"1990-09-15",10.1 -"1990-09-16",10.4 -"1990-09-17",3.7 -"1990-09-18",7.3 -"1990-09-19",11.6 -"1990-09-20",16.3 -"1990-09-21",9.6 -"1990-09-22",6.8 -"1990-09-23",5.2 -"1990-09-24",10.6 -"1990-09-25",16.3 -"1990-09-26",9.8 -"1990-09-27",4.6 -"1990-09-28",11.1 -"1990-09-29",8.7 -"1990-09-30",10.0 -"1990-10-01",11.3 -"1990-10-02",10.5 -"1990-10-03",9.9 -"1990-10-04",11.0 -"1990-10-05",14.0 -"1990-10-06",9.2 -"1990-10-07",9.8 -"1990-10-08",6.0 -"1990-10-09",9.8 -"1990-10-10",9.2 -"1990-10-11",11.8 -"1990-10-12",10.3 -"1990-10-13",7.5 -"1990-10-14",7.7 -"1990-10-15",15.8 -"1990-10-16",14.6 -"1990-10-17",10.5 -"1990-10-18",11.3 -"1990-10-19",10.9 -"1990-10-20",6.4 -"1990-10-21",10.9 -"1990-10-22",9.0 -"1990-10-23",10.9 -"1990-10-24",12.4 -"1990-10-25",11.6 -"1990-10-26",13.3 -"1990-10-27",14.4 -"1990-10-28",18.4 -"1990-10-29",13.6 -"1990-10-30",14.9 -"1990-10-31",14.8 -"1990-11-01",15.4 -"1990-11-02",11.8 -"1990-11-03",13.0 -"1990-11-04",11.1 -"1990-11-05",12.5 -"1990-11-06",18.3 -"1990-11-07",19.2 -"1990-11-08",15.4 -"1990-11-09",13.1 -"1990-11-10",11.5 -"1990-11-11",8.6 -"1990-11-12",12.6 -"1990-11-13",13.8 -"1990-11-14",14.6 -"1990-11-15",13.2 -"1990-11-16",12.3 -"1990-11-17",8.8 -"1990-11-18",10.7 -"1990-11-19",9.9 -"1990-11-20",8.3 -"1990-11-21",15.0 -"1990-11-22",12.2 -"1990-11-23",10.5 -"1990-11-24",11.1 -"1990-11-25",13.0 -"1990-11-26",12.9 -"1990-11-27",8.8 -"1990-11-28",14.7 -"1990-11-29",14.7 -"1990-11-30",12.7 -"1990-12-01",13.3 -"1990-12-02",13.2 -"1990-12-03",16.2 -"1990-12-04",17.3 -"1990-12-05",20.5 -"1990-12-06",20.2 -"1990-12-07",19.4 -"1990-12-08",15.5 -"1990-12-09",14.1 -"1990-12-10",11.0 -"1990-12-11",11.1 -"1990-12-12",14.0 -"1990-12-13",11.4 -"1990-12-14",12.5 -"1990-12-15",13.4 -"1990-12-16",13.6 -"1990-12-17",13.9 -"1990-12-18",17.2 -"1990-12-19",14.7 -"1990-12-20",15.4 -"1990-12-21",13.1 -"1990-12-22",13.2 -"1990-12-23",13.9 -"1990-12-24",10.0 -"1990-12-25",12.9 -"1990-12-26",14.6 -"1990-12-27",14.0 -"1990-12-28",13.6 -"1990-12-29",13.5 -"1990-12-30",15.7 -"1990-12-31",13.0 - -Daily minimum temperatures in Melbourne, Australia, 1981-1990 - diff --git a/tf2.0/exercises.txt b/tf2.0/exercises.txt deleted file mode 100644 index 6fdee299..00000000 --- a/tf2.0/exercises.txt +++ /dev/null @@ -1,36 +0,0 @@ -Logistic Regression -https://www.kaggle.com/uciml/pima-indians-diabetes-database -https://lazyprogrammer.me/course_files/exercises/diabetes.csv - -Linear Regression -https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html -https://lazyprogrammer.me/course_files/exercises/boston.txt - -ANN -https://archive.ics.uci.edu/ml/datasets/ecoli (orig) -https://www.kaggle.com/elikplim/ecoli-data-set (alt) -https://lazyprogrammer.me/course_files/exercises/ecoli.csv - -CNN -https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge -https://archive.org/download/fer2013_202311/fer2013.csv - -RNN -Find your own stock price dataset! - -NLP -https://www.kaggle.com/crowdflower/twitter-airline-sentiment -https://lazyprogrammer.me/course_files/exercises/AirlineSentimentTweets.csv - -Recommender Systems -http://www2.informatik.uni-freiburg.de/~cziegler/BX/ -http://lazyprogrammer.me/course_files/exercises/BX-CSV-Dump.zip - -Transfer Learning -https://www.kaggle.com/c/dogs-vs-cats - -GAN -https://www.kaggle.com/c/dogs-vs-cats - -DeepRL -Find your own stock price dataset! \ No newline at end of file diff --git a/tf2.0/extra_reading.txt b/tf2.0/extra_reading.txt deleted file mode 100644 index a23d273c..00000000 --- a/tf2.0/extra_reading.txt +++ /dev/null @@ -1,36 +0,0 @@ -Gradient Descent: Convergence Analysis -http://www.stat.cmu.edu/~ryantibs/convexopt-F13/scribes/lec6.pdf - -Deep learning improved by biological activation functions -https://arxiv.org/pdf/1804.11237.pdf - -Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift -Sergey Ioffe, Christian Szegedy -https://arxiv.org/abs/1502.03167 - -Dropout: A Simple Way to Prevent Neural Networks from Overfitting -https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf - -Convolution arithmetic tutorial -https://theano-pymc.readthedocs.io/en/latest/tutorial/conv_arithmetic.html - -On the Practical Computational Power of Finite Precision RNNs for Language Recognition -https://arxiv.org/abs/1805.04908 - -Massive Exploration of Neural Machine Translation Architectures -https://arxiv.org/abs/1703.03906 - -Practical Deep Reinforcement Learning Approach for Stock Trading -https://arxiv.org/abs/1811.07522 - -Inceptionism: Going Deeper into Neural Networks -https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html - -The Loss Surfaces of Multilayer Networks -https://arxiv.org/pdf/1412.0233.pdf - -Tensorflow Developer Certificate Installation Guide -https://www.tensorflow.org/static/extras/cert/Setting_Up_TF_Developer_Certificate_Exam.pdf - -Tensorflow Developer Certificate Candidate Handbook -https://www.tensorflow.org/extras/cert/TF_Certificate_Candidate_Handbook.pdf diff --git a/tf2.0/fake_util.py b/tf2.0/fake_util.py deleted file mode 100644 index 49dd3a3f..00000000 --- a/tf2.0/fake_util.py +++ /dev/null @@ -1,4 +0,0 @@ -# Used for an example only - -def my_useful_function(): - print("hello world") \ No newline at end of file diff --git a/tf2.0/keras_trader.py b/tf2.0/keras_trader.py deleted file mode 100644 index 18545989..00000000 --- a/tf2.0/keras_trader.py +++ /dev/null @@ -1,421 +0,0 @@ -import numpy as np -import pandas as pd - -# must do this BEFORE importing keras -import os -os.environ["KERAS_BACKEND"] = "jax" - -from keras.models import Model -from keras.layers import Dense, Input -from keras.optimizers import Adam - -from datetime import datetime -import itertools -import argparse -import re -import pickle - -from sklearn.preprocessing import StandardScaler - - -import keras.backend as K -print("Using backend:", K.backend()) - -# import tensorflow as tf -# if tf.__version__.startswith('2'): -# tf.compat.v1.disable_eager_execution() - - - -# Let's use AAPL (Apple), MSI (Motorola), SBUX (Starbucks) -def get_data(): - # returns a T x 3 list of stock prices - # each row is a different stock - # 0 = AAPL - # 1 = MSI - # 2 = SBUX - df = pd.read_csv('aapl_msi_sbux.csv') - return df.values - - - -### The experience replay memory ### -class ReplayBuffer: - def __init__(self, obs_dim, act_dim, size): - self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32) - self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32) - self.acts_buf = np.zeros(size, dtype=np.uint8) - self.rews_buf = np.zeros(size, dtype=np.float32) - self.done_buf = np.zeros(size, dtype=np.uint8) - self.ptr, self.size, self.max_size = 0, 0, size - - def store(self, obs, act, rew, next_obs, done): - self.obs1_buf[self.ptr] = obs - self.obs2_buf[self.ptr] = next_obs - self.acts_buf[self.ptr] = act - self.rews_buf[self.ptr] = rew - self.done_buf[self.ptr] = done - self.ptr = (self.ptr+1) % self.max_size - self.size = min(self.size+1, self.max_size) - - def sample_batch(self, batch_size=32): - idxs = np.random.randint(0, self.size, size=batch_size) - return dict(s=self.obs1_buf[idxs], - s2=self.obs2_buf[idxs], - a=self.acts_buf[idxs], - r=self.rews_buf[idxs], - d=self.done_buf[idxs]) - - - - - -def get_scaler(env): - # return scikit-learn scaler object to scale the states - # Note: you could also populate the replay buffer here - - states = [] - for _ in range(env.n_step): - action = np.random.choice(env.action_space) - state, reward, done, info = env.step(action) - states.append(state) - if done: - break - - scaler = StandardScaler() - scaler.fit(states) - return scaler - - - - -def maybe_make_dir(directory): - if not os.path.exists(directory): - os.makedirs(directory) - - - - -def mlp(input_dim, n_action, n_hidden_layers=1, hidden_dim=32): - """ A multi-layer perceptron """ - - # input layer - i = Input(shape=(input_dim,)) - x = i - - # hidden layers - for _ in range(n_hidden_layers): - x = Dense(hidden_dim, activation='relu')(x) - - # final layer - x = Dense(n_action)(x) - - # make the model - model = Model(i, x) - - model.compile(loss='mse', optimizer='adam') - print((model.summary())) - return model - - - - -class MultiStockEnv: - """ - A 3-stock trading environment. - State: vector of size 7 (n_stock * 2 + 1) - - # shares of stock 1 owned - - # shares of stock 2 owned - - # shares of stock 3 owned - - price of stock 1 (using daily close price) - - price of stock 2 - - price of stock 3 - - cash owned (can be used to purchase more stocks) - Action: categorical variable with 27 (3^3) possibilities - - for each stock, you can: - - 0 = sell - - 1 = hold - - 2 = buy - """ - def __init__(self, data, initial_investment=20000): - # data - self.stock_price_history = data - self.n_step, self.n_stock = self.stock_price_history.shape - - # instance attributes - self.initial_investment = initial_investment - self.cur_step = None - self.stock_owned = None - self.stock_price = None - self.cash_in_hand = None - - self.action_space = np.arange(3**self.n_stock) - - # action permutations - # returns a nested list with elements like: - # [0,0,0] - # [0,0,1] - # [0,0,2] - # [0,1,0] - # [0,1,1] - # etc. - # 0 = sell - # 1 = hold - # 2 = buy - self.action_list = list(map(list, itertools.product([0, 1, 2], repeat=self.n_stock))) - - # calculate size of state - self.state_dim = self.n_stock * 2 + 1 - - self.reset() - - - def reset(self): - self.cur_step = 0 - self.stock_owned = np.zeros(self.n_stock) - self.stock_price = self.stock_price_history[self.cur_step] - self.cash_in_hand = self.initial_investment - return self._get_obs() - - - def step(self, action): - assert action in self.action_space - - # get current value before performing the action - prev_val = self._get_val() - - # perform the trade - self._trade(action) - - # update price, i.e. go to the next day - self.cur_step += 1 - self.stock_price = self.stock_price_history[self.cur_step] - - # get the new value after taking the action - cur_val = self._get_val() - - # reward is the increase in porfolio value - reward = cur_val - prev_val - - # done if we have run out of data - done = self.cur_step == self.n_step - 1 - - # store the current value of the portfolio here - info = {'cur_val': cur_val} - - # conform to the Gym API - return self._get_obs(), reward, done, info - - - def _get_obs(self): - obs = np.empty(self.state_dim) - obs[:self.n_stock] = self.stock_owned - obs[self.n_stock:2*self.n_stock] = self.stock_price - obs[-1] = self.cash_in_hand - return obs - - - - def _get_val(self): - return self.stock_owned.dot(self.stock_price) + self.cash_in_hand - - - def _trade(self, action): - # index the action we want to perform - # 0 = sell - # 1 = hold - # 2 = buy - # e.g. [2,1,0] means: - # buy first stock - # hold second stock - # sell third stock - action_vec = self.action_list[action] - - # determine which stocks to buy or sell - sell_index = [] # stores index of stocks we want to sell - buy_index = [] # stores index of stocks we want to buy - for i, a in enumerate(action_vec): - if a == 0: - sell_index.append(i) - elif a == 2: - buy_index.append(i) - - # sell any stocks we want to sell - # then buy any stocks we want to buy - if sell_index: - # NOTE: to simplify the problem, when we sell, we will sell ALL shares of that stock - for i in sell_index: - self.cash_in_hand += self.stock_price[i] * self.stock_owned[i] - self.stock_owned[i] = 0 - if buy_index: - # NOTE: when buying, we will loop through each stock we want to buy, - # and buy one share at a time until we run out of cash - can_buy = True - while can_buy: - for i in buy_index: - if self.cash_in_hand > self.stock_price[i]: - self.stock_owned[i] += 1 # buy one share - self.cash_in_hand -= self.stock_price[i] - else: - can_buy = False - - - - - -class DQNAgent(object): - def __init__(self, state_size, action_size): - self.state_size = state_size - self.action_size = action_size - self.memory = ReplayBuffer(state_size, action_size, size=500) - self.gamma = 0.95 # discount rate - self.epsilon = 1.0 # exploration rate - self.epsilon_min = 0.01 - self.epsilon_decay = 0.995 - self.model = mlp(state_size, action_size) - - - def update_replay_memory(self, state, action, reward, next_state, done): - self.memory.store(state, action, reward, next_state, done) - - - def act(self, state): - if np.random.rand() <= self.epsilon: - return np.random.choice(self.action_size) - act_values = self.model.predict(state, verbose=0) - return np.argmax(act_values[0]) # returns action - - - def replay(self, batch_size=32): - # first check if replay buffer contains enough data - if self.memory.size < batch_size: - return - - # sample a batch of data from the replay memory - minibatch = self.memory.sample_batch(batch_size) - states = minibatch['s'] - actions = minibatch['a'] - rewards = minibatch['r'] - next_states = minibatch['s2'] - done = minibatch['d'] - - # Calculate the tentative target: Q(s',a) - target = rewards + (1 - done) * self.gamma * np.amax(self.model.predict(next_states, verbose=0), axis=1) - - # With the Keras API, the target (usually) must have the same - # shape as the predictions. - # However, we only need to update the network for the actions - # which were actually taken. - # We can accomplish this by setting the target to be equal to - # the prediction for all values. - # Then, only change the targets for the actions taken. - # Q(s,a) - target_full = self.model.predict(states, verbose=0) - target_full[np.arange(batch_size), actions] = target - - # Run one training step - self.model.train_on_batch(states, target_full) - - if self.epsilon > self.epsilon_min: - self.epsilon *= self.epsilon_decay - - - def load(self, name): - self.model.load_weights(name) - - - def save(self, name): - self.model.save_weights(name) - - - -def play_one_episode(agent, env, is_train): - # note: after transforming states are already 1xD - state = env.reset() - state = scaler.transform([state]) - done = False - - while not done: - action = agent.act(state) - next_state, reward, done, info = env.step(action) - next_state = scaler.transform([next_state]) - if is_train == 'train': - agent.update_replay_memory(state, action, reward, next_state, done) - agent.replay(batch_size) - state = next_state - - return info['cur_val'] - - - -if __name__ == '__main__': - - # config - models_folder = 'rl_trader_models' - rewards_folder = 'rl_trader_rewards' - model_file = 'dqn.weights.h5' - num_episodes = 2000 - batch_size = 32 - initial_investment = 20000 - - - parser = argparse.ArgumentParser() - parser.add_argument('-m', '--mode', type=str, required=True, - help='either "train" or "test"') - args = parser.parse_args() - - maybe_make_dir(models_folder) - maybe_make_dir(rewards_folder) - - data = get_data() - n_timesteps, n_stocks = data.shape - - n_train = n_timesteps // 2 - - train_data = data[:n_train] - test_data = data[n_train:] - - env = MultiStockEnv(train_data, initial_investment) - state_size = env.state_dim - action_size = len(env.action_space) - agent = DQNAgent(state_size, action_size) - scaler = get_scaler(env) - - # store the final value of the portfolio (end of episode) - portfolio_value = [] - - if args.mode == 'test': - # then load the previous scaler - with open(f'{models_folder}/scaler.pkl', 'rb') as f: - scaler = pickle.load(f) - - # remake the env with test data - env = MultiStockEnv(test_data, initial_investment) - - # make sure epsilon is not 1! - # no need to run multiple episodes if epsilon = 0, it's deterministic - agent.epsilon = 0.01 - - # load trained weights - agent.load(f'{models_folder}/{model_file}') - - # play the game num_episodes times - for e in range(num_episodes): - t0 = datetime.now() - val = play_one_episode(agent, env, args.mode) - dt = datetime.now() - t0 - print(f"episode: {e + 1}/{num_episodes}, episode end value: {val:.2f}, duration: {dt}") - portfolio_value.append(val) # append episode end portfolio value - - # save the weights when we are done - if args.mode == 'train': - # save the DQN - agent.save(f'{models_folder}/{model_file}') - - # save the scaler - with open(f'{models_folder}/scaler.pkl', 'wb') as f: - pickle.dump(scaler, f) - - - # save portfolio value for each episode - np.save(f'{rewards_folder}/{args.mode}.npy', portfolio_value) diff --git a/tf2.0/mlp_trader.py b/tf2.0/mlp_trader.py deleted file mode 100644 index e5d53e56..00000000 --- a/tf2.0/mlp_trader.py +++ /dev/null @@ -1,401 +0,0 @@ -import numpy as np -import pandas as pd - -from sklearn.neural_network import MLPRegressor -from sklearn.preprocessing import StandardScaler - -from datetime import datetime -import itertools -import argparse -import re -import os -import pickle - - -# Let's use AAPL (Apple), MSI (Motorola), SBUX (Starbucks) -def get_data(): - # returns a T x 3 list of stock prices - # each row is a different stock - # 0 = AAPL - # 1 = MSI - # 2 = SBUX - df = pd.read_csv('aapl_msi_sbux.csv') - return df.values - - - -### The experience replay memory ### -class ReplayBuffer: - def __init__(self, obs_dim, act_dim, size): - self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32) - self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32) - self.acts_buf = np.zeros(size, dtype=np.uint8) - self.rews_buf = np.zeros(size, dtype=np.float32) - self.done_buf = np.zeros(size, dtype=np.uint8) - self.ptr, self.size, self.max_size = 0, 0, size - - def store(self, obs, act, rew, next_obs, done): - self.obs1_buf[self.ptr] = obs - self.obs2_buf[self.ptr] = next_obs - self.acts_buf[self.ptr] = act - self.rews_buf[self.ptr] = rew - self.done_buf[self.ptr] = done - self.ptr = (self.ptr+1) % self.max_size - self.size = min(self.size+1, self.max_size) - - def sample_batch(self, batch_size=32): - idxs = np.random.randint(0, self.size, size=batch_size) - return dict(s=self.obs1_buf[idxs], - s2=self.obs2_buf[idxs], - a=self.acts_buf[idxs], - r=self.rews_buf[idxs], - d=self.done_buf[idxs]) - - - - - -def get_scaler(env): - # return scikit-learn scaler object to scale the states - # Note: you could also populate the replay buffer here - - states = [] - for _ in range(env.n_step): - action = np.random.choice(env.action_space) - state, reward, done, info = env.step(action) - states.append(state) - if done: - break - - scaler = StandardScaler() - scaler.fit(states) - return scaler - - - - -def maybe_make_dir(directory): - if not os.path.exists(directory): - os.makedirs(directory) - - - - -def mlp(input_dim, n_action, n_hidden_layers=1, hidden_dim=32): - """ A multi-layer perceptron """ - - model = MLPRegressor( - hidden_layer_sizes=n_hidden_layers * [hidden_dim], - ) - - # since we'll be first using this to make a prediction with random weights - # we need to know the output size - - # so we'll just start by fitting on some dummy data - X = np.random.randn(100, input_dim) - Y = np.random.randn(100, n_action) - model.partial_fit(X, Y) - - return model - - - - -class MultiStockEnv: - """ - A 3-stock trading environment. - State: vector of size 7 (n_stock * 2 + 1) - - # shares of stock 1 owned - - # shares of stock 2 owned - - # shares of stock 3 owned - - price of stock 1 (using daily close price) - - price of stock 2 - - price of stock 3 - - cash owned (can be used to purchase more stocks) - Action: categorical variable with 27 (3^3) possibilities - - for each stock, you can: - - 0 = sell - - 1 = hold - - 2 = buy - """ - def __init__(self, data, initial_investment=20000): - # data - self.stock_price_history = data - self.n_step, self.n_stock = self.stock_price_history.shape - - # instance attributes - self.initial_investment = initial_investment - self.cur_step = None - self.stock_owned = None - self.stock_price = None - self.cash_in_hand = None - - self.action_space = np.arange(3**self.n_stock) - - # action permutations - # returns a nested list with elements like: - # [0,0,0] - # [0,0,1] - # [0,0,2] - # [0,1,0] - # [0,1,1] - # etc. - # 0 = sell - # 1 = hold - # 2 = buy - self.action_list = list(map(list, itertools.product([0, 1, 2], repeat=self.n_stock))) - - # calculate size of state - self.state_dim = self.n_stock * 2 + 1 - - self.reset() - - - def reset(self): - self.cur_step = 0 - self.stock_owned = np.zeros(self.n_stock) - self.stock_price = self.stock_price_history[self.cur_step] - self.cash_in_hand = self.initial_investment - return self._get_obs() - - - def step(self, action): - assert action in self.action_space - - # get current value before performing the action - prev_val = self._get_val() - - # perform the trade - self._trade(action) - - # update price, i.e. go to the next day - self.cur_step += 1 - self.stock_price = self.stock_price_history[self.cur_step] - - # get the new value after taking the action - cur_val = self._get_val() - - # reward is the increase in porfolio value - reward = cur_val - prev_val - - # done if we have run out of data - done = self.cur_step == self.n_step - 1 - - # store the current value of the portfolio here - info = {'cur_val': cur_val} - - # conform to the Gym API - return self._get_obs(), reward, done, info - - - def _get_obs(self): - obs = np.empty(self.state_dim) - obs[:self.n_stock] = self.stock_owned - obs[self.n_stock:2*self.n_stock] = self.stock_price - obs[-1] = self.cash_in_hand - return obs - - - - def _get_val(self): - return self.stock_owned.dot(self.stock_price) + self.cash_in_hand - - - def _trade(self, action): - # index the action we want to perform - # 0 = sell - # 1 = hold - # 2 = buy - # e.g. [2,1,0] means: - # buy first stock - # hold second stock - # sell third stock - action_vec = self.action_list[action] - - # determine which stocks to buy or sell - sell_index = [] # stores index of stocks we want to sell - buy_index = [] # stores index of stocks we want to buy - for i, a in enumerate(action_vec): - if a == 0: - sell_index.append(i) - elif a == 2: - buy_index.append(i) - - # sell any stocks we want to sell - # then buy any stocks we want to buy - if sell_index: - # NOTE: to simplify the problem, when we sell, we will sell ALL shares of that stock - for i in sell_index: - self.cash_in_hand += self.stock_price[i] * self.stock_owned[i] - self.stock_owned[i] = 0 - if buy_index: - # NOTE: when buying, we will loop through each stock we want to buy, - # and buy one share at a time until we run out of cash - can_buy = True - while can_buy: - for i in buy_index: - if self.cash_in_hand > self.stock_price[i]: - self.stock_owned[i] += 1 # buy one share - self.cash_in_hand -= self.stock_price[i] - else: - can_buy = False - - - - - -class DQNAgent(object): - def __init__(self, state_size, action_size): - self.state_size = state_size - self.action_size = action_size - self.memory = ReplayBuffer(state_size, action_size, size=500) - self.gamma = 0.95 # discount rate - self.epsilon = 1.0 # exploration rate - self.epsilon_min = 0.01 - self.epsilon_decay = 0.995 - self.model = mlp(state_size, action_size) - - - def update_replay_memory(self, state, action, reward, next_state, done): - self.memory.store(state, action, reward, next_state, done) - - - def act(self, state): - if np.random.rand() <= self.epsilon: - return np.random.choice(self.action_size) - act_values = self.model.predict(state) - return np.argmax(act_values[0]) # returns action - - def replay(self, batch_size=32): - # first check if replay buffer contains enough data - if self.memory.size < batch_size: - return - - # sample a batch of data from the replay memory - minibatch = self.memory.sample_batch(batch_size) - states = minibatch['s'] - actions = minibatch['a'] - rewards = minibatch['r'] - next_states = minibatch['s2'] - done = minibatch['d'] - - # Calculate the tentative target: Q(s',a) - target = rewards + (1 - done) * self.gamma * np.amax(self.model.predict(next_states), axis=1) - - # With the Keras API, the target (usually) must have the same - # shape as the predictions. - # However, we only need to update the network for the actions - # which were actually taken. - # We can accomplish this by setting the target to be equal to - # the prediction for all values. - # Then, only change the targets for the actions taken. - # Q(s,a) - target_full = self.model.predict(states) - target_full[np.arange(batch_size), actions] = target - - # Run one training step - self.model.partial_fit(states, target_full) - - if self.epsilon > self.epsilon_min: - self.epsilon *= self.epsilon_decay - - - def load(self, name): - with open(name, "rb") as f: - self.model = pickle.load(f) - - - def save(self, name): - with open(name, "wb") as f: - pickle.dump(self.model, f) - - -def play_one_episode(agent, env, is_train): - # note: after transforming states are already 1xD - state = env.reset() - state = scaler.transform([state]) - done = False - - while not done: - action = agent.act(state) - next_state, reward, done, info = env.step(action) - next_state = scaler.transform([next_state]) - if is_train == 'train': - agent.update_replay_memory(state, action, reward, next_state, done) - agent.replay(batch_size) - state = next_state - - return info['cur_val'] - - - -if __name__ == '__main__': - - # config - models_folder = 'rl_trader_models' - rewards_folder = 'rl_trader_rewards' - num_episodes = 2000 - batch_size = 32 - initial_investment = 20000 - - - parser = argparse.ArgumentParser() - parser.add_argument('-m', '--mode', type=str, required=True, - help='either "train" or "test"') - args = parser.parse_args() - - maybe_make_dir(models_folder) - maybe_make_dir(rewards_folder) - - data = get_data() - n_timesteps, n_stocks = data.shape - - n_train = n_timesteps // 2 - - train_data = data[:n_train] - test_data = data[n_train:] - - env = MultiStockEnv(train_data, initial_investment) - state_size = env.state_dim - action_size = len(env.action_space) - agent = DQNAgent(state_size, action_size) - scaler = get_scaler(env) - - # store the final value of the portfolio (end of episode) - portfolio_value = [] - - if args.mode == 'test': - # then load the previous scaler - with open(f'{models_folder}/scaler.pkl', 'rb') as f: - scaler = pickle.load(f) - - # remake the env with test data - env = MultiStockEnv(test_data, initial_investment) - - # make sure epsilon is not 1! - # no need to