From 38110699a754b425a901d655862a1969b41d9e5b Mon Sep 17 00:00:00 2001 From: Min Date: Sun, 27 Aug 2017 19:59:07 +1200 Subject: [PATCH 01/40] updated Readme --- README.md | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 1f9383a..f974c58 100644 --- a/README.md +++ b/README.md @@ -2,8 +2,9 @@ Tensorflow implementation of https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf -Currently I haven't trained with the full SQuAD dataset.\n -The dataset used for this task is Stanford Question Answering Dataset (https://rajpurkar.github.io/SQuAD-explorer/).\n +Currently I haven't trained with the full SQuAD dataset. + +The dataset used for this task is Stanford Question Answering Dataset (https://rajpurkar.github.io/SQuAD-explorer/). ## Requirements * NumPy @@ -20,7 +21,7 @@ $ python process.py --process True ``` # Training -You can change the hyperparameters from params.py file.\n +You can change the hyperparameters from params.py file. To train the model, run the following line. ```shell $ python model.py @@ -33,6 +34,8 @@ $ tensorboard --logdir=r-net:r_net/ ``` # Note -As a sanity check I trained the network with 3000 independent randomly sampled question-answering pairs. It took about 4 hours and a half for the model to get the gist of what's going on with the data. With full dataset (90,000+ pairs) we are expecting longer time for convergence.\n -Some sort of normalization method might help speed up convergence (though the authors of the original paper didn't mention anything about the normalization).\n +As a sanity check I trained the network with 3000 independent randomly sampled question-answering pairs. It took about 4 hours and a half for the model to get the gist of what's going on with the data. With full dataset (90,000+ pairs) we are expecting longer time for convergence. + +Some sort of normalization method might help speed up convergence (though the authors of the original paper didn't mention anything about the normalization). + ![Alt text](/../dev/screenshots/figure.png?raw=true "Training error") From c8d5318ccc61630ff1f96c6107adbffea1edc153 Mon Sep 17 00:00:00 2001 From: Min Date: Mon, 28 Aug 2017 11:34:54 +1200 Subject: [PATCH 02/40] fixed minor errors --- README.md | 2 +- params.py | 4 ++-- process.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index f974c58..ee7681b 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,7 @@ $ tensorboard --logdir=r-net:r_net/ ``` # Note -As a sanity check I trained the network with 3000 independent randomly sampled question-answering pairs. It took about 4 hours and a half for the model to get the gist of what's going on with the data. With full dataset (90,000+ pairs) we are expecting longer time for convergence. +As a sanity check I trained the network with 3000 independent randomly sampled question-answering pairs. With my GTX 1080, it took about 4 hours and a half for the model to get the gist of what's going on with the data. With full dataset (90,000+ pairs) we are expecting longer time for convergence. Some sort of normalization method might help speed up convergence (though the authors of the original paper didn't mention anything about the normalization). diff --git a/params.py b/params.py index 98193a3..bb8f5a3 100644 --- a/params.py +++ b/params.py @@ -1,7 +1,7 @@ class Params(): # data - data_size = 3000 + data_size = 80000 num_epochs = 100 data_dir = "./data/" logdir = "./train/train" @@ -20,7 +20,7 @@ class Params(): learning_rate = 1 vocab_size = 2196018 char_vocab_size = 74 - batch_size = 16 + batch_size = 32 train_prop = 0.9 emb_size = 300 attn_size = 75 diff --git a/process.py b/process.py index 3649768..dcc5a5c 100644 --- a/process.py +++ b/process.py @@ -160,7 +160,7 @@ def add_to_dict(self, line): if self.append_dict: self.process_word(splitted_line) - self.process_char(splitted_line) + self.process_char("".join(splitted_line)) words = [] chars = [] for i,word in enumerate(splitted_line): From 8ec13338c3b5fc59e82aa3ddc5253c00e0aaefb2 Mon Sep 17 00:00:00 2001 From: Min Date: Mon, 28 Aug 2017 14:07:29 +1200 Subject: [PATCH 03/40] fixed preprocess --- model.py | 2 +- params.py | 6 +++--- process.py | 9 ++++++--- 3 files changed, 10 insertions(+), 7 deletions(-) diff --git a/model.py b/model.py index 273132e..4ab2d68 100644 --- a/model.py +++ b/model.py @@ -120,7 +120,7 @@ def loss_function(self): self.global_step = tf.Variable(0, name='global_step', trainable=False) # gradient clipping by norm - gradients, variables = zip(self.optimizer.compute_gradients(self.mean_loss)) + gradients, variables = zip(*self.optimizer.compute_gradients(self.mean_loss)) gradients, _ = tf.clip_by_global_norm(gradients, 5.0) self.train_op = self.optimizer.apply_gradients(zip(gradients, variables), global_step = self.global_step) diff --git a/params.py b/params.py index bb8f5a3..1f57eab 100644 --- a/params.py +++ b/params.py @@ -1,7 +1,7 @@ class Params(): # data - data_size = 80000 + data_size = 3000 num_epochs = 100 data_dir = "./data/" logdir = "./train/train" @@ -14,12 +14,12 @@ class Params(): coreNLP_dir = "./stanford-corenlp-full-2017-06-09" # model - debug = False + debug = False max_len = 200 save_steps = 50 learning_rate = 1 vocab_size = 2196018 - char_vocab_size = 74 + char_vocab_size = 77 batch_size = 32 train_prop = 0.9 emb_size = 300 diff --git a/process.py b/process.py index dcc5a5c..59c8a21 100644 --- a/process.py +++ b/process.py @@ -1,9 +1,10 @@ # -*- coding: utf-8 -*- #/usr/bin/python2 -import codecs +import cPickle as pickle import numpy as np import json +import codecs import unicodedata import re import nltk @@ -317,8 +318,10 @@ def max_value(inputlist): return max_val def main(): - loader = data_loader(pretrained = Params.glove_dir) - loader.process_json(Params.data_dir + "train-v1.1.json") + with open(Params.data_dir + 'dictionary.pkl','wb') as dictionary: + loader = data_loader(pretrained = Params.glove_dir) + loader.process_json(Params.data_dir + "train-v1.1.json") + pickle.dump(loader, dictionary, pickle.HIGHEST_PROTOCOL) 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29/40] cleaning up some lines --- model.py | 33 ++++++++++++++------------------- params.py | 10 +++++++--- 2 files changed, 21 insertions(+), 22 deletions(-) diff --git a/model.py b/model.py index d629289..77bc0f7 100644 --- a/model.py +++ b/model.py @@ -14,10 +14,10 @@ import cPickle as pickle from process import * -optimizer_factory = {"adadelta":tf.train.AdadeltaOptimizer(learning_rate = Params.learning_rate, epsilon = 1e-06), - "adam":tf.train.AdamOptimizer(learning_rate = Params.learning_rate), - "gradientdescent":tf.train.GradientDescentOptimizer(learning_rate = Params.learning_rate), - "adagrad":tf.train.AdagradOptimizer(learning_rate = Params.learning_rate)} +optimizer_factory = {"adadelta":tf.train.AdadeltaOptimizer, + "adam":tf.train.AdamOptimizer, + "gradientdescent":tf.train.GradientDescentOptimizer, + "adagrad":tf.train.AdagradOptimizer} class Model(object): def __init__(self,is_training = True): @@ -60,14 +60,17 @@ def encode_ids(self): self.word_embeddings_placeholder = tf.placeholder(tf.float32,[Params.vocab_size, Params.emb_size],"word_embeddings_placeholder") self.emb_assign = tf.group(tf.assign(self.word_embeddings, self.word_embeddings_placeholder),tf.assign(self.char_embeddings, self.char_embeddings_placeholder)) + # Embed the question and passage information for word and character tokens self.passage_word_encoded, self.passage_char_encoded = encoding(self.passage_w, self.passage_c, word_embeddings = self.word_embeddings, - char_embeddings = self.char_embeddings) + char_embeddings = self.char_embeddings, + scope = "passage_embeddings") self.question_word_encoded, self.question_char_encoded = encoding(self.question_w, self.question_c, word_embeddings = self.word_embeddings, - char_embeddings = self.char_embeddings) + char_embeddings = self.char_embeddings, + scope = "question_embeddings") self.passage_char_encoded = bidirectional_GRU(self.passage_char_encoded, self.passage_c_len, scope = "passage_char_encoding", @@ -122,7 +125,7 @@ def attention_match_rnn(self): cell, bidirection = True, scope = scopes[i]) - memory = inputs # self_matching + memory = inputs # self matching (attention over itself) inputs = apply_dropout(inputs, is_training = self.is_training) self.self_matching_output = inputs @@ -155,7 +158,7 @@ def loss_function(self): # Use non-sparse softmax self.indices_prob = tf.one_hot(self.indices, shapes[1]) self.mean_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = self.indices_prob, logits = self.points_logits)) - self.optimizer = optimizer_factory[Params.optimizer] + self.optimizer = optimizer_factory[Params.optimizer](Params.opt_arg[Params.optimizer]) if Params.clip: # gradient clipping by norm @@ -163,7 +166,7 @@ def loss_function(self): gradients, _ = tf.clip_by_global_norm(gradients, Params.norm) self.train_op = self.optimizer.apply_gradients(zip(gradients, variables), global_step = self.global_step) else: - self.train_op = self.optimizer.minimize(self.mean_loss, global_step = self.global_step)#, aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N) + self.train_op = self.optimizer.minimize(self.mean_loss, global_step = self.global_step) def summary(self): self.F1 = tf.Variable(tf.constant(0.0, shape=(), dtype = tf.float32),trainable=False, name="F1") @@ -172,16 +175,8 @@ def summary(self): self.EM_placeholder = tf.placeholder(tf.float32, shape = (), name = "EM_placeholder") self.metric_assign = tf.group(tf.assign(self.F1, self.F1_placeholder),tf.assign(self.EM, self.EM_placeholder)) tf.summary.scalar('mean_loss', self.mean_loss) - tf.summary.scalar("F1",self.F1) - tf.summary.scalar("EM",self.EM) - tf.summary.scalar('passage_word_encoded',tf.reduce_mean(self.passage_word_encoded)) - tf.summary.scalar('passage_char_encoded',tf.reduce_mean(self.passage_char_encoded)) - tf.summary.scalar('question_word_encoded',tf.reduce_mean(self.question_word_encoded)) - tf.summary.scalar('question_char_encoded',tf.reduce_mean(self.question_char_encoded)) - tf.summary.scalar('question_encoding',tf.reduce_mean(self.question_encoding)) - tf.summary.scalar('passage_encoding',tf.reduce_mean(self.passage_encoding)) - tf.summary.scalar('self_matching',tf.reduce_mean(self.self_matching_output)) - tf.summary.scalar('pointer',tf.reduce_mean(self.points_logits)) + tf.summary.scalar("training_F1_Score",self.F1) + tf.summary.scalar("training_Exact_Match",self.EM) tf.summary.scalar('learning_rate', Params.learning_rate) self.merged = tf.summary.merge_all() diff --git a/params.py b/params.py index 1a0a52c..216c79b 100644 --- a/params.py +++ b/params.py @@ -20,15 +20,19 @@ class Params(): p_chars_dir = "chars_context.txt" # Training - debug = False # Set it to True to debug the computation graph + debug = True # Set it to True to debug the computation graph test = False # Test the model on dev-set - learning_rate = 1 # Adadelta doesn't require initial learning rate dropout = 0.2 # dropout probability optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] - batch_size = 56 # Size of the mini-batch for training + batch_size = 50 # Size of the mini-batch for training save_steps = 50 # Save the model at every 50 steps clip = False # clip gradient norm norm = 5.0 # global norm + # Change the hyperparameters of your learning algorithms here + opt_arg = {'adadelta':{'learning_rate':1, 'rho': 0.95, 'epsilon':1e-6}, + 'adam':{'learning_rate':1e-3, 'beta1':0.9, 'beta2':0.999, 'epsilon':1e-8}, + 'gradientdescent':{'learning_rate':1}, + 'adagrad':{'learning_rate':1}} # Architecture max_len = 200 # Maximum number of words in each passage context From bc777a89b8191977907499967bdc56590a808a40 Mon Sep 17 00:00:00 2001 From: Min Date: Wed, 6 Sep 2017 12:06:39 +1200 Subject: [PATCH 30/40] fixed a typo --- model.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/model.py b/model.py index 77bc0f7..3265f14 100644 --- a/model.py +++ b/model.py @@ -158,7 +158,7 @@ def loss_function(self): # Use non-sparse softmax self.indices_prob = tf.one_hot(self.indices, shapes[1]) self.mean_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = self.indices_prob, logits = self.points_logits)) - self.optimizer = optimizer_factory[Params.optimizer](Params.opt_arg[Params.optimizer]) + self.optimizer = optimizer_factory[Params.optimizer](**Params.opt_arg[Params.optimizer]) if Params.clip: # gradient clipping by norm @@ -177,11 +177,11 @@ def summary(self): tf.summary.scalar('mean_loss', self.mean_loss) tf.summary.scalar("training_F1_Score",self.F1) tf.summary.scalar("training_Exact_Match",self.EM) - tf.summary.scalar('learning_rate', Params.learning_rate) + tf.summary.scalar('learning_rate', Params.opt_arg[Params.optimizer]['learning_rate']) self.merged = tf.summary.merge_all() def debug(): - model = Model(is_training = False) + model = Model(is_training = True) print("Built model") def test(): From 836bfd17b247a763e4e0c927b6fb72d307b8b5ba Mon Sep 17 00:00:00 2001 From: Min Date: Wed, 6 Sep 2017 22:45:06 +1200 Subject: [PATCH 31/40] minor bug fix --- model.py | 6 +++--- params.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/model.py b/model.py index 77bc0f7..3265f14 100644 --- a/model.py +++ b/model.py @@ -158,7 +158,7 @@ def loss_function(self): # Use non-sparse softmax self.indices_prob = tf.one_hot(self.indices, shapes[1]) self.mean_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = self.indices_prob, logits = self.points_logits)) - self.optimizer = optimizer_factory[Params.optimizer](Params.opt_arg[Params.optimizer]) + self.optimizer = optimizer_factory[Params.optimizer](**Params.opt_arg[Params.optimizer]) if Params.clip: # gradient clipping by norm @@ -177,11 +177,11 @@ def summary(self): tf.summary.scalar('mean_loss', self.mean_loss) tf.summary.scalar("training_F1_Score",self.F1) tf.summary.scalar("training_Exact_Match",self.EM) - tf.summary.scalar('learning_rate', Params.learning_rate) + tf.summary.scalar('learning_rate', Params.opt_arg[Params.optimizer]['learning_rate']) self.merged = tf.summary.merge_all() def debug(): - model = Model(is_training = False) + model = Model(is_training = True) print("Built model") def test(): diff --git a/params.py b/params.py index 216c79b..e5eda2f 100644 --- a/params.py +++ b/params.py @@ -20,7 +20,7 @@ class Params(): p_chars_dir = "chars_context.txt" # Training - debug = True # Set it to True to debug the computation graph + debug = False # Set it to True to debug the computation graph test = False # Test the model on dev-set dropout = 0.2 # dropout probability optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] From ae187a143d6dd69bb0b5c53fddc2e054e83bded7 Mon Sep 17 00:00:00 2001 From: Min Date: Thu, 7 Sep 2017 17:28:49 +1200 Subject: [PATCH 32/40] minor fix --- data_load.py | 4 +-- model.py | 100 +++++++++++++++++++++++++-------------------------- params.py | 2 +- process.py | 12 ++++--- 4 files changed, 60 insertions(+), 58 deletions(-) diff --git a/data_load.py b/data_load.py index 4982bab..36229b7 100644 --- a/data_load.py +++ b/data_load.py @@ -187,12 +187,12 @@ def get_data(ind): data = get_data(inputs=ind_list, dtypes=[np.int32]*9, capacity=Params.batch_size*32, - num_threads=8) + num_threads=6) # create batch queues batch = tf.train.batch(data, shapes=shapes, - num_threads=8, + num_threads=6, batch_size=Params.batch_size, capacity=Params.batch_size*32, dynamic_pad=True) diff --git a/model.py b/model.py index 3265f14..45ecc70 100644 --- a/model.py +++ b/model.py @@ -15,9 +15,9 @@ from process import * optimizer_factory = {"adadelta":tf.train.AdadeltaOptimizer, - "adam":tf.train.AdamOptimizer, - "gradientdescent":tf.train.GradientDescentOptimizer, - "adagrad":tf.train.AdagradOptimizer} + "adam":tf.train.AdamOptimizer, + "gradientdescent":tf.train.GradientDescentOptimizer, + "adagrad":tf.train.AdagradOptimizer} class Model(object): def __init__(self,is_training = True): @@ -62,41 +62,41 @@ def encode_ids(self): # Embed the question and passage information for word and character tokens self.passage_word_encoded, self.passage_char_encoded = encoding(self.passage_w, - self.passage_c, - word_embeddings = self.word_embeddings, - char_embeddings = self.char_embeddings, - scope = "passage_embeddings") + self.passage_c, + word_embeddings = self.word_embeddings, + char_embeddings = self.char_embeddings, + scope = "passage_embeddings") self.question_word_encoded, self.question_char_encoded = encoding(self.question_w, - self.question_c, - word_embeddings = self.word_embeddings, - char_embeddings = self.char_embeddings, - scope = "question_embeddings") + self.question_c, + word_embeddings = self.word_embeddings, + char_embeddings = self.char_embeddings, + scope = "question_embeddings") self.passage_char_encoded = bidirectional_GRU(self.passage_char_encoded, - self.passage_c_len, - scope = "passage_char_encoding", - output = 1, - is_training = self.is_training) + self.passage_c_len, + scope = "passage_char_encoding", + output = 1, + is_training = self.is_training) self.question_char_encoded = bidirectional_GRU(self.question_char_encoded, - self.question_c_len, - scope = "question_char_encoding", - output = 1, - is_training = self.is_training) + self.question_c_len, + scope = "question_char_encoding", + output = 1, + is_training = self.is_training) self.passage_encoding = tf.concat((self.passage_word_encoded, self.passage_char_encoded),axis = 2) self.question_encoding = tf.concat((self.question_word_encoded, self.question_char_encoded),axis = 2) # Passage and question encoding self.passage_encoding = bidirectional_GRU(self.passage_encoding, - self.passage_w_len, - layers = Params.num_layers, - scope = "passage_encoding", - output = 0, - is_training = self.is_training) + self.passage_w_len, + layers = Params.num_layers, + scope = "passage_encoding", + output = 0, + is_training = self.is_training) self.question_encoding = bidirectional_GRU(self.question_encoding, - self.question_w_len, - layers = Params.num_layers, - scope = "question_encoding", - output = 0, - is_training = self.is_training) + self.question_w_len, + layers = Params.num_layers, + scope = "question_encoding", + output = 0, + is_training = self.is_training) def attention_match_rnn(self): with tf.variable_scope("attention_match_rnn"): @@ -104,13 +104,13 @@ def attention_match_rnn(self): inputs = self.passage_encoding scopes = ["question_passage_matching", "self_matching"] params = [([[self.params["W_u_Q"], - self.params["W_u_P"], - self.params["W_v_P"]], - self.params["v"]],self.params["W_g"]), - ([[tf.concat((self.params["W_v_P"], - self.params["W_v_P"]),axis = 0), - self.params["W_v_Phat"]], - self.params["v"]],self.params["W_g"])] + self.params["W_u_P"], + self.params["W_v_P"]], + self.params["v"]],self.params["W_g"]), + ([[tf.concat((self.params["W_v_P"], + self.params["W_v_P"]),axis = 0), + self.params["W_v_Phat"]], + self.params["v"]],self.params["W_g"])] for i in range(2): if scopes[i] == "question_passage_matching": cell_fw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False) @@ -120,26 +120,26 @@ def attention_match_rnn(self): cell_bw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True) cell = (cell_fw, cell_bw) inputs = attention_rnn(inputs, - self.passage_w_len, - Params.attn_size, - cell, - bidirection = True, - scope = scopes[i]) + self.passage_w_len, + Params.attn_size, + cell, + bidirection = True, + scope = scopes[i]) memory = inputs # self matching (attention over itself) inputs = apply_dropout(inputs, is_training = self.is_training) self.self_matching_output = inputs def bidirectional_readout(self): self.final_bidirectional_outputs = bidirectional_GRU(self.self_matching_output, - self.passage_w_len, - layers = Params.num_layers, - scope = "bidirectional_readout", - output = 0, - is_training = self.is_training) + self.passage_w_len, + layers = Params.num_layers, + scope = "bidirectional_readout", + output = 0, + is_training = self.is_training) def pointer_network(self): params = (([self.params["W_u_Q"],self.params["W_v_Q"]],self.params["v"]), - ([self.params["W_h_P"],self.params["W_h_a"]],self.params["v"])) + ([self.params["W_h_P"],self.params["W_h_a"]],self.params["v"])) cell = apply_dropout(tf.contrib.rnn.GRUCell(Params.attn_size*2), is_training = self.is_training) self.points_logits = pointer_net(self.final_bidirectional_outputs, self.passage_w_len, self.question_encoding, cell, params, scope = "pointer_network") @@ -218,9 +218,9 @@ def main(): config = tf.ConfigProto() config.gpu_options.allow_growth = True sv = tf.train.Supervisor(logdir=Params.logdir, - save_model_secs=0, - global_step = model.global_step, - init_op = model.init_op) + save_model_secs=0, + global_step = model.global_step, + init_op = model.init_op) with sv.managed_session(config = config) as sess: sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) for epoch in range(1, Params.num_epochs+1): diff --git a/params.py b/params.py index e5eda2f..216c79b 100644 --- a/params.py +++ b/params.py @@ -20,7 +20,7 @@ class Params(): p_chars_dir = "chars_context.txt" # Training - debug = False # Set it to True to debug the computation graph + debug = True # Set it to True to debug the computation graph test = False # Test the model on dev-set dropout = 0.2 # dropout probability optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] diff --git a/process.py b/process.py index 7de9468..318a784 100644 --- a/process.py +++ b/process.py @@ -134,11 +134,11 @@ def loop(self, data, dir_ = Params.train_dir): if start_i == -1: self.invalid_q += 1 continue - write_file([str(start_i),str(finish_i)],dir_ + "indices.txt","\n") - write_file(words,dir_ + "words_questions.txt","\n") - write_file(chars,dir_ + "chars_questions.txt","\n") - write_file(words_c,dir_ + "words_context.txt") - write_file(chars_c,dir_ + "chars_context.txt") + write_file([str(start_i),str(finish_i)],dir_ + Params.target_dir) + write_file(words,dir_ + Params.q_word_dir) + write_file(chars,dir_ + Params.q_chars_dir) + write_file(words_c,dir_ + Params.p_word_dir) + write_file(chars_c,dir_ + Params.p_chars_dir) def process_word(self,line): for word in splitted_line: @@ -328,8 +328,10 @@ def main(): loader.process_json(Params.data_dir + "train-v1.1.json", out_dir = Params.train_dir) loader.process_json(Params.data_dir + "dev-v1.1.json", out_dir = Params.dev_dir) pickle.dump(loader, dictionary, pickle.HIGHEST_PROTOCOL) + print("Tokenizing completed successfully") load_glove(Params.glove_dir,"glove",vocab_size = Params.vocab_size) load_glove(Params.glove_char,"glove_char", vocab_size = Params.char_vocab_size) + print("Processing completed successfully") if __name__ == "__main__": main() From 5c06c0ae73d0359c1c9339fccc1cd6613647c2c2 Mon Sep 17 00:00:00 2001 From: Min Date: Tue, 12 Sep 2017 17:44:19 +1200 Subject: [PATCH 33/40] params api change --- layers.py | 4 ++-- model.py | 28 +++++++++++++++------------- params.py | 8 ++++---- process.py | 24 +++++++++++++----------- 4 files changed, 34 insertions(+), 30 deletions(-) diff --git a/layers.py b/layers.py index 948d37b..b80b232 100644 --- a/layers.py +++ b/layers.py @@ -55,8 +55,8 @@ def bidirectional_GRU(inputs, inputs_len, cell = None, units = Params.attn_size, cell_fw = MultiRNNCell([apply_dropout(tf.contrib.rnn.GRUCell(units),is_training = is_training) for _ in range(layers)]) cell_bw = MultiRNNCell([apply_dropout(tf.contrib.rnn.GRUCell(units),is_training = is_training) for _ in range(layers)]) else: - cell_fw = tf.contrib.rnn.GRUCell(units) - cell_bw = tf.contrib.rnn.GRUCell(units) + cell_fw = apply_dropout(tf.contrib.rnn.GRUCell(units), is_training = is_training) + cell_bw = apply_dropout(tf.contrib.rnn.GRUCell(units), is_training = is_training) shapes = inputs.get_shape().as_list() if len(shapes) > 3: diff --git a/model.py b/model.py index 45ecc70..0e26086 100644 --- a/model.py +++ b/model.py @@ -113,11 +113,11 @@ def attention_match_rnn(self): self.params["v"]],self.params["W_g"])] for i in range(2): if scopes[i] == "question_passage_matching": - cell_fw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False) - cell_bw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False) + cell_fw = apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False), is_training = self.is_training) + cell_bw = apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False), is_training = self.is_training) elif scopes[i] == "self_matching": - cell_fw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True) - cell_bw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True) + cell_fw = apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True), is_training = self.is_training) + cell_bw = apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True), is_training = self.is_training) cell = (cell_fw, cell_bw) inputs = attention_rnn(inputs, self.passage_w_len, @@ -126,13 +126,13 @@ def attention_match_rnn(self): bidirection = True, scope = scopes[i]) memory = inputs # self matching (attention over itself) - inputs = apply_dropout(inputs, is_training = self.is_training) + #inputs = apply_dropout(inputs, is_training = self.is_training) self.self_matching_output = inputs def bidirectional_readout(self): self.final_bidirectional_outputs = bidirectional_GRU(self.self_matching_output, self.passage_w_len, - layers = Params.num_layers, + # layers = Params.num_layers, # or 1? not specified in the original paper scope = "bidirectional_readout", output = 0, is_training = self.is_training) @@ -192,9 +192,8 @@ def test(): char_glove = np.memmap(Params.data_dir + "glove_char.np",dtype = np.float32, mode = "r") char_glove = np.reshape(char_glove,(Params.char_vocab_size,Params.emb_size)) with model.graph.as_default(): - sv = tf.train.Supervisor() + sv = tf.train.Supervisor(logdir=Params.logdir) with sv.managed_session() as sess: - sv.saver.restore(sess, tf.train.latest_checkpoint(Params.logdir)) sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) EM, F1 = 0.0, 0.0 for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): @@ -220,13 +219,14 @@ def main(): sv = tf.train.Supervisor(logdir=Params.logdir, save_model_secs=0, global_step = model.global_step, - init_op = model.init_op) + init_op = model.init_op, + summary_op = None) with sv.managed_session(config = config) as sess: sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) for epoch in range(1, Params.num_epochs+1): if sv.should_stop(): break for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): - sess.run(model.train_op) + sess.run([model.train_op, model.merged] if step % Params.summary_steps == 0 else model.train_op) if step % Params.save_steps == 0: sv.saver.save(sess, Params.logdir + '/model_epoch_%d_step_%d'%(epoch,step)) index, ground_truth, passage = sess.run([model.points_logits, model.indices, model.passage_w]) @@ -242,12 +242,14 @@ def main(): print("\nExact_match: {}\nF1_score: {}".format(EM,F1)) if __name__ == '__main__': - if Params.debug == True: + if Params.mode.lower() == "debug": print("Debugging...") debug() - elif Params.test == True: + elif Params.mode.lower() == "test": print("Testing on dev set...") test() - else: + elif Params.mode.lower() == "train": print("Training...") main() + else: + print("Invalid mode.") diff --git a/params.py b/params.py index 216c79b..d7d765c 100644 --- a/params.py +++ b/params.py @@ -7,7 +7,7 @@ class Params(): data_dir = "./data/" train_dir = data_dir + "trainset/" dev_dir = data_dir + "devset/" - logdir = "./train/train_fulldata" + logdir = "./train/train_" glove_dir = "glove.840B.300d.txt" # Glove file name (If you want to use your own glove, replace the file name here) glove_char = "glove.840B.300d.char.txt" # Character Glove file name coreNLP_dir = "./stanford-corenlp-full-2017-06-09" # Directory to pycorenlp wrapper @@ -20,12 +20,12 @@ class Params(): p_chars_dir = "chars_context.txt" # Training - debug = True # Set it to True to debug the computation graph - test = False # Test the model on dev-set + mode = "train" # case-insensitive options: ["train", "test", "debug"] dropout = 0.2 # dropout probability optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] - batch_size = 50 # Size of the mini-batch for training + batch_size = 50 if mode is not "test" else 100# Size of the mini-batch for training save_steps = 50 # Save the model at every 50 steps + summary_steps = 10 # Flush summary every N steps clip = False # clip gradient norm norm = 5.0 # global norm # Change the hyperparameters of your learning algorithms here diff --git a/process.py b/process.py index 318a784..f1a3e51 100644 --- a/process.py +++ b/process.py @@ -35,12 +35,12 @@ def str2bool(v): from stanford_corenlp_pywrapper import CoreNLP proc = CoreNLP("ssplit",corenlp_jars=[Params.coreNLP_dir + "/*"]) -def tokenize_corenlp(text): - parsed = proc.parse_doc(text) - tokens = [] - for sent in parsed['sentences']: - tokens.extend(sent['tokens']) - return tokens + def tokenize_corenlp(text): + parsed = proc.parse_doc(text) + tokens = [] + for sent in parsed['sentences']: + tokens.extend(sent['tokens']) + return tokens class data_loader(object): def __init__(self,use_pretrained = None): @@ -160,8 +160,10 @@ def process_char(self,line): def add_to_dict(self, line): splitted_line = re.split(r'[`\--=~!@#$%^&*\"“”()_+ \[\]{};\\:"|<,./<>?]', line.strip()) splitted_line = [sl for sl in splitted_line if sl] - splitted_line = " ".join(splitted_line) - splitted_line = tokenize_corenlp(splitted_line) + if args.process: + splitted_line = " ".join(splitted_line) + splitted_line = tokenize_corenlp(splitted_line) + if self.append_dict: self.process_word(splitted_line) self.process_char("".join(splitted_line)) @@ -268,7 +270,7 @@ def load_target(dir): count = 0 with codecs.open(dir,"rb","utf-8") as f: line = f.readline() - while count < 1000 if Params.debug else line: + while count < 1000 if Params.mode == "debug" else line: # while count < 1000: line = [int(w) for w in line.split()] data.append(line) @@ -282,7 +284,7 @@ def load_word(dir): count = 0 with codecs.open(dir,"rb","utf-8") as f: line = f.readline() - while count < 1000 if Params.debug else line: + while count < 1000 if Params.mode == "debug" else line: # while count < 1000: line = [int(w) for w in line.split()] data.append(line) @@ -298,7 +300,7 @@ def load_char(dir): count = 0 with codecs.open(dir,"rb","utf-8") as f: line = f.readline() - while count < 1000 if Params.debug else line: + while count < 1000 if Params.mode == "debug" else line: # while count < 1000: c_len = [] chars = [] From 68248262a7af3623788cc69ab4d5c49690338eef Mon Sep 17 00:00:00 2001 From: Min Date: Wed, 13 Sep 2017 12:39:50 +1200 Subject: [PATCH 34/40] fixed tensorboard summary --- model.py | 9 +++++++-- params.py | 2 +- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/model.py b/model.py index 0e26086..722a34d 100644 --- a/model.py +++ b/model.py @@ -220,13 +220,18 @@ def main(): save_model_secs=0, global_step = model.global_step, init_op = model.init_op, - summary_op = None) + summary_op = None, + save_summaries_secs = 15) with sv.managed_session(config = config) as sess: sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) for epoch in range(1, Params.num_epochs+1): if sv.should_stop(): break for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): - sess.run([model.train_op, model.merged] if step % Params.summary_steps == 0 else model.train_op) + if step % Params.summary_steps == 0: + _, summary = sess.run([model.train_op, model.merged]) + sv.summary_computed(sess,summary) + else: + sess.run(model.train_op) if step % Params.save_steps == 0: sv.saver.save(sess, Params.logdir + '/model_epoch_%d_step_%d'%(epoch,step)) index, ground_truth, passage = sess.run([model.points_logits, model.indices, model.passage_w]) diff --git a/params.py b/params.py index d7d765c..5d2bf79 100644 --- a/params.py +++ b/params.py @@ -7,7 +7,7 @@ class Params(): data_dir = "./data/" train_dir = data_dir + "trainset/" dev_dir = data_dir + "devset/" - logdir = "./train/train_" + logdir = "./train/train" glove_dir = "glove.840B.300d.txt" # Glove file name (If you want to use your own glove, replace the file name here) glove_char = "glove.840B.300d.char.txt" # Character Glove file name coreNLP_dir = "./stanford-corenlp-full-2017-06-09" # Directory to pycorenlp wrapper From 2d9d27431ed813696af947dc87b8c93f50a71356 Mon Sep 17 00:00:00 2001 From: Min Date: Thu, 14 Sep 2017 16:40:18 +1200 Subject: [PATCH 35/40] fixed testing bug --- README.md | 1 + model.py | 16 ++++++---------- params.py | 1 - 3 files changed, 7 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 7fd5147..5c01da6 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,7 @@ The dataset used for this task is Stanford Question Answering Dataset (https://r ## Requirements * Python2.7 * NumPy + * nltk * tqdm * TensorFlow == 1.2 diff --git a/model.py b/model.py index 722a34d..81a65a0 100644 --- a/model.py +++ b/model.py @@ -192,8 +192,9 @@ def test(): char_glove = np.memmap(Params.data_dir + "glove_char.np",dtype = np.float32, mode = "r") char_glove = np.reshape(char_glove,(Params.char_vocab_size,Params.emb_size)) with model.graph.as_default(): - sv = tf.train.Supervisor(logdir=Params.logdir) + sv = tf.train.Supervisor() with sv.managed_session() as sess: + sv.saver.restore(sess, tf.train.latest_checkpoint(Params.logdir)) sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) EM, F1 = 0.0, 0.0 for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): @@ -219,21 +220,16 @@ def main(): sv = tf.train.Supervisor(logdir=Params.logdir, save_model_secs=0, global_step = model.global_step, - init_op = model.init_op, - summary_op = None, - save_summaries_secs = 15) + init_op = model.init_op) with sv.managed_session(config = config) as sess: sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) for epoch in range(1, Params.num_epochs+1): if sv.should_stop(): break for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): - if step % Params.summary_steps == 0: - _, summary = sess.run([model.train_op, model.merged]) - sv.summary_computed(sess,summary) - else: - sess.run(model.train_op) + sess.run(model.train_op) if step % Params.save_steps == 0: - sv.saver.save(sess, Params.logdir + '/model_epoch_%d_step_%d'%(epoch,step)) + gs = sess.run(model.global_step) + sv.saver.save(sess, Params.logdir + '/model_epoch_%d_step_%d'%(gs//model.num_batch, gs%model.num_batch)) index, ground_truth, passage = sess.run([model.points_logits, model.indices, model.passage_w]) index = np.argmax(index, axis = 2) F1, EM = 0.0, 0.0 diff --git a/params.py b/params.py index 5d2bf79..7229a4d 100644 --- a/params.py +++ b/params.py @@ -25,7 +25,6 @@ class Params(): optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] batch_size = 50 if mode is not "test" else 100# Size of the mini-batch for training save_steps = 50 # Save the model at every 50 steps - summary_steps = 10 # Flush summary every N steps clip = False # clip gradient norm norm = 5.0 # global norm # Change the hyperparameters of your learning algorithms here From a4d6921e6fe020a76b00c4b9f8fa69fff95df6f2 Mon Sep 17 00:00:00 2001 From: Min Date: Fri, 15 Sep 2017 14:07:41 +1200 Subject: [PATCH 36/40] added recurrent dropout and stopped sharing additive attention parameters --- layers.py | 7 +++---- model.py | 15 ++++++--------- 2 files changed, 9 insertions(+), 13 deletions(-) diff --git a/layers.py b/layers.py index b80b232..7c4b574 100644 --- a/layers.py +++ b/layers.py @@ -28,8 +28,7 @@ def get_attn_params(attn_size,initializer = tf.truncated_normal_initializer): "W_h_P":tf.get_variable("W_h_P",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), "W_v_Phat":tf.get_variable("W_v_Phat",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), "W_h_a":tf.get_variable("W_h_a",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), - "W_v_Q":tf.get_variable("W_v_Q",dtype = tf.float32, shape = (attn_size, attn_size), initializer = initializer()), - "v":tf.get_variable("v", dtype = tf.float32, shape = attn_size, initializer = initializer())} + "W_v_Q":tf.get_variable("W_v_Q",dtype = tf.float32, shape = (attn_size, attn_size), initializer = initializer())} return params def encoding(word, char, word_embeddings, char_embeddings, scope = "embedding"): @@ -42,7 +41,7 @@ def apply_dropout(inputs, dropout = Params.dropout, is_training = True): if not is_training: return inputs if isinstance(inputs, RNNCell): - return tf.contrib.rnn.DropoutWrapper(inputs, output_keep_prob=1.0 - dropout) + return tf.contrib.rnn.DropoutWrapper(inputs, output_keep_prob=1.0 - dropout, variational_recurrent=True, dtype = tf.float32) else: return tf.nn.dropout(inputs, keep_prob = 1.0 - dropout) @@ -127,7 +126,7 @@ def gated_attention(memory, inputs, states, units, params, self_matching = False def attention(inputs, units, weights, scope = "attention", output_fn = "softmax", reuse = None): with tf.variable_scope(scope, reuse = reuse): - weights, v = weights + v = tf.get_variable("v", shape = Params.attn_size, dtype = tf.float32, initializer = tf.contrib.layers.xavier_initializer()) outputs_ = [] for i, (inp,w) in enumerate(zip(inputs,weights)): shapes = inp.shape.as_list() diff --git a/model.py b/model.py index 81a65a0..ca806a1 100644 --- a/model.py +++ b/model.py @@ -103,14 +103,12 @@ def attention_match_rnn(self): memory = self.question_encoding inputs = self.passage_encoding scopes = ["question_passage_matching", "self_matching"] - params = [([[self.params["W_u_Q"], + params = [([self.params["W_u_Q"], self.params["W_u_P"], - self.params["W_v_P"]], - self.params["v"]],self.params["W_g"]), - ([[tf.concat((self.params["W_v_P"], + self.params["W_v_P"]],self.params["W_g"]), + ([tf.concat((self.params["W_v_P"], self.params["W_v_P"]),axis = 0), - self.params["W_v_Phat"]], - self.params["v"]],self.params["W_g"])] + self.params["W_v_Phat"]],self.params["W_g"])] for i in range(2): if scopes[i] == "question_passage_matching": cell_fw = apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False), is_training = self.is_training) @@ -126,7 +124,6 @@ def attention_match_rnn(self): bidirection = True, scope = scopes[i]) memory = inputs # self matching (attention over itself) - #inputs = apply_dropout(inputs, is_training = self.is_training) self.self_matching_output = inputs def bidirectional_readout(self): @@ -138,8 +135,8 @@ def bidirectional_readout(self): is_training = self.is_training) def pointer_network(self): - params = (([self.params["W_u_Q"],self.params["W_v_Q"]],self.params["v"]), - ([self.params["W_h_P"],self.params["W_h_a"]],self.params["v"])) + params = ((self.params["W_u_Q"],self.params["W_v_Q"]), + (self.params["W_h_P"],self.params["W_h_a"])) cell = apply_dropout(tf.contrib.rnn.GRUCell(Params.attn_size*2), is_training = self.is_training) self.points_logits = pointer_net(self.final_bidirectional_outputs, self.passage_w_len, self.question_encoding, cell, params, scope = "pointer_network") From 488686b232ff2aedbffed78875415d6928fb0e87 Mon Sep 17 00:00:00 2001 From: Min Date: Fri, 15 Sep 2017 17:47:55 +1200 Subject: [PATCH 37/40] Added weight sharing option --- layers.py | 5 ++++- model.py | 4 ++-- params.py | 1 + 3 files changed, 7 insertions(+), 3 deletions(-) diff --git a/layers.py b/layers.py index 7c4b574..21aa27d 100644 --- a/layers.py +++ b/layers.py @@ -111,13 +111,14 @@ def question_pooling(memory, units, weights, scope = "question_pooling"): def gated_attention(memory, inputs, states, units, params, self_matching = False, output_argmax = None, scope="gated_attention"): with tf.variable_scope(scope): weights, W_g = params + if W_g is None: + W_g = tf.get_variable("W_g", dtype = tf.float32, shape = (4 * Params.attn_size, 4 * Params.attn_size), initializer = tf.contrib.layers.xavier_initializer()) inputs_ = [memory, inputs] states = tf.reshape(states,(Params.batch_size,Params.attn_size)) if not self_matching: inputs_.append(states) scores = attention(inputs_, units, weights) - # scores = tf.reshape(scores,(shapes[0],shapes[1],1)) scores = tf.expand_dims(scores,-1) attention_pool = tf.reduce_sum(scores * memory, 1) inputs = tf.concat((inputs,attention_pool),axis = 1) @@ -131,6 +132,8 @@ def attention(inputs, units, weights, scope = "attention", output_fn = "softmax" for i, (inp,w) in enumerate(zip(inputs,weights)): shapes = inp.shape.as_list() inp = tf.reshape(inp, (-1, shapes[-1])) + if w is None: + w = tf.get_variable("w_%d"%i, dtype = tf.float32, shape = [shapes[-1],Params.attn_size], initializer = tf.contrib.layers.xavier_initializer()) outputs = tf.matmul(inp, w) if len(shapes) > 2: outputs = tf.reshape(outputs, (shapes[0], shapes[1], -1)) diff --git a/model.py b/model.py index ca806a1..89b4eca 100644 --- a/model.py +++ b/model.py @@ -108,7 +108,7 @@ def attention_match_rnn(self): self.params["W_v_P"]],self.params["W_g"]), ([tf.concat((self.params["W_v_P"], self.params["W_v_P"]),axis = 0), - self.params["W_v_Phat"]],self.params["W_g"])] + self.params["W_v_Phat"]],self.params["W_g"]) if Params.weight_sharing else ([None, self.params["W_v_Phat"]], None)] for i in range(2): if scopes[i] == "question_passage_matching": cell_fw = apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False), is_training = self.is_training) @@ -135,7 +135,7 @@ def bidirectional_readout(self): is_training = self.is_training) def pointer_network(self): - params = ((self.params["W_u_Q"],self.params["W_v_Q"]), + params = ((self.params["W_u_Q"],self.params["W_v_Q"]) if Params.weight_sharing else (None, self.params["W_v_Q"]), (self.params["W_h_P"],self.params["W_h_a"])) cell = apply_dropout(tf.contrib.rnn.GRUCell(Params.attn_size*2), is_training = self.is_training) self.points_logits = pointer_net(self.final_bidirectional_outputs, self.passage_w_len, self.question_encoding, cell, params, scope = "pointer_network") diff --git a/params.py b/params.py index 7229a4d..a6fda08 100644 --- a/params.py +++ b/params.py @@ -23,6 +23,7 @@ class Params(): mode = "train" # case-insensitive options: ["train", "test", "debug"] dropout = 0.2 # dropout probability optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] + weight_sharing = True # Use weight sharing batch_size = 50 if mode is not "test" else 100# Size of the mini-batch for training save_steps = 50 # Save the model at every 50 steps clip = False # clip gradient norm From 179ac21dd50edb38c5400da43545e48fecc69021 Mon Sep 17 00:00:00 2001 From: Min Date: Mon, 18 Sep 2017 17:42:08 +1200 Subject: [PATCH 38/40] Pointer network initial state / Question pooling fix --- layers.py | 11 +++++++---- params.py | 2 +- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/layers.py b/layers.py index 21aa27d..29ac55a 100644 --- a/layers.py +++ b/layers.py @@ -28,7 +28,7 @@ def get_attn_params(attn_size,initializer = tf.truncated_normal_initializer): "W_h_P":tf.get_variable("W_h_P",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), "W_v_Phat":tf.get_variable("W_v_Phat",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), "W_h_a":tf.get_variable("W_h_a",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), - "W_v_Q":tf.get_variable("W_v_Q",dtype = tf.float32, shape = (attn_size, attn_size), initializer = initializer())} + "W_v_Q":tf.get_variable("W_v_Q",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer())} return params def encoding(word, char, word_embeddings, char_embeddings, scope = "embedding"): @@ -102,7 +102,7 @@ def attention_rnn(inputs, inputs_len, units, attn_cell, bidirection = True, scop def question_pooling(memory, units, weights, scope = "question_pooling"): with tf.variable_scope(scope): shapes = memory.get_shape().as_list() - V_r = tf.get_variable("question_param", shape = units, dtype = tf.float32) + V_r = tf.get_variable("question_param", shape = (shapes[1], 2 * units), dtype = tf.float32) inputs_ = [memory, V_r] attn = attention(inputs_, units, weights, scope = "question_attention_pooling") attn = tf.expand_dims(attn, -1) @@ -127,7 +127,6 @@ def gated_attention(memory, inputs, states, units, params, self_matching = False def attention(inputs, units, weights, scope = "attention", output_fn = "softmax", reuse = None): with tf.variable_scope(scope, reuse = reuse): - v = tf.get_variable("v", shape = Params.attn_size, dtype = tf.float32, initializer = tf.contrib.layers.xavier_initializer()) outputs_ = [] for i, (inp,w) in enumerate(zip(inputs,weights)): shapes = inp.shape.as_list() @@ -135,12 +134,16 @@ def attention(inputs, units, weights, scope = "attention", output_fn = "softmax" if w is None: w = tf.get_variable("w_%d"%i, dtype = tf.float32, shape = [shapes[-1],Params.attn_size], initializer = tf.contrib.layers.xavier_initializer()) outputs = tf.matmul(inp, w) + # Hardcoded attention output reshaping. Equation (4), (8), (9) and (11) in the original paper. if len(shapes) > 2: outputs = tf.reshape(outputs, (shapes[0], shapes[1], -1)) - elif len(shapes) == 2: + elif len(shapes) == 2 and shapes[0] is Params.batch_size: outputs = tf.reshape(outputs, (shapes[0],1,-1)) + else: + outputs = tf.reshape(outputs, (1, shapes[0],-1)) outputs_.append(outputs) outputs = sum(outputs_) + v = tf.get_variable("v", shape = Params.attn_size, dtype = tf.float32, initializer = tf.contrib.layers.xavier_initializer()) scores = tf.reduce_sum(tf.tanh(outputs) * v, [-1]) if output_fn == "softmax": return tf.nn.softmax(scores) diff --git a/params.py b/params.py index a6fda08..671f736 100644 --- a/params.py +++ b/params.py @@ -7,7 +7,7 @@ class Params(): data_dir = "./data/" train_dir = data_dir + "trainset/" dev_dir = data_dir + "devset/" - logdir = "./train/train" + logdir = "./train/train_weights" glove_dir = "glove.840B.300d.txt" # Glove file name (If you want to use your own glove, replace the file name here) glove_char = "glove.840B.300d.char.txt" # Character Glove file name coreNLP_dir = "./stanford-corenlp-full-2017-06-09" # Directory to pycorenlp wrapper From b84a42484cd339d21df7804d5b43ee7f8cb5914c Mon Sep 17 00:00:00 2001 From: Min Date: Wed, 20 Sep 2017 14:48:33 +1200 Subject: [PATCH 39/40] changed sequence masking method --- data_load.py | 2 +- layers.py | 23 ++++++++++++++++------- model.py | 23 +++++++++-------------- params.py | 9 +++++---- 4 files changed, 31 insertions(+), 26 deletions(-) diff --git a/data_load.py b/data_load.py index 36229b7..452b845 100644 --- a/data_load.py +++ b/data_load.py @@ -129,7 +129,7 @@ def load_data(dir_): # Get max length to pad p_max_word = np.max(p_word_len) p_max_char = max_value(p_char_len) - q_max_word = np.max(q_word_len) + q_max_word = Params.max_q_len#np.max(q_word_len) q_max_char = max_value(q_char_len) # pad_data diff --git a/layers.py b/layers.py index 29ac55a..517f64f 100644 --- a/layers.py +++ b/layers.py @@ -22,13 +22,15 @@ def get_attn_params(attn_size,initializer = tf.truncated_normal_initializer): with tf.variable_scope("attention_weights"): params = {"W_u_Q":tf.get_variable("W_u_Q",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), + "W_ru_Q":tf.get_variable("W_ru_Q",dtype = tf.float32, shape = (2 * attn_size, 2 * attn_size), initializer = initializer()), "W_u_P":tf.get_variable("W_u_P",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), "W_v_P":tf.get_variable("W_v_P",dtype = tf.float32, shape = (attn_size, attn_size), initializer = initializer()), + "W_v_P_2":tf.get_variable("W_v_P_2",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), "W_g":tf.get_variable("W_g",dtype = tf.float32, shape = (4 * attn_size, 4 * attn_size), initializer = initializer()), "W_h_P":tf.get_variable("W_h_P",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), "W_v_Phat":tf.get_variable("W_v_Phat",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), "W_h_a":tf.get_variable("W_h_a",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), - "W_v_Q":tf.get_variable("W_v_Q",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer())} + "W_v_Q":tf.get_variable("W_v_Q",dtype = tf.float32, shape = (attn_size, 2 * attn_size), initializer = initializer())} return params def encoding(word, char, word_embeddings, char_embeddings, scope = "embedding"): @@ -38,7 +40,7 @@ def encoding(word, char, word_embeddings, char_embeddings, scope = "embedding"): return word_encoding, char_encoding def apply_dropout(inputs, dropout = Params.dropout, is_training = True): - if not is_training: + if not is_training or Params.dropout is None: return inputs if isinstance(inputs, RNNCell): return tf.contrib.rnn.DropoutWrapper(inputs, output_keep_prob=1.0 - dropout, variational_recurrent=True, dtype = tf.float32) @@ -102,7 +104,7 @@ def attention_rnn(inputs, inputs_len, units, attn_cell, bidirection = True, scop def question_pooling(memory, units, weights, scope = "question_pooling"): with tf.variable_scope(scope): shapes = memory.get_shape().as_list() - V_r = tf.get_variable("question_param", shape = (shapes[1], 2 * units), dtype = tf.float32) + V_r = tf.get_variable("question_param", shape = (Params.max_q_len, units), dtype = tf.float32) inputs_ = [memory, V_r] attn = attention(inputs_, units, weights, scope = "question_attention_pooling") attn = tf.expand_dims(attn, -1) @@ -111,13 +113,10 @@ def question_pooling(memory, units, weights, scope = "question_pooling"): def gated_attention(memory, inputs, states, units, params, self_matching = False, output_argmax = None, scope="gated_attention"): with tf.variable_scope(scope): weights, W_g = params - if W_g is None: - W_g = tf.get_variable("W_g", dtype = tf.float32, shape = (4 * Params.attn_size, 4 * Params.attn_size), initializer = tf.contrib.layers.xavier_initializer()) inputs_ = [memory, inputs] states = tf.reshape(states,(Params.batch_size,Params.attn_size)) if not self_matching: inputs_.append(states) - scores = attention(inputs_, units, weights) scores = tf.expand_dims(scores,-1) attention_pool = tf.reduce_sum(scores * memory, 1) @@ -130,6 +129,7 @@ def attention(inputs, units, weights, scope = "attention", output_fn = "softmax" outputs_ = [] for i, (inp,w) in enumerate(zip(inputs,weights)): shapes = inp.shape.as_list() + # print(inp,w) inp = tf.reshape(inp, (-1, shapes[-1])) if w is None: w = tf.get_variable("w_%d"%i, dtype = tf.float32, shape = [shapes[-1],Params.attn_size], initializer = tf.contrib.layers.xavier_initializer()) @@ -143,13 +143,22 @@ def attention(inputs, units, weights, scope = "attention", output_fn = "softmax" outputs = tf.reshape(outputs, (1, shapes[0],-1)) outputs_.append(outputs) outputs = sum(outputs_) - v = tf.get_variable("v", shape = Params.attn_size, dtype = tf.float32, initializer = tf.contrib.layers.xavier_initializer()) + v = tf.get_variable("v", shape = outputs.shape[-1], dtype = tf.float32, initializer = tf.contrib.layers.xavier_initializer()) scores = tf.reduce_sum(tf.tanh(outputs) * v, [-1]) if output_fn == "softmax": return tf.nn.softmax(scores) else: return scores +def cross_entropy_with_sequence_mask(output, target): + cross_entropy = target * tf.log(output + 1e-8) + cross_entropy = -tf.reduce_sum(cross_entropy, 2) + mask = tf.sign(tf.reduce_max(tf.abs(target), 2)) + cross_entropy *= mask + cross_entropy = tf.reduce_sum(cross_entropy, 1) + cross_entropy /= tf.reduce_sum(mask, 1) + return tf.reduce_mean(cross_entropy) + def total_params(): total_parameters = 0 for variable in tf.trainable_variables(): diff --git a/model.py b/model.py index 89b4eca..47f1472 100644 --- a/model.py +++ b/model.py @@ -104,11 +104,12 @@ def attention_match_rnn(self): inputs = self.passage_encoding scopes = ["question_passage_matching", "self_matching"] params = [([self.params["W_u_Q"], - self.params["W_u_P"], - self.params["W_v_P"]],self.params["W_g"]), - ([tf.concat((self.params["W_v_P"], - self.params["W_v_P"]),axis = 0), - self.params["W_v_Phat"]],self.params["W_g"]) if Params.weight_sharing else ([None, self.params["W_v_Phat"]], None)] + self.params["W_u_P"], + self.params["W_v_P"]], + self.params["W_g"]), + ([self.params["W_v_P_2"], + self.params["W_v_Phat"]], + self.params["W_g"]) if Params.weight_sharing else ([None, self.params["W_v_Phat"]], None)] for i in range(2): if scopes[i] == "question_passage_matching": cell_fw = apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False), is_training = self.is_training) @@ -135,7 +136,7 @@ def bidirectional_readout(self): is_training = self.is_training) def pointer_network(self): - params = ((self.params["W_u_Q"],self.params["W_v_Q"]) if Params.weight_sharing else (None, self.params["W_v_Q"]), + params = ((self.params["W_ru_Q"],self.params["W_v_Q"]) if Params.weight_sharing else (None, self.params["W_v_Q"]), (self.params["W_h_P"],self.params["W_h_a"])) cell = apply_dropout(tf.contrib.rnn.GRUCell(Params.attn_size*2), is_training = self.is_training) self.points_logits = pointer_net(self.final_bidirectional_outputs, self.passage_w_len, self.question_encoding, cell, params, scope = "pointer_network") @@ -146,15 +147,9 @@ def outputs(self): def loss_function(self): with tf.variable_scope("loss"): shapes = self.passage_w.shape - self.mask = tf.to_float(tf.sequence_mask(self.passage_w_len, shapes[1])) - self.points_logits *= tf.expand_dims(self.mask,1) - - # Causes NaN error - # self.mean_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = self.indices, logits = self.points_logits)) - - # Use non-sparse softmax self.indices_prob = tf.one_hot(self.indices, shapes[1]) - self.mean_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = self.indices_prob, logits = self.points_logits)) + self.mean_loss = cross_entropy_with_sequence_mask(tf.nn.softmax(self.points_logits), self.indices_prob) + #self.mean_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = self.indices_prob, logits = self.points_logits)) self.optimizer = optimizer_factory[Params.optimizer](**Params.opt_arg[Params.optimizer]) if Params.clip: diff --git a/params.py b/params.py index 671f736..2d09c09 100644 --- a/params.py +++ b/params.py @@ -7,9 +7,9 @@ class Params(): data_dir = "./data/" train_dir = data_dir + "trainset/" dev_dir = data_dir + "devset/" - logdir = "./train/train_weights" - glove_dir = "glove.840B.300d.txt" # Glove file name (If you want to use your own glove, replace the file name here) - glove_char = "glove.840B.300d.char.txt" # Character Glove file name + logdir = "./train/train" + glove_dir = "./glove.840B.300d.txt" # Glove file name (If you want to use your own glove, replace the file name here) + glove_char = "./glove.840B.300d.char.txt" # Character Glove file name coreNLP_dir = "./stanford-corenlp-full-2017-06-09" # Directory to pycorenlp wrapper # Data dir @@ -20,8 +20,9 @@ class Params(): p_chars_dir = "chars_context.txt" # Training + max_q_len = 37 mode = "train" # case-insensitive options: ["train", "test", "debug"] - dropout = 0.2 # dropout probability + dropout = 0.2 # dropout probability if None, don't use dropout optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] weight_sharing = True # Use weight sharing batch_size = 50 if mode is not "test" else 100# Size of the mini-batch for training From e5d482d4be345f6c79eaba1e3693f158b2c2dea8 Mon Sep 17 00:00:00 2001 From: Min Date: Fri, 22 Sep 2017 17:44:10 +1200 Subject: [PATCH 40/40] Updated apis --- README.md | 4 +-- layers.py | 14 +++++----- model.py | 76 ++++++++++++++++++++++++++----------------------------- params.py | 5 ++-- 4 files changed, 47 insertions(+), 52 deletions(-) diff --git a/README.md b/README.md index 5c01da6..3d36f8a 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ $ bash setup.sh $ python process.py --process True ``` -# Training / Testing / Debug +# Training / Testing / Debugging You can change the hyperparameters from params.py file to fit the model in your GPU. To train the model, run the following line. ```shell $ python model.py @@ -36,7 +36,7 @@ $ tensorboard --logdir=r-net:train/ # Log **05/09/17** -After rewriting some part of the architectures, the model converges with full dataset and it takes about 20 hours to reach F1/EM=67/60 on training set and 40/30 on dev set. with batch size of 54. Reproducing the results obtained by R-Net in the original paper is a new work in progress. +After rewriting the architectures, the model converges with full dataset and it takes about 20 hours to reach F1/EM=67/60 on training set and 40/30 on dev set. with batch size of 54. Reproducing the results obtained by R-Net in the original paper is a new work in progress. **02/09/17** One of the challenges I faced while training was to fit a minibatch of size 32 or larger into my GTX 1080. Since SQuAD dataset displayed high variance in data, higher batch size was essential in training (otherwise the model doesn't converge). Reducing GPU memory usage significantly to fit batch size of 32 and higher is a work in progress. If you have any suggestions on reducing the GPU memory usage, please put forward a pr. diff --git a/layers.py b/layers.py index 517f64f..7b7a0e6 100644 --- a/layers.py +++ b/layers.py @@ -43,7 +43,7 @@ def apply_dropout(inputs, dropout = Params.dropout, is_training = True): if not is_training or Params.dropout is None: return inputs if isinstance(inputs, RNNCell): - return tf.contrib.rnn.DropoutWrapper(inputs, output_keep_prob=1.0 - dropout, variational_recurrent=True, dtype = tf.float32) + return tf.contrib.rnn.DropoutWrapper(inputs, output_keep_prob=1.0 - dropout, dtype = tf.float32) else: return tf.nn.dropout(inputs, keep_prob = 1.0 - dropout) @@ -53,11 +53,11 @@ def bidirectional_GRU(inputs, inputs_len, cell = None, units = Params.attn_size, (cell_fw, cell_bw) = cell else: if layers > 1: - cell_fw = MultiRNNCell([apply_dropout(tf.contrib.rnn.GRUCell(units),is_training = is_training) for _ in range(layers)]) - cell_bw = MultiRNNCell([apply_dropout(tf.contrib.rnn.GRUCell(units),is_training = is_training) for _ in range(layers)]) + cell_fw = MultiRNNCell([tf.contrib.rnn.GRUCell(units) for _ in range(layers)]) + cell_bw = MultiRNNCell([tf.contrib.rnn.GRUCell(units) for _ in range(layers)]) else: - cell_fw = apply_dropout(tf.contrib.rnn.GRUCell(units), is_training = is_training) - cell_bw = apply_dropout(tf.contrib.rnn.GRUCell(units), is_training = is_training) + cell_fw = tf.contrib.rnn.GRUCell(units) + cell_bw = tf.contrib.rnn.GRUCell(units) shapes = inputs.get_shape().as_list() if len(shapes) > 3: @@ -104,7 +104,7 @@ def attention_rnn(inputs, inputs_len, units, attn_cell, bidirection = True, scop def question_pooling(memory, units, weights, scope = "question_pooling"): with tf.variable_scope(scope): shapes = memory.get_shape().as_list() - V_r = tf.get_variable("question_param", shape = (Params.max_q_len, units), dtype = tf.float32) + V_r = tf.get_variable("question_param", shape = (Params.max_q_len, units), initializer = tf.contrib.layers.xavier_initializer(), dtype = tf.float32) inputs_ = [memory, V_r] attn = attention(inputs_, units, weights, scope = "question_attention_pooling") attn = tf.expand_dims(attn, -1) @@ -129,7 +129,6 @@ def attention(inputs, units, weights, scope = "attention", output_fn = "softmax" outputs_ = [] for i, (inp,w) in enumerate(zip(inputs,weights)): shapes = inp.shape.as_list() - # print(inp,w) inp = tf.reshape(inp, (-1, shapes[-1])) if w is None: w = tf.get_variable("w_%d"%i, dtype = tf.float32, shape = [shapes[-1],Params.attn_size], initializer = tf.contrib.layers.xavier_initializer()) @@ -159,6 +158,7 @@ def cross_entropy_with_sequence_mask(output, target): cross_entropy /= tf.reduce_sum(mask, 1) return tf.reduce_mean(cross_entropy) + def total_params(): total_parameters = 0 for variable in tf.trainable_variables(): diff --git a/model.py b/model.py index 47f1472..aa2878a 100644 --- a/model.py +++ b/model.py @@ -21,6 +21,7 @@ class Model(object): def __init__(self,is_training = True): + # Build the computational graph when initializing self.is_training = is_training self.graph = tf.Graph() with self.graph.as_default(): @@ -35,6 +36,7 @@ def __init__(self,is_training = True): self.passage_c_len, self.question_c_len, self.indices) = data + self.passage_w_len = tf.squeeze(self.passage_w_len) self.question_w_len = tf.squeeze(self.question_w_len) @@ -72,33 +74,34 @@ def encode_ids(self): char_embeddings = self.char_embeddings, scope = "question_embeddings") self.passage_char_encoded = bidirectional_GRU(self.passage_char_encoded, - self.passage_c_len, - scope = "passage_char_encoding", - output = 1, - is_training = self.is_training) + self.passage_c_len, + scope = "passage_char_encoding", + output = 1, + is_training = self.is_training) self.question_char_encoded = bidirectional_GRU(self.question_char_encoded, - self.question_c_len, - scope = "question_char_encoding", - output = 1, - is_training = self.is_training) + self.question_c_len, + scope = "question_char_encoding", + output = 1, + is_training = self.is_training) self.passage_encoding = tf.concat((self.passage_word_encoded, self.passage_char_encoded),axis = 2) self.question_encoding = tf.concat((self.question_word_encoded, self.question_char_encoded),axis = 2) # Passage and question encoding self.passage_encoding = bidirectional_GRU(self.passage_encoding, - self.passage_w_len, - layers = Params.num_layers, - scope = "passage_encoding", - output = 0, - is_training = self.is_training) + self.passage_w_len, + layers = Params.num_layers, + scope = "passage_encoding", + output = 0, + is_training = self.is_training) self.question_encoding = bidirectional_GRU(self.question_encoding, - self.question_w_len, - layers = Params.num_layers, - scope = "question_encoding", - output = 0, - is_training = self.is_training) + self.question_w_len, + layers = Params.num_layers, + scope = "question_encoding", + output = 0, + is_training = self.is_training) def attention_match_rnn(self): + # Apply gated attention recurrent network for both query-passage matching and self matching networks with tf.variable_scope("attention_match_rnn"): memory = self.question_encoding inputs = self.passage_encoding @@ -109,34 +112,28 @@ def attention_match_rnn(self): self.params["W_g"]), ([self.params["W_v_P_2"], self.params["W_v_Phat"]], - self.params["W_g"]) if Params.weight_sharing else ([None, self.params["W_v_Phat"]], None)] + self.params["W_g"])] for i in range(2): - if scopes[i] == "question_passage_matching": - cell_fw = apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False), is_training = self.is_training) - cell_bw = apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False), is_training = self.is_training) - elif scopes[i] == "self_matching": - cell_fw = apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True), is_training = self.is_training) - cell_bw = apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True), is_training = self.is_training) - cell = (cell_fw, cell_bw) + cell_fw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False if i == 0 else True) + cell_bw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False if i == 0 else True) inputs = attention_rnn(inputs, self.passage_w_len, Params.attn_size, - cell, - bidirection = True, + (cell_fw, cell_bw), scope = scopes[i]) memory = inputs # self matching (attention over itself) self.self_matching_output = inputs def bidirectional_readout(self): self.final_bidirectional_outputs = bidirectional_GRU(self.self_matching_output, - self.passage_w_len, - # layers = Params.num_layers, # or 1? not specified in the original paper - scope = "bidirectional_readout", - output = 0, - is_training = self.is_training) + self.passage_w_len, + # layers = Params.num_layers, # or 1? not specified in the original paper + scope = "bidirectional_readout", + output = 0, + is_training = self.is_training) def pointer_network(self): - params = ((self.params["W_ru_Q"],self.params["W_v_Q"]) if Params.weight_sharing else (None, self.params["W_v_Q"]), + params = ((self.params["W_ru_Q"],self.params["W_v_Q"]), (self.params["W_h_P"],self.params["W_h_a"])) cell = apply_dropout(tf.contrib.rnn.GRUCell(Params.attn_size*2), is_training = self.is_training) self.points_logits = pointer_net(self.final_bidirectional_outputs, self.passage_w_len, self.question_encoding, cell, params, scope = "pointer_network") @@ -149,7 +146,6 @@ def loss_function(self): shapes = self.passage_w.shape self.indices_prob = tf.one_hot(self.indices, shapes[1]) self.mean_loss = cross_entropy_with_sequence_mask(tf.nn.softmax(self.points_logits), self.indices_prob) - #self.mean_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = self.indices_prob, logits = self.points_logits)) self.optimizer = optimizer_factory[Params.optimizer](**Params.opt_arg[Params.optimizer]) if Params.clip: @@ -203,16 +199,16 @@ def main(): model = Model(is_training = True); print("Built model") dict_ = pickle.load(open(Params.data_dir + "dictionary.pkl","r")) glove = np.memmap(Params.data_dir + "glove.np", dtype = np.float32, mode = "r") - glove = np.reshape(glove,(Params.vocab_size,300)) + glove = np.reshape(glove,(Params.vocab_size,Params.emb_size)) char_glove = np.memmap(Params.data_dir + "glove_char.np",dtype = np.float32, mode = "r") - char_glove = np.reshape(char_glove,(Params.char_vocab_size,300)) + char_glove = np.reshape(char_glove,(Params.char_vocab_size,Params.emb_size)) with model.graph.as_default(): config = tf.ConfigProto() config.gpu_options.allow_growth = True sv = tf.train.Supervisor(logdir=Params.logdir, - save_model_secs=0, - global_step = model.global_step, - init_op = model.init_op) + save_model_secs=0, + global_step = model.global_step, + init_op = model.init_op) with sv.managed_session(config = config) as sess: sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) for epoch in range(1, Params.num_epochs+1): diff --git a/params.py b/params.py index 2d09c09..da7d37c 100644 --- a/params.py +++ b/params.py @@ -10,7 +10,7 @@ class Params(): logdir = "./train/train" glove_dir = "./glove.840B.300d.txt" # Glove file name (If you want to use your own glove, replace the file name here) glove_char = "./glove.840B.300d.char.txt" # Character Glove file name - coreNLP_dir = "./stanford-corenlp-full-2017-06-09" # Directory to pycorenlp wrapper + coreNLP_dir = "./stanford-corenlp-full-2017-06-09" # Directory to Stanford coreNLP tool # Data dir target_dir = "indices.txt" @@ -22,9 +22,8 @@ class Params(): # Training max_q_len = 37 mode = "train" # case-insensitive options: ["train", "test", "debug"] - dropout = 0.2 # dropout probability if None, don't use dropout + dropout = None # dropout probability if None, don't use dropout optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] - weight_sharing = True # Use weight sharing batch_size = 50 if mode is not "test" else 100# Size of the mini-batch for training save_steps = 50 # Save the model at every 50 steps clip = False # clip gradient norm

