diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md index 617efab..5072075 100644 --- a/.github/ISSUE_TEMPLATE.md +++ b/.github/ISSUE_TEMPLATE.md @@ -1,10 +1,16 @@ # STOP! +**Please don't waste my time!** + Most of the problems people are having are already described in the [installation instructions](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/README.md). -## Python 3.5 +You should first make a serious attempt to solve your problem. +If you ask a question that has already been answered elsewhere, or if you do not +give enough details about your problem, then your issue may be closed immediately. + +## Python 3 -These tutorials were developed in **Python 3.5** and may give strange errors in Python 2.7 +These tutorials were developed in **Python 3.5** (and higher) and may give strange errors in Python 2.7 ## Missing Files @@ -12,7 +18,13 @@ You need to **download the whole repository**, either using `git clone` or as a ## Questions about TensorFlow -General questions about TensorFlow should either be asked on [StackOverflow](http://stackoverflow.com/questions/tagged/tensorflow) or [GitHub](https://github.com/tensorflow/tensorflow/issues). +General questions about TensorFlow should either be asked on [StackOverflow](http://stackoverflow.com/questions/tagged/tensorflow) or the [official TensorFlow repository](https://github.com/tensorflow/tensorflow/issues). + +## Modifications + +Questions about modifications or how to use these tutorials on your own data-set should also be asked on [StackOverflow](http://stackoverflow.com/questions/tagged/tensorflow). + +Thousands of people are using these tutorials. It is impossible for me to give individual support for your project. ## Suggestions for Changes diff --git a/01_Simple_Linear_Model.ipynb b/01_Simple_Linear_Model.ipynb index d6dacad..6df8479 100644 --- a/01_Simple_Linear_Model.ipynb +++ b/01_Simple_Linear_Model.ipynb @@ -22,6 +22,15 @@ "You should be familiar with basic linear algebra, Python and the Jupyter Notebook editor. It also helps if you have a basic understanding of Machine Learning and classification." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TensorFlow 2\n", + "\n", + "This tutorial was developed using TensorFlow v.1 back in the year 2016. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\", which is still useful for learning how TensorFlow works, but you would have to implement it slightly differently in TF2 (see Tutorial 03C on the Keras API). It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -32,39 +41,56 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", - "import tensorflow as tf\n", "import numpy as np\n", "from sklearn.metrics import confusion_matrix" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n" + ] + } + ], + "source": [ + "# Use TensorFlow v.2 with this old v.1 code.\n", + "# E.g. placeholder variables and sessions have changed in TF2.\n", + "import tensorflow.compat.v1 as tf\n", + "tf.disable_v2_behavior()" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:" + "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'0.12.0-rc1'" + "'2.1.0'" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -89,40 +115,25 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting data/MNIST/train-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n", - "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "execution_count": 4, + "metadata": {}, + "outputs": [], "source": [ - "from tensorflow.examples.tutorials.mnist import input_data\n", - "data = input_data.read_data_sets(\"data/MNIST/\", one_hot=True)" + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The MNIST data-set has now been loaded and consists of 70.000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -130,94 +141,68 @@ "text": [ "Size of:\n", "- Training-set:\t\t55000\n", - "- Test-set:\t\t10000\n", - "- Validation-set:\t5000\n" + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" ] } ], "source": [ "print(\"Size of:\")\n", - "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n", - "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n", - "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))" + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### One-Hot Encoding" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data-set has been loaded as so-called One-Hot encoding. This means the labels have been converted from a single number to a vector whose length equals the number of possible classes. All elements of the vector are zero except for the $i$'th element which is one and means the class is $i$. For example, the One-Hot encoded labels for the first 5 images in the test-set are:" + "Copy some of the data-dimensions for convenience." ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n", - " [ 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 6, + "metadata": {}, + "outputs": [], "source": [ - "data.test.labels[0:5, :]" + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", + "\n", + "# Tuple with height and width of images used to reshape arrays.\n", + "img_shape = data.img_shape\n", + "\n", + "# Number of classes, one class for each of 10 digits.\n", + "num_classes = data.num_classes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We also need the classes as single numbers for various comparisons and performance measures, so we convert the One-Hot encoded vectors to a single number by taking the index of the highest element. Note that the word 'class' is a keyword used in Python so we need to use the name 'cls' instead." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "data.test.cls = np.array([label.argmax() for label in data.test.labels])" + "### One-Hot Encoding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can now see the class for the first five images in the test-set. Compare these to the One-Hot encoded vectors above. For example, the class for the first image is 7, which corresponds to a One-Hot encoded vector where all elements are zero except for the element with index 7." + "The output-data is loaded as both integer class-numbers and so-called One-Hot encoded arrays. This means the class-numbers have been converted from a single integer to a vector whose length equals the number of possible classes. All elements of the vector are zero except for the $i$'th element which is 1 and means the class is $i$. For example, the One-Hot encoded labels for the first 5 images in the test-set are:" ] }, { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([7, 2, 1, 0, 4])" + "array([[0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n", + " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]])" ] }, "execution_count": 7, @@ -226,42 +211,34 @@ } ], "source": [ - "data.test.cls[0:5]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data dimensions" + "data.y_test[0:5, :]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The data dimensions are used in several places in the source-code below. In computer programming it is generally best to use variables and constants rather than having to hard-code specific numbers every time that number is used. This means the numbers only have to be changed in one single place. Ideally these would be inferred from the data that has been read, but here we just write the numbers." + "We also need the classes as integers for various comparisons and performance measures. These can be found from the One-Hot encoded arrays by taking the index of the highest element using the `np.argmax()` function. But this has already been done for us when the data-set was loaded, so we can see the class-number for the first five images in the test-set. Compare these to the One-Hot encoded arrays above." ] }, { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([7, 2, 1, 0, 4])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# We know that MNIST images are 28 pixels in each dimension.\n", - "img_size = 28\n", - "\n", - "# Images are stored in one-dimensional arrays of this length.\n", - "img_size_flat = img_size * img_size\n", - "\n", - "# Tuple with height and width of images used to reshape arrays.\n", - "img_shape = (img_size, img_size)\n", - "\n", - "# Number of classes, one class for each of 10 digits.\n", - "num_classes = 10" + "data.y_test_cls[0:5]" ] }, { @@ -281,9 +258,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_images(images, cls_true, cls_pred=None):\n", @@ -307,7 +282,11 @@ " \n", " # Remove ticks from the plot.\n", " ax.set_xticks([])\n", - " ax.set_yticks([])" + " ax.set_yticks([])\n", + " \n", + " # Ensure the plot is shown correctly with multiple plots\n", + " # in a single Notebook cell.\n", + " plt.show()" ] }, { @@ -320,15 +299,13 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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ANzuvq96Vd4EErN/vzxz4UwG4NbuSQkvW2jq6z6u7OhK5eDyOSCSCQCDA7vIE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\n", 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" ] }, "metadata": {}, @@ -337,10 +314,10 @@ ], "source": [ "# Get the first images from the test-set.\n", - "images = data.test.images[0:9]\n", + "images = data.x_test[0:9]\n", "\n", "# Get the true classes for those images.\n", - "cls_true = data.test.cls[0:9]\n", + "cls_true = data.y_test_cls[0:9]\n", "\n", "# Plot the images and labels using our helper-function above.\n", "plot_images(images=images, cls_true=cls_true)" @@ -356,11 +333,11 @@ "\n", "TensorFlow can also automatically calculate the gradients that are needed to optimize the variables of the graph so as to make the model perform better. This is because the graph is a combination of simple mathematical expressions so the gradient of the entire graph can be calculated using the chain-rule for derivatives.\n", "\n", - "TensorFlow can also take advantage of multi-core CPUs as well as GPUs - and Google has even built special chips just for TensorFlow which are called TPUs (Tensor Processing Units) and are even faster than GPUs.\n", + "TensorFlow can also take advantage of multi-core CPUs as well as GPUs - and Google has even built special chips just for TensorFlow which are called TPUs (Tensor Processing Units) that are even faster than GPUs.\n", "\n", "A TensorFlow graph consists of the following parts which will be detailed below:\n", "\n", - "* Placeholder variables used to change the input to the graph.\n", + "* Placeholder variables used to feed input into the graph.\n", "* Model variables that are going to be optimized so as to make the model perform better.\n", "* The model which is essentially just a mathematical function that calculates some output given the input in the placeholder variables and the model variables.\n", "* A cost measure that can be used to guide the optimization of the variables.\n", @@ -388,9 +365,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "x = tf.placeholder(tf.float32, [None, img_size_flat])" @@ -406,9 +381,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "y_true = tf.placeholder(tf.float32, [None, num_classes])" @@ -424,9 +397,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "y_true_cls = tf.placeholder(tf.int64, [None])" @@ -451,9 +422,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "weights = tf.Variable(tf.zeros([img_size_flat, num_classes]))" @@ -469,9 +438,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "biases = tf.Variable(tf.zeros([num_classes]))" @@ -498,9 +465,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "logits = tf.matmul(x, weights) + biases" @@ -518,9 +483,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "y_pred = tf.nn.softmax(logits)" @@ -536,12 +499,10 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ - "y_pred_cls = tf.argmax(y_pred, dimension=1)" + "y_pred_cls = tf.argmax(y_pred, axis=1)" ] }, { @@ -565,13 +526,11 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ - "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,\n", - " labels=y_true)" + "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits,\n", + " labels=y_true)" ] }, { @@ -584,9 +543,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "cost = tf.reduce_mean(cross_entropy)" @@ -611,9 +568,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(cost)" @@ -638,9 +593,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" @@ -656,9 +609,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" @@ -683,9 +634,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "session = tf.Session()" @@ -703,9 +652,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" @@ -722,15 +669,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "There are 50.000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore use Stochastic Gradient Descent which only uses a small batch of images in each iteration of the optimizer." + "There are 55.000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore use Stochastic Gradient Descent which only uses a small batch of images in each iteration of the optimizer." ] }, { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "batch_size = 100" @@ -746,9 +691,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def optimize(num_iterations):\n", @@ -756,7 +699,7 @@ " # Get a batch of training examples.\n", " # x_batch now holds a batch of images and\n", " # y_true_batch are the true labels for those images.\n", - " x_batch, y_true_batch = data.train.next_batch(batch_size)\n", + " x_batch, y_true_batch, _ = data.random_batch(batch_size=batch_size)\n", " \n", " # Put the batch into a dict with the proper names\n", " # for placeholder variables in the TensorFlow graph.\n", @@ -788,14 +731,12 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ - "feed_dict_test = {x: data.test.images,\n", - " y_true: data.test.labels,\n", - " y_true_cls: data.test.cls}" + "feed_dict_test = {x: data.x_test,\n", + " y_true: data.y_test,\n", + " y_true_cls: data.y_test_cls}" ] }, { @@ -808,9 +749,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def print_accuracy():\n", @@ -831,14 +770,12 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def print_confusion_matrix():\n", " # Get the true classifications for the test-set.\n", - " cls_true = data.test.cls\n", + " cls_true = data.y_test_cls\n", " \n", " # Get the predicted classifications for the test-set.\n", " cls_pred = session.run(y_pred_cls, feed_dict=feed_dict_test)\n", @@ -860,7 +797,11 @@ " plt.xticks(tick_marks, range(num_classes))\n", " plt.yticks(tick_marks, range(num_classes))\n", " plt.xlabel('Predicted')\n", - " plt.ylabel('True')" + " plt.ylabel('True')\n", + " \n", + " # Ensure the plot is shown correctly with multiple plots\n", + " # in a single Notebook cell.\n", + " plt.show()" ] }, { @@ -873,9 +814,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_example_errors():\n", @@ -890,13 +829,13 @@ " \n", " # Get the images from the test-set that have been\n", " # incorrectly classified.\n", - " images = data.test.images[incorrect]\n", + " images = data.x_test[incorrect]\n", " \n", " # Get the predicted classes for those images.\n", " cls_pred = cls_pred[incorrect]\n", "\n", " # Get the true classes for those images.\n", - " cls_true = data.test.cls[incorrect]\n", + " cls_true = data.y_test_cls[incorrect]\n", " \n", " # Plot the first 9 images.\n", " plot_images(images=images[0:9],\n", @@ -921,9 +860,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_weights():\n", @@ -956,7 +893,11 @@ "\n", " # Remove ticks from each sub-plot.\n", " ax.set_xticks([])\n", - " ax.set_yticks([])" + " ax.set_yticks([])\n", + " \n", + " # Ensure the plot is shown correctly with multiple plots\n", + " # in a single Notebook cell.\n", + " plt.show()" ] }, { @@ -971,9 +912,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -990,15 +929,13 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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Ix8LvkkV6Z8STxuo0m00Wz0ajwW4Im82GaDSK+/fv47333sPa2hrC4TDXLt2l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\nAElFTkSuQmCC\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1015,15 +952,13 @@ "source": [ "## Performance after 1 optimization iteration\n", "\n", - "Already after a single optimization iteration, the model has increased its accuracy on the test-set to 40.7% up from 9.8%. This means that it mis-classifies the images about 6 out of 10 times, as demonstrated on a few examples below." + "Already after a single optimization iteration, the model has increased its accuracy on the test-set significantly." ] }, { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "optimize(num_iterations=1)" @@ -1032,15 +967,13 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on test-set: 40.7%\n" + "Accuracy on test-set: 22.2%\n" ] } ], @@ -1051,15 +984,13 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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YLLLAUV2ccdzTiyCXq7GRNgCuxyPxpGzceagYPpVKwel0ygQ5yQzUtJ1K9ejQ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\n", 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" ] }, "metadata": {}, @@ -1086,15 +1017,13 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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u2daehCMp8ZRPzxlg10PkXDR3liG0BaTp1P7f+MCSALIUGZYI422M724K7u9d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\nFdxsID8AAAAASUVORK5CYII=\n", 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\n", 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" ] }, "metadata": {}, @@ -1115,9 +1044,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# We have already performed 1 iteration.\n", @@ -1127,15 +1054,13 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on test-set: 78.2%\n" + "Accuracy on test-set: 77.6%\n" ] } ], @@ -1146,15 +1071,13 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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v1wshBDY3N9Hv9yGE4N1Qp9Ph7DGDwYBEIoG9vT08ePAA6XQa0WiUS13ow6Pd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\n1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FKemfju3Cxl7TSzTbifGIKnpdfiQcMoy/Gg7Jp7GLak03Fh1GUj5CCoNqHPoDMjqR7P\ndPU3s+y6ZU1Ri/e2rQcWxcAUdFJCn0Fnph54sNYdJ9TgzDMIgiAIWkstzjyDIAiCoFWE8wyCIAiC\nnITzDIIgCIKchPMMgiAIgpyE8wyCIAiCnITzDIIgCIKchPMMgiAIgpyE8wyCIAiCnITzDIIgCIKc\n/Ad6wg1N9mmYjwAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, "metadata": {}, @@ -1168,15 +1091,13 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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UHlgBfz1bf51DZhAhfLp5coCbHLC6E9x2a+3mppuPVMJs09MCpAxBl+DkxPGZ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CyxdsR2W9O2012p0mYDnQHbOXxn8LmLBv0JntyzXMvwcNXiCeS9xON3APxcN+pTAHwYfjokXuXW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\nLkHK/8ynkaZ77j8+7xw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\n", 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" ] }, "metadata": {}, @@ -1199,9 +1120,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# We have already performed 10 iterations.