run multiple episodes if epsilon = 0, it's deterministic - agent.epsilon = 0.01 - - # load trained weights - agent.load(f'{models_folder}/mlp.pkl') - - # play the game num_episodes times - for e in range(num_episodes): - t0 = datetime.now() - val = play_one_episode(agent, env, args.mode) - dt = datetime.now() - t0 - print(f"episode: {e + 1}/{num_episodes}, episode end value: {val:.2f}, duration: {dt}") - portfolio_value.append(val) # append episode end portfolio value - - # save the weights when we are done - if args.mode == 'train': - # save the DQN - agent.save(f'{models_folder}/mlp.pkl') - - # save the scaler - with open(f'{models_folder}/scaler.pkl', 'wb') as f: - pickle.dump(scaler, f) - - - # save portfolio value for each episode - np.save(f'{rewards_folder}/{args.mode}.npy', portfolio_value) diff --git a/tf2.0/moore.csv b/tf2.0/moore.csv deleted file mode 100644 index ea97403f..00000000 --- a/tf2.0/moore.csv +++ /dev/null @@ -1,162 +0,0 @@ -1971,2300 -1972,3500 -1973,2500 -1973,2500 -1974,4100 -1974,4500 -1974,8000 -1975,3510 -1976,5000 -1976,8500 -1976,6500 -1978,9000 -1978,29000 -1979,17500 -1979,29000 -1979,68000 -1981,11500 -1982,55000 -1982,134000 -1983,22000 -1984,63000 -1984,190000 -1985,275000 -1985,25000 -1985,16000 -1986,110000 -1986,375000 -1986,30000 -1987,385000 -1987,730000 -1987,273000 -1987,553000 -1988,180000 -1988,250000 -1989,600000 -1989,1000000 -1989,1180235 -1989,310000 -1990,1200000 -1991,1350000 -1991,35000 -1992,600000 -1992,900000 -1993,2800000 -1993,3100000 -1994,578977 -1994,2500000 -1995,2500000 -1999,111000 -1995,5500000 -1996,4300000 -1997,10000000 -1997,7500000 -1997,8800000 -1998,7500000 -1999,9500000 -1999,13500000 -2000,21000000 -2000,21000000 -1999,27400000 -1999,21300000 -1999,22000000 -2000,42000000 -2001,191000000 -2001,45000000 -2002,55000000 -2004,112000000 -2004,400000000 -2005,169000000 -2006,184000000 -2005,228000000 -2006,362000000 -2007,540000000 -2008,47000000 -2003,54300000 -2003,105900000 -2002,220000000 -2005,165000000 -2005,250000000 -2006,291000000 -2007,169000000 -2003,410000000 -2008,600000000 -2009,760000000 -2011,1870000000 -2012,432000000 -2007,463000000 -2007,26000000 -2008,230000000 -2004,592000000 -2007,411000000 -2008,731000000 -2008,758000000 -2007,789000000 -2009,904000000 -2010,1000000000 -2012,2990000000 -2013,1000000000 -2011,1160000000 -2010,1170000000 -2010,1200000000 -2012,1200000000 -2012,1303000000 -2010,1400000000 -2012,1400000000 -2014,1400000000 -2006,1700000000 -2015,1750000000 -2013,1860000000 -2015,1900000000 -2008,1900000000 -2010,2000000000 -2014,2000000000 -2015,2000000000 -2015,3000000000 -2012,2100000000 -2011,2270000000 -2010,2300000000 -2014,2600000000 -2011,2600000000 -2012,2750000000 -2014,3000000000 -2016,3000000000 -2017,5300000000 -2017,5300000000 -2018,8500000000 -2012,3100000000 -2016,3200000000 -2016,3300000000 -2015,3990000000 -2013,4200000000 -2017,4300000000 -2014,4310000000 -2017,4800000000 -2017,4800000000 -2017,4800000000 -2012,5000000000 -2013,5000000000 -2014,5560000000 -2017,6100000000 -2018,6900000000 -2016,4000000000 -2018,6900000000 -2017,5500000000 -2018,5500000000 -2017,7000000000 -2015,7100000000 -2017,8000000000 -2016,7200000000 -2017,8000000000 -2016,8000000000 -2017,9700000000 -2017,250000000 -2015,10000000000 -2017,5450000000 -2018,10000000000 -2017,4300000000 -2017,18000000000 -2017,19200000000 -2018,8876000000 -2018,23600000000 -2018,9000000000 diff --git a/tf2.0/plot_rl_rewards.py b/tf2.0/plot_rl_rewards.py deleted file mode 100644 index 3eb8e171..00000000 --- a/tf2.0/plot_rl_rewards.py +++ /dev/null @@ -1,22 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import argparse - -parser = argparse.ArgumentParser() -parser.add_argument('-m', '--mode', type=str, required=True, - help='either "train" or "test"') -args = parser.parse_args() - -a = np.load(f'rl_trader_rewards/{args.mode}.npy') - -print(f"average reward: {a.mean():.2f}, min: {a.min():.2f}, max: {a.max():.2f}") - -if args.mode == 'train': - # show the training progress - plt.plot(a) -else: - # test - show a histogram of rewards - plt.hist(a, bins=20) - -plt.title(args.mode) -plt.show() \ No newline at end of file diff --git a/tf2.0/rl_trader.py b/tf2.0/rl_trader.py deleted file mode 100644 index b5849494..00000000 --- a/tf2.0/rl_trader.py +++ /dev/null @@ -1,414 +0,0 @@ -import numpy as np -import pandas as pd - -from tensorflow.keras.models import Model -from tensorflow.keras.layers import Dense, Input -from tensorflow.keras.optimizers import Adam - -from datetime import datetime -import itertools -import argparse -import re -import os -import pickle - -from sklearn.preprocessing import StandardScaler - - -import tensorflow as tf -# if tf.__version__.startswith('2'): -# tf.compat.v1.disable_eager_execution() - - -# Let's use AAPL (Apple), MSI (Motorola), SBUX (Starbucks) -def get_data(): - # returns a T x 3 list of stock prices - # each row is a different stock - # 0 = AAPL - # 1 = MSI - # 2 = SBUX - df = pd.read_csv('aapl_msi_sbux.csv') - return df.values - - - -### The experience replay memory ### -class ReplayBuffer: - def __init__(self, obs_dim, act_dim, size): - self.obs1_buf = np.zeros([size, obs_dim], dtype=np.float32) - self.obs2_buf = np.zeros([size, obs_dim], dtype=np.float32) - self.acts_buf = np.zeros(size, dtype=np.uint8) - self.rews_buf = np.zeros(size, dtype=np.float32) - self.done_buf = np.zeros(size, dtype=np.uint8) - self.ptr, self.size, self.max_size = 0, 0, size - - def store(self, obs, act, rew, next_obs, done): - self.obs1_buf[self.ptr] = obs - self.obs2_buf[self.ptr] = next_obs - self.acts_buf[self.ptr] = act - self.rews_buf[self.ptr] = rew - self.done_buf[self.ptr] = done - self.ptr = (self.ptr+1) % self.max_size - self.size = min(self.size+1, self.max_size) - - def sample_batch(self, batch_size=32): - idxs = np.random.randint(0, self.size, size=batch_size) - return dict(s=self.obs1_buf[idxs], - s2=self.obs2_buf[idxs], - a=self.acts_buf[idxs], - r=self.rews_buf[idxs], - d=self.done_buf[idxs]) - - - - - -def get_scaler(env): - # return scikit-learn scaler object to scale the states - # Note: you could also populate the replay buffer here - - states = [] - for _ in range(env.n_step): - action = np.random.choice(env.action_space) - state, reward, done, info = env.step(action) - states.append(state) - if done: - break - - scaler = StandardScaler() - scaler.fit(states) - return scaler - - - - -def maybe_make_dir(directory): - if not os.path.exists(directory): - os.makedirs(directory) - - - - -def mlp(input_dim, n_action, n_hidden_layers=1, hidden_dim=32): - """ A multi-layer perceptron """ - - # input layer - i = Input(shape=(input_dim,)) - x = i - - # hidden layers - for _ in range(n_hidden_layers): - x = Dense(hidden_dim, activation='relu')(x) - - # final layer - x = Dense(n_action)(x) - - # make the model - model = Model(i, x) - - model.compile(loss='mse', optimizer='adam') - print((model.summary())) - return model - - - - -class MultiStockEnv: - """ - A 3-stock trading environment. - State: vector of size 7 (n_stock * 2 + 1) - - # shares of stock 1 owned - - # shares of stock 2 owned - - # shares of stock 3 owned - - price of stock 1 (using daily close price) - - price of stock 2 - - price of stock 3 - - cash owned (can be used to purchase more stocks) - Action: categorical variable with 27 (3^3) possibilities - - for each stock, you can: - - 0 = sell - - 1 = hold - - 2 = buy - """ - def __init__(self, data, initial_investment=20000): - # data - self.stock_price_history = data - self.n_step, self.n_stock = self.stock_price_history.shape - - # instance attributes - self.initial_investment = initial_investment - self.cur_step = None - self.stock_owned = None - self.stock_price = None - self.cash_in_hand = None - - self.action_space = np.arange(3**self.n_stock) - - # action permutations - # returns a nested list with elements like: - # [0,0,0] - # [0,0,1] - # [0,0,2] - # [0,1,0] - # [0,1,1] - # etc. - # 0 = sell - # 1 = hold - # 2 = buy - self.action_list = list(map(list, itertools.product([0, 1, 2], repeat=self.n_stock))) - - # calculate size of state - self.state_dim = self.n_stock * 2 + 1 - - self.reset() - - - def reset(self): - self.cur_step = 0 - self.stock_owned = np.zeros(self.n_stock) - self.stock_price = self.stock_price_history[self.cur_step] - self.cash_in_hand = self.initial_investment - return self._get_obs() - - - def step(self, action): - assert action in self.action_space - - # get current value before performing the action - prev_val = self._get_val() - - # perform the trade - self._trade(action) - - # update price, i.e. go to the next day - self.cur_step += 1 - self.stock_price = self.stock_price_history[self.cur_step] - - # get the new value after taking the action - cur_val = self._get_val() - - # reward is the increase in porfolio value - reward = cur_val - prev_val - - # done if we have run out of data - done = self.cur_step == self.n_step - 1 - - # store the current value of the portfolio here - info = {'cur_val': cur_val} - - # conform to the Gym API - return self._get_obs(), reward, done, info - - - def _get_obs(self): - obs = np.empty(self.state_dim) - obs[:self.n_stock] = self.stock_owned - obs[self.n_stock:2*self.n_stock] = self.stock_price - obs[-1] = self.cash_in_hand - return obs - - - - def _get_val(self): - return self.stock_owned.dot(self.stock_price) + self.cash_in_hand - - - def _trade(self, action): - # index the action we want to perform - # 0 = sell - # 1 = hold - # 2 = buy - # e.g. [2,1,0] means: - # buy first stock - # hold second stock - # sell third stock - action_vec = self.action_list[action] - - # determine which stocks to buy or sell - sell_index = [] # stores index of stocks we want to sell - buy_index = [] # stores index of stocks we want to buy - for i, a in enumerate(action_vec): - if a == 0: - sell_index.append(i) - elif a == 2: - buy_index.append(i) - - # sell any stocks we want to sell - # then buy any stocks we want to buy - if sell_index: - # NOTE: to simplify the problem, when we sell, we will sell ALL shares of that stock - for i in sell_index: - self.cash_in_hand += self.stock_price[i] * self.stock_owned[i] - self.stock_owned[i] = 0 - if buy_index: - # NOTE: when buying, we will loop through each stock we want to buy, - # and buy one share at a time until we run out of cash - can_buy = True - while can_buy: - for i in buy_index: - if self.cash_in_hand > self.stock_price[i]: - self.stock_owned[i] += 1 # buy one share - self.cash_in_hand -= self.stock_price[i] - else: - can_buy = False - - - - - -class DQNAgent(object): - def __init__(self, state_size, action_size): - self.state_size = state_size - self.action_size = action_size - self.memory = ReplayBuffer(state_size, action_size, size=500) - self.gamma = 0.95 # discount rate - self.epsilon = 1.0 # exploration rate - self.epsilon_min = 0.01 - self.epsilon_decay = 0.995 - self.model = mlp(state_size, action_size) - - - def update_replay_memory(self, state, action, reward, next_state, done): - self.memory.store(state, action, reward, next_state, done) - - - def act(self, state): - if np.random.rand() <= self.epsilon: - return np.random.choice(self.action_size) - act_values = self.model.predict(state, verbose=0) - return np.argmax(act_values[0]) # returns action - - @tf.function - def replay(self, batch_size=32): - # first check if replay buffer contains enough data - if self.memory.size < batch_size: - return - - # sample a batch of data from the replay memory - minibatch = self.memory.sample_batch(batch_size) - states = minibatch['s'] - actions = minibatch['a'] - rewards = minibatch['r'] - next_states = minibatch['s2'] - done = minibatch['d'] - - # Calculate the tentative target: Q(s',a) - target = rewards + (1 - done) * self.gamma * np.amax(self.model.predict(next_states, verbose=0), axis=1) - - # With the Keras API, the target (usually) must have the same - # shape as the predictions. - # However, we only need to update the network for the actions - # which were actually taken. - # We can accomplish this by setting the target to be equal to - # the prediction for all values. - # Then, only change the targets for the actions taken. - # Q(s,a) - target_full = self.model.predict(states, verbose=0) - target_full[np.arange(batch_size), actions] = target - - # Run one training step - self.model.train_on_batch(states, target_full) - - if self.epsilon > self.epsilon_min: - self.epsilon *= self.epsilon_decay - - - def load(self, name): - self.model.load_weights(name) - - - def save(self, name): - self.model.save_weights(name) - - - -def play_one_episode(agent, env, is_train): - # note: after transforming states are already 1xD - state = env.reset() - state = scaler.transform([state]) - done = False - - while not done: - action = agent.act(state) - next_state, reward, done, info = env.step(action) - next_state = scaler.transform([next_state]) - if is_train == 'train': - agent.update_replay_memory(state, action, reward, next_state, done) - agent.replay(batch_size) - state = next_state - - return info['cur_val'] - - - -if __name__ == '__main__': - - # config - models_folder = 'rl_trader_models' - rewards_folder = 'rl_trader_rewards' - model_file = 'dqn.weights.h5' - num_episodes = 2000 - batch_size = 32 - initial_investment = 20000 - - - parser = argparse.ArgumentParser() - parser.add_argument('-m', '--mode', type=str, required=True, - help='either "train" or "test"') - args = parser.parse_args() - - maybe_make_dir(models_folder) - maybe_make_dir(rewards_folder) - - data = get_data() - n_timesteps, n_stocks = data.shape - - n_train = n_timesteps // 2 - - train_data = data[:n_train] - test_data = data[n_train:] - - env = MultiStockEnv(train_data, initial_investment) - state_size = env.state_dim - action_size = len(env.action_space) - agent = DQNAgent(state_size, action_size) - scaler = get_scaler(env) - - # store the final value of the portfolio (end of episode) - portfolio_value = [] - - if args.mode == 'test': - # then load the previous scaler - with open(f'{models_folder}/scaler.pkl', 'rb') as f: - scaler = pickle.load(f) - - # remake the env with test data - env = MultiStockEnv(test_data, initial_investment) - - # make sure epsilon is not 1! - # no need to run multiple episodes if epsilon = 0, it's deterministic - agent.epsilon = 0.01 - - # load trained weights - agent.load(f'{models_folder}/{model_file}') - - # play the game num_episodes times - for e in range(num_episodes): - t0 = datetime.now() - val = play_one_episode(agent, env, args.mode) - dt = datetime.now() - t0 - print(f"episode: {e + 1}/{num_episodes}, episode end value: {val:.2f}, duration: {dt}") - portfolio_value.append(val) # append episode end portfolio value - - # save the weights when we are done - if args.mode == 'train': - # save the DQN - agent.save(f'{models_folder}/{model_file}') - - # save the scaler - with open(f'{models_folder}/scaler.pkl', 'wb') as f: - pickle.dump(scaler, f) - - - # save portfolio value for each episode - np.save(f'{rewards_folder}/{args.mode}.npy', portfolio_value) \ No newline at end of file diff --git a/tf2.0/sbux.csv b/tf2.0/sbux.csv deleted file mode 100644 index 05576b6e..00000000 --- a/tf2.0/sbux.csv +++ /dev/null @@ -1,1260 +0,0 @@ -date,open,high,low,close,volume,Name -2013-02-08,27.92,28.325,27.92,28.185,7146296,SBUX -2013-02-11,28.26,28.26,27.93,28.07,5457354,SBUX -2013-02-12,28.0,28.275,27.975,28.13,8665592,SBUX -2013-02-13,28.23,28.23,27.75,27.915,7022056,SBUX -2013-02-14,27.765,27.905,27.675,27.775,8899188,SBUX -2013-02-15,27.805,27.85,27.085,27.17,18195730,SBUX -2013-02-19,27.18,27.305,27.01,27.225,11760912,SBUX -2013-02-20,27.3,27.42,26.59,26.655,12472506,SBUX -2013-02-21,26.535,26.82,26.26,26.675,13896450,SBUX -2013-02-22,26.85,27.105,26.64,27.085,11487316,SBUX -2013-02-25,27.2,27.355,26.6,26.605,12333954,SBUX -2013-02-26,26.715,26.93,26.425,26.64,10607724,SBUX -2013-02-27,26.625,27.4875,26.54,27.285,12056302,SBUX -2013-02-28,27.325,27.585,27.225,27.425,10394356,SBUX -2013-03-01,27.315,27.465,27.0,27.435,8451764,SBUX -2013-03-04,27.385,27.86,27.33,27.85,10193852,SBUX -2013-03-05,28.0,28.4,28.0,28.255,12931844,SBUX -2013-03-06,28.38,28.745,28.325,28.55,14925144,SBUX -2013-03-07,28.55,29.25,28.545,29.125,18237018,SBUX -2013-03-08,29.335,29.485,29.0725,29.335,14215718,SBUX -2013-03-11,29.2,29.465,29.165,29.305,9897766,SBUX -2013-03-12,29.225,29.275,28.99,29.14,11670100,SBUX -2013-03-13,29.165,29.4,29.14,29.2925,7435340,SBUX -2013-03-14,29.26,29.375,28.705,28.84,14723066,SBUX -2013-03-15,28.68,28.93,28.63,28.83,15102742,SBUX -2013-03-18,28.53,28.71,28.375,28.465,10521204,SBUX -2013-03-19,28.255,28.49,27.98,28.415,13337034,SBUX -2013-03-20,28.61,28.805,28.5,28.715,9620874,SBUX -2013-03-21,28.65,28.71,28.375,28.525,8307328,SBUX -2013-03-22,28.65,28.875,28.58,28.69,8720670,SBUX -2013-03-25,28.765,28.915,28.075,28.345,10580234,SBUX -2013-03-26,28.495,28.58,28.355,28.525,6128410,SBUX -2013-03-27,28.43,28.475,28.105,28.455,7456828,SBUX -2013-03-28,28.465,28.63,28.43,28.475,7620390,SBUX -2013-04-01,28.565,28.67,28.325,28.435,7009632,SBUX -2013-04-02,28.595,29.165,28.575,29.13,13495550,SBUX -2013-04-03,29.2,29.45,28.69,28.85,11272606,SBUX -2013-04-04,28.805,29.155,28.805,29.055,7568480,SBUX -2013-04-05,28.605,28.9382,28.3592,28.9,8993596,SBUX -2013-04-08,28.915,29.06,28.73,29.06,7343972,SBUX -2013-04-09,28.98,29.0,28.59,28.705,8361158,SBUX -2013-04-10,28.82,29.155,28.8045,28.9,8577388,SBUX -2013-04-11,28.975,29.495,28.9,29.2875,10416656,SBUX -2013-04-12,29.355,29.855,29.3,29.545,10418310,SBUX -2013-04-15,29.33,29.72,28.8,28.855,10700276,SBUX -2013-04-16,28.97,29.47,28.86,29.28,8849328,SBUX -2013-04-17,29.015,29.275,28.825,29.085,7207368,SBUX -2013-04-18,29.18,29.24,28.75,28.86,8776706,SBUX -2013-04-19,28.79,29.29,28.77,29.2025,8605504,SBUX -2013-04-22,29.21,29.435,28.99,29.32,5547302,SBUX -2013-04-23,29.42,29.95,29.3899,29.695,11323422,SBUX -2013-04-24,29.845,29.985,29.565,29.915,10410890,SBUX -2013-04-25,30.0,30.32,29.955,30.25,16550532,SBUX -2013-04-26,29.65,30.195,29.6,30.0,14970972,SBUX -2013-04-29,30.175,30.46,30.065,30.29,7400546,SBUX -2013-04-30,30.315,30.455,30.155,30.42,8041092,SBUX -2013-05-01,30.275,30.34,29.975,30.07,6294596,SBUX -2013-05-02,30.22,30.34,29.8,30.19,6237460,SBUX -2013-05-03,30.425,30.985,30.35,30.935,9835550,SBUX -2013-05-06,31.0,31.24,30.96,31.24,6997134,SBUX -2013-05-07,31.265,31.265,30.855,31.095,7495628,SBUX -2013-05-08,31.17,31.22,30.865,31.205,5507696,SBUX -2013-05-09,31.33,31.45,31.02,31.18,8276152,SBUX -2013-05-10,31.285,31.595,31.195,31.5485,7480820,SBUX -2013-05-13,31.6,31.615,31.305,31.41,5906892,SBUX -2013-05-14,31.46,31.785,31.39,31.76,7510580,SBUX -2013-05-15,31.6592,32.1,31.61,32.035,9654546,SBUX -2013-05-16,32.0,32.0325,31.725,31.775,6661036,SBUX -2013-05-17,31.885,32.07,31.75,32.065,6934282,SBUX -2013-05-20,32.015,32.305,31.89,31.915,7211790,SBUX -2013-05-21,32.02,32.23,31.94,32.125,6588946,SBUX -2013-05-22,32.075,32.465,31.98,32.075,11012050,SBUX -2013-05-23,31.845,31.91,31.515,31.76,8258348,SBUX -2013-05-24,31.54,31.7,31.305,31.68,6507242,SBUX -2013-05-28,32.105,32.35,32.02,32.13,7438516,SBUX -2013-05-29,31.895,32.05,31.5575,31.815,7560250,SBUX -2013-05-30,31.935,32.0221,31.7275,31.735,8798456,SBUX -2013-05-31,31.605,32.1725,31.395,31.57,10205308,SBUX -2013-06-03,31.63,31.77,31.31,31.73,11417578,SBUX -2013-06-04,31.305,32.07,31.305,31.665,8622564,SBUX -2013-06-05,31.52,31.65,31.155,31.17,8408538,SBUX -2013-06-06,31.175,31.5275,31.16,31.51,8625308,SBUX -2013-06-07,31.705,32.545,31.705,32.52,13752898,SBUX -2013-06-10,32.83,33.155,32.74,33.055,12412340,SBUX -2013-06-11,32.735,33.0525,32.655,32.71,7897496,SBUX -2013-06-12,32.91,33.0,32.13,32.225,10069920,SBUX -2013-06-13,32.27,33.095,32.035,32.985,11909482,SBUX -2013-06-14,32.975,33.335,32.725,32.8,9854434,SBUX -2013-06-17,33.095,33.28,32.8575,33.015,8115800,SBUX -2013-06-18,33.0,33.565,32.975,33.5475,7451352,SBUX -2013-06-19,33.51,33.74,33.205,33.205,10644698,SBUX -2013-06-20,32.91,33.16,32.52,32.61,11606540,SBUX -2013-06-21,32.855,32.96,32.115,32.345,14709502,SBUX -2013-06-24,32.0,32.289,31.59,32.005,9913028,SBUX -2013-06-25,32.265,32.68,32.22,32.37,9856852,SBUX -2013-06-26,32.685,33.3546,32.4455,32.9,9545442,SBUX -2013-06-27,33.135,33.25,32.825,32.845,6292594,SBUX -2013-06-28,32.755,33.125,32.7,32.755,10836506,SBUX -2013-07-01,33.045,33.245,32.91,33.12,6692040,SBUX -2013-07-02,33.12,33.6,33.04,33.395,6765528,SBUX -2013-07-03,33.215,33.84,33.2,33.65,5635188,SBUX -2013-07-05,33.935,34.0,33.45,33.86,5189244,SBUX -2013-07-08,33.97,34.41,33.94,34.145,8029920,SBUX -2013-07-09,34.45,34.5,33.995,34.065,7459914,SBUX -2013-07-10,33.935,34.06,33.725,34.05,8090792,SBUX -2013-07-11,34.34,34.7575,34.175,34.67,8385798,SBUX -2013-07-12,34.62,34.86,34.39,34.86,9706492,SBUX -2013-07-15,34.845,34.95,34.765,34.83,6180564,SBUX -2013-07-16,34.82,34.86,34.54,34.76,6068146,SBUX -2013-07-17,34.87,34.9076,33.945,34.1,12147310,SBUX -2013-07-18,34.225,34.505,34.02,34.24,8537754,SBUX -2013-07-19,34.285,34.555,34.2,34.395,7196954,SBUX -2013-07-22,34.54,34.605,34.275,34.51,6559682,SBUX -2013-07-23,34.62,34.66,33.725,33.83,8465332,SBUX -2013-07-24,34.06,34.215,33.15,33.305,14033776,SBUX -2013-07-25,33.47,34.15,33.365,34.085,15971984,SBUX -2013-07-26,36.3,36.76,35.925,36.68,32293248,SBUX -2013-07-29,36.375,36.54,36.05,36.225,12801986,SBUX -2013-07-30,36.45,36.49,35.855,35.965,9808846,SBUX -2013-07-31,36.07,36.25,35.61,35.6445,11248506,SBUX -2013-08-01,36.135,36.865,36.04,36.74,12849582,SBUX -2013-08-02,36.75,37.135,36.58,37.115,9394576,SBUX -2013-08-05,37.11,37.135,36.855,36.985,7606188,SBUX -2013-08-06,36.83,36.925,36.39,36.4,7956060,SBUX -2013-08-07,36.18,36.575,36.0825,36.095,7068922,SBUX -2013-08-08,36.385,36.7,36.2,36.47,5923772,SBUX -2013-08-09,36.295,36.6154,36.26,36.4,5366324,SBUX -2013-08-12,36.14,36.565,36.125,36.465,5473182,SBUX -2013-08-13,36.545,36.545,35.9674,36.32,5377794,SBUX -2013-08-14,36.29,36.29,35.895,35.925,4932052,SBUX -2013-08-15,35.655,35.675,35.155,35.37,7198878,SBUX -2013-08-16,35.49,35.695,35.275,35.355,6929830,SBUX -2013-08-19,35.29,35.6475,35.09,35.145,6349108,SBUX -2013-08-20,35.18,35.5125,35.0558,35.33,6046820,SBUX -2013-08-21,35.21,35.7,35.14,35.3565,6917088,SBUX -2013-08-22,35.54,36.13,35.505,35.95,5399676,SBUX -2013-08-23,36.0325,36.095,35.79,35.985,5869740,SBUX -2013-08-26,36.035,36.105,35.8,35.94,4928868,SBUX -2013-08-27,35.45,35.685,35.055,35.08,8021076,SBUX -2013-08-28,35.045,35.66,34.93,35.48,6249812,SBUX -2013-08-29,35.365,35.92,35.25,35.59,5509920,SBUX -2013-08-30,35.69,35.7,35.165,35.26,5666988,SBUX -2013-09-03,35.85,36.07,35.595,35.8,7249600,SBUX -2013-09-04,35.71,36.135,35.5455,36.07,6502234,SBUX -2013-09-05,36.125,36.38,36.005,36.025,4931220,SBUX -2013-09-06,36.185,36.325,35.465,35.785,6356670,SBUX -2013-09-09,35.84,36.245,35.835,36.22,4936828,SBUX -2013-09-10,36.49,37.16,36.465,37.1075,11779224,SBUX -2013-09-11,37.165,37.7499,37.165,37.695,9982644,SBUX -2013-09-12,37.75,37.955,37.5603,37.835,7628560,SBUX -2013-09-13,37.795,37.85,37.325,37.785,5767336,SBUX -2013-09-16,38.285,38.3,37.43,37.62,8787132,SBUX -2013-09-17,37.63,38.12,37.585,38.02,6661016,SBUX -2013-09-18,37.94,38.81,37.775,38.665,9488762,SBUX -2013-09-19,38.76,38.9225,38.15,38.175,8042062,SBUX -2013-09-20,38.13,38.5,38.035,38.06,9620500,SBUX -2013-09-23,38.055,38.19,37.535,37.68,6783882,SBUX -2013-09-24,37.765,38.655,37.739000000000004,38.275,7633976,SBUX -2013-09-25,38.42,38.505,38.0352,38.17,7261490,SBUX -2013-09-26,38.32,38.6395,38.16,38.59,4988814,SBUX -2013-09-27,38.455,38.74,38.2,38.665,6200278,SBUX -2013-09-30,38.25,38.5425,38.025,38.485,8828424,SBUX -2013-10-01,38.49,38.67,38.28,38.58,5205160,SBUX -2013-10-02,38.295,38.615,38.105,38.595,6088422,SBUX -2013-10-03,38.48,38.73,38.16,38.435,8164122,SBUX -2013-10-04,38.4,38.73,38.255,38.7,5226696,SBUX -2013-10-07,38.3,38.695,38.205,38.4305,5106784,SBUX -2013-10-08,38.555,39.015,37.72,37.765,12663250,SBUX -2013-10-09,37.695,37.9,37.2263,37.63,9482078,SBUX -2013-10-10,38.145,38.655,38.065,38.56,6638096,SBUX -2013-10-11,38.61,38.92,38.44,38.91,5530376,SBUX -2013-10-14,38.7675,39.16,38.625,39.05,5986662,SBUX -2013-10-15,38.915,38.9475,38.3,38.355,8146874,SBUX -2013-10-16,38.6,39.1125,38.445,39.02,10103938,SBUX -2013-10-17,38.835,39.425,38.725,39.3675,6562580,SBUX -2013-10-18,39.635,39.835,39.485,39.655,9049968,SBUX -2013-10-21,39.555,39.955,39.335,39.73,6770290,SBUX -2013-10-22,39.95,40.54,39.8,40.45,8419570,SBUX -2013-10-23,40.225,40.425,39.945,40.025,7908094,SBUX -2013-10-24,39.68,39.72,39.255,39.525,13508366,SBUX -2013-10-25,39.735,40.0,39.635,39.98,7251840,SBUX -2013-10-28,40.1,40.215,39.2625,39.355,11242732,SBUX -2013-10-29,39.605,39.81,39.525,39.81,7898436,SBUX -2013-10-30,40.315,40.425,39.88,40.415,17518716,SBUX -2013-10-31,39.345,40.81,39.2859,40.525,20491552,SBUX -2013-11-01,40.77,40.77,39.835,40.185,12005206,SBUX -2013-11-04,40.375,40.39,40.105,40.185,8285184,SBUX -2013-11-05,40.18,41.17,40.075,40.995,10092844,SBUX -2013-11-06,41.195,41.25,40.485,40.565,8562294,SBUX -2013-11-07,40.65,40.675,39.5,39.535,13283288,SBUX -2013-11-08,39.755,40.625,39.7,40.6,11487736,SBUX -2013-11-11,40.6,40.71,40.49,40.495,5146208,SBUX -2013-11-12,40.38,40.5249,40.06,40.3075,6613072,SBUX -2013-11-13,39.62,40.75,39.595,40.7305,12519058,SBUX -2013-11-14,40.765,40.77,40.3925,40.57,7647688,SBUX -2013-11-15,40.625,40.725,40.39,40.595,6445400,SBUX -2013-11-18,40.51,40.67,40.105,40.27,8322628,SBUX -2013-11-19,40.005,40.46,39.93,39.96,8725944,SBUX -2013-11-20,40.065,40.125,39.66,39.845,8734150,SBUX -2013-11-21,39.9,40.825,39.875,40.765,13139094,SBUX -2013-11-22,40.85,40.85,40.55,40.675,7926170,SBUX -2013-11-25,40.685,40.74,40.255,40.355,9001126,SBUX -2013-11-26,40.43,40.825,40.2025,40.755,8767864,SBUX -2013-11-27,40.695,40.955,40.6,40.81,4752000,SBUX -2013-11-29,40.925,41.185,40.685,40.73,4387144,SBUX -2013-12-02,40.745,40.8445,40.455,40.535,5773800,SBUX -2013-12-03,40.37,40.5775,40.145,40.275,7893264,SBUX -2013-12-04,40.195,40.36,39.72,39.75,10206454,SBUX -2013-12-05,39.78,40.125,39.665,39.86,6569194,SBUX -2013-12-06,40.2975,40.375,39.855,39.97,6736632,SBUX -2013-12-09,40.13,40.23,39.8025,39.865,7936162,SBUX -2013-12-10,39.495,39.535,38.34,38.69,26329504,SBUX -2013-12-11,38.8,38.9975,38.145,38.2,15751996,SBUX -2013-12-12,38.14,38.545,38.0,38.24,9651038,SBUX -2013-12-13,38.415,38.465,38.045,38.175,8052582,SBUX -2013-12-16,38.015,38.49,37.955,38.23,9348824,SBUX -2013-12-17,38.29,38.29,37.955,38.045,7008900,SBUX -2013-12-18,38.065,38.845,38.05,38.84,10268256,SBUX -2013-12-19,39.12,39.25,38.51,38.575,11406282,SBUX -2013-12-20,38.76,38.99,38.622,38.83,12707400,SBUX -2013-12-23,38.985,39.17,38.675,39.16,7624552,SBUX 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numpy as np -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D - - - -def get_label(x, i1, i2, i3): - # x = sequence - if x[i1] < 0 and x[i2] < 0 and x[i3] < 0: - return 1 - if x[i1] < 0 and x[i2] > 0 and x[i3] > 0: - return 1 - if x[i1] > 0 and x[i2] < 0 and x[i3] > 0: - return 1 - if x[i1] > 0 and x[i2] > 0 and x[i3] < 0: - return 1 - return 0 - - -N = 2000 -X = np.random.random((N, 3))*2 - 1 - -Y = np.zeros(N) -for i in range(N): - x = X[i] - y = get_label(x, 0, 1, 2) - Y[i] = y - - -fig = plt.figure() -ax = fig.add_subplot(111, projection='3d') -ax.scatter(X[:,0], X[:,1], X[:,2], c=Y) -plt.show() \ No newline at end of file diff --git a/tf_xor.py b/tf_xor.py new file mode 100644 index 00000000..d4f8d6d6 --- /dev/null +++ b/tf_xor.py @@ -0,0 +1,79 @@ +import tensorflow as tf +import numpy as np + +### xor ### +# trX = np.array([[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]]) +# trY = np.array([[0, 1], [1, 0], [1, 0], [0, 1]]) + + +### donut ### +N = 1000 +R_inner = 5 +R_outer = 10 +ones = np.array([[1]*N]).T + +# distance from origin is radius + random normal +# angle theta is uniformly distributed between (0, 2pi) +R1 = np.random.randn(N/2) + R_inner +theta = 2*np.pi*np.random.random(N/2) +X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T + +R2 = np.random.randn(N/2) + R_outer +theta = 2*np.pi*np.random.random(N/2) +X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T + +trX = np.concatenate([ X_inner, X_outer ]) +trX = np.concatenate((ones, trX), axis=1) +trY = np.array([[0, 1]]*(N/2) + [[1, 0]]*(N/2)) + + + + + +def init_weights(shape): + return tf.Variable(tf.random_normal(shape, stddev=0.01)) + + +def model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden): # this network is the same as the previous one except with an extra hidden layer + dropout + X = tf.nn.dropout(X, p_drop_input) + h = tf.nn.relu(tf.matmul(X, w_h)) + + h = tf.nn.dropout(h, p_drop_hidden) + h2 = tf.nn.relu(tf.matmul(h, w_h2)) + + h2 = tf.nn.dropout(h2, p_drop_hidden) + + return tf.matmul(h2, w_o) + + +X = tf.placeholder("float", [None, 3]) +Y = tf.placeholder("float", [None, 2]) + +w_h = init_weights([3, 8]) +w_h2 = init_weights([8, 2]) +w_o = init_weights([2, 2]) + +p_keep_input = tf.placeholder("float") +p_keep_hidden = tf.placeholder("float") +py_x = model(X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden) + + +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) +train_op = tf.train.RMSPropOptimizer(0.00001, 1.0).minimize(cost) +predict_op = tf.argmax(py_x, 1) + +sess = tf.Session() +init = tf.initialize_all_variables() +sess.run(init) + + +for i in range(1000): + # for + sess.run(train_op, feed_dict={X: trX, Y: trY, p_keep_input: 0.8, p_keep_hidden: 0.5}) + + print i, np.mean(np.argmax(trY, axis=1) == sess.run(predict_op, feed_dict={X: trX, Y: trY, + p_keep_input: 0.8, + p_keep_hidden: 0.5})) + + + diff --git a/timeseries/WHERE ARE THE NOTEBOOKS.txt b/timeseries/WHERE ARE THE NOTEBOOKS.txt deleted file mode 100644 index 5446ce25..00000000 --- a/timeseries/WHERE ARE THE NOTEBOOKS.txt +++ /dev/null @@ -1,5 +0,0 @@ -As stated in the "where to get the code" / "where to get the notebooks" lecture, the notebooks are NOT on Github. - -If you missed this, please review the lecture for the actual location of the notebooks. - -If, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com. \ No newline at end of file diff --git a/timeseries/extra_reading.txt b/timeseries/extra_reading.txt deleted file mode 100644 index 5e20b991..00000000 --- a/timeseries/extra_reading.txt +++ /dev/null @@ -1,30 +0,0 @@ -Estimating Box-Cox power transformation parameter via goodness of fit tests -https://arxiv.org/pdf/1401.3812.pdf - -Linear Regression -https://deeplearningcourses.com/c/data-science-linear-regression-in-python/ - -Logistic Regression -https://deeplearningcourses.com/c/data-science-logistic-regression-in-python/ - -Support Vector Machines -https://deeplearningcourses.com/c/support-vector-machines-in-python - -Random Forests -https://deeplearningcourses.com/c/machine-learning-in-python-random-forest-adaboost - -Deep Learning and Tensorflow 2 -https://deeplearningcourses.com/c/deep-learning-tensorflow-2 - -Gaussian Processes for Regression and Classification -https://www.cs.toronto.edu/~radford/ftp/val6gp.pdf - -How Does Backpropagation Work? (In-Depth) -https://deeplearningcourses.com/c/data-science-deep-learning-in-python/ -https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow/ - -Forecasting at Scale (Facebook Prophet) -https://peerj.com/preprints/3190.pdf - -Statistical and Machine Learning forecasting methods: Concerns and ways forward -https://journals.plos.org/plosone/article%3Fid%3D10.1371/journal.pone.0194889 \ No newline at end of file diff --git a/transformers/WHERE ARE THE NOTEBOOKS.txt b/transformers/WHERE ARE THE NOTEBOOKS.txt deleted file mode 100644 index 5446ce25..00000000 --- a/transformers/WHERE ARE THE NOTEBOOKS.txt +++ /dev/null @@ -1,5 +0,0 @@ -As stated in the "where to get the code" / "where to get the notebooks" lecture, the notebooks are NOT on Github. - -If you missed this, please review the lecture for the actual location of the notebooks. - -If, after reviewing it, you still need assistance, please contact info@deeplearningcourses.com. \ No newline at end of file diff --git a/transformers/extra_reading.txt b/transformers/extra_reading.txt deleted file mode 100644 index 718e2963..00000000 --- a/transformers/extra_reading.txt +++ /dev/null @@ -1,32 +0,0 @@ -Attention Is All You Need -https://arxiv.org/abs/1706.03762 - -BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding -https://arxiv.org/abs/1810.04805v2 - -Improving Language Understanding by Generative Pre-Training (GPT) -https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf - -Improving Language Understanding with Unsupervised Learning -https://openai.com/blog/language-unsupervised/ - -Language Models are Unsupervised Multitask Learners (GPT-2) -https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf - -Better Language Models and Their Implications -https://openai.com/blog/better-language-models/ - -Language Models are Few-Shot Learners (GPT-3) -https://arxiv.org/abs/2005.14165 - -List of Hugging Face Pipelines for NLP -https://lazyprogrammer.me/list-of-hugging-face-pipelines-for-nlp/ - -BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models -https://arxiv.org/abs/2106.10199 - -Translation Datasets -https://opus.nlpl.eu/KDE4.php - -Layer Normalization -https://arxiv.org/abs/1607.06450 \ No newline at end of file diff --git a/unsupervised_class/__init__.py b/unsupervised_class/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/unsupervised_class/books.py b/unsupervised_class/books.py deleted file mode 100644 index 47ad973d..00000000 --- a/unsupervised_class/books.py +++ /dev/null @@ -1,253 +0,0 @@ -# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python -# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import networkx as nx -import nltk -import numpy as np -import matplotlib.pyplot as plt -from nltk.stem import WordNetLemmatizer -from sklearn.decomposition import PCA -from sklearn.manifold import TSNE, LocallyLinearEmbedding as LLE -from sklearn.feature_extraction.text import TfidfTransformer - -wordnet_lemmatizer = WordNetLemmatizer() - -titles = [line.rstrip() for line in open('../nlp_class/all_book_titles.txt')] - -# copy tokenizer from sentiment example -stopwords = set(w.rstrip() for w in open('../nlp_class/stopwords.txt')) -# add more stopwords specific to this problem -stopwords = stopwords.union({ - 'introduction', 'edition', 'series', 'application', - 'approach', 'card', 'access', 'package', 'plus', 'etext', - 'brief', 'vol', 'fundamental', 'guide', 'essential', 'printed', - 'third', 'second', 'fourth', }) -def my_tokenizer(s): - s = s.lower() # downcase - tokens = nltk.tokenize.word_tokenize(s) # split string into words (tokens) - tokens = [t for t in tokens if len(t) > 2] # remove short words, they're probably not useful - tokens = [wordnet_lemmatizer.lemmatize(t) for t in tokens] # put words into base form - tokens = [t for t in tokens if t not in stopwords] # remove stopwords - tokens = [t for t in tokens if not any(c.isdigit() for c in t)] # remove any digits, i.e. "3rd edition" - return tokens - - -# create a word-to-index map so that we can create our word-frequency vectors later -# let's also save the tokenized versions so we don't have to tokenize again later -word_index_map = {} -current_index = 0 -all_tokens = [] -all_titles = [] -index_word_map = [] -print("num titles:", len(titles)) -print("first title:", titles[0]) -for title in titles: - try: - title = title.encode('ascii', 'ignore') # this will throw exception if bad characters - title = title.decode('utf-8') - all_titles.append(title) - tokens = my_tokenizer(title) - all_tokens.append(tokens) - for token in tokens: - if token not in word_index_map: - word_index_map[token] = current_index - current_index += 1 - index_word_map.append(token) - except Exception as e: - print(e) - - - -# now let's create our input matrices - just indicator variables for this example - works better than proportions -def tokens_to_vector(tokens): - x = np.zeros(len(word_index_map)) - for t in tokens: - i = word_index_map[t] - x[i] += 1 - return x - -N = len(all_tokens) -D = len(word_index_map) -X = np.zeros((D, N)) # terms will go along rows, documents along columns -i = 0 -for tokens in all_tokens: - X[:,i] = tokens_to_vector(tokens) - i += 1 - -def d(u, v): - diff = u - v - return diff.dot(diff) - -def cost(X, R, M): - cost = 0 - for k in range(len(M)): - # method 1 - # for n in range(len(X)): - # cost += R[n,k]*d(M[k], X[n]) - - # method 2 - diff = X - M[k] - sq_distances = (diff * diff).sum(axis=1) - cost += (R[:,k] * sq_distances).sum() - return cost - -def plot_k_means(X, K, index_word_map, max_iter=20, beta=1.0, show_plots=True): - N, D = X.shape - M = np.zeros((K, D)) - R = np.zeros((N, K)) - exponents = np.empty((N, K)) - - # initialize M to random - for k in range(K): - M[k] = X[np.random.choice(N)] - - costs = np.zeros(max_iter) - for i in range(max_iter): - # step 1: determine assignments / resposibilities - # is this inefficient? - for k in range(K): - for n in range(N): - # R[n,k] = np.exp(-beta*d(M[k], X[n])) / np.sum( np.exp(-beta*d(M[j], X[n])) for j in range(K) ) - exponents[n,k] = np.exp(-beta*d(M[k], X[n])) - - R = exponents / exponents.sum(axis=1, keepdims=True) - - # step 2: recalculate means - for k in range(K): - M[k] = R[:,k].dot(X) / R[:,k].sum() - - costs[i] = cost(X, R, M) - if i > 0: - if np.abs(costs[i] - costs[i-1]) < 10e-5: - break - - if show_plots: - # plt.plot(costs) - # plt.title("Costs") - # plt.show() - - random_colors = np.random.random((K, 3)) - colors = R.dot(random_colors) - plt.figure(figsize=(80.0, 80.0)) - plt.scatter(X[:,0], X[:,1], s=300, alpha=0.9, c=colors) - annotate1(X, index_word_map) - # plt.show() - plt.savefig("test.png") - - - # print out the clusters - hard_responsibilities = np.argmax(R, axis=1) # is an N-size array of cluster identities - # let's "reverse" the order so it's cluster identity -> word index - cluster2word = {} - for i in range(len(hard_responsibilities)): - word = index_word_map[i] - cluster = hard_responsibilities[i] - if cluster not in cluster2word: - cluster2word[cluster] = [] - cluster2word[cluster].append(word) - - # print out the words grouped by cluster - for cluster, wordlist in cluster2word.items(): - print("cluster", cluster, "->", wordlist) - - return M, R - -# def annotate2(X, index_word_map, k=0.1): -# N, D = X.shape - -# # create graph -# G = nx.DiGraph() -# data_nodes = [] -# init_pos = {} -# for i in range(N): -# x, y = X[i] -# label = index_word_map[i] -# data_str = 'data_{0}'.format(label) -# G.add_node(data_str) -# G.add_node(label) -# G.add_edge(label, data_str) -# data_nodes.append(data_str) -# init_pos[data_str] = (x, y) -# init_pos[label] = (x, y) -# pos = nx.spring_layout(G, pos=init_pos, fixed=data_nodes, k=k) - -# # undo spring_layout's rescaling -# pos_after = np.vstack([pos[d] for d in data_nodes]) -# pos_before = np.vstack([init_pos[d] for d in data_nodes]) -# scale, shift_x = np.polyfit(pos_after[:,0], pos_before[:,0], 1) -# scale, shift_y = np.polyfit(pos_after[:,1], pos_before[:,1], 1) -# shift = np.array([shift_x, shift_y]) -# for key, val in pos.items(): -# pos[key] = (val*scale) + shift - - -# for label, data_str in G.edges(): -# plt.annotate( -# label, -# xy=pos[data_str], xycoords='data', -# xytext=pos[label], textcoords='data', -# arrowprops=dict(arrowstyle="->", color='black'), -# ) - -# # expand limits -# all_pos = np.vstack(pos.values()) -# x_span, y_span = np.ptp(all_pos, axis=0) -# mins = np.min(all_pos-x_span*0.15, 0) -# maxs = np.max(all_pos+y_span*0.15, 0) -# plt.xlim([mins[0], maxs[0]]) -# plt.ylim([mins[1], maxs[1]]) - - -def annotate1(X, index_word_map, eps=0.1): - N, D = X.shape - placed = np.empty((N, D)) - for i in range(N): - x, y = X[i] - - # if x, y is too close to something already plotted, move it - close = [] - - x, y = X[i] - for retry in range(3): - for j in range(i): - diff = np.array([x, y]) - placed[j] - - # if something is close, append it to the close list - if diff.dot(diff) < eps: - close.append(placed[j]) - - if close: - # then the close list is not empty - x += (np.random.randn() + 0.5) * (1 if np.random.rand() < 0.5 else -1) - y += (np.random.randn() + 0.5) * (1 if np.random.rand() < 0.5 else -1) - close = [] # so we can start again with an empty list - else: - # nothing close, let's break - break - - placed[i] = (x, y) - - plt.annotate( - s=index_word_map[i], - xy=(X[i,0], X[i,1]), - xytext=(x, y), - arrowprops={ - 'arrowstyle' : '->', - 'color' : 'black', - } - ) - -print("vocab size:", current_index) - -transformer = TfidfTransformer() -X = transformer.fit_transform(X).toarray() - -reducer = TSNE() -Z = reducer.fit_transform(X) -plot_k_means(Z[:,:2], current_index//10, index_word_map, show_plots=True) diff --git a/unsupervised_class/choose_k.py b/unsupervised_class/choose_k.py deleted file mode 100644 index e0719e1c..00000000 --- a/unsupervised_class/choose_k.py +++ /dev/null @@ -1,34 +0,0 @@ -# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python -# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt -from kmeans import plot_k_means, get_simple_data, cost - - -def main(): - X = get_simple_data() - - plt.scatter(X[:,0], X[:,1]) - plt.show() - - costs = np.empty(10) - costs[0] = None - for k in range(1, 10): - M, R = plot_k_means(X, k, show_plots=False) - c = cost(X, R, M) - costs[k] = c - - plt.plot(costs) - plt.title("Cost vs K") - plt.show() - - -if __name__ == '__main__': - main() diff --git a/unsupervised_class/evolution.py b/unsupervised_class/evolution.py deleted file mode 100644 index 7b99dc3c..00000000 --- a/unsupervised_class/evolution.py +++ /dev/null @@ -1,106 +0,0 @@ -# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python -# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import random -import numpy as np -import scipy.spatial.distance as ssd -import matplotlib.pyplot as plt -from scipy.cluster.hierarchy import dendrogram, linkage - - -# our genetic code -code = ['A', 'T', 'C', 'G'] - - -# convert list of integers to corresponding letters -def to_code(a): - return [code[i] for i in a] - - -# distance between 2 DNA strands -def dist(a, b): - return sum(i != j for i, j in zip(a, b)) - - -# generate offspring by modifying some characters in the code -def generate_offspring(parent): - return [maybe_modify(c) for c in parent] - - -# modify letter c with probability ~1/1000 -def maybe_modify(c): - if np.random.random() < 0.001: - return np.random.choice(code) - return c - - - -# create 3 distinct ancestors -p1 = to_code(np.random.randint(4, size=1000)) -p2 = to_code(np.random.randint(4, size=1000)) -p3 = to_code(np.random.randint(4, size=1000)) - - -# create offspring -num_generations = 99 -max_offspring_per_generation = 1000 -current_generation = [p1, p2, p3] -for i in range(num_generations): - - next_generation = [] - for parent in current_generation: - # each parent will have between 1 and 3 children - num_offspring = np.random.randint(3) + 1 - - # generate the offspring - for _ in range(num_offspring): - child = generate_offspring(parent) - next_generation.append(child) - - current_generation = next_generation - - # limit the number of offspring - random.shuffle(current_generation) - current_generation = current_generation[:max_offspring_per_generation] - - print("Finished creating generation %d / %d, size = %d" % (i + 2, num_generations + 1, len(current_generation))) - - - -# create distance matrix -# note: you can also use scipy's pdist for this -# but NOT sklearn's pairwise_distances function -# which does not return a symmetric matrix -N = len(current_generation) -dist_matrix = np.zeros((N, N)) -for i in range(N): - for j in range(N): - if i == j: - continue - elif j > i: - a = current_generation[i] - b = current_generation[j] - dist_matrix[i,j] = dist(a, b) - else: - dist_matrix[i,j] = dist_matrix[j,i] - -dist_array = ssd.squareform(dist_matrix) - -Z = linkage(dist_array, 'ward') -plt.title("Ward") -dendrogram(Z) -plt.show() - -Z = linkage(dist_array, 'single') -plt.title("Single") -dendrogram(Z) -plt.show() - -Z = linkage(dist_array, 'complete') -plt.title("Complete") -dendrogram(Z) -plt.show() diff --git a/unsupervised_class/gmm.py b/unsupervised_class/gmm.py index 4bd94c79..314a87f2 100644 --- a/unsupervised_class/gmm.py +++ b/unsupervised_class/gmm.py @@ -1,19 +1,10 @@ -# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python -# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import matplotlib.pyplot as plt from scipy.stats import multivariate_normal -def gmm(X, K, max_iter=20, smoothing=1e-2): +def gmm(X, K, max_iter=20): N, D = X.shape M = np.zeros((K, D)) R = np.zeros((N, K)) @@ -21,50 +12,37 @@ def gmm(X, K, max_iter=20, smoothing=1e-2): pi = np.ones(K) / K # uniform # initialize M to random, initialize C to spherical with variance 1 - for k in range(K): + for k in xrange(K): M[k] = X[np.random.choice(N)] C[k] = np.eye(D) - lls = [] + costs = np.zeros(max_iter) weighted_pdfs = np.zeros((N, K)) # we'll use these to store the PDF value of sample n and Gaussian k - for i in range(max_iter): + for i in xrange(max_iter): # step 1: determine assignments / resposibilities - # this is the slow way - # for k in range(K): - # for n in range(N): - # weighted_pdfs[n,k] = pi[k]*multivariate_normal.pdf(X[n], M[k], C[k]) - - # for k in range(K): - # for n in range(N): - # R[n,k] = weighted_pdfs[n,k] / weighted_pdfs[n,:].sum() + for k in xrange(K): + for n in xrange(N): + weighted_pdfs[n,k] = pi[k]*multivariate_normal.pdf(X[n], M[k], C[k]) - # a faster way to do step 1: "vectorization" - for k in range(K): - weighted_pdfs[:,k] = pi[k]*multivariate_normal.pdf(X, M[k], C[k]) - R = weighted_pdfs / weighted_pdfs.sum(axis=1, keepdims=True) + for k in xrange(K): + for n in xrange(N): + R[n,k] = weighted_pdfs[n,k] / weighted_pdfs[n,:].sum() # step 2: recalculate params - for k in range(K): + for k in xrange(K): Nk = R[:,k].sum() pi[k] = Nk / N M[k] = R[:,k].dot(X) / Nk + C[k] = np.sum(R[n,k]*np.outer(X[n] - M[k], X[n] - M[k]) for n in xrange(N)) / Nk + np.eye(D)*0.001 - ## faster - delta = X - M[k] # N x D - Rdelta = np.expand_dims(R[:,k], -1) * delta # multiplies R[:,k] by each col. of delta - N x D - C[k] = Rdelta.T.dot(delta) / Nk + np.eye(D)*smoothing # D x D - ## slower - # C[k] = np.sum(R[n,k]*np.outer(X[n] - M[k], X[n] - M[k]) for n in range(N)) / Nk + np.eye(D)*smoothing - - ll = np.log(weighted_pdfs.sum(axis=1)).sum() - lls.append(ll) + costs[i] = np.log(weighted_pdfs.sum(axis=1)).sum() if i > 0: - if np.abs(lls[i] - lls[i-1]) < 0.1: + if np.abs(costs[i] - costs[i-1]) < 0.1: break - plt.plot(lls) - plt.title("Log-Likelihood") + plt.plot(costs) + plt.title("Costs") plt.show() random_colors = np.random.random((K, 3)) @@ -72,10 +50,10 @@ def gmm(X, K, max_iter=20, smoothing=1e-2): plt.scatter(X[:,0], X[:,1], c=colors) plt.show() - print("pi:", pi) - print("means:", M) - print("covariances:", C) - return R + print "pi:", pi + print "means:", M + print "covariances:", C + def main(): diff --git a/unsupervised_class/gmm_mnist.py b/unsupervised_class/gmm_mnist.py deleted file mode 100644 index a1b19a26..00000000 --- a/unsupervised_class/gmm_mnist.py +++ /dev/null @@ -1,39 +0,0 @@ -# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python -# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python - -# data is from https://www.kaggle.com/c/digit-recognizer -# each image is a D = 28x28 = 784 dimensional vector -# there are N = 42000 samples -# you can plot an image by reshaping to (28,28) and using plt.imshow() -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -# from kmeans import plot_k_means, get_simple_data -from datetime import datetime -# from gmm import gmm -from sklearn.mixture import GaussianMixture -from kmeans_mnist import get_data, purity, DBI - - -def main(): - X, Y = get_data(10000) - print("Number of data points:", len(Y)) - - model = GaussianMixture(n_components=10) - model.fit(X) - M = model.means_ - R = model.predict_proba(X) - - print("Purity:", purity(Y, R)) # max is 1, higher is better - print("DBI:", DBI(X, M, R)) # lower is better - - -if __name__ == "__main__": - main() diff --git a/unsupervised_class/hcluster.py b/unsupervised_class/hcluster.py index 98a468a7..131eeddc 100644 --- a/unsupervised_class/hcluster.py +++ b/unsupervised_class/hcluster.py @@ -1,11 +1,3 @@ -# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python -# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import matplotlib.pyplot as plt @@ -27,18 +19,9 @@ def main(): X[600:, :] = np.random.randn(300, D) + mu3 Z = linkage(X, 'ward') - print("Z.shape:", Z.shape) + print "Z.shape:", Z.shape # Z has the format [idx1, idx2, dist, sample_count] # therefore, its size will be (N-1, 4) - - # from documentation: - # A (n-1) by 4 matrix Z is returned. At the i-th iteration, - # clusters with indices Z[i, 0] and Z[i, 1] are combined to - # form cluster n + i. A cluster with an index less than n - # corresponds to one of the original observations. - # The distance between clusters Z[i, 0] and Z[i, 1] is given - # by Z[i, 2]. The fourth value Z[i, 3] represents the number - # of original observations in the newly formed cluster. plt.title("Ward") dendrogram(Z) plt.show() diff --git a/unsupervised_class/kmeans.py b/unsupervised_class/kmeans.py index bb2e2659..6da74219 100644 --- a/unsupervised_class/kmeans.py +++ b/unsupervised_class/kmeans.py @@ -1,15 +1,5 @@ -# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python -# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np import matplotlib.pyplot as plt -from sklearn.metrics.pairwise import pairwise_distances def d(u, v): @@ -19,77 +9,52 @@ def d(u, v): def cost(X, R, M): cost = 0 - for k in range(len(M)): - # method 1 - # for n in range(len(X)): - # cost += R[n,k]*d(M[k], X[n]) - - # method 2 - diff = X - M[k] - sq_distances = (diff * diff).sum(axis=1) - cost += (R[:,k] * sq_distances).sum() + for k in xrange(len(M)): + for n in xrange(len(X)): + cost += R[n,k]*d(M[k], X[n]) return cost -def plot_k_means(X, K, max_iter=20, beta=3.0, show_plots=False): +def plot_k_means(X, K, max_iter=20, beta=1.0): N, D = X.shape - # R = np.zeros((N, K)) - exponents = np.empty((N, K)) + M = np.zeros((K, D)) + R = np.zeros((N, K)) # initialize M to random - initial_centers = np.random.choice(N, K, replace=False) - M = X[initial_centers] + for k in xrange(K): + M[k] = X[np.random.choice(N)] - costs = [] - k = 0 - for i in range(max_iter): - k += 1 + costs = np.zeros(max_iter) + for i in xrange(max_iter): # step 1: determine assignments / resposibilities # is this inefficient? - for k in range(K): - for n in range(N): - exponents[n,k] = np.exp(-beta*d(M[k], X[n])) - R = exponents / exponents.sum(axis=1, keepdims=True) - + for k in xrange(K): + for n in xrange(N): + R[n,k] = np.exp(-beta*d(M[k], X[n])) / np.sum( np.exp(-beta*d(M[j], X[n])) for j in xrange(K) ) # step 2: recalculate means - # decent vectorization - # for k in range(K): - # M[k] = R[:,k].dot(X) / R[:,k].sum() - # oldM = M - - # full vectorization - M = R.T.dot(X) / R.sum(axis=0, keepdims=True).T - # print("diff M:", np.abs(M - oldM).sum()) + for k in xrange(K): + M[k] = R[:,k].dot(X) / R[:,k].sum() - c = cost(X, R, M) - costs.append(c) + costs[i] = cost(X, R, M) if i > 0: - if np.abs(costs[-1] - costs[-2]) < 1e-5: + if np.abs(costs[i] - costs[i-1]) < 0.01: break - if len(costs) > 1: - if costs[-1] > costs[-2]: - pass - # print("cost increased!") - # print("M:", M) - # print("R.min:", R.min(), "R.max:", R.max()) + plt.plot(costs) + plt.title("Costs") + plt.show() + + random_colors = np.random.random((K, 3)) + colors = R.dot(random_colors) + plt.scatter(X[:,0], X[:,1], c=colors) + plt.show() - if show_plots: - plt.plot(costs) - plt.title("Costs") - plt.show() - random_colors = np.random.random((K, 3)) - colors = R.dot(random_colors) - plt.scatter(X[:,0], X[:,1], c=colors) - plt.show() - print("Final cost", costs[-1]) - return M, R -def get_simple_data(): +def main(): # assume 3 means D = 2 # so we can visualize it more easily s = 4 # separation so we can control how far apart the means are @@ -102,30 +67,19 @@ def get_simple_data(): X[:300, :] = np.random.randn(300, D) + mu1 