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zLH)SLiWGBIEcZL%j!d_FL`=F8!4VC05~T-pk(#FF^W-}9OM5pl zfIznd^u>!o?pQM)`c(%^p90(LNay8;-|AoXX2N;DJ@SEG;mb?teY))Ugnz%4!XOSV t4|+ySLnY>j{e0Qq3IF>4`oZIiD}g)foG)shVcLJOq{QSA1tNOy{s$RSdJzBs literal 0 HcmV?d00001 From 15715943da4acfa8b5e9ec99a987f63ac825c9b0 Mon Sep 17 00:00:00 2001 From: Min Date: Mon, 28 Aug 2017 14:28:13 +1200 Subject: [PATCH 05/40] README --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index ee7681b..191b9b6 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,9 @@ # R-NET: MACHINE READING COMPREHENSION WITH SELF MATCHING NETWORKS Tensorflow implementation of https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf +![Alt text](/../dev/screenshots/architecture.png?raw=true "R-NET") -Currently I haven't trained with the full SQuAD dataset. +Training with full SQuAD dataset is currently a work in progress. The dataset used for this task is Stanford Question Answering Dataset (https://rajpurkar.github.io/SQuAD-explorer/). @@ -32,6 +33,7 @@ Run tensorboard for visualisation. ```shell $ tensorboard --logdir=r-net:r_net/ ``` +![Alt text](/../dev/screenshots/graph.png?raw=true "Tensorboard Graph") # Note As a sanity check I trained the network with 3000 independent randomly sampled question-answering pairs. With my GTX 1080, it took about 4 hours and a half for the model to get the gist of what's going on with the data. With full dataset (90,000+ pairs) we are expecting longer time for convergence. From c3c49daa4698c91206e8f64b7dec85b0c61670bc Mon Sep 17 00:00:00 2001 From: Min Date: Mon, 28 Aug 2017 14:44:19 +1200 Subject: [PATCH 06/40] added LICENSE --- LICENSE | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..927cb60 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2017 Min Sang Kim + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. From ca76ea4175d598b9b7f5d82e44f8f9b2fbbd6262 Mon Sep 17 00:00:00 2001 From: Min Date: Tue, 29 Aug 2017 12:20:27 +1200 Subject: [PATCH 07/40] fixed README --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index 191b9b6..ab4ae40 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,6 @@ The dataset used for this task is Stanford Question Answering Dataset (https://r ## Requirements * NumPy - * librosa * tqdm * TensorFlow == 1.2 From 426d9ab4c55fa03d536c0e38d59d6d32bbff7de4 Mon Sep 17 00:00:00 2001 From: Min Date: Wed, 30 Aug 2017 14:16:55 +1200 Subject: [PATCH 08/40] updated to use pretrained character embeddings --- layers.py | 27 +++++++-------------------- model.py | 21 +++++++++++++++------ params.py | 9 +++++---- process.py | 53 ++++++++++++++++++++++++++++------------------------- 4 files changed, 55 insertions(+), 55 deletions(-) diff --git a/layers.py b/layers.py index 3a06a13..b61a51e 100644 --- a/layers.py +++ b/layers.py @@ -11,10 +11,10 @@ W_u^Q.shape: (2 * attn_size, attn_size) W_u^P.shape: (2 * attn_size, attn_size) W_v^P.shape: (attn_size, attn_size) -W_g.shape: (2 * attn_size, 2 * attn_size) +W_g.shape: (4 * attn_size, 4 * attn_size) W_h^P.shape: (2 * attn_size, attn_size) -W_v^Phat.shape: (attn_size, attn_size) -W_h^a.shape: (attn_size, attn_size) +W_v^Phat.shape: (2 * attn_size, attn_size) +W_h^a.shape: (2 * attn_size, attn_size) W_v^Q.shape: (attn_size, attn_size) ''' @@ -31,23 +31,10 @@ def get_attn_params(attn_size,initializer = tf.contrib.layers.xavier_initializer "v":tf.get_variable("v", dtype = tf.float32, shape = attn_size, initializer = initializer)} return params -def encoding(word, char, units = 75, word_embedding = None, scope = "embedding", reuse = None): - with tf.variable_scope(scope, reuse = reuse): - if word_embedding is not None: - word_encoding = tf.nn.embedding_lookup(word_embedding, word) - else: - with tf.device('/cpu:0'): - embedding = tf.get_variable("lookup_table", - dtype = tf.float32, - shape = [Params.vocab_size, units], - initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.01)) - word_encoding = tf.nn.embedding_lookup(embedding, word) - - char_embedding = tf.get_variable("char_lookup_table", - dtype = tf.float32, - shape = [Params.char_vocab_size+1, units], - initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.01)) - char_encoding = tf.nn.embedding_lookup(char_embedding, char) +def encoding(word, char, word_embeddings, char_embeddings, scope = "embedding"): + with tf.variable_scope(scope): + word_encoding = tf.nn.embedding_lookup(word_embeddings, word) + char_encoding = tf.nn.embedding_lookup(char_embeddings, char) return word_encoding, char_encoding def apply_dropout(cell,dropout = 0.2, is_training = True): diff --git a/model.py b/model.py index 4ab2d68..f9d0a82 100644 --- a/model.py +++ b/model.py @@ -43,14 +43,18 @@ def encode_ids(self): with tf.device('/cpu:0'): self.word_embeddings = tf.Variable(tf.constant(0.0, shape=[Params.vocab_size, Params.emb_size]),trainable=False, name="word_embeddings") self.word_embeddings_placeholder = tf.placeholder(tf.float32,[Params.vocab_size, Params.emb_size],"word_embeddings_placeholder") - self.emb_assign = self.word_embeddings.assign(self.word_embeddings_placeholder) + self.char_embeddings = tf.Variable(tf.constant(0.0, shape=[Params.char_vocab_size, Params.emb_size]),trainable=False, name="char_embeddings") + self.char_embeddings_placeholder = tf.placeholder(tf.float32,[Params.char_vocab_size, Params.emb_size],"char_embeddings_placeholder") + self.emb_assign = tf.group(tf.assign(self.word_embeddings, self.word_embeddings_placeholder),tf.assign(self.char_embeddings, self.char_embeddings_placeholder)) self.passage_word_encoded, self.passage_char_encoded = encoding(self.passage_w, self.passage_c, - word_embedding = self.word_embeddings) + word_embeddings = self.word_embeddings, + char_embeddings = self.char_embeddings) self.question_word_encoded, self.question_char_encoded = encoding(self.question_w, self.question_c, - word_embedding = self.word_embeddings, reuse = True) + word_embeddings = self.word_embeddings, + char_embeddings = self.char_embeddings) self.passage_char_encoded = bidirectional_GRU(self.passage_char_encoded, self.passage_c_len, scope = "passage_char_encoding", @@ -142,15 +146,20 @@ def debug(): def main(): model = Model(is_training = True); print("Built model") - glove = np.memmap(Params.data_dir + "glove.np",dtype = np.float32,mode = "r") - glove = np.asarray(np.reshape(glove,(Params.vocab_size,300))) + glove = np.memmap(Params.data_dir + "glove.np", dtype = np.float32, mode = "r") + glove = np.reshape(glove,(Params.vocab_size,300)) + char_glove = np.memmap(Params.data_dir + "glove_char.np",dtype = np.float32, mode = "r") + char_glove = np.reshape(char_glove,(Params.char_vocab_size,300)) with model.graph.as_default(): + print("loading model...") sv = tf.train.Supervisor(logdir=Params.logdir, save_model_secs=0, global_step = model.global_step, init_op = model.init_op) with sv.managed_session() as sess: - sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove}) + print("assign embeddings...") + sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) + print("embeddings assigned...") for epoch in range(1, Params.num_epochs+1): if sv.should_stop(): break for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): diff --git a/params.py b/params.py index 1f57eab..91a97fd 100644 --- a/params.py +++ b/params.py @@ -1,11 +1,12 @@ class Params(): # data - data_size = 3000 + data_size = 80000 num_epochs = 100 data_dir = "./data/" - logdir = "./train/train" + logdir = "./train/fix_char" glove_dir = "glove.840B.300d.txt" + glove_char = "glove.840B.300d.char.txt" target_dir = data_dir + "/indices.txt" q_word_dir = data_dir + "/words_questions.txt" q_chars_dir = data_dir + "/chars_questions.txt" @@ -14,12 +15,12 @@ class Params(): coreNLP_dir = "./stanford-corenlp-full-2017-06-09" # model - debug = False + debug = False max_len = 200 save_steps = 50 learning_rate = 1 vocab_size = 2196018 - char_vocab_size = 77 + char_vocab_size = 95 batch_size = 32 train_prop = 0.9 emb_size = 300 diff --git a/process.py b/process.py index 59c8a21..b7648c8 100644 --- a/process.py +++ b/process.py @@ -42,7 +42,7 @@ def tokenize_corenlp(text): return tokens class data_loader(object): - def __init__(self,pretrained = None): + def __init__(self,use_pretrained = None): self.c_dict = {"_UNK":0} self.w_dict = {"_UNK":0} self.w_occurence = 0 @@ -54,10 +54,12 @@ def __init__(self,pretrained = None): self.append_dict = True self.invalid_q = 0 - if pretrained: + if use_pretrained: self.append_dict = False - self.process_vocab(pretrained) + self.w_dict, self.w_count = self.process_glove(Params.glove_dir, self.w_dict, self.w_count) + self.c_dict, self.c_count = self.process_glove(Params.glove_char, self.c_dict, self.c_count) self.ids2word = {v: k for k, v in self.w_dict.iteritems()} + self.ids2char = {v: k for k, v in self.c_dict.iteritems()} def ind2word(self,ids): output = [] @@ -73,27 +75,26 @@ def ind2char(self,ids): output.append(" ") return "".join(output) - def process_vocab(self,wordvecs): + def process_glove(self, wordvecs, dict_, count): + print("Reading GloVe from: {}".format(wordvecs)) with codecs.open(wordvecs,"rb","utf-8") as f: line = f.readline() i = 0 while line: vocab = line.split(" ") - if len(vocab) != 301: + if len(vocab) != Params.emb_size + 1: line = f.readline() continue - vocab = normalize_text(''.join(vocab[0:-300]).decode("utf-8")) - self.process_char(vocab) - if vocab in self.w_dict: - self.w_count += 1 - if vocab not in self.w_dict: - self.w_dict[vocab] = self.w_count - self.w_count += 1 + vocab = normalize_text(''.join(vocab[0:-Params.emb_size]).decode("utf-8")) + if vocab not in dict_: + dict_[vocab] = count line = f.readline() + count += 1 i += 1 if i % 100 == 0: sys.stdout.write("\rProcessing line %d"%i) - print("\n") + print("") + return dict_, count def process_json(self,dir): self.data = json.load(codecs.open(dir,"rb","utf-8")) @@ -146,7 +147,7 @@ def process_word(self,line): self.w_count += 1 def process_char(self,line): - for char in re.sub(r"[^a-zA-Z0-9'()_;:\[\]\-\"\)\(.,?]", "", line.strip()): + for char in line.strip(): if char: if char != " ": if not char in self.c_dict: @@ -160,8 +161,8 @@ def add_to_dict(self, line): splitted_line = tokenize_corenlp(splitted_line) if self.append_dict: self.process_word(splitted_line) + self.process_char("".join(splitted_line)) - self.process_char("".join(splitted_line)) words = [] chars = [] for i,word in enumerate(splitted_line): @@ -184,8 +185,8 @@ def add_to_dict(self, line): self.w_unknown_count += 1 return (words, chars) -def load_glove(dir_): - glove = np.zeros((Params.vocab_size,300),dtype = np.float32) +def load_glove(dir_, name, vocab_size): + glove = np.zeros((vocab_size,Params.emb_size),dtype = np.float32) with codecs.open(dir_,"rb","utf-8") as f: line = f.readline() i = 1 @@ -193,10 +194,10 @@ def load_glove(dir_): if i % 100 == 0: sys.stdout.write("\rProcessing %d vocabs"%i) vector = line.split(" ") - if len(vector) != 301: + if len(vector) != Params.emb_size + 1: line = f.readline() continue - vector = vector[-300:] + vector = vector[-Params.emb_size:] if vector: try: vector = [float(n) for n in vector] @@ -209,9 +210,10 @@ def load_glove(dir_): assert 0 line = f.readline() i += 1 - print("\n") - glove_map = np.memmap(Params.data_dir + "glove.np", dtype='float32', mode='write', shape=(Params.vocab_size,300)) + print("") + glove_map = np.memmap(Params.data_dir + name + ".np", dtype='float32', mode='write', shape=(vocab_size,Params.emb_size)) glove_map[:] = glove + del glove_map def find_answer_index(context, answer): window_len = len(answer) @@ -319,10 +321,11 @@ def max_value(inputlist): def main(): with open(Params.data_dir + 'dictionary.pkl','wb') as dictionary: - loader = data_loader(pretrained = Params.glove_dir) - loader.process_json(Params.data_dir + "train-v1.1.json") - pickle.dump(loader, dictionary, pickle.HIGHEST_PROTOCOL) - load_glove(Params.glove_dir) + loader = data_loader(use_pretrained = True) + loader.process_json(Params.data_dir + "train-v1.1.json") + pickle.dump(loader, dictionary, pickle.HIGHEST_PROTOCOL) + load_glove(Params.glove_dir,"glove",vocab_size = Params.vocab_size) + load_glove(Params.glove_char,"glove_char", vocab_size = Params.char_vocab_size) if __name__ == "__main__": main() From 05ee774df39bcfae6d1b072db34d318c751d62ae Mon Sep 17 00:00:00 2001 From: Min Date: Wed, 30 Aug 2017 14:24:25 +1200 Subject: [PATCH 09/40] added character embeddings --- README.md | 2 +- glove.840B.300d.char.txt | 94 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 95 insertions(+), 1 deletion(-) create mode 100644 glove.840B.300d.char.txt diff --git a/README.md b/README.md index ab4ae40..082e2eb 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ Tensorflow implementation of https://www.microsoft.com/en-us/research/wp-content Training with full SQuAD dataset is currently a work in progress. -The dataset used for this task is Stanford Question Answering Dataset (https://rajpurkar.github.io/SQuAD-explorer/). +The dataset used for this task is Stanford Question Answering Dataset (https://rajpurkar.github.io/SQuAD-explorer/). Pretrained GloVe embeddings are used for both words (https://nlp.stanford.edu/projects/glove/) and characters (https://github.com/minimaxir/char-embeddings/blob/master/glove.840B.300d-char.txt). ## Requirements * NumPy diff --git a/glove.840B.300d.char.txt b/glove.840B.300d.char.txt new file mode 100644 index 0000000..2ff2303 --- /dev/null +++ b/glove.840B.300d.char.txt @@ -0,0 +1,94 @@ +$ 0.144288 -0.460809 0.205078 0.061188 -0.057054 -0.048921 -0.164738 0.132241 0.139505 -1.45673 0.396798 0.277961 -0.174046 0.278037 0.336294 0.098585 0.17377 -0.848967 0.396099 -0.202508 -0.160806 0.241847 -0.138796 0.136647 -0.041472 -0.287252 0.121254 -0.026738 -0.162418 0.044436 0.091555 -0.136861 0.178595 -0.000123 -0.14282 0.219414 0.032894 -0.059294 0.282107 0.438588 0.097691 -0.132703 0.079615 0.09284 -0.133715 -0.028065 0.219812 -0.021644 -0.15124 -0.064424 0.188657 -0.140944 -0.015666 0.301707 0.176221 -0.228171 0.117744 0.024572 0.151532 0.130388 -0.093589 -0.063591 -0.213963 -0.664897 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tensorboard --logdir=r-net:r_net/ +$ tensorboard --logdir=r-net:train/ ``` ![Alt text](/../dev/screenshots/graph.png?raw=true "Tensorboard Graph") diff --git a/params.py b/params.py index 91a97fd..adf8710 100644 --- a/params.py +++ b/params.py @@ -4,7 +4,7 @@ class Params(): data_size = 80000 num_epochs = 100 data_dir = "./data/" - logdir = "./train/fix_char" + logdir = "./train/train" glove_dir = "glove.840B.300d.txt" glove_char = "glove.840B.300d.char.txt" target_dir = data_dir + "/indices.txt" @@ -15,14 +15,14 @@ class Params(): coreNLP_dir = "./stanford-corenlp-full-2017-06-09" # model - debug = False - max_len = 200 - save_steps = 50 - learning_rate = 1 - vocab_size = 2196018 - char_vocab_size = 95 - batch_size = 32 - train_prop = 0.9 - emb_size = 300 - attn_size = 75 - num_layers = 3 + debug = False # Set it to True to debug the computation graph + max_len = 200 # Maximum number of words in each passage context + save_steps = 50 # Save the model at every 50 steps + learning_rate = 1 # Adadelta doesn't require initial learning rate + vocab_size = 2196018 # Number of vocabs in glove.840B.300d.txt + 1 for an unknown token + char_vocab_size = 95 # Number of characters in glove.840B.300d.char.txt + 1 for an unknown character + batch_size = 16 + train_prop = 0.9 # Not implemented + emb_size = 300 # Embeddings size for both words and characters + attn_size = 75 # RNN celland attention module size + num_layers = 3 # Number of layers at question-passage matching and self matching network From a13165f8e8dc9a35ea7397dce28afe5a532f7745 Mon Sep 17 00:00:00 2001 From: Min Date: Wed, 30 Aug 2017 15:03:52 +1200 Subject: [PATCH 11/40] Changed training scheme --- model.py | 8 +++----- params.py | 5 +++-- 2 files changed, 6 insertions(+), 7 deletions(-) diff --git a/model.py b/model.py index f9d0a82..f64afd2 100644 --- a/model.py +++ b/model.py @@ -17,6 +17,7 @@ def __init__(self,is_training = True): self.graph = tf.Graph() with self.graph.as_default(): if is_training: + self.global_step = tf.Variable(0, name='global_step', trainable=False) data, self.num_batch = get_batch() (self.passage_w, self.question_w, @@ -120,8 +121,8 @@ def loss_function(self): self.points_logits *= tf.expand_dims(mask,1) self.mean_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = self.indices, logits = self.points_logits)) - self.optimizer = tf.train.AdadeltaOptimizer(learning_rate = Params.learning_rate, epsilon = 1e-06) - self.global_step = tf.Variable(0, name='global_step', trainable=False) + # self.optimizer = tf.train.AdadeltaOptimizer(learning_rate = Params.learning_rate, epsilon = 1e-06) + self.optimizer = tf.train.AdamOptimizer(learning_rate = Params.learning_rate) # gradient clipping by norm gradients, variables = zip(*self.optimizer.compute_gradients(self.mean_loss)) @@ -151,15 +152,12 @@ def main(): char_glove = np.memmap(Params.data_dir + "glove_char.np",dtype = np.float32, mode = "r") char_glove = np.reshape(char_glove,(Params.char_vocab_size,300)) with model.graph.as_default(): - print("loading model...") sv = tf.train.Supervisor(logdir=Params.logdir, save_model_secs=0, global_step = model.global_step, init_op = model.init_op) with sv.managed_session() as sess: - print("assign embeddings...") sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) - print("embeddings assigned...") for epoch in range(1, Params.num_epochs+1): if sv.should_stop(): break for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): diff --git a/params.py b/params.py index adf8710..6a1863b 100644 --- a/params.py +++ b/params.py @@ -4,7 +4,7 @@ class Params(): data_size = 80000 num_epochs = 100 data_dir = "./data/" - logdir = "./train/train" + logdir = "./train/adam" glove_dir = "glove.840B.300d.txt" glove_char = "glove.840B.300d.char.txt" target_dir = data_dir + "/indices.txt" @@ -18,7 +18,8 @@ class Params(): debug = False # Set it to True to debug the computation graph max_len = 200 # Maximum number of words in each passage context save_steps = 50 # Save the model at every 50 steps - learning_rate = 1 # Adadelta doesn't require initial learning rate + # NOTE Used AdamOptimizer for now, as AdadeltaOptimizer causes NaN error + learning_rate = 0.001 # Adadelta doesn't require initial learning rate vocab_size = 2196018 # Number of vocabs in glove.840B.300d.txt + 1 for an unknown token char_vocab_size = 95 # Number of characters in glove.840B.300d.char.txt + 1 for an unknown character batch_size = 16 From 6aa83dbd6e196a921d3737a2b4c5943f5f53dc21 Mon Sep 17 00:00:00 2001 From: Min Date: Wed, 30 Aug 2017 16:27:54 +1200 Subject: [PATCH 12/40] Important fix --- process.