\n", @@ -1211,15 +1130,13 @@ { "cell_type": "code", "execution_count": 44, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on test-set: 91.7%\n" + "Accuracy on test-set: 91.6%\n" ] } ], @@ -1230,15 +1147,13 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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1J9L5JIzP84aRaJK7Api6PKjTEbnjPB4P4vH4bf06hAWDsrDp5FkqlVCtVrmH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\nTkSuQmCC\n", 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\n", 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r4X4rrNfCag11LVxfJxtMUx1aqJsbAnm2Ile0efYCJl0TQjqn3Ep+bB4RnF+b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gBTjvv0l4dHPZfbyZGLnDw3DF+iHDlZTklwdbZprAQsASue3hvawaarnUpHYZpDSEb99lY47AyL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\ndg3rtMMAAAAASUVORK5CYII=\n", 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\n", 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" ] }, "metadata": {}, @@ -1282,40 +1195,40 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also print and plot the so-called confusion matrix which lets us see more details about the mis-classifications. For example, it shows that images actually depicting a 5 have sometimes been mis-classified as all other possible digits, but mostly either 3, 6 or 8." + "We can also print and plot the so-called confusion matrix which lets us see more details about the mis-classifications. For example, it shows that images actually depicting a 5 have sometimes been mis-classified as all other possible digits, but mostly as 6 or 8." ] }, { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[ 957 0 3 2 0 5 11 1 1 0]\n", - " [ 0 1108 2 2 1 2 4 2 14 0]\n", - " [ 4 9 914 19 15 5 13 14 35 4]\n", - " [ 1 0 16 928 0 28 2 14 13 8]\n", - " [ 1 1 3 2 939 0 10 2 6 18]\n", - " [ 10 3 3 33 10 784 17 6 19 7]\n", - " [ 8 3 3 2 11 14 915 1 1 0]\n", - " [ 3 9 21 9 7 1 0 959 2 17]\n", - " [ 8 8 8 38 11 40 14 18 825 4]\n", - " [ 11 7 1 13 75 13 1 39 4 845]]\n" + "[[ 958 0 1 4 0 7 5 2 3 0]\n", + " [ 0 1103 3 5 1 1 3 2 17 0]\n", + " [ 7 6 917 25 12 3 8 11 38 5]\n", + " [ 1 0 12 943 0 19 1 11 17 6]\n", + " [ 2 3 4 2 921 1 8 2 8 31]\n", + " [ 10 2 4 53 8 754 10 8 35 8]\n", + " [ 15 3 5 2 23 17 885 2 6 0]\n", + " [ 3 8 20 8 7 1 0 946 4 31]\n", + " [ 7 4 6 40 9 25 9 12 858 4]\n", + " [ 11 5 2 12 49 8 0 31 11 880]]\n" ] }, { "data": { - "image/png": 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BgW1mZpY5FrgxIhZ3LYiIi6vWPyxpMXCbpA9FxKJ+jlfXk9cd2GZmlq5BdGu/dP9tvPzg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\nm5mZNYD/Dy0JOPxBG9Q8AAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1333,9 +1246,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -1384,9 +1295,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:tf]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1398,9 +1309,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02_Convolutional_Neural_Network.ipynb b/02_Convolutional_Neural_Network.ipynb index e89e963..c4f3446 100644 --- a/02_Convolutional_Neural_Network.ipynb +++ b/02_Convolutional_Neural_Network.ipynb @@ -37,32 +37,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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fRQSBVwVeQjwfexAl5q9q6WsOcDDx/E0F/gZ8OOtzHvF87pLsB/g89WtrjyZ3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\n+y1Eps78iraSJI02M4D9iWV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q8jjjI4AO8HeiBP47gQOAB3s7HEmSJElj2F4UVcyuJSbr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/lzD2OJXvT2WbN7e0sGLFChKJxBHZeqIT9HeCutaDg4NhH7GhoSEUa7XWOI5Da2trxW968Lqr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\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4gVqtxunTp3WAweTkJBMTE3iex86dO7njjjuwbZuJiQmy2SywPdvm+pCEnjyRz6hVng1+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\n4DgO+4+kSaWEtmfs7CJ79v1TY8Q5MB8b5mRul3ZckBKef97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VBHUqlaKvr49cLqdT6S5X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- "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image('images/02_network_flowchart.png')" + "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below.\n", + "\n", + "![Flowchart](images/02_network_flowchart.png)" ] }, { @@ -97,30 +74,9 @@ "\n", "The red filter-weights means that the filter has a positive reaction to black pixels in the input image, while blue pixels means the filter has a negative reaction to black pixels.\n", "\n", - "In this case it appears that the filter recognizes the horizontal line of the 7-digit, as can be seen from its stronger reaction to that line in the output image." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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Ybeb5b6i68weoCYmXUYrZY6h9QnFU3XkTqu60oPbsTSZfQQngn0W1ic+iVljv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\n5cRrWJaF4zg4jkM2m8W27dojemwYhmNes2274X03R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- "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('images/02_convolution.png')" + "In this case it appears that the filter recognizes the horizontal line of the 7-digit, as can be seen from its stronger reaction to that line in the output image.\n", + "\n", + "![Convolution example](images/02_convolution.png)" ] }, { @@ -138,6 +94,15 @@ "Note that the second convolutional layer is more complicated because it takes 16 input channels. We want a separate filter for each input channel, so we need 16 filters instead of just one. Furthermore, we want 36 output channels from the second convolutional layer, so in total we need 16 x 36 = 576 filters for the second convolutional layer. It can be a bit challenging to understand how this works." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TensorFlow 2\n", + "\n", + "This tutorial was developed using TensorFlow v.1 back in the year 2016. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\", which is still useful for learning how TensorFlow works, but you would have to implement it slightly differently in TF2 (see Tutorial 03C on the Keras API). It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -147,15 +112,12 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", - "import tensorflow as tf\n", "import numpy as np\n", "from sklearn.metrics import confusion_matrix\n", "import time\n", @@ -163,27 +125,47 @@ "import math" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n" + ] + } + ], + "source": [ + "# Use TensorFlow v.2 with this old v.1 code.\n", + "# E.g. placeholder variables and sessions have changed in TF2.\n", + "import tensorflow.compat.v1 as tf\n", + "tf.disable_v2_behavior()" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:" + "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'0.12.0-rc0'" + "'2.1.0'" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -203,10 +185,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "# Convolutional Layer 1.\n", @@ -237,39 +217,26 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting data/MNIST/train-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n", - "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "execution_count": 5, + "metadata": {}, + "outputs": [], "source": [ - "from tensorflow.examples.tutorials.mnist import input_data\n", - "data = input_data.read_data_sets('data/MNIST/', one_hot=True)\n" + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { - "collapsed": false + "scrolled": true }, "outputs": [ { @@ -278,72 +245,45 @@ "text": [ "Size of:\n", "- Training-set:\t\t55000\n", - "- Test-set:\t\t10000\n", - "- Validation-set:\t5000\n" + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" ] } ], "source": [ "print(\"Size of:\")\n", - "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n", - "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n", - "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))" + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now." + "Copy some of the data-dimensions for convenience." ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "data.test.cls = np.argmax(data.test.labels, axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Dimensions" - ] - }, - { - "cell_type": "markdown", + "execution_count": 7, "metadata": {}, - "source": [ - "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, "outputs": [], "source": [ - "# We know that MNIST images are 28 pixels in each dimension.\n", - "img_size = 28\n", + "# The number of pixels in each dimension of an image.\n", + "img_size = data.img_size\n", "\n", - "# Images are stored in one-dimensional arrays of this length.\n", - "img_size_flat = img_size * img_size\n", + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", "\n", "# Tuple with height and width of images used to reshape arrays.\n", - "img_shape = (img_size, img_size)\n", - "\n", - "# Number of colour channels for the images: 1 channel for gray-scale.\n", - "num_channels = 1\n", + "img_shape = data.img_shape\n", "\n", "# Number of classes, one class for each of 10 digits.\n", - "num_classes = 10" + "num_classes = data.num_classes\n", + "\n", + "# Number of colour channels for the images: 1 channel for gray-scale.\n", + "num_channels = data.num_channels" ] }, { @@ -362,10 +302,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "def plot_images(images, cls_true, cls_pred=None):\n", @@ -406,16 +344,14 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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xGFs30RsuCNcxHo/R6/V4IUZZxJRoMp1O4XK52GQglUohlUohkUiwwLnd7oVeIQurx2Qy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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -424,10 +360,10 @@ ], "source": [ "# Get the first images from the test-set.\n", - "images = data.test.images[0:9]\n", + "images = data.x_test[0:9]\n", "\n", "# Get the true classes for those images.\n", - "cls_true = data.test.cls[0:9]\n", + "cls_true = data.y_test_cls[0:9]\n", "\n", "# Plot the images and labels using our helper-function above.\n", "plot_images(images=images, cls_true=cls_true)" @@ -472,10 +408,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "def new_weights(shape):\n", @@ -484,10 +418,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "def new_biases(length):\n", @@ -526,10 +458,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "def new_conv_layer(input, # The previous layer.\n", @@ -602,10 +532,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "def flatten_layer(layer):\n", @@ -651,10 +579,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "def new_fc_layer(input, # The previous layer.\n", @@ -695,10 +621,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')" @@ -713,10 +637,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, + "execution_count": 16, + "metadata": {}, "outputs": [], "source": [ "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])" @@ -731,13 +653,11 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, + "execution_count": 17, + "metadata": {}, "outputs": [], "source": [ - "y_true = tf.placeholder(tf.float32, shape=[None, 10], name='y_true')" + "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')" ] }, { @@ -749,13 +669,11 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [], "source": [ - "y_true_cls = tf.argmax(y_true, dimension=1)" + "y_true_cls = tf.argmax(y_true, axis=1)" ] }, { @@ -769,10 +687,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ "layer_conv1, weights_conv1 = \\\n", @@ -792,10 +708,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { @@ -803,7 +717,7 @@ "" ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -823,10 +737,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, + "execution_count": 21, + "metadata": {}, "outputs": [], "source": [ "layer_conv2, weights_conv2 = \\\n", @@ -846,10 +758,8 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "data": { @@ -857,7 +767,7 @@ "" ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -877,10 +787,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [], "source": [ "layer_flat, num_features = flatten_layer(layer_conv2)" @@ -895,10 +803,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "data": { @@ -906,7 +812,7 @@ "" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -917,10 +823,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "data": { @@ -928,7 +832,7 @@ "1764" ] }, - "execution_count": 27, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -948,10 +852,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": true - }, + "execution_count": 26, + "metadata": {}, "outputs": [], "source": [ "layer_fc1 = new_fc_layer(input=layer_flat,\n", @@ -969,10 +871,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": 27, + "metadata": {}, "outputs": [ { "data": { @@ -980,7 +880,7 @@ "" ] }, - "execution_count": 29, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1000,10 +900,8 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": true - }, + "execution_count": 28, + "metadata": {}, "outputs": [], "source": [ "layer_fc2 = new_fc_layer(input=layer_fc1,\n", @@ -1014,10 +912,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, + "execution_count": 29, + "metadata": {}, "outputs": [ { "data": { @@ -1025,7 +921,7 @@ "" ] }, - "execution_count": 31, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1050,10 +946,8 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": true - }, + "execution_count": 30, + "metadata": {}, "outputs": [], "source": [ "y_pred = tf.nn.softmax(layer_fc2)" @@ -1068,13 +962,11 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": true - }, + "execution_count": 31, + "metadata": {}, "outputs": [], "source": [ - "y_pred_cls = tf.argmax(y_pred, dimension=1)" + "y_pred_cls = tf.argmax(y_pred, axis=1)" ] }, { @@ -1097,11 +989,24 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :2: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "\n", + "Future major versions of TensorFlow will allow gradients to flow\n", + "into the labels input on backprop by default.\n", + "\n", + "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n", + "\n" + ] + } + ], "source": [ "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,\n", " labels=y_true)" @@ -1116,10 +1021,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": true - }, + "execution_count": 33, + "metadata": {}, "outputs": [], "source": [ "cost = tf.reduce_mean(cross_entropy)" @@ -1143,10 +1046,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": true - }, + "execution_count": 34, + "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost)" @@ -1170,10 +1071,8 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": true - }, + "execution_count": 35, + "metadata": {}, "outputs": [], "source": [ "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" @@ -1188,10 +1087,8 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": true - }, + "execution_count": 36, + "metadata": {}, "outputs": [], "source": [ "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" @@ -1215,10 +1112,8 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": true - }, + "execution_count": 37, + "metadata": {}, "outputs": [], "source": [ "session = tf.Session()" @@ -1235,10 +1130,8 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false - }, + "execution_count": 38, + "metadata": {}, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" @@ -1262,10 +1155,8 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": true - }, + "execution_count": 39, + "metadata": {}, "outputs": [], "source": [ "train_batch_size = 64" @@ -1280,10 +1171,8 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false - }, + "execution_count": 40, + "metadata": {}, "outputs": [], "source": [ "# Counter for total number of iterations performed so far.\n", @@ -1302,7 +1191,7 @@ " # Get a batch of training examples.\n", " # x_batch now holds a batch of images and\n", " # y_true_batch are the true labels for those images.\n", - " x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n", + " x_batch, y_true_batch, _ = data.random_batch(batch_size=train_batch_size)\n", "\n", " # Put the batch into a dict with the proper names\n", " # for placeholder variables in the TensorFlow graph.\n", @@ -1354,10 +1243,8 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": true - }, + "execution_count": 41, + "metadata": {}, "outputs": [], "source": [ "def plot_example_errors(cls_pred, correct):\n", @@ -1374,13 +1261,13 @@ " \n", " # Get the images from the test-set that have been\n", " # incorrectly classified.\n", - " images = data.test.images[incorrect]\n", + " images = data.x_test[incorrect]\n", " \n", " # Get the predicted classes for those images.\n", " cls_pred = cls_pred[incorrect]\n", "\n", " # Get the true classes for those images.\n", - " cls_true = data.test.cls[incorrect]\n", + " cls_true = data.y_test_cls[incorrect]\n", " \n", " # Plot the first 9 images.\n", " plot_images(images=images[0:9],\n", @@ -1397,10 +1284,8 @@ }, { "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": true - }, + "execution_count": 42, + "metadata": {}, "outputs": [], "source": [ "def plot_confusion_matrix(cls_pred):\n", @@ -1410,7 +1295,7 @@ " # all images in the test-set.\n", "\n", " # Get the true classifications for the test-set.\n", - " cls_true = data.test.cls\n", + " cls_true = data.y_test_cls\n", " \n", " # Get the confusion matrix using sklearn.\n", " cm = confusion_matrix(y_true=cls_true,\n", @@ -1455,10 +1340,8 @@ }, { "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": false - }, + "execution_count": 43, + "metadata": {}, "outputs": [], "source": [ "# Split the test-set into smaller batches of this size.\n", @@ -1468,7 +1351,7 @@ " show_confusion_matrix=False):\n", "\n", " # Number of images in the test-set.\n", - " num_test = len(data.test.images)\n", + " num_test = data.num_test\n", "\n", " # Allocate an array for the predicted classes which\n", " # will be calculated in batches and filled into this array.\n", @@ -1486,10 +1369,10 @@ " j = min(i + test_batch_size, num_test)\n", "\n", " # Get the images from the test-set between index i and j.\n", - " images = data.test.images[i:j, :]\n", + " images = data.x_test[i:j, :]\n", "\n", " # Get the associated labels.\n", - " labels = data.test.labels[i:j, :]\n", + " labels = data.y_test[i:j, :]\n", "\n", " # Create a feed-dict with these images and labels.\n", " feed_dict = {x: images,\n", @@ -1503,7 +1386,7 @@ " i = j\n", "\n", " # Convenience variable for the true class-numbers of the test-set.\n", - " cls_true = data.test.cls\n", + " cls_true = data.y_test_cls\n", "\n", " # Create a boolean array whether each image is correctly classified.