X[300:600, :] = np.random.randn(300, D) + mu2 X[600:, :] = np.random.randn(300, D) + mu3 - return X - - -def main(): - X = get_simple_data() # what does it look like without clustering? plt.scatter(X[:,0], X[:,1]) plt.show() K = 3 # luckily, we already know this - plot_k_means(X, K, beta=1.0, show_plots=True) - - K = 3 # luckily, we already know this - plot_k_means(X, K, beta=3.0, show_plots=True) - - K = 3 # luckily, we already know this - plot_k_means(X, K, beta=10.0, show_plots=True) + plot_k_means(X, K) K = 5 # what happens if we choose a "bad" K? - plot_k_means(X, K, max_iter=30, show_plots=True) + plot_k_means(X, K, max_iter=30) K = 5 # what happens if we change beta? - plot_k_means(X, K, max_iter=30, beta=0.3, show_plots=True) + plot_k_means(X, K, max_iter=30, beta=0.3) if __name__ == '__main__': diff --git a/unsupervised_class/kmeans_fail.py b/unsupervised_class/kmeans_fail.py index 9b090c28..ba2ee242 100644 --- a/unsupervised_class/kmeans_fail.py +++ b/unsupervised_class/kmeans_fail.py @@ -1,12 +1,3 @@ -# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python -# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - import numpy as np from kmeans import plot_k_means @@ -20,12 +11,12 @@ def donut(): # distance from origin is radius + random normal # angle theta is uniformly distributed between (0, 2pi) - R1 = np.random.randn(N//2) + R_inner - theta = 2*np.pi*np.random.random(N//2) + R1 = np.random.randn(N/2) + R_inner + theta = 2*np.pi*np.random.random(N/2) X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T - R2 = np.random.randn(N//2) + R_outer - theta = 2*np.pi*np.random.random(N//2) + R2 = np.random.randn(N/2) + R_outer + theta = 2*np.pi*np.random.random(N/2) X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T X = np.concatenate([ X_inner, X_outer ]) @@ -35,19 +26,19 @@ def donut(): def main(): # donut X = donut() - plot_k_means(X, 2, beta=0.1, show_plots=True) + plot_k_means(X, 2) # elongated clusters X = np.zeros((1000, 2)) X[:500,:] = np.random.multivariate_normal([0, 0], [[1, 0], [0, 20]], 500) X[500:,:] = np.random.multivariate_normal([5, 0], [[1, 0], [0, 20]], 500) - plot_k_means(X, 2, beta=0.1, show_plots=True) + plot_k_means(X, 2) # different density X = np.zeros((1000, 2)) X[:950,:] = np.array([0,0]) + np.random.randn(950, 2) X[950:,:] = np.array([3,0]) + np.random.randn(50, 2) - plot_k_means(X, 2, show_plots=True) + plot_k_means(X, 2) diff --git a/unsupervised_class/kmeans_mnist.py b/unsupervised_class/kmeans_mnist.py deleted file mode 100644 index b399afab..00000000 --- a/unsupervised_class/kmeans_mnist.py +++ /dev/null @@ -1,158 +0,0 @@ -# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python -# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python - -# data is from https://www.kaggle.com/c/digit-recognizer -# each image is a D = 28x28 = 784 dimensional vector -# there are N = 42000 samples -# you can plot an image by reshaping to (28,28) and using plt.imshow() - -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -from .kmeans import plot_k_means, get_simple_data -from datetime import datetime - -def get_data(limit=None): - print("Reading in and transforming data...") - df = pd.read_csv('../large_files/train.csv') - data = df.values - np.random.shuffle(data) - X = data[:, 1:] / 255.0 # data is from 0..255 - Y = data[:, 0] - if limit is not None: - X, Y = X[:limit], Y[:limit] - return X, Y - - -# hard labels -def purity2(Y, R): - # maximum purity is 1, higher is better - C = np.argmax(R, axis=1) # cluster assignments - - N = len(Y) # number of data pts - K = len(set(Y)) # number of labels - - total = 0.0 - for k in range(K): - max_intersection = 0 - for j in range(K): - intersection = ((C == k) & (Y == j)).sum() - if intersection > max_intersection: - max_intersection = intersection - total += max_intersection - return total / N - - -def purity(Y, R): - # maximum purity is 1, higher is better - N, K = R.shape - p = 0 - for k in range(K): - best_target = -1 # we don't strictly need to store this - max_intersection = 0 - for j in range(K): - intersection = R[Y==j, k].sum() - if intersection > max_intersection: - max_intersection = intersection - best_target = j - p += max_intersection - return p / N - - -# hard labels -def DBI2(X, R): - N, D = X.shape - _, K = R.shape - - # get sigmas, means first - sigma = np.zeros(K) - M = np.zeros((K, D)) - assignments = np.argmax(R, axis=1) - for k in range(K): - Xk = X[assignments == k] - M[k] = Xk.mean(axis=0) - # assert(Xk.mean(axis=0).shape == (D,)) - n = len(Xk) - diffs = Xk - M[k] - sq_diffs = diffs * diffs - sigma[k] = np.sqrt( sq_diffs.sum() / n ) - - - # calculate Davies-Bouldin Index - dbi = 0 - for k in range(K): - max_ratio = 0 - for j in range(K): - if k != j: - numerator = sigma[k] + sigma[j] - denominator = np.linalg.norm(M[k] - M[j]) - ratio = numerator / denominator - if ratio > max_ratio: - max_ratio = ratio - dbi += max_ratio - return dbi / K - - - -def DBI(X, M, R): - # ratio between sum of std deviations between 2 clusters / distance between cluster means - # lower is better - N, D = X.shape - K, _ = M.shape - - # get sigmas first - sigma = np.zeros(K) - for k in range(K): - diffs = X - M[k] # should be NxD - squared_distances = (diffs * diffs).sum(axis=1) # now just N - weighted_squared_distances = R[:,k]*squared_distances - sigma[k] = np.sqrt( weighted_squared_distances.sum() / R[:,k].sum() ) - - # calculate Davies-Bouldin Index - dbi = 0 - for k in range(K): - max_ratio = 0 - for j in range(K): - if k != j: - numerator = sigma[k] + sigma[j] - denominator = np.linalg.norm(M[k] - M[j]) - ratio = numerator / denominator - if ratio > max_ratio: - max_ratio = ratio - dbi += max_ratio - return dbi / K - - -def main(): - # mnist data - X, Y = get_data(10000) - - # simple data - # X = get_simple_data() - # Y = np.array([0]*300 + [1]*300 + [2]*300) - - print("Number of data points:", len(Y)) - M, R = plot_k_means(X, len(set(Y))) - # Exercise: Try different values of K and compare the evaluation metrics - print("Purity:", purity(Y, R)) - print("Purity 2 (hard clusters):", purity2(Y, R)) - print("DBI:", DBI(X, M, R)) - print("DBI 2 (hard clusters):", DBI2(X, R)) - - # plot the mean images - # they should look like digits - for k in range(len(M)): - im = M[k].reshape(28, 28) - plt.imshow(im, cmap='gray') - plt.show() - - -if __name__ == "__main__": - main() diff --git a/unsupervised_class/kmeans_visualize.py b/unsupervised_class/kmeans_visualize.py deleted file mode 100644 index d10b8503..00000000 --- a/unsupervised_class/kmeans_visualize.py +++ /dev/null @@ -1,94 +0,0 @@ -# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python -# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -import matplotlib.pyplot as plt - - -def d(u, v): - diff = u - v - return diff.dot(diff) - - -def cost(X, R, M): - cost = 0 - for k in range(len(M)): - for n in range(len(X)): - cost += R[n,k]*d(M[k], X[n]) - return cost - - -def plot_k_means(X, K, max_iter=20, beta=1.0): - N, D = X.shape - M = np.zeros((K, D)) - R = np.ones((N, K)) / K - - # initialize M to random - for k in range(K): - M[k] = X[np.random.choice(N)] - - grid_width = 5 - grid_height = max_iter / grid_width - random_colors = np.random.random((K, 3)) - plt.figure() - - costs = np.zeros(max_iter) - for i in range(max_iter): - # moved the plot inside the for loop - colors = R.dot(random_colors) - plt.subplot(grid_width, grid_height, i+1) - plt.scatter(X[:,0], X[:,1], c=colors) - - # step 1: determine assignments / resposibilities - # is this inefficient? - for k in range(K): - for n in range(N): - R[n,k] = np.exp(-beta*d(M[k], X[n])) / np.sum( np.exp(-beta*d(M[j], X[n])) for j in range(K) ) - - # step 2: recalculate means - for k in range(K): - M[k] = R[:,k].dot(X) / R[:,k].sum() - - costs[i] = cost(X, R, M) - if i > 0: - if np.abs(costs[i] - costs[i-1]) < 1e-5: - break - plt.show() - - -def main(): - # assume 3 means - D = 2 # so we can visualize it more easily - s = 4 # separation so we can control how far apart the means are - mu1 = np.array([0, 0]) - mu2 = np.array([s, s]) - mu3 = np.array([0, s]) - - N = 900 # number of samples - X = np.zeros((N, D)) - X[:300, :] = np.random.randn(300, D) + mu1 - X[300:600, :] = np.random.randn(300, D) + mu2 - X[600:, :] = np.random.randn(300, D) + mu3 - - # what does it look like without clustering? - plt.scatter(X[:,0], X[:,1]) - plt.show() - - K = 3 # luckily, we already know this - plot_k_means(X, K) - - # K = 5 # what happens if we choose a "bad" K? - # plot_k_means(X, K, max_iter=30) - - # K = 5 # what happens if we change beta? - # plot_k_means(X, K, max_iter=30, beta=0.3) - - -if __name__ == '__main__': - main() diff --git a/unsupervised_class/neural_kmeans.py b/unsupervised_class/neural_kmeans.py deleted file mode 100644 index 5e9a2d20..00000000 --- a/unsupervised_class/neural_kmeans.py +++ /dev/null @@ -1,47 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from kmeans import get_simple_data -from sklearn.preprocessing import StandardScaler - - -# get the data and standardize it -X = get_simple_data() -scaler = StandardScaler() -X = scaler.fit_transform(X) - -# get shapes -N, D = X.shape -K = 3 - -# initialize parameters -W = np.random.randn(D, K) - -# set hyperparameters -n_epochs = 100 -learning_rate = 0.001 -losses = [] - -# training loop -for i in range(n_epochs): - loss = 0 - for j in range(N): - h = W.T.dot(X[j]) # K-length vector - k = np.argmax(h) # winning neuron - - # accumulate loss - loss += (W[:,k] - X[j]).dot(W[:,k] - X[j]) - - # weight update - W[:,k] += learning_rate * (X[j] - W[:,k]) - - losses.append(loss) - - -# plot losses -plt.plot(losses) -plt.show() - -# show cluster assignments -H = np.argmax(X.dot(W), axis=1) -plt.scatter(X[:,0], X[:,1], c=H, alpha=0.5) -plt.show() diff --git a/unsupervised_class/tweets.py b/unsupervised_class/tweets.py deleted file mode 100644 index 6ffba008..00000000 --- a/unsupervised_class/tweets.py +++ /dev/null @@ -1,201 +0,0 @@ -# https://deeplearningcourses.com/c/cluster-analysis-unsupervised-machine-learning-python -# https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python -# data from https://www.kaggle.com/benhamner/clinton-trump-tweets -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import re -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -from scipy.spatial.distance import pdist -from scipy.cluster.hierarchy import dendrogram, linkage, fcluster -from sklearn.feature_extraction.text import TfidfVectorizer -# from sklearn.metrics.pairwise import pairwise_distances ### fast, but result not symmetric - - - -# load stopwords -# selected after observing results without stopwords -stopwords = [ - 'the', - 'about', - 'an', - 'and', - 'are', - 'at', - 'be', - 'can', - 'for', - 'from', - 'if', - 'in', - 'is', - 'it', - 'of', - 'on', - 'or', - 'that', - 'this', - 'to', - 'you', - 'your', - 'with', -] - - -# find urls and twitter usernames within a string -url_finder = re.compile(r"(?:\@|https?\://)\S+") - - -def filter_tweet(s): - s = s.lower() # downcase - s = url_finder.sub("", s) # remove urls and usernames - return s - - - -### load data ### -df = pd.read_csv('../large_files/tweets.csv') -text = df.text.tolist() -text = [filter_tweet(s) for s in text] - - -# transform the text into a data matrix -tfidf = TfidfVectorizer(max_features=100, stop_words=stopwords) -X = tfidf.fit_transform(text).asformat('array') - - -# subsample for efficiency -# remember: calculating distances is O(N^2) -N = X.shape[0] -idx = np.random.choice(N, size=2000, replace=False) -x = X[idx] -labels = df.handle[idx].tolist() - - -# proportions of each label -# so we can be confident that each is represented equally -pTrump = sum(1.0 if e == 'realDonaldTrump' else 0.0 for e in labels) / len(labels) -print("proportion @realDonaldTrump: %.3f" % pTrump) -print("proportion @HillaryClinton: %.3f" % (1 - pTrump)) - - -# transform the data matrix into pairwise distances list -dist_array = pdist(x) - - -# calculate hierarchy -Z = linkage(dist_array, 'ward') -plt.title("Ward") -dendrogram(Z, labels=labels) -plt.show() - -### hits max recursion depth -# Z = linkage(dist_array, 'single') -# plt.title("Single") -# dendrogram(Z, labels=labels) -# plt.show() - -# Z = linkage(dist_array, 'complete') -# plt.title("Complete") -# dendrogram(Z, labels=labels) -# plt.show() - - -# convert labels to (1, 2), not (0, 1) -# since that's what's returned by fcluster -Y = np.array([1 if e == 'realDonaldTrump' else 2 for e in labels]) - - -# get cluster assignments -# threshold 9 was chosen empirically to yield 2 clusters -C = fcluster(Z, 9, criterion='distance') # returns 1, 2, ..., K -categories = set(C) -# sanity check: should be {1, 2} -print("values in C:", categories) - - -### calculate the purity of our clusters ### -def purity(true_labels, cluster_assignments, categories): - # maximum purity is 1, higher is better - N = len(true_labels) - - total = 0.0 - for k in categories: - max_intersection = 0 - for j in categories: - intersection = ((cluster_assignments == k) & (true_labels == j)).sum() - if intersection > max_intersection: - max_intersection = intersection - total += max_intersection - return total / N - -print("purity:", purity(Y, C, categories)) - - -# we know the smaller cluster is the trump cluster -# -# important note: we call it the trump cluster from -# observing AFTER the fact that most tweets in this -# cluster are by trump -# we do NOT use those labels to create the cluster -# -# we create the clusters using a distance-based -# algorithm which knows nothing about the labels, -# just the tf-idf scores. -# -# it just so happens that most of the tweets in -# one of the clusters is by trump, and that this -# cluster is very small -if (C == 1).sum() < (C == 2).sum(): - d = 1 - h = 2 -else: - d = 2 - h = 1 - -actually_donald = ((C == d) & (Y == 1)).sum() -donald_cluster_size = (C == d).sum() -print("purity of @realDonaldTrump cluster:", float(actually_donald) / donald_cluster_size) - -actually_hillary = ((C == h) & (Y == 2)).sum() -hillary_cluster_size = (C == h).sum() -print("purity of @HillaryClinton cluster:", float(actually_hillary) / hillary_cluster_size) - - -# just for interest, how would a classifier do? -# note: classification is always easier -from sklearn.ensemble import RandomForestClassifier - -rf = RandomForestClassifier() -rf.fit(X, df.handle) -print("classifier score:", rf.score(X, df.handle)) - -# same as mnist -# classifier yields almost 100% accuracy -# but purity of clustering is much lower - - - -# what words have the highest tf-idf in cluster 1? in cluster 2? -w2i = tfidf.vocabulary_ - -# tf-idf vectorizer todense() returns a matrix rather than array -# matrix always wants to be 2-D, so we convert to array in order to flatten -d_avg = np.array(x[C == d].mean(axis=0)).flatten() -d_sorted = sorted(w2i.keys(), key=lambda w: -d_avg[w2i[w]]) - -print("\nTop 10 'Donald cluster' words:") -print("\n".join(d_sorted[:10])) - -h_avg = np.array(x[C == h].mean(axis=0)).flatten() -h_sorted = sorted(w2i.keys(), key=lambda w: -h_avg[w2i[w]]) - -print("\nTop 10 'Hillary cluster' words:") -print("\n".join(h_sorted[:10])) - - diff --git a/unsupervised_class2/__init__.py b/unsupervised_class2/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/unsupervised_class2/autoencoder.py b/unsupervised_class2/autoencoder.py index 87633fa1..6c370fc4 100644 --- a/unsupervised_class2/autoencoder.py +++ b/unsupervised_class2/autoencoder.py @@ -1,10 +1,4 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python # https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import theano import theano.tensor as T @@ -14,42 +8,19 @@ from util import relu, error_rate, getKaggleMNIST, init_weights -def T_shared_zeros_like32(p): - # p is a Theano shared itself - return theano.shared(np.zeros_like(p.get_value(), dtype=np.float32)) - -def momentum_updates(cost, params, mu, learning_rate): - # momentum changes - dparams = [T_shared_zeros_like32(p) for p in params] - - updates = [] - grads = T.grad(cost, params) - for p, dp, g in zip(params, dparams, grads): - dp_update = mu*dp - learning_rate*g - p_update = p + dp_update - - updates.append((dp, dp_update)) - updates.append((p, p_update)) - return updates - - class AutoEncoder(object): def __init__(self, M, an_id): self.M = M self.id = an_id def fit(self, X, learning_rate=0.5, mu=0.99, epochs=1, batch_sz=100, show_fig=False): - # cast to float - mu = np.float32(mu) - learning_rate = np.float32(learning_rate) - N, D = X.shape - n_batches = N // batch_sz + n_batches = N / batch_sz W0 = init_weights((D, self.M)) self.W = theano.shared(W0, 'W_%s' % self.id) - self.bh = theano.shared(np.zeros(self.M, dtype=np.float32), 'bh_%s' % self.id) - self.bo = theano.shared(np.zeros(D, dtype=np.float32), 'bo_%s' % self.id) + self.bh = theano.shared(np.zeros(self.M), 'bh_%s' % self.id) + self.bo = theano.shared(np.zeros(D), 'bo_%s' % self.id) self.params = [self.W, self.bh, self.bo] self.forward_params = [self.W, self.bh] @@ -71,39 +42,33 @@ def fit(self, X, learning_rate=0.5, mu=0.99, epochs=1, batch_sz=100, show_fig=Fa outputs=H, ) - # save this for later so we can call it to - # create reconstructions of input - self.predict = theano.function( - inputs=[X_in], - outputs=X_hat, - ) - - cost = -(X_in * T.log(X_hat) + (1 - X_in) * T.log(1 - X_hat)).flatten().mean() + # cost = ((X_in - X_hat) * (X_in - X_hat)).sum() / N + cost = -(X_in * T.log(X_hat) + (1 - X_in) * T.log(1 - X_hat)).sum() / N cost_op = theano.function( inputs=[X_in], outputs=cost, ) - - - updates = momentum_updates(cost, self.params, mu, learning_rate) + updates = [ + (p, p + mu*dp - learning_rate*T.grad(cost, p)) for p, dp in zip(self.params, self.dparams) + ] + [ + (dp, mu*dp - learning_rate*T.grad(cost, p)) for p, dp in zip(self.params, self.dparams) + ] train_op = theano.function( inputs=[X_in], updates=updates, ) costs = [] - print("training autoencoder: %s" % self.id) - print("epochs to do:", epochs) - for i in range(epochs): - print("epoch:", i) + print "training autoencoder: %s" % self.id + for i in xrange(epochs): + print "epoch:", i X = shuffle(X) - for j in range(n_batches): + for j in xrange(n_batches): batch = X[j*batch_sz:(j*batch_sz + batch_sz)] train_op(batch) - the_cost = cost_op(batch) # technically we could also get the cost for Xtest here - if j % 10 == 0: - print("j / n_batches:", j, "/", n_batches, "cost:", the_cost) + the_cost = cost_op(X) # technically we could also get the cost for Xtest here + print "j / n_batches:", j, "/", n_batches, "cost:", the_cost costs.append(the_cost) if show_fig: plt.plot(costs) @@ -111,6 +76,8 @@ def fit(self, X, learning_rate=0.5, mu=0.99, epochs=1, batch_sz=100, show_fig=Fa def forward_hidden(self, X): Z = T.nnet.sigmoid(X.dot(self.W) + self.bh) + # Z = T.tanh(X.dot(self.W) + self.bh) + # Z = relu(X.dot(self.W) + self.bh) return Z def forward_output(self, X): @@ -139,22 +106,9 @@ def __init__(self, hidden_layer_sizes, UnsupervisedModel=AutoEncoder): count += 1 - def fit(self, X, Y, Xtest, Ytest, - pretrain=True, - train_head_only=False, - learning_rate=0.1, - mu=0.99, - reg=0.0, - epochs=1, - batch_sz=100): - - # cast to float32 - learning_rate = np.float32(learning_rate) - mu = np.float32(mu) - reg = np.float32(reg) - + def fit(self, X, Y, Xtest, Ytest, pretrain=True, learning_rate=0.01, mu=0.99, reg=0.1, epochs=1, batch_sz=100): # greedy layer-wise training of autoencoders - pretrain_epochs = 2 + pretrain_epochs = 1 if not pretrain: pretrain_epochs = 0 @@ -170,45 +124,56 @@ def fit(self, X, Y, Xtest, Ytest, K = len(set(Y)) W0 = init_weights((self.hidden_layers[-1].M, K)) self.W = theano.shared(W0, "W_logreg") - self.b = theano.shared(np.zeros(K, dtype=np.float32), "b_logreg") + self.b = theano.shared(np.zeros(K), "b_logreg") self.params = [self.W, self.b] - if not train_head_only: - for ae in self.hidden_layers: - self.params += ae.forward_params + for ae in self.hidden_layers: + self.params += ae.forward_params + + # for momentum + self.dW = theano.shared(np.zeros(W0.shape), "dW_logreg") + self.db = theano.shared(np.zeros(K), "db_logreg") + self.dparams = [self.dW, self.db] + for ae in self.hidden_layers: + self.dparams += ae.forward_dparams X_in = T.matrix('X_in') targets = T.ivector('Targets') pY = self.forward(X_in) - squared_magnitude = [(p*p).sum() for p in self.params] - reg_cost = T.sum(squared_magnitude) - cost = -T.mean( T.log(pY[T.arange(pY.shape[0]), targets]) ) + reg*reg_cost + # squared_magnitude = [(p*p).sum() for p in self.params] + # reg_cost = T.sum(squared_magnitude) + cost = -T.mean( T.log(pY[T.arange(pY.shape[0]), targets]) ) #+ reg*reg_cost prediction = self.predict(X_in) cost_predict_op = theano.function( inputs=[X_in, targets], outputs=[cost, prediction], ) - updates = momentum_updates(cost, self.params, mu, learning_rate) + updates = [ + (p, p + mu*dp - learning_rate*T.grad(cost, p)) for p, dp in zip(self.params, self.dparams) + ] + [ + (dp, mu*dp - learning_rate*T.grad(cost, p)) for p, dp in zip(self.params, self.dparams) + ] + # updates = [(p, p - learning_rate*T.grad(cost, p)) for p in self.params] train_op = theano.function( inputs=[X_in, targets], updates=updates, ) - n_batches = N // batch_sz + n_batches = N / batch_sz costs = [] - print("supervised training...") - for i in range(epochs): - print("epoch:", i) + print "supervised training..." + for i in xrange(epochs): + print "epoch:", i X, Y = shuffle(X, Y) - for j in range(n_batches): + for j in xrange(n_batches): Xbatch = X[j*batch_sz:(j*batch_sz + batch_sz)] Ybatch = Y[j*batch_sz:(j*batch_sz + batch_sz)] train_op(Xbatch, Ybatch) the_cost, the_prediction = cost_predict_op(Xtest, Ytest) error = error_rate(the_prediction, Ytest) - print("j / n_batches:", j, "/", n_batches, "cost:", the_cost, "error:", error) + print "j / n_batches:", j, "/", n_batches, "cost:", the_cost, "error:", error costs.append(the_cost) plt.plot(costs) plt.show() @@ -233,36 +198,8 @@ def main(): # dnn.fit(Xtrain, Ytrain, Xtest, Ytest, epochs=3) # vs dnn = DNN([1000, 750, 500]) - dnn.fit(Xtrain, Ytrain, Xtest, Ytest, pretrain=True, train_head_only=False, epochs=3) - # note: try training the head only too! what does that mean? - - -def test_single_autoencoder(): - Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - - autoencoder = AutoEncoder(300, 0) - autoencoder.fit(Xtrain, epochs=2, show_fig=True) - - done = False - while not done: - i = np.random.choice(len(Xtest)) - x = Xtest[i] - y = autoencoder.predict([x]) - plt.subplot(1,2,1) - plt.imshow(x.reshape(28,28), cmap='gray') - plt.title('Original') - - plt.subplot(1,2,2) - plt.imshow(y.reshape(28,28), cmap='gray') - plt.title('Reconstructed') - - plt.show() - - ans = input("Generate another?") - if ans and ans[0] in ('n' or 'N'): - done = True + dnn.fit(Xtrain, Ytrain, Xtest, Ytest, pretrain=False, epochs=10) if __name__ == '__main__': main() - # test_single_autoencoder() diff --git a/unsupervised_class2/autoencoder_tf.py b/unsupervised_class2/autoencoder_tf.py deleted file mode 100644 index 93d2b87b..00000000 --- a/unsupervised_class2/autoencoder_tf.py +++ /dev/null @@ -1,233 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python -# https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt - -from sklearn.utils import shuffle -from util import error_rate, getKaggleMNIST - -if tf.