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/process.py b/process.py index b7648c8..a13d897 100644 --- a/process.py +++ b/process.py @@ -120,7 +120,7 @@ def loop(self,data): para['context'] = pattern.sub(lambda m: cond[re.escape(m.group(0))], para['context']) words_c,chars_c = self.add_to_dict(para['context']) - if len(words_c) > Params.max_len: + if len(words_c) >= Params.max_len: continue for qas in para['qas']: From 15e1c8de99a03cd8fef61c52d022e97ce04581fa Mon Sep 17 00:00:00 2001 From: Min Date: Wed, 30 Aug 2017 17:48:28 +1200 Subject: [PATCH 13/40] clean up --- model.py | 25 +++++++++++++++++++------ params.py | 16 +++++++++------- 2 files changed, 28 insertions(+), 13 deletions(-) diff --git a/model.py b/model.py index f64afd2..dad7780 100644 --- a/model.py +++ b/model.py @@ -11,6 +11,11 @@ from GRU import gated_attention_GRUCell import numpy as np +optimizer_factory = {"adadelta":tf.train.AdadeltaOptimizer(learning_rate = Params.learning_rate, epsilon = 1e-06), + "adam":tf.train.AdamOptimizer(learning_rate = Params.learning_rate), + "gradientdescent":tf.train.GradientDescentOptimizer(learning_rate = Params.learning_rate), + "adagrad":tf.train.AdagradOptimizer(learning_rate = Params.learning_rate)} + class Model(object): def __init__(self,is_training = True): self.is_training = is_training @@ -34,6 +39,7 @@ def __init__(self,is_training = True): self.encode_ids() self.params = get_attn_params(Params.attn_size) self.attention_match_rnn() + self.bidirectional_readout() self.pointer_network() self.loss_function() self.summary() @@ -42,10 +48,10 @@ def __init__(self,is_training = True): def encode_ids(self): with tf.device('/cpu:0'): - self.word_embeddings = tf.Variable(tf.constant(0.0, shape=[Params.vocab_size, Params.emb_size]),trainable=False, name="word_embeddings") - self.word_embeddings_placeholder = tf.placeholder(tf.float32,[Params.vocab_size, Params.emb_size],"word_embeddings_placeholder") self.char_embeddings = tf.Variable(tf.constant(0.0, shape=[Params.char_vocab_size, Params.emb_size]),trainable=False, name="char_embeddings") self.char_embeddings_placeholder = tf.placeholder(tf.float32,[Params.char_vocab_size, Params.emb_size],"char_embeddings_placeholder") + self.word_embeddings = tf.Variable(tf.constant(0.0, shape=[Params.vocab_size, Params.emb_size]),trainable=False, name="word_embeddings") + self.word_embeddings_placeholder = tf.placeholder(tf.float32,[Params.vocab_size, Params.emb_size],"word_embeddings_placeholder") self.emb_assign = tf.group(tf.assign(self.word_embeddings, self.word_embeddings_placeholder),tf.assign(self.char_embeddings, self.char_embeddings_placeholder)) self.passage_word_encoded, self.passage_char_encoded = encoding(self.passage_w, @@ -108,21 +114,28 @@ def attention_match_rnn(self): memory = inputs # self_matching self.self_matching_output = inputs + def bidirectional_readout(self): + self.final_bidirectional_outputs = bidirectional_GRU(self.self_matching_output, + self.passage_w_len, + layers = 3, + scope = "bidirectional_readout", + output = 0, + is_training = self.is_training) + def pointer_network(self): params = ((tf.concat((self.params["W_u_Q"],self.params["W_v_Q"]),axis = 0),self.params["v"]), (tf.concat((self.params["W_h_P"],self.params["W_h_a"]),axis = 0),self.params["v"])) cell = tf.contrib.rnn.GRUCell(Params.attn_size*2) - self.points_logits = pointer_net(self.self_matching_output, self.passage_w_len, self.question_encoding, cell, params, scope = "pointer_network") + self.points_logits = pointer_net(self.final_bidirectional_outputs, self.passage_w_len, self.question_encoding, cell, params, scope = "pointer_network") def loss_function(self): with tf.variable_scope("loss"): shapes = self.passage_w.shape mask = tf.to_float(tf.sequence_mask(self.passage_w_len, shapes[1])) self.points_logits *= tf.expand_dims(mask,1) - self.mean_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = self.indices, logits = self.points_logits)) - # self.optimizer = tf.train.AdadeltaOptimizer(learning_rate = Params.learning_rate, epsilon = 1e-06) - self.optimizer = tf.train.AdamOptimizer(learning_rate = Params.learning_rate) + + self.optimizer = optimizer_factory[Params.optimizer] # gradient clipping by norm gradients, variables = zip(*self.optimizer.compute_gradients(self.mean_loss)) diff --git a/params.py b/params.py index 6a1863b..35e9762 100644 --- a/params.py +++ b/params.py @@ -3,8 +3,9 @@ class Params(): # data data_size = 80000 num_epochs = 100 + train_prop = 0.9 # Not implemented atm data_dir = "./data/" - logdir = "./train/adam" + logdir = "./train/adadelta" glove_dir = "glove.840B.300d.txt" glove_char = "glove.840B.300d.char.txt" target_dir = data_dir + "/indices.txt" @@ -14,16 +15,17 @@ class Params(): p_chars_dir = data_dir + "/chars_context.txt" coreNLP_dir = "./stanford-corenlp-full-2017-06-09" - # model + # Training debug = False # Set it to True to debug the computation graph - max_len = 200 # Maximum number of words in each passage context + learning_rate = 1 # Adadelta doesn't require initial learning rate + optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] + batch_size = 16 save_steps = 50 # Save the model at every 50 steps - # NOTE Used AdamOptimizer for now, as AdadeltaOptimizer causes NaN error - learning_rate = 0.001 # Adadelta doesn't require initial learning rate + + # Architecture + max_len = 200 # Maximum number of words in each passage context vocab_size = 2196018 # Number of vocabs in glove.840B.300d.txt + 1 for an unknown token char_vocab_size = 95 # Number of characters in glove.840B.300d.char.txt + 1 for an unknown character - batch_size = 16 - train_prop = 0.9 # Not implemented emb_size = 300 # Embeddings size for both words and characters attn_size = 75 # RNN celland attention module size num_layers = 3 # Number of layers at question-passage matching and self matching network From 1b632dfec6ac08efb05c3f8c8807fbe3209935b5 Mon Sep 17 00:00:00 2001 From: Min Date: Wed, 30 Aug 2017 21:00:57 +1200 Subject: [PATCH 14/40] updated apis --- layers.py | 2 +- model.py | 9 ++++----- params.py | 6 +++--- 3 files changed, 8 insertions(+), 9 deletions(-) diff --git a/layers.py b/layers.py index b61a51e..c3a3be1 100644 --- a/layers.py +++ b/layers.py @@ -37,7 +37,7 @@ def encoding(word, char, word_embeddings, char_embeddings, scope = "embedding"): char_encoding = tf.nn.embedding_lookup(char_embeddings, char) return word_encoding, char_encoding -def apply_dropout(cell,dropout = 0.2, is_training = True): +def apply_dropout(cell, dropout = 0.2, is_training = True): if is_training: return tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=1.0 - dropout) else: diff --git a/model.py b/model.py index dad7780..ab424b7 100644 --- a/model.py +++ b/model.py @@ -78,13 +78,13 @@ def encode_ids(self): # Passage and question encoding self.passage_encoding = bidirectional_GRU(self.passage_encoding, self.passage_w_len, - layers = 3, + layers = Params.num_layers, scope = "passage_encoding", output = 0, is_training = self.is_training) self.question_encoding = bidirectional_GRU(self.question_encoding, self.question_w_len, - layers = 3, + layers = Params.num_layers, scope = "question_encoding", output = 0, is_training = self.is_training) @@ -117,7 +117,7 @@ def attention_match_rnn(self): def bidirectional_readout(self): self.final_bidirectional_outputs = bidirectional_GRU(self.self_matching_output, self.passage_w_len, - layers = 3, + layers = Params.num_layers, scope = "bidirectional_readout", output = 0, is_training = self.is_training) @@ -134,7 +134,6 @@ def loss_function(self): mask = tf.to_float(tf.sequence_mask(self.passage_w_len, shapes[1])) self.points_logits *= tf.expand_dims(mask,1) self.mean_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = self.indices, logits = self.points_logits)) - self.optimizer = optimizer_factory[Params.optimizer] # gradient clipping by norm @@ -176,7 +175,7 @@ def main(): for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): sess.run(model.train_op) if step % Params.save_steps == 0: - sv.saver.save(sess, Params.logdir + '/model_step_%d'%step) + sv.saver.save(sess, Params.logdir + '/model_epoch_%d_step_%d'%(epoch,step)) if __name__ == '__main__': if Params.debug == True: diff --git a/params.py b/params.py index 35e9762..16b63c0 100644 --- a/params.py +++ b/params.py @@ -5,7 +5,7 @@ class Params(): num_epochs = 100 train_prop = 0.9 # Not implemented atm data_dir = "./data/" - logdir = "./train/adadelta" + logdir = "./train/adam" glove_dir = "glove.840B.300d.txt" glove_char = "glove.840B.300d.char.txt" target_dir = data_dir + "/indices.txt" @@ -17,8 +17,8 @@ class Params(): # Training debug = False # Set it to True to debug the computation graph - learning_rate = 1 # Adadelta doesn't require initial learning rate - optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] + learning_rate = 0.001 # Adadelta doesn't require initial learning rate + optimizer = "adam" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] batch_size = 16 save_steps = 50 # Save the model at every 50 steps From 373263143512009310856448282fd97ae294885c Mon Sep 17 00:00:00 2001 From: Min Date: Thu, 31 Aug 2017 10:50:36 +1200 Subject: [PATCH 15/40] reduced grad norm clipping to 2.0 --- model.py | 4 ++-- params.py | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/model.py b/model.py index dad7780..17a3e19 100644 --- a/model.py +++ b/model.py @@ -139,7 +139,7 @@ def loss_function(self): # gradient clipping by norm gradients, variables = zip(*self.optimizer.compute_gradients(self.mean_loss)) - gradients, _ = tf.clip_by_global_norm(gradients, 5.0) + gradients, _ = tf.clip_by_global_norm(gradients, 2.0) self.train_op = self.optimizer.apply_gradients(zip(gradients, variables), global_step = self.global_step) def summary(self): @@ -176,7 +176,7 @@ def main(): for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): sess.run(model.train_op) if step % Params.save_steps == 0: - sv.saver.save(sess, Params.logdir + '/model_step_%d'%step) + sv.saver.save(sess, Params.logdir + '/model_epoch_%d_step_%d'%(epoch,step)) if __name__ == '__main__': if Params.debug == True: diff --git a/params.py b/params.py index 35e9762..16b63c0 100644 --- a/params.py +++ b/params.py @@ -5,7 +5,7 @@ class Params(): num_epochs = 100 train_prop = 0.9 # Not implemented atm data_dir = "./data/" - logdir = "./train/adadelta" + logdir = "./train/adam" glove_dir = "glove.840B.300d.txt" glove_char = "glove.840B.300d.char.txt" target_dir = data_dir + "/indices.txt" @@ -17,8 +17,8 @@ class Params(): # Training debug = False # Set it to True to debug the computation graph - learning_rate = 1 # Adadelta doesn't require initial learning rate - optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] + learning_rate = 0.001 # Adadelta doesn't require initial learning rate + optimizer = "adam" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] batch_size = 16 save_steps = 50 # Save the model at every 50 steps From 198121131906e265d09a3eca3d4476386a66163b Mon Sep 17 00:00:00 2001 From: Min Date: Thu, 31 Aug 2017 17:45:07 +1200 Subject: [PATCH 16/40] fixed preprocessing step --- data_load.py | 102 ++++++++-------- layers.py | 13 +- model.py | 333 ++++++++++++++++++++++++++------------------------- params.py | 22 ++-- process.py | 43 ++++--- 5 files changed, 263 insertions(+), 250 deletions(-) diff --git a/data_load.py b/data_load.py index 19bd828..5a4acee 100644 --- a/data_load.py +++ b/data_load.py @@ -112,47 +112,59 @@ def _run(self, sess, enqueue_op, coord=None): with self._lock: self._runs_per_session[sess] -= 1 +def load_data(dir_): + # Target indices + indices = load_target(dir_ + Params.target_dir) + + # Load question data + print("Loading question data...") + q_word_ids, _ = load_word(dir_ + Params.q_word_dir) + q_char_ids, q_char_len, q_word_len = load_char(dir_ + Params.q_chars_dir) + + # Load passage data + print("Loading passage data...") + p_word_ids, _ = load_word(dir_ + Params.p_word_dir) + p_char_ids, p_char_len, p_word_len = load_char(dir_ + Params.p_chars_dir) + + # Get max length to pad + p_max_word = np.max(p_word_len) + p_max_char = max_value(p_char_len) + q_max_word = np.max(q_word_len) + q_max_char = max_value(q_char_len) + + # pad_data + print("Preparing training data...") + p_word_ids = pad_data(p_word_ids,p_max_word) + q_word_ids = pad_data(q_word_ids,q_max_word) + p_char_ids = pad_char_data(p_char_ids,p_max_char,p_max_word) + q_char_ids = pad_char_data(q_char_ids,q_max_char,q_max_word) + + # to numpy + indices = np.reshape(np.asarray(indices,np.int32),(-1,2)) + p_word_len = np.reshape(np.asarray(p_word_len,np.int32),(-1,1)) + q_word_len = np.reshape(np.asarray(q_word_len,np.int32),(-1,1)) + p_char_len = pad_data(p_char_len,p_max_word) + q_char_len = pad_data(q_char_len,q_max_word) + + # shapes of each data + shapes=[(p_max_word,),(q_max_word,), + (p_max_word,p_max_char,),(q_max_word,q_max_char,), + (1,),(1,), + (p_max_word,),(q_max_word,), + (2,)] + + return ([p_word_ids, q_word_ids, + p_char_ids, q_char_ids, + p_word_len, q_word_len, + p_char_len, q_char_len, + indices], shapes) + def get_batch(): """Loads training data and put them in queues""" with tf.device('/cpu:0'): - # Target indices - indices = load_target(Params.target_dir) - - # Load question data - print("Loading question data...") - q_word_ids, _ = load_word(Params.q_word_dir) - q_char_ids, q_char_len, q_word_len = load_char(Params.q_chars_dir) - - # Load passage data - print("Loading passage data...") - p_word_ids, _ = load_word(Params.p_word_dir) - p_char_ids, p_char_len, p_word_len = load_char(Params.p_chars_dir) - - # Get max length to pad - p_max_word = np.max(p_word_len) - p_max_char = max_value(p_char_len) - q_max_word = np.max(q_word_len) - q_max_char = max_value(q_char_len) - - # pad_data - print("Preparing training data...") - p_word_ids = pad_data(p_word_ids,p_max_word) - q_word_ids = pad_data(q_word_ids,q_max_word) - p_char_ids = pad_char_data(p_char_ids,p_max_char,p_max_word) - q_char_ids = pad_char_data(q_char_ids,q_max_char,q_max_word) - - # to numpy - indices = np.reshape(np.asarray(indices,np.int32),(-1,2)) - p_word_len = np.reshape(np.asarray(p_word_len,np.int32),(-1,1)) - q_word_len = np.reshape(np.asarray(q_word_len,np.int32),(-1,1)) - p_char_len = pad_data(p_char_len,p_max_word) - q_char_len = pad_data(q_char_len,q_max_word) - - input_list = [p_word_ids, q_word_ids, - p_char_ids, q_char_ids, - p_word_len, q_word_len, - p_char_len, q_char_len, - indices] + # Training set + input_list, shapes = load_data(Params.train_dir) + indices = input_list[-1] train_ind = np.arange(indices.shape[0],dtype = np.int32) np.random.shuffle(train_ind) @@ -160,20 +172,12 @@ def get_batch(): # Create Queues ind_list = tf.train.slice_input_producer([ind_list], shuffle=True) - shapes=[(p_max_word,),(q_max_word,), - (p_max_word,p_max_char,),(q_max_word,q_max_char,), - (1,),(1,), - (p_max_word,),(q_max_word,), - (2,)] @producer_func def get_data(ind): '''From `_inputs`, which has been fetched from slice queues, then enqueue them again. ''' - - while (indices[ind][0] == -1).any(): - ind = np.random.choice(train_ind[:Params.data_size]) return [np.reshape(input_[ind], shapes[i]) for i,input_ in enumerate(input_list)] data = get_data(inputs=ind_list, @@ -183,11 +187,7 @@ def get_data(ind): # create batch queues batch = tf.train.batch(data, - shapes=[(p_max_word,),(q_max_word,), - (p_max_word,p_max_char,),(q_max_word,q_max_char,), - (1,),(1,), - (p_max_word,),(q_max_word,), - (2,)], + shapes=shapes, num_threads=8, batch_size=Params.batch_size, capacity=Params.batch_size*32, diff --git a/layers.py b/layers.py index c3a3be1..0741a1c 100644 --- a/layers.py +++ b/layers.py @@ -2,6 +2,7 @@ #/usr/bin/python2 from tensorflow.contrib.rnn import MultiRNNCell +from tensorflow.contrib.rnn import RNNCell from params import Params import tensorflow as tf import numpy as np @@ -18,7 +19,7 @@ W_v^Q.shape: (attn_size, attn_size) ''' -def get_attn_params(attn_size,initializer = tf.contrib.layers.xavier_initializer()): +def get_attn_params(attn_size,initializer = tf.truncated_normal_initializer()): with tf.variable_scope("attention_weights"): params = {"W_u_Q":tf.get_variable("W_u_Q",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer), "W_u_P":tf.get_variable("W_u_P",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer), @@ -37,11 +38,13 @@ def encoding(word, char, word_embeddings, char_embeddings, scope = "embedding"): char_encoding = tf.nn.embedding_lookup(char_embeddings, char) return word_encoding, char_encoding -def apply_dropout(cell, dropout = 0.2, is_training = True): - if is_training: - return tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=1.0 - dropout) +def apply_dropout(inputs, dropout = 0.2, is_training = True): + if not is_training: + return inputs + if isinstance(inputs, RNNCell): + return tf.contrib.rnn.DropoutWrapper(inputs, output_keep_prob=1.0 - dropout) else: - return cell + return tf.nn.dropout(inputs, keep_prob = 1.0 - dropout) def bidirectional_GRU(inputs, inputs_len, cell = None, units = 75, layers = 1, scope = "Bidirectional_GRU", output = 0, is_training = True, reuse = None): with tf.variable_scope(scope, reuse = reuse): diff --git a/model.py b/model.py index 75a8bb4..f470f26 100644 --- a/model.py +++ b/model.py @@ -12,177 +12,178 @@ import numpy as np optimizer_factory = {"adadelta":tf.train.AdadeltaOptimizer(learning_rate = Params.learning_rate, epsilon = 1e-06), - "adam":tf.train.AdamOptimizer(learning_rate = Params.learning_rate), - "gradientdescent":tf.train.GradientDescentOptimizer(learning_rate = Params.learning_rate), - "adagrad":tf.train.AdagradOptimizer(learning_rate = Params.learning_rate)} + "adam":tf.train.AdamOptimizer(learning_rate = Params.learning_rate), + "gradientdescent":tf.train.GradientDescentOptimizer(learning_rate = Params.learning_rate), + "adagrad":tf.train.AdagradOptimizer(learning_rate = Params.learning_rate)} class Model(object): - def __init__(self,is_training = True): - self.is_training = is_training - self.graph = tf.Graph() - with self.graph.as_default(): - if is_training: - self.global_step = tf.Variable(0, name='global_step', trainable=False) - data, self.num_batch = get_batch() - (self.passage_w, - self.question_w, - self.passage_c, - self.question_c, - self.passage_w_len, - self.question_w_len, - self.passage_c_len, - self.question_c_len, - self.indices) = data - self.passage_w_len = tf.squeeze(self.passage_w_len) - self.question_w_len = tf.squeeze(self.question_w_len) - - self.encode_ids() - self.params = get_attn_params(Params.attn_size) - self.attention_match_rnn() - self.bidirectional_readout() - self.pointer_network() - self.loss_function() - self.summary() - self.init_op = tf.global_variables_initializer() - total_params() - - def encode_ids(self): - with tf.device('/cpu:0'): - self.char_embeddings = tf.Variable(tf.constant(0.0, shape=[Params.char_vocab_size, Params.emb_size]),trainable=False, name="char_embeddings") - self.char_embeddings_placeholder = tf.placeholder(tf.float32,[Params.char_vocab_size, Params.emb_size],"char_embeddings_placeholder") - self.word_embeddings = tf.Variable(tf.constant(0.0, shape=[Params.vocab_size, Params.emb_size]),trainable=False, name="word_embeddings") - self.word_embeddings_placeholder = tf.placeholder(tf.float32,[Params.vocab_size, Params.emb_size],"word_embeddings_placeholder") - self.emb_assign = tf.group(tf.assign(self.word_embeddings, self.word_embeddings_placeholder),tf.assign(self.char_embeddings, self.char_embeddings_placeholder)) - - self.passage_word_encoded, self.passage_char_encoded = encoding(self.passage_w, - self.passage_c, - word_embeddings = self.word_embeddings, - char_embeddings = self.char_embeddings) - self.question_word_encoded, self.question_char_encoded = encoding(self.question_w, - self.question_c, - word_embeddings = self.word_embeddings, - char_embeddings = self.char_embeddings) - self.passage_char_encoded = bidirectional_GRU(self.passage_char_encoded, - self.passage_c_len, - scope = "passage_char_encoding", - output = 1, - is_training = self.is_training) - self.question_char_encoded = bidirectional_GRU(self.question_char_encoded, - self.question_c_len, - scope = "question_char_encoding", - output = 1, - is_training = self.is_training) - self.passage_encoding = tf.concat((self.passage_word_encoded, self.passage_char_encoded),axis = 2) - self.question_encoding = tf.concat((self.question_word_encoded, self.question_char_encoded),axis = 2) - - # Passage and question encoding - self.passage_encoding = bidirectional_GRU(self.passage_encoding, - self.passage_w_len, - layers = Params.num_layers, - scope = "passage_encoding", - output = 0, - is_training = self.is_training) - self.question_encoding = bidirectional_GRU(self.question_encoding, - self.question_w_len, - layers = Params.num_layers, - scope = "question_encoding", - output = 0, - is_training = self.is_training) - - def attention_match_rnn(self): - memory = self.question_encoding - inputs = self.passage_encoding - scopes = ["question_passage_matching", "self_matching"] - params = [((tf.concat((self.params["W_u_Q"], - self.params["W_u_P"], - self.params["W_v_P"]),axis = 0), - self.params["v"]),self.params["W_g"]), - ((tf.concat((self.params["W_v_P"], - self.params["W_v_P"], - self.params["W_v_Phat"]),axis = 0), - self.params["v"]),self.params["W_g"])] - for i in range(2): - # cell_fw = MultiRNNCell([apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i]),is_training = self.is_training) for _ in range(Params.num_layers)]) - # cell_bw = MultiRNNCell([apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i]),is_training = self.is_training) for _ in range(Params.num_layers)]) - cell_fw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True if i == 1 else False) - cell_bw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True if i == 1 else False) - inputs = attention_rnn(inputs, - self.passage_w_len, - Params.attn_size, - (cell_fw,cell_bw), - scope = scopes[i]) - memory = inputs # self_matching - self.self_matching_output = inputs - - def bidirectional_readout(self): - self.final_bidirectional_outputs = bidirectional_GRU(self.self_matching_output, - self.passage_w_len, - layers = Params.num_layers, - scope = "bidirectional_readout", - output = 0, - is_training = self.is_training) - - def pointer_network(self): - params = ((tf.concat((self.params["W_u_Q"],self.params["W_v_Q"]),axis = 0),self.params["v"]), - (tf.concat((self.params["W_h_P"],self.params["W_h_a"]),axis = 0),self.params["v"])) - cell = tf.contrib.rnn.GRUCell(Params.attn_size*2) - self.points_logits = pointer_net(self.final_bidirectional_outputs, self.passage_w_len, self.question_encoding, cell, params, scope = "pointer_network") - - def loss_function(self): - with tf.variable_scope("loss"): - shapes = self.passage_w.shape - mask = tf.to_float(tf.sequence_mask(self.passage_w_len, shapes[1])) - self.points_logits *= tf.expand_dims(mask,1) - self.mean_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = self.indices, logits = self.points_logits)) - self.optimizer = optimizer_factory[Params.optimizer] - - # gradient clipping by norm - gradients, variables = zip(*self.optimizer.compute_gradients(self.mean_loss)) - gradients, _ = tf.clip_by_global_norm(gradients, 2.0) - self.train_op = self.optimizer.apply_gradients(zip(gradients, variables), global_step = self.global_step) - - def