\n", " correct = (cls_true == cls_pred)\n", @@ -1542,16 +1425,14 @@ }, { "cell_type": "code", - "execution_count": 46, - "metadata": { - "collapsed": false - }, + "execution_count": 44, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 10.9% (1093 / 10000)\n" + "Accuracy on Test-Set: 9.2% (915 / 10000)\n" ] } ], @@ -1570,16 +1451,14 @@ }, { "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": false - }, + "execution_count": 45, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Optimization Iteration: 1, Training Accuracy: 6.2%\n", + "Optimization Iteration: 1, Training Accuracy: 4.7%\n", "Time usage: 0:00:00\n" ] } @@ -1590,9 +1469,8 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 46, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1600,7 +1478,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 13.0% (1296 / 10000)\n" + "Accuracy on Test-Set: 8.7% (873 / 10000)\n" ] } ], @@ -1619,9 +1497,8 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 47, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1629,7 +1506,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Time usage: 0:00:00\n" + "Time usage: 0:00:02\n" ] } ], @@ -1639,24 +1516,22 @@ }, { "cell_type": "code", - "execution_count": 50, - "metadata": { - "collapsed": false - }, + "execution_count": 48, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 66.6% (6656 / 10000)\n", + "Accuracy on Test-Set: 61.2% (6117 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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/oAkZgUBg4qRMSUeyQYPkTb9/ii9S8pnWQ0deD5p2QjZNzWWm7es6tkazQRuN\nBiqVCruLM5kMwuEwd36jdXtWNoJzJ560cy8UCshmszg9PeVYZ7PZRLfbBYCJno00p3NnZwcrKyu8\nsEgkN4HmvvZ6PVgsFvh8PkSjUaRSKZ4/WK1WJ1LttaJ6X42s6TQghECtVuMEpFAoxHMaqa+ox+OR\nm0bJa7FYLIhEItje3ubknFarBUVROM9Ep9MhnU7j6OiIbYsaglDS0HXtrN/vI5/P89jI/f19VKtV\n6HQ6zlJfW1tDIpGAx+OZmRyV2XgVN2A0GqFWqyGdTuPw8JDFs1wuc9yIsg7dbjf8fj+L5/b2NmKx\nGDwejxRPyY0xm80IBoMsnJFIBEtLS0ilUtjb28OrV68mGnJQXB7ARFLRXUICrXX90hQN7YDjxcVF\nCCHgdDqleEpei8ViQTgc5lijoijIZrPIZDKcENfv95FOp3F8fAyn04loNIpIJMJN3ElAr3PyHAwG\nyOfz2N3dxde//nWcnp6iUqlMDO9YW1tDPB6X4nkb6OSZSqWwv7+Ps7Mz5PN51Gq1iftZLBZ4vV7E\nYjEsLi5iZWUF6+vrXIs0K78AyfxA7lESzng8jmq1ilQqBYPBwKEEymikBAhyZWlPpCR22n+/LSSa\nFKcCxu4y6nRECUculwuxWOyuPg7JjKK1JW11wnVtzWKxIBQKwePxwGKxIJPJ4PDwEHa7neOgrVYL\nuVwOJycnMJvN6PV6POaRuEw8p1+HqqpotVrIZDLY3d3FV7/6VSiKgkajAaPRCI/Hg0gkgpWVFUSj\nUbhcrplZu2fjVdyA0WiEer2OdDqN/f19ZLNZKIpy4X4ulwsLCwt49913sbGxgXA4DKvVKjNsJbeC\nFgPtLMTBYIDt7W2YTCYsLi5OuGwbjQaXqGjFstFocMYuZS5Sq7+7glL+qe6ZmoZInj6UvUolUDRE\nmpJ8XmcH2obxTqcTKysraDQasNvtyOVyyGazKBaL6Pf7yOVyGAwGE238qEk8NQOhGOpgMODXQsMT\n6vU68vk8PvzwQ5yenqJWq8FkMnHv8eXlZW7A4HK5ZqqOee7Ek06e6XQaBwcHqNfrl4qn0+lk8Vxc\nXEQoFJoYbyOTJiRvCy0u1JiapqaEQiE0Go2JzMRKpcLt+Ug8R6MRMpkMUqkUzs7OUKvVuGbzLqEJ\nL9MdtyRPm+nsVRLO65aXkH2Tm395eRlGoxHBYBAvX76E0WhEt9tFr9dDNptFPp9HuVxGLpdDOp3G\nxsYGVFVLdkMvAAAgAElEQVSF2+3mNnpCiAvD5FOpFFKpFJLJJE5PT3F6eop6vY5QKMQTr7Ti6XQ6\nZcLQbZg+eZLLahrtyTMcDnO9nPaDv+5CQi63N90medpoN1zk+qdM7nA4fOH+w+EQhUKB+4NqxfPV\nq1cwmUzodDrscqXm8ncBZaWTeMqT5/OCOmFphfO6J08AnCjkcrlgMpm4M5vJZGKXbbVa5SuXy3Gm\nN3W9Wlpa4gEJOp2OpxIVi0Wcnp5id3cXu7u7ODw8RLVa5XmjNO1qc3OTxTMQCMBisTzQp3c95k48\nrwstSNSHlE6b10mW0PZQvOyEOh2zmhZvbfKGtvfum04WQgiOTblcLjgcDtnke44RQnDiGjAZ72k0\nGrzw0EJXq9XYRoQQMJlME82wKXtcr9ezTSmKwuUCVD4jkVAnLG2bR6fTyWuiEAK9Xg+NRgOlUok7\ns1HoQFsHSolDTqeTy7QcDgdKpRLK5TJKpdLEyLC1tTX4/X7uqtVqtSbmgqZSKaTTaWQyGWQyGbRa\nLZjNZk442tra4mHa8Xgcbrd7JtfAJy2e5LrodrsTQ1bfBBkMXXQbMLkAUlxh+rSgLU8gd0Y2m31t\njR89v9VqRSKRwMLCAqxW60w0QJa8HVSD6XK5YDabJ5I3FEXhmCeNftLaGj3W6/XyWDO6jEYj2xQN\nQ1BVVYqnBAAmBG80GsFqtfJAam2tcb/f5+Hu5XKZXfvUDo8Ei0JdDocDi4uLsNlsWFhYYI9KoVCA\n0+mE2+2G2+1GMBiEz+fj0qlsNotsNsthirOzM+RyOSiKAkVRMBgMeNpQMBicEE96zlnMEH+y4kkt\npigR4yYNhalAGMDEyW9aQKm+bno+nvbEWywWcXx8jP39fdTr9Ut/nrbrh9PphKqqcLlcCAQC/Hok\n84nFYpkQTqLZbE60+MvlchN9O6lNn8/nQyKRwOLiIpaXl7G8vAyLxYKDgwMcHBzAaDRyxqJEQmgb\nYtDJ0+FwcCYrnTxJPLUnTzoMkD3Sc1GP8IWFBQwGAxbFbDYLu93OHdyocxadPM/OzrC3t4eDgwOc\nnJzg+PgYpVKJn5+mXUWjUayvr7Nwbm1tzXSOypMVz3K5jFevXsFut8PtdnOT7OsIETU/djgcsFqt\n3GVDr9fziZKKhcl9pl0cteN/SqUS0uk00un0RLNwLdPiabFY+OeSC1cyf2h/r9OQOzcUCiGbzbI7\nVns67fV6nI1IWeJUw3x2doZ0Oo1CoYB6vc4lKlp0Oh2MRiMsFgsnWsziIiS5W7S/Y51OB6/Xi6Wl\nJTQaDY5B9no9GAwGdDodjkFSDXA+n4fT6eQa4csOHjQAnq5Op4NqtQq9Xs/r42Aw4BnLNLiDmnjQ\ngGs6JKyurvJF9Zx3MaHlPnmy4lkqlfDixQvU63XOirxuazLa8VPnDJpMYTAY+ETZ7/dRKpX40sY9\nKd45Go0mUrKpYP4y6HVRXR695lgsNpMuC8ntMJlMcLlcCAaD8Hq9vEgRo9EI3W4XlUqF21FS/Nxs\nNnPcKJvNotlsXuqyJdcdiees1MdJHg6q911ZWeF1bDAYcLiA2kuS7TWbTaTTaYTDYUQiEV7/6DRJ\njEYjlMtlvrRJSVpR1cb2W60W+v0+9Ho9vF4vEokE4vE4e1aWlpawsLAAt9sNp9M58xu9J/ttKhaL\nqNVqePXq1QW365uwWq2IxWKIxWJc4kIF8lQk3O12OfidSqUuTRoCPhbS69RWAYDdbuefRSdeefJ8\nepjNZk7g8Hq9fPIEPrYdaspdrVZRqVS496fZbOYsXiqBuSxLl1xtVquVeznP+oIkuVtIPKmUajgc\nolarIZVK8egxGqHXbDaRz+eRSqWwurqKdruNSCTC032oexAwKZ5UekIHiUqlwoJJ2eTD4RB6vZ4T\nmKjn8vb2NjY2NhCLxSbGRM7DgWHuxFObhOH3+yd2PFouS+S5Lp1OB3q9nk+ONA7HaDTyqbPX63HA\nnPqZ0uu7CsqAM5vNvJObdk3YbDYEAgG43e6JRU/ytDAYDLDZbBgOh/B6vZwsoZ1moU1Io1FNwLj1\nmbZ2k6AMXRpnFovFsLS0hPX1dcRiMbhcrpl2g0nuHrIJh8MBo9GIeDyOjY0NdDodnJ6eckhpNBpB\nUZSJRMhWq4V0Oj2x/hGj0WiihrlSqXD8Xju3FgCHodxuNwKBAAKBAGKxGFZWVrCysoLFxUX4/X5e\n8+aFuRNPmpLi9XoRDodZuO4y05DimcDYjaHNvNX68xVFmUjUuGzMj/bvZrOZ/fw0eZ1mixIWi4Xd\nGcFgEE6nc2IQsuRpYDAYYLFYoKrqxGDh4XDIM2m1mz+qA6X5hu12e6KsBRifNClxw+VyYXl5GZub\nm3j33XcRiUTg9XqleD5DyH2v0+kQiUQwGAzgcDjg8/lgNpu5FzJ1CqLmCoVCAXa7/dKxeJSkRpNV\nKO7ZarV4Dihl+lIJC/WCXlpa4vUtFArB7/fDbrfPXB3nm5g78RRCwGq1wuPxcGF6p9O50Nv2NpAw\nttvtiQzI6f6k2sns2ixJep3Tp1ASTzIYSsPW9oOk1lTUZYOKlCVPCxJPiv+EQiHEYjEecE27doJO\nn5QYNN0chGzPZrPxxnJpaQmbm5t45513OJYuvRjPDwr/GI1GRCIR2O12xGIxWCwWtNttZLPZiTZ+\n1WoVxWKRBfOytUxby/66y2w2w+PxIBaL8XCOnZ0dLCws8OGBmtfMm23OnXjq9Xr4fD6srKyg3W7z\nfE4qB3hT5wzyv1OZCZWyXFarORwOJ+Ylkgv1spIXSsygga20W9Mand1uRyAQ4LmilNGm3XEZDAa4\n3W54PB4+oUrxfHpoW6C5XC5Eo1FsbGywCPZ6PW6gMBgMJpq/0+mT7IwSzGw2GxKJBF/krvV6vTLm\n+UyZzvim2DqNaiyVSmg2m/D5fBN1x9qB6xRGGAwGHBYwmUxX2pI2dEAu2ng8jqWlJayuriKRSCAU\nCl2aiDRPzN2rNhgMCIVC2N7ehsvlwvHxMWfFahtvX0W/32e3ArUuq9VqV8ZHaSyO1+udyLzV+v+B\ncTtAEj2r1crGo91NUeyBOsWQ2Gqfi9zSdFGmpORpoe1g5XK5kEgkOD5FNXhGo5GzbGnxGgwGAD6O\nn9P0IIqbrqysYHV1FSsrK4hEIrxIyYEIEuDj8iWdTodwOIydnR3Y7XZks1kUCgUUi0U0Gg1uLtNq\ntTiu2W634fF4eC0ELs/xcLlcbJPU4EP7J5UOzvtmbi7FMxgMcu9a6rZvt9uvbJenpdPpcIYZlZiQ\nv/4ydDodnE4nwuEwotEoC9p0YDsYDHJ6t9PpZJHU7qrINaGdzn7ZKZbiq3R/ueg9PbRhAJfLxSJK\nTbdpCku9Xp/oo0w2bjAYOGOXXL6JRAJbW1vY2trC5uYmb+CMRuPMFppLHhYSTzqEUEezfD7P7fLK\n5TLHM6vVKoxGI/fE9Xq9iMfjiEajV9YxU/w+Ho/zwYbCU+TBm+XmB9dl7sST4jo2mw0ul4ubXRuN\nxmuLJ6VlVyoVFr3pGBNhMpm4bCUSibCLbDq4TTurUCgEp9PJp8t5dUlI7hftwmM2m7kPbqfT4Z2/\n0+nk9H9yp1GCmsfjgcfjgc/nY/uMx+NYWVnBwsICIpHIY749yYwyndlvMpngdrt5vXI6nahWq2xr\ntVqN7axWq7GtUb7JZQJIrtpoNAqPx3NpYuRTYK5XdurIE41GYTabWTRf57olHz6Na6IGBt1u99Jy\nE71ezwsVnQwu89NT/JJimLPeHUMyO2jbQfr9fqytrcFmsyEejyObzSKXy6FcLk+k/9MiFo1GuaGH\nz+dDIBCA0+l8zLcjmRO0STqUj2EymeD3+7kkr9VqYXl5mTdv2nwM4HK3rcPhuHDafIpr4ZMQT7PZ\nDL/fz7e/LuapzZKlHrTUcu+qn0ExThLEy9qcUc0m3Wfe/fmSh4PEk5LhrFYrotEoSqUSkskkUqkU\nj4CqVCrQ6XRYX1/H+vo6FhcXYbfb+eRABe0SyZugpDVa0+gUSuujdrgGuW1pjdPmaVy1Fr4uwfIp\nMPfieZkLVSKZJ7Sj8qh5AgDeGNpsNng8Hk5u0+l0WF1dxdraGhKJBMeRnppbTHK/aF2uVAsquT5z\nLZ4SyVOGTgKqqsJut3NBuhACoVAIXq+XS6Oe4s5eIpllpHhKJDOK0WjklmWBQIBLVcjjQu0b5bQU\nieThkeIpkcwoBoNhwo0rkUhmB+nrkUgkEonkhkjxlEgkEonkhkjxlEgkEonkhkjxlEgkEonkhkjx\nlEgkEonkhkjxlEgkEonkhkjxlEgkEonkhkjxlEgkEonkhkjxlEgkEonkhtxHhyELALx48eIenvr5\novk8ZRf82yHt8x6Q9nknSNu8J+7DPsXrxne91RMK8cMAfu1On1Si5UdUVf3CY7+IeUXa570j7fMt\nkbb5INyZfd6HePoBfC+AYwCdO33y540FwDKAL6qqWnrk1zK3SPu8N6R93hJpm/fKndvnnYunRCKR\nSCRPHZkwJJFIJBLJDZHiKZFIJBLJDZHiKZFIJBLJDZHiKZFIJBLJDZHiKZFIJBLJDZHi+cgIIbaE\nECMhxOZjvxaJZBppn5JZRghhPrfP73non31t8Tx/gcPzP6evoRDiJ+/zhV7zNb4vhPgNIcSZEKIp\nhPimEOLH3uJ5fkPzvrpCiF0hxH9yH6/5nBvXCwkhflEI8cH56/vd+3hR88Sc2Kf5itf2gzd8npm3\nT0IIERJC5M5fq+kuX9Q8MQ/2SQgh/g0hxIdCiI4QIiOE+C9u+Pif1byvvhDiUAjxc0II63295psi\nhAgIIf6WEKIuhCgJIX7ppq/vJu35Ipq//zEAPw1gE4A4v0254kXqVVUd3uRF3YJvB5AE8K+c//n7\nAPySEKKrquqv3uB5VAB/B8C/BcAK4AcB/BUhRFtV1f92+s5CCB0AVX3YotkRgP8BwD8NYOUBf+6s\nMg/2SfwxAP+P5t+VGz5+HuyT+OsAvgzg+x/hZ88Sc2GfQog/D+DfBPBnAXwAwAFg4S2e6gMA/xwA\nE8Zr1K8CMAL496/4uQ/9PfyfAdgB/P7zP/8mgP8OwJ++9jOoqnrjC8CfAFC+5PbvxXhR/2cBfBVA\nF8B3APh1AF+Yuu9/D+Dva/6tA/CTAI4ANDH+8H/wbV7f1M/5ZQB/74aPuez1/haAf3j+9x8FkAHw\nLwF4CaAHIHT+fz92flsbwLcA/Omp5/mnAHz9/P+/BOCHAAwBbL7l+/tZAL9728/pKV2zap8AzOc/\n/3tu+f7mwj4xXih/E8D3nT+H6bFtYxauGbbPIMadjX7PLd/fhTUJwN8AcHD+9++77H2e/98PAfja\nuf3tAfgJnDfzOf//bQC/c/7/39B8Ztf+TgF479wedzS3/fPn3xPfdZ/nvmKePwPg3wOwA2D3mo/5\naQD/MoB/HcA7AH4RwN8SQnwH3eHchfAf3fC1uAGUb/iYy2hjvIsCxjt/D4B/B8AfB/BJABUhxJ8C\n8B9jvGvbxtiYf04I8YcBQAjhAvB3Md6Jv4fx5/Tz0z/oLd+n5Po8tn3+shAiL4T4khDiczd76Vcy\nU/YphPg0gP8QY6GQbcxuxmPZ5/dhbEc7QoiXQohTIcQXhBDRt3kTU0zbJzD5Pl8KIf4ZAH8NwH9+\nftuPY+xd+bPnr1+HsX2WAXwWY/v+OUzZ1/n36hdf81p+D4CcqqraDvxfxNgT++3XfUP3MVVFBfAT\nqqr+Ft0ghHjN3QEhhB3jL9p3qar69fObf0UI8fsxdiH84/Pb9gBcuy/h+eN/EMAfvO5jLnkOgbHL\n6Q9gvKMiTBjv2vc19/0pAD+uqurfO7/pRAjxGYwN4G8D+JMY7+x+VFXVAcYGswrgv5r6sTd6n5Ib\n8Zj2OQTw5zF22XYwtqtfEUJYVFX95Ru/E8ymfZ7Hjr4A4M+oqpp70+crmeAx7XMV4zDAf4Cxh6KF\nsZD9phDiPVVVRzd+N+PX9x0A/gjGwkdc9j7/UwB/UVXVXz+/6VgI8Zcw/s78PIAfAJDA+GRcPn/M\nTwL4X6Z+5BGA7GteUgRATnuDqqodIUQDk+7113If4gmMXQY3YQvjxr2/LSYtxYix6wgAoKrq77vu\nEwoh3sP4Q/0JVVX/3xu+HgD4ISHEHzp/DcDY7fAzmv9XphYmL4A4gM9PGbseH/8itwF89XxhIr6E\nKW7yPiVvxaPY5/nv/S9rbvqaEMID4M9hHF64CbNsn/8lgH+kqur/Sj9+6k/J63ms9VN3/pgfVVX1\ndwCe9JLE2J3/2zd4Td9xLkaG8+vvYCzKWqbf56cAvC+E+M80t+kBGM5PndsADkk4z/kSpuxKVdUf\nvsHr1CJwAy/JfYlnc+rfI1zM7DVq/u7A+EX/QVzcGd14usC5y+gfAPh5VVWnd83X5TcB/LsY+8HT\n6rljXMP0e3Se//mvYhwz0kKL0Y1+OZJ741Htc4p/hIuLynWYZfv8AwDWhRB/XPO8AkBDCPGTqqr+\n5asfKsHj2Wfm/E92Z6qqmhZC1AEs3uB5gLGNUbw8pV6eDMTv81z07Ri7cf/+9B1VVR2d3+cu7DML\nIKy9QQhhwfhzzF36iEu4L/GcpgDgM1O3fQZA/vzvH2L8BV5UVfXLt/lB526o/wPAL6iq+rNvuv9r\nUFRVPbrB/c8AFAGsanbc03wE4AenMsu+6xavUXI3PJh9XsJ7uMEXVsMs2+cPYJwcRfxejBNcKBte\ncjMeyj5/5/zPLZyfWIUQEQAuACc3fK7uTexTVVVVCPE1AFuqqv7CFXf7CMCaEMKnOX1+F24uqF8C\nEBZC7Gjint+D8Wd47c/vocTz/wLwbwsh/iiAfwLgXwOwjvNfvqqqFSHEXwHwC+c7gC9hnPDwewHk\nVVX9DQAQQvw2gL+uquqvXPZDzoXz/8TYXftLQgjaXQzUe54xeP7L/2kAPyOEaJ2/DgvG2XIWVVX/\nKsbp0D8F4K+d105tYhz0nn4fr32f5/dZx3inFAJgOz9tA8CHbxubeMY8lH3+C+eP+8cYnxi/H+NY\n1U/d31sb85D2qarqwdT9qdThhaqqvTt6S8+JB7FPVVU/FEL8g/Pn+TGMk3x+/vxn/s5lj7ljfhrA\n3xZCZADQBu8zGGd6/zTGJ9IkgL8pxnXNAVzy3RFC/AaAj1RV/YuX/RBVVb8mhPgtAL8qhPhxjE+8\n/zWAvzHlEn4tD9JhSFXVv4txVtR/g4991L8+dZ8/d36fv4DxDuN/x3g3cKy52xoA/2t+1B8F4AXw\npwCkNRf76sXHHVO+4/KneHvOF6AfxzhI/w2Mjf6HMQ5gQ1XVGsYJTN+OcYr2X8A4+3GaN71PAPif\nMI4Z/EmMsyn/yfkVuOXbeHY8oH0OMHZL/X8Y/+7+BIAfU1X15+gOT8g+JXfEA9onMK5B/RDjsMA/\nxLgG+QcoLCA+bvTxR273ri6iqur/BuBfBPCHAHwFY8H+M/jYPocYl5R4MT4h/gKAy5qDLOLNiT9/\nGOPT9P+NsVB/8fxnXZtnNwxbCPH9AP5HAGuqqk7HFiSSR0Xap2SWEULsYLzx21JV9eyxX89j8hx7\n234/gL8kFybJjCLtUzLLfD+Av/rchRN4hidPiUQikUhuy3M8eUokEolEciukeEokEolEckOkeEok\nEolEckPuvM5TCOHHuNP9MW7ffUXyMRYAywC+eN81q08ZaZ/3hrTPWyJt8165c/u8jyYJ3wvg1+7h\neSVjfgTjptuSt0Pa5/0i7fPtkbZ5/9yZfd6HeB4DwOc//3ns7Ozcw9M/T168eIHPfe5zwGTRs+Tm\nHAPSPu8aaZ93wjEgbfM+uA/7vA/x7ADAzs4O3n///Xt4+mePdOfcDmmf94u0z7dH2ub9c2f2KROG\nJBKJRCK5IVI8JRKJRCK5IVI8JRKJRCK5IVI8JRKJRCK5IQ81z1MikUgkM0S320W73Uan00Gr1eJL\np9PB7XbD5XLBbrfDYDDwJfkY+WlIJBLJM6TVaqFYLKJYLCKXyyGTySCTycBkMmFtbQ1ra2uIxWKw\n2Wyw2WxSPKeQn4ZEIpE8Q9rtNgqFAk5OTvDq1Svs7u5id3cXVqsV3/md3wmDwQCr1QpVVWE0GmGx\nWB77Jc8Uz0I8h8MhX/1+/8LV6/UwGAwwGo0wHA7xujFtBoMBer2eL51OB71eD7PZDLPZDIvFAoPB\nwLcLIR7wnUoeClVV2U5UVcVoNGL76fV66Ha76PV6E48xmUx8ae1I2ojkMSDxPDo6wv7+Pg4PD3F0\ndASXy4W1tTUoisJroxxdeZFnIZ6DwYB9+4qioF6vo9FooF6v89VqtXjB6/f7lz6PTqeDxWKB1WqF\nxWJhwTSbzfD7/QgEAggGg7BarTCbzdDpdHJhfMKQaGo3Zd1uF+VyGeVyGbVabeL+Ho8HHo8HXq+X\nbchisUgbkTwK7XYbxWIRx8fHSCaTqFQqFzZ8hBTPizwr8azX6yiVSsjn8xNXLpdDpVJBq9VCs9lE\nt9vlx6qqyoubTqeDy+WCy+WC0+mE3W6H3W6Hw+HA0tISlpeXYTQa+TFGoxE6nUxofopMC2en0+HN\nWTqdxtnZGTKZzMRjYrEY4vE4AGA0GkGn08FsNj/Gy5dI+ORJ4lmr1a48OEguMnfiORqNJlyuw+Fw\nwmVGl3an1Gg0UK1WUa1WUSwWXyuerVYLvV4PJpMJRqMRRqMRBoMBRqMRJpMJZrMZw+EQQgi+CHmC\neNpo3bSDwQCtVgvtdhvNZhO1Wg31eh2VSgXJZPJS8aT71Go1+P1+eL1e+Hw+2Gw2dvmTG3fatiSS\nu2A0GvHGr9VqoVaroVAooNFoQAgBj8eDYDAIj8cDh8MBi8UCk8kEvV7/2C995phL8SQxrNVq6PV6\nfNFi1mq1rhTPWq2GRqNxpdt2OBzCaDTC5XLB4/HA7XbD6XTyRe5Zv9/PLluTycS3ezweWK1WmEwm\neep8gtBGrd1uo1Qq8WYsm80im80il8uhXC6jUqmgWq1OPLZcLiOVSsHr9SIYDCIYDCIUCiEUCiEc\nDiMUCsFms8l4ueTeGA6HGAwG6Pf7aLfbfOl0Oni9Xng8HqytrWF5eRmRSIRDDDLT9iJz94mMRiMo\nisKp1XRapN0/ieRoNOLHaMWz3W6/NmFoNBrBZDLB7XYjGo0iGo3yQkeLHf39TQlDcvF7WmhdtZ1O\nB6VSCaenpzg+Psbh4SEODg6QTCY5dj4dP9LGyCORCKLRKGKxGNbW1jAcDuFwONjbITdekvtgNBpx\nQhsdNEg8/X4/VldXsbm5iZWVFYTDYXg8Huj1eimelzB3n8hwOES1WkUymcT+/j6azSYURUGz2eTd\nfqVSATB2o+p0OnatKYqC4XAInU7H4qZdrKgQ2OFwYGFhAYlEAvF4HOFwmE8GdOoMBAKP/ElIHprh\ncMg79UKhgGQyicPDQ+zt7eHg4ACHh4dIJpNXPl7rjq1UKqhUKiiXy+j1ehBCwGQyIRgMwuFwwG63\nw2w2T2R1SyS3hTx05Hkjj5vZbIbL5UIsFkMikWDXrc1me+yXPLPMpXiWSiUcHBzgq1/9KrrdLl+0\nsLVaLRgMBi4LsFqtsFqtCAQCE7dTuQAlbjgcDjgcDrjdbvh8Pvh8Pni9XrhcLu644XA4ZJLHM6Xb\n7XLCWTKZxN7eHvb29nB4eIh8Po9ms/nax2tDCa1WC+VymePzrVYL+Xwe8XicE4u8Xi8XqJtMpvt+\ne5JnQKfTQaVSQaFQQLFYnDhQGAwG9p4ZjUbpOXsDcy+elDCkTRwajUawWq18srTZbLybt9vtvCCZ\nzWY+eTocDi418Xg8bEQU09QmEMmF7HnS6/U4tX9/fx97e3vY3d3F2dkZb9zeBNWHttttDIdDKIqC\nRqOBXC6Hg4MDrKysYGdnh+1ZVVW2P4nktpB4ptNpFAoFNJtNFk9qhEDrovR2vJ65E0/KEiuVSkil\nUhOCSW5XEkyr1cqnRzpJUr9GbSaZyWSCy+VCJBJBJBKBx+N57LcpmUEGgwEURUGxWEQmk0EqlUIq\nlbqQVTvdAIHCB8DHp09qpkAutGKxCJPJxFmPJpMJqqqi3+9DCAFVVSeel5CnA8lN0Irn9MnTZDJN\neDqkeL6euRNPnU4Hm80Gn8+HaDTKyULkqqXi80Qiwf0ZKe2aBFN7oqQFyWq1wuVywWg0PvZblMwo\n5MmgMMBlC4xer4fT6eQsba3XAvj45Fmr1TgrV9uZqFQq4fDwEMPhEMViEYuLi1hYWEA4HIbb7YbH\n44Hdbp8QZInkupB4ZjKZiZOnwWCAzWaD2+2G2+2WGbbXYO4+HRJPv9+PWCyGUqnEp1Fa2Cjh55Of\n/CQ++9nPwu12s7t1ur0eLULk75fuMclV0O6cukxdVv+m1+vhcrkQjUYRiUR4J2+32ydq7FKpFAwG\nA3e+Go1GGAwGHAel022xWES1WkWj0UAikWDXGiW8yZOn5Ca02+1LT54kntQFSzaCfzNz9+nodDo4\nHA6EQiEsLi5Cp9Oh2+2iWq1yMpDVakUwGMTa2ho+85nPwO128yIjFxvJ20JipU0003afosQzn8+H\nRCKBlZWViRphbQ9cq9WKwWDATROohKper3OXomKxyOJK7lsKR9CJVjbpkLwObWMPYCye1WoV2WwW\npVKJa+Lp4EEd1Khhh+Rq5k489Xo9/H4/1tbWMBqNYDab2d1lMBi4nIDqP9vtNtdeysJzyW3o9/tQ\nFIU7U9XrdfR6Pej1ek5G83q9WFtbw87ODra2tiZ6IZPLVjulwu12I5VKIZvNIpPJoN1uTyQV5fN5\nCCG4by6VZFGtsdfrBSCFU3I11BiBEtQoZNBsNrmu3Wq18saMsm1lWOD1zK14DodD2Gw2Fs6TkxPo\ndDoMh8OJwa6tVgs2mw2qqkpjkNyKwWCARqOBQqGAXC53QTwDgQA3PdjZ2cG77757YZAwnQDsdjvc\nbjdCoRD29vag1+tRr9e55ST1Y87n85xURH82Gg2sr6+z+Er3reR1DAYDbtrRbDa5jWSz2eTkNBJO\nqsTfch0AACAASURBVEKQtcVvZi7F0+fzwW63IxgMsnA6HA7uFKRt1ddut9HtdjleJZG8LVrxnD55\nOhwOBINBLCwsYHV1FVtbW3jnnXeufC63281xe6PRyK7aTqfDbSK73S53MiIXW6PRQLvdhtlsRjAY\nRDgcZveadrGTYiohqCMW2U+tVuMJKlrRtNlsnFB5G6ZdxdrbyS61/0e3T4fWZt2G5048KcHHaDTC\nbDbD4/EgEolgeXmZu7aQYVDzhH6/LxsbSG4NlapQT9tGo4F+vw+DwQCv14vFxUVsbm4iEonA4XC8\n9rmothgAFhYWUK/XOZGIxFJRFLZhio9ms1kYDAbY7XYYDAZ0Oh34/X74/X54PJ6ZX3AkD4uqqmg0\nGsjn8ygUCkin02xrtH76fD7ubHUX1QbU8pQOM+Qy1gomZZf3ej32Imqz2Cl5c5btee7EEwDHLslt\nFYlEsLKyAr1ej1arxfEh+uWQK0zOpJPchsFggGazyeJJiTxmsxlerxcLCwtYX19HJBKB3W5/7XOZ\nTCY4HA6YTCYsLCxgNBrBZrMhFArh5OQEJpOJp12QG7fRaPBiZDQauUH92toaDAYD3G43gNnfsUse\nDlVVoSgKstksjo6OkMlkWDypNCUSiSAYDMLpdN5Jhi1NHFIUBe12e0IktfdRFAWKoqDf7090dHM6\nnexOnmXmTjzp5EmZjnTyrNVq3OKMdj7ak6cUT8lt0Z48C4UC3z598qQ2jq+DulUB480gCafP54PJ\nZJrY9FH/UZoARIJK/Zr1ej0CgcCEfUsBlQAfnzyz2Sz29/cvPXlGo1GEQqE7E89+v8+JbSSglE1O\n9Ho9Hhrf7XYRj8fR6XT4/6lJyCzb8dyJJ3BxYZj+N81bpOHE9XqdfepXuSXo/7RZuTIRQ6KFRI46\nVpF3AwBP17luVrf2/+kUSj1uqXCd2qR1Oh30ej2uEx0Oh+yKM5lMCIfDiEaj8Pv9EzEsiYTshXp/\nUwiAMr6pttPlcsFqtb51eQpt9nq9HjKZDM7OznB2doZqtcr5J4PBgO9Pmet08iwWi0gmk/D7/Vhe\nXsbKygpUVWX37Sxm/86leE6jDVDT30k8G43GhAvtqp2VwWCYKH7Xnm4lEgAca/R6vfD7/XwKpE0W\niedN7YYK1PV6PceHqKFHt9vl+Cr9H52ASUwjkQhisRiCwSB8Ph/Xg0okwMfxRXKhkheO7JnE8za1\nnSSGzWYTqVQKu7u7ePnyJQqFAp88teJJr4mS4yhhyel0olqtQlVVro92OBwz2bBh9l7RWzLtkqVU\nfzp5UoH6VSdPiiFpe4dK4ZRoocXG5/PB7/djNBqxq4lOntrOVdeFPB7U1YV6LQshuBF9oVCYmCfa\naDSgKAo6nQ6i0SiPzpPCKZmGKhC04glg4uRJLfluc/JsNpsol8tIJpPY3d3FBx98gGw2e+nJczoj\nlzagJpMJo9EITqcT8Xh84nXOGnMnntouLb1eD/V6HYVCgbMUqWNGvV5HMpmE3W5HMplkd9ZVxmGx\nWHiSutvt5pZqVPdEl+T5Mj26rtFoXLAnrWhSowO6Op0OX2azmbMLte4oCjX0ej3uphUMBnn6Sr1e\n55PDZeEJr9c7EVuSSAhtkw6CPGy39bK1Wi2ecXt2doZsNotyuYx6vc7hDSrpcjqd7OGjw0ytVuNO\nW7VaDalUCi9evMDy8jJ0Oh1cLtfMnT5n69VcE+oD2u12UavVkMvleHfebDZZPE9OTrjDkHbo9fQC\nBwA2mw3BYJDHktHpwufzcb9HKZ7PGxJP2lhRMflVqKqKZrOJYrGIUqnEw68rlQrcbjcCgQACgcCE\nN4QyxTudDobDIbea7HQ60Ol0LL7an9Hv99nLQo+TSB6SZrOJfD6Pw8NDnJ2dcd9cirFqs8nj8TiC\nwSAfToQQOD4+xvHxMVKpFNrtNpLJJH+3nE4nEonEI7/Di8ydeFIAvN/vo9PpcJ/G4+NjdmXR1IpW\nq4VUKsU7+9clANntdkSjUUSjUR5GHI/HOSHEarU+2HuUzCbUaING2pF4XpXFTWUC+XweZ2dnPMIs\nnU4jHA5jaWkJS0tLEyn52tMBxYLC4TBvFqvV6oWfQd+FZrOJTqcz4R6TSB6CZrPJM2lPT09RKBR4\nMweM7dRisSAUCmFjYwPLy8vweDzwer0QQsBut/OweVq36/U6HA4HEonETG4I50I8yVWrqioPJC4W\ni8hms3j16hXS6TRqtRr6/T63SqPYk7Y9Gk1HJwGluCh1danX6zAYDBzLolMtnUapEJ1cu9PZlfPS\nGUPyduj1elgsFjidzonsxH6/PyFg2qS1fD6P4+NjvHjxArlcjvviKoqCVquFWq12ZRyebLBer3PW\n4rQw0kShYrGIVCoFl8sFr9cLr9c7McBdO4VF2qfktlDIoN/v8zSgXC6HZDLJ3beoBpoSf+LxOLa3\nt7G1tYXFxUVuQj8ajZDJZBAIBLhWudlscrlLq9XCaDR65Hd8kbkRz+FwyDVvFJDe29vDwcEBkskk\nms0mdx2iUwHN7SQ3G3VlIWioNtUa6XQ6tNttFItF1Ot1pFIpWCwWjnG53W6sr69jc3MT6+vrEz9D\nO95M8jQh8aQvPc08pJhOs9lErVZjN5XRaEQmk8GrV6/wta99baJOs9lsolqtIp1OX+n6JUFut9v8\n3FqXLQBOHsrlcrxx1E5yoRgTZQHLSRmSu6Lb7XL/8Hw+j2w2i1QqxafO4XAIj8eDhYUFLC4uYmVl\nhWcsR6NRbgXY7XY5ROb1erl+meL83W53Jmv050I8gfEOm4pvz87O8I1vfANf+cpXWPxarRacTifv\ndEjwSPSoNk+7y69Wqzg7O4Ner+ehxO12G41Gg+uWBoMBn2Ltdju++7u/G0IIBAIBLoEhQZYL09NG\ne/LUDgwmjwgJ3HA4nBDP/f19fP3rX2ebIg+JtmH8ZWiT46hE5bKTJ5XL9Pt9PhVT71wA3KuUvCQS\nyW0hm1cUBdVqFYVCgcWTktqGwyHsdjsWFhbwqU99CltbW1hYWMDCwgL8fj8nKjWbzYkOQ9SRiJos\nSPG8Bdq4jqIoKJfLXIhLBb8ulwuRSASRSATRaBQOh4NPnLSYeDyeCfGs1WpwuVzw+XyoVCp8gmi1\nWnxKoCJeKlRPJpPY29uD1Wrl3T2dROjvRqPxrUoWJLMNbaD8fj8ikQgSiQTK5TLcbjfC4TDcbjeP\nvwPAcUoaA0ULyl26oEajEbrdLhqNBlRVRTKZ5PFntCD5/X62UZfLdaHwXIYbJDeFGnrQ0PZcLsdj\nzqjzm91ux/r6OtbX17GxsYGVlRVOxnQ6nfxcg8Fgop+tTqfjagpqZl+pVDjngO7z2MyFeNKpUzuS\niQpyyY1msViwsbHBl81m4w+aSgKmS1Xa7Ta39qPyANpNFYtFFAoFlEolLhEg99nu7i5KpRKLpdPp\nxOLiIhYXF7G0tMSZmG/KxpTMF0ajEU6ncyJhDQAajQbvqgOBAO+o+/3+RP3naDS6F4EikQaAdDqN\nfr+PQqEw4YGJx+O866eieKfTyQI6C4uRZH54Xds/l8uFhYUFJBIJbGxsYGtrC8vLywiHw2xz16Xd\nbqNSqSCdTmM0GsHtdsPtds+Evc6FeJKLgNLxyReuKApPPw8Gg9ja2sJ7772H9957DxaLhRcFWrym\ny1RIlLVTAKh2lLIi0+k0J3kUi0UuBP7www95AXI6nfjkJz+J4XDIv1g5Au3pYTAYOH5ImyKr1Ypm\ns4nFxUUWT3L3DwaDiTo62lHfJZS4obXlYrHIu3i6tre38c477/COHgB3lBFCzHwfUclsoW04v7+/\nP9Fw3uVyYWlpCf9/e28eJNl+1Xd+f7nv+15Za6/vPelpCaSBwDFYYVsgD2gwyDYhhGEAAxrEYhZj\nGEKDwBZYYBsIwMMEGmMGhAgmbA82hEQYB4RGI5YB9CQ9ve7XXfuS+76vv/nj5vn1zeyq7sruyqzM\nqvOJuFHdWVk3b1aeuuf3O8v3vPzyy7h586aqKg8EAk9NVUzSbreV86S/O6fTeSHTX56XpXCewCMF\nF6vVikAggNXVVVQqFUQiEXXcuXMHN27ceKz8/zzQbqLX66FWqympKOrHC4fDyolms1mV1KYqXVrl\n2+12xONxhMNhhMNh1QTPw2WXH7I/cjjD4RAWiwXtdlv1CDudzrEpEjTwem1tbUwsgarHyfnR8Sy5\nHX1BnV4gYVL1iIqGaJ5jpVKB1+tVu1OLxaLslG316kB2QOkk+mzJbsj2yCbPguxVL1CTyWSwv7+P\nfD6Pbrer7s8rKyu4desWtra2EIlE4Pf7nzpp6DT0IiA0MWtR8p9L4TwNBgPsdrv6pb300ktwuVy4\ndevWWOg0mUyqsNm0kDYpoAkmBINBmEwmeDwexONxNUT25OQEx8fHSKVSYwO3y+UyHjx4gGq1is3N\nTdy8eVPF/qmqjG9Iy4/BYFA6yNSuRIU6DodDtUORg43H47h79y663S7K5TLK5bJqq9JXkFNE5SL7\n2fQtXsViETs7O2i1Wjg8PFQCDfF4HMlkEslkUgmBWK1WttUrhNFohMVieawzgKIV1Kr3NMekb0+h\nex6pu7XbbQgh1GZDr7c8bah2WVga50mFGFSos7GxgWazqfrYSP/Q4XA8l/OkVbfZbIbH41G7UTKy\ng4MD7O/v4+DgAOl0Wh3lchnVahXb29solUoAoG6uyzCbjnk61IpEs2RJbowqa/WFYvT/RCKhVuTp\ndBqZTAaZTEYpr/R6PZRKJSXlNwvnCQDFYhGtVgupVEpVn/t8Pty8eRPtdnus0Okq3uiuM+Q8SRKP\nPmfaQZLzpN3nkyCdXOpRJudJ919yntFoFCsrK4hEIkq7+aqxFO9I79Aox3nR6JvHydgmwwz9fl9N\nXqH+Obrp6OXXbDabUs/o9XoIh8OQUirhbwqfMcvFpI08zckYjUYEg0EMBgOlnUwHrfSpJD+bzcLj\n8ajCH+B0LdLJa5mUmtQPgKfcK+0Ums0mAM2RUkFRv99Xf1PU7kLn1OdqmeVECAGr1arqQvL5vIqC\nUcopk8kgGAwiEok8cfFGghyVSgX5fB7ZbBaFQgGVSkVFLSYFZfRVtU9CH0Km/LvJZFIa0JQSo8jO\nIrAUznNREELA5XIhGo3CbDYr0e61tTXs7OxgZ2dHqczs7+9DSolsNov19XU0m02EQiGVX2Kd3KsP\n7VD9fr+amhIIBJBMJseKiqiVhcK5hL6/c3LQtT6HqX9+oVBQWrrValUpFFGLjF5MXkqJXC6H/f19\n2Gw2tFotJJNJ1VvKIdzlRwgBt9uNWCyGXq+HcrmsJEsbjYaqYrVYLAiHw2OLt0kGgwFKpRIODg6w\nt7eHg4MDVCoVJb3n9/uRSCRU7v+8nQZkk3pNZ6PRCLvdrnayFAJ2uVwL08HAznMKaMqFyWSC1+tV\n0y7q9TocDgc6nY4awbO/v49MJoNcLodms6lyDGRo7DyvPkII1R5Fo8xoV0jOjPo0SUlIv/KnmYdU\nfKTfEVKfpj4c1u/3sbe3h93dXezu7iKdTgOA2nHSOUmWstvtIpvNwmq1YjAYqEImGqRAMx85jLu8\n0II/FovBarUilUrB4/HAYDCgXq/j5OQEpVIJDocDN27ceEzBSg/J8O3u7uLVV1/FwcGBmr2pd57T\nOjm986S/AUrR0X12ZWUFgUBAFbUtAuw8p4B2EtSErlfDIIm0g4MDFAoF1Ot1ZDIZ1QBMPyOlhMVi\nGds9LIoxMBcPOTh9CFY/w9BgMIxVPOp3mIPBAJ1ORw0MJiitoB/pBGhyfjROj3Jc1F9KovG0sqdw\nbrlchslkUsUiVqtVyVjS/0mhiFk+aL4rfQ2FQkoog7STS6US4vG4kiVtNBoqvaS/Nw2HQ9TrdeRy\nORwcHCCfz6sRkKTsRuHayVF7k+iVsyilQB0MFPUwmUxj061mka57Hth5PgdUeQkAwWAQm5ubajVH\nhSEkDi6EUGILzWYT0Wh0bHYoczWhG4S+Farb7Y5VP5IdUb8lQQVsNKwAeJTrPO3mZjAYEAqF0O/3\n1Y2SlJBIlD6TyahdJ+Vd6/W6yo3RTa/X62E4HCqtXGZ5ocUW7UJJVU0f9Wi328jn89jf31c7Pp/P\nd+57E0VD9EVJT8pNDgYDJXSTyWSQTqeRz+dRLpfV9VGng9vtXsjUATvP54BubFQYsrm5CYPBALfb\nDaPRqHr6SLqK5Kv0uwmbzcbO8wpDTrPf74+1NjmdTuWwqIJ38gZBVbxWq/WxnCc9fzLnGQ6H4XA4\nlEwl9SU/fPgQDx48QLfbVTc1cuakriWlfMxxxuPx+fyimJlB/ckmk2lM65t6NanitlAoKOe5srIy\n1b2JCuhoQXhe50nTgNLpNHK5nJp1GwgEsLGxgXg8Do/Hs5DROXaez4j+BgYAPp8Pw+FQrdwplEvj\neejQtw9YrVYl3s1cDfS9lRSSorAUrbTr9Tr8fj8AjOl5XoQWssVigc/ng5QSwWAQ0WgUpVJJ5TUp\nJEeThOjGSbkmIQS63a5qsyFt57McPLPYUHqInJvL5YLf71fFQf1+X4X08/k8dnd31S6Vwv/08xTq\np1SVvrWFKmOdTqfKmU/ain7QAcnupVIp1fZHAz70m5FYLMY7z6uOxWKBy+UCAKyvr6sQycHBgeoN\nHQwGyOfz6ubq9XqxtrZ2yVfOXCR0Y6AcDoVKSZWKHk8mk9jY2ECn01Ghe2pluigsFgvcbjeEENjY\n2FBN7FQZTuPM6KZIzr5cLqNQKCCXyyGbzcLv9yttaC50W26cTicikQhu3LgBACrv3Wq1kE6nYTQa\nx3KhiURibO5mNptFKpVShUatVgsAxhwz5VQnHR5NxWo0GigUCsoOd3Z2kE6n0e12Ybfb4fP5VJFQ\nMBhUUZpFg53nBUE3Kn14JBaLqR0GhW1zuRyKxSKGwyHW19eV8TFXA5o2USwWUSgUsL29rQ592PbW\nrVvodDowm82qUOiiC3NoB0E7W6/Xq4Thqa2F2laoiIgqc6nlJZvNqlFRFP5jlhen04loNIqtrS2l\nG3t0dIRms4l0Oo16vY5SqYRSqYR8Po9kMolEIoFEIgGLxTI2eozEFagI0ul0Kud5mqIaSZ9SqHZ7\nexv37t3Dzs4OKpUKOp2OElrQO89nFb6ZNew8LwiqfqSh26FQSIW/6CZKQgq1Wg0AkMvlnlgaziwf\nw+EQ7XYb5XIZ6XQa29vbeOWVV/DKK6+owox2u63UsahtgP6tzxU9z2qbnCbZpNvtRjweV/NAC4UC\n9vb20Gq1lLgCtatQS0Iul1MN9NRuwyw3TqcT4XAYvV5PyY3a7XY0m82xHuFyuYx8Po98Pq+iEy6X\nSymqZTKZsfPq1d9IpnI4HKqFoX5RmUqlsL+/j4cPH+L111/H7u6uyt9TvjMSiajRkk+r3L0s2HnO\nAH0VLqm3BINBlMvlsdU9czXRixtQVSvNg6U8UalUwvb2NgBtrmy73YbJZILf75/JODvKV1L5/+bm\npqpsPDg4UCE1uuE1m03kcjns7e0pKUKfz3dh18NcDtSPCUAJc9Cuk8Y9CiFUDpQWVKRMROMYJ6FR\njqVSaWzhple3KhQKOD4+xvHxMY6OjnB0dIRcLoder4doNIpoNIpYLIa7d+8iHo/D4XAs9FhHdp4z\ngG5SJGjvdrvh9/vh9XrRaDRgNBoXZjIAc/HonWev11POk6puB4MBisUiAKBUKqHdbsNsNqvQ6OTY\ns4tAr93s9/uxubkJAOoGl8lklPOkXQI5T6vVCp/Ph2QyeWHXw1wO5DztdruaT9zv9+F2u9WukgqI\nqBqXohQmkwn5fF7Zrh5afJVKJaUQZDQa1QjHYrGITCajnGcmkxkrovR6vbhx4wZefPFFbGxsIBaL\nqR3sRRTSzQJ2njNAX+HmcDhU35LP50OpVFrYlRRzMVBVob46kVqTSFmI8kqAVrbv8/mwurqqBArs\ndvuFXpM+7EU7D6/XqyQkKd+qd575fB6A5mCTySTn568AVPgFPGqjMhgMaodHeUzKzdPs16dBO0+q\n4iaKxaKaREUzko+Pj5XtA1A9pbdu3cLb3vY2BAIBBAKBhc11Euw8Z8Ckgks+n1el2L1eD1arVenb\nsiO9WgghVM6bnGStVlPFGTSW7LTIw1ki8BcNyfPRQPlOp/PYLEcqDiItZgrdMlcHu92OcDiMwWCg\noguxWGysL71SqagWqyctnsrlMnZ3d1XLCkEOlc7V7XZhs9kQi8VUIVosFsPLL7+Mra0tBAIBdW9c\nxN2mHnaeM4DyBFQ4oneeNJ7K6XSy6PYVxGAwjAkg1Ot1tFot9Ho9HB4eQkqphNr1nCbfNytOc550\nPfTatBuZnB7EXB3IedIUqGg0io2NDWQymbG5xel0WrUxnQU5z2q1OmYntIttNpsYDAYqleXxeHDz\n5k1sbW1ha2sLyWQSq6urSr92GUY48l/DDNBPP6eBsel0GoVCARaLhXeeVxhynk6nEz6fT+U6aRVd\nrVZxfHw85jzn6TiBR/ZJOqan7TxPc55sq1cLh8MBm82GUCiETqeDZrOJZrOJTCaD7e1teL1eJa5R\nrVbHQq2TlEolVCoV7O7uPjYmj+yKnLTP50M8HseLL76IL/mSL8FLL72k/mZI0WjRd53AEjrPwWCg\nGs1brdbYzEGatXlaj9E8rosOms+YyWSwt7enJquYzWZVUXb79m0kEgmW5rti6GdsmkwmeDwexGIx\n9Pt9VCoVFAoFVZRBSi31eh3Hx8f44he/iFqthlAohGAwCK/Xq24qtPOjXPrToLyl/m+FdgCZTAap\nVArHx8fY3d1FPp9/LLelnxdK6kLLcENjzg/VZgCPIg5Go1FN8zGZTHC73YhEItjY2EAqlUI+n0cu\nl0OhUBjL4ZOiFgAl60fRNRoM73a71bzPlZUV3LlzR4kw6JW2loWlc57D4RDVahXZbBb5fF4JZFMJ\nfiAQuJRGbv34qGKxiMPDQ6Wekcvl0Gq14HA4sLKygpdeegm3b99WBSLM1YRUpiKRiOr3zWazOD4+\nhslkQq1WU43jh4eHMBgMyGQyCIVCCIVCiEQiiMViiEajqoVlmh1gvV5HNptFLpdTPXvUx0c9fXQz\nfNIcR+bqQ73GpEKlnwN68+ZNVKtV5HI5fOELX8AXvvAFNJtNdb8jiT6CwsGRSESpZ9FIPto8RKNR\nhMNhhMNh2O32x2bTLgNL5zwphHBycoKDgwMVH7darWoWIVUTzvu6ut2u6mc6ODjAvXv3cHBwoJxn\nIBDAysoKXn75Zdy5cwd+v5+d5xWGiodMJhN8Pp/KJUUiEQyHQ1XeX6vV1GzEQCCAcDiMUCiE1dVV\n1f9JakG0ij8PNBZvd3cX+/v7SiqyXC6rnj7St9UP4WauHxRdIFsjUQ3aWVJEzeFwoFarYX9/HwCU\nHesh57mxsaEWfj6fD5FIBCsrK2o25+RIxmWLbCyF8yTH1Ol0UK1WcXBwgAcPHuDhw4dwu93weDxw\nu91wu91ot9vPnTfSCxjT2B4a36R/Dl0Thd5I9Pvo6Ai7u7vIZrPo9Xpwu93Y3NzE+vo61tfXVaUZ\n9TExVxcqnjCZTIhGo7hx4wY6nQ62t7dhNpuVNF6320WlUlH/rtfrSs6vXC4jFAqpNoPzFlMcHR2p\nFgHq4aOQMYVx9UO5afdBzj4SiSCZTCKZTF5aRIeZD/qw/JN2gBR2jUQiSv/2rEIiIQSGw+HY8AFS\nspqcabuMLIXzpGG+5XIZ2WwWOzs7uHfvHl577TVEIpGx4yLCTxS/J4UYUuIgWT16Dj1eqVRQrVbV\nvE56rFwuw2KxIBaLwev1Yn19HRsbGwiFQnA6nUsX42emh1bzBoMBkUhENaTb7XY1KIAGCtMkHvpK\nxWY7OztwuVwwm81qVuLTkFKqtphyuTxmn7TT1OerAC3fZbfb4XA4EA6Hsbq6ips3b+LGjRuIRCKc\nn7/mGAwGOBwOBINBrKyswGAwoNPpoFKpjG1YaDfabrdVaoLEGGhmLMk9XvQwhHmyFFdNs99I3ml3\ndxf37t3DF77wBaytraHX68FoNKow1EXsPKmBmEbn0LBW/XNoiGsmk1E3qUqlolbxQgjE43FEo1Hc\nvHkTGxsbWFtbG5sUwM7zakOfr8FgQDgchsvlwurqqhJm397eHhvzRDcc4NGMRFJZocK484a39MO3\nqbhDrzU6Wd1L4gwejwfhcBjJZBI3b97EzZs34XA4Lly4gVkuqJI8GAwikUgoxzk5xF3vPElliObE\n2u121TdMMn7LylI4T3Jm3W5XSUrVajVVvUj9kk6nU1WLUXiLckUExe9pxU0OjMILdLOhr61WSxVW\nTMpSFYtFlEolFItF1dc5HA7hcDhUOHl1dVX1MiUSCQSDQZUgZ642erujvk+TyQSHw4HV1VXcvXtX\nzXylHGSz2VQqL2SDZFf6WaHn4az2F3LKJpNJOWcSpqcijs3NTWxsbKiiDyrKY64vRqNRTebpdDow\nGAyq7Ynk/KhKl0K2w+FwrEr8KlVuL/Vfw3A4VEUR9AHmcjlsb2/D5/PB4/HA6/WOlWP3+32Vw5RS\njg16Ja3FRqOhVuq9Xg/1el3loAg6F63uTSYTvF4vzGYzwuEwYrGY2nVGIhGVOHe5XHwTuqbQLlQI\ngVgshje+8Y3weDzIZDJjC7RyuYxSqaQ0RimvTrvGyerGszirb9RsNqsKSJq44nK5EAqF1PiplZUV\nrK+vw+/3L7S+KDM/qKNhMBioeg3adVJqoFKpqEUipSwoakHjyp40MHuZWOq7uJRSKaTQ/DnKD1EO\nNBqNjhXltNttNZB1OByqVXen00E2m0U2m1UxfFrlk5j3pCoMtQ5QhS9p2K6vr+PGjRu4ceMGAoEA\nnE7nmCgCO8/rCUU5DAYDotEo3G43bty4gUwmo4p7Tk5OlKpLsVhUKkDAowXbeZ3nWZjNZjV7MRAI\nqNaYRCKBtbU1rK+vqx2nx+NRN0l2ntcbo9Go5POi0aiK2PX7faRSKWQyGZXX1DtPu92u9GvZPps0\nKwAAIABJREFUec4Zg8EwNmyVqgDL5bLa+dFNpdVqqZmF3W5XxdvJGVIYjOZoUnNur9dDoVBAsVhE\nvV4faxA3m82q2Ve/ktdX+gaDQQSDQdVisLGxgfX1dbjd7rG8FXN90Tsgh8OhCnDsdjusVqsaIuD3\n+xEMBpXzrNVqSglIvxOl3SiFVCllQYdeQETfFkCvQX3RZLvRaBQrKytIJBLw+/0q7cF2ywBQQjQk\ngrCysoJms4nhcIhgMKg2K1arVS289F/J1kixatnviUvjPJ1Op8pRNhoNAIDH41E3l1qtpkKw9IH0\nej1ks1lVKEFOFXikrqEfPOx2u5UcFZ2Hep5oxaSH2mMmD1rNL/pIHWYxsNlsqu9Nr+hCTpNaS8hh\nNptNJTLfbDZVCNZms6HVaqnFITk/i8Uypr5F9uz1elXIlh7z+Xxwu92qqpftljkNGly9trYGm82m\nqrlrtZrabdJBtud0OtVmgxSzeOc5Y4xGowp70mrd4XAgEokgl8spFRW9pBj1ING8RH0ymz5UvXK/\nXm/WYrGo3aLdblch4EAgoK6JnK1e+5PEGugcFKblkBfzJKxWq8qHRyIRFU3Rj4aiAiLKwVP/Zrlc\nht/vh9/vh9vtVnn7Wq2mdre0o9UvAl0ul5JP0ztZaochu13mmxszO4QQ8Pl8sNlsiEajYxFA/UhG\n/aEvUNMXDy0rS+E8KWxLDqnf78NiscDr9SKXy6lwqZ58Pq96KfVSUhT+pXJpulnYbDZVQEGVulRY\nQc4zGAyOvQY9n1RkKDy2zAbBzJ+zVIOouI2qvyl6Uq/X4fV64fV6USqVVAiWnCf1JNOOlJwnHbQb\n0K/+l/1GxswXIcTYbNDryFI4Tz30ofl8PlXJRbJ3eur1ulqFU59bv9+H0WiEzWZTOUxaFVFek1bi\ntFLSh21dLtfYa9Dz9TcghrkoqOAC0KIv1GZlMpkgpYTdbkej0RgTj/f5fGq3qo+A6MO2tDC8Cqt/\nhrksls550jw4k8kEl8uFQCCgQgZ66DFSUqGCIdod6lfbk4UVk987S9nlKvYuMYuDwWBQCzN92wmt\n+EOhEHq9nurZpIIhqg7X27O+105v48DyaYoyzCKwdM6TVCmWWZmCYc6DPnc0CUvlMczlwnFGhmEY\nhpkSdp4MwzAMMyXsPBmGYRhmSth5MgzDMMyUsPNkGIZhmClh58kwDMMwU8LOk2EYhmGmhJ0nwzAM\nw0wJO0+GYRiGmZJZKAzZAOC1116bwamvL7rfp+0yr+MKwPY5A9g+LwS2zRkxC/sU+uHOF3JCId4L\n4Lcu9KSMnm+UUn7ssi9iWWH7nDlsn88I2+ZcuDD7nIXzDAL4SgB7ANoXevLrjQ3ABoBPSikLl3wt\nSwvb58xg+3xO2DZnyoXb54U7T4ZhGIa56nDBEMMwDMNMCTtPhmEYhpkSdp4MwzAMMyXsPBmGYRhm\nSth5MgzDMMyUsPO8ZIQQd4QQQyHE7cu+FoaZhO2TWWSEENaRfb5z3q99buc5usDB6OvkMRBCfHCW\nF3rOa7SecW3vnvI8H9f9bEcIcV8I8c9mdd0ApuoXEkJEhRCfFEKcCCHaQoh9IcS/EUI4ZnWBi86S\n2OeFfG6Lbp+EEOIfCyE+P3qvKSHEz130hS0Ly2CfxPN+bkKIn9a9r54QYkcI8REhhH1W1/ysCCFs\nQogvPssCcRp5vpju398A4EMAbgMQo8fqZ1ycUUo5mOaiLoBvAPDHuv+Xpvx5CeA/AfhOAHYA7wbw\ni0KIlpTyFyafLIQwAJByfk2zAwD/F4AfAVCA9jn8KgA3gG+f0zUsGstgnxf1uS26fUII8WMAvgPA\nDwH4SwAuAKvzev0FZBns8yI/t78E8HcBWAD89wD+DwBmAP/kjNe9DD8BAD8PYAfAnal/Uko59QHg\nmwEUT3n8KwEMAfwdAH8NoAPg7QB+G8DHJp77bwH8ge7/BgAfBLALoAHtl//uKa/LOnr9dz7L+9Kd\n57Tr/RMAfzT693cBSAH4OgD3AHQBREbfe//osRaAVwF8+8R5vhzAK6PvfwbAe6DdVG8/5zX/MID7\nz3OOq3Isqn1e1Oe26PYJIAxNIedLL9sWFvFYVPu8qM8NwE8D+H8nHvv3ALZH//6q097n6HvvAfDZ\nkf29DuBHMRLzGX3/LoBPj77/Od3vbOp7PoCvHb3WG0fnmOoePKuc54cBfD+AFwDcP+fPfAjA1wP4\nVgAvAfgVAL8jhHg7PWEUQvin5zjXrwkhskKIzwgh3jfdpZ9JC9oqCtBW/j4A3wvgm6D98ktCiG+D\ntqv4IWgf8gcBfEQI8fdH1+8B8HsA/gLAW6D9nn528oWmeJ/0/CQ0Q/jjZ3lj15DLtk96/kV+botk\nn181up4XhBD3hBAHQoiPCSHiz/82rwWXZZ+z/Nwm7RMYf5/3hBB/G1ok5l+OHvsAtOjKD42u3wDN\nPosAvgSafX8EE2mF0X3/V550MUKIFQC/DOAboS0up2YWU1UkgB+VUv4JPSCEeMLTASGEE8APAvgy\nKeUro4c/KoT4m9BCCH8+eux1aOGusxgA+DFoN6M2gHeNzmOTUv7a1O9EuzYxOs87oK2oCAu0VftD\n3XN/AsAHpJT/ZfTQvhDizdAM4HcBfMvour5LStmHZjBbAP71xMs+7X3S6/0HaAZvgxYO/O5p3981\n5DLtk853YZ/bgtrnFrRw8g9A2+k2od0QPyGEeIuUcvgMb/W6cJn2OZPPbeTA/wE0x0ec9j7/VwA/\nKaX87dFDe0KIn4J2T/9ZAF8NIAltZ1wc/cwHAfyHiZfcBZB+wvUIAL8B4OeklK8KIe7gGfL6s3Ce\ngBYymIY70G4knxLjlmKGFjoCAEgpv+JJJxn9wf+M7qHPCiF80EJj0zrP9wghvmZ0DYAWdviw7vv1\niRuTH8AKgN+cMHYjHn2QdwH89eg6ic9ggqe9Tx3vB+CFtkr7GWiG/oPn/NnrzKXYp46L+NwW2T4N\no+v6Linlp0ev/14AR9DCwp96ys9fdy7LPi/yc3u7EKIGzceYoOXof2DiOZPv82UAbxVC/HPdY0YA\nptGu8y6AHXKcIz6DR3ljAICU8r1PubYf1p4m/83o/09enZzBrJxnY+L/Qzxe2WvW/dsFzfP/LTy+\nMnre6QJ/hsc/tPPwCQDfB21LfyJHQXIdk+/RPfr6j6DljPTQzUjgGSsXT0NKmQGQAfC6EKIO4A+F\nED8lpSxf1GtcUS7VPi/oc1tk+0yNvqohilLKEyFEFcDaBZz/qnNZ9nmRn9sreJQvP5anFwOp9zly\n+k5oYdw/mHyilHI4es5F2Oc7AHyFEKKne0wA+IIQ4qNSyvef5ySzcp6T5AC8eeKxNwPIjv79eWh/\nwGtSyr+44Nd+C7Qb1bTUpZS7Uzz/EEAewJaU8j+e8ZwvAnj3RGXZlz3DtZ2GcfTV8sRnMadxmfb5\nrJ/bItvnp0df72C08xFCxAB4AOw/w/muO/Oyz4v83DrT2KeUUgohPgvgjpTyl8542hcB3BBCBHS7\nzy/D9A71O/BoMQlo4er/G1r9wV+d9yTzcp7/DcB3CyH+IbSL+58A3MTow5dSloQQvwjgl4QQNmgf\nnA/A3wCQlVJ+HACEEJ8C8OtSyo+e9iJCiK8d/dyfQ1uRvwtaOOwnZvfWNEYf/ocAfFgI0QTwX6GF\nUt4OwCal/GVocfafAPCrQuudug0t6T35Pp72Pr8G2vv8S2irtzdBywn8Vyll9rSfYZ7IvOzz0j63\nedqnlPLzQog/hPb7ej+0YpGfhfa7/fRpP8M8kbnY5wJ8bh8C8LtCiBQAWuC9GVoV7Ieg7UiPAPyG\n0PqaQzjl3i6E+DiAL0opf/K0F5FSHk48fwBt5/lQSnlmrnSSuSgMSSl/D1pV1M/jUYz6tyee88Oj\n5/w4tBXG7wN4J7TBsMQNAMEnvFQf2rb/T6HdoL4ZwPullB+hJ4hHiilvP+Mcz8zoBvQBaCubz0Ez\n+vdCS2BDSlmB1pP3Nmgl2j8Orfpxkqe9zw6A/xmaQb8KLWf2cWjVdsyUzNE+n/q5XRH7BLRexs9D\nCy//EbRe668+JbzMPIU52ifwlM9NPBKi+QfP964eR0r5nwH8PQBfA+D/g/Z38j14ZJ8DAP8jAD+0\nivBfAnCaOMgaxvtqz/Xy017vtRuGLYR4F4B/B+CGlHIyt8AwlwrbJ7PICCFegLYxuTO5g7tuXEdt\n23cB+Cm+MTELCtsns8i8C8AvX3fHCVzDnSfDMAzDPC/XcefJMAzDMM8FO0+GYRiGmRJ2ngzDMAwz\nJRfe5ymECEJTut/D86sDMY+wAdgA8Ekp5VP1U5nTYfucGWyfzwnb5ky5cPuchUjCVwL4rRmcl9H4\nRgAfu+yLWGLYPmcL2+ezw7Y5ey7MPmfhPPcA4Dd/8zfxwgsvzOD015PXXnsN73vf+4DxpmdmevYA\nts+Lhu3zQtgD2DZnwSzscxbOsw0AL7zwAt761rfO4PTXHg7nPB9sn7OF7fPZYducPRdmn1wwxDAM\nwzBTws6TYRiGYaaEnSfDMAzDTAk7T4ZhGIaZEnaeDMMwDDMl7DwZhmEYZkrYeTIMwzDMlMyiz5Nh\nGIZZIqSU6gAAIQSEEGPPmfz/dYedJ8MwzDVHSonBYIDBYAAAMBqNMBgMMBg4OHkW7DwZhmGuOeQ8\n+/3+2OOn7UAZDXaepzAcDpUh0dHr9R77CmAs1DENZrMZTqcTDocDNpsNRqNRrfYYhmHmSafTQbVa\nRbVaRb/fh8lkgslkgtlshsVigcVigdlsVo+bTPN3HVJKDIdDSCmVQ5907vN09Ow8T2E4HKLZbKLR\naKDRaKBWq40d1WoVtVoNg8FAOdppEELA6/ViY2MD6+vriEQisNvtsNlssFgsM3pXDMMwp9NoNHB4\neIj9/X3UajXlIG02G3w+H3w+HzweD1wuF1wu16U4z+FwiH6/j8FgACEEDAbD2IZj3jtkdp6nMBgM\n0Gw2US6XUSgUkM1mkcvlkM1mxw7ahfb7/XPtPvWrpXg8jre97W0QQsBms0FKCZPJxM6TYZi5U6/X\ncXR0hM997nPIZrMwGo0wmUxwuVxIJBKIx+OIx+MYDAYqajZvyHn2ej0IIWAymdTO8zJCy9fOeVI4\nlpLj+n/T0Wq1kM/nkc/nkcvlkMlkkE6nkclkkM1m1Vd9GIGS65PHYDBAr9dTjpZeM5/Pw+/3Ix6P\nIxQKwWAwwG63X/avh2GYa0i320WlUkEmk8Hh4SGGwyGGwyFcLhfabW0QidVqhdVqhcfjuZRr7PV6\naDabqNfrkFLCbDaPhZWtVivMZvPcrufaOc9ut4tarYZ6vY56va5Cs3Q0m00Vmq1Wq6hUKuqoVqto\nNpvodruwWq2w2Wwq3Ko/rFar+jCbzSZKpRLK5TLK5bI6b6/XQ61WQzabRTqdhtlsvjSjZBjmemMy\nmWC32+HxeGC329U9r16vw+PxIBgMotFooNPpTJ2muiharZbazHS7XRW2dTqdCIfDCIfD7DxnCa2w\ncrmc2l0WCgUUCgUUi0UUCgVUKhW0Wi20Wi202210u110Oh30ej1V2GOxWOD3+xEMBhEIBOD1euF2\nu+HxeOB0OtVRKpVwdHSEw8NDnJycwGAwoNVqod/vo1qtKmPweDyIRCKX/ethGOYaYjQaYbPZ4Ha7\n4XA4UCwWUSqVMBwOEQgEEIlElPOcrMidF+Q89/b2UK/X1eOBQEDtkt1u99yu58o6T32VrD5EWywW\ncXJyglQqhXQ6rcKwuVxOOdRKpaJ+ZjgcwmQyqRyA3W6H2+2Gy+VCLBZDNBpFNBqF3+9/LLHudruR\nzWbhdDpVCLfVaqFcLsNoNKrr7Ha7586bMgzDXDTkPF0uF2w2G4bDIRqNBtrtNmq1GprNJtrtNnq9\nHobD4dyui8LHg8EA1WoVmUwGu7u7KJfL6nuxWAxutxsrKytzuy7gCjvPWq02tpOkEGy5XFarqnK5\nrL5Xr9fRarUgpYTD4VDO0mKxwOv1qoN2l263WzlLr9erdpoOh2MslNtut+H1euH3+xEIBFTI2Gq1\nYn19Hbdv38atW7cQi8XgcDgu+9fGMMw1xGAwwGw2q/uW2WxeiP7OTqejooAnJyfY39/H9vY2isWi\ncp6dTgfJZFLlZufFlXaex8fH2N3dxdHREVKpFE5OTs4MyQ6HQxWStdvtyhF6PB6srKxgZWUF8Xgc\nXq8XHo8HXq93LM9JPVD0lZyv3nlSDqHRaMDpdGJjYwO3b9/G7du34XK52HkyDHMpnOY8F6HnnPpP\ny+UyTk5OcHBwgO3tbeTzeeU8pZS4e/cuOp3OXK/tyjrPRqOBVCqF+/fv4+HDh9jd3cXOzg4ajcZj\n+o0GgwFWqxVutxtOp1M5R6/Xi1AohK2tLWxtbWFjY2Pse/SzTyqVbjabynnW63WVM/B4PFhbW8Pm\n5ibW1tZYyWPJ0IfYJ5u2z/sz+j/+074+C/qFm76Mn22LOQ+LZjNU4JnL5ZBOp3F8fIyDgwPk83kl\nUON2u1GtVtHtdud6bVfWebZaLRQKBRweHiKdTqNSqaDb7aq8pd1uh8vlgsfjGTvcbrdKmtPOMxKJ\nqLym3W6H1Wo9t3GZTCY4nU4EAgEAgMfjQSKRUDtPt9u9EEbKTAc5uMFgACmlatamXPZZP0M/R9EP\nqvCePFqtllKxOi9GoxGxWEzl4qny22azPe/bZa44nU4HxWIRh4eHODw8RLFYnPtO7qzrqtVqyOfz\nKJfLaDab6Pf7aqdsMpngdrths9nmLtxwpZ1nsVjE0dERMpkMKpUKer2e2mEGg0FEo1EkEgkkEgmE\nw2G1q3Q6nap3yGazweFwqDAufWC043waZrMZLpcLgUAADodDNRrb7XaEQiG4XC52nksIaYFSAQX1\nnD3tZ8jhNpvNsQpv/Vc6KIejj5RMno8QQsBiseCll17CG97wBhiNRrUgpMUew5yF3nkeHR0p53nZ\noVvaeRYKBZTLZTQaDeU8rVar2uCQxOk8ubLOs91uo1QqIZVKIZfLqVYTCstGo1Fsbm7i1q1buHXr\nFlZWVlQ41uFwjAkdPA+Tahx6GT7aqfCNbfmQUqLf74/1vZ228yQHR8+nCvBKpYJsNqsqv+krHel0\nGo1G44mvPxnatdvtqNfrsFgsiEQiEELAarVe8Dtnrgr6xRfdL4+Pj3F8fIxarYZut3vpUQtynqft\nPG0229g9m53nBWGxWNSOj8qtu90uLBYL3G43wuEwotEowuEwgsGg+gAoUX5RMX+DwQCLxaIM1WKx\nqJ3rZa/qmGen1+uhWCyqhVkoFEIoFILFYlG7S2pNokMvtkGtUdlsVlV9VyoVtNtt2O12JBIJDAaD\nUxdYvV5P6SuTgxVCQEqJZrOp2rEoZcAtUMxp6NXPyJZarZaqy5hnS8pZUMFQPp9HqVRCq9VSkbtI\nJIK1tTVsbW0hHA7P3dFfWedJO75AIIBGo4Fer4d6vQ6z2Qy3241QKKScJ4kcUOjtIp0aCSqQQ9bP\nyVuUpDwzPeQ8Dw4OUK/XMRwO4XA44PP5VE9xt9tFuVxGqVRCqVQa211Su1SpVEK73Va7UsrJBwIB\nWK1WJdCtF79uNps4OTlBv99Ho9FQNiSlRKvVUhEXl8uFYDDIzpM5lX6/j3a7rSTvFtF56guGyuUy\nWq0WhsOhcp5UzBkOh+cub3plnSftPPUtIpRk1u88Q6GQ2nnquSinRjvPswTf2XkuJ91uVznPUqkE\nh8OBSCQylgttt9sol8vKYT58+FAdlUpF9fzqbYQUqxKJBLxer5J61BdDVKtVDAYDNbiAIOdJO89Q\nKIRWq3UZvx5mCej3+2i1WqjX66rXvdlsqhTXIiy6JneezWZT7TzD4TBu3LjBO8+LQD9/s1KpqLAa\n3WxI1DgYDCIWiyEcDsPtdquy/lnAzvHqQLu6crmMbDaLw8ND1TeczWbx8OFDFf5qNBqo1+tjEpD5\nfB7NZhNWq1U5SQBwOp0qd+Pz+dT3JgvUiGq1quzKbDYrnWZKT6RSKQCA3+9HNBpVzp0iK2yTDKDd\nL0l7u1KpjMnv6edmTo7/mqX96GsE9O1ctAum6J0+Lef3++F0Orna9nmgMES73UalUlHjxCqVCvr9\n/pnOc55iwszy0mq1kEqlsLu7i5OTkzGRfxKrPj4+VkpWtLukg/KgVqtVKVK5XC4VBYlGo/B4PKpN\nymw2n3rDIudJ03xSqZRqf6lWq5BSotFoIBqNYnV1VUmZORyOmS4UmeWCppSQrZ7mPIFHhXDzcJ7A\n6Y5T78yp0pYii36/X9n2PLmSzrPRaKiQViaTUav9SecZiUTO1WLAMACURNirr76Kw8NDdSMxGAxq\n5utwOBwbNkAKVp1OZ6yXOBwOIxaLIR6PY21tDevr69jY2IDT6VTnJCZvVpVKBcCjm4yUUhUhUSg4\nm81ibW0N6XQapVJJCSewihVD9Ho9da+kNpDThN/JHqdp0XseTnOc+p0npTn0znMytTEPrpTzpN45\nEnuvVCqqV8nhcMDr9SIQCMDtdsNut8NisXCrCPNEer0eOp0O2u020uk0Tk5O1JScs4q+6Obj8/nU\n4oxGzlEvcSAQQDAYRDAYRCQSUVGQ8+RtqEd4bW0NvV5PCYLkcrmxdph6vY5SqYR0Oq1CXR6PZ+4l\n/cziQDs4ik7kcjns7+/j+PgYpVJJTY6izQYt8JLJJBKJBAKBwMxzi51ORwmFUK6zWq2i3+8rgZtI\nJAKfzweHw3Fp9/Er5TwbjQay2Sz29vaQSqXGhBGcTqcqDnK73bBarXMLQzDLC7WFkLYmHcfHxwDw\nWFGF0WhUwwL0AwH8fj9cLpc69IMESNXqvE7NbDbD7/djOBzCYDCoSt7j42Ol2TwYDMZ6nWnc1CIU\ngTCXh35XV6/X1ZSSw8NDlEolpcLmdrvh9XqRSCSwurqK9fV1rK+vz1yDm4reaIBHNptVAgkAlLZ4\nPB5Xim/z2hFPcqWcZ7PZVPPe9M6TckuTznPe23xm+eh2u2oU0qTzpH5OfUk/CWDE43Gsrq5idXUV\na2trWF1dVc6SGrqpZek80n56TCaTKpJwOBxIpVLY2dlRswwHg4GaRkGO1ev1IhKJLET7AXN5kPPs\n9/vKee7s7ODg4EBJmJIqWjgcxsrKikorrK2tKVud5fVRxXgqlUImk1HOkxx3PB5/zHlexgboSnkP\nUnzRl1vTtBSakk7z6jhcy5wHEjogUQLS+6QWEgrJUpiLNItppU5hr0mt2eexPWq5MhqNcDqdSm+Z\nKnPp3OREaS7jtFq5zNWj0Wio8Yx7e3s4OTlBPp9HtVpVAgTUH08CBLFYTE2RmgeUfjs8PEQ2m1VF\neaQhTW1dNAThssRmrpTz1GuH6qvFKIav16dlx8mcB70DajabkFLCZrOpUCwNQdeHaUOhEMLhMEKh\nkMpz6ttOLgK6YVAVJB36HCz1m3a7XbWQ5LDt9aZWq+Hw8BAHBwd48OCBynU2m010u10Mh0NYrVaE\nQiFsbm7i5s2biEajcys0m8zFZrNZJUKyaFw750mFQiyNx5wHcp40LB2Ailwkk0msrq5iZWVFtZqE\nw2E1SIBCSpMjwi4C6ncjBaJJ5Sp9eK7b7S6MYgxzuVSrVRwdHeHVV1/FgwcPcHJyojRjyWYsFsuY\n8wyFQuw8T+FKOU993xJJ8pE4Nk1SoZ4grjhkzoNe5rHT6SiFKimlmshDPcMk9aivsJ0FegcshIDJ\nZILVaoXdbkez2VQ70MmSf951Xj+klEq/ttfrIZvN4vj4WIVsS6USOp2OKj4zm81qUkkwGEQoFILb\n7T5TIe2irpEOSr3V63V1H+92uwtpu1fKeZIcWiaTUUZBbSo+n0/J8V2GGgWznNhsNoTDYRgMBgSD\nQaXmA0CFar1er6qipbL5eUU2hBDqhud2u9FsNsdsm9MT1xsqwCGhDip2Ozo6Qi6XQ71eH5uPabFY\nVOSEqsKpM2GW0AKP0gzUH93r9dTM3EXjSnmQTqeDSqWCTCaDcrmMTqcDo9EIh8MBv9+PWCymZmjy\nzpM5D+Q8vV4ver0e+v2++mPW7zD1IdR5Cv7THE+73Q63241KpaKcJztOBng0biyXy6nxd5TrJKF1\nEh6w2WzKcZLzJPueJfqRfXphkW63q0b+LRpL7TwndRC73S6azeZjVZHUWEv5TiHEU4WP9T2gdBPi\nm9H1gxzioirz6NVWfD4fSqUSK2ZdcyZnyNZqNWQyGezv7+Po6EhtLmiwNEk3kmjH+vq6Eu2wWq0z\nH59I10mCH61WS2lD08QhWqxSFJEGYF9m7cpSO08AY/kcvboKABVCowKP4XCo4uekpHEa+hAGVeby\n+DBmERFCwGazwePxIBAIIJfLqfzUIoa6mPmgD4OWSiUcHR3h/v37ODg4QLFYfEzD1uVyYXV1Fbdv\n38bdu3exvr4Ot9s9l9GJdO/Wj0er1WpKb5fu2VTlnkwmEY/H1RjJy2Lpnae+wlY/VYXCWTab7THn\nSUnpswyC+kL10wTYcTKLCIlkk/OkvCvBDvR6oq+01jtPmiXb6XTGcolOpxNra2t405vehLt37yIe\nj485z1miLxRqNBqo1WpK1YvCtuw8Z4A+Rk6DXGnnSeXN7XYbxWIRh4eHqFarTz2nyWQak1KjxnZK\nnFNrwGTVI8PMGyoYolwV2Sg7zevLZJtStVpFNptVs2drtZq6R1Lvsd/vRzwex9bWFjY3N5X+97zC\novpiIdKSppAtQcpHwWBQ6dpeZu3KUjtPynPSJIlqtYpmszlWodXtdnF4eIjhcIhisXiuaePUjkAi\n3lSyHQgExqrQOJzLLCKcp2dIIIMU12q1GsrlMur1OjqdjtrJ0QaBiil9Pp/aMCxaUSVNdtEX6V2m\nfS+18wSgeoIKhYKSmCJFlV6vB4PBgE6ng0KhgO3t7XO1qJjNZtWCQAn09fV1rK6uIhQKqTyTvimd\nYRYNtsvriZRSOU8a0VitVlEqldTQACklrFYrvF4vQqGQcp40wICia4uEfhA2O8/nhIwHVXkBAAAY\nrElEQVSk2+2i1WqpClqTyaTKm4fDIZrNpgrdnnaOyeGrRqNRTboIBAKo1Wpot9uqmpfEF/RapXr4\npsXMC/2NclJJiFbqdLNhVa2rC9kBhT+p4KZQKKBYLKJSqSilHnJCTqcT4XAYq6urSCaTCIfDqoqV\nohf68P/TUgH6+95Zz5323qgfxE0dEzR8gaapXBZL7Tz16ipOp1MVTUQiEdX4q9/qT/6yKS9AFbrU\nWzQYDCCEUHMSjUYj2u02crkcEokEVlZWkEgklCRbNBp9TFeUYeaBlBKdTgfVahWFQgH1eh3dbhcA\nVAsLzbC1Wq1sn1eUwWCgZmBWq1Wk02nV0/nw4UMUi0UlrE5hz3A4jI2NDbzwwgu4deuWGl4waSNU\nkfskibzJe5++XUb/nGmrdymX73Q6EYlE1M7YZrNdukb5UjtPAMp50rDrYDCIaDSKwWCgQg9UcUtV\nt8RksRGpx1B5NIU8Wq2WGnW2srKCZDKJZDKJO3fuwGQyIRgMjhkGw8yL4XCIdrutnKe+x5mcJ43h\ne95pLsziQs6TxBC2t7exvb2NnZ0dHB4eolAoqKiczWaD3W5HOBzG5uYm3vCGN2B9fR2hUOgxG5mU\neDwLff2HPpo3+Rxy4NM4T5/Pp4bG+/1+Vcx0mRNVgCvgPPWi76QitLa2huFwqHabNEPR6XSOOU/K\nCVBTbqVSUUe1WkW1Wh3LFwghVDikWCzCbDbD5/NhZWUFdrtd9YYyzLyQUqLdbqNWq6FUKp2686Qb\nDgmEMFcPSk/R8PO9vT3cv38f9+/fV/c0KaXqYafe4FAohJWVFYTDYVitVvT7fTSbTQCPHKe+huQs\n9DNpydFOyurRRofG51FkkELOlH7QD/awWCxwu92qYJMmFFmt1pn/Tp/GUjtPfZm+EALJZBJSSng8\nHrXCoTAFfWj6lYo+V0Q9RqRsQc6T+o3K5TJqtRosFgvq9TqOjo4QDAaVril9uH6/n3VzmblBgh9U\nUUkFIYB2syLZPr26FnP1GAwGaDQayOfzSKVSyGazaog01WkQ5BTr9TrS6TQePnyolKnIqZHT63a7\nKJfLSrDgLCwWi6r/0KfA9LtVp9Op0lyBQEBtagwGAxqNBorFIrLZLCqVioqekEOmjdBlFwnpWfq7\nPIUhzGYzVlZW4Ha7sbq6qlZZlBzXzzskJpPs+hAuDT+m/tDDw0OcnJwoBYx8Pg+fzwe32w2Hw4Fu\ntwuj0Qiv13uJvw3mukHOk9IO1BtHC0tynjzH9mpDzrNQKCCdTiOXy6FYLKJcLo+prulFZer1OjKZ\nDLa3t5HNZpWjIqSUaDabSKVSSKfTKBQKZ74+5SVdLpfqgCD5PyIUCuH27du4c+cO1tbWEAgEAGgS\nqo1GA6VSSQ2/brfbAB7VtZxWs3LZLLXzpF8s7fScTidisdgznUs/B7TT6agpBLlcDl6vF0ajUQkV\nN5tNZDIZ+P1+eDwelcD2+/0LOXeOubropc0oV68X+rbb7WPDuNl5Xk30zjOVSinnWavVxp6nz2HS\nzhPAWMeAPmdZr9ext7eHvb099dzToJ54n8+HVqulQsX6HW8ikVATiQwGgyrqdDqdKJfLyOfzSne3\n3W6PFThNVtgugh0vtfO8SKjgB4Ba6VAogoTkado6raYmWwEWKaTAXD9IktLpdMJgMMDn88Hv98Pn\n88HpdHLY9grT7/dVRCyVSqmezkmotU8IgXK5DEBTYJuUuaOwLXUcnHauyedT9wIwHhGk3KbRaESx\nWMS9e/dQrVYRiUQQDofhcrmQTqfVkclk0Gg0IKWEw+FQVcGJRAIej2dh+k/ZeerQa9lSnpQKgGjH\nSa0swCMBeX0CnGEuC3KeAFQkxOfzKSkz3nleXSgMS85TnzecfB5FJwaDgZqBPOmQyHn2+300Go1z\nO08qEiLnCWAsbVYoFNBoNHB0dIRQKIRgMAin04lKpaJyqzRnFAAcDgdCoRDW19eVnu2i1JQsxlUs\nCFRqTfq1FotF3Yz0O09ynvQ8cp6LsiJirjZPalYnmxVCKMfp8/m4CvyKoy8YSqfTqmBnEv20FQqv\nXgR650npNNpxUr6SJrxUq1UMBoOxqAilxEhOsNlsPrbzjMfj8Hg87DwXDcp1UtVtLpdDLpfD8fEx\n7t27h+PjY9RqNQwGA9jtdlitVkSjUSQSCSXbR+Eyhpk1dKMim6WBCFS4Qa1bNB2IudqYzWb4/X6s\nra2pStt8Pv+Yqpp+F0hRNr3M6KSAAU3toSgcpagmw7ykyObxeCCEUPUj5DzNZjO63a7aYVI7DPBo\nN0wbFJIOJJlUqiuhdsBFsWd2niOoybhWqyGfz2N3d1cdBwcHynnScG2bzYZIJIJEIoFkMqnCD+w8\nmVmjl+Qjx0k3H7rhRCIRBAKBcw1CYJYfs9mMQCCA9fV1VCoVmM1mtFqtU53npBOkqNlpWt1msxke\nj0c5R1qYTQ6HJy1wr9ernOdgMBjTou12uygWiygWiyiVSiiXyyiVSqqHnnbDVGtisVjGXpud54Iy\nGAzQarVQLpeRSqXw4MEDfO5zn8P9+/dVn2e9XkcwGITdbldKRolEAmtra+qDZefJzJpJ50nhrl6v\nB4vFAp/Ph0QiwTvPa4TJZEIgEMDa2hra7TY6nQ5yudxjzyNnRkIDetECSkPp7cVmsyEUCiEcDqtR\nYH6//7GWPHrc7/cr59nv95WIjcViUQM6KLS8s7OD3d1dNJvNMedJdSRUwavfeVosloW5x15Z5zkp\nDzXZ30maoCTJR8oc6XQaR0dH2NnZwfHxMcrlslrR+3w+xONxJc+3vr6OYDDIOU9mrlCKgVSxarWa\nKuqg1T6V9rNNXg9MJhM8Hg8SiYTKa1IoVw+Jq1P0jBzbWc7TYrGoHSU5MRrJqEc///i0sC21FA4G\nAyXCQML1eqdIqkJOp/MxRaFF25xceecppRybbajvc2o2m6o8+vj4eEwMIZ/Pq6ovaja32+1IJpO4\ndesWbt68iY2NDQSDQXWTWqQPlrm60MKPpCKr1Srq9TqazaYSSLBarQvXVM7MDpPJpEKmpPRDYVw9\npPFNdRsUIiX1nknnSdNM6Of0IVU99DjJ5tF9Vt+mIoRQ486GwyGy2ezYjpKeox9ooO9RpnvsokRS\nrqzzBB59gHrRdn1VWKPRQDqdxuuvv65ElHd2dpBOp1UoTEoJn88Hu92OUCiknOcb3/hGBINBBIPB\nhVsRMVcb2nnW63WUy2XlPFutlmo8p4Zy7j2+HpDzpB1bMBhEMplUvZyEfkgG5T71tnLaJoDunWcV\nFQE483H996kv3ul0QggxJvKuj9yRnm0gEIDX64XD4VDXuEgs1tVMiX53KaVEt9tVB0lS9fv9x55D\nYvDZbHasKCiVSqFYLKLdbsNqtaoEeSQSQTQaRTwex82bN7G+vo5IJDI2qYJvUMy86PV6KBQK2NnZ\nweuvv66K2UhMG3h8RBRztaEWO704OzkqPRSipa8kCKMvGJrVRmAylxoIBLCysoJOpwO73Q6fz4dk\nMqmKMGn8o8/nW8hF4FI7T2Bcn7Zer6NWqyltREqck1I/qXBQuTTJQWUyGTXOqdVqqeR7OBxGJBLB\n6uqqGhhLjtTv96vwxaJ9qMzVptvtIpPJ4P79+3jllVdweHiIarXKdnjNoc/fYDCoRf1keJVCs/pd\npv6Ylw3pq4PtdjsSiQRu3ryJarWqIno0m9nv9y9k7n7pnSflL0kJQ5+rpGZbcqKdTgelUgmZTAbZ\nbFY17NI0CtqdUo/c2toabty4oY719XWVL7DZbDNdpTHMWXS7XWSzWdy/fx+f/exnlQg3w9AOlBb2\nk1rb+ojE5L/nCTlPq9WKcDis7tHdble1wjidTlXUxM7zGdFXzdLwXwq90m6T8j8k8UQViPV6fayR\nnCq8CoWCGtXT7/dVnJ1mx21ubmJrawsbGxtYXV1VM+9oxbZo8Xfm+kDFbuVyGYVCAf1+H4PBAGaz\nWS38IpEIvF7vmOA3c3WZdH6L6Gz0kPgC7ZIpMjgcDsdysadNw1oUlsoDUIi2UqmoHWYqlVJHv99X\nh342J4Vuafg1fY/Kue12O7xeL5LJJFZWVrCysoJEIoFEIoF4PA6fz6dkoXi3yVw2+j5PEvmm9hRq\np9rc3FQFJIt442EYcvAGg0FFEGmSCrW2LHLufmmcp16ouFKp4OjoCHt7e3jw4AFef/11PHjwQFWR\nWa3WsV0p5TzpoGpbGtnkdrsRj8dx584dvPjii9ja2lJxd6/Xq1Q4aLe5qB8mcz2YdJ5kn1arFT6f\nD7FYDJubm6oYhO2VWTT0BU50X50sdltkxwksifMcDodotVpoNpuoVqvKaT548AA7OzvY3t7G7u6u\nmkxOQsN6gWH6IKiHyGazwePxqLLulZUV3L17F7dv38ba2poK4U7KUDHMZUArc9Kz1VeS6/viSP3F\n4/Fc9iUzzJksumM8D0vhPKk0P5PJ4OTkRO02t7e3kcvlUKlUMBwOVQiL4ue9Xk+Nx6EGXp/Pp+bI\nhcNhhEIhhEIhRCIRxGIxxGIxeDwe2Gw2zmsyCwO1WVF/J6kJGQwGOBwOBINBxONxJcnHMMxsWQrv\n0O/3USwWx3ac5DypEIjG7NCMOuBRH6jJZFLh2UQigRs3bmBra0u1nkQiEfh8PlVJy4pBzKJBi8Nm\ns4lGo6HmyhoMBjidTgSDQSQSCWXHDMPMlqVwnvobB7WWkKIKSUCRYgblf/T/drlc8Hg88Hg8SCaT\nqvUkkUionedkMzHDLBI09adcLqNYLKLRaKDX68FoNMLlciEcDqvpPrzzZJjZsxTOk5yjx+NBKBRC\ntVpFo9FQoVk6SNWfZJ9ovpzD4VDatNR4S7tN0lpkmEWGBD5yuRwymQwqlQo6nQ4MBgNcLhei0SjW\n1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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1678,9 +1553,8 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 49, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1688,16 +1562,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimization Iteration: 101, Training Accuracy: 71.9%\n", - "Optimization Iteration: 201, Training Accuracy: 76.6%\n", - "Optimization Iteration: 301, Training Accuracy: 71.9%\n", - "Optimization Iteration: 401, Training Accuracy: 85.9%\n", + "Optimization Iteration: 101, Training Accuracy: 62.5%\n", + "Optimization Iteration: 201, Training Accuracy: 68.8%\n", + "Optimization Iteration: 301, Training Accuracy: 85.9%\n", + "Optimization Iteration: 401, Training Accuracy: 81.2%\n", "Optimization Iteration: 501, Training Accuracy: 89.1%\n", - "Optimization Iteration: 601, Training Accuracy: 95.3%\n", - "Optimization Iteration: 701, Training Accuracy: 90.6%\n", - "Optimization Iteration: 801, Training Accuracy: 92.2%\n", - "Optimization Iteration: 901, Training Accuracy: 95.3%\n", - "Time usage: 0:00:03\n" + "Optimization Iteration: 601, Training Accuracy: 90.6%\n", + "Optimization Iteration: 701, Training Accuracy: 92.2%\n", + "Optimization Iteration: 801, Training Accuracy: 90.6%\n", + "Optimization Iteration: 901, Training Accuracy: 92.2%\n", + "Time usage: 0:00:17\n" ] } ], @@ -1707,9 +1581,8 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 50, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1717,15 +1590,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 93.1% (9308 / 10000)\n", + "Accuracy on Test-Set: 94.1% (9409 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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qtRAMBrG5uYlHjx5xFm6320WhUBDxFN4ao+u12+2iUqkglUphd3cXz549w7e+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\n1Gg0Go1mTLTy1Gg0Go1mTP5/ha2J4E3+0vMAAAAASUVORK5CYII=\n", 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\n", 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" ] }, "metadata": {}, @@ -1747,9 +1620,8 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 51, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1757,97 +1629,97 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimization Iteration: 1001, Training Accuracy: 98.4%\n", - "Optimization Iteration: 1101, Training Accuracy: 93.8%\n", - "Optimization Iteration: 1201, Training Accuracy: 92.2%\n", - "Optimization Iteration: 1301, Training Accuracy: 95.3%\n", - "Optimization Iteration: 1401, Training Accuracy: 93.8%\n", - "Optimization Iteration: 1501, Training Accuracy: 93.8%\n", - "Optimization Iteration: 1601, Training Accuracy: 92.2%\n", - "Optimization Iteration: 1701, Training Accuracy: 92.2%\n", - "Optimization Iteration: 1801, Training Accuracy: 89.1%\n", - "Optimization Iteration: 1901, Training Accuracy: 95.3%\n", - "Optimization Iteration: 2001, Training Accuracy: 93.8%\n", - "Optimization Iteration: 2101, Training Accuracy: 98.4%\n", - "Optimization Iteration: 2201, Training Accuracy: 92.2%\n", - "Optimization Iteration: 2301, Training Accuracy: 95.3%\n", - "Optimization Iteration: 2401, Training Accuracy: 100.0%\n", + "Optimization Iteration: 1001, Training Accuracy: 92.2%\n", + "Optimization Iteration: 1101, Training Accuracy: 96.9%\n", + "Optimization Iteration: 1201, Training Accuracy: 95.3%\n", + "Optimization Iteration: 1301, Training Accuracy: 96.9%\n", + "Optimization Iteration: 1401, Training Accuracy: 96.9%\n", + "Optimization Iteration: 1501, Training Accuracy: 95.3%\n", + "Optimization Iteration: 1601, Training Accuracy: 96.9%\n", + "Optimization Iteration: 1701, Training Accuracy: 98.4%\n", + "Optimization Iteration: 1801, Training Accuracy: 96.9%\n", + "Optimization Iteration: 1901, Training Accuracy: 98.4%\n", + "Optimization Iteration: 2001, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2101, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2201, Training Accuracy: 96.9%\n", + "Optimization Iteration: 2301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 2401, Training Accuracy: 98.4%\n", "Optimization Iteration: 2501, Training Accuracy: 96.9%\n", - "Optimization Iteration: 2601, Training Accuracy: 93.8%\n", - "Optimization Iteration: 2701, Training Accuracy: 100.0%\n", - "Optimization Iteration: 2801, Training Accuracy: 95.3%\n", - "Optimization Iteration: 2901, Training Accuracy: 95.3%\n", - "Optimization Iteration: 3001, Training Accuracy: 96.9%\n", - "Optimization Iteration: 3101, Training Accuracy: 96.9%\n", - "Optimization Iteration: 3201, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2601, Training Accuracy: 92.2%\n", + "Optimization Iteration: 2701, Training Accuracy: 96.9%\n", + "Optimization Iteration: 2801, Training Accuracy: 98.4%\n", + "Optimization Iteration: 2901, Training Accuracy: 98.4%\n", + "Optimization Iteration: 3001, Training Accuracy: 98.4%\n", + "Optimization Iteration: 3101, Training Accuracy: 92.2%\n", + "Optimization Iteration: 3201, Training Accuracy: 96.9%\n", "Optimization Iteration: 3301, Training Accuracy: 96.9%\n", - "Optimization Iteration: 3401, Training Accuracy: 98.4%\n", + "Optimization Iteration: 3401, Training Accuracy: 100.0%\n", "Optimization Iteration: 3501, Training Accuracy: 100.0%\n", "Optimization Iteration: 3601, Training Accuracy: 98.4%\n", - "Optimization Iteration: 3701, Training Accuracy: 95.3%\n", - "Optimization Iteration: 3801, Training Accuracy: 95.3%\n", - "Optimization Iteration: 3901, Training Accuracy: 95.3%\n", - "Optimization Iteration: 4001, Training Accuracy: 100.0%\n", - "Optimization Iteration: 4101, Training Accuracy: 93.8%\n", - "Optimization Iteration: 4201, Training Accuracy: 95.3%\n", - "Optimization Iteration: 4301, Training Accuracy: 100.0%\n", + "Optimization Iteration: 3701, Training Accuracy: 100.0%\n", + "Optimization Iteration: 3801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 3901, Training Accuracy: 98.4%\n", + "Optimization Iteration: 4001, Training Accuracy: 96.9%\n", + "Optimization Iteration: 4101, Training Accuracy: 100.0%\n", + "Optimization Iteration: 4201, Training Accuracy: 93.8%\n", + "Optimization Iteration: 4301, Training Accuracy: 96.9%\n", "Optimization Iteration: 4401, Training Accuracy: 96.9%\n", "Optimization Iteration: 4501, Training Accuracy: 100.0%\n", - "Optimization Iteration: 4601, Training Accuracy: 100.0%\n", - "Optimization Iteration: 4701, Training Accuracy: 100.0%\n", - "Optimization Iteration: 4801, Training Accuracy: 98.4%\n", - "Optimization Iteration: 4901, Training Accuracy: 98.4%\n", - "Optimization Iteration: 5001, Training Accuracy: 98.4%\n", - "Optimization Iteration: 5101, Training Accuracy: 100.0%\n", - "Optimization Iteration: 5201, Training Accuracy: 95.3%\n", - "Optimization Iteration: 5301, Training Accuracy: 96.9%\n", - "Optimization Iteration: 5401, Training Accuracy: 100.0%\n", - "Optimization Iteration: 5501, Training Accuracy: 100.0%\n", + "Optimization Iteration: 4601, Training Accuracy: 98.4%\n", + "Optimization Iteration: 4701, Training Accuracy: 93.8%\n", + "Optimization Iteration: 4801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 4901, Training Accuracy: 96.9%\n", + "Optimization Iteration: 5001, Training Accuracy: 95.3%\n", + "Optimization Iteration: 5101, Training Accuracy: 93.8%\n", + "Optimization Iteration: 5201, Training Accuracy: 100.0%\n", + "Optimization Iteration: 5301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 5401, Training Accuracy: 96.9%\n", + "Optimization Iteration: 5501, Training Accuracy: 96.9%\n", "Optimization Iteration: 5601, Training Accuracy: 100.0%\n", "Optimization Iteration: 5701, Training Accuracy: 96.9%\n", - "Optimization Iteration: 5801, Training Accuracy: 98.4%\n", + "Optimization Iteration: 5801, Training Accuracy: 96.9%\n", "Optimization Iteration: 5901, Training Accuracy: 100.0%\n", - "Optimization Iteration: 6001, Training Accuracy: 95.3%\n", - "Optimization Iteration: 6101, Training Accuracy: 96.9%\n", + "Optimization Iteration: 6001, Training Accuracy: 96.9%\n", + "Optimization Iteration: 6101, Training Accuracy: 95.3%\n", "Optimization Iteration: 6201, Training Accuracy: 100.0%\n", - "Optimization Iteration: 6301, Training Accuracy: 96.9%\n", + "Optimization Iteration: 6301, Training Accuracy: 98.4%\n", "Optimization Iteration: 6401, Training Accuracy: 100.0%\n", "Optimization Iteration: 6501, Training Accuracy: 98.4%\n", - "Optimization Iteration: 6601, Training Accuracy: 98.4%\n", - "Optimization Iteration: 6701, Training Accuracy: 95.3%\n", - "Optimization Iteration: 6801, Training Accuracy: 100.0%\n", - "Optimization Iteration: 6901, Training Accuracy: 98.4%\n", - "Optimization Iteration: 7001, Training Accuracy: 95.3%\n", + "Optimization Iteration: 6601, Training Accuracy: 100.0%\n", + "Optimization Iteration: 6701, Training Accuracy: 96.9%\n", + "Optimization Iteration: 6801, Training Accuracy: 96.9%\n", + 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"Optimization Iteration: 8101, Training Accuracy: 98.4%\n", - "Optimization Iteration: 8201, Training Accuracy: 98.4%\n", + "Optimization Iteration: 8001, Training Accuracy: 98.4%\n", + "Optimization Iteration: 8101, Training Accuracy: 95.3%\n", + "Optimization Iteration: 8201, Training Accuracy: 100.0%\n", "Optimization Iteration: 8301, Training Accuracy: 100.0%\n", - "Optimization Iteration: 8401, Training Accuracy: 96.9%\n", - "Optimization Iteration: 8501, Training Accuracy: 98.4%\n", - "Optimization Iteration: 8601, Training Accuracy: 98.4%\n", + "Optimization Iteration: 8401, Training Accuracy: 98.4%\n", + "Optimization Iteration: 8501, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8601, Training Accuracy: 100.0%\n", "Optimization Iteration: 8701, Training Accuracy: 100.0%\n", "Optimization Iteration: 8801, Training Accuracy: 100.0%\n", "Optimization Iteration: 8901, Training Accuracy: 98.4%\n", - "Optimization Iteration: 9001, Training Accuracy: 95.3%\n", - "Optimization Iteration: 9101, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9001, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9101, Training Accuracy: 95.3%\n", "Optimization Iteration: 9201, Training Accuracy: 100.0%\n", - "Optimization Iteration: 9301, Training Accuracy: 96.9%\n", - "Optimization Iteration: 9401, Training Accuracy: 96.9%\n", - "Optimization Iteration: 9501, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9401, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9501, Training Accuracy: 100.0%\n", "Optimization Iteration: 9601, Training Accuracy: 100.0%\n", - "Optimization Iteration: 9701, Training Accuracy: 96.9%\n", - "Optimization Iteration: 9801, Training Accuracy: 98.4%\n", - "Optimization Iteration: 9901, Training Accuracy: 98.4%\n", - "Time usage: 0:00:26\n" + "Optimization Iteration: 9701, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9901, Training Accuracy: 96.9%\n", + "Time usage: 0:02:51\n" ] } ], @@ -1857,9 +1729,8 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 52, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1867,15 +1738,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 98.8% (9880 / 10000)\n", + "Accuracy on Test-Set: 98.7% (9867 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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kkvOOLRbLTBrM/HiWgnq3oM92MpmwpaNWq73wHIrLWFTMhsMhGo0Gcrkcjo6O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wghRj4A/F0AuSOefyv0P5q/AX1gbwJ4I4DfAfDbQ+/9NwA+Zfi/CcD7AWwCqEIXr7c/zfX1z/ch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\n8JQkqZLhKUlSJcNTkqRKhqckSZW2QsESFjEbpuAAAAAASUVORK5CYII=\n", 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\n", 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" ] }, "metadata": {}, @@ -1886,26 +1757,28 @@ "output_type": "stream", "text": [ "Confusion Matrix:\n", - "[[ 973 0 1 0 0 1 1 0 3 1]\n", - " [ 0 1129 2 1 0 0 1 1 1 0]\n", - " [ 1 2 1023 2 0 0 0 2 2 0]\n", - " [ 1 0 1 1002 0 3 0 1 2 0]\n", - " [ 0 1 0 0 974 0 1 0 2 4]\n", - " [ 2 0 0 3 0 882 2 0 1 2]\n", - " [ 4 1 0 0 1 4 948 0 0 0]\n", - " [ 1 4 11 2 0 0 0 1004 2 4]\n", - " [ 3 0 4 2 1 2 0 0 960 2]\n", - " [ 3 4 1 0 7 5 0 2 2 985]]\n" + "[[ 975 0 0 0 0 0 1 1 3 0]\n", + " [ 0 1128 3 0 0 0 2 1 1 0]\n", + " [ 5 2 1015 1 0 0 0 6 3 0]\n", + " [ 1 0 0 1003 0 3 0 1 2 0]\n", + " [ 0 0 1 1 975 0 1 0 1 3]\n", + " [ 2 0 0 8 0 879 2 0 1 0]\n", + " [ 6 2 0 0 2 3 942 0 3 0]\n", + " [ 1 2 5 2 0 0 0 1012 1 5]\n", + " [ 5 0 3 1 0 0 0 2 960 3]\n", + " [ 4 5 0 6 8 2 0 4 2 978]]\n" ] }, { "data": { - "image/png": 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FkqZLGtfvWWZmvViZ5dzzbU2Fw/rW+mvAgaTRL7OB7wDXA4e/cXBED/BxoBu4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\nZnk/cB/pIafDefM387ukoPw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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1932,10 +1805,8 @@ }, { "cell_type": "code", - "execution_count": 55, - "metadata": { - "collapsed": true - }, + "execution_count": 53, + "metadata": {}, "outputs": [], "source": [ "def plot_conv_weights(weights, input_channel=0):\n", @@ -1993,10 +1864,8 @@ }, { "cell_type": "code", - "execution_count": 56, - "metadata": { - "collapsed": true - }, + "execution_count": 54, + "metadata": {}, "outputs": [], "source": [ "def plot_conv_layer(layer, image):\n", @@ -2060,10 +1929,8 @@ }, { "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": true - }, + "execution_count": 55, + "metadata": {}, "outputs": [], "source": [ "def plot_image(image):\n", @@ -2083,24 +1950,24 @@ }, { "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": false - }, + "execution_count": 56, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "image1 = data.test.images[0]\n", + "image1 = data.x_test[0]\n", "plot_image(image1)" ] }, @@ -2113,24 +1980,24 @@ }, { "cell_type": "code", - "execution_count": 59, - "metadata": { - "collapsed": false - }, + "execution_count": 57, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "image2 = data.test.images[13]\n", + "image2 = data.x_test[13]\n", "plot_image(image2)" ] }, @@ -2152,17 +2019,16 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 58, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ { "data": { - "image/png": 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" ] }, "metadata": {}, @@ -2182,17 +2048,16 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 59, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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s9/CW4skQAOA8iiEAwHkUQwCA89rlQ/f//Oc/PdfHjx9X96Snp6uspKREZdaa\n04kTJ1RWX1+vsu3bt3uuZ82ape5hzbBlRo0apbKmJn2EWF1dncr8c2r9LjQ2NqrMWhNau1YfZp2W\nlqYy1gxTO3bsmMpWrFjhuf7444/VPdaakPUhbWs9yfq7tfoQ3nzzTc/1pZdequ5Batb6v//9ubi4\nONBrffrppyqz1vorKytVVlRUpDL/wcRPPPGEumfOnDmBxhYET4YAAOdRDAEAzqMYAgCcRzEEADgv\n9AYa66QC/yKt1SBRU1OjMmu3dKtZxlqIf+ONN1S2c+dOz/XcuXPVPUjNWhS3PvQ8YMAAlVkbKfi/\n1nota3f9V155RWX79+9XmfU7g9Ryc3NVNmPGDM/1lVdeqe6xmiEOHjyostraWpUdOHBAZVbzTSKR\n8FyXlZWpe8aNG6cyeFkbUvhPlpk9e7a6Z8+ePSqzNt6wGh6tzRysTVL8DXjWa4WJJ0MAgPMohgAA\n51EMAQDOoxgCAJwXegPNZZddprJbb73Vc23tUGE1SFgNNNZuIlZDh7VbgX9X9WuuuUbdg9SCNqRk\nZWW16PWt34/u3bur7P3331eZtYPQtdde26JxuM7aVWjp0qUpv87aLchqiqqoqFCZ/4QbEZF+/fqp\nzL/zzcSJE1OOC5r13jlmzJiUX3fdddcFei1r9xrrd8E6gcbfaDllypSU42oNngwBAM6jGAIAnEcx\nBAA4j2IIAHBeuxzh5Gc1wXTt2jVQZh3ncuedd6qsSxf9n/bQQw95rgcNGnTecSIaVrPMsmXLVNap\nk/5/ucmTJ6ts7Nix4QwMgVjHAlnNFdZuVf7dT0REduzYkfL1Bg4ceCFDRCtZ7+HW3Fk71ezdu1dl\n1vu1/4i9wYMHX8gQLxhPhgAA51EMAQDOoxgCAJxHMQQAOC+SBpqgrAaaffv2qcx/NJOIvXuGfycc\na8EX7c9qhPF7+eWXVWYd7WMdN2M15KB9WUc4WU0vvXr1UllDQ4PK/LtHBfkdQtvasmWLyj766COV\nWcc1WfO+aNEiz7XVtBMmfoMAAM6jGAIAnEcxBAA4j2IIAHBeh26gsRZMb7/9dpVZxz8tWbJEZdnZ\n2eEMDKHy7yq0ePFidY/VIDF16lSV5eXlhTcwtEh5ebnK6urqVDZy5EiV/f3vf1dZ3759Vda/f/8W\njg5hqKqqUtnGjRtV9u6776rMOuJr4cKFKrOOA2xLPBkCAJxHMQQAOI9iCABwHsUQAOC8DtNAYx3N\n9MQTT6iQp/FyAAAC2klEQVTsyJEjKps2bZrKbrzxRpW19Q4GSM063mfr1q2e60Qioe6xGia+/vWv\nq8w6CgZtx2peq6mpUVlzc7PKXnrpJZXV19erzL9zFKLn/5sVsf9uKysrVTZ69GiV3X333aGMqzV4\nMgQAOI9iCABwHsUQAOC8SBZYgq7r/OIXv1CZtbv5I488ojLrRAO0r6AnCdx2222ea+s0kQcffFBl\n1toD2ldZWZnKamtrVTZp0iSVnThxQmXWSTXt/eFreBUXF6vs1VdfVZl1skyfPn1UZr1f9+zZs4Wj\nCw9PhgAA51EMAQDOoxgCAJxHMQQAOC+SBhqrQWL58uUqa2pqUtmdd96pspycHJXxAfvoWRspWA4f\nPuy5njx5srrHyoI26KDtfPTRRyqzmit+/vOfq8zfOCUismDBgnAGhtBYp0wcOHBAZdb7tXWKzJgx\nY8IZWMh4NwEAOI9iCABwHsUQAOA8iiEAwHmRNNAcP35cZYcOHVKZdSrBwoULVZaRkRHOwNDmjh07\nprKHH37Yc52fn6/u6d27d5uNCS03aNAglVnNFeXl5SqbOXOmytLT08MZGEIzfPhwlU2fPl1l1vvw\nXXfdFei+joAnQwCA8yiGAADnUQwBAM4LumaYIWLvXt4S1qnXFRUVKrN2v9+zZ4/KevTooTLrg/1h\nOOtn0DH/4bvlQp1jEXttwJpT/2nYWVlZ6h7rw91tub4U93kuKSkJ5cVKS0tV1tDQoLJTp06pzFpH\ntOa5rTbQOOtnEMs5LioqCuXF6urqVHbw4EGV1dTUqCyRSKhs27ZtKmurOT7rZ5ByjtOSyWTKF0xL\nS5snImtaN6zYmZ9MJtdGPYiwMMfnxDzHH3McfynnOGgxzBaRmSKSEBG9545bMkRkuIi8kUwmP4t4\nLKFhjhXmOf6Y4/gLPMeBiiEAAHFGAw0AwHkUQwCA8yiGAADnUQwBAM6jGAIAnEcxBAA4j2IIAHDe\n/wB1n632HVyUlQAAAABJRU5ErkJggg==\n", 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3wbAaAALoHAEggM4RAALoHAEggM4RAALoHAEggM4RAAISWpgYiUSi/tGIuaK+vl5aWlrSt/nbx1RSUhL11+XmisOHD0tbW1uXvbe5/NyKiLz++ustXXkSeC7f33j9QkKdY2VlpXPmSy6xC9m7ovLyclm/fn22m5GUJUuWZLsJceXycysiUlxc3KVXn1RWVsrLL7+c7WYkxd8Ix2JYDQABdI4AEEDnCAABdI4AEEDnCAABGTkmAR+PPdq2ra0tZq5///5OrqSkRGP/aFFcZbfq9++t3YLf3ksRkT59+qS3YXmiqalJ45aWFidnt0u0RwyLiJSWlmqczqMQ4uHNEQAC6BwBICBl76vvvfeexo2NjU5uxIgRGk+aNClVf6WyJ+tdunQp5T8/24qKijT2Txu0Q5W9e/c6OTukKSjo3AIW+3eJiAwbNixmLh9s2LBBY//+2aFzJBJxcv369dPY3tsePdz3DTsknD59upMbO3asxleuXEmk2TljzZo1Gu/evdvJ2T7D3k8R91krLCzU2H+Obblo/vz5Tm7atGkajxkzJpFmiwhvjgAQROcIAAF0jgAQkLKaY01Njca/+MUvnJydInHmzBknZ2tmtp4wcOBA57qJEydq/KUvfcnJffrTn9Z46NChiTQ7J9h7e+rUKSd38803a7xw4cKU/91HjhzR+OLFiyn/+dlmp5P49UL/GbRsPddOp/Jrhw0NDRoXFxc7OftMt7e3d7LFucVOyfGnmg0aNEhj/xTUd955R2N7T0+cOOFcZ79v8O9vWVmZxtQcASBF6BwBICBlw+rPfe5zGt91111Obv/+/Rrv27fPyb355psa21dk+8otItK7d2+N/VdwO3TJx2H1pk2bNH766aednB1m+NNNbMnCTnEaPXq0c91XvvIVjZctW+bk8nFqlFVVVaWxnXImInL99ddr7Jcz7BSSc+fOaWynroi4U3kWLFjg5Do7vSqXzZs3T2N/Q1w7TcyWKUTcqT32/v7hD39wrjt48KDGAwYMcHJTpkxJosUdeHMEgAA6RwAIoHMEgICU1RxtbcqvU9klb/7yt0WLFmls64p79uxxrrPTSI4ePerk7Ff2+ejb3/52MBYRuXz5ssbNzc1Ozn629dwbb7zRuW7cuHEa+zvT2KlX+bgTzcyZM2Pm7BQSf5qIrRe+8sorGq9du9a5ztbB/J197BSgfGXrijYWcb878OuR9t+7PVvJ1hhF3Oe6urraydl7739P0Rm8OQJAAJ0jAAR0qc1uW1tbNfZXDNiv8/3hib9RZnfSs2dPjf3ygp3WNHjw4GAs4g6/6+vrnZx/bXdiV8z07dvXydnncfXq1Rrb34eIyBe/+MWYPz9fd+LpLFua8DdjtiUHW7bwN76109fuvvtuJ5fMUNrizREAAugcASAg68Nq+w21Hcb4K2Tst6gjR450cpyPclW8YYTdPPTkyZNOzg5h7MaiItk7v6Or8e/tSy+9pLFd9eWXIR5++GGN7Xk1cPklhnXr1mlsN17x2RVdqZ5NwZsjAATQOQJAAJ0jAARkvaBkD+N6//33NT5+/Lhzna1B5vuKmGT5K5OGDx+ucbxdjey0KXsdOrz11lvO5yeeeELjCxcuaHz//fdnrE35ZMeOHc7njRs3anz69GmN7aFkIiKLFy9OW5t4cwSAADpHAAjI+LDaX0Fgh9J2c1b/vBJ/I1JcZYfI/uapdrMEO32nrq7Ouc5OhWJaVAc79WbVqlVOzp6tc+2112rsr9Jg+k5sdnre5s2bnVxtba3G9jl+8MEHnevSuWEwb44AEEDnCAABdI4AEJCRmqOtM9rzaEXcpYDxzrHtzjvvxGPvk382r90I19bI/Ck//kauuGrnzp0a/+53v3NydjeYxx9/XGN7zxHfli1bNP75z3/u5Gwt0R4AN3v2bOe6dN5v3hwBIIDOEQACMjKstmf5PvPMM07OrshYuHChxnfeeWf6G5YH4m1Ga8sZn/jEJzS20yREusf5ycmwqzbs5rYi7pnW9hxwpu50np3G5z+Ddpeeb3zjGxpnsmzBmyMABNA5AkAAnSMABGSk5miX/k2ZMsXJNTU1aWynovjLDBH25JNPavyPf/zDyZWXl2tsl7XZepkIu33HYnc1snUvkQ8vY0PibB185cqVTm7ixIka23PWM3nWN2+OABBA5wgAAQWJnO1aUFBwTETqPvLCrqkqGo122WU23Nv0yfF7K8L9TaeY9zahzhEAuguG1QAQQOcIAAF0jgAQQOcIAAF0jgAQQOcIAAF0jgAQkNCi2kgkEq2urk5TU9KrtrZWWlpauuzGhdzb9CkpKYnadea5pqampqUrTwKPRCLRqqqqbDcjKXV1dTGf3YQ6x+rqannttddS06oMmzZtWrabEBf3Nn3Ky8vl+eefz3YzkjZq1KguvfqkqqpKtm/fnu1mJGXmzJkxcwyrASCAzhEAAugcASCAzhEAAlK2BfSpU6c0bm9vd3L9+vXTuH///k6uRw/654/S3NyssX9v7emDAwcOzFib8tHbb7/tfN6wYYPG/imPt956q8a5/E14up05c0Zj/9+6/ezvRm9PAsjW6Zj0TAAQQOcIAAEpG1a//vrrGvtzyuywOhKJOLmysjKN7au0/wpuX63Hjh3r5CZNmpREi3PHnj17NP7Pf/7j5N59912N/WH1ddddp3FhYaHG/uFldsPjGTNmOLlx48Zp3KdPn0SanRPsfbH3S0TkoYce0tgeQC8i8uyzz2psnz+/7HH27FmNN2/e7OTsYXNr165NpNk541//+pfGv/nNb5zc8ePHNZ4wYYKTs4dvFRUVaRxviO0fHGcP5iopKelkizvw5ggAAXSOABBA5wgAASmrOdbX12vc0tLi5GydoK2tzcnV1NRobA/sPnHihHNdU1OTxkuXLnVydtH7gAEDEml2TigtLdX45MmTTu7gwYMa+/UYW4+8dOlS8L+LuL+7L3/5y07unnvu0XjhwoWJNDsnHDhwQOO5c+c6uf3796f077L1dRG3Tp+vWltbNfaf3YaGBo1ra2ud3KZNmzS2NXI7ZVBE5Pz58xpPnjzZyf3yl7/UeNasWQm0+ireHAEggM4RAAJSNqy221YVFxc7OftqbVd7iLhTduwr8n//+1/nOjt8vHjxopPLxykm1tChQzVetmyZk5s/f77Gx44dc3JHjhzR2JYs/Gkpw4cP19hOPRH58FAo31y+fFnjnTt3Ojk7xclfwWGHyHao508HsmyJQsQtZ+Sre++9V+P77rvPye3evVtjf1hdV9exS5v9Pbz66qvOdVu2bNHYL8V93GeXN0cACKBzBIAAOkcACEhZzdEu97GxiFszsDUeEbfOaKeY/PWvf3WuszUef5lQvtcc7a4v/g4wdglavHu7a9cuje2yLRGRK1euaDxixAgnN378+CRanDtGjRoVM2fvy4ULF5xcvN1mYl3nL9tcsmRJp9uZq+y/ff8eTpw4MRj7bC1448aNTs7WeL/whS84uWHDhiXWWA9vjgAQQOcIAAEpG1bHY1du+FMi7NBl1apVwT8jIrJgwQKNP/nJT6a6iTkr3r21U57syYb+vbVTr/xhe74Pq+Oxw+W+ffvGvM7fCNf69a9/rbFfsvBXdKCDLQnZYbX/jNvVcSNHjnRydkepZPDmCAABdI4AEJCRYXU8b7zxhsZbt27V2D9r5vbbb9fYruhAB/vNoIjIv//9b43tqhh/U1y7Wce8efOcXL7PBEhWr169gv+9sbHR+WxLG/434/5Ksu7Mf3btphz2ObYbZ4u4z6tfEvq4m9Dw5ggAAXSOABBA5wgAARmvOdpNa0VEfvzjH2tsN8n1d5+ZOnVqWtuVD/xNbLdt26ax3RnJn3pSXV2t8ejRo9PTuDwzaNAgjW39cf369c51tnburxxDB7vxrYjIn//852DOrggTcadHxdsRKRm8OQJAAJ0jAARkZFhtZ7uvXr3aydlzOuxC8dtuu825LplzZ7sDuzntiy++6OTs5p920wN/mtTs2bM1jncucHdmh9Ei7lnK69at07h3797OdXZDBX91R3dnN2C203VE3BKRXal06623OtfZaX2pnnbGmyMABNA5AkAAnSMABGSkCGLPBn700UfdBpg6zHe+8x2N421+iQ72kKLnnnvOydmDn+xBXP4SwcrKyjS1LrfZZ9OvOdo6+ooVKzT+6U9/6lw3duzYNLUu99kz6x977DEnZ+/v8uXLNfZ33knnrlG8OQJAAJ0jAARkZFhtz5q1q2BE3NdiO/u9sLAw/Q3LA/Z+trW1OTm7+44dpvirDBBm75nPThuxm9baM8ZF4p8v090dOnRI4/b2didnz5uxq7v8MkU6p0fxmwOAADpHAAigcwSAgIwfsLV06VInZ5eu2d1h0DkVFRUa++cgNzc3azx37lyN/d2UEWZrYnZalIhbt125cqXG9nx1xDdkyBCNv/vd7zo5+4zaQ7Qy+V0Eb44AEEDnCAABBf7BNnEvLig4JiJ16WtOWlVFo9EhH31ZdnBv0yfH760I9zedYt7bhDpHAOguGFYDQACdIwAE0DkCQACdIwAE0DkCQACdIwAE0DkCQACdIwAE0DkCQMD/Ac1XaUVQDdoeAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, @@ -2212,17 +2077,16 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 60, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ { "data": { - "image/png": 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73whTan6GDOK/cudldxffHAkhJACdIyGEBKBzJISQAHHLOWJpQ7169YwNc2a+mSp+ssf8I86zFrF5TN+U1O+KKU5gTtDvxli3bp3q5s2bq8byChGRBg0aqK5Tp46xYd4SBx2lCjhH/eDBg8aGXaL8DhacxY47u3xnn/3790e9Pj63PpcWLRefbGAO1nfMwvw2PqsittktPuN+7j3mDrt3725seL99CVy0nKa59nXPIISQYgidIyGEBIjbuzu+pvbr18/YcG7MgQMHjG3mzJmqsfmnD6uxJALPExHp2rVr/hecIvzqV79SjY1VRUQmTpyoeuTIkap9yLZhw4aottatW6v2JVSpAO4Iev/9940NGyxjiC1i7yfq0aNHm/MwzMY5PiK29CpVwmgPpmk6duxobLijxc+zb9iwoept27apfuedd8x5GKo/9dRTxoalQ3kJoz18cySEkAB0joQQEoDOkRBCAsQt0YGlCPgZXsTma1577TVjW7FihWr87I/b4kRErl69qhrnBIvElk9IVvA+iIgMGzZM9fz5840NG4FiXtF3RsKtcL7cAnM/qQhu/cMhZCIib7/9tmq/ve0f//iHapxpjZ12PD7fHsss5WSjZs2aqgcOHGhsCxYsUO23Xc6ePVs1+hZfJvjII4+o9h2QCuoX+OZICCEB6BwJISRAQuoHfDNaDDVwZowHP703adLE2PD1vEePHgVdYtLiw2rsNvL3v//d2LDTC4YtOFtGRGTVqlWq/Q4Z/1umMvfff7853rJli+pJkyYZ29ixY1X37NlTtQ+V27RpE/X6lSpVin2xSYjfyYZhsJ87hY1xsRuUT9nhTCrf9aegzy7fHAkhJACdIyGEBKBzJISQADdkzxLO+L399tuNDXMLp0+fVu1LSnCbV6rM9I0HmHP0HdhxABZqPxO8UaNGqrHjenHDl34888wzqn2+cNq0aaqx+8uAAQPMeYMHD1bty4GKG36LJA7V8iVQ2M0In2s/AA7nhMd7eyvfHAkhJACdIyGEBEjDRpLXPTkt7biI7E3cchJKdiQSqVLYi4gG723iSPJ7K8L7m0ii3tt8OUdCCCkuMKwmhJAAdI6EEBKAzpEQQgLQORJCSAA6R0IICUDnSAghAegcCSEkQL72VmdkZET8WNRkYd++fZKTk5N2/TMLh/T09EhmZmZhLyMmDh48KCdPniyy9zaZn1sRkTVr1uQU5SLwjIyMCPZPSCb27NkT1S/kyzlmZWXJf//73/is6gbjZwYXNTIzM783kzdZuO+++wp7CbmSlZUln332WWEvI2bKli1bpHef1K5dW1auXFnYy4iJVq1aRbUxrCaEkAA3pGUZuTH4VmSnTp1SjZPvzp07Z87DERTYAkrk+22miiu53dszZ85E/XOYKinO7eCuh58+eOjQIdU47uPy5cvmPAznfTsznFoYC3xzJISQAHSOhBASgM6REEIC3PCEUl5bmftWajie0XP+/PkCrSmZwVGgfjTlTTd99/8+/KLsczGrV69Wjfkdke/ncYoT2Kp/7ty5xoajETDniPdcRGTo0KGqO3XqZGw44qI4guNY582bZ2zz589XvXPnTtV+RMqQIUNU49gFEZFu3boVaH18cySEkAB0joQQEuCGhNUYSs+aNcvYZs6cqfrDDz9U7UNEnAQ3ceLEeC8xafAlJbfddpvqxo0bx3TNli1bqvZlPocPH1adW2ojFTh79qw5fuWVV1T757ZcuXKqK1WqpBrLokRELly4oNqX8ly8eDH2xSYhR48eNcdvvPGGavQDIva3yMnJUV2lit0ohCF3ly5djA1Tc7E8u3xzJISQAHSOhBASgM6REEIC3JCc47Jly1SfPn3a2G6//XbVvXv3Vv3ll1+a89asWaN6xYoVxoabxzHHk4qULFnSHNepUyfquS+88ILqJ554QnXVqlXNeZgLwlyaiJiGAj6flmr4pirTp0+Peu7AgQNVP/jgg6pffvllc96+fftU+9IdLEvx2+dSkYULF5pjzDPidkwRkXr16qmeNGmS6vfff9+ch/lIf38XLVqkunv37vleL98cCSEkAJ0jIYQESEhY7XdZHDlyRLXfIdO/f3/VDRo0UL1p0yZz3kcffaT6f//7n7FhaJmKnU+wJCE9PT3qeVevXjXHWGLSq1cv1bt27TLn/e53v1M9YcIEY/vhD3+oGn9HkdQo7cEdRr6c5Ouvv1bdrl07Y3vxxRdV4w4t/3y/++67qhs1amRs99xzj+pUDauxJM+nw7CUKSMjw9jGjx+vul+/fqqxW4+IyKeffhr1+hhK52fH3bfwzZEQQgLQORJCSAA6R0IICRC3nCPmZ7BjiYjN6/hYH3OEWGJSpkwZc1758uVV+84cqQ7eM1/Kg/i8SosWLYIa82Ai389VIlja43OOqcCxY8dUr1+/3tgwf/jqq69GvQb+Pq1btzY27ObzwQcfGBvmHFMVfGb8/a1QoYLqrl27Ght22MEuUlg2JSIye/Zs1cuXLzc2/J6B/knEbruNBt8cCSEkAJ0jIYQEiFtYjd1cfPNZ7KThP9lHG+CEobiILQnw3VPwFdnPzy3okJ2iht9hhGkJ3I0hYsNlDGEGDRpkzsutjARDxlQo3fHs2bNHtb+3OGzMdzzyXaO+xc/HxnAOh5wVF06ePKnad+VBX4ClZiLRw95SpUpFPfZ+B32ITznlBb45EkJIADpHQggJELewGjeA+6YR2FwCN5SLiFSsWDF4PT+LA1+LfUiDX8GqV69ubP6rd7LjvyxjWqJy5crGFi308w1zc/tanergPfL3BZsAe6LNf/GhOT63/vrFAby/fuY0hsQ+HRatUS1WF4jk/tW5oBUufHMkhJAAdI6EEBKAzpEQQgLELeeIsb/vynP8+HHVvmsJdjvB3JfvPoNlFbnt1IhWGpTMrFu3TrXP544bN061z99imQreF7/LBvO7OFDLXwPLgULXSUbwefTPXG55KrxnWDKyatUqcx7m29u3bx/zOpMVzCX6/H9u5X/4zGMZ1Y4dO6Je339vwLKqWEr6+OZICCEB6BwJISRA3GJQfL3FprUidg4JNqcUEenWrVuero+hud9Enp2drToVm1Jg+IqzdEREnn/+edVPPvmksWG4h7tnfMMDDBH9DJ7SpUsH15Eq4HPbt29fY8OdQzgHScTu7li7dq1q/3zj8/joo48aW3GYW43lZb5pxCeffKLaP9fYqBlTFdjIQ8SmRbC5ikjBy/j45kgIIQHoHAkhJACdIyGEBEhI3Qs2qhSxpTdLly41Nhw6VL9+fdV+S1vTpk1V+5KIzMzM2BebBGApA94jEXs/fa733nvvzdP1cWbw/v37jS3aNrlUAcs/HnjgAWPDPKO/L1h6snnzZtV+C2fnzp1V+1KrVJ+xLmJL/MaMGWNs6Bd8xyLMx2L5js97Y84RB3HFA745EkJIADpHQggJkJCw2lejP/zww6r9LgQMT1avXq3avyK3bdtWdZMmTYzNN8BMZe6//35zjLN6f/3rXxvbqFGjVONv4sud7rvvPtUDBw40NtyZlOr4LjyY2vENlqPNPho6dKg5D49TdTZ1XsF5RCIikyZNUu1n0WM3n7p166r2DZf79Omj2u++Kyh8cySEkAB0joQQEoDOkRBCAtyQFjbY1deXl+S13IT8Hz5fOGXKFNVffPGFsb300kuqcctl7969zXk//vGPVdeoUSMu60xGfK4c89zY6V7E5spHjBgR9ZrFoVwnr/j7iznIDh06GBuW8qD/wPyuyPcnBsQTvjkSQkgAOkdCCAmQlp95rmlpacdFZG/ilpNQsiORSJXrn1Y48N4mjiS/tyK8v4kk6r3Nl3MkhJDiAsNqQggJQOdICCEB6BwJISQAnSMhhASgcySEkAB0joQQEoDOkRBCAuRrb3VGRkYER1kmE3v27JGcnJy0659ZOPDeJo7KlStHkrkv5bp163KKchF4qj67+XKOtWvXNjOok4lWrVoV9hJyhfc2cdSqVUs+/vjjwl5GzFStWrVI7z5J1Wf3hnTlQfzgrJMnT6rG7r/YaVnEDvn2Q3Z8d+DiypUrV8zxsWPHVGOXEz8EyndnJ/kDHQMO5RIRycjIUO0Hw2VlZSV2YUnEtWvXzDF2kcIhb/7vPh77rj8FhTlHQggJQOdICCEB6BwJISTADck5Hj9+XLXvVr1o0SLVu3fvVu0nCt5zzz2qhw8fbmw+P1mc2Ldvn+p169YZ2/bt24M27GItInL33Xer/slPfmJsmOstzviO01u2bFGNg+sxh+7Pe/vtt43trbfeiucSkw68V5s3bza2N998UzU+u74TPk4S6N+/v7EV9As63xwJISQAnSMhhARISFjtw7aZM2eqfu6556L+Ofwsv3//fmPDkHvAgAHGhiF4IgfuFAVOnz5tjpcsWaJ6wYIFxrZnzx7Vhw8fVr1161ZzHpaiVK9e3dgGDRoU81qTnfLly6v24Vz9+vVV43PbtWtXc960adOCWkRk27Ztqu+8884CrTUZwHIyEZtSmzx5srHhc3706NHgPxcR2bFjh+oKFSoYG8NqQghJAHSOhBASgM6REEICJCTnuGbNGnOMecYjR44YW82aNVX36NFD9ezZs815mMfEz/wiIiNHjlSNg8JTkQ0bNpjjDz/8UDWWTInYEqfc9pCeOHFCtS836dy5s2q/7TDV8OVjmGf029uwRKdMmTKqz507Z8775S9/qXrs2LHGNm7cONXvvfdeDCtOLvyzi98i/Bbg7Oxs1aNHj1Y9b948cx7mIP3++W7duqlGP5NX+OZICCEB6BwJISRA3MJqDDt82IuhtP/cPmXKFNVt2rRR7Xe9zJkzR/X8+fON7ZFHHolhxckDdtvZuHGjsWFYcfPN9uds1KiR6qFDh6o+ePCgOe+Pf/yj6jNnzhjb559/rtrvQEg1/P1D1q5da46xvATLcHxqA8PDf/3rX8Y2depU1b5bVW5rSSbwvwtTESIiFy5cUO13Yg0ePFg1hsetW7c25z399NOq/e4kLG0bNWpU3hf9/+GbIyGEBKBzJISQAHSOhBASIG6JDcxVLV++3NiwJAJzXyIiXbp0UV2tWjXVvvMO5hwPHDhgbIcOHVJ9xx135GfZSQGWh2CnHRGRsmXLRv1zHTp0UN2iRQvVPu+LOTNfsoK5Nd9p3HdlTnZy23qKW/1EbE4cn1u/dfbs2bOq/bOP2wn9M52sM1k8+N+/c+dOY8Ptmb5MDJ9J3NLqn3fMR2JJmojN/2I3cZG8bTPmmyMhhASgcySEkABxC6sxtMUGrCJ218qwYcOMDUMSpEoVO4kSG4riq7qIDWUikYixpcLwLQyrc3JyjA1TFnfddZexNW/eXDWGEbijQ8SWjfiwGu81Dj0SKV5htd/B8tRTT6nG++fDvtyuiX/Od6xJFfDZxdIdERsuN2jQwNjQL5QuXTp4PRGRS5cuqfblUDiwD88Tsf4kGnxzJISQAHSOhBASIG5hNYZcPkSoUaOGaj+rF8NgDIF9CIf4sBBDwVQIoz2XL18OahF7r/Hrn0j0ucj+iyo2XPA7k3yYnWrg/fOpHMTPo/Yh3Lf4nS3+KynSsmVL1blVHSQzWOHgQ1s89s9ZtK/1GCqL2JDbP9f4LEf7vXKDb46EEBKAzpEQQgLQORJCSIC45RxLlCih2pfnYB7G526wCSVew3dBwV0dP/jBD4wtMzMzhhUnD5hL9AOwcFiW73qCHXvwGv68qlWrBs/zNj9kKhXAnOP69euNrVmzZqpxQJmIHQj1pz/9SbUvJcN8ri8Hwuc4WklbsoPPEz5LInZo3q5du4wNfxcsBfS/A95f/LYhYhs8o2/JK3xzJISQAHSOhBASIG5hNX56f/DBB41t8eLFQS0i0rhxY9X4Kd43Bq1YsaLqXr16GVtGRkb+F5xEYGgycOBAY5s+fbpqv7EfwzhsyOF3MGFo4mfwYOjnZ6ykApgq8GVSGCI///zzxoah9F/+8hfVnTp1MudhaO7LVdLT04PrSCUwlO7Tp4+x4bPrmyy/8847qjt27Kjaz5bCUqHu3bsbG4bZWPKTV/jmSAghAegcCSEkAJ0jIYQEiFvOEQfk+Kae2I3D5xawMS4OzvJbgbDjTPv27Y0tls/0yQR2EMEcloAaZRYAAAHDSURBVIid2T1r1ixjw+FYOOQMG9iK2G1W9erVM7ZUbB6MYI7Vb3vFYWZ+5nTTpk1V79ixQzUO1BKxeUW/PbFWrVoxrDh58bPThwwZotrPS8dvEziP2udtsSmu//ZQ0PIovjkSQkgAOkdCCAmQkOG4+KorYkO/hQsXGhvOmsU5J760oV+/fqqLWziCYJgmItKuXTvVfvfMihUrVJ86dUq173iCvxfOuhZJ3RKTED7sxZkkfoYMzk/GWT2+uS0+3z7k9h2QUh2f/howYIBqn77BFBF21OnZs6c5D38z33zZ/13JL3xzJISQAHSOhBASgM6REEICJCTn6PMuTZo0Ue27U2NpD5b8+PwPbh/My8zZVMV3Osd5v35oUN26dVVjCYQ/D/OKqTY0Kz/45wpLQ3xp2cGDB1VjGZsHtyAWtxxjfsBtxCIiEydOVI3bOn25VW7DzQpK8fUyhBCSC3SOhBASIM0358z15LS04yKyN3HLSSjZkUgk+gSlQob3NnEk+b0V4f1NJFHvbb6cIyGEFBcYVhNCSAA6R0IICUDnSAghAegcCSEkAJ0jIYQEoHMkhJAAdI6EEBKAzpEQQgLQORJCSID/B1hPZ+7tOyL8AAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, @@ -2260,17 +2124,16 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 61, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ { "data": { - "image/png": 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v6B/9c718t9yrkKSSBQuUGDbMnfXkk2ahB/debeZM33abfUTf/rYzlFq9WsXT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\nOAEACPD/AOULKX5U7BsBAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, @@ -2290,16 +2153,14 @@ }, { "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": false - }, + "execution_count": 62, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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Nmg9JIyVlm+FjpM/r+64f1O97WTvq18xrR/2on+uRt/fFRcrLy+so6QxJGUkRv/ViI19S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\nAALQOAEACOC1AcK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\n", 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" ] }, "metadata": {}, @@ -2323,17 +2184,16 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 63, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ { "data": { - "image/png": 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UYxPxiRMAAAcMnAAAOGDgBADAAQMnAAAOGDgBAHDgNKu2oaEhMNNT6zAiIvLl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\nR3VcQRMvAAAAAElFTkSuQmCC\n", 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\n", 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" ] }, "metadata": {}, @@ -2353,17 +2213,16 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 64, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ { "data": { - "image/png": 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/fr0xblsk3rSovIj+HzoRCYxnMaz1Y3IQAAAOGDgBAHDAwAkAgIN4/8YpIqeW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\nrkJggg==\n", 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\n", 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" ] }, "metadata": {}, @@ -2399,10 +2258,8 @@ }, { "cell_type": "code", - "execution_count": 67, - "metadata": { - "collapsed": false - }, + "execution_count": 65, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -2467,9 +2324,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2481,9 +2338,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/03B_Layers_API.ipynb b/03B_Layers_API.ipynb new file mode 100644 index 0000000..58d8fbc --- /dev/null +++ b/03B_Layers_API.ipynb @@ -0,0 +1,2197 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #03-B\n", + "# Layers API\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**The Layers API was intended to be a basic builder API for creating Neural Networks in TensorFlow, but the Layers API was never fully completed. Although it still works in TensorFlow v. 1.9, it seems quite possible that it may be deprecated in the future. It is recommended that you use the more complete _Keras API_ instead, see Tutorial #03-C.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "It is important to use a builder API when constructing Neural Networks in TensorFlow because it makes it easier to implement and modify the source-code. This also lowers the risk of bugs.\n", + "\n", + "Many of the other tutorials used the TensorFlow builder API called PrettyTensor for easy construction of Neural Networks. But there are several other builder APIs available for TensorFlow. PrettyTensor was used in these tutorials, because at the time in mid-2016, PrettyTensor was the most complete and polished builder API available for TensorFlow. But PrettyTensor is only developed by a single person working at Google and although it has some unique and elegant features, it is possible that it may become deprecated in the future.\n", + "\n", + "This tutorial is about a small builder API that has recently been added to TensorFlow version 1.1. It is simply called *Layers* or the *Layers API* or by its Python name `tf.layers`. This builder API is automatically installed as part of TensorFlow, so you no longer have to install a separate Python package as was needed with PrettyTensor.\n", + "\n", + "This tutorial is very similar to Tutorial #03 on PrettyTensor and shows how to implement the same Convolutional Neural Network using the Layers API. It is recommended that you are familiar with Tutorial #02 on Convolutional Neural Networks." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flowchart" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. See Tutorial #02 for a more detailed description of convolution." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Flowchart](images/02_network_flowchart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The input image is processed in the first convolutional layer using the filter-weights. This results in 16 new images, one for each filter in the convolutional layer. The images are also down-sampled using max-pooling so the image resolution is decreased from 28x28 to 14x14.\n", + "\n", + "These 16 smaller images are then processed in the second convolutional layer. We need filter-weights for each of these 16 channels, and we need filter-weights for each output channel of this layer. There are 36 output channels so there are a total of 16 x 36 = 576 filters in the second convolutional layer. The resulting images are also down-sampled using max-pooling to 7x7 pixels.\n", + "\n", + "The output of the second convolutional layer is 36 images of 7x7 pixels each. These are then flattened to a single vector of length 7 x 7 x 36 = 1764, which is used as the input to a fully-connected layer with 128 neurons (or elements). This feeds into another fully-connected layer with 10 neurons, one for each of the classes, which is used to determine the class of the image, that is, which number is depicted in the image.\n", + "\n", + "The convolutional filters are initially chosen at random, so the classification is done randomly. The error between the predicted and true class of the input image is measured as the so-called cross-entropy. The optimizer then automatically propagates this error back through the Convolutional Network using the chain-rule of differentiation and updates the filter-weights so as to improve the classification error. This is done iteratively thousands of times until the classification error is sufficiently low.\n", + "\n", + "These particular filter-weights and intermediate images are the results of one optimization run and may look different if you re-run this Notebook.\n", + "\n", + "Note that the computation in TensorFlow is actually done on a batch of images instead of a single image, which makes the computation more efficient. This means the flowchart actually has one more data-dimension when implemented in TensorFlow." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "from sklearn.metrics import confusion_matrix\n", + "import math" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.1.0'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting data/MNIST/train-images-idx3-ubyte.gz\n", + "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n", + "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n", + "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from tensorflow.examples.tutorials.mnist import input_data\n", + "data = input_data.read_data_sets('data/MNIST/', one_hot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Size of:\n", + "- Training-set:\t\t55000\n", + "- Test-set:\t\t10000\n", + "- Validation-set:\t5000\n" + ] + } + ], + "source": [ + "print(\"Size of:\")\n", + "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n", + "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n", + "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data.test.cls = np.argmax(data.test.labels, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Dimensions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# We know that MNIST images are 28 pixels in each dimension.\n", + "img_size = 28\n", + "\n", + "# Images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = img_size * img_size\n", + "\n", + "# Tuple with height and width of images used to reshape arrays.\n", + "img_shape = (img_size, img_size)\n", + "\n", + "# Number of colour channels for the images: 1 channel for gray-scale.\n", + "num_channels = 1\n", + "\n", + "# Number of classes, one class for each of 10 digits.\n", + "num_classes = 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for plotting images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_images(images, cls_true, cls_pred=None):\n", + " assert len(images) == len(cls_true) == 9\n", + " \n", + " # Create figure with 3x3 sub-plots.\n", + " fig, axes = plt.subplots(3, 3)\n", + " fig.subplots_adjust(hspace=0.3, wspace=0.3)\n", + "\n", + " for i, ax in enumerate(axes.flat):\n", + " # Plot image.\n", + " ax.imshow(images[i].reshape(img_shape), cmap='binary')\n", + "\n", + " # Show true and predicted classes.\n", + " if cls_pred is None:\n", + " xlabel = \"True: {0}\".format(cls_true[i])\n", + " else:\n", + " xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n", + "\n", + " # Show the classes as the label on the x-axis.\n", + " ax.set_xlabel(xlabel)\n", + " \n", + " # Remove ticks from the plot.\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " \n", + " # Ensure the plot is shown correctly with multiple plots\n", + " # in a single Notebook cell.\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot a few images to see if data is correct" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the first images from the test-set.\n", + "images = data.test.images[0:9]\n", + "\n", + "# Get the true classes for those images.\n", + "cls_true = data.test.cls[0:9]\n", + "\n", + "# Plot the images and labels using our helper-function above.\n", + "plot_images(images=images, cls_true=cls_true)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TensorFlow Graph\n", + "\n", + "The entire purpose of TensorFlow is to have a so-called computational graph that can be executed much more efficiently than if the same calculations were to be performed directly in Python. TensorFlow can be more efficient than NumPy because TensorFlow knows the entire computation graph that must be executed, while NumPy only knows the computation of a single mathematical operation at a time.\n", + "\n", + "TensorFlow can also automatically calculate the gradients that are needed to optimize the variables of the graph so as to make the model perform better. This is because the graph is a combination of simple mathematical expressions so the gradient of the entire graph can be calculated using the chain-rule for derivatives.\n", + "\n", + "TensorFlow can also take advantage of multi-core CPUs as well as GPUs - and Google has even built special chips just for TensorFlow which are called TPUs (Tensor Processing Units) and are even faster than GPUs.\n", + "\n", + "A TensorFlow graph consists of the following parts which will be detailed below:\n", + "\n", + "* Placeholder variables used for inputting data to the graph.\n", + "* Variables that are going to be optimized so as to make the convolutional network perform better.\n", + "* The mathematical formulas for the convolutional neural network.\n", + "* A so-called cost-measure or loss-function that can be used to guide the optimization of the variables.\n", + "* An optimization method which updates the variables.\n", + "\n", + "In addition, the TensorFlow graph may also contain various debugging statements e.g. for logging data to be displayed using TensorBoard, which is not covered in this tutorial." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Placeholder variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph. We call this feeding the placeholder variables and it is demonstrated further below.\n", + "\n", + "First we define the placeholder variable for the input images. This allows us to change the images that are input to the TensorFlow graph. This is a so-called tensor, which just means that it is a multi-dimensional array. The data-type is set to `float32` and the shape is set to `[None, img_size_flat]`, where `None` means that the tensor may hold an arbitrary number of images with each image being a vector of length `img_size_flat`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The convolutional layers expect `x` to be encoded as a 4-dim tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y_true_cls = tf.argmax(y_true, dimension=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PrettyTensor Implementation\n", + "\n", + "This section shows the implementation of a Convolutional Neural Network using PrettyTensor taken from Tutorial #03 so it can be compared to the implementation using the Layers API below. This code has been enclosed in an `if False:` block so it does not run here.\n", + "\n", + "The basic idea is to wrap the input tensor `x_image` in a PrettyTensor object which has helper-functions for adding new computational layers so as to create an entire Convolutional Neural Network. This is a fairly simple and elegant syntax." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "if False:\n", + " x_pretty = pt.wrap(x_image)\n", + "\n", + " with pt.defaults_scope(activation_fn=tf.nn.relu):\n", + " y_pred, loss = x_pretty.\\\n", + " conv2d(kernel=5, depth=16, name='layer_conv1').