__version__.startswith('2'): - tf.compat.v1.disable_eager_execution() - - -class AutoEncoder(object): - def __init__(self, D, M, an_id): - self.M = M - self.id = an_id - self.build(D, M) - - def set_session(self, session): - self.session = session - - def build(self, D, M): - self.W = tf.Variable(tf.random.normal(shape=(D, M))) - self.bh = tf.Variable(np.zeros(M).astype(np.float32)) - self.bo = tf.Variable(np.zeros(D).astype(np.float32)) - - self.X_in = tf.compat.v1.placeholder(tf.float32, shape=(None, D)) - self.Z = self.forward_hidden(self.X_in) # for transform() later - self.X_hat = self.forward_output(self.X_in) - - - # using the naive formulation for cross-entropy - # will have numerical stability issues if X_hat = 0 or 1 - logits = self.forward_logits(self.X_in) - self.cost = tf.reduce_mean( - input_tensor=tf.nn.sigmoid_cross_entropy_with_logits( - labels=self.X_in, - logits=logits, - ) - ) - - self.train_op = tf.compat.v1.train.AdamOptimizer(1e-1).minimize(self.cost) - # self.train_op = tf.train.MomentumOptimizer(1e-3, momentum=0.9).minimize(self.cost) - - def fit(self, X, epochs=1, batch_sz=100, show_fig=False): - N, D = X.shape - n_batches = N // batch_sz - - costs = [] - print("training autoencoder: %s" % self.id) - for i in range(epochs): - print("epoch:", i) - X = shuffle(X) - for j in range(n_batches): - batch = X[j*batch_sz:(j*batch_sz + batch_sz)] - _, c = self.session.run((self.train_op, self.cost), feed_dict={self.X_in: batch}) - if j % 10 == 0: - print("j / n_batches:", j, "/", n_batches, "cost:", c) - costs.append(c) - if show_fig: - plt.plot(costs) - plt.show() - - def transform(self, X): - # accepts and returns a real numpy array - # unlike forward_hidden and forward_output - # which deal with tensorflow variables - return self.session.run(self.Z, feed_dict={self.X_in: X}) - - def predict(self, X): - # accepts and returns a real numpy array - # unlike forward_hidden and forward_output - # which deal with tensorflow variables - return self.session.run(self.X_hat, feed_dict={self.X_in: X}) - - def forward_hidden(self, X): - Z = tf.nn.sigmoid(tf.matmul(X, self.W) + self.bh) - return Z - - def forward_logits(self, X): - Z = self.forward_hidden(X) - return tf.matmul(Z, tf.transpose(a=self.W)) + self.bo - - def forward_output(self, X): - return tf.nn.sigmoid(self.forward_logits(X)) - - -class DNN(object): - def __init__(self, D, hidden_layer_sizes, K, UnsupervisedModel=AutoEncoder): - self.hidden_layers = [] - count = 0 - input_size = D - for output_size in hidden_layer_sizes: - ae = UnsupervisedModel(input_size, output_size, count) - self.hidden_layers.append(ae) - count += 1 - input_size = output_size - self.build_final_layer(D, hidden_layer_sizes[-1], K) - - def set_session(self, session): - self.session = session - for layer in self.hidden_layers: - layer.set_session(session) - - def build_final_layer(self, D, M, K): - # initialize logistic regression layer - self.W = tf.Variable(tf.random.normal(shape=(M, K))) - self.b = tf.Variable(np.zeros(K).astype(np.float32)) - - self.X = tf.compat.v1.placeholder(tf.float32, shape=(None, D)) - labels = tf.compat.v1.placeholder(tf.int32, shape=(None,)) - self.Y = labels - logits = self.forward(self.X) - - self.cost = tf.reduce_mean( - input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits( - logits=logits, - labels=labels - ) - ) - self.train_op = tf.compat.v1.train.AdamOptimizer(1e-2).minimize(self.cost) - self.prediction = tf.argmax(input=logits, axis=1) - - def fit(self, X, Y, Xtest, Ytest, pretrain=True, epochs=1, batch_sz=100): - N = len(X) - - # greedy layer-wise training of autoencoders - pretrain_epochs = 1 - if not pretrain: - pretrain_epochs = 0 - - current_input = X - for ae in self.hidden_layers: - ae.fit(current_input, epochs=pretrain_epochs) - - # create current_input for the next layer - current_input = ae.transform(current_input) - - n_batches = N // batch_sz - costs = [] - print("supervised training...") - for i in range(epochs): - print("epoch:", i) - X, Y = shuffle(X, Y) - for j in range(n_batches): - Xbatch = X[j*batch_sz:(j*batch_sz + batch_sz)] - Ybatch = Y[j*batch_sz:(j*batch_sz + batch_sz)] - self.session.run( - self.train_op, - feed_dict={self.X: Xbatch, self.Y: Ybatch} - ) - c, p = self.session.run( - (self.cost, self.prediction), - feed_dict={self.X: Xtest, self.Y: Ytest - }) - error = error_rate(p, Ytest) - if j % 10 == 0: - print("j / n_batches:", j, "/", n_batches, "cost:", c, "error:", error) - costs.append(c) - plt.plot(costs) - plt.show() - - def forward(self, X): - current_input = X - for ae in self.hidden_layers: - Z = ae.forward_hidden(current_input) - current_input = Z - - # logistic layer - logits = tf.matmul(current_input, self.W) + self.b - return logits - - -def test_pretraining_dnn(): - Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - # dnn = DNN([1000, 750, 500]) - # dnn.fit(Xtrain, Ytrain, Xtest, Ytest, epochs=3) - # vs - Xtrain = Xtrain.astype(np.float32) - Xtest = Xtest.astype(np.float32) - _, D = Xtrain.shape - K = len(set(Ytrain)) - dnn = DNN(D, [1000, 750, 500], K) - init_op = tf.compat.v1.global_variables_initializer() - with tf.compat.v1.Session() as session: - session.run(init_op) - dnn.set_session(session) - dnn.fit(Xtrain, Ytrain, Xtest, Ytest, pretrain=True, epochs=10) - - -def test_single_autoencoder(): - Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - Xtrain = Xtrain.astype(np.float32) - Xtest = Xtest.astype(np.float32) - - _, D = Xtrain.shape - autoencoder = AutoEncoder(D, 300, 0) - init_op = tf.compat.v1.global_variables_initializer() - with tf.compat.v1.Session() as session: - session.run(init_op) - autoencoder.set_session(session) - autoencoder.fit(Xtrain, show_fig=True) - - done = False - while not done: - i = np.random.choice(len(Xtest)) - x = Xtest[i] - y = autoencoder.predict([x]) - plt.subplot(1,2,1) - plt.imshow(x.reshape(28,28), cmap='gray') - plt.title('Original') - - plt.subplot(1,2,2) - plt.imshow(y.reshape(28,28), cmap='gray') - plt.title('Reconstructed') - - plt.show() - - ans = input("Generate another?") - if ans and ans[0] in ('n' or 'N'): - done = True - - - -if __name__ == '__main__': - # test_single_autoencoder() - test_pretraining_dnn() diff --git a/unsupervised_class2/compare_pca_svd.py b/unsupervised_class2/compare_pca_svd.py deleted file mode 100644 index 8d08e38d..00000000 --- a/unsupervised_class2/compare_pca_svd.py +++ /dev/null @@ -1,31 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python -# https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - -from sklearn.decomposition import PCA, TruncatedSVD -from util import getKaggleMNIST - - -X, Y, _, _ = getKaggleMNIST() -m = X.mean(axis=0) -s = X.std(axis=0) -np.place(s, s == 0, 1) -X = (X - m) / s - -pca = PCA() -svd = TruncatedSVD() - -Z1 = pca.fit_transform(X) -Z2 = svd.fit_transform(X) - -plt.subplot(1,2,1) -plt.scatter(Z1[:,0], Z1[:,1], c=Y) -plt.subplot(1,2,2) -plt.scatter(Z2[:,0], Z2[:,1], c=Y) -plt.show() diff --git a/unsupervised_class2/extra_reading.txt b/unsupervised_class2/extra_reading.txt deleted file mode 100644 index 24c20d3e..00000000 --- a/unsupervised_class2/extra_reading.txt +++ /dev/null @@ -1,32 +0,0 @@ -Visualizing Data using t-SNE -http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf - -Reducing the Dimensionality of Data with Neural Networks -https://www.cs.toronto.edu/~hinton/science.pdf - -A fast learning algorithm for deep belief nets -https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf - -Why Does Unsupervised Pre-training Help Deep Learning? -http://www.jmlr.org/papers/volume11/erhan10a/erhan10a.pdf - -A Better Way to Pretrain Deep Boltzmann Machines -http://www.cs.toronto.edu/~hinton/absps/DBM_pretrain.pdf - -On Deep Generative Models with Applications to Recognition -http://www.cs.toronto.edu/~hinton/absps/ranzato_cvpr2011.pdf - -LEARNING A BETTER REPRESENTATION OF SPEECH SOUND WAVES USING RESTRICTED BOLTZMANN MACHINES -http://www.cs.toronto.edu/~hinton/absps/jaitly_ICASSP2011.pdf - -Rectified Linear Units Improve Restricted Boltzmann Machines -http://www.cs.toronto.edu/~hinton/absps/reluICML.pdf - -Generative versus discriminative training of RBMs for classification of fMRI images -http://www.cs.toronto.edu/~hinton/absps/fmrinips.pdf - -Restricted Boltzmann Machines for Collaborative Filtering -http://www.cs.toronto.edu/~hinton/absps/netflix.pdf - -On Contrastive Divergence Learning -http://www.cs.toronto.edu/~hinton/absps/cdmiguel.pdf \ No newline at end of file diff --git a/unsupervised_class2/gaussian_nb.py b/unsupervised_class2/gaussian_nb.py deleted file mode 100644 index e4a1601f..00000000 --- a/unsupervised_class2/gaussian_nb.py +++ /dev/null @@ -1,62 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from sklearn.decomposition import PCA -# from sklearn.naive_bayes import GaussianNB # doesn't have smoothing -from scipy.stats import norm -from scipy.stats import multivariate_normal as mvn -from util import getKaggleMNIST - - -class GaussianNB(object): - def fit(self, X, Y, smoothing=1e-2): - self.gaussians = dict() - self.priors = dict() - labels = set(Y) - for c in labels: - current_x = X[Y == c] - self.gaussians[c] = { - 'mean': current_x.mean(axis=0), - 'var': current_x.var(axis=0) + smoothing, - } - self.priors[c] = float(len(Y[Y == c])) / len(Y) - - def score(self, X, Y): - P = self.predict(X) - return np.mean(P == Y) - - def predict(self, X): - N, D = X.shape - K = len(self.gaussians) - P = np.zeros((N, K)) - for c, g in iteritems(self.gaussians): - mean, var = g['mean'], g['var'] - P[:,c] = mvn.logpdf(X, mean=mean, cov=var) + np.log(self.priors[c]) - return np.argmax(P, axis=1) - - -# get data -Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - -# try NB by itself -model1 = GaussianNB() -model1.fit(Xtrain, Ytrain) -print("NB train score:", model1.score(Xtrain, Ytrain)) -print("NB test score:", model1.score(Xtest, Ytest)) - -# try NB with PCA first -pca = PCA(n_components=50) -Ztrain = pca.fit_transform(Xtrain) -Ztest = pca.transform(Xtest) - -model2 = GaussianNB() -model2.fit(Ztrain, Ytrain) -print("NB+PCA train score:", model2.score(Ztrain, Ytrain)) -print("NB+PCA test score:", model2.score(Ztest, Ytest)) diff --git a/unsupervised_class2/pca.py b/unsupervised_class2/pca.py index 7c88431f..afb1e70b 100644 --- a/unsupervised_class2/pca.py +++ b/unsupervised_class2/pca.py @@ -1,10 +1,4 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python # https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import matplotlib.pyplot as plt diff --git a/unsupervised_class2/pca_impl.py b/unsupervised_class2/pca_impl.py deleted file mode 100644 index 3bbedd84..00000000 --- a/unsupervised_class2/pca_impl.py +++ /dev/null @@ -1,43 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python -# https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - -from util import getKaggleMNIST - -# get the data -Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - -# decompose covariance -covX = np.cov(Xtrain.T) -lambdas, Q = np.linalg.eigh(covX) - - -# lambdas are sorted from smallest --> largest -# some may be slightly negative due to precision -idx = np.argsort(-lambdas) -lambdas = lambdas[idx] # sort in proper order -lambdas = np.maximum(lambdas, 0) # get rid of negatives -Q = Q[:,idx] - - -# plot the first 2 columns of Z -Z = Xtrain.dot(Q) -plt.scatter(Z[:,0], Z[:,1], s=100, c=Ytrain, alpha=0.3) -plt.show() - - -# plot variances -plt.plot(lambdas) -plt.title("Variance of each component") -plt.show() - -# cumulative variance -plt.plot(np.cumsum(lambdas)) -plt.title("Cumulative variance") -plt.show() \ No newline at end of file diff --git a/unsupervised_class2/rbm.py b/unsupervised_class2/rbm.py index 2f3f9991..6fa3dfd4 100644 --- a/unsupervised_class2/rbm.py +++ b/unsupervised_class2/rbm.py @@ -1,10 +1,4 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python # https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import theano import theano.tensor as T @@ -23,12 +17,8 @@ def __init__(self, M, an_id): self.rng = RandomStreams() def fit(self, X, learning_rate=0.1, epochs=1, batch_sz=100, show_fig=False): - # cast to float32 - learning_rate = np.float32(learning_rate) - - N, D = X.shape - n_batches = N // batch_sz + n_batches = N / batch_sz W0 = init_weights((D, self.M)) self.W = theano.shared(W0, 'W_%s' % self.id) @@ -37,6 +27,14 @@ def fit(self, X, learning_rate=0.1, epochs=1, batch_sz=100, show_fig=False): self.params = [self.W, self.c, self.b] self.forward_params = [self.W, self.c] + # we won't use this to fit the RBM but we will use these for backpropagation later + # TODO: technically they should be reset before doing backprop + self.dW = theano.shared(np.zeros(W0.shape), 'dW_%s' % self.id) + self.dc = theano.shared(np.zeros(self.M), 'dbh_%s' % self.id) + self.db = theano.shared(np.zeros(D), 'dbo_%s' % self.id) + self.dparams = [self.dW, self.dc, self.db] + self.forward_dparams = [self.dW, self.dc] + X_in = T.matrix('X_%s' % self.id) # attach it to the object so it can be used later @@ -51,7 +49,7 @@ def fit(self, X, learning_rate=0.1, epochs=1, batch_sz=100, show_fig=False): # but we would like to see how this cost function changes # as we do contrastive divergence X_hat = self.forward_output(X_in) - cost = -(X_in * T.log(X_hat) + (1 - X_in) * T.log(1 - X_hat)).mean() + cost = -(X_in * T.log(X_hat) + (1 - X_in) * T.log(1 - X_hat)).sum() / N cost_op = theano.function( inputs=[X_in], outputs=cost, @@ -72,15 +70,15 @@ def fit(self, X, learning_rate=0.1, epochs=1, batch_sz=100, show_fig=False): ) costs = [] - print("training rbm: %s" % self.id) - for i in range(epochs): - print("epoch:", i) + print "training rbm: %s" % self.id + for i in xrange(epochs): + print "epoch:", i X = shuffle(X) - for j in range(n_batches): + for j in xrange(n_batches): batch = X[j*batch_sz:(j*batch_sz + batch_sz)] train_op(batch) the_cost = cost_op(X) # technically we could also get the cost for Xtest here - print("j / n_batches:", j, "/", n_batches, "cost:", the_cost) + print "j / n_batches:", j, "/", n_batches, "cost:", the_cost costs.append(the_cost) if show_fig: plt.plot(costs) diff --git a/unsupervised_class2/rbm_tf.py b/unsupervised_class2/rbm_tf.py deleted file mode 100644 index 3b3516ce..00000000 --- a/unsupervised_class2/rbm_tf.py +++ /dev/null @@ -1,139 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python -# https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt -from sklearn.utils import shuffle -from util import getKaggleMNIST -from autoencoder_tf import DNN - - -class RBM(object): - def __init__(self, D, M, an_id): - self.D = D - self.M = M - self.id = an_id - self.build(D, M) - - def set_session(self, session): - self.session = session - - def build(self, D, M): - # params - self.W = tf.Variable(tf.random.normal(shape=(D, M)) * np.sqrt(2.0 / M)) - # note: without limiting variance, you get numerical stability issues - self.c = tf.Variable(np.zeros(M).astype(np.float32)) - self.b = tf.Variable(np.zeros(D).astype(np.float32)) - - # data - self.X_in = tf.compat.v1.placeholder(tf.float32, shape=(None, D)) - - # conditional probabilities - # NOTE: tf.contrib.distributions.Bernoulli API has changed in Tensorflow v1.2 - V = self.X_in - p_h_given_v = tf.nn.sigmoid(tf.matmul(V, self.W) + self.c) - self.p_h_given_v = p_h_given_v # save for later - # self.rng_h_given_v = tf.contrib.distributions.Bernoulli( - # probs=p_h_given_v, - # dtype=tf.float32 - # ) - r = tf.random.uniform(shape=tf.shape(input=p_h_given_v)) - H = tf.cast(r < p_h_given_v, dtype=tf.float32) - - p_v_given_h = tf.nn.sigmoid(tf.matmul(H, tf.transpose(a=self.W)) + self.b) - # self.rng_v_given_h = tf.contrib.distributions.Bernoulli( - # probs=p_v_given_h, - # dtype=tf.float32 - # ) - r = tf.random.uniform(shape=tf.shape(input=p_v_given_h)) - X_sample = tf.cast(r < p_v_given_h, dtype=tf.float32) - - - # build the objective - objective = tf.reduce_mean(input_tensor=self.free_energy(self.X_in)) - tf.reduce_mean(input_tensor=self.free_energy(X_sample)) - self.train_op = tf.compat.v1.train.AdamOptimizer(1e-2).minimize(objective) - # self.train_op = tf.train.GradientDescentOptimizer(1e-3).minimize(objective) - - # build the cost - # we won't use this to optimize the model parameters - # just to observe what happens during training - logits = self.forward_logits(self.X_in) - self.cost = tf.reduce_mean( - input_tensor=tf.nn.sigmoid_cross_entropy_with_logits( - labels=self.X_in, - logits=logits, - ) - ) - - def fit(self, X, epochs=1, batch_sz=100, show_fig=False): - N, D = X.shape - n_batches = N // batch_sz - - costs = [] - print("training rbm: %s" % self.id) - for i in range(epochs): - print("epoch:", i) - X = shuffle(X) - for j in range(n_batches): - batch = X[j*batch_sz:(j*batch_sz + batch_sz)] - _, c = self.session.run((self.train_op, self.cost), feed_dict={self.X_in: batch}) - if j % 10 == 0: - print("j / n_batches:", j, "/", n_batches, "cost:", c) - costs.append(c) - if show_fig: - plt.plot(costs) - plt.show() - - def free_energy(self, V): - b = tf.reshape(self.b, (self.D, 1)) - first_term = -tf.matmul(V, b) - first_term = tf.reshape(first_term, (-1,)) - - second_term = -tf.reduce_sum( - # tf.log(1 + tf.exp(tf.matmul(V, self.W) + self.c)), - input_tensor=tf.nn.softplus(tf.matmul(V, self.W) + self.c), - axis=1 - ) - - return first_term + second_term - - def forward_hidden(self, X): - return tf.nn.sigmoid(tf.matmul(X, self.W) + self.c) - - def forward_logits(self, X): - Z = self.forward_hidden(X) - return tf.matmul(Z, tf.transpose(a=self.W)) + self.b - - def forward_output(self, X): - return tf.nn.sigmoid(self.forward_logits(X)) - - def transform(self, X): - # accepts and returns a real numpy array - # unlike forward_hidden and forward_output - # which deal with tensorflow variables - return self.session.run(self.p_h_given_v, feed_dict={self.X_in: X}) - - -def main(): - Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - - # same as autoencoder_tf.py - Xtrain = Xtrain.astype(np.float32) - Xtest = Xtest.astype(np.float32) - _, D = Xtrain.shape - K = len(set(Ytrain)) - dnn = DNN(D, [1000, 750, 500], K, UnsupervisedModel=RBM) - init_op = tf.compat.v1.global_variables_initializer() - with tf.compat.v1.Session() as session: - session.run(init_op) - dnn.set_session(session) - dnn.fit(Xtrain, Ytrain, Xtest, Ytest, pretrain=True, epochs=10) - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/unsupervised_class2/sk_mlp.py b/unsupervised_class2/sk_mlp.py deleted file mode 100644 index f2e1d393..00000000 --- a/unsupervised_class2/sk_mlp.py +++ /dev/null @@ -1,33 +0,0 @@ -# https://deeplearningcourses.com/c/data-science-deep-learning-in-theano-tensorflow -# https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow -from __future__ import print_function, division -from future.utils import iteritems -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - - -import numpy as np -from sklearn.neural_network import MLPRegressor -from util import getKaggleMNIST - - - -# get data -X, _, Xt, _ = getKaggleMNIST() - -# create the model and train it -model = MLPRegressor() -model.fit(X, X) - -# test the model -print("Train R^2:", model.score(X, X)) -print("Test R^2:", model.score(Xt, Xt)) - -Xhat = model.predict(X) -mse = ((Xhat - X)**2).mean() -print("Train MSE:", mse) - -Xhat = model.predict(Xt) -mse = ((Xhat - Xt)**2).mean() -print("Test MSE:", mse) \ No newline at end of file diff --git a/unsupervised_class2/tsne_books.py b/unsupervised_class2/tsne_books.py index d31b8f2a..4c15efe5 100644 --- a/unsupervised_class2/tsne_books.py +++ b/unsupervised_class2/tsne_books.py @@ -1,10 +1,3 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python -# https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import nltk import numpy as np import matplotlib.pyplot as plt @@ -44,7 +37,6 @@ def my_tokenizer(s): for title in titles: try: title = title.encode('ascii', 'ignore') # this will throw exception if bad characters - title = title.decode('utf-8') all_titles.append(title) tokens = my_tokenizer(title) all_tokens.append(tokens) @@ -53,8 +45,8 @@ def my_tokenizer(s): word_index_map[token] = current_index current_index += 1 index_word_map.append(token) - except Exception as e: - print(e) + except: + pass @@ -73,13 +65,13 @@ def tokens_to_vector(tokens): for tokens in all_tokens: X[:,i] = tokens_to_vector(tokens) i += 1 -print("X.shape:", X.shape) +print "X.shape:", X.shape def main(): tsne = TSNE(perplexity=40) Z = tsne.fit_transform(X) plt.scatter(Z[:,0], Z[:,1]) - for i in range(D): + for i in xrange(D): plt.annotate(s=index_word_map[i], xy=(Z[i,0], Z[i,1])) plt.show() diff --git a/unsupervised_class2/tsne_donut.py b/unsupervised_class2/tsne_donut.py index 5e0724f3..6a5da62e 100644 --- a/unsupervised_class2/tsne_donut.py +++ b/unsupervised_class2/tsne_donut.py @@ -1,10 +1,4 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python # https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import matplotlib.pyplot as plt @@ -18,16 +12,16 @@ def get_donut_data(): # distance from origin is radius + random normal # angle theta is uniformly distributed between (0, 2pi) - R1 = np.random.randn(N//2) + R_inner - theta = 2*np.pi*np.random.random(N//2) + R1 = np.random.randn(N/2) + R_inner + theta = 2*np.pi*np.random.random(N/2) X_inner = np.concatenate([[R1 * np.cos(theta)], [R1 * np.sin(theta)]]).T - R2 = np.random.randn(N//2) + R_outer - theta = 2*np.pi*np.random.random(N//2) + R2 = np.random.randn(N/2) + R_outer + theta = 2*np.pi*np.random.random(N/2) X_outer = np.concatenate([[R2 * np.cos(theta)], [R2 * np.sin(theta)]]).T X = np.concatenate([ X_inner, X_outer ]) - Y = np.array([0]*(N//2) + [1]*(N//2)) + Y = np.array([0]*(N/2) + [1]*(N/2)) return X, Y diff --git a/unsupervised_class2/tsne_mnist.py b/unsupervised_class2/tsne_mnist.py index d2ee1239..c4b58348 100644 --- a/unsupervised_class2/tsne_mnist.py +++ b/unsupervised_class2/tsne_mnist.py @@ -1,22 +1,10 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python # https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import matplotlib.pyplot as plt from sklearn.manifold import TSNE from util import getKaggleMNIST -import os -import sys -sys.path.append(os.path.abspath('..')) -from unsupervised_class.kmeans_mnist import purity -from sklearn.mixture import GaussianMixture - def main(): Xtrain, Ytrain, _, _ = getKaggleMNIST() @@ -30,18 +18,5 @@ def main(): plt.scatter(Z[:,0], Z[:,1], s=100, c=Y, alpha=0.5) plt.show() - # purity measure from unsupervised machine learning pt 1 - # maximum purity is 1, higher is better - gmm = GaussianMixture(n_components=10) - gmm.fit(X) - Rfull = gmm.predict_proba(X) - print("Rfull.shape:", Rfull.shape) - print("full purity:", purity(Y, Rfull)) - - # now try the same thing on the reduced data - gmm.fit(Z) - Rreduced = gmm.predict_proba(Z) - print("reduced purity:", purity(Y, Rreduced)) - if __name__ == '__main__': main() \ No newline at end of file diff --git a/unsupervised_class2/tsne_visualization.py b/unsupervised_class2/tsne_visualization.py deleted file mode 100644 index 727e2860..00000000 --- a/unsupervised_class2/tsne_visualization.py +++ /dev/null @@ -1,55 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python -# https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - -from mpl_toolkits.mplot3d import Axes3D -from sklearn.manifold import TSNE - - -if __name__ == '__main__': - # define the centers of each Gaussian cloud - centers = np.array([ - [ 1, 1, 1], - [ 1, 1, -1], - [ 1, -1, 1], - [ 1, -1, -1], - [-1, 1, 1], - [-1, 1, -1], - [-1, -1, 1], - [-1, -1, -1], - ])*3 - - # create the clouds, Gaussian samples centered at - # each of the centers we just made - data = [] - pts_per_cloud = 100 - for c in centers: - cloud = np.random.randn(pts_per_cloud, 3) + c - data.append(cloud) - data = np.concatenate(data) - - # visualize the clouds in 3-D - # add colors / labels so we can track where the points go - colors = np.array([[i]*pts_per_cloud for i in range(len(centers))]).flatten() - fig = plt.figure() - ax = fig.add_subplot(111, projection='3d') - ax.scatter(data[:,0], data[:,1], data[:,2], c=colors) - plt.show() - - - # perform dimensionality reduction - tsne = TSNE() - transformed = tsne.fit_transform(data) - - # visualize the clouds in 2-D - plt.scatter(transformed[:,0], transformed[:,1], c=colors) - plt.show() - - - diff --git a/unsupervised_class2/tsne_xor.py b/unsupervised_class2/tsne_xor.py index 58e59001..8b03c2ca 100644 --- a/unsupervised_class2/tsne_xor.py +++ b/unsupervised_class2/tsne_xor.py @@ -1,10 +1,4 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python # https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import matplotlib.pyplot as plt diff --git a/unsupervised_class2/umap_transformer.py b/unsupervised_class2/umap_transformer.py deleted file mode 100644 index 51e1a86b..00000000 --- a/unsupervised_class2/umap_transformer.py +++ /dev/null @@ -1,40 +0,0 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python -# https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt - -from datetime import datetime -from util import getKaggleMNIST -from sklearn.linear_model import LogisticRegression -from umap import UMAP - -# get the data -Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() - -print("Score without transformation:") -model = LogisticRegression() -model.fit(Xtrain, Ytrain) -print(model.score(Xtrain, Ytrain)) -print(model.score(Xtest, Ytest)) - - -umapper = UMAP(n_neighbors=5, n_components=10) -t0 = datetime.now() -Ztrain = umapper.fit_transform(Xtrain) -print("umap fit_transform took:", datetime.now() - t0) -t0 = datetime.now() -Ztest = umapper.transform(Xtest) -print("umap transform took:", datetime.now() - t0) - -print("Score with transformation") -model = LogisticRegression() -t0 = datetime.now() -model.fit(Ztrain, Ytrain) -print("logistic regression fit took:", datetime.now() - t0) -print(model.score(Ztrain, Ytrain)) -print(model.score(Ztest, Ytest)) \ No newline at end of file diff --git a/unsupervised_class2/unsupervised.py b/unsupervised_class2/unsupervised.py index 9b3485b6..b012b5ce 100644 --- a/unsupervised_class2/unsupervised.py +++ b/unsupervised_class2/unsupervised.py @@ -1,10 +1,4 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python # https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import theano import theano.tensor as T @@ -15,7 +9,7 @@ from sklearn.decomposition import PCA from theano.tensor.shared_randomstreams import RandomStreams from util import relu, error_rate, getKaggleMNIST, init_weights -from autoencoder import AutoEncoder, momentum_updates +from autoencoder import AutoEncoder from rbm import RBM @@ -47,37 +41,28 @@ def forward(self, X): Z = ae.forward_hidden(Z) return Z - def fit_to_input(self, k, learning_rate=1.0, mu=0.99, epochs=100000): + def fit_to_input(self, k, learning_rate=0.00001, mu=0.99, reg=10e-10, epochs=20000): # This is not very flexible, as you would ideally # like to be able to activate any node in any hidden # layer, not just the last layer. # Exercise for students: modify this function to be able # to activate neurons in the middle layers. - - # cast hyperperams - learning_rate = np.float32(learning_rate) - mu = np.float32(mu) - - # randomly initialize an image X0 = init_weights((1, self.D)) - - # make the image a shared so theano can update it X = theano.shared(X0, 'X_shared') - - # get the output of the neural network + dX = theano.shared(np.zeros(X0.shape), 'dX_shared') Y = self.forward(X) - # t = np.zeros(self.hidden_layers[-1].M) # t[k] = 1 # # choose Y[0] b/c it's shape 1xD, we want just a D-size vector, not 1xD matrix - # cost = -(t*T.log(Y[0]) + (1 - t)*(T.log(1 - Y[0]))).sum() + # cost = -(t*T.log(Y[0]) + (1 - t)*(T.log(1 - Y[0]))).sum() + reg*(X * X).sum() - # k = which output node to look at - # there is only 1 image, so we select the 0th row of X - cost = -T.log(Y[0,k]) + cost = -T.log(Y[0,k]) + reg*(X * X).sum() - updates = momentum_updates(cost, [X], mu, learning_rate) + updates = [ + (X, X + mu*dX - learning_rate*T.grad(cost, X)), + (dX, mu*dX - learning_rate*T.grad(cost, X)), + ] train = theano.function( inputs=[], outputs=[cost, Y], @@ -85,17 +70,25 @@ def fit_to_input(self, k, learning_rate=1.0, mu=0.99, epochs=100000): ) costs = [] - for i in range(epochs): - if i % 10000 == 0: - print("epoch:", i) + bestX = None + for i in xrange(epochs): + if i % 1000 == 0: + print "epoch:", i the_cost, out = train() if i == 0: - print("out.shape:", out.shape) + print "out.shape:", out.shape costs.append(the_cost) + # if the_cost < 10: + # break + if the_cost > costs[-1] or np.isnan(the_cost): + break + + bestX = X.get_value() + print "len(costs):", len(costs), "max:", np.max(costs), "min:", np.min(costs) plt.plot(costs) plt.show() - return X.get_value() + return bestX def save(self, filename): arrays = [p.get_value() for layer in self.hidden_layers for p in layer.params] @@ -107,7 +100,7 @@ def load(filename, UnsupervisedModel=AutoEncoder): npz = np.load(filename) dbn.hidden_layers = [] count = 0 - for i in range(0, len(npz.files), 3): + for i in xrange(0, len(npz.files), 3): W = npz['arr_%s' % i] bh = npz['arr_%s' % (i + 1)] bo = npz['arr_%s' % (i + 2)] @@ -126,26 +119,26 @@ def main(): dbn = DBN([1000, 750, 500], UnsupervisedModel=AutoEncoder) # dbn = DBN([1000, 750, 500, 10]) output = dbn.fit(Xtrain, pretrain_epochs=2) - print("output.shape", output.shape) + print "output.shape", output.shape # sample before using t-SNE because it requires lots of RAM sample_size = 600 tsne = TSNE() reduced = tsne.fit_transform(output[:sample_size]) plt.scatter(reduced[:,0], reduced[:,1], s=100, c=Ytrain[:sample_size], alpha=0.5) - plt.title("t-SNE visualization on data transformed by DBN") + plt.title("t-SNE visualization") plt.show() # t-SNE on raw data reduced = tsne.fit_transform(Xtrain[:sample_size]) plt.scatter(reduced[:,0], reduced[:,1], s=100, c=Ytrain[:sample_size], alpha=0.5) - plt.title("t-SNE visualization on raw data") + plt.title("t-SNE visualization") plt.show() pca = PCA() reduced = pca.fit_transform(output) plt.scatter(reduced[:,0], reduced[:,1], s=100, c=Ytrain, alpha=0.5) - plt.title("PCA visualization on data transformed by DBN") + plt.title("PCA visualization") plt.show() if __name__ == '__main__': diff --git a/unsupervised_class2/util.py b/unsupervised_class2/util.py index 24ed683e..59835132 100644 --- a/unsupervised_class2/util.py +++ b/unsupervised_class2/util.py @@ -1,10 +1,4 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python # https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import pandas as pd @@ -23,7 +17,7 @@ def getKaggleMNIST(): # column 0 is labels # column 1-785 is data, with values 0 .. 