summary(self): - tf.summary.scalar('mean_loss', self.mean_loss) - tf.summary.scalar('passage_word_encoded',tf.reduce_mean(self.passage_word_encoded)) - tf.summary.scalar('passage_char_encoded',tf.reduce_mean(self.passage_char_encoded)) - tf.summary.scalar('question_word_encoded',tf.reduce_mean(self.question_word_encoded)) - tf.summary.scalar('question_char_encoded',tf.reduce_mean(self.question_char_encoded)) - tf.summary.scalar('question_encoding',tf.reduce_mean(self.question_encoding)) - tf.summary.scalar('passage_encoding',tf.reduce_mean(self.passage_encoding)) - tf.summary.scalar('self_matching',tf.reduce_mean(self.self_matching_output)) - tf.summary.scalar('pointer',tf.reduce_mean(self.points_logits)) - tf.summary.scalar('learning_rate', Params.learning_rate) - self.merged = tf.summary.merge_all() + def __init__(self,is_training = True): + self.is_training = is_training + self.graph = tf.Graph() + with self.graph.as_default(): + if is_training: + self.global_step = tf.Variable(0, name='global_step', trainable=False) + data, self.num_batch = get_batch() + (self.passage_w, + self.question_w, + self.passage_c, + self.question_c, + self.passage_w_len, + self.question_w_len, + self.passage_c_len, + self.question_c_len, + self.indices) = data + self.passage_w_len = tf.squeeze(self.passage_w_len) + self.question_w_len = tf.squeeze(self.question_w_len) + + self.encode_ids() + self.params = get_attn_params(Params.attn_size) + self.attention_match_rnn() + self.bidirectional_readout() + self.pointer_network() + self.loss_function() + self.summary() + self.init_op = tf.global_variables_initializer() + total_params() + + def encode_ids(self): + with tf.device('/cpu:0'): + self.char_embeddings = tf.Variable(tf.constant(0.0, shape=[Params.char_vocab_size, Params.emb_size]),trainable=False, name="char_embeddings") + self.char_embeddings_placeholder = tf.placeholder(tf.float32,[Params.char_vocab_size, Params.emb_size],"char_embeddings_placeholder") + self.word_embeddings = tf.Variable(tf.constant(0.0, shape=[Params.vocab_size, Params.emb_size]),trainable=False, name="word_embeddings") + self.word_embeddings_placeholder = tf.placeholder(tf.float32,[Params.vocab_size, Params.emb_size],"word_embeddings_placeholder") + self.emb_assign = tf.group(tf.assign(self.word_embeddings, self.word_embeddings_placeholder),tf.assign(self.char_embeddings, self.char_embeddings_placeholder)) + + self.passage_word_encoded, self.passage_char_encoded = encoding(self.passage_w, + self.passage_c, + word_embeddings = self.word_embeddings, + char_embeddings = self.char_embeddings) + self.question_word_encoded, self.question_char_encoded = encoding(self.question_w, + self.question_c, + word_embeddings = self.word_embeddings, + char_embeddings = self.char_embeddings) + self.passage_char_encoded = bidirectional_GRU(self.passage_char_encoded, + self.passage_c_len, + scope = "passage_char_encoding", + output = 1, + is_training = self.is_training) + self.question_char_encoded = bidirectional_GRU(self.question_char_encoded, + self.question_c_len, + scope = "question_char_encoding", + output = 1, + is_training = self.is_training) + self.passage_encoding = tf.concat((self.passage_word_encoded, self.passage_char_encoded),axis = 2) + self.question_encoding = tf.concat((self.question_word_encoded, self.question_char_encoded),axis = 2) + + # Passage and question encoding + self.passage_encoding = bidirectional_GRU(self.passage_encoding, + self.passage_w_len, + layers = Params.num_layers, + scope = "passage_encoding", + output = 0, + is_training = self.is_training) + self.question_encoding = bidirectional_GRU(self.question_encoding, + self.question_w_len, + layers = Params.num_layers, + scope = "question_encoding", + output = 0, + is_training = self.is_training) + + def attention_match_rnn(self): + memory = self.question_encoding + inputs = self.passage_encoding + scopes = ["question_passage_matching", "self_matching"] + params = [((tf.concat((self.params["W_u_Q"], + self.params["W_u_P"], + self.params["W_v_P"]),axis = 0), + self.params["v"]),self.params["W_g"]), + ((tf.concat((self.params["W_v_P"], + self.params["W_v_P"], + self.params["W_v_Phat"]),axis = 0), + self.params["v"]),self.params["W_g"])] + for i in range(2): + # cell_fw = MultiRNNCell([apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i]),is_training = self.is_training) for _ in range(Params.num_layers)]) + # cell_bw = MultiRNNCell([apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i]),is_training = self.is_training) for _ in range(Params.num_layers)]) + cell_fw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True if i == 1 else False) + cell_bw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True if i == 1 else False) + inputs = attention_rnn(inputs, + self.passage_w_len, + Params.attn_size, + (cell_fw,cell_bw), + scope = scopes[i]) + memory = inputs # self_matching + inputs = apply_dropout(inputs, is_training = self.is_training) + self.self_matching_output = inputs + + def bidirectional_readout(self): + self.final_bidirectional_outputs = bidirectional_GRU(self.self_matching_output, + self.passage_w_len, + layers = Params.num_layers, + scope = "bidirectional_readout", + output = 0, + is_training = self.is_training) + + def pointer_network(self): + params = ((tf.concat((self.params["W_u_Q"],self.params["W_v_Q"]),axis = 0),self.params["v"]), + (tf.concat((self.params["W_h_P"],self.params["W_h_a"]),axis = 0),self.params["v"])) + cell = apply_dropout(tf.contrib.rnn.GRUCell(Params.attn_size*2), is_training = self.is_training) + self.points_logits = pointer_net(self.final_bidirectional_outputs, self.passage_w_len, self.question_encoding, cell, params, scope = "pointer_network") + + def loss_function(self): + with tf.variable_scope("loss"): + shapes = self.passage_w.shape + self.mask = tf.to_float(tf.sequence_mask(self.passage_w_len, shapes[1])) + self.points_logits *= tf.expand_dims(self.mask,1) + + self.mean_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = self.indices, logits = self.points_logits)) + self.optimizer = optimizer_factory[Params.optimizer] + + # gradient clipping by norm + gradients, variables = zip(*self.optimizer.compute_gradients(self.mean_loss)) + gradients, _ = tf.clip_by_global_norm(gradients, 5.0) + self.train_op = self.optimizer.apply_gradients(zip(gradients, variables), global_step = self.global_step) + + def summary(self): + tf.summary.scalar('mean_loss', self.mean_loss) + tf.summary.scalar('passage_word_encoded',tf.reduce_mean(self.passage_word_encoded)) + tf.summary.scalar('passage_char_encoded',tf.reduce_mean(self.passage_char_encoded)) + tf.summary.scalar('question_word_encoded',tf.reduce_mean(self.question_word_encoded)) + tf.summary.scalar('question_char_encoded',tf.reduce_mean(self.question_char_encoded)) + tf.summary.scalar('question_encoding',tf.reduce_mean(self.question_encoding)) + tf.summary.scalar('passage_encoding',tf.reduce_mean(self.passage_encoding)) + tf.summary.scalar('self_matching',tf.reduce_mean(self.self_matching_output)) + tf.summary.scalar('pointer',tf.reduce_mean(self.points_logits)) + tf.summary.scalar('learning_rate', Params.learning_rate) + self.merged = tf.summary.merge_all() def debug(): - model = Model(is_training = True) + model = Model(is_training = True) + print("Built graph") def main(): - model = Model(is_training = True); print("Built model") - glove = np.memmap(Params.data_dir + "glove.np", dtype = np.float32, mode = "r") - glove = np.reshape(glove,(Params.vocab_size,300)) - char_glove = np.memmap(Params.data_dir + "glove_char.np",dtype = np.float32, mode = "r") - char_glove = np.reshape(char_glove,(Params.char_vocab_size,300)) - with model.graph.as_default(): - sv = tf.train.Supervisor(logdir=Params.logdir, - save_model_secs=0, - global_step = model.global_step, - init_op = model.init_op) - with sv.managed_session() as sess: - sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) - for epoch in range(1, Params.num_epochs+1): - if sv.should_stop(): break - for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): - sess.run(model.train_op) - if step % Params.save_steps == 0: - sv.saver.save(sess, Params.logdir + '/model_epoch_%d_step_%d'%(epoch,step)) + model = Model(is_training = True); print("Built model") + glove = np.memmap(Params.data_dir + "glove.np", dtype = np.float32, mode = "r") + glove = np.reshape(glove,(Params.vocab_size,300)) + char_glove = np.memmap(Params.data_dir + "glove_char.np",dtype = np.float32, mode = "r") + char_glove = np.reshape(char_glove,(Params.char_vocab_size,300)) + with model.graph.as_default(): + sv = tf.train.Supervisor(logdir=Params.logdir, + save_model_secs=0, + global_step = model.global_step, + init_op = model.init_op) + with sv.managed_session() as sess: + sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) + for epoch in range(1, Params.num_epochs+1): + if sv.should_stop(): break + for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): + sess.run(model.train_op) + if step % Params.save_steps == 0: + sv.saver.save(sess, Params.logdir + '/model_epoch_%d_step_%d'%(epoch,step)) if __name__ == '__main__': - if Params.debug == True: - print("debugging...") - debug() - else: - print("Running...") - main() - - print("Done") + if Params.debug == True: + print("debugging...") + debug() + else: + print("Running...") + main() diff --git a/params.py b/params.py index 16b63c0..88c1848 100644 --- a/params.py +++ b/params.py @@ -5,20 +5,24 @@ class Params(): num_epochs = 100 train_prop = 0.9 # Not implemented atm data_dir = "./data/" - logdir = "./train/adam" + train_dir = data_dir + "trainset/" + dev_dir = data_dir + "devset/" + logdir = "./train/adadelta" glove_dir = "glove.840B.300d.txt" glove_char = "glove.840B.300d.char.txt" - target_dir = data_dir + "/indices.txt" - q_word_dir = data_dir + "/words_questions.txt" - q_chars_dir = data_dir + "/chars_questions.txt" - p_word_dir = data_dir + "/words_context.txt" - p_chars_dir = data_dir + "/chars_context.txt" coreNLP_dir = "./stanford-corenlp-full-2017-06-09" + # Data dir + target_dir = "indices.txt" + q_word_dir = "words_questions.txt" + q_chars_dir = "chars_questions.txt" + p_word_dir = "words_context.txt" + p_chars_dir = "chars_context.txt" + # Training - debug = False # Set it to True to debug the computation graph - learning_rate = 0.001 # Adadelta doesn't require initial learning rate - optimizer = "adam" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] + debug = True # Set it to True to debug the computation graph + learning_rate = 1 # Adadelta doesn't require initial learning rate + optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] batch_size = 16 save_steps = 50 # Save the model at every 50 steps diff --git a/process.py b/process.py index a13d897..5526a80 100644 --- a/process.py +++ b/process.py @@ -9,6 +9,7 @@ import re import nltk import sys +import os import argparse from tqdm import tqdm @@ -96,15 +97,17 @@ def process_glove(self, wordvecs, dict_, count): print("") return dict_, count - def process_json(self,dir): - self.data = json.load(codecs.open(dir,"rb","utf-8")) - self.loop(self.data) + def process_json(self,file_dir,out_dir): + if not os.path.exists(out_dir): + os.makedirs(out_dir) + self.data = json.load(codecs.open(file_dir,"rb","utf-8")) + self.loop(self.data, out_dir) self.ids2char = {v: k for k, v in self.c_dict.iteritems()} with codecs.open("dictionary.txt","wb","utf-8") as f: for key, value in sorted(self.w_dict.iteritems(), key=lambda (k,v): (v,k)): f.write("%s: %s" % (key, value) + "\n") - def loop(self,data): + def loop(self, data, dir_ = Params.train_dir): watch_list = ["one"] for topic in data['data']: for para in topic['paragraphs']: @@ -126,16 +129,17 @@ def loop(self,data): for qas in para['qas']: question = qas['question'] words,chars = self.add_to_dict(question) - write_file(words,"words_questions.txt","\n") - write_file(chars,"chars_questions.txt","\n") - write_file(words_c,"words_context.txt") - write_file(chars_c,"chars_context.txt") - for ans in qas['answers']: - ans_ids,_ = self.add_to_dict(ans['text']) - (start_i, finish_i) = find_answer_index(words_c, ans_ids) - if start_i == -1: - self.invalid_q += 1 - write_file([str(start_i),str(finish_i)],"indices.txt","\n") + ans = qas['answers'][0] + ans_ids,_ = self.add_to_dict(ans['text']) + (start_i, finish_i) = find_answer_index(words_c, ans_ids) + if start_i == -1: + self.invalid_q += 1 + continue + write_file([str(start_i),str(finish_i)],dir_ + "indices.txt","\n") + write_file(words,dir_ + "words_questions.txt","\n") + write_file(chars,dir_ + "chars_questions.txt","\n") + write_file(words_c,dir_ + "words_context.txt") + write_file(chars_c,dir_ + "chars_context.txt") def process_word(self,line): for word in splitted_line: @@ -221,11 +225,11 @@ def find_answer_index(context, answer): if answer[0] in context: return (context.index(answer[0]), context.index(answer[0])) else: - return(-1,-1) + return (-1, -1) for i in range(len(context)): if context[i:i+window_len] == answer: return (i, i + window_len) - return(-1,-1) + return (-1, -1) def normalize_text(text): return unicodedata.normalize('NFD', text) @@ -241,8 +245,8 @@ def to_text_file(line, dir): with codecs.open(dir,"ab","utf-8") as f: f.write(line + "\n") -def write_file(indices, dir, separate = "\n"): - with codecs.open(Params.data_dir + dir,"ab","utf-8") as f: +def write_file(indices, dir_, separate = "\n"): + with codecs.open(dir_,"ab","utf-8") as f: f.write(" ".join(indices) + separate) def pad_data(data, max_word): @@ -322,7 +326,8 @@ def max_value(inputlist): def main(): with open(Params.data_dir + 'dictionary.pkl','wb') as dictionary: loader = data_loader(use_pretrained = True) - loader.process_json(Params.data_dir + "train-v1.1.json") + loader.process_json(Params.data_dir + "train-v1.1.json", out_dir = Params.train_dir) + loader.process_json(Params.data_dir + "dev-v1.1.json", out_dir = Params.dev_dir) pickle.dump(loader, dictionary, pickle.HIGHEST_PROTOCOL) load_glove(Params.glove_dir,"glove",vocab_size = Params.vocab_size) load_glove(Params.glove_char,"glove_char", vocab_size = Params.char_vocab_size) From 98052f43bc3c3134e8c49795da04e0131ab20801 Mon Sep 17 00:00:00 2001 From: Min Date: Thu, 31 Aug 2017 20:18:58 +1200 Subject: [PATCH 17/40] changed loss function api --- model.py | 11 ++++++++--- params.py | 2 +- process.py | 1 - 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/model.py b/model.py index f470f26..eaaf39d 100644 --- a/model.py +++ b/model.py @@ -135,10 +135,15 @@ def loss_function(self): self.mask = tf.to_float(tf.sequence_mask(self.passage_w_len, shapes[1])) self.points_logits *= tf.expand_dims(self.mask,1) - self.mean_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = self.indices, logits = self.points_logits)) - self.optimizer = optimizer_factory[Params.optimizer] + # Causes NaN error + # self.mean_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = self.indices, logits = self.points_logits)) + + # Use non-sparse softmax + self.indices_prob = tf.one_hot(self.indices, shapes[1]) + self.mean_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = self.indices_prob, logits = self.points_logits)) # gradient clipping by norm + self.optimizer = optimizer_factory[Params.optimizer] gradients, variables = zip(*self.optimizer.compute_gradients(self.mean_loss)) gradients, _ = tf.clip_by_global_norm(gradients, 5.0) self.train_op = self.optimizer.apply_gradients(zip(gradients, variables), global_step = self.global_step) @@ -158,7 +163,7 @@ def summary(self): def debug(): model = Model(is_training = True) - print("Built graph") + print("Built model") def main(): model = Model(is_training = True); print("Built model") diff --git a/params.py b/params.py index 88c1848..e358fd3 100644 --- a/params.py +++ b/params.py @@ -20,7 +20,7 @@ class Params(): p_chars_dir = "chars_context.txt" # Training - debug = True # Set it to True to debug the computation graph + debug = False # Set it to True to debug the computation graph learning_rate = 1 # Adadelta doesn't require initial learning rate optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] batch_size = 16 diff --git a/process.py b/process.py index 5526a80..7de9468 100644 --- a/process.py +++ b/process.py @@ -102,7 +102,6 @@ def process_json(self,file_dir,out_dir): os.makedirs(out_dir) self.data = json.load(codecs.open(file_dir,"rb","utf-8")) self.loop(self.data, out_dir) - self.ids2char = {v: k for k, v in self.c_dict.iteritems()} with codecs.open("dictionary.txt","wb","utf-8") as f: for key, value in sorted(self.w_dict.iteritems(), key=lambda (k,v): (v,k)): f.write("%s: %s" % (key, value) + "\n") From 2d7767889d94b3b1539c60c4a6ee7af5e49bb59f Mon Sep 17 00:00:00 2001 From: Min Date: Fri, 1 Sep 2017 15:17:21 +1200 Subject: [PATCH 18/40] added clip gradient option --- model.py | 13 ++++++++----- params.py | 2 ++ 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/model.py b/model.py index eaaf39d..c5f7486 100644 --- a/model.py +++ b/model.py @@ -141,12 +141,15 @@ def loss_function(self): # Use non-sparse softmax self.indices_prob = tf.one_hot(self.indices, shapes[1]) self.mean_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = self.indices_prob, logits = self.points_logits)) - - # gradient clipping by norm self.optimizer = optimizer_factory[Params.optimizer] - gradients, variables = zip(*self.optimizer.compute_gradients(self.mean_loss)) - gradients, _ = tf.clip_by_global_norm(gradients, 5.0) - self.train_op = self.optimizer.apply_gradients(zip(gradients, variables), global_step = self.global_step) + + if Params.clip: + # gradient clipping by norm + gradients, variables = zip(*self.optimizer.compute_gradients(self.mean_loss)) + gradients, _ = tf.clip_by_global_norm(gradients, Params.norm) + self.train_op = self.optimizer.apply_gradients(zip(gradients, variables), global_step = self.global_step) + else: + self.train_op = self.optimizer.minimize(self.mean_loss, global_step = self.global_step) def summary(self): tf.summary.scalar('mean_loss', self.mean_loss) diff --git a/params.py b/params.py index e358fd3..feca935 100644 --- a/params.py +++ b/params.py @@ -25,6 +25,8 @@ class Params(): optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] batch_size = 16 save_steps = 50 # Save the model at every 50 steps + clip = False # clip gradient norm + norm = 5.0 # global norm # Architecture max_len = 200 # Maximum number of words in each passage context From 72b56279cf46343d3cf3d947cdcdff787ea79335 Mon Sep 17 00:00:00 2001 From: Min Date: Sat, 2 Sep 2017 11:23:29 +1200 Subject: [PATCH 19/40] updated params --- data_load.py | 6 +++++- layers.py | 2 +- model.py | 2 +- params.py | 11 ++++++----- 4 files changed, 13 insertions(+), 8 deletions(-) diff --git a/data_load.py b/data_load.py index 5a4acee..d6c3c81 100644 --- a/data_load.py +++ b/data_load.py @@ -168,7 +168,11 @@ def get_batch(): train_ind = np.arange(indices.shape[0],dtype = np.int32) np.random.shuffle(train_ind) - ind_list = tf.convert_to_tensor(train_ind[:Params.data_size]) + + size = Params.data_size + if Params.data_size > inidices.shape[0] or Params.data_size == -1: + size = indices.shape + ind_list = tf.convert_to_tensor(train_ind[:size]) # Create Queues ind_list = tf.train.slice_input_producer([ind_list], shuffle=True) diff --git a/layers.py b/layers.py index 0741a1c..2092da7 100644 --- a/layers.py +++ b/layers.py @@ -38,7 +38,7 @@ def encoding(word, char, word_embeddings, char_embeddings, scope = "embedding"): char_encoding = tf.nn.embedding_lookup(char_embeddings, char) return word_encoding, char_encoding -def apply_dropout(inputs, dropout = 0.2, is_training = True): +def apply_dropout(inputs, dropout = Params.dropout, is_training = True): if not is_training: return inputs if isinstance(inputs, RNNCell): diff --git a/model.py b/model.py index c5f7486..f5ed2b7 100644 --- a/model.py +++ b/model.py @@ -37,7 +37,7 @@ def __init__(self,is_training = True): self.question_w_len = tf.squeeze(self.question_w_len) self.encode_ids() - self.params = get_attn_params(Params.attn_size) + self.params = get_attn_params(Params.attn_size, initializer = tf.contrib.layers.xavier_initializer()) self.attention_match_rnn() self.bidirectional_readout() self.pointer_network() diff --git a/params.py b/params.py index feca935..f8b3e78 100644 --- a/params.py +++ b/params.py @@ -1,13 +1,13 @@ class Params(): # data - data_size = 80000 + data_size = -1 # -1 to use all data num_epochs = 100 train_prop = 0.9 # Not implemented atm data_dir = "./data/" train_dir = data_dir + "trainset/" dev_dir = data_dir + "devset/" - logdir = "./train/adadelta" + logdir = "./train/adadelta_dALL_x" glove_dir = "glove.840B.300d.txt" glove_char = "glove.840B.300d.char.txt" coreNLP_dir = "./stanford-corenlp-full-2017-06-09" @@ -22,16 +22,17 @@ class Params(): # Training debug = False # Set it to True to debug the computation graph learning_rate = 1 # Adadelta doesn't require initial learning rate + dropout = 0.2 # dropout probability optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] - batch_size = 16 + batch_size = 48 save_steps = 50 # Save the model at every 50 steps clip = False # clip gradient norm norm = 5.0 # global norm # Architecture - max_len = 200 # Maximum number of words in each passage context + max_len = 100 # Maximum number of words in each passage context vocab_size = 2196018 # Number of vocabs in glove.840B.300d.txt + 1 for an unknown token char_vocab_size = 95 # Number of characters in glove.840B.300d.char.txt + 1 for an unknown character emb_size = 300 # Embeddings size for both words and characters attn_size = 75 # RNN celland attention module size - num_layers = 3 # Number of layers at question-passage matching and self matching network + num_layers = 1 # Number of layers at question-passage matching and self matching network From e5afd548dcc29cffb648ec62d0514a97864ef588 Mon Sep 17 00:00:00 2001 From: Min Date: Sun, 3 Sep 2017 22:59:37 +1200 Subject: [PATCH 20/40] added evaluation --- GRU.py | 3 ++ README.md | 6 +++- data_load.py | 12 ++++---- model.py | 86 ++++++++++++++++++++++++++++++++++++++-------------- params.py | 1 + 5 files changed, 79 insertions(+), 29 deletions(-) diff --git a/GRU.py b/GRU.py index bf2acc3..1988725 100644 --- a/GRU.py +++ b/GRU.py @@ -1,3 +1,6 @@ +# -*- coding: utf-8 -*- +#/usr/bin/python2 + from __future__ import absolute_import from __future__ import division from __future__ import print_function diff --git a/README.md b/README.md index 95b973f..8c00680 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ Tensorflow implementation of https://www.microsoft.com/en-us/research/wp-content Training with full SQuAD dataset is currently a work in progress. -The dataset used for this task is Stanford Question Answering Dataset (https://rajpurkar.github.io/SQuAD-explorer/). Pretrained GloVe embeddings are used for both words (https://nlp.stanford.edu/projects/glove/) and characters (https://github.com/minimaxir/char-embeddings/blob/master/glove.840B.300d-char.txt). +The dataset used for this task is Stanford Question Answering Dataset (https://rajpurkar.github.io/SQuAD-explorer/). Pretrained GloVe embeddings are used for both words (https://nlp.stanford.edu/projects/glove/) and characters (https://github.com/minimaxir/char-embeddings/blob/master/glove.840B.300d-char.txt). ## Requirements * NumPy @@ -35,6 +35,10 @@ $ tensorboard --logdir=r-net:train/ ![Alt text](/../dev/screenshots/graph.png?raw=true "Tensorboard Graph") # Note +**02/09/17** +One of the challenges I faced while training was to fit a minibatch of size 32 or larger into my GTX 1080. Since SQuAD dataset displayed high variance in data, higher batch size was essential in training (otherwise the model doesn't converge). Reducing GPU memory usage significantly to fit batch size of 32 and higher is a work in progress. If you have any suggestions on reducing the GPU memory usage, please put forward a pr. + +**27/08/17** As a sanity check I trained the network with 3000 independent randomly sampled question-answering pairs. With my GTX 1080, it took about 4 hours and a half for the model to get the gist of what's going on with the data. With full dataset (90,000+ pairs) we are expecting longer time for convergence. Some sort of normalization method might help speed up convergence (though the authors of the original paper didn't mention anything about the normalization). diff --git a/data_load.py b/data_load.py index d6c3c81..4982bab 100644 --- a/data_load.py +++ b/data_load.py @@ -159,19 +159,19 @@ def load_data(dir_): p_char_len, q_char_len, indices], shapes) -def get_batch(): +def get_batch(is_training = True): """Loads training data and put them in queues""" with tf.device('/cpu:0'): - # Training set - input_list, shapes = load_data(Params.train_dir) + # Load dataset + input_list, shapes = load_data(Params.train_dir if is_training else Params.dev_dir) indices = input_list[-1] train_ind = np.arange(indices.shape[0],dtype = np.int32) np.random.shuffle(train_ind) size = Params.data_size - if Params.data_size > inidices.shape[0] or Params.data_size == -1: - size = indices.shape + if Params.data_size > indices.shape[0] or Params.data_size == -1: + size = indices.shape[0] ind_list = tf.convert_to_tensor(train_ind[:size]) # Create Queues @@ -197,4 +197,4 @@ def get_data(ind): capacity=Params.batch_size*32, dynamic_pad=True) - return batch, Params.data_size // Params.batch_size + return batch, size // Params.batch_size diff --git a/model.py b/model.py index f5ed2b7..4df4935 100644 --- a/model.py +++ b/model.py @@ -8,8 +8,13 @@ from data_load import get_batch from params import Params from layers import * +from sklearn.metrics import f1_score from GRU import gated_attention_GRUCell +from evaluate import * import numpy as np +from utils import * +import cPickle as pickle +from process import * optimizer_factory = {"adadelta":tf.train.AdadeltaOptimizer(learning_rate = Params.learning_rate, epsilon = 1e-06), "adam":tf.train.AdamOptimizer(learning_rate = Params.learning_rate), @@ -21,30 +26,33 @@ def __init__(self,is_training = True): self.is_training = is_training self.graph = tf.Graph() with self.graph.as_default(): + self.global_step = tf.Variable(0, name='global_step', trainable=False) + data, self.num_batch = get_batch(is_training = is_training) + (self.passage_w, + self.question_w, + self.passage_c, + self.question_c, + self.passage_w_len, + self.question_w_len, + self.passage_c_len, + self.question_c_len, + self.indices) = data + self.passage_w_len = tf.squeeze(self.passage_w_len) + self.question_w_len = tf.squeeze(self.question_w_len) + + self.encode_ids() + self.params = get_attn_params(Params.attn_size, initializer = tf.contrib.layers.xavier_initializer()) + self.attention_match_rnn() + self.bidirectional_readout() + self.pointer_network() + if is_training: - self.global_step = tf.Variable(0, name='global_step', trainable=False) - data, self.num_batch = get_batch() - (self.passage_w, - self.question_w, - self.passage_c, - self.question_c, - self.passage_w_len, - self.question_w_len, - self.passage_c_len, - self.question_c_len, - self.indices) = data - self.passage_w_len = tf.squeeze(self.passage_w_len) - self.question_w_len = tf.squeeze(self.question_w_len) - - self.encode_ids() - self.params = get_attn_params(Params.attn_size, initializer = tf.contrib.layers.xavier_initializer()) - self.attention_match_rnn() - self.bidirectional_readout() - self.pointer_network() self.loss_function() self.summary() self.init_op = tf.global_variables_initializer() - total_params() + else: + self.outputs() + total_params() def encode_ids(self): with tf.device('/cpu:0'): @@ -129,6 +137,9 @@ def pointer_network(self): cell = apply_dropout(tf.contrib.rnn.GRUCell(Params.attn_size*2), is_training = self.is_training) self.points_logits = pointer_net(self.final_bidirectional_outputs, self.passage_w_len, self.question_encoding, cell, params, scope = "pointer_network") + def outputs(self): + self.output_index = tf.argmax(self.points_logits, axis = 2) + def loss_function(self): with tf.variable_scope("loss"): shapes = self.passage_w.shape @@ -165,9 +176,37 @@ def summary(self): self.merged = tf.summary.merge_all() def debug(): - model = Model(is_training = True) + model = Model(is_training = False) print("Built model") +def test(): + model = Model(is_training = False); print("Built model") + dict_ = pickle.load(open(Params.data_dir + "dictionary.pkl","r")) + glove = np.memmap(Params.data_dir + "glove.np", dtype = np.float32, mode = "r") + glove = np.reshape(glove,(Params.vocab_size,300)) + char_glove = np.memmap(Params.data_dir + "glove_char.np",dtype = np.float32, mode = "r") + char_glove = np.reshape(char_glove,(Params.char_vocab_size,300)) + with model.graph.as_default(): + sv = tf.train.Supervisor() + with sv.managed_session() as sess: + sv.saver.restore(sess, tf.train.latest_checkpoint(Params.logdir)) + sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) + EM = 0.0 + f1 = 0.0 + for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): + index, ground_truth, passage = sess.run([model.output_index, model.indices, model.passage_w]) + for batch in range(Params.batch_size): + answer = " ".join([dict_.ind2word(a) for a in passage[batch,index[batch,0]:index[batch,1]].tolist() if len(a) == 1 else [passage[batch,index[batch,0]:index[batch,1]].tolist()]]) + answer_ = " ".join([dict_.ind2word(a) for a in passage[batch,ground_truth[batch,0]:index[batch,1]].tolist() if len(a) == 1 else [passage[batch,ground_truth[batch,0]:index[batch,1]].tolist()]]) + f1 += f1_score(answer,answer_) + EM = exact_match_score(answer,answer_) + f1 /= Params.batch_size + EM /= Params.batch_size + f1 /= model.num_batch + EM /= model.num_batch + print("Exact_match: {}\nf1_score: {}".format(EM,f1)) + + def main(): model = Model(is_training = True); print("Built model") glove = np.memmap(Params.data_dir + "glove.np", dtype = np.float32, mode = "r") @@ -190,8 +229,11 @@ def main(): if __name__ == '__main__': if Params.debug == True: - print("debugging...") + print("Debugging...") debug() + elif Params.test == True: + print("Testing on dev set...") + test() else: print("Running...") main() diff --git a/params.py b/params.py index f8b3e78..a85acd1 100644 --- a/params.py +++ b/params.py @@ -21,6 +21,7 @@ class Params(): # Training debug = False # Set it to True to debug the computation graph + test = True learning_rate = 1 # Adadelta doesn't require initial learning rate dropout = 0.2 # dropout probability optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] From 0523897bbf61db0df75b60dd09ff56cdff7d8310 Mon Sep 17 00:00:00 2001 From: Min Date: Mon, 4 Sep 2017 16:23:07 +1200 Subject: [PATCH 21/40] EM and F1 measure while training --- evaluate.py | 110 ++++++++++++++++++++++++++++++++++++++++++++++++++++ layers.py | 43 +++++++++++--------- model.py | 67 +++++++++++++++++++------------- params.py | 12 +++--- 4 files changed, 181 insertions(+), 51 deletions(-) create mode 100644 evaluate.py diff --git a/evaluate.py b/evaluate.py new file mode 100644 index 0000000..b98620e --- /dev/null +++ b/evaluate.py @@ -0,0 +1,110 @@ +""" Official evaluation script for v1.1 of the SQuAD dataset. """ +from __future__ import print_function +from collections import Counter +import string +import re +import argparse +import json +import sys + +def f1_and_EM(index, ground_truth, passage, dict_): + if index[0] == index[1]: + pred_ind = [passage[index[0]].tolist()] + else: + pred_ind = passage[index[0]:index[1]].tolist() + if pred_ind is None: + pred_ind = [0] + if ground_truth[0] == ground_truth[1]: + answer_ind = [passage[ground_truth[0]].tolist()] + else: + answer_ind = passage[ground_truth[0]:ground_truth[1]].tolist() + answer = dict_.ind2word(pred_ind) + answer_ = dict_.ind2word(answer_ind) + f1 = f1_score(answer,answer_) + EM = exact_match_score(answer,answer_) + return f1, EM + +def normalize_answer(s): + """Lower text and remove punctuation, articles and extra whitespace.""" + def remove_articles(text): + return re.sub(r'\b(a|an|the)\b', ' ', text) + + def white_space_fix(text): + return ' '.join(text.split()) + + def remove_punc(text): + exclude = set(string.punctuation) + return ''.join(ch for ch in text if ch not in exclude) + + def lower(text): + return text.lower() + + return white_space_fix(remove_articles(remove_punc(lower(s)))) + + +def f1_score(prediction, ground_truth): + prediction_tokens = normalize_answer(prediction).split() + ground_truth_tokens = normalize_answer(ground_truth).split() + common = Counter(prediction_tokens) & Counter(ground_truth_tokens) + num_same = sum(common.values()) + if num_same == 0: + return 0 + precision = 1.0 * num_same / len(prediction_tokens) + recall = 1.0 * num_same / len(ground_truth_tokens) + f1 = (2 * precision * recall) / (precision + recall) + return f1 + + +def exact_match_score(prediction, ground_truth): + return (normalize_answer(prediction) == normalize_answer(ground_truth)) + + +def metric_max_over_ground_truths(metric_fn, prediction, ground_truths): + scores_for_ground_truths = [] + for ground_truth in ground_truths: + score = metric_fn(prediction, ground_truth) + scores_for_ground_truths.append(score) + return max(scores_for_ground_truths) + + +def evaluate(dataset, predictions): + f1 = exact_match = total = 0 + for article in dataset: + for paragraph in article['paragraphs']: + for qa in paragraph['qas']: + total += 1 + if qa['id'] not in predictions: + message = 'Unanswered question ' + qa['id'] + \ + ' will receive score 0.' + print(message, file=sys.stderr) + continue + ground_truths = list(map(lambda x: x['text'], qa['answers'])) + prediction = predictions[qa['id']] + exact_match += metric_max_over_ground_truths( + exact_match_score, prediction, ground_truths) + f1 += metric_max_over_ground_truths( + f1_score, prediction, ground_truths) + + exact_match = 100.0 * exact_match / total + f1 = 100.0 * f1 / total + + return {'exact_match': exact_match, 'f1': f1} + + +if __name__ == '__main__': + expected_version = '1.1' + parser = argparse.ArgumentParser( + description='Evaluation for SQuAD ' + expected_version) + parser.add_argument('dataset_file', help='Dataset file') + parser.add_argument('prediction_file', help='Prediction File') + args = parser.parse_args() + with open(args.dataset_file) as dataset_file: + dataset_json = json.load(dataset_file) + if (dataset_json['version'] != expected_version): + print('Evaluation expects v-' + expected_version + + ', but got dataset with v-' + dataset_json['version'], + file=sys.stderr) + dataset = dataset_json['data'] + with open(args.prediction_file) as prediction_file: + predictions = json.load(prediction_file) + print(json.dumps(evaluate(dataset, predictions))) diff --git a/layers.py b/layers.py index 2092da7..bdd03d4 100644 --- a/layers.py +++ b/layers.py @@ -19,17 +19,17 @@ W_v^Q.shape: (attn_size, attn_size) ''' -def get_attn_params(attn_size,initializer = tf.truncated_normal_initializer()): +def get_attn_params(attn_size,initializer = tf.truncated_normal_initializer): with tf.variable_scope("attention_weights"): - params = {"W_u_Q":tf.get_variable("W_u_Q",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer), - "W_u_P":tf.get_variable("W_u_P",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer), - "W_v_P":tf.get_variable("W_v_P",dtype = tf.float32, shape = (attn_size, attn_size), initializer = initializer), - "W_g":tf.get_variable("W_g",dtype = tf.float32, shape = (4 * attn_size, 4 * attn_size), initializer = initializer), - "W_h_P":tf.get_variable("W_h_P",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer), - "W_v_Phat":tf.get_variable("W_v_Phat",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer), - "W_h_a":tf.get_variable("W_h_a",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer), - "W_v_Q":tf.get_variable("W_v_Q",dtype = tf.float32, shape = (attn_size, attn_size), initializer = initializer), - "v":tf.get_variable("v", dtype = tf.float32, shape = attn_size, initializer = initializer)} + params = {"W_u_Q":tf.get_variable("W_u_Q",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), + "W_u_P":tf.get_variable("W_u_P",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), + "W_v_P":tf.get_variable("W_v_P",dtype = tf.float32, shape = (attn_size, attn_size), initializer = initializer()), + "W_g":tf.get_variable("W_g",dtype = tf.float32, shape = (4 * attn_size, 4 * attn_size), initializer = initializer()), + "W_h_P":tf.get_variable("W_h_P",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), + "W_v_Phat":tf.get_variable("W_v_Phat",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), + "W_h_a":tf.get_variable("W_h_a",dtype = tf.float32, shape = (2 * attn_size, attn_size), initializer = initializer()), + "W_v_Q":tf.get_variable("W_v_Q",dtype = tf.float32, shape = (attn_size, attn_size), initializer = initializer()), + "v":tf.get_variable("v", dtype = tf.float32, shape = attn_size, initializer = initializer())} return params def encoding(word, char, word_embeddings, char_embeddings, scope = "embedding"): @@ -46,7 +46,7 @@ def apply_dropout(inputs, dropout = Params.dropout, is_training = True): else: return tf.nn.dropout(inputs, keep_prob = 1.0 - dropout) -def bidirectional_GRU(inputs, inputs_len, cell = None, units = 75, layers = 1, scope = "Bidirectional_GRU", output = 0, is_training = True, reuse = None): +def bidirectional_GRU(inputs, inputs_len, cell = None, units = Params.attn_size, layers = 1, scope = "Bidirectional_GRU", output = 0, is_training = True, reuse = None): with tf.variable_scope(scope, reuse = reuse): if cell is not None: (cell_fw, cell_bw) = cell @@ -89,15 +89,19 @@ def pointer_net(passage, passage_len, question, cell, params, scope = "pointer_n p2_logits = tf.reshape(scores,(shapes[0],shapes[1])) return tf.stack((p1_logits,p2_logits),1) -def attention_rnn(inputs, inputs_len, units, attn_cell, scope = "gated_attention_rnn", is_training = True): +def attention_rnn(inputs, inputs_len, units, attn_cell, bidirection = True, scope = "gated_attention_rnn", is_training = True): with tf.variable_scope(scope): - (cell_fw, cell_bw) = attn_cell - outputs = bidirectional_GRU(inputs, - inputs_len, - cell = (cell_fw,cell_bw), - scope = scope + "_bidirectional", - output = 0, - is_training = is_training) + if bidirection: + outputs = bidirectional_GRU(inputs, + inputs_len, + cell = attn_cell, + scope = scope + "_bidirectional", + output = 0, + is_training = is_training) + else: + outputs, _ = tf.nn.dynamic_rnn(attn_cell, inputs, + sequence_length = inputs_len, + dtype=tf.float32) return outputs def question_pooling(memory, units, weights, scope = "question_pooling"): @@ -125,6 +129,7 @@ def gated_attention(memory, inputs, states, units, params, self_matching = False inputs_ = tf.concat((inputs_,states),axis = 2) # The order matters inputs_ = tf.reshape(inputs_,(shapes[0]*shapes[1],-1)) + # print(inputs_) scores = attention(inputs_, units, weights) scores = tf.reshape(scores,(shapes[0],shapes[1],1)) attention_pool = tf.reduce_sum(scores * memory, 1) diff --git a/model.py b/model.py index 4df4935..2edab88 100644 --- a/model.py +++ b/model.py @@ -8,11 +8,9 @@ from data_load import get_batch from params import Params from layers import * -from sklearn.metrics import f1_score from GRU import gated_attention_GRUCell from evaluate import * import numpy as np -from utils import * import cPickle as pickle from process import * @@ -41,7 +39,7 @@ def __init__(self,is_training = True): self.question_w_len = tf.squeeze(self.question_w_len) self.encode_ids() - self.params = get_attn_params(Params.attn_size, initializer = tf.contrib.layers.xavier_initializer()) + self.params = get_attn_params(Params.attn_size, initializer = tf.contrib.layers.xavier_initializer) self.attention_match_rnn() self.bidirectional_readout() self.pointer_network() @@ -110,14 +108,17 @@ def attention_match_rnn(self): self.params["W_v_Phat"]),axis = 0), self.params["v"]),self.params["W_g"])] for i in range(2): - # cell_fw = MultiRNNCell([apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i]),is_training = self.is_training) for _ in range(Params.num_layers)]) - # cell_bw = MultiRNNCell([apply_dropout(gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i]),is_training = self.is_training) for _ in range(Params.num_layers)]) - cell_fw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True if i == 1 else False) - cell_bw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True if i == 1 else False) + if scopes[i] == "question_passage_matching": + cell_fw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False) + cell_bw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False) + cell = (cell_fw, cell_bw) + elif scopes[i] == "self_matching": + cell = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True) inputs = attention_rnn(inputs, self.passage_w_len, Params.attn_size, - (cell_fw,cell_bw), + cell, + bidirection = True if i == 0 else False, scope = scopes[i]) memory = inputs # self_matching inputs = apply_dropout(inputs, is_training = self.is_training) @@ -126,7 +127,7 @@ def attention_match_rnn(self): def bidirectional_readout(self): self.final_bidirectional_outputs = bidirectional_GRU(self.self_matching_output, self.passage_w_len, - layers = Params.num_layers, + # layers = Params.num_layers, scope = "bidirectional_readout", output = 0, is_training = self.is_training) @@ -160,10 +161,17 @@ def loss_function(self): gradients, _ = tf.clip_by_global_norm(gradients, Params.norm) self.train_op = self.optimizer.apply_gradients(zip(gradients, variables), global_step = self.global_step) else: - self.train_op = self.optimizer.minimize(self.mean_loss, global_step = self.global_step) + self.train_op = self.optimizer.minimize(self.mean_loss, global_step = self.global_step)#, aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N) def summary(self): + self.F1 = tf.Variable(tf.constant(0.0, shape=(), dtype = tf.float32),trainable=False, name="F1") + self.F1_placeholder = tf.placeholder(tf.float32, shape = (), name = "F1_placeholder") + self.EM = tf.Variable(tf.constant(0.0, shape=(), dtype = tf.float32),trainable=False, name="EM") + self.EM_placeholder = tf.placeholder(tf.float32, shape = (), name = "EM_placeholder") + self.metric_assign = tf.group(tf.assign(self.F1, self.F1_placeholder),tf.assign(self.EM, self.EM_placeholder)) tf.summary.scalar('mean_loss', self.mean_loss) + tf.summary.scalar("F1",self.F1) + tf.summary.scalar("EM",self.EM) tf.summary.scalar('passage_word_encoded',tf.reduce_mean(self.passage_word_encoded)) tf.summary.scalar('passage_char_encoded',tf.reduce_mean(self.passage_char_encoded)) tf.summary.scalar('question_word_encoded',tf.reduce_mean(self.question_word_encoded)) @@ -183,42 +191,38 @@ def test(): model = Model(is_training = False); print("Built model") dict_ = pickle.load(open(Params.data_dir + "dictionary.pkl","r")) glove = np.memmap(Params.data_dir + "glove.np", dtype = np.float32, mode = "r") - glove = np.reshape(glove,(Params.vocab_size,300)) + glove = np.reshape(glove,(Params.vocab_size,Params.emb_size)) char_glove = np.memmap(Params.data_dir + "glove_char.np",dtype = np.float32, mode = "r") - char_glove = np.reshape(char_glove,(Params.char_vocab_size,300)) + char_glove = np.reshape(char_glove,(Params.char_vocab_size,Params.emb_size)) with model.graph.as_default(): sv = tf.train.Supervisor() with sv.managed_session() as sess: sv.saver.restore(sess, tf.train.latest_checkpoint(Params.logdir)) sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) - EM = 0.0 - f1 = 0.0 + EM, f1 = 0.0, 0.0 for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): index, ground_truth, passage = sess.run([model.output_index, model.indices, model.passage_w]) for batch in range(Params.batch_size): - answer = " ".join([dict_.ind2word(a) for a in passage[batch,index[batch,0]:index[batch,1]].tolist() if len(a) == 1 else [passage[batch,index[batch,0]:index[batch,1]].tolist()]]) - answer_ = " ".join([dict_.ind2word(a) for a in passage[batch,ground_truth[batch,0]:index[batch,1]].tolist() if len(a) == 1 else [passage[batch,ground_truth[batch,0]:index[batch,1]].tolist()]]) - f1 += f1_score(answer,answer_) - EM = exact_match_score(answer,answer_) - f1 /= Params.batch_size - EM /= Params.batch_size - f1 /= model.num_batch - EM /= model.num_batch - print("Exact_match: {}\nf1_score: {}".format(EM,f1)) - + f1_, EM_ = f1_and_EM(index[batch], ground_truth[batch], passage[batch], dict_) + f1 /= float(model.num_batch * Params.batch_size) + EM /= float(model.num_batch * Params.batch_size) + print("Exact_match: {}\nF1_score: {}".format(EM,f1)) def main(): model = Model(is_training = True); print("Built model") + dict_ = pickle.load(open(Params.data_dir + "dictionary.pkl","r")) glove = np.memmap(Params.data_dir + "glove.np", dtype = np.float32, mode = "r") glove = np.reshape(glove,(Params.vocab_size,300)) char_glove = np.memmap(Params.data_dir + "glove_char.np",dtype = np.float32, mode = "r") char_glove = np.reshape(char_glove,(Params.char_vocab_size,300)) with model.graph.as_default(): + config = tf.ConfigProto() + config.gpu_options.allow_growth = True sv = tf.train.Supervisor(logdir=Params.logdir, save_model_secs=0, global_step = model.global_step, init_op = model.init_op) - with sv.managed_session() as sess: + with sv.managed_session(config = config) as sess: sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) for epoch in range(1, Params.num_epochs+1): if sv.should_stop(): break @@ -226,6 +230,17 @@ def main(): sess.run(model.train_op) if step % Params.save_steps == 0: sv.saver.save(sess, Params.logdir + '/model_epoch_%d_step_%d'%(epoch,step)) + index, ground_truth, passage = sess.run([model.points_logits, model.indices, model.passage_w]) + index = np.argmax(index, axis = 2) + F1, EM = 0.0, 0.0 + for batch in range(Params.batch_size): + f1, em = f1_and_EM(index[batch], ground_truth[batch], passage[batch], dict_) + F1 += f1 + EM += em + F1 /= float(Params.batch_size) + EM /= float(Params.batch_size) + sess.run(model.metric_assign,{model.F1_placeholder: F1, model.EM_placeholder: EM}) + print("Exact_match: {}\nF1_score: {}".format(EM,F1)) if __name__ == '__main__': if Params.debug == True: @@ -235,5 +250,5 @@ def main(): print("Testing on dev set...") test() else: - print("Running...") + print("Training...") main() diff --git a/params.py b/params.py index a85acd1..48106bc 100644 --- a/params.py +++ b/params.py @@ -7,7 +7,7 @@ class Params(): data_dir = "./data/" train_dir = data_dir + "trainset/" dev_dir = data_dir + "devset/" - logdir = "./train/adadelta_dALL_x" + logdir = "./train/adadelta" glove_dir = "glove.840B.300d.txt" glove_char = "glove.840B.300d.char.txt" coreNLP_dir = "./stanford-corenlp-full-2017-06-09" @@ -20,8 +20,8 @@ class Params(): p_chars_dir = "chars_context.txt" # Training - debug = False # Set it to True to debug the computation graph - test = True + debug = True # Set it to True to debug the computation graph + test = False learning_rate = 1 # Adadelta doesn't require initial learning rate dropout = 0.2 # dropout probability optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] @@ -31,9 +31,9 @@ class Params(): norm = 5.0 # global norm # Architecture - max_len = 100 # Maximum number of words in each passage context + max_len = 200 # Maximum number of words in each passage context vocab_size = 2196018 # Number of vocabs in glove.840B.300d.txt + 1 for an unknown token char_vocab_size = 95 # Number of characters in glove.840B.300d.char.txt + 1 for an unknown character emb_size = 300 # Embeddings size for both words and characters - attn_size = 75 # RNN celland attention module size - num_layers = 1 # Number of layers at question-passage matching and self matching network + attn_size = 64 # RNN celland attention module size + num_layers = 3 # Number of layers at question-passage matching and self matching network From ec238d6f7ac57890423298acaf71befbceddf012 Mon Sep 17 00:00:00 2001 From: Min Date: Mon, 4 Sep 2017 17:15:21 +1200 Subject: [PATCH 22/40] reduced gpu memory usage --- layers.py | 49 +++++++++++++++++++++++-------------------------- model.py | 34 ++++++++++++++++++++++------------ params.py | 8 ++++---- 3 files changed, 49 insertions(+), 42 deletions(-) diff --git a/layers.py b/layers.py index bdd03d4..948d37b 100644 --- a/layers.py +++ b/layers.py @@ -75,18 +75,14 @@ def pointer_net(passage, passage_len, question, cell, params, scope = "pointer_n weights_q, weights_p = params shapes = passage.get_shape().as_list() initial_state = question_pooling(question, units = Params.attn_size, weights = weights_q, scope = "question_pooling") - stacked_state = tf.stack([initial_state] * shapes[1],1) - inputs = tf.reshape(tf.concat((passage, stacked_state),axis = 2),(shapes[0]*shapes[1],-1)) - scores = attention(inputs, Params.attn_size, weights_p, output_fn = None, scope = "attention") - scores = tf.reshape(scores,(shapes[0],shapes[1],1)) - p1_logits = tf.squeeze(scores,-1) + inputs = [passage, initial_state] + p1_logits = attention(inputs, Params.attn_size, weights_p, output_fn = None, scope = "attention") + scores = tf.expand_dims(p1_logits, -1) scores = tf.nn.softmax(scores) attention_pool = tf.reduce_sum(scores * passage,1) _, state = cell(attention_pool, initial_state) - stacked_state = tf.stack([state] * shapes[1],1) - inputs = tf.reshape(tf.concat((passage, stacked_state),axis = 2),(shapes[0]*shapes[1],-1)) - scores = attention(inputs, Params.attn_size, weights_p, output_fn = None, scope = "attention", reuse = True) - p2_logits = tf.reshape(scores,(shapes[0],shapes[1])) + inputs = [passage, state] + p2_logits = attention(inputs, Params.attn_size, weights_p, output_fn = None, scope = "attention", reuse = True) return tf.stack((p1_logits,p2_logits),1) def attention_rnn(inputs, inputs_len, units, attn_cell, bidirection = True, scope = "gated_attention_rnn", is_training = True): @@ -108,30 +104,22 @@ def question_pooling(memory, units, weights, scope = "question_pooling"): with tf.variable_scope(scope): shapes = memory.get_shape().as_list() V_r = tf.get_variable("question_param", shape = units, dtype = tf.float32) - V_r = tf.stack([V_r] * shapes[1], axis = 0) - V_r = tf.stack([V_r] * Params.batch_size, axis = 0) - inputs = tf.concat((memory, V_r), axis = 2) - inputs = tf.reshape(inputs, (shapes[0]*shapes[1],-1)) - attn = attention(inputs, units, weights, scope = "question_attention_pooling") - attn = tf.reshape(attn,(shapes[0],shapes[1],1)) + inputs_ = [memory, V_r] + attn = attention(inputs_, units, weights, scope = "question_attention_pooling") + attn = tf.expand_dims(attn, -1) return tf.reduce_sum(attn * memory, 1) def gated_attention(memory, inputs, states, units, params, self_matching = False, output_argmax = None, scope="gated_attention"): with tf.variable_scope(scope): weights, W_g = params - shapes = memory.get_shape().as_list() - inputs_ = tf.stack([inputs] * shapes[1],1) - inputs_ = tf.concat((memory,inputs_),axis = 2) # The order doesn't matter + inputs_ = [memory, inputs] states = tf.reshape(states,(Params.batch_size,Params.attn_size)) - if not self_matching: - states = tf.stack([states]*shapes[1],1) - inputs_ = tf.concat((inputs_,states),axis = 2) # The order matters + inputs_.append(states) - inputs_ = tf.reshape(inputs_,(shapes[0]*shapes[1],-1)) - # print(inputs_) scores = attention(inputs_, units, weights) - scores = tf.reshape(scores,(shapes[0],shapes[1],1)) + # scores = tf.reshape(scores,(shapes[0],shapes[1],1)) + scores = tf.expand_dims(scores,-1) attention_pool = tf.reduce_sum(scores * memory, 1) inputs = tf.concat((inputs,attention_pool),axis = 1) g_t = tf.sigmoid(tf.matmul(inputs,W_g)) @@ -140,8 +128,17 @@ def gated_attention(memory, inputs, states, units, params, self_matching = False def attention(inputs, units, weights, scope = "attention", output_fn = "softmax", reuse = None): with tf.variable_scope(scope, reuse = reuse): weights, v = weights - shapes = inputs.get_shape().as_list() - outputs = tf.matmul(inputs, weights) + outputs_ = [] + for i, (inp,w) in enumerate(zip(inputs,weights)): + shapes = inp.shape.as_list() + inp = tf.reshape(inp, (-1, shapes[-1])) + outputs = tf.matmul(inp, w) + if len(shapes) > 2: + outputs = tf.reshape(outputs, (shapes[0], shapes[1], -1)) + elif len(shapes) == 2: + outputs = tf.reshape(outputs, (shapes[0],1,-1)) + outputs_.append(outputs) + outputs = sum(outputs_) scores = tf.reduce_sum(tf.tanh(outputs) * v, [-1]) if output_fn == "softmax": return tf.nn.softmax(scores) diff --git a/model.py b/model.py index 2edab88..70586b6 100644 --- a/model.py +++ b/model.py @@ -99,14 +99,22 @@ def attention_match_rnn(self): memory = self.question_encoding inputs = self.passage_encoding scopes = ["question_passage_matching", "self_matching"] - params = [((tf.concat((self.params["W_u_Q"], - self.params["W_u_P"], - self.params["W_v_P"]),axis = 0), - self.params["v"]),self.params["W_g"]), - ((tf.concat((self.params["W_v_P"], - self.params["W_v_P"], - self.params["W_v_Phat"]),axis = 0), - self.params["v"]),self.params["W_g"])] + params = [([[self.params["W_u_Q"], + self.params["W_u_P"], + self.params["W_v_P"]], + self.params["v"]],self.params["W_g"]), + ([[tf.concat((self.params["W_v_P"], + self.params["W_v_P"]),axis = 0), + self.params["W_v_Phat"]], + self.params["v"]],self.params["W_g"])] + # params = [((tf.concat((self.params["W_u_Q"], + # self.params["W_u_P"], + # self.params["W_v_P"]),axis = 0), + # self.params["v"]),self.params["W_g"]), + # ((tf.concat((self.params["W_v_P"], + # self.params["W_v_P"], + # self.params["W_v_Phat"]),axis = 0), + # self.params["v"]),self.params["W_g"])] for i in range(2): if scopes[i] == "question_passage_matching": cell_fw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False) @@ -133,8 +141,8 @@ def bidirectional_readout(self): is_training = self.is_training) def pointer_network(self): - params = ((tf.concat((self.params["W_u_Q"],self.params["W_v_Q"]),axis = 0),self.params["v"]), - (tf.concat((self.params["W_h_P"],self.params["W_h_a"]),axis = 0),self.params["v"])) + params = (([self.params["W_u_Q"],self.params["W_v_Q"]],self.params["v"]), + ([self.params["W_h_P"],self.params["W_h_a"]],self.params["v"])) cell = apply_dropout(tf.contrib.rnn.GRUCell(Params.attn_size*2), is_training = self.is_training) self.points_logits = pointer_net(self.final_bidirectional_outputs, self.passage_w_len, self.question_encoding, cell, params, scope = "pointer_network") @@ -203,8 +211,10 @@ def test(): for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): index, ground_truth, passage = sess.run([model.output_index, model.indices, model.passage_w]) for batch in range(Params.batch_size): - f1_, EM_ = f1_and_EM(index[batch], ground_truth[batch], passage[batch], dict_) - f1 /= float(model.num_batch * Params.batch_size) + f1, em = f1_and_EM(index[batch], ground_truth[batch], passage[batch], dict_) + F1 += f1 + EM += em + F1 /= float(model.num_batch * Params.batch_size) EM /= float(model.num_batch * Params.batch_size) print("Exact_match: {}\nF1_score: {}".format(EM,f1)) diff --git a/params.py b/params.py index 48106bc..1b49afc 100644 --- a/params.py +++ b/params.py @@ -7,7 +7,7 @@ class Params(): data_dir = "./data/" train_dir = data_dir + "trainset/" dev_dir = data_dir + "devset/" - logdir = "./train/adadelta" + logdir = "./train/train" glove_dir = "glove.840B.300d.txt" glove_char = "glove.840B.300d.char.txt" coreNLP_dir = "./stanford-corenlp-full-2017-06-09" @@ -20,12 +20,12 @@ class Params(): p_chars_dir = "chars_context.txt" # Training - debug = True # Set it to True to debug the computation graph + debug = False # Set it to True to debug the computation graph test = False learning_rate = 1 # Adadelta doesn't require initial learning rate dropout = 0.2 # dropout probability optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] - batch_size = 48 + batch_size = 64 save_steps = 50 # Save the model at every 50 steps clip = False # clip gradient norm norm = 5.0 # global norm @@ -35,5 +35,5 @@ class Params(): vocab_size = 2196018 # Number of vocabs in glove.840B.300d.txt + 1 for an unknown token char_vocab_size = 95 # Number of characters in glove.840B.300d.char.txt + 1 for an unknown character emb_size = 300 # Embeddings size for both words and characters - attn_size = 64 # RNN celland attention module size + attn_size = 75 # RNN celland attention module size num_layers = 3 # Number of layers at question-passage matching and self matching network From 682149ee4e098914d0c70a82fbcd8b6c4bf6e919 Mon Sep 17 00:00:00 2001 From: Min Date: Tue, 5 Sep 2017 14:47:39 +1200 Subject: [PATCH 23/40] use bidirectional for all layers --- README.md | 1 + model.py | 78 +++++++++++++++++++++++++------------------------------ params.py | 4 +-- 3 files changed, 39 insertions(+), 44 deletions(-) diff --git a/README.md b/README.md index 8c00680..b25ab2a 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,7 @@ Training with full SQuAD dataset is currently a work in progress. The dataset used for this task is Stanford Question Answering Dataset (https://rajpurkar.github.io/SQuAD-explorer/). Pretrained GloVe embeddings are used for both words (https://nlp.stanford.edu/projects/glove/) and characters (https://github.com/minimaxir/char-embeddings/blob/master/glove.840B.300d-char.txt). ## Requirements + * Python2.7 * NumPy * tqdm * TensorFlow == 1.2 diff --git a/model.py b/model.py index 70586b6..d629289 100644 --- a/model.py +++ b/model.py @@ -96,49 +96,43 @@ def encode_ids(self): is_training = self.is_training) def attention_match_rnn(self): - memory = self.question_encoding - inputs = self.passage_encoding - scopes = ["question_passage_matching", "self_matching"] - params = [([[self.params["W_u_Q"], - self.params["W_u_P"], - self.params["W_v_P"]], - self.params["v"]],self.params["W_g"]), - ([[tf.concat((self.params["W_v_P"], - self.params["W_v_P"]),axis = 0), - self.params["W_v_Phat"]], - self.params["v"]],self.params["W_g"])] - # params = [((tf.concat((self.params["W_u_Q"], - # self.params["W_u_P"], - # self.params["W_v_P"]),axis = 0), - # self.params["v"]),self.params["W_g"]), - # ((tf.concat((self.params["W_v_P"], - # self.params["W_v_P"], - # self.params["W_v_Phat"]),axis = 0), - # self.params["v"]),self.params["W_g"])] - for i in range(2): - if scopes[i] == "question_passage_matching": - cell_fw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False) - cell_bw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False) + with tf.variable_scope("attention_match_rnn"): + memory = self.question_encoding + inputs = self.passage_encoding + scopes = ["question_passage_matching", "self_matching"] + params = [([[self.params["W_u_Q"], + self.params["W_u_P"], + self.params["W_v_P"]], + self.params["v"]],self.params["W_g"]), + ([[tf.concat((self.params["W_v_P"], + self.params["W_v_P"]),axis = 0), + self.params["W_v_Phat"]], + self.params["v"]],self.params["W_g"])] + for i in range(2): + if scopes[i] == "question_passage_matching": + cell_fw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False) + cell_bw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = False) + elif scopes[i] == "self_matching": + cell_fw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True) + cell_bw = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True) cell = (cell_fw, cell_bw) - elif scopes[i] == "self_matching": - cell = gated_attention_GRUCell(Params.attn_size, memory = memory, params = params[i], self_matching = True) - inputs = attention_rnn(inputs, - self.passage_w_len, - Params.attn_size, - cell, - bidirection = True if i == 0 else False, - scope = scopes[i]) - memory = inputs # self_matching - inputs = apply_dropout(inputs, is_training = self.is_training) - self.self_matching_output = inputs + inputs = attention_rnn(inputs, + self.passage_w_len, + Params.attn_size, + cell, + bidirection = True, + scope = scopes[i]) + memory = inputs # self_matching + inputs = apply_dropout(inputs, is_training = self.is_training) + self.self_matching_output = inputs def bidirectional_readout(self): self.final_bidirectional_outputs = bidirectional_GRU(self.self_matching_output, - self.passage_w_len, - # layers = Params.num_layers, - scope = "bidirectional_readout", - output = 0, - is_training = self.is_training) + self.passage_w_len, + layers = Params.num_layers, + scope = "bidirectional_readout", + output = 0, + is_training = self.is_training) def pointer_network(self): params = (([self.params["W_u_Q"],self.params["W_v_Q"]],self.params["v"]), @@ -207,7 +201,7 @@ def test(): with sv.managed_session() as sess: sv.saver.restore(sess, tf.train.latest_checkpoint(Params.logdir)) sess.run(model.emb_assign, {model.word_embeddings_placeholder:glove, model.char_embeddings_placeholder:char_glove}) - EM, f1 = 0.0, 0.0 + EM, F1 = 0.0, 0.0 for step in tqdm(range(model.num_batch), total = model.num_batch, ncols=70, leave=False, unit='b'): index, ground_truth, passage = sess.run([model.output_index, model.indices, model.passage_w]) for batch in range(Params.batch_size): @@ -216,7 +210,7 @@ def test(): EM += em F1 /= float(model.num_batch * Params.batch_size) EM /= float(model.num_batch * Params.batch_size) - print("Exact_match: {}\nF1_score: {}".format(EM,f1)) + print("Exact_match: {}\nF1_score: {}".format(EM,F1)) def main(): model = Model(is_training = True); print("Built model") @@ -250,7 +244,7 @@ def main(): F1 /= float(Params.batch_size) EM /= float(Params.batch_size) sess.run(model.metric_assign,{model.F1_placeholder: F1, model.EM_placeholder: EM}) - print("Exact_match: {}\nF1_score: {}".format(EM,F1)) + print("\nExact_match: {}\nF1_score: {}".format(EM,F1)) if __name__ == '__main__': if Params.debug == True: diff --git a/params.py b/params.py index 1b49afc..43a17b2 100644 --- a/params.py +++ b/params.py @@ -7,7 +7,7 @@ class Params(): data_dir = "./data/" train_dir = data_dir + "trainset/" dev_dir = data_dir + "devset/" - logdir = "./train/train" + logdir = "./train/draw" glove_dir = "glove.840B.300d.txt" glove_char = "glove.840B.300d.char.txt" coreNLP_dir = "./stanford-corenlp-full-2017-06-09" @@ -25,7 +25,7 @@ class Params(): learning_rate = 1 # Adadelta doesn't require initial learning rate dropout = 0.2 # dropout probability optimizer = "adadelta" # Options: ["adadelta", "adam", "gradientdescent", "adagrad"] - batch_size = 64 + batch_size = 50 save_steps = 50 # Save the model at every 50 steps clip = False # clip gradient norm norm = 5.0 # global norm From a2e4f850538be4ce854ed7e0db13ace05c51b32f Mon Sep 17 00:00:00 2001 From: Min Date: Tue, 5 Sep 2017 17:42:37 +1200 Subject: [PATCH 24/40] Updated README --- README.md | 13 +++++++------ params.py | 12 ++++++------ screenshots/architecture.png | Bin 80972 -> 223759 bytes 3 files changed, 13 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index b25ab2a..7fd5147 100644 --- a/README.md +++ b/README.md @@ -3,8 +3,6 @@ Tensorflow implementation of https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf ![Alt text](/../dev/screenshots/architecture.png?raw=true "R-NET") -Training with full SQuAD dataset is currently a work in progress. - The dataset used for this task is Stanford Question Answering Dataset (https://rajpurkar.github.io/SQuAD-explorer/). Pretrained GloVe embeddings are used for both words (https://nlp.stanford.edu/projects/glove/) and characters (https://github.com/minimaxir/char-embeddings/blob/master/glove.840B.300d-char.txt). ## Requirements @@ -21,12 +19,12 @@ $ bash setup.sh $ python process.py --process True ``` -# Training -You can change the hyperparameters from params.py file. -To train the model, run the following line. +# Training / Testing / Debug +You can change the hyperparameters from params.py file to fit the model in your GPU. To train the model, run the following line. ```shell $ python model.py ``` +To test or debug your model after training, put debug/test=True from params.py file and run the model. # Tensorboard Run tensorboard for visualisation. @@ -35,7 +33,10 @@ $ tensorboard --logdir=r-net:train/ ``` ![Alt text](/../dev/screenshots/graph.png?raw=true "Tensorboard Graph") -# Note +# Log +**05/09/17** +After rewriting some part of the architectures, the model converges with full dataset and it takes about 20 hours to reach F1/EM=67/60 on training set and 40/30 on dev set. with batch size of 54. Reproducing the results obtained by R-Net in the original paper is a new work in progress. + **02/09/17** One of the challenges I faced while training was to fit a minibatch of size 32 or larger into my GTX 1080. Since SQuAD dataset displayed high variance in data, higher batch size was essential in training (otherwise the model doesn't converge). Reducing GPU memory usage significantly to fit batch size of 32 and higher is a work in progress. If you have any suggestions on reducing the GPU memory usage, please put forward a pr. diff --git a/params.py b/params.py index 43a17b2..1a0a52c 100644 --- a/params.py +++ b/params.py @@ -7,10 +7,10 @@ class Params(): data_dir = "./data/" train_dir = data_dir + "trainset/" dev_dir = data_dir + "devset/" - logdir = "./train/draw" - glove_dir = "glove.840B.300d.txt" - glove_char = "glove.840B.300d.char.txt" - coreNLP_dir = "./stanford-corenlp-full-2017-06-09" + logdir = "./train/train_fulldata" + glove_dir = "glove.840B.300d.txt" # Glove file name (If you want to use your own glove, replace the file name here) + glove_char = "glove.840B.300d.char.txt" # Character Glove file name + coreNLP_dir = "./stanford-corenlp-full-2017-06-09" # Directory to pycorenlp wrapper # Data dir target_dir = "indices.txt" @@ -21,11 +21,11 @@ class Params(): # Training debug = False # Set it to True to debug the computation graph - test = False + test = False # Test the model on dev-set learning_rate = 1 # 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