\\\n", + " max_pool(kernel=2, stride=2).\\\n", + " conv2d(kernel=5, depth=36, name='layer_conv2').\\\n", + " max_pool(kernel=2, stride=2).\\\n", + " flatten().\\\n", + " fully_connected(size=128, name='layer_fc1').\\\n", + " softmax_classifier(num_classes=num_classes, labels=y_true)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Layers Implementation\n", + "\n", + "We now implement the same Convolutional Neural Network using the Layers API that is included in TensorFlow version 1.1. This requires more code than PrettyTensor, although a lot of the following are just comments.\n", + "\n", + "We use the `net`-variable to refer to the last layer while building the Neural Network. This makes it easy to add or remove layers in the code if you want to experiment. First we set the `net`-variable to the reshaped input image." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "net = x_image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The input image is then input to the first convolutional layer, which has 16 filters each of size 5x5 pixels. The activation-function is the Rectified Linear Unit (ReLU) described in more detail in Tutorial #02." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "net = tf.layers.conv2d(inputs=net, name='layer_conv1', padding='same',\n", + " filters=16, kernel_size=5, activation=tf.nn.relu)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the advantages of constructing neural networks in this fashion, is that we can now easily pull out a reference to a layer. This was more complicated in PrettyTensor.\n", + "\n", + "Further below we want to plot the output of the first convolutional layer, so we create another variable for holding a reference to that layer." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "layer_conv1 = net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now do the max-pooling on the output of the convolutional layer. This was also described in more detail in Tutorial #02." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now add the second convolutional layer which has 36 filters each with 5x5 pixels, and a ReLU activation function again." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "net = tf.layers.conv2d(inputs=net, name='layer_conv2', padding='same',\n", + " filters=36, kernel_size=5, activation=tf.nn.relu)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also want to plot the output of this convolutional layer, so we keep a reference for later use." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "layer_conv2 = net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output of the second convolutional layer is also max-pooled for down-sampling the images." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tensors that are being output by this max-pooling are 4-rank, as can be seen from this:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we want to add fully-connected layers to the Neural Network, but these require 2-rank tensors as input, so we must first flatten the tensors.\n", + "\n", + "The `tf.layers` API was first located in `tf.contrib.layers` before it was moved into TensorFlow Core. But even though it has taken the TensorFlow developers a year to move these fairly simple functions, they have somehow forgotten to move the even simpler `flatten()` function. So we still need to use the one in `tf.contrib.layers`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "net = tf.contrib.layers.flatten(net)\n", + "\n", + "# This should eventually be replaced by:\n", + "# net = tf.layers.flatten(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This has now flattened the data to a 2-rank tensor, as can be seen from this:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now add fully-connected layers to the neural network. These are called *dense* layers in the Layers API." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "net = tf.layers.dense(inputs=net, name='layer_fc1',\n", + " units=128, activation=tf.nn.relu)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need the neural network to classify the input images into 10 different classes. So the final fully-connected layer has `num_classes=10` output neurons." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "net = tf.layers.dense(inputs=net, name='layer_fc_out',\n", + " units=num_classes, activation=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output of the final fully-connected layer are sometimes called logits, so we have a convenience variable with that name." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "logits = net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the softmax function to 'squash' the outputs so they are between zero and one, and so they sum to one." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y_pred = tf.nn.softmax(logits=logits)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us how likely the neural network thinks the input image is of each possible class. The one that has the highest value is considered the most likely so its index is taken to be the class-number." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y_pred_cls = tf.argmax(y_pred, dimension=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now created the exact same Convolutional Neural Network in a few lines of code that required many complex lines of code in the direct TensorFlow implementation.\n", + "\n", + "The Layers API is perhaps not as elegant as PrettyTensor, but it has some other advantages, e.g. that we can more easily refer to intermediate layers, and it is also easier to construct neural networks with branches and multiple outputs using the Layers API." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loss-Function to be Optimized" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make the model better at classifying the input images, we must somehow change the variables of the Convolutional Neural Network.\n", + "\n", + "The cross-entropy is a performance measure used in classification. The cross-entropy is a continuous function that is always positive and if the predicted output of the model exactly matches the desired output then the cross-entropy equals zero. The goal of optimization is therefore to minimize the cross-entropy so it gets as close to zero as possible by changing the variables of the model.\n", + "\n", + "TensorFlow has a function for calculating the cross-entropy, which uses the values of the `logits`-layer because it also calculates the softmax internally, so as to to improve numerical stability." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=logits)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now calculated the cross-entropy for each of the image classifications so we have a measure of how well the model performs on each image individually. But in order to use the cross-entropy to guide the optimization of the model's variables we need a single scalar value, so we simply take the average of the cross-entropy for all the image classifications." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "loss = tf.reduce_mean(cross_entropy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimization Method\n", + "\n", + "Now that we have a cost measure that must be minimized, we can then create an optimizer. In this case it is the Adam optimizer with a learning-rate of 1e-4.\n", + "\n", + "Note that optimization is not performed at this point. In fact, nothing is calculated at all, we just add the optimizer-object to the TensorFlow graph for later execution." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classification Accuracy\n", + "\n", + "We need to calculate the classification accuracy so we can report progress to the user.\n", + "\n", + "First we create a vector of booleans telling us whether the predicted class equals the true class of each image." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Getting the Weights\n", + "\n", + "Further below, we want to plot the weights of the convolutional layers. In the TensorFlow implementation we had created the variables ourselves so we could just refer to them directly. But when the network is constructed using a builder API such as `tf.layers`, all the variables of the layers are created indirectly by the builder API. We therefore have to retrieve the variables from TensorFlow.\n", + "\n", + "First we need a list of the variable names in the TensorFlow graph:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "for var in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):\n", + " print(var)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each of the convolutional layers has two variables. For the first convolutional layer they are named `layer_conv1/kernel:0` and `layer_conv1/bias:0`. The `kernel` variables are the ones we want to plot further below.\n", + "\n", + "It is somewhat awkward to get references to these variables, because we have to use the TensorFlow function `get_variable()` which was designed for another purpose; either creating a new variable or re-using an existing variable. The easiest thing is to make the following helper-function." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def get_weights_variable(layer_name):\n", + " # Retrieve an existing variable named 'kernel' in the scope\n", + " # with the given layer_name.\n", + " # This is awkward because the TensorFlow function was\n", + " # really intended for another purpose.\n", + "\n", + " with tf.variable_scope(layer_name, reuse=True):\n", + " variable = tf.get_variable('kernel')\n", + "\n", + " return variable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using this helper-function we can retrieve the variables. These are TensorFlow objects. In order to get the contents of the variables, you must do something like: `contents = session.run(weights_conv1)` as demonstrated further below." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "weights_conv1 = get_weights_variable(layer_name='layer_conv1')\n", + "weights_conv2 = get_weights_variable(layer_name='layer_conv2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TensorFlow Run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create TensorFlow session\n", + "\n", + "Once the TensorFlow graph has been created, we have to create a TensorFlow session which is used to execute the graph." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "session = tf.Session()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize variables\n", + "\n", + "The variables for the TensorFlow graph must be initialized before we start optimizing them." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "session.run(tf.global_variables_initializer())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function to perform optimization iterations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n", + "\n", + "If your computer crashes or becomes very slow because you run out of RAM, then you may try and lower this number, but you may then need to do more optimization iterations." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "train_batch_size = 64" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function performs a number of optimization iterations so as to gradually improve the variables of the neural network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# Counter for total number of iterations performed so far.\n", + "total_iterations = 0\n", + "\n", + "def optimize(num_iterations):\n", + " # Ensure we update the global variable rather than a local copy.\n", + " global total_iterations\n", + "\n", + " for i in range(total_iterations,\n", + " total_iterations + num_iterations):\n", + "\n", + " # Get a batch of training examples.\n", + " # x_batch now holds a batch of images and\n", + " # y_true_batch are the true labels for those images.\n", + " x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n", + "\n", + " # Put the batch into a dict with the proper names\n", + " # for placeholder variables in the TensorFlow graph.\n", + " feed_dict_train = {x: x_batch,\n", + " y_true: y_true_batch}\n", + "\n", + " # Run the optimizer using this batch of training data.\n", + " # TensorFlow assigns the variables in feed_dict_train\n", + " # to the placeholder variables and then runs the optimizer.\n", + " session.run(optimizer, feed_dict=feed_dict_train)\n", + "\n", + " # Print status every 100 iterations.\n", + " if i % 100 == 0:\n", + " # Calculate the accuracy on the training-set.\n", + " acc = session.run(accuracy, feed_dict=feed_dict_train)\n", + "\n", + " # Message for printing.\n", + " msg = \"Optimization Iteration: {0:>6}, Training Accuracy: {1:>6.1%}\"\n", + "\n", + " # Print it.\n", + " print(msg.format(i + 1, acc))\n", + "\n", + " # Update the total number of iterations performed.\n", + " total_iterations += num_iterations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function to plot example errors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function for plotting examples of images from the test-set that have been mis-classified." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_example_errors(cls_pred, correct):\n", + " # This function is called from print_test_accuracy() below.\n", + "\n", + " # cls_pred is an array of the predicted class-number for\n", + " # all images in the test-set.\n", + "\n", + " # correct is a boolean array whether the predicted class\n", + " # is equal to the true class for each image in the test-set.\n", + "\n", + " # Negate the boolean array.\n", + " incorrect = (correct == False)\n", + " \n", + " # Get the images from the test-set that have been\n", + " # incorrectly classified.\n", + " images = data.test.images[incorrect]\n", + " \n", + " # Get the predicted classes for those images.\n", + " cls_pred = cls_pred[incorrect]\n", + "\n", + " # Get the true classes for those images.\n", + " cls_true = data.test.cls[incorrect]\n", + " \n", + " # Plot the first 9 images.\n", + " plot_images(images=images[0:9],\n", + " cls_true=cls_true[0:9],\n", + " cls_pred=cls_pred[0:9])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function to plot confusion matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_confusion_matrix(cls_pred):\n", + " # This is called from print_test_accuracy() below.\n", + "\n", + " # cls_pred is an array of the predicted class-number for\n", + " # all images in the test-set.\n", + "\n", + " # Get the true classifications for the test-set.\n", + " cls_true = data.test.cls\n", + " \n", + " # Get the confusion matrix using sklearn.\n", + " cm = confusion_matrix(y_true=cls_true,\n", + " y_pred=cls_pred)\n", + "\n", + " # Print the confusion matrix as text.\n", + " print(cm)\n", + "\n", + " # Plot the confusion matrix as an image.\n", + " plt.matshow(cm)\n", + "\n", + " # Make various adjustments to the plot.\n", + " plt.colorbar()\n", + " tick_marks = np.arange(num_classes)\n", + " plt.xticks(tick_marks, range(num_classes))\n", + " plt.yticks(tick_marks, range(num_classes))\n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('True')\n", + "\n", + " # Ensure the plot is shown correctly with multiple plots\n", + " # in a single Notebook cell.\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for showing the performance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is a function for printing the classification accuracy on the test-set.\n", + "\n", + "It takes a while to compute the classification for all the images in the test-set, that's why the results are re-used by calling the above functions directly from this function, so the classifications don't have to be recalculated by each function.\n", + "\n", + "Note that this function can use a lot of computer memory, which is why the test-set is split into smaller batches. If you have little RAM in your computer and it crashes, then you can try and lower the batch-size." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the test-set into smaller batches of this size.\n", + "test_batch_size = 256\n", + "\n", + "def print_test_accuracy(show_example_errors=False,\n", + " show_confusion_matrix=False):\n", + "\n", + " # Number of images in the test-set.\n", + " num_test = len(data.test.images)\n", + "\n", + " # Allocate an array for the predicted classes which\n", + " # will be calculated in batches and filled into this array.\n", + " cls_pred = np.zeros(shape=num_test, dtype=np.int)\n", + "\n", + " # Now calculate the predicted classes for the batches.\n", + " # We will just iterate through all the batches.\n", + " # There might be a more clever and Pythonic way of doing this.\n", + "\n", + " # The starting index for the next batch is denoted i.\n", + " i = 0\n", + "\n", + " while i < num_test:\n", + " # The ending index for the next batch is denoted j.\n", + " j = min(i + test_batch_size, num_test)\n", + "\n", + " # Get the images from the test-set between index i and j.\n", + " images = data.test.images[i:j, :]\n", + "\n", + " # Get the associated labels.\n", + " labels = data.test.labels[i:j, :]\n", + "\n", + " # Create a feed-dict with these images and labels.\n", + " feed_dict = {x: images,\n", + " y_true: labels}\n", + "\n", + " # Calculate the predicted class using TensorFlow.\n", + " cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)\n", + "\n", + " # Set the start-index for the next batch to the\n", + " # end-index of the current batch.\n", + " i = j\n", + "\n", + " # Convenience variable for the true class-numbers of the test-set.\n", + " cls_true = data.test.cls\n", + "\n", + " # Create a boolean array whether each image is correctly classified.\n", + " correct = (cls_true == cls_pred)\n", + "\n", + " # Calculate the number of correctly classified images.\n", + " # When summing a boolean array, False means 0 and True means 1.\n", + " correct_sum = correct.sum()\n", + "\n", + " # Classification accuracy is the number of correctly classified\n", + " # images divided by the total number of images in the test-set.\n", + " acc = float(correct_sum) / num_test\n", + "\n", + " # Print the accuracy.\n", + " msg = \"Accuracy on Test-Set: {0:.1%} ({1} / {2})\"\n", + " print(msg.format(acc, correct_sum, num_test))\n", + "\n", + " # Plot some examples of mis-classifications, if desired.\n", + " if show_example_errors:\n", + " print(\"Example errors:\")\n", + " plot_example_errors(cls_pred=cls_pred, correct=correct)\n", + "\n", + " # Plot the confusion matrix, if desired.\n", + " if show_confusion_matrix:\n", + " print(\"Confusion Matrix:\")\n", + " plot_confusion_matrix(cls_pred=cls_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance before any optimization\n", + "\n", + "The accuracy on the test-set is very low because the variables for the neural network have only been initialized and not optimized at all, so it just classifies the images randomly." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on Test-Set: 5.8% (577 / 10000)\n" + ] + } + ], + "source": [ + "print_test_accuracy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance after 1 optimization iteration\n", + "\n", + "The classification accuracy does not improve much from just 1 optimization iteration, because the learning-rate for the optimizer is set very low." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization Iteration: 1, Training Accuracy: 9.4%\n" + ] + } + ], + "source": [ + "optimize(num_iterations=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on Test-Set: 6.6% (659 / 10000)\n" + ] + } + ], + "source": [ + "print_test_accuracy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance after 100 optimization iterations\n", + "\n", + "After 100 optimization iterations, the model has significantly improved its classification accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 368 ms, sys: 56 ms, total: 424 ms\n", + "Wall time: 308 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "optimize(num_iterations=99) # We already performed 1 iteration above." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on Test-Set: 81.2% (8125 / 10000)\n", + "Example errors:\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_test_accuracy(show_example_errors=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance after 1000 optimization iterations\n", + "\n", + "After 1000 optimization iterations, the model has greatly increased its accuracy on the test-set to more than 90%." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization Iteration: 101, Training Accuracy: 89.1%\n", + "Optimization Iteration: 201, Training Accuracy: 89.1%\n", + "Optimization Iteration: 301, Training Accuracy: 90.6%\n", + "Optimization Iteration: 401, Training Accuracy: 90.6%\n", + "Optimization Iteration: 501, Training Accuracy: 89.1%\n", + "Optimization Iteration: 601, Training Accuracy: 93.8%\n", + "Optimization Iteration: 701, Training Accuracy: 92.2%\n", + "Optimization Iteration: 801, Training Accuracy: 92.2%\n", + "Optimization Iteration: 901, Training Accuracy: 98.4%\n", + "CPU times: user 3.55 s, sys: 500 ms, total: 4.05 s\n", + "Wall time: 2.96 s\n" + ] + } + ], + "source": [ + "%%time\n", + "optimize(num_iterations=900) # We performed 100 iterations above." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on Test-Set: 94.5% (9455 / 10000)\n", + "Example errors:\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_test_accuracy(show_example_errors=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance after 10,000 optimization iterations\n", + "\n", + "After 10,000 optimization iterations, the model has a classification accuracy on the test-set of about 99%." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization Iteration: 1001, Training Accuracy: 92.2%\n", + "Optimization Iteration: 1101, Training Accuracy: 92.2%\n", + "Optimization Iteration: 1201, Training Accuracy: 92.2%\n", + "Optimization Iteration: 1301, Training Accuracy: 95.3%\n", + "Optimization Iteration: 1401, Training Accuracy: 96.9%\n", + "Optimization Iteration: 1501, Training Accuracy: 96.9%\n", + "Optimization Iteration: 1601, Training Accuracy: 93.8%\n", + "Optimization Iteration: 1701, Training Accuracy: 92.2%\n", + "Optimization Iteration: 1801, Training Accuracy: 96.9%\n", + "Optimization Iteration: 1901, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2001, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2101, Training Accuracy: 98.4%\n", + "Optimization Iteration: 2201, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 2401, Training Accuracy: 98.4%\n", + "Optimization Iteration: 2501, Training Accuracy: 93.8%\n", + "Optimization Iteration: 2601, Training Accuracy: 96.9%\n", + "Optimization Iteration: 2701, Training Accuracy: 100.0%\n", + "Optimization Iteration: 2801, Training Accuracy: 96.9%\n", + "Optimization Iteration: 2901, Training Accuracy: 98.4%\n", + "Optimization Iteration: 3001, Training Accuracy: 96.9%\n", + "Optimization Iteration: 3101, Training Accuracy: 96.9%\n", + "Optimization Iteration: 3201, Training Accuracy: 96.9%\n", + "Optimization Iteration: 3301, Training Accuracy: 96.9%\n", + "Optimization Iteration: 3401, Training Accuracy: 100.0%\n", + "Optimization Iteration: 3501, Training Accuracy: 95.3%\n", + "Optimization Iteration: 3601, Training Accuracy: 100.0%\n", + "Optimization Iteration: 3701, Training Accuracy: 98.4%\n", + "Optimization Iteration: 3801, Training Accuracy: 98.4%\n", + "Optimization Iteration: 3901, Training Accuracy: 98.4%\n", + "Optimization Iteration: 4001, Training Accuracy: 98.4%\n", + "Optimization Iteration: 4101, Training Accuracy: 96.9%\n", + "Optimization Iteration: 4201, Training Accuracy: 98.4%\n", + "Optimization Iteration: 4301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 4401, Training Accuracy: 98.4%\n", + "Optimization Iteration: 4501, Training Accuracy: 95.3%\n", + "Optimization Iteration: 4601, Training Accuracy: 100.0%\n", + "Optimization Iteration: 4701, Training Accuracy: 98.4%\n", + "Optimization Iteration: 4801, Training Accuracy: 98.4%\n", + "Optimization Iteration: 4901, Training Accuracy: 100.0%\n", + "Optimization Iteration: 5001, Training Accuracy: 98.4%\n", + "Optimization Iteration: 5101, Training Accuracy: 96.9%\n", + "Optimization Iteration: 5201, Training Accuracy: 100.0%\n", + "Optimization Iteration: 5301, Training Accuracy: 100.0%\n", + "Optimization Iteration: 5401, Training Accuracy: 100.0%\n", + "Optimization Iteration: 5501, Training Accuracy: 98.4%\n", + "Optimization Iteration: 5601, Training Accuracy: 100.0%\n", + "Optimization Iteration: 5701, Training Accuracy: 96.9%\n", + "Optimization Iteration: 5801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 5901, Training Accuracy: 96.9%\n", + "Optimization Iteration: 6001, Training Accuracy: 98.4%\n", + "Optimization Iteration: 6101, Training Accuracy: 100.0%\n", + "Optimization Iteration: 6201, Training Accuracy: 95.3%\n", + "Optimization Iteration: 6301, Training Accuracy: 96.9%\n", + "Optimization Iteration: 6401, Training Accuracy: 100.0%\n", + "Optimization Iteration: 6501, Training Accuracy: 100.0%\n", + "Optimization Iteration: 6601, Training Accuracy: 96.9%\n", + "Optimization Iteration: 6701, Training Accuracy: 98.4%\n", + "Optimization Iteration: 6801, Training Accuracy: 98.4%\n", + "Optimization Iteration: 6901, Training Accuracy: 100.0%\n", + "Optimization Iteration: 7001, Training Accuracy: 100.0%\n", + "Optimization Iteration: 7101, Training Accuracy: 100.0%\n", + "Optimization Iteration: 7201, Training Accuracy: 100.0%\n", + "Optimization Iteration: 7301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 7401, Training Accuracy: 100.0%\n", + "Optimization Iteration: 7501, Training Accuracy: 96.9%\n", + "Optimization Iteration: 7601, Training Accuracy: 93.8%\n", + "Optimization Iteration: 7701, Training Accuracy: 96.9%\n", + "Optimization Iteration: 7801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 7901, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8001, Training Accuracy: 95.3%\n", + "Optimization Iteration: 8101, Training Accuracy: 98.4%\n", + "Optimization Iteration: 8201, Training Accuracy: 96.9%\n", + "Optimization Iteration: 8301, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8401, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8501, Training Accuracy: 96.9%\n", + "Optimization Iteration: 8601, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8701, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8901, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9001, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9101, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9201, Training Accuracy: 96.9%\n", + "Optimization Iteration: 9301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9401, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9501, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9601, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9701, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9901, Training Accuracy: 100.0%\n", + "CPU times: user 34.6 s, sys: 4.08 s, total: 38.7 s\n", + "Wall time: 28.2 s\n" + ] + } + ], + "source": [ + "%%time\n", + "optimize(num_iterations=9000) # We performed 1000 iterations above." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on Test-Set: 98.8% (9884 / 10000)\n", + "Example errors:\n" + ] + }, + { + "data": { + "image/png": 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QfDA3zIjrVFHPnpvZFKBneLkkDUyYkTzrnDu+1LFIYTSEMq6oSlNEpNT0GKWI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\nRK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix:\n", + "[[ 975 0 0 0 0 1 2 1 1 0]\n", + " [ 0 1127 2 0 0 0 2 2 2 0]\n", + " [ 2 0 1024 1 1 0 0 4 0 0]\n", + " [ 0 0 0 1006 0 1 0 1 2 0]\n", + " [ 0 0 1 0 979 0 1 0 0 1]\n", + " [ 2 0 0 7 0 879 3 0 1 0]\n", + " [ 5 2 0 1 1 3 946 0 0 0]\n", + " [ 0 1 4 2 0 0 0 1018 1 2]\n", + " [ 1 0 3 10 1 1 1 2 951 4]\n", + " [ 1 3 0 6 8 4 0 6 2 979]]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_test_accuracy(show_example_errors=True,\n", + " show_confusion_matrix=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization of Weights and Layers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for plotting convolutional weights" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_conv_weights(weights, input_channel=0):\n", + " # Assume weights are TensorFlow ops for 4-dim variables\n", + " # e.g. weights_conv1 or weights_conv2.\n", + " \n", + " # Retrieve the values of the weight-variables from TensorFlow.\n", + " # A feed-dict is not necessary because nothing is calculated.\n", + " w = session.run(weights)\n", + "\n", + " # Get the lowest and highest values for the weights.\n", + " # This is used to correct the colour intensity across\n", + " # the images so they can be compared with each other.\n", + " w_min = np.min(w)\n", + " w_max = np.max(w)\n", + "\n", + " # Number of filters used in the conv. layer.\n", + " num_filters = w.shape[3]\n", + "\n", + " # Number of grids to plot.\n", + " # Rounded-up, square-root of the number of filters.\n", + " num_grids = math.ceil(math.sqrt(num_filters))\n", + " \n", + " # Create figure with a grid of sub-plots.\n", + " fig, axes = plt.subplots(num_grids, num_grids)\n", + "\n", + " # Plot all the filter-weights.\n", + " for i, ax in enumerate(axes.flat):\n", + " # Only plot the valid filter-weights.\n", + " if i" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "image1 = data.test.images[0]\n", + "plot_image(image1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot another example image from the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "image2 = data.test.images[13]\n", + "plot_image(image2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convolution Layer 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now plot the filter-weights for the first convolutional layer.\n", + "\n", + "Note that positive weights are red and negative weights are blue." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_conv_weights(weights=weights_conv1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Applying each of these convolutional filters to the first input image gives the following output images, which are then used as input to the second convolutional layer." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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U2Gcj/5PJteyht+M4TgRZlV/r168Hgp1D3r2jowOA5uZmADo7O20XKSoqsp8d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\nTiSxq6qqJoTLJSUlppj0/95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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_conv_layer(layer=layer_conv1, image=image1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following images are the results of applying the convolutional filters to the second image." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Er5VInS52fD6feCWMm2/fvl32ZdNG3OLp8XgkjpZowwT7WHZ0dMTF0NasWRO3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\nL3NmEENEnAPKy8ul+1gqSO9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_conv_layer(layer=layer_conv1, image=image2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convolution Layer 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now plot the filter-weights for the second convolutional layer.\n", + "\n", + "There are 16 output channels from the first conv-layer, which means there are 16 input channels to the second conv-layer. The second conv-layer has a set of filter-weights for each of its input channels. We start by plotting the filter-weigths for the first channel.\n", + "\n", + "Note again that positive weights are red and negative weights are blue." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_conv_weights(weights=weights_conv2, input_channel=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are 16 input channels to the second convolutional layer, so we can make another 15 plots of filter-weights like this. We just make one more with the filter-weights for the second channel. " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6KhAAsG2bWbKwQC6cMmdOcvbUyc/3cP/98hpvv92u6dFDz1ML7N9/WNmDqeQP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\nCbwTyk8Td2ZOAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_conv_weights(weights=weights_conv2, input_channel=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It can be difficult to understand and keep track of how these filters are applied because of the high dimensionality.\n", + "\n", + "Applying these convolutional filters to the images that were ouput from the first conv-layer gives the following images.\n", + "\n", + "Note that these are down-sampled to 14 x 14 pixels which is half the resolution of the original input images, because the first convolutional layer was followed by a max-pooling layer with stride 2. Max-pooling is also done after the second convolutional layer, but we retrieve these images before that has been applied." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Dq+KSEyohW2fHMAxjgFK1ZCpPmjyHHR0dQDhAWNKqikP4XQ7l6c6irVSUc2xX\nX3014NL0Tj311OC9cmxWHxai9qo0C36Ug2y7Kpgh6VORGuC82bIj+lK7bMpZ0qCSsF/6UqfGK4lP\nkmoW7KRJojHLvyE7eH/HaZKpYRhGAthiahiGkQBVq/kKDVHYT6nwH6lSftiQVAjfYTVQmDNnTvBa\nyQaf+cxngP438KoXUm2j7W79Vs2qEylHnFpe+GRNrYewc0hqvcJ/Xn311dD/PgrI92t9ap41zrgA\n/XqrwzomVfCSKW7VqlXBPi+99BLgkg98s5zu4aw5npI+rzL5RM0Z/SUbZ8kwDGOAk6skpCOXy60D\nVva5Y21oKxQKxcvZJ4SNMRU+CuO0MSbIQBhnRYupYRiGEY+p+YZhGAlgi6lhGEYC2GJqGIaRALaY\nGoZhJIAtpoZhGAlQUdB+a2trwW/96xONCkg6wLajo4POzs6aR0OXGmOtycIYNY/6W4uA7aVLl3am\nEVLzUZ3LciJ0krg/0xojVDaX/viTuI7LHWdFi2l7eztPP/10aJuyCJRtoWwCZYokRVq95tva2li0\naFGoQ+GIESNS+e20xhg3j2rZoCy1aF95cFk/ymCLtgCB8op853K5VOIF48aZFvWcy2L4RWjUOija\ncx7cPKsotObSb80C6Y0RajuXKmijUot+y6RRo0ZxwAEHlPU9puYbhmEkgC2mhmEYCVB1oZNosYCk\n1fu0KRQK9PT0BDVYARYtWgTAypW92qk6VPoVvvfff3/AFcXw613OmDEDyGYRELFu3TrAqTyq2+mr\nPCoSot47qlAOToVUcYws1af11VsVNFGBHhX6+CjgX38afxZ7WMVRKBTYvHnzNqaGJNB9/OijjwLh\ngiczZ84su6eUSaaGYRgJ0C/JVOXZwDks/BJeA5l8Ps92220X6vek0mWSTB955BEgXLbt1ltvBVzH\nAd+gr7J8Bx10EOC6lY4fP74mY+iLQqFAV1dXqOvm888/DzhJTWXc1qxZE+wjSVSeYJVxA+eMGjt2\nbNHfLbfTadL4Y9A4f/e73wGwfPlyIOxMU2cE/ZVzDmDy5MmAm8MsUCgU6l72r9Z0d3fzwQcfsGLF\nimCbuhxHHaJ+D6e1a9cCTpuaOXNm8J6iA+69914Abr/9dsD1o4Pe69m/l0thkqlhGEYC9Esy9SUM\nhUSpp45sh/3t8JdFjjrqKAAmTZoEuOLIvl318MMPB5yt0O9o+fLLLwPOnlxvO113dzfvvfdeqO+R\nnurqvimQUykyAAAZX0lEQVSbr/9UHjNmDOD6yPuSqc6NH0oV/XxcIeVaUigU2Lp1a8h+O3XqVMDZ\nDyXdPPXUU8E+6qKrfmbSNsCFA0lLOe644wCYOHFisM+gQYNS61aqudQxg7snFd6nufRt/LoW1clU\nhbCzTNSXsXTp0tD7CmGSvR+cdqHr2u8/J04++WTA+Tb867qxsbFsqd8kU8MwjATol/joe6r1hFd7\ni8WLFwPhVg+yM8leNXfu3OC9yy+/HHBdSrNMNAPDD+zfc889Q+/JOw5w5513Ak4KkO20Xp7UXC5H\nPp8PPaUlqey2225Aac1C8+jbImfPnl30t0S9WtP4moBey7YrrSMOSZ++lKnrWpENQ4cOBcKRDS0t\nLalJprlcjsbGxpD0rTYlDz/8MAALFy4EnPYIrk2NOpEedthhwXtf+MIXQtvkI1GAvz6XpqbR09PD\nxo0bA6kb3Npz4oknArD77rtX9RuSaH3JFsqPTDHJ1DAMIwFsMTUMw0iAfqn5ftEAGXilWsip4asE\nCiX68Y9/DDiHDMA111zTn0OoK+UUXPDDK3SOFI4jx0a9jP65XI6mpqZQgoVUmXIch3fffTfgxlWK\nenadlQrcX6Lqnk9U9fOdO5BeR898Ps/QoUNDoXya1z322AOA0047DQibZZScIdOTTHHgxq39FRoo\nJw701qtIs+WR5tIPJ9x3330BF2Ko4PpSyTG+OUbmC52DajHJ1DAMIwEqlkwLhULoaa9wGTlTFGLj\np2D95je/AZyx/rvf/W7wXlzv9Q8bCtmQhO4/4etBPp9n8ODBoSd4OeEfN9xwAwD3338/APPmzavN\nARpVoetLziXNrcLAYNtQuDj8AHkIO0ybmppSk76hV9psaWkJOXqV6qyxSBuOCz3UvXfjjTcG2/7z\nP/8z0WM0ydQwDCMBKpZMoxKMpE39FX6wtoKejz32WACuvPLKin4zK+2olZJWyg6nffQkBLj55psB\nOProo4HqQziSoKGhoazCK7/+9a+D19dffz0A06dPB2DChAm1Obg64NvxlZQybdq0eh1ORURDlHSP\n6q/e9xMqSmkiSp+VXVUSrl/XN+2knHw+T0tLS8lwwuga5PP9738fcNcubBvAL8m9v/WLTTI1DMNI\nAFtMDcMwEqBmsrqcTgCvvfYaANdee23Zn/cdWPWuAyo1qZzj0HH7xnmFQKl61EBAYTFyOvnbrrvu\nurocUy1QJtObb74ZbFOG2kAlqsLL9FRuLVBluCn8S9evX9u2oaFhQFSqmjNnDgCvvvoqADfddFPR\nfaPhbZVikqlhGEYCJC6ZynitfGBwtU7jKrYUI82wi76QM82vSRBFSQsKzFd9U3COJz+wOuvcd999\ngMvvBrjggguAbesQDGRUU8J3qGSpS0ASlCOR+mFQqiuh+1YSWz0TMPqLHMGXXHIJULqGcLUacHZW\nLMMwjAFM4pLpCy+8AISfYpVUJS8nJSxtohKp7C9+PUVte/bZZwHYddddg/eyVJW9LyShqPq4L4X+\n8Ic/rMsx1QJVYFdqr58aK+2qWhtavankXvJtxgp6V0q0JPWs2UhV+UqVpHTv3XbbbcE+qkZ3/vnn\nb/N5pb4r8ahaTDI1DMNIgMQkU6V0qRK2An2hvMIgsksOBLuMgu79Y9VTTk/4c845J/0DqwIFaC9Z\nsgRwRSCK1SkdqCgBRDZuVc73A9qfeOIJwJ2DKVOmBO/Vq29XX/hSo8bmFzaBcOdg1TZ98cUXgXAP\nLF3fCl7XdZ41yVTHrGv2q1/9KuDGDy71OY7+BucXwyRTwzCMBLDF1DAMIwESU/Oj+cC+inHHHXcA\nrr1AXKhGVlWJUvjmC4WTKPxLYx0oKDFBThclGPzlX/5l3Y6pFsicpLmLu97kjMqiM7QYfv0KzaUc\nab///e8Bp9KDc9rIyXTwwQcH7yk4v6mpCXDnKCs1MoTaBslkoXm65557yvq8n4SQBCaZGoZhJECu\nkqdNLpdbB6ys3eGUpK1QKOzY927VYWNMhY/COG2MCTIQxlnRYmoYhmHEY2q+YRhGAthiahiGkQC2\nmBqGYSSALaaGYRgJUFGcaWtra6Gc1NBa0NHRQWdnZ82DUD+qY1RsohySKoFYi7jfpUuXdqbhBdY4\nfSerXkfjopMm7bmMcyTXOmY7rTFC/DWrMUev2aQpd5wVLaZtbW08/vjjoeZjO+20E+CqcdcK5VDX\nmvb2dp5++ulUfstvOtjQ0BC0hK4148ePZ+HChUEHBHAV51VbQQHbqmkJsM8++wDlV2wvRi6XSyXE\npa2tjcWLFwcB6uDGOWnSJMBVRPLbIOu1FiO/vmlra2vsbymPH3pro6Y5l4899ljo93Vd6QGpRcZv\ngZxEDYy07kmIvy81PuXiK8nCf7Bo7Aro9+vW6j1d68Uod5ym5huGYSSALaaGYRgJUJGav3nzZlau\nXMm8efOCbWPHjgVg1qxZRT+nhl5RkTuOjRs3AoRUs3Hjxm3TG7xWdHd38+677wZFrsHlLyfd7375\n8uXB69bW1pDaX2sKhUKoTKLUX82NGiKuXr062EfqvcrQSe2HcDFsgAMPPLAGR10ZPT09/OlPfwpy\nuMEVAo62JvHLsUkFlrpY6tqTiqkSjNBb6s5vCJkG/j2lMpCLFy8OHdumTZuCfaTyKz/dL8+ncoS7\n7bYbALvssgsQPkdp19AoFAohUwa467GlpSW03TfZiKFDhwLhc7BhwwbAmUV0P/hmgqamprJrEphk\nahiGkQAVSaZdXV2sX78+ZLz+9Kc/HdpHFVzUFgJc6ws5rko1llu/fj1ASJpIs61sd3c37733Hg8+\n+GCw7bHHHgPgi1/8IgAnnHAC4JqxgSsorIK1hx12WPBe1BkhScdvVnfwwQcHEnytyefzDB06NCSd\nScKaMGEC4I7ZPw+33HILAHPnzgV6HTxCc6qqU3IWXHzxxTUZQzkUCgW6urpCDgY5TEshaU1tMUp5\nid944w0gLJkOGTIkNU0qn88zePDgUHFnjVdzqWNbsGBBsI80L0l70jDBXZ9yvMycORMIX8d77bVX\natcr9EqPq1evDtpQg9MYVeVL96DWIIDJkycDTjL1HVDSHnTNas2p9HoRJpkahmEkQEWS6bBhw5g5\nc2ZJe9gOO+wAxIeQ6CleSjJ96qmnADj55JND29OSTBsaGhg+fHioLbWegAcddBDgnmR+3Nvbb78N\nwKpVq4BwSFEUSTovvfRSsG3q1Kmp29l8iUuhQlHOPffc2NflsnKli4JK287W0NDAyJEjg/krF9UB\nld3etw1HmT9/fvBbYtKkSanW/szlciFtcd999w39Pf744wG49tprg300L3HXotp86zqXPd33Y4wZ\nMybV67WpqYmxY8duY5sHF86n1jt+nVJJ7K+88goQtvvKJiwJW9L6/vvvH/rdcq9bk0wNwzASoOJK\n+31lGWgV95905513HuDsqMcee+w2n4u2aY1Kpmkhaea0004Ltqk5nu/9hnB7YD3F5f3234vy+OOP\nA+HK51nh9ddfB5w04kuW0joktc+ZMyd4T5X5ozZ0eUwh+crmfZHL5UI2snKRrbScwHZJptOmTQu2\nVSLNJEGhUKj493x7N4Q1Kc2h5k6V+n1bZNpaRi6XK5osonvtggsuKPp53Zf+ccuW/K//+q+As49+\n8pOf7NcxmmRqGIaRALaYGoZhJEC/Gur5QbEKJ5CIrABYXwVcuHAh4JrOxaH9/fzheuKHipSDAp19\ntbYY//Vf/wWEwy7GjBmTSL50f1mxYgXgHIcdHR0AoeQFqUiPPPIIEA7UV7KFUN6/HzIUNQFkATkO\npcqCOxelHG6333474Mwgp5xySvDeyJEj+2Ve6A8KV9Q4wN2DcrxVEt7jo2B4ObL8a2HIkCE1KyxS\nC+LuZ82d6op87WtfA8JrUCWOxIFzNgzDMDJMxY/PQqEQciT4oQbgnvB+gPDll18OFK+4A07CvfDC\nCys9pEygp1w5EokkOyUBQK9DoK/qNbVk4sSJof+POOKI0F+fr3/9631+3x//+EfApW/WEz+ER+mE\nSg5ZtmwZAPfee2+wj0JmSkle119/PQCDBw8GwgHtzc3NqYbytbS0hK47SaYaqwLd/cpuuhflbPND\nAaPoHvfv35EjR6beArtQKITSQRX2JK2wUn76058CcNZZZwHx61Ml82iSqWEYRgJULJlGA4SF7KE/\n+tGPAJeeCHDdddcV/b5///d/B1y4VJo1EvuLUvCUYOBz8MEHF/3c7NmzAWeT8aW+lpaWVJ/0hUIh\nZA+S9CapRqFRlUqWug4UQF3qfKSFf14VwiaJQ3M5derUYJ9S2pHs3dLArrjiCiCcwJEmuVyOpqam\nkFajeZXUqbRhX/JSAo1srUr5BhcSFtWUfFvidtttl7rNNJfLlQyvU7KFv/ZE7cUXXXRR8HrRokWA\nC42qFpNMDcMwEsAWU8MwjASoOn5DRuAHHngAgDvvvBOAX/ziF0U/47c9UTWXU089tdpDSQ2FzviV\ngQ455JA+P6dqS8oIU8gJpJ9REkXzKFUpLp9dudmqbxmHMqimTJmS9CEmgswZytBTFtoZZ5xR1ue/\n+93vAjB9+nQATj/9dMA5orKATDVy6ipzz6+Xq/DGGTNmAE7tB1dtSqxZswYIO6nSrORWjGg/L43B\nv6+ExnDjjTcG20qtUf3BJFPDMIwEqFoyVVC2DPMyEB9++OFFP/M///M/wWs/Bz7ryOnw6KOPAsUr\nLUU56aSTAFd96Oijjwa2rRBeTyRl65gUauMnIZSSSIWqZsU1lPNrbtYLhdZIypaDolQXhfPPPz94\nLcn7kksuAcKOm6yg8ywHkebW1wilechJpboLcciBl3YoVLlI6oyTSIUqZ33iE58ItqkucVKYZGoY\nhpEAVUumSjsUf/d3f1d0X1WW91O7VAk7y0iK+e///m8A/vEf/xGAn//850U/47dRlm1GYWNZlGZk\nu47iS8/SQhQu5SdmSCL10yqhdP+keqC0VwWiS2KJQ3UuFb4HLpztxBNPBMpL0kgbHZOC9GVbVKIC\nOOlVknpcjWJVidI1kLX0UY3n1VdfBeLTZtWvTp0f/M4RSZOts2MYhjFAqfqxqmDl733ve0C4SnUU\neRNLdTLNIrIVyWMr76Gkkzj8MR555JGAK/SRJc9vJUQD+H17qgLCo0HVvmRab+8vuKB9JROUsgN+\n5StfAcLS+Ze+9CWgdCeFehO9vnTe41KD4zriqrupogCyKH2DC84vlRii6IurrroKqG2xHZNMDcMw\nEsAWU8MwjASoWn6XulOO2iMVq57VkfqDDPlyVuyxxx59fsZXDRXYrfYKWVB3obzjWLx4cfBaTgpV\nhPLVyU996lOhz0UDqsv9vVpTSesUORH98D21tMjCWJIg7l6U403qfdYcTyLaekXccccdwWs17/zG\nN77R5/dV2wQxm2fJMAxjgJGrZDXO5XLrgJV97lgb2gqFQmU9e/uBjTEVPgrjtDEmyEAYZ0WLqWEY\nhhGPqfmGYRgJYIupYRhGAthiahiGkQC2mBqGYSRARXGmra2thWK9buTIKuXQqiZeraOjg87OzpoH\n95UaY63JwhiV/qm//nwqtlLz2N8Y0qVLl3am4QUuZy7j4mGTIAtzWauxibTGCANjLitaTNvb24Pq\nK0I3nQK5VWHJb2oVbdDl52srQFj7qGmX3/xrxIgRsfUxa0FbWxuLFy8OtQdWQ7Jak1YzwXHjxjF/\n/nzeeuutYJsqQqn6k6or+RX3NTfKyVczNnAV7JWsoMpg48ePD/ZR8sN2222XSohL3PUq1DBQgenl\nBvOXe8OmPZfRluvg2o+rVqmSZpIizeaXcXOpZoiqHtXc3AzE369RIcF/rXVJ10BUICxVb8TH1HzD\nMIwEsMXUMAwjAfqVm