255 # total size of CSV: (42000, 1, 28, 28) - train = pd.read_csv('../large_files/train.csv').values.astype(np.float32) + train = pd.read_csv('../large_files/train.csv').as_matrix().astype(np.float32) train = shuffle(train) Xtrain = train[:-1000,1:] / 255 @@ -35,5 +29,4 @@ def getKaggleMNIST(): def init_weights(shape): - w = np.random.randn(*shape) / np.sqrt(sum(shape)) - return w.astype(np.float32) \ No newline at end of file + return np.random.randn(*shape) / np.sqrt(sum(shape)) \ No newline at end of file diff --git a/unsupervised_class2/vanishing.py b/unsupervised_class2/vanishing.py index 0f3b9775..5cff7e62 100644 --- a/unsupervised_class2/vanishing.py +++ b/unsupervised_class2/vanishing.py @@ -1,31 +1,22 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python # https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import theano import theano.tensor as T import matplotlib.pyplot as plt from sklearn.utils import shuffle -from autoencoder import T_shared_zeros_like32, momentum_updates from util import relu, error_rate, getKaggleMNIST, init_weights class HiddenLayer(object): def __init__(self, D, M): W = init_weights((D, M)) - b = np.zeros(M, dtype=np.float32) + b = np.zeros(M) self.W = theano.shared(W) self.b = theano.shared(b) self.params = [self.W, self.b] def forward(self, X): - # we want to use the sigmoid so we can observe - # the vanishing gradient! return T.nnet.sigmoid(X.dot(self.W) + self.b) @@ -34,10 +25,6 @@ def __init__(self, hidden_layer_sizes): self.hidden_layer_sizes = hidden_layer_sizes def fit(self, X, Y, learning_rate=0.01, mu=0.99, epochs=30, batch_sz=100): - # cast to float32 - learning_rate = np.float32(learning_rate) - mu = np.float32(mu) - N, D = X.shape K = len(set(Y)) @@ -50,7 +37,7 @@ def fit(self, X, Y, learning_rate=0.01, mu=0.99, epochs=30, batch_sz=100): # initialize logistic regression layer W = init_weights((mo, K)) - b = np.zeros(K, dtype=np.float32) + b = np.zeros(K) self.W = theano.shared(W) self.b = theano.shared(b) @@ -67,28 +54,39 @@ def fit(self, X, Y, learning_rate=0.01, mu=0.99, epochs=30, batch_sz=100): cost = -T.mean( T.log(pY[T.arange(pY.shape[0]), targets]) ) prediction = self.predict(X_in) - - updates = momentum_updates(cost, self.params, mu, learning_rate) + # cost_predict_op = theano.function( + # inputs=[X_in, targets], + # outputs=[cost, prediction], + # ) + + dparams = [theano.shared(p.get_value()*0) for p in self.params] + grads = T.grad(cost, self.params) + + updates = [ + (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads) + ] + [ + (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads) + ] train_op = theano.function( inputs=[X_in, targets], outputs=[cost, prediction], updates=updates, ) - n_batches = N // batch_sz + n_batches = N / batch_sz costs = [] lastWs = [W.get_value() for W in self.allWs] W_changes = [] - print("supervised training...") - for i in range(epochs): - print("epoch:", i) + print "supervised training..." + for i in xrange(epochs): + print "epoch:", i X, Y = shuffle(X, Y) - for j in range(n_batches): + for j in xrange(n_batches): Xbatch = X[j*batch_sz:(j*batch_sz + batch_sz)] Ybatch = Y[j*batch_sz:(j*batch_sz + batch_sz)] c, p = train_op(Xbatch, Ybatch) if j % 100 == 0: - print("j / n_batches:", j, "/", n_batches, "cost:", c, "error:", error_rate(p, Ybatch)) + print "j / n_batches:", j, "/", n_batches, "cost:", c, "error:", error_rate(p, Ybatch) costs.append(c) # log changes in all Ws @@ -98,7 +96,7 @@ def fit(self, X, Y, learning_rate=0.01, mu=0.99, epochs=30, batch_sz=100): W_changes = np.array(W_changes) plt.subplot(2,1,1) - for i in range(W_changes.shape[1]): + for i in xrange(W_changes.shape[1]): plt.plot(W_changes[:,i], label='layer %s' % i) plt.legend() # plt.show() @@ -120,6 +118,9 @@ def forward(self, X): def main(): Xtrain, Ytrain, Xtest, Ytest = getKaggleMNIST() + # dnn = DNN([1000, 750, 500]) + # dnn.fit(Xtrain, Ytrain, Xtest, Ytest, epochs=3) + # vs dnn = ANN([1000, 750, 500]) dnn.fit(Xtrain, Ytrain) diff --git a/unsupervised_class2/visualize_features.py b/unsupervised_class2/visualize_features.py index 18139579..6553b7f0 100644 --- a/unsupervised_class2/visualize_features.py +++ b/unsupervised_class2/visualize_features.py @@ -1,10 +1,4 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python # https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import theano import theano.tensor as T @@ -23,40 +17,40 @@ def main(loadfile=None, savefile=None): dbn = DBN.load(loadfile) else: dbn = DBN([1000, 750, 500, 10]) # AutoEncoder is default - # dbn = DBN([1000, 750, 500, 10], UnsupervisedModel=RBM) - dbn.fit(Xtrain, pretrain_epochs=2) + dbn = DBN([1000, 750, 500, 10], UnsupervisedModel=RBM) + dbn.fit(Xtrain, pretrain_epochs=15) if savefile: dbn.save(savefile) # first layer features # initial weight is D x M - W = dbn.hidden_layers[0].W.eval() - for i in range(dbn.hidden_layers[0].M): - imgplot = plt.imshow(W[:,i].reshape(28, 28), cmap='gray') - plt.show() - should_quit = input("Show more? Enter 'n' to quit\n") - if should_quit == 'n': - break + # W = dbn.hidden_layers[0].W.eval() + # for i in xrange(dbn.hidden_layers[0].M): + # imgplot = plt.imshow(W[:,i].reshape(28, 28), cmap='gray') + # plt.show() + # should_quit = raw_input("Show more? Enter 'n' to quit\n") + # if should_quit == 'n': + # break # features learned in the last layer - for k in range(dbn.hidden_layers[-1].M): + for k in xrange(dbn.hidden_layers[-1].M): # activate the kth node X = dbn.fit_to_input(k) imgplot = plt.imshow(X.reshape(28, 28), cmap='gray') plt.show() if k < dbn.hidden_layers[-1].M - 1: - should_quit = input("Show more? Enter 'n' to quit\n") + should_quit = raw_input("Show more? Enter 'n' to quit\n") if should_quit == 'n': break if __name__ == '__main__': # to load a saved file - # main(loadfile='rbm15.npz') + main(loadfile='rbm15.npz') # to neither load nor save - main() + # main() # to save a trained unsupervised deep network # main(savefile='rbm15.npz') \ No newline at end of file diff --git a/unsupervised_class2/xwing.py b/unsupervised_class2/xwing.py index 146ebea0..70e480e0 100644 --- a/unsupervised_class2/xwing.py +++ b/unsupervised_class2/xwing.py @@ -1,10 +1,4 @@ -# https://deeplearningcourses.com/c/unsupervised-deep-learning-in-python # https://www.udemy.com/unsupervised-deep-learning-in-python -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - import numpy as np import theano import theano.tensor as T @@ -12,20 +6,13 @@ from sklearn.utils import shuffle from util import relu, error_rate, getKaggleMNIST, init_weights -from autoencoder import momentum_updates -# new additions used to compare purity measure using GMM -import os -import sys -sys.path.append(os.path.abspath('..')) -from unsupervised_class.kmeans_mnist import purity -from sklearn.mixture import GaussianMixture class Layer(object): def __init__(self, m1, m2): W = init_weights((m1, m2)) - bi = np.zeros(m2, dtype=np.float32) - bo = np.zeros(m1, dtype=np.float32) + bi = np.zeros(m2) + bo = np.zeros(m1) self.W = theano.shared(W) self.bi = theano.shared(bi) self.bo = theano.shared(bo) @@ -43,12 +30,8 @@ def __init__(self, hidden_layer_sizes): self.hidden_layer_sizes = hidden_layer_sizes def fit(self, X, learning_rate=0.5, mu=0.99, epochs=50, batch_sz=100, show_fig=False): - # cast hyperparams - learning_rate = np.float32(learning_rate) - mu = np.float32(mu) - N, D = X.shape - n_batches = N // batch_sz + n_batches = N / batch_sz mi = D self.layers = [] @@ -68,7 +51,14 @@ def fit(self, X, learning_rate=0.5, mu=0.99, epochs=50, batch_sz=100, show_fig=F outputs=cost, ) - updates = momentum_updates(cost, self.params, mu, learning_rate) + dparams = [theano.shared(p.get_value()*0) for p in self.params] + grads = T.grad(cost, self.params) + + updates = [ + (p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads) + ] + [ + (dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads) + ] train_op = theano.function( inputs=[X_in], outputs=cost, @@ -76,14 +66,14 @@ def fit(self, X, learning_rate=0.5, mu=0.99, epochs=50, batch_sz=100, show_fig=F ) costs = [] - for i in range(epochs): - print("epoch:", i) + for i in xrange(epochs): + print "epoch:", i X = shuffle(X) - for j in range(n_batches): + for j in xrange(n_batches): batch = X[j*batch_sz:(j*batch_sz + batch_sz)] c = train_op(batch) if j % 100 == 0: - print("j / n_batches:", j, "/", n_batches, "cost:", c) + print "j / n_batches:", j, "/", n_batches, "cost:", c costs.append(c) if show_fig: plt.plot(costs) @@ -99,7 +89,7 @@ def forward(self, X): outputs=Z, ) - for i in range(len(self.layers)-1, -1, -1): + for i in xrange(len(self.layers)-1, -1, -1): Z = self.layers[i].forwardT(Z) return Z @@ -113,18 +103,6 @@ def main(): plt.scatter(mapping[:,0], mapping[:,1], c=Ytrain, s=100, alpha=0.5) plt.show() - # purity measure from unsupervised machine learning pt 1 - # NOTE: this will take a long time (i.e. just leave it overnight) - gmm = GaussianMixture(n_components=10) - gmm.fit(Xtrain) - print("Finished GMM training") - responsibilities_full = gmm.predict_proba(Xtrain) - print("full purity:", purity(Ytrain, responsibilities_full)) - - gmm.fit(mapping) - responsibilities_reduced = gmm.predict_proba(mapping) - print("reduced purity:", purity(Ytrain, responsibilities_reduced)) - if __name__ == '__main__': main() diff --git a/unsupervised_class3/autoencoder_tf.py b/unsupervised_class3/autoencoder_tf.py deleted file mode 100644 index 6ed108e9..00000000 --- a/unsupervised_class3/autoencoder_tf.py +++ /dev/null @@ -1,95 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders -# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import util -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt - - -class Autoencoder: - def __init__(self, D, M): - # represents a batch of training data - self.X = tf.placeholder(tf.float32, shape=(None, D)) - - # input -> hidden - self.W = tf.Variable(tf.random_normal(shape=(D, M)) * np.sqrt(2.0 / M)) - self.b = tf.Variable(np.zeros(M).astype(np.float32)) - - # hidden -> output - self.V = tf.Variable(tf.random_normal(shape=(M, D)) * np.sqrt(2.0 / D)) - self.c = tf.Variable(np.zeros(D).astype(np.float32)) - - # construct the reconstruction - self.Z = tf.nn.relu(tf.matmul(self.X, self.W) + self.b) - logits = tf.matmul(self.Z, self.V) + self.c - self.X_hat = tf.nn.sigmoid(logits) - - # compute the cost - self.cost = tf.reduce_sum( - tf.nn.sigmoid_cross_entropy_with_logits( - labels=self.X, - logits=logits - ) - ) - - # make the trainer - self.train_op = tf.train.RMSPropOptimizer(learning_rate=0.001).minimize(self.cost) - - # set up session and variables for later - self.init_op = tf.global_variables_initializer() - self.sess = tf.InteractiveSession() - self.sess.run(self.init_op) - - def fit(self, X, epochs=30, batch_sz=64): - costs = [] - n_batches = len(X) // batch_sz - print("n_batches:", n_batches) - for i in range(epochs): - print("epoch:", i) - np.random.shuffle(X) - for j in range(n_batches): - batch = X[j*batch_sz:(j+1)*batch_sz] - _, c, = self.sess.run((self.train_op, self.cost), feed_dict={self.X: batch}) - c /= batch_sz # just debugging - costs.append(c) - if j % 100 == 0: - print("iter: %d, cost: %.3f" % (j, c)) - plt.plot(costs) - plt.show() - - def predict(self, X): - return self.sess.run(self.X_hat, feed_dict={self.X: X}) - - -def main(): - X, Y = util.get_mnist() - - model = Autoencoder(784, 300) - model.fit(X) - - # plot reconstruction - done = False - while not done: - i = np.random.choice(len(X)) - x = X[i] - im = model.predict([x]).reshape(28, 28) - plt.subplot(1,2,1) - plt.imshow(x.reshape(28, 28), cmap='gray') - plt.title("Original") - plt.subplot(1,2,2) - plt.imshow(im, cmap='gray') - plt.title("Reconstruction") - plt.show() - - ans = input("Generate another?") - if ans and ans[0] in ('n' or 'N'): - done = True - -if __name__ == '__main__': - main() - diff --git a/unsupervised_class3/autoencoder_theano.py b/unsupervised_class3/autoencoder_theano.py deleted file mode 100644 index c4f38bad..00000000 --- a/unsupervised_class3/autoencoder_theano.py +++ /dev/null @@ -1,115 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders -# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import util -import numpy as np -import theano -import theano.tensor as T -import matplotlib.pyplot as plt - - -class Autoencoder: - def __init__(self, D, M): - # represents a batch of training data - self.X = T.matrix('X') - - # input -> hidden - self.W = theano.shared(np.random.randn(D, M) * np.sqrt(2.0 / M)) - self.b = theano.shared(np.zeros(M)) - - # hidden -> output - self.V = theano.shared(np.random.randn(M, D) * np.sqrt(2.0 / D)) - self.c = theano.shared(np.zeros(D)) - - # construct the reconstruction - self.Z = T.nnet.relu(self.X.dot(self.W) + self.b) - self.X_hat = T.nnet.sigmoid(self.Z.dot(self.V) + self.c) - - # compute the cost - self.cost = T.sum( - T.nnet.binary_crossentropy( - output=self.X_hat, - target=self.X, - ) - ) - - # define the updates - params = [self.W, self.b, self.V, self.c] - grads = T.grad(self.cost, params) - - # rmsprop - decay = 0.9 - learning_rate = 0.001 - - # for rmsprop - cache = [theano.shared(np.ones_like(p.get_value())) for p in params] - new_cache = [decay*c + (1-decay)*g*g for p, c, g in zip(params, cache, grads)] - - updates = [ - (c, new_c) for c, new_c in zip(cache, new_cache) - ] + [ - (p, p - learning_rate*g/T.sqrt(new_c + 1e-10)) for p, new_c, g in zip(params, new_cache, grads) - ] - - - # now define callable functions - self.train_op = theano.function( - inputs=[self.X], - outputs=self.cost, - updates=updates - ) - - self.predict = theano.function( - inputs=[self.X], - outputs=self.X_hat - ) - - def fit(self, X, epochs=30, batch_sz=64): - costs = [] - n_batches = len(X) // batch_sz - print("n_batches:", n_batches) - for i in range(epochs): - print("epoch:", i) - np.random.shuffle(X) - for j in range(n_batches): - batch = X[j*batch_sz:(j+1)*batch_sz] - c = self.train_op(batch) - c /= batch_sz # just debugging - costs.append(c) - if j % 100 == 0: - print("iter: %d, cost: %.3f" % (j, c)) - plt.plot(costs) - plt.show() - - -def main(): - X, Y = util.get_mnist() - - model = Autoencoder(784, 300) - model.fit(X) - - # plot reconstruction - done = False - while not done: - i = np.random.choice(len(X)) - x = X[i] - im = model.predict([x]).reshape(28, 28) - plt.subplot(1,2,1) - plt.imshow(x.reshape(28, 28), cmap='gray') - plt.title("Original") - plt.subplot(1,2,2) - plt.imshow(im, cmap='gray') - plt.title("Reconstruction") - plt.show() - - ans = input("Generate another?") - if ans and ans[0] in ('n' or 'N'): - done = True - -if __name__ == '__main__': - main() - diff --git a/unsupervised_class3/bayes_classifier_gaussian.py b/unsupervised_class3/bayes_classifier_gaussian.py deleted file mode 100644 index d9098ef3..00000000 --- a/unsupervised_class3/bayes_classifier_gaussian.py +++ /dev/null @@ -1,70 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders -# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import util -import numpy as np -import matplotlib.pyplot as plt -from scipy.stats import multivariate_normal as mvn - - -def clamp_sample(x): - x = np.minimum(x, 1) - x = np.maximum(x, 0) - return x - - -class BayesClassifier: - def fit(self, X, Y): - # assume classes are numbered 0...K-1 - self.K = len(set(Y)) - - self.gaussians = [] - self.p_y = np.zeros(self.K) - for k in range(self.K): - Xk = X[Y == k] - self.p_y[k] = len(Xk) - mean = Xk.mean(axis=0) - cov = np.cov(Xk.T) - g = {'m': mean, 'c': cov} - self.gaussians.append(g) - # normalize p(y) - self.p_y /= self.p_y.sum() - - def sample_given_y(self, y): - g = self.gaussians[y] - return clamp_sample( mvn.rvs(mean=g['m'], cov=g['c']) ) - - def sample(self): - y = np.random.choice(self.K, p=self.p_y) - return clamp_sample( self.sample_given_y(y) ) - - -if __name__ == '__main__': - X, Y = util.get_mnist() - clf = BayesClassifier() - clf.fit(X, Y) - - for k in range(clf.K): - # show one sample for each class - # also show the mean image learned - - sample = clf.sample_given_y(k).reshape(28, 28) - mean = clf.gaussians[k]['m'].reshape(28, 28) - - plt.subplot(1,2,1) - plt.imshow(sample, cmap='gray') - plt.title("Sample") - plt.subplot(1,2,2) - plt.imshow(mean, cmap='gray') - plt.title("Mean") - plt.show() - - # generate a random sample - sample = clf.sample().reshape(28, 28) - plt.imshow(sample, cmap='gray') - plt.title("Random Sample from Random Class") - plt.show() diff --git a/unsupervised_class3/bayes_classifier_gmm.py b/unsupervised_class3/bayes_classifier_gmm.py deleted file mode 100644 index f3c7dd0f..00000000 --- a/unsupervised_class3/bayes_classifier_gmm.py +++ /dev/null @@ -1,82 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders -# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import util -import numpy as np -import matplotlib.pyplot as plt -from sklearn.mixture import BayesianGaussianMixture - - -def clamp_sample(x): - x = np.minimum(x, 1) - x = np.maximum(x, 0) - return x - - -class BayesClassifier: - def fit(self, X, Y): - # assume classes are numbered 0...K-1 - self.K = len(set(Y)) - - self.gaussians = [] - self.p_y = np.zeros(self.K) - for k in range(self.K): - print("Fitting gmm", k) - Xk = X[Y == k] - self.p_y[k] = len(Xk) - gmm = BayesianGaussianMixture(n_components=10) - gmm.fit(Xk) - self.gaussians.append(gmm) - # normalize p(y) - self.p_y /= self.p_y.sum() - - def sample_given_y(self, y): - gmm = self.gaussians[y] - sample = gmm.sample() - # note: sample returns a tuple containing 2 things: - # 1) the sample - # 2) which cluster it came from - # we'll use (2) to obtain the means so we can plot - # them like we did in the previous script - # we cheat by looking at "non-public" params in - # the sklearn source code - mean = gmm.means_[sample[1]] - return clamp_sample( sample[0].reshape(28, 28) ), mean.reshape(28, 28) - - def sample(self): - y = np.random.choice(self.K, p=self.p_y) - return clamp_sample( self.sample_given_y(y) ) - - -if __name__ == '__main__': - X, Y = util.get_mnist() - clf = BayesClassifier() - clf.fit(X, Y) - - for k in range(clf.K): - # show one sample for each class - # also show the mean image learned - - sample, mean = clf.sample_given_y(k) - - plt.subplot(1,2,1) - plt.imshow(sample, cmap='gray') - plt.title("Sample") - plt.subplot(1,2,2) - plt.imshow(mean, cmap='gray') - plt.title("Mean") - plt.show() - - # generate a random sample - sample, mean = clf.sample() - plt.subplot(1,2,1) - plt.imshow(sample, cmap='gray') - plt.title("Random Sample from Random Class") - plt.subplot(1,2,2) - plt.imshow(mean, cmap='gray') - plt.title("Corresponding Cluster Mean") - plt.show() diff --git a/unsupervised_class3/dcgan_tf.py b/unsupervised_class3/dcgan_tf.py deleted file mode 100644 index b325aa94..00000000 --- a/unsupervised_class3/dcgan_tf.py +++ /dev/null @@ -1,576 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders -# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import os -import util -import scipy as sp -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt -from datetime import datetime - - -# some constants -LEARNING_RATE = 0.0002 -BETA1 = 0.5 -BATCH_SIZE = 64 -EPOCHS = 2 -SAVE_SAMPLE_PERIOD = 50 - - -# make dir to save samples -if not os.path.exists('samples'): - os.mkdir('samples') - - -def lrelu(x, alpha=0.2): - return tf.maximum(alpha*x, x) - - -class ConvLayer: - def __init__(self, name, mi, mo, apply_batch_norm, filtersz=5, stride=2, f=tf.nn.relu): - # mi = input feature map size - # mo = output feature map size - # self.W = tf.Variable(0.02*tf.random_normal(shape=(filtersz, filtersz, mi, mo))) - # self.b = tf.Variable(np.zeros(mo, dtype=np.float32)) - self.W = tf.get_variable( - "W_%s" % name, - shape=(filtersz, filtersz, mi, mo), - # initializer=tf.contrib.layers.xavier_initializer(), - initializer=tf.truncated_normal_initializer(stddev=0.02), - ) - self.b = tf.get_variable( - "b_%s" % name, - shape=(mo,), - initializer=tf.zeros_initializer(), - ) - self.name = name - self.f = f - self.stride = stride - self.apply_batch_norm = apply_batch_norm - self.params = [self.W, self.b] - - def forward(self, X, reuse, is_training): - # print("**************** reuse:", reuse) - conv_out = tf.nn.conv2d( - X, - self.W, - strides=[1, self.stride, self.stride, 1], - padding='SAME' - ) - conv_out = tf.nn.bias_add(conv_out, self.b) - - # apply batch normalization - if self.apply_batch_norm: - conv_out = tf.contrib.layers.batch_norm( - conv_out, - decay=0.9, - updates_collections=None, - epsilon=1e-5, - scale=True, - is_training=is_training, - reuse=reuse, - scope=self.name, - ) - return self.f(conv_out) - - -class FractionallyStridedConvLayer: - def __init__(self, name, mi, mo, output_shape, apply_batch_norm, filtersz=5, stride=2, f=tf.nn.relu): - # mi = input feature map size - # mo = output feature map size - # NOTE!!! shape is specified in the OPPOSITE way from regular conv - # self.W = tf.Variable(0.02*tf.random_normal(shape=(filtersz, filtersz, mo, mi))) - # self.b = tf.Variable(np.zeros(mo, dtype=np.float32)) - self.W = tf.get_variable( - "W_%s" % name, - shape=(filtersz, filtersz, mo, mi), - # initializer=tf.contrib.layers.xavier_initializer(), - initializer=tf.random_normal_initializer(stddev=0.02), - ) - self.b = tf.get_variable( - "b_%s" % name, - shape=(mo,), - initializer=tf.zeros_initializer(), - ) - self.f = f - self.stride = stride - self.name = name - self.output_shape = output_shape - self.apply_batch_norm = apply_batch_norm - self.params = [self.W, self.b] - - def forward(self, X, reuse, is_training): - conv_out = tf.nn.conv2d_transpose( - value=X, - filter=self.W, - output_shape=self.output_shape, - strides=[1, self.stride, self.stride, 1], - ) - conv_out = tf.nn.bias_add(conv_out, self.b) - - # apply batch normalization - if self.apply_batch_norm: - conv_out = tf.contrib.layers.batch_norm( - conv_out, - decay=0.9, - updates_collections=None, - epsilon=1e-5, - scale=True, - is_training=is_training, - reuse=reuse, - scope=self.name, - ) - - return self.f(conv_out) - - -class DenseLayer(object): - def __init__(self, name, M1, M2, apply_batch_norm, f=tf.nn.relu): - self.W = tf.get_variable( - "W_%s" % name, - shape=(M1, M2), - initializer=tf.random_normal_initializer(stddev=0.02), - ) - self.b = tf.get_variable( - "b_%s" % name, - shape=(M2,), - initializer=tf.zeros_initializer(), - ) - self.f = f - self.name = name - self.apply_batch_norm = apply_batch_norm - self.params = [self.W, self.b] - - def forward(self, X, reuse, is_training): - a = tf.matmul(X, self.W) + self.b - - # apply batch normalization - if self.apply_batch_norm: - a = tf.contrib.layers.batch_norm( - a, - decay=0.9, - updates_collections=None, - epsilon=1e-5, - scale=True, - is_training=is_training, - reuse=reuse, - scope=self.name, - ) - return self.f(a) - - -class DCGAN: - def __init__(self, img_length, num_colors, d_sizes, g_sizes): - - # save for later - self.img_length = img_length - self.num_colors = num_colors - self.latent_dims = g_sizes['z'] - - # define the input data - self.X = tf.placeholder( - tf.float32, - shape=(None, img_length, img_length, num_colors), - name='X' - ) - self.Z = tf.placeholder( - tf.float32, - shape=(None, self.latent_dims), - name='Z' - ) - - # note: by making batch_sz a placeholder, we can specify a variable - # number of samples in the FS-conv operation where we are required - # to pass in output_shape - # we need only pass in the batch size via feed_dict - self.batch_sz = tf.placeholder(tf.int32, shape=(), name='batch_sz') - - # build the discriminator - logits = self.build_discriminator(self.X, d_sizes) - - # build generator - self.sample_images = self.build_generator(self.Z, g_sizes) - - # get sample logits - with tf.variable_scope("discriminator") as scope: - scope.reuse_variables() - sample_logits = self.d_forward(self.sample_images, True) - - # get sample images for test time (batch norm is different) - with tf.variable_scope("generator") as scope: - scope.reuse_variables() - self.sample_images_test = self.g_forward( - self.Z, reuse=True, is_training=False - ) - - # build costs - self.d_cost_real = tf.nn.sigmoid_cross_entropy_with_logits( - logits=logits, - labels=tf.ones_like(logits) - ) - self.d_cost_fake = tf.nn.sigmoid_cross_entropy_with_logits( - logits=sample_logits, - labels=tf.zeros_like(sample_logits) - ) - self.d_cost = tf.reduce_mean(self.d_cost_real) + tf.reduce_mean(self.d_cost_fake) - self.g_cost = tf.reduce_mean( - tf.nn.sigmoid_cross_entropy_with_logits( - logits=sample_logits, - labels=tf.ones_like(sample_logits) - ) - ) - real_predictions = tf.cast(logits > 0, tf.float32) - fake_predictions = tf.cast(sample_logits < 0, tf.float32) - num_predictions = 2.0*BATCH_SIZE - num_correct = tf.reduce_sum(real_predictions) + tf.reduce_sum(fake_predictions) - self.d_accuracy = num_correct / num_predictions - - - # optimizers - self.d_params = [t for t in tf.trainable_variables() if t.name.startswith('d')] - self.g_params = [t for t in tf.trainable_variables() if t.name.startswith('g')] - - self.d_train_op = tf.train.AdamOptimizer( - LEARNING_RATE, beta1=BETA1 - ).minimize( - self.d_cost, var_list=self.d_params - ) - self.g_train_op = tf.train.AdamOptimizer( - LEARNING_RATE, beta1=BETA1 - ).minimize( - self.g_cost, var_list=self.g_params - ) - - # show_all_variables() - # exit() - - # set up session and variables for later - self.init_op = tf.global_variables_initializer() - self.sess = tf.InteractiveSession() - self.sess.run(self.init_op) - - - def build_discriminator(self, X, d_sizes): - with tf.variable_scope("discriminator") as scope: - - # build conv layers - self.d_convlayers = [] - mi = self.num_colors - dim = self.img_length - count = 0 - for mo, filtersz, stride, apply_batch_norm in d_sizes['conv_layers']: - # make up a name - used for get_variable - name = "convlayer_%s" % count - count += 1 - - layer = ConvLayer(name, mi, mo, apply_batch_norm, filtersz, stride, lrelu) - self.d_convlayers.append(layer) - mi = mo - print("dim:", dim) - dim = int(np.ceil(float(dim) / stride)) - - - mi = mi * dim * dim - # build dense layers - self.d_denselayers = [] - for mo, apply_batch_norm in d_sizes['dense_layers']: - name = "denselayer_%s" % count - count += 1 - - layer = DenseLayer(name, mi, mo, apply_batch_norm, lrelu) - mi = mo - self.d_denselayers.append(layer) - - - # final logistic layer - name = "denselayer_%s" % count - self.d_finallayer = DenseLayer(name, mi, 1, False, lambda x: x) - - # get the logits - logits = self.d_forward(X) - - # build the cost later - return logits - - - def d_forward(self, X, reuse=None, is_training=True): - # encapsulate this because we use it twice - output = X - for layer in self.d_convlayers: - output = layer.forward(output, reuse, is_training) - output = tf.contrib.layers.flatten(output) - for layer in self.d_denselayers: - output = layer.forward(output, reuse, is_training) - logits = self.d_finallayer.forward(output, reuse, is_training) - return logits - - - def build_generator(self, Z, g_sizes): - with tf.variable_scope("generator") as scope: - - # determine the size of the data at each step - dims = [self.img_length] - dim = self.img_length - for _, _, stride, _ in reversed(g_sizes['conv_layers']): - dim = int(np.ceil(float(dim) / stride)) - dims.append(dim) - - # note: dims is actually backwards - # the first layer of the generator is actually last - # so let's reverse it - dims = list(reversed(dims)) - print("dims:", dims) - self.g_dims = dims - - - # dense layers - mi = self.latent_dims - self.g_denselayers = [] - count = 0 - for mo, apply_batch_norm in g_sizes['dense_layers']: - name = "g_denselayer_%s" % count - count += 1 - - layer = DenseLayer(name, mi, mo, apply_batch_norm) - self.g_denselayers.append(layer) - mi = mo - - # final dense layer - mo = g_sizes['projection'] * dims[0] * dims[0] - name = "g_denselayer_%s" % count - layer = DenseLayer(name, mi, mo, not g_sizes['bn_after_project']) - self.g_denselayers.append(layer) - - - # fs-conv layers - mi = g_sizes['projection'] - self.g_convlayers = [] - - # output may use tanh or sigmoid - num_relus = len(g_sizes['conv_layers']) - 1 - activation_functions = [tf.nn.relu]*num_relus + [g_sizes['output_activation']] - - for i in range(len(g_sizes['conv_layers'])): - name = "fs_convlayer_%s" % i - mo, filtersz, stride, apply_batch_norm = g_sizes['conv_layers'][i] - f = activation_functions[i] - output_shape = [self.batch_sz, dims[i+1], dims[i+1], mo] - print("mi:", mi, "mo:", mo, "outp shape:", output_shape) - layer = FractionallyStridedConvLayer( - name, mi, mo, output_shape, apply_batch_norm, filtersz, stride, f - ) - self.g_convlayers.append(layer) - mi = mo - - # get the output - self.g_sizes = g_sizes - return self.g_forward(Z) - - - def g_forward(self, Z, reuse=None, is_training=True): - # dense layers - output = Z - for layer in self.g_denselayers: - output = layer.forward(output, reuse, is_training) - - # project and reshape - output = tf.reshape( - output, - [-1, self.g_dims[0], self.g_dims[0], self.g_sizes['projection']], - ) - - # apply batch norm - if self.g_sizes['bn_after_project']: - output = tf.contrib.layers.batch_norm( - output, - decay=0.9, - updates_collections=None, - epsilon=1e-5, - scale=True, - is_training=is_training, - reuse=reuse, - scope='bn_after_project' - ) - - # pass through fs-conv layers - for layer in self.g_convlayers: - output = layer.forward(output, reuse, is_training) - - return output - - - def fit(self, X): - d_costs = [] - g_costs = [] - - N = len(X) - n_batches = N // BATCH_SIZE - total_iters = 0 - for i in range(EPOCHS): - print("epoch:", i) - np.random.shuffle(X) - for j in range(n_batches): - t0 = datetime.now() - - if type(X[0]) is str: - # is celeb dataset - batch = util.files2images( - X[j*BATCH_SIZE:(j+1)*BATCH_SIZE] - ) - - else: - # is mnist dataset - batch = X[j*BATCH_SIZE:(j+1)*BATCH_SIZE] - - Z = np.random.uniform(-1, 1, size=(BATCH_SIZE, self.latent_dims)) - - # train the discriminator - _, d_cost, d_acc = self.sess.run( - (self.d_train_op, self.d_cost, self.d_accuracy), - feed_dict={self.X: batch, self.Z: Z, self.batch_sz: BATCH_SIZE}, - ) - d_costs.append(d_cost) - - # train the generator - _, g_cost1 = self.sess.run( - (self.g_train_op, self.g_cost), - feed_dict={self.Z: Z, self.batch_sz: BATCH_SIZE}, - ) - # g_costs.append(g_cost1) - _, g_cost2 = self.sess.run( - (self.g_train_op, self.g_cost), - feed_dict={self.Z: Z, self.batch_sz: BATCH_SIZE}, - ) - g_costs.append((g_cost1 + g_cost2)/2) # just use the avg - - print(" batch: %d/%d - dt: %s - d_acc: %.2f" % (j+1, n_batches, datetime.now() - t0, d_acc)) - - - # save samples periodically - total_iters += 1 - if total_iters % SAVE_SAMPLE_PERIOD == 0: - print("saving a sample...") - samples = self.sample(64) # shape is (64, D, D, color) - - # for convenience - d = self.img_length - - if samples.shape[-1] == 1: - # if color == 1, we want a 2-D image (N x N) - samples = samples.reshape(64, d, d) - flat_image = np.empty((8*d, 8*d)) - - k = 0 - for i in range(8): - for j in range(8): - flat_image[i*d:(i+1)*d, j*d:(j+1)*d] = samples[k].reshape(d, d) - k += 1 - - # plt.imshow(flat_image, cmap='gray') - else: - # if color == 3, we want a 3-D image (N x N x 3) - flat_image = np.empty((8*d, 8*d, 3)) - k = 0 - for i in range(8): - for j in range(8): - flat_image[i*d:(i+1)*d, j*d:(j+1)*d] = samples[k] - k += 1 - # plt.imshow(flat_image) - - # plt.savefig('samples/samples_at_iter_%d.png' % total_iters) - sp.misc.imsave( - 'samples/samples_at_iter_%d.png' % total_iters, - flat_image, - ) - - # save a plot of the costs - plt.clf() - plt.plot(d_costs, label='discriminator cost') - plt.plot(g_costs, label='generator cost') - plt.legend() - plt.savefig('cost_vs_iteration.png') - - def sample(self, n): - Z = np.random.uniform(-1, 1, size=(n, self.latent_dims)) - samples = self.sess.run(self.sample_images_test, feed_dict={self.Z: Z, self.batch_sz: n}) - return samples - - - -def celeb(): - X = util.get_celeb() - # just loads a list of filenames, we will load them in dynamically - # because there are many - dim = 64 - colors = 3 - - # for celeb - d_sizes = { - 'conv_layers': [ - (64, 5, 2, False), - (128, 5, 2, True), - (256, 5, 2, True), - (512, 5, 2, True) - ], - 'dense_layers': [], - } - g_sizes = { - 'z': 100, - 'projection': 512, - 'bn_after_project': True, - 'conv_layers': [ - (256, 5, 2, True), - (128, 5, 2, True), - (64, 5, 2, True), - (colors, 5, 2, False) - ], - 'dense_layers': [], - 'output_activation': tf.tanh, - } - - # setup gan - # note: assume square images, so only need 1 dim - gan = DCGAN(dim, colors, d_sizes, g_sizes) - gan.fit(X) - - -def mnist(): - X, Y = util.get_mnist() - X = X.reshape(len(X), 28, 28, 1) - dim = X.shape[1] - colors = X.shape[-1] - - # for mnist - d_sizes = { - 'conv_layers': [(2, 5, 2, False), (64, 5, 2, True)], - 'dense_layers': [(1024, True)], - } - g_sizes = { - 'z': 100, - 'projection': 128, - 'bn_after_project': False, - 'conv_layers': [(128, 5, 2, True), (colors, 5, 2, False)], - 'dense_layers': [(1024, True)], - 'output_activation': tf.sigmoid, - } - - - # setup gan - # note: assume square images, so only need 1 dim - gan = DCGAN(dim, colors, d_sizes, g_sizes) - gan.fit(X) - # samples = gan.sample(1) # just making sure it works - - # since training will take a considerable - # amount of time, let's just save some - # samples to disk rather than plotting now - - -if __name__ == '__main__': - # celeb() - mnist() diff --git a/unsupervised_class3/dcgan_theano.py b/unsupervised_class3/dcgan_theano.py deleted file mode 100644 index b5ae5461..00000000 --- a/unsupervised_class3/dcgan_theano.py +++ /dev/null @@ -1,663 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders -# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import os -import util -import scipy as sp -import numpy as np -import theano -import theano.tensor as T -import matplotlib.pyplot as plt - -from datetime import datetime -from theano.tensor.nnet.bn import batch_normalization_train, batch_normalization_test -from theano.tensor.nnet import conv2d - - -# some constants -LEARNING_RATE = 0.0002 -BETA1 = 0.5 -BETA2 = 0.999 -EPSILON = 1e-8 -BATCH_SIZE = 64 -EPOCHS = 2 -BATCH_SIZE = 64 -SAVE_SAMPLE_PERIOD = 50 - - -# make dir to save samples -if not os.path.exists('samples'): - os.mkdir('samples') - - -# make it callable with only 1 input -def lrelu(x, alpha=0.2): - return T.nnet.relu(x, alpha) - - -# helper for adam optimizer -def adam(params, grads): - updates = [] - time = theano.shared(0) - new_time = time + 1 - updates.append((time, new_time)) - lr = LEARNING_RATE*T.sqrt(1 - BETA2**new_time) / (1 - BETA1**new_time) - for p, g in zip(params, grads): - m = theano.shared(p.get_value() * 0.) - v = theano.shared(p.get_value() * 0.) - new_m = BETA1*m + (1 - BETA1)*g - new_v = BETA2*v + (1 - BETA2)*g*g - new_p = p - lr*new_m / (T.sqrt(new_v) + EPSILON) - updates.append((m, new_m)) - updates.append((v, new_v)) - updates.append((p, new_p)) - return updates - - -# helper for batch norm -def batch_norm( - input_, - gamma, - beta, - running_mean, - running_var, - is_training, - axes='per-activation'): - - if is_training: - # returns: - # batch-normalized output - # batch mean - # batch variance - # running mean (for later use as population mean estimate) - # running var (for later use as population var estimate) - out, _, _, new_running_mean, new_running_var = batch_normalization_train( - input_, - gamma, - beta, - running_mean=running_mean, - running_var=running_var, - axes=axes, - running_average_factor=0.9, - ) - else: - new_running_mean = None - new_running_var = None # just to ensure we don't try to use them - out = batch_normalization_test( - input_, - gamma, - beta, - running_mean, - running_var, - axes=axes, - ) - return out, new_running_mean, new_running_var - - -class ConvLayer: - def __init__(self, mi, mo, apply_batch_norm, filtersz=5, stride=2, f=T.nnet.relu): - # mi = input feature map size - # mo = output feature map size - W = 0.02*np.random.randn(mo, mi, filtersz, filtersz) - self.W = theano.shared(W) - self.b = theano.shared(np.zeros(mo)) - self.params = [self.W, self.b] - self.updates = [] # in case we do batch norm - - if apply_batch_norm: - self.gamma = theano.shared(np.ones(mo)) - self.beta = theano.shared(np.zeros(mo)) - self.params += [self.gamma, self.beta] - - self.running_mean = theano.shared(np.zeros(mo)) - self.running_var = theano.shared(np.zeros(mo)) - - self.f = f - self.stride = stride - self.apply_batch_norm = apply_batch_norm - - - def forward(self, X, is_training): - conv_out = conv2d( - input=X, - filters=self.W, - subsample=(self.stride, self.stride), - border_mode='half', - ) - conv_out += self.b.dimshuffle('x', 0, 'x', 'x') - - # apply batch normalization - if self.apply_batch_norm: - conv_out, new_running_mean, new_running_var = batch_norm( - conv_out, - self.gamma, - self.beta, - self.running_mean, - self.running_var, - is_training, - 'spatial' - ) - if is_training: - self.updates = [ - (self.running_mean, new_running_mean), - (self.running_var, new_running_var), - ] - return self.f(conv_out) - - -# regular convolution expects output size to be: -# new_dim = floor( (old_dim - filter_sz) / stride ) + 1 - -# therefore, for fs-conv, output size should be: -# new_dim = stride * (old_dim - 1) + filter_sz - - -class FractionallyStridedConvLayer: - def __init__(self, mi, mo, output_shape, apply_batch_norm, filtersz=5, stride=2, f=T.nnet.relu): - # mi = input feature map size - # mo = output feature map size - self.filter_shape = (mi, mo, filtersz, filtersz) - W = 0.02*np.random.randn(*self.filter_shape) - self.W = theano.shared(W) - self.b = theano.shared(np.zeros(mo)) - self.params = [self.W, self.b] - self.updates = [] # in case we do batch norm - - if apply_batch_norm: - self.gamma = theano.shared(np.ones(mo)) - self.beta = theano.shared(np.zeros(mo)) - self.params += [self.gamma, self.beta] - - self.running_mean = theano.shared(np.zeros(mo)) - self.running_var = theano.shared(np.zeros(mo)) - - self.f = f - self.stride = stride - self.output_shape = output_shape - self.apply_batch_norm = apply_batch_norm - self.params = [self.W, self.b] - - def forward(self, X, is_training): - conv_out = T.nnet.abstract_conv.conv2d_grad_wrt_inputs( - X, self.W, - input_shape=self.output_shape, - filter_shape=self.filter_shape, - border_mode='half', - subsample=(self.stride, self.stride) - ) - conv_out += self.b.dimshuffle('x', 0, 'x', 'x') - - # apply batch normalization - if self.apply_batch_norm: - conv_out, new_running_mean, new_running_var = batch_norm( - conv_out, - self.gamma, - self.beta, - self.running_mean, - self.running_var, - is_training, - 'spatial' - ) - if is_training: - self.updates = [ - (self.running_mean, new_running_mean), - (self.running_var, new_running_var), - ] - - return self.f(conv_out) - - -class DenseLayer(object): - def __init__(self, M1, M2, apply_batch_norm, f=T.nnet.relu): - W = 0.02*np.random.randn(M1, M2) - self.W = theano.shared(W) - self.b = theano.shared(np.zeros(M2)) - self.params = [self.W, self.b] - self.updates = [] # in case we do batch norm - - if apply_batch_norm: - self.gamma = theano.shared(np.ones(M2)) - self.beta = theano.shared(np.zeros(M2)) - self.params += [self.gamma, self.beta] - - self.running_mean = theano.shared(np.zeros(M2)) - self.running_var = theano.shared(np.zeros(M2)) - - self.f = f - self.apply_batch_norm = apply_batch_norm - self.params = [self.W, self.b] - - def forward(self, X, is_training): - a = X.dot(self.W) + self.b - - # apply batch normalization - if self.apply_batch_norm: - a, new_running_mean, new_running_var = batch_norm( - a, - self.gamma, - self.beta, - self.running_mean, - self.running_var, - is_training, - 'spatial' - ) - if is_training: - self.updates = [ - (self.running_mean, new_running_mean), - (self.running_var, new_running_var), - ] - - return self.f(a) - - -class DCGAN: - def __init__(self, img_length, num_colors, d_sizes, g_sizes): - - # save for later - self.img_length = img_length - self.num_colors = num_colors - self.latent_dims = g_sizes['z'] - - # define the input data - self.X = T.tensor4('placeholderX') - self.Z = T.matrix('placeholderZ') - - # build the discriminator - p_real_given_real = self.build_discriminator(self.X, d_sizes) - - # build generator - self.sample_images = self.build_generator(self.Z, g_sizes) - - # get sample predictions - p_real_given_fake = self.d_forward(self.sample_images, True) - - # sample with batch norm in test mode - self.sample_images_test = self.g_forward(self.Z, False) - - # build costs - self.d_cost_real = T.nnet.binary_crossentropy( - output=p_real_given_real, - target=T.ones_like(p_real_given_real), - ) - self.d_cost_fake = T.nnet.binary_crossentropy( - output=p_real_given_fake, - target=T.zeros_like(p_real_given_fake), - ) - self.d_cost = T.mean(self.d_cost_real) + T.mean(self.d_cost_fake) - - self.g_cost = T.mean( - T.nnet.binary_crossentropy( - output=p_real_given_fake, - target=T.ones_like(p_real_given_fake), - ) - ) - real_predictions = T.sum(T.eq(T.round(p_real_given_real), 1)) - fake_predictions = T.sum(T.eq(T.round(p_real_given_fake), 0)) - num_predictions = 2.0*BATCH_SIZE - num_correct = real_predictions + fake_predictions - self.d_accuracy = num_correct / num_predictions - - - # optimizers - d_grads = T.grad(self.d_cost, self.d_params) - d_updates = adam(self.d_params, d_grads) - # add batch norm updates - for layer in self.d_convlayers + self.d_denselayers + [self.d_finallayer]: - d_updates += layer.updates - self.train_d = theano.function( - inputs=[self.X, self.Z], - outputs=[self.d_cost, self.d_accuracy], - updates=d_updates, - ) - - g_grads = T.grad(self.g_cost, self.g_params) - g_updates = adam(self.g_params, g_grads) - # add batch norm updates - for layer in self.g_denselayers + self.g_convlayers: - g_updates += layer.updates - g_updates += self.g_bn_updates - self.train_g = theano.function( - inputs=[self.Z], - outputs=self.g_cost, - updates=g_updates, - ) - - # make a function to get sample images - self.get_sample_images = theano.function( - inputs=[self.Z], - outputs=self.sample_images_test, - ) - - - def build_discriminator(self, X, d_sizes): - self.d_params = [] - - # build conv layers - self.d_convlayers = [] - mi = self.num_colors - dim = self.img_length - print("*** conv layer image sizes:") - for mo, filtersz, stride, apply_batch_norm in d_sizes['conv_layers']: - layer = ConvLayer(mi, mo, apply_batch_norm, filtersz, stride, lrelu) - self.d_convlayers.append(layer) - self.d_params += layer.params - mi = mo - print("dim:", dim) - dim = int(np.ceil(float(dim) / stride)) - - # for 'valid' border mode - # dim = int(np.floor( (dim - filtersz) / stride ) ) + 1 - - # for 'full' border mode - # dim = int(np.ceil( (dim + filtersz - 1) / stride ) ) - - print("final dim before flatten:", dim) - - - mi = mi * dim * dim - # build dense layers - self.d_denselayers = [] - for mo, apply_batch_norm in d_sizes['dense_layers']: - layer = DenseLayer(mi, mo, apply_batch_norm, lrelu) - mi = mo - self.d_denselayers.append(layer) - self.d_params += layer.params - - - # final logistic layer - self.d_finallayer = DenseLayer(mi, 1, False, T.nnet.sigmoid) - self.d_params += self.d_finallayer.params - - # get the logits - p_real_given_x = self.d_forward(X, True) - - # build the cost later - return p_real_given_x - - - def d_forward(self, X, is_training): - # encapsulate this because we use it twice - output = X - for layer in self.d_convlayers: - output = layer.forward(output, is_training) - output = output.flatten(ndim=2) - for layer in self.d_denselayers: - output = layer.forward(output, is_training) - output = self.d_finallayer.forward(output, is_training) - return output - - - def build_generator(self, Z, g_sizes): - self.g_params = [] - - # determine the size of the data at each step - dims = [self.img_length] - dim = self.img_length - for _, filtersz, stride, _ in reversed(g_sizes['conv_layers']): - dim = int(np.ceil(float(dim) / stride)) - - # for 'valid' border mode - # dim = int(np.floor( (dim - filtersz) / stride ) ) + 1 - - # for 'full' border mode - # dim = int(np.ceil( (dim + filtersz - 1) / stride ) ) - - dims.append(dim) - - # note: dims is actually backwards - # the first layer of the generator is actually last - # so let's reverse it - dims = list(reversed(dims)) - print("dims:", dims) - self.g_dims = dims - - - # dense layers - mi = self.latent_dims - self.g_denselayers = [] - for mo, apply_batch_norm in g_sizes['dense_layers']: - layer = DenseLayer(mi, mo, apply_batch_norm) - self.g_denselayers.append(layer) - self.g_params += layer.params - mi = mo - - # final dense layer - mo = g_sizes['projection'] * dims[0] * dims[0] - layer = DenseLayer(mi, mo, not g_sizes['bn_after_project']) - self.g_denselayers.append(layer) - self.g_params += layer.params - - - # fs-conv layers - mi = g_sizes['projection'] - self.g_convlayers = [] - - # output may use tanh or sigmoid - num_relus = len(g_sizes['conv_layers']) - 1 - activation_functions = [T.nnet.relu]*num_relus + [g_sizes['output_activation']] - - for i in range(len(g_sizes['conv_layers'])): - mo, filtersz, stride, apply_batch_norm = g_sizes['conv_layers'][i] - f = activation_functions[i] - output_shape = [BATCH_SIZE, mo, dims[i+1], dims[i+1]] - print("mi:", mi, "mo:", mo, "outp shape:", output_shape) - layer = FractionallyStridedConvLayer( - mi, mo, output_shape, apply_batch_norm, filtersz, stride, f - ) - self.g_convlayers.append(layer) - self.g_params += layer.params - mi = mo - - # apply batch norm - if g_sizes['bn_after_project']: - self.gamma = theano.shared(np.ones(g_sizes['projection'])) - self.beta = theano.shared(np.zeros(g_sizes['projection'])) - self.running_mean = theano.shared(np.zeros(g_sizes['projection'])) - self.running_var = theano.shared(np.zeros(g_sizes['projection'])) - self.g_params += [self.gamma, self.beta] - - # get the output - self.g_sizes = g_sizes - return self.g_forward(Z, True) - - - def g_forward(self, Z, is_training): - # dense layers - output = Z - for layer in self.g_denselayers: - output = layer.forward(output, is_training) - - # project and reshape - # remember! (N, color, D, D) - output = output.reshape( - [-1, self.g_sizes['projection'], self.g_dims[0], self.g_dims[0]], - ) - - # apply batch norm - if self.g_sizes['bn_after_project']: - output, new_running_mean, new_running_var = batch_norm( - output, - self.gamma, - self.beta, - self.running_mean, - self.running_var, - is_training, - 'spatial' - ) - if is_training: - self.g_bn_updates = [ - (self.running_mean, new_running_mean), - (self.running_var, new_running_var), - ] - else: - self.g_bn_updates = [] - - # pass through fs-conv layers - for layer in self.g_convlayers: - output = layer.forward(output, is_training) - - return output - - - def fit(self, X): - d_costs = [] - g_costs = [] - - N = len(X) - n_batches = N // BATCH_SIZE - total_iters = 0 - for i in range(EPOCHS): - print("epoch:", i) - np.random.shuffle(X) - for j in range(n_batches): - t0 = datetime.now() - - if type(X[0]) is str: - # is celeb dataset - batch = util.files2images_theano( - X[j*BATCH_SIZE:(j+1)*BATCH_SIZE] - ) - - else: - # is mnist dataset - batch = X[j*BATCH_SIZE:(j+1)*BATCH_SIZE] - - Z = np.random.uniform(-1, 1, size=(BATCH_SIZE, self.latent_dims)) - - # train the discriminator - d_cost, d_acc = self.train_d(batch, Z) - d_costs.append(d_cost) - - # train the generator - g_cost1 = self.train_g(Z) - g_cost2 = self.train_g(Z) - g_costs.append((g_cost1 + g_cost2)/2) # just use the avg - - print(" batch: %d/%d - dt: %s - d_acc: %.2f" % (j+1, n_batches, datetime.now() - t0, d_acc)) - - # save samples periodically - total_iters += 1 - if total_iters % SAVE_SAMPLE_PERIOD == 0: - print("saving a sample...") - samples = self.sample(64) # shape is (64, D, D, color) - - # for convenience - d = self.img_length - - if samples.shape[-1] == 1: - # if color == 1, we want a 2-D image (N x N) - samples = samples.reshape(64, d, d) - flat_image = np.empty((8*d, 8*d)) - - k = 0 - for i in range(8): - for j in range(8): - flat_image[i*d:(i+1)*d, j*d:(j+1)*d] = samples[k].reshape(d, d) - k += 1 - - # plt.imshow(flat_image, cmap='gray') - else: - # if color == 3, we want a 3-D image (N x N x 3) - flat_image = np.empty((8*d, 8*d, 3)) - k = 0 - for i in range(8): - for j in range(8): - # note: we have to change it back to (D, D, color) - flat_image[i*d:(i+1)*d, j*d:(j+1)*d] = samples[k].transpose((1, 2, 0)) - k += 1 - # plt.imshow(flat_image) - - # plt.savefig('samples/samples_at_iter_%d.png' % total_iters) - sp.misc.imsave( - 'samples/samples_at_iter_%d.png' % total_iters, - flat_image, - ) - - # save a plot of the costs - plt.clf() - plt.plot(d_costs, label='discriminator cost') - plt.plot(g_costs, label='generator cost') - plt.legend() - plt.savefig('cost_vs_iteration.png') - - def sample(self, n): - Z = np.random.uniform(-1, 1, size=(n, self.latent_dims)) - return self.get_sample_images(Z) - - - -def celeb(): - X = util.get_celeb() - # just loads a list of filenames, we will load them in dynamically - # because there are many - dim = 64 - colors = 3 - - # for celeb - d_sizes = { - 'conv_layers': [ - (64, 5, 2, False), - (128, 5, 2, True), - (256, 5, 2, True), - (512, 5, 2, True) - ], - 'dense_layers': [], - } - g_sizes = { - 'z': 100, - 'projection': 512, - 'bn_after_project': True, - 'conv_layers': [ - (256, 5, 2, True), - (128, 5, 2, True), - (64, 5, 2, True), - (colors, 5, 2, False) - ], - 'dense_layers': [], - 'output_activation': T.tanh, - } - - # setup gan - # note: assume square images, so only need 1 dim - gan = DCGAN(dim, colors, d_sizes, g_sizes) - gan.fit(X) - - -def mnist(): - X, Y = util.get_mnist() - X = X.reshape(len(X), 1, 28, 28) # remember! (N, color, D, D) - dim = X.shape[2] - colors = X.shape[1] - - # for mnist - d_sizes = { - 'conv_layers': [(2, 5, 2, False), (64, 5, 2, True)], - 'dense_layers': [(1024, True)], - } - g_sizes = { - 'z': 100, - 'projection': 128, - 'bn_after_project': False, - 'conv_layers': [(128, 5, 2, True), (colors, 5, 2, False)], - 'dense_layers': [(1024, True)], - 'output_activation': T.nnet.sigmoid, - } - - - # setup gan - # note: assume square images, so only need 1 dim - gan = DCGAN(dim, colors, d_sizes, g_sizes) - gan.fit(X) - - # since training will take a considerable - # amount of time, let's just save some - # samples to disk rather than plotting now - - -if __name__ == '__main__': - celeb() - # mnist() diff --git a/unsupervised_class3/extra_reading.txt b/unsupervised_class3/extra_reading.txt deleted file mode 100644 index 7c7628e9..00000000 --- a/unsupervised_class3/extra_reading.txt +++ /dev/null @@ -1,12 +0,0 @@ -Generative Visual Manipulation on the Natural Image Manifold -https://www.youtube.com/watch?v=9c4z6YsBGQ0 - -Generative Adversarial Nets (Goodfellow 2014) -https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf - -Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks -https://arxiv.org/abs/1511.06434v2 - -Striving for Simplicity: The All Convolutional Net (Springenberg et al., 2015) -https://arxiv.org/abs/1412.6806 - diff --git a/unsupervised_class3/parameterize_guassian.py b/unsupervised_class3/parameterize_guassian.py deleted file mode 100644 index 497a7b88..00000000 --- a/unsupervised_class3/parameterize_guassian.py +++ /dev/null @@ -1,54 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders -# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import matplotlib.pyplot as plt -from scipy.stats import multivariate_normal as mvn - - -def softplus(x): - # log1p(x) == log(1 + x) - return np.log1p(np.exp(x)) - - -# we're going to make a neural network -# with the layer sizes (4, 3, 2) -# like a toy version of a encoder - -W1 = np.random.randn(4, 3) -W2 = np.random.randn(3, 2*2) - -# why 2 * 2? -# we need 2 components for the mean, -# and 2 components for the standard deviation! - -# ignore bias terms for simplicity. - -def forward(x, W1, W2): - hidden = np.tanh(x.dot(W1)) - output = hidden.dot(W2) # no activation! - mean = output[:2] - stddev = softplus(output[2:]) - return mean, stddev - - -# make a random input -x = np.random.randn(4) - -# get the parameters of the Gaussian -mean, stddev = forward(x, W1, W2) -print("mean:", mean) -print("stddev:", stddev) - -# draw samples -samples = mvn.rvs(mean=mean, cov=stddev**2, size=10000) - -# plot the samples -plt.scatter(samples[:,0], samples[:,1], alpha=0.5) -plt.show() - - diff --git a/unsupervised_class3/test_stochastic_tensor.py b/unsupervised_class3/test_stochastic_tensor.py deleted file mode 100644 index 9347f077..00000000 --- a/unsupervised_class3/test_stochastic_tensor.py +++ /dev/null @@ -1,43 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders -# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders - -# a simple script to see what StochasticTensor outputs -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt - -st = tf.contrib.bayesflow.stochastic_tensor -Normal = tf.contrib.distributions.Normal - -# sample N samples from N(5,3*3) -N = 10000 -mean = np.ones(N)*5 -scale = np.ones(N)*3 - - -I = tf.Variable(np.ones(N)) - - -with st.value_type(st.SampleValue()): - X = st.StochasticTensor(Normal(loc=mean, scale=scale)) - -# cannot session.run a stochastic tensor -# but we can session.run a tensor -Y = I * X - - -init_op = tf.global_variables_initializer() -with tf.Session() as session: - session.run(init_op) - Y_val = session.run(Y) - - print("Sample mean:", Y_val.mean()) - print("Sample std dev:", Y_val.std()) - - plt.hist(Y_val, bins=20) - plt.show() diff --git a/unsupervised_class3/util.py b/unsupervised_class3/util.py deleted file mode 100755 index 935eb634..00000000 --- a/unsupervised_class3/util.py +++ /dev/null @@ -1,172 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders -# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders - -from __future__ import print_function, division -from builtins import range -# Note: you may need to update your version of future -# sudo pip install -U future - -import os -import requests -import zipfile -import numpy as np -import pandas as pd - -try: - # new version doesn't support - from scipy.misc import imread, imsave, imresize -except: - from PIL import Image - def imread(fn): - im = Image.open(fn) - return np.array(im) - - def imsave(fn, arr): - im = Image.fromarray(arr) - im.save(fn) - - def imresize(arr, sz): - im = Image.fromarray(arr) - im.resize(sz) - return np.array(im) - - -from glob import glob -from tqdm import tqdm -from sklearn.utils import shuffle - - -def get_mnist(limit=None): - if not os.path.exists('../large_files'): - print("You must create a folder called large_files adjacent to the class folder first.") - if not os.path.exists('../large_files/train.csv'): - print("Looks like you haven't downloaded the data or it's not in the right spot.") - print("Please get train.csv from https://www.kaggle.com/c/digit-recognizer") - print("and place it in the large_files folder.") - - print("Reading in and transforming data...") - df = pd.read_csv('../large_files/train.csv') - data = df.values - # np.random.shuffle(data) - X = data[:, 1:] / 255.0 # data is from 0..255 - Y = data[:, 0] - X, Y = shuffle(X, Y) - if limit is not None: - X, Y = X[:limit], Y[:limit] - return X, Y - - -def get_celeb(limit=None): - if not os.path.exists('../large_files'): - os.mkdir('../large_files') - - # eventual place where our final data will reside - if not os.path.exists('../large_files/img_align_celeba-cropped'): - - # check for original data - if not os.path.exists('../large_files/img_align_celeba'): - # download the file and place it here - if not os.path.exists('../large_files/img_align_celeba.zip'): - print("Downloading img_align_celeba.zip...") - download_file( - '0B7EVK8r0v71pZjFTYXZWM3FlRnM', - '../large_files/img_align_celeba.zip' - ) - - # unzip the file - print("Extracting img_align_celeba.zip...") - with zipfile.ZipFile('../large_files/img_align_celeba.zip') as zf: - zip_dir = zf.namelist()[0] - zf.extractall('../large_files') - - - # load in the original images - filenames = glob("../large_files/img_align_celeba/*.jpg") - N = len(filenames) - print("Found %d files!" % N) - - - # crop the images to 64x64 - os.mkdir('../large_files/img_align_celeba-cropped') - print("Cropping images, please wait...") - - for i in range(N): - crop_and_resave(filenames[i], '../large_files/img_align_celeba-cropped') - if i % 1000 == 0: - print("%d/%d" % (i, N)) - - - # make sure to return the cropped version - filenames = glob("../large_files/img_align_celeba-cropped/*.jpg") - return filenames - - -def crop_and_resave(inputfile, outputdir): - # theoretically, we could try to find the face - # but let's be lazy - # we assume that the middle 108 pixels will contain the face - im = imread(inputfile) - height, width, color = im.shape - edge_h = int( round( (height - 108) / 2.0 ) ) - edge_w = int( round( (width - 108) / 2.0 ) ) - - cropped = im[edge_h:(edge_h + 108), edge_w:(edge_w + 108)] - small = imresize(cropped, (64, 64)) - - filename = inputfile.split('/')[-1] - imsave("%s/%s" % (outputdir, filename), small) - - -def scale_image(im): - # scale to (-1, +1) - return (im / 255.0)*2 - 1 - - -def files2images_theano(filenames): - # theano wants images to be of shape (C, D, D) - # tensorflow wants (D, D, C) which is what scipy imread - # uses by default - return [scale_image(imread(fn).transpose((2, 0, 1))) for fn in filenames] - - -def files2images(filenames): - return [scale_image(imread(fn)) for fn in filenames] - - -# functions for downloading file from google drive -def save_response_content(r, dest): - # unfortunately content-length is not provided in header - total_iters = 1409659 # in KB - print("Note: units are in KB, e.g. KKB = MB") - # because we are reading 1024 bytes at a time, hence - # 1KB == 1 "unit" for tqdm - with open(dest, 'wb') as f: - for chunk in tqdm( - r.iter_content(1024), - total=total_iters, - unit='KB', - unit_scale=True): - if chunk: # filter out keep-alive new chunks - f.write(chunk) - - -def get_confirm_token(response): - for key, value in response.cookies.items(): - if key.startswith('download_warning'): - return value - return None - - -def download_file(file_id, dest): - drive_url = "https://docs.google.com/uc?export=download" - session = requests.Session() - response = session.get(drive_url, params={'id': file_id}, stream=True) - token = get_confirm_token(response) - - if token: - params = {'id': file_id, 'confirm': token} - response = session.get(drive_url, params=params, stream=True) - - save_response_content(response, dest) - - diff --git a/unsupervised_class3/vae_tf.py b/unsupervised_class3/vae_tf.py deleted file mode 100644 index d26b0114..00000000 --- a/unsupervised_class3/vae_tf.py +++ /dev/null @@ -1,286 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders -# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import util -import numpy as np -import tensorflow as tf -import matplotlib.pyplot as plt - -st = None -try: - st = tf.contrib.bayesflow.stochastic_tensor -except: - # doesn't exist in later versions of TF - # we will use the reparameterization trick instead - # watch the later lecture on the reparameterization trick - # to learn about it. - pass -Normal = tf.contrib.distributions.Normal -Bernoulli = tf.contrib.distributions.Bernoulli - - - -class DenseLayer(object): - def __init__(self, M1, M2, f=tf.nn.relu): - # self.M1 = M1 - # self.M2 = M2 - - self.W = tf.Variable(tf.random_normal(shape=(M1, M2)) * 2 / np.sqrt(M1)) - self.b = tf.Variable(np.zeros(M2).astype(np.float32)) - self.f = f - - def forward(self, X): - return self.f(tf.matmul(X, self.W) + self.b) - - -class VariationalAutoencoder: - def __init__(self, D, hidden_layer_sizes): - # hidden_layer_sizes specifies the size of every layer - # in the encoder - # up to the final hidden layer Z - # the decoder will have the reverse shape - - - # represents a batch of training data - self.X = tf.placeholder(tf.float32, shape=(None, D)) - - # encoder - self.encoder_layers = [] - M_in = D - for M_out in hidden_layer_sizes[:-1]: - h = DenseLayer(M_in, M_out) - self.encoder_layers.append(h) - M_in = M_out - - - # for convenience, we'll refer to the final encoder size as M - # also the input to the decoder size - M = hidden_layer_sizes[-1] - - # the encoder's final layer output is unbounded - # so there is no activation function - # we also need 2 times as many units as specified by M_out - # since there needs to be M_out means + M_out variances - h = DenseLayer(M_in, 2 * M, f=lambda x: x) - self.encoder_layers.append(h) - - # get the mean and variance / std dev of Z. - # note that the variance must be > 0 - # we can get a sigma (standard dev) > 0 from an unbounded variable by - # passing it through the softplus function. - # add a small amount for smoothing. - current_layer_value = self.X - for layer in self.encoder_layers: - current_layer_value = layer.forward(current_layer_value) - self.means = current_layer_value[:, :M] - self.stddev = tf.nn.softplus(current_layer_value[:, M:]) + 1e-6 - - # get a sample of Z - # we need to use a stochastic tensor - # in order for the errors to be backpropagated past this point - if st is None: - # doesn't exist in later versions of Tensorflow - # we'll use the same trick we use in Theano - standard_normal = Normal( - loc=np.zeros(M, dtype=np.float32), - scale=np.ones(M, dtype=np.float32) - ) - e = standard_normal.sample(tf.shape(self.means)[0]) - self.Z = e * self.stddev + self.means - - # note: this also works because Tensorflow - # now does the "magic" for you - # n = Normal( - # loc=self.means, - # scale=self.stddev, - # ) - # self.Z = n.sample() - else: - with st.value_type(st.SampleValue()): - self.Z = st.StochasticTensor(Normal(loc=self.means, scale=self.stddev)) - # to get back Q(Z), the distribution of Z - # we will later use self.Z.distribution - - - # decoder - self.decoder_layers = [] - M_in = M - for M_out in reversed(hidden_layer_sizes[:-1]): - h = DenseLayer(M_in, M_out) - self.decoder_layers.append(h) - M_in = M_out - - # the decoder's final layer should technically go through a sigmoid - # so that the final output is a binary probability (e.g. Bernoulli) - # but Bernoulli accepts logits (pre-sigmoid) so we will take those - # so no activation function is needed at the final layer - h = DenseLayer(M_in, D, f=lambda x: x) - self.decoder_layers.append(h) - - # get the logits - current_layer_value = self.Z - for layer in self.decoder_layers: - current_layer_value = layer.forward(current_layer_value) - logits = current_layer_value - posterior_predictive_logits = logits # save for later - - # get the output - self.X_hat_distribution = Bernoulli(logits=logits) - - # take samples from X_hat - # we will call this the posterior predictive sample - self.posterior_predictive = self.X_hat_distribution.sample() - self.posterior_predictive_probs = tf.nn.sigmoid(logits) - - # take sample from a Z ~ N(0, 1) - # and put it through the decoder - # we will call this the prior predictive sample - standard_normal = Normal( - loc=np.zeros(M, dtype=np.float32), - scale=np.ones(M, dtype=np.float32) - ) - - Z_std = standard_normal.sample(1) - current_layer_value = Z_std - for layer in self.decoder_layers: - current_layer_value = layer.forward(current_layer_value) - logits = current_layer_value - - prior_predictive_dist = Bernoulli(logits=logits) - self.prior_predictive = prior_predictive_dist.sample() - self.prior_predictive_probs = tf.nn.sigmoid(logits) - - - # prior predictive from input - # only used for generating visualization - self.Z_input = tf.placeholder(tf.float32, shape=(None, M)) - current_layer_value = self.Z_input - for layer in self.decoder_layers: - current_layer_value = layer.forward(current_layer_value) - logits = current_layer_value - self.prior_predictive_from_input_probs = tf.nn.sigmoid(logits) - - - # now build the cost - if st is None: - kl = -tf.log(self.stddev) + 0.5*(self.stddev**2 + self.means**2) - 0.5 - kl = tf.reduce_sum(kl, axis=1) - else: - kl = tf.reduce_sum( - tf.contrib.distributions.kl_divergence( - self.Z.distribution, standard_normal - ), - 1 - ) - expected_log_likelihood = tf.reduce_sum( - self.X_hat_distribution.log_prob(self.X), - 1 - ) - - # equivalent - # expected_log_likelihood = -tf.nn.sigmoid_cross_entropy_with_logits( - # labels=self.X, - # logits=posterior_predictive_logits - # ) - # expected_log_likelihood = tf.reduce_sum(expected_log_likelihood, 1) - - - - self.elbo = tf.reduce_sum(expected_log_likelihood - kl) - self.train_op = tf.train.RMSPropOptimizer(learning_rate=0.001).minimize(-self.elbo) - - # set up session and variables for later - self.init_op = tf.global_variables_initializer() - self.sess = tf.InteractiveSession() - self.sess.run(self.init_op) - - - def fit(self, X, epochs=30, batch_sz=64): - costs = [] - n_batches = len(X) // batch_sz - print("n_batches:", n_batches) - for i in range(epochs): - print("epoch:", i) - np.random.shuffle(X) - for j in range(n_batches): - batch = X[j*batch_sz:(j+1)*batch_sz] - _, c, = self.sess.run((self.train_op, self.elbo), feed_dict={self.X: batch}) - c /= batch_sz # just debugging - costs.append(c) - if j % 100 == 0: - print("iter: %d, cost: %.3f" % (j, c)) - plt.plot(costs) - plt.show() - - def transform(self, X): - return self.sess.run( - self.means, - feed_dict={self.X: X} - ) - - def prior_predictive_with_input(self, Z): - return self.sess.run( - self.prior_predictive_from_input_probs, - feed_dict={self.Z_input: Z} - ) - - def posterior_predictive_sample(self, X): - # returns a sample from p(x_new | X) - return self.sess.run(self.posterior_predictive, feed_dict={self.X: X}) - - def prior_predictive_sample_with_probs(self): - # returns a sample from p(x_new | z), z ~ N(0, 1) - return self.sess.run((self.prior_predictive, self.prior_predictive_probs)) - - -def main(): - X, Y = util.get_mnist() - # convert X to binary variable - X = (X > 0.5).astype(np.float32) - - vae = VariationalAutoencoder(784, [200, 100]) - vae.fit(X) - - # plot reconstruction - done = False - while not done: - i = np.random.choice(len(X)) - x = X[i] - im = vae.posterior_predictive_sample([x]).reshape(28, 28) - plt.subplot(1,2,1) - plt.imshow(x.reshape(28, 28), cmap='gray') - plt.title("Original") - plt.subplot(1,2,2) - plt.imshow(im, cmap='gray') - plt.title("Sampled") - plt.show() - - ans = input("Generate another?") - if ans and ans[0] in ('n' or 'N'): - done = True - - # plot output from random samples in latent space - done = False - while not done: - im, probs = vae.prior_predictive_sample_with_probs() - im = im.reshape(28, 28) - probs = probs.reshape(28, 28) - plt.subplot(1,2,1) - plt.imshow(im, cmap='gray') - plt.title("Prior predictive sample") - plt.subplot(1,2,2) - plt.imshow(probs, cmap='gray') - plt.title("Prior predictive probs") - plt.show() - - ans = input("Generate another?") - if ans and ans[0] in ('n' or 'N'): - done = True - - -if __name__ == '__main__': - main() diff --git a/unsupervised_class3/vae_theano.py b/unsupervised_class3/vae_theano.py deleted file mode 100644 index fd1c4a29..00000000 --- a/unsupervised_class3/vae_theano.py +++ /dev/null @@ -1,261 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders -# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import util -import numpy as np -import theano -import theano.tensor as T -import matplotlib.pyplot as plt - -from theano.tensor.shared_randomstreams import RandomStreams - - -class DenseLayer(object): - def __init__(self, M1, M2, f=T.nnet.relu): - self.W = theano.shared(np.random.randn(M1, M2) * np.sqrt(2.0 / M1)) - self.b = theano.shared(np.zeros(M2)) - self.f = f - self.params = [self.W, self.b] - - def forward(self, X): - return self.f(X.dot(self.W) + self.b) - - -class VariationalAutoencoder: - def __init__(self, D, hidden_layer_sizes): - # hidden_layer_sizes specifies the size of every layer - # in the encoder - # up to the final hidden layer Z - # the decoder will have the reverse shape - - - # represents a batch of training data - self.X = T.matrix('X') - - # encoder - self.encoder_layers = [] - M_in = D - for M_out in hidden_layer_sizes[:-1]: - h = DenseLayer(M_in, M_out) - self.encoder_layers.append(h) - M_in = M_out - - - # for convenience, we'll refer to the final encoder size as M - # also the input to the decoder size - M = hidden_layer_sizes[-1] - - # the encoder's final layer output is unbounded - # so there is no activation function - # we also need 2 times as many units as specified by M_out - # since there needs to be M_out means + M_out variances - h = DenseLayer(M_in, 2 * M, f=lambda x: x) - self.encoder_layers.append(h) - - # get the mean and variance / std dev of Z. - # note that the variance must be > 0 - # we can get a sigma (standard dev) > 0 from an unbounded variable by - # passing it through the softplus function. - # add a small amount for smoothing. - current_layer_value = self.X - for layer in self.encoder_layers: - current_layer_value = layer.forward(current_layer_value) - self.means = current_layer_value[:, :M] - self.stddev = T.nnet.softplus(current_layer_value[:, M:]) + 1e-6 - - # get a sample of Z - self.rng = RandomStreams() - eps = self.rng.normal((self.means.shape[0], M)) - self.Z = self.means + self.stddev * eps - - - # decoder - self.decoder_layers = [] - M_in = M - for M_out in reversed(hidden_layer_sizes[:-1]): - h = DenseLayer(M_in, M_out) - self.decoder_layers.append(h) - M_in = M_out - - # the decoder's final layer should go through a sigmoid - h = DenseLayer(M_in, D, f=T.nnet.sigmoid) - self.decoder_layers.append(h) - - # get the posterior predictive - current_layer_value = self.Z - for layer in self.decoder_layers: - current_layer_value = layer.forward(current_layer_value) - self.posterior_predictive_probs = current_layer_value - - # take samples from X_hat - # we will call this the posterior predictive sample - self.posterior_predictive = self.rng.binomial( - size=self.posterior_predictive_probs.shape, - n=1, - p=self.posterior_predictive_probs - ) - - # take sample from a Z ~ N(0, 1) - # and put it through the decoder - # we will call this the prior predictive sample - Z_std = self.rng.normal((1, M)) - current_layer_value = Z_std - for layer in self.decoder_layers: - current_layer_value = layer.forward(current_layer_value) - self.prior_predictive_probs = current_layer_value - - self.prior_predictive = self.rng.binomial( - size=self.prior_predictive_probs.shape, - n=1, - p=self.prior_predictive_probs - ) - - # prior predictive from input - # only used for generating visualization - Z_input = T.matrix('Z_input') - current_layer_value = Z_input - for layer in self.decoder_layers: - current_layer_value = layer.forward(current_layer_value) - prior_predictive_probs_from_Z_input = current_layer_value - - - # now build the cost - # https://stats.stackexchange.com/questions/7440/kl-divergence-between-two-univariate-gaussians - # https://stats.stackexchange.com/questions/60680/kl-divergence-between-two-multivariate-gaussians - kl = -T.log(self.stddev) + 0.5*(self.stddev**2 + self.means**2) - 0.5 - kl = T.sum(kl, axis=1) - expected_log_likelihood = -T.nnet.binary_crossentropy( - output=self.posterior_predictive_probs, - target=self.X, - ) - expected_log_likelihood = T.sum(expected_log_likelihood, axis=1) - self.elbo = T.sum(expected_log_likelihood - kl) - - - - # define the updates - params = [] - for layer in self.encoder_layers: - params += layer.params - for layer in self.decoder_layers: - params += layer.params - - grads = T.grad(-self.elbo, params) - - # rmsprop - decay = 0.9 - learning_rate = 0.001 - - # for rmsprop - cache = [theano.shared(np.ones_like(p.get_value())) for p in params] - - new_cache = [decay*c + (1-decay)*g*g for p, c, g in zip(params, cache, grads)] - - updates = [ - (c, new_c) for c, new_c in zip(cache, new_cache) - ] + [ - (p, p - learning_rate*g/T.sqrt(new_c + 1e-10)) for p, new_c, g in zip(params, new_cache, grads) - ] - - - # now define callable functions - self.train_op = theano.function( - inputs=[self.X], - outputs=self.elbo, - updates=updates - ) - - # returns a sample from p(x_new | X) - self.posterior_predictive_sample = theano.function( - inputs=[self.X], - outputs=self.posterior_predictive, - ) - - # returns a sample from p(x_new | z), z ~ N(0, 1) - self.prior_predictive_sample_with_probs = theano.function( - inputs=[], - outputs=[self.prior_predictive, self.prior_predictive_probs] - ) - - # return mean of q(z | x) - self.transform = theano.function( - inputs=[self.X], - outputs=self.means - ) - - # returns a sample from p(x_new | z), from a given z - self.prior_predictive_with_input = theano.function( - inputs=[Z_input], - outputs=prior_predictive_probs_from_Z_input - ) - - - def fit(self, X, epochs=30, batch_sz=64): - costs = [] - n_batches = len(X) // batch_sz - print("n_batches:", n_batches) - for i in range(epochs): - print("epoch:", i) - np.random.shuffle(X) - for j in range(n_batches): - batch = X[j*batch_sz:(j+1)*batch_sz] - c = self.train_op(batch) - c /= batch_sz # just debugging - costs.append(c) - if j % 100 == 0: - print("iter: %d, cost: %.3f" % (j, c)) - plt.plot(costs) - plt.show() - - -def main(): - X, Y = util.get_mnist() - # convert X to binary variable - X = (X > 0.5).astype(np.float32) - - vae = VariationalAutoencoder(784, [200, 100]) - vae.fit(X) - - # plot reconstruction - done = False - while not done: - i = np.random.choice(len(X)) - x = X[i] - im = vae.posterior_predictive_sample([x]).reshape(28, 28) - plt.subplot(1,2,1) - plt.imshow(x.reshape(28, 28), cmap='gray') - plt.title("Original") - plt.subplot(1,2,2) - plt.imshow(im, cmap='gray') - plt.title("Sampled") - plt.show() - - ans = input("Generate another?") - if ans and ans[0] in ('n' or 'N'): - done = True - - # plot output from random samples in latent space - done = False - while not done: - im, probs = vae.prior_predictive_sample_with_probs() - im = im.reshape(28, 28) - probs = probs.reshape(28, 28) - plt.subplot(1,2,1) - plt.imshow(im, cmap='gray') - plt.title("Prior predictive sample") - plt.subplot(1,2,2) - plt.imshow(probs, cmap='gray') - plt.title("Prior predictive probs") - plt.show() - - ans = input("Generate another?") - if ans and ans[0] in ('n' or 'N'): - done = True - - -if __name__ == '__main__': - main() diff --git a/unsupervised_class3/visualize_latent_space.py b/unsupervised_class3/visualize_latent_space.py deleted file mode 100644 index 4693d83a..00000000 --- a/unsupervised_class3/visualize_latent_space.py +++ /dev/null @@ -1,66 +0,0 @@ -# https://deeplearningcourses.com/c/deep-learning-gans-and-variational-autoencoders -# https://www.udemy.com/deep-learning-gans-and-variational-autoencoders -from __future__ import print_function, division -from builtins import range, input -# Note: you may need to update your version of future -# sudo pip install -U future - -import util -import numpy as np -import matplotlib.pyplot as plt - -from vae_tf import VariationalAutoencoder -# from vae_theano import VariationalAutoencoder - -if __name__ == '__main__': - X, Y = util.get_mnist() - # convert X to binary variable - X = (X > 0.5).astype(np.float32) - - for i in range(len(X)): - plt.imshow(X[i].reshape(28, 28), cmap='gray') - plt.title("Label: %s" % Y[i]) - plt.show() - ans = input("Show another? [Y/n]") - if ans and ans[0].lower().startswith('n'): - break - - - vae = VariationalAutoencoder(784, [200, 100, 2]) - vae.fit(X.copy()) - # fit will shuffle the data - # so we need to copy to prevent messing up the order - # for plotting later, we need Z and Y to correspond - - Z = vae.transform(X) - plt.scatter(Z[:,0], Z[:,1], c=Y, s=10) - plt.show() - - - # plot what image is reproduced for different parts of Z - n = 20 # number of images per side - x_values = np.linspace(-3, 3, n) - y_values = np.linspace(-3, 3, n) - image = np.empty((28 * n, 28 * n)) - - # build Z first so we don't have to keep - # re-calling the predict function - # it is particularly slow in theano - Z2 = [] - for i, x in enumerate(x_values): - for j, y in enumerate(y_values): - z = [x, y] - Z2.append(z) - X_recon = vae.prior_predictive_with_input(Z2) - - k = 0 - for i, x in enumerate(x_values): - for j, y in enumerate(y_values): - x_recon = X_recon[k] - k += 1 - # convert from NxD == 1 x 784 --> 28 x 28 - x_recon = x_recon.reshape(28, 28) - image[(n - i - 1) * 28:(n - i) * 28, j * 28:(j + 1) * 28] = x_recon - plt.imshow(image, cmap='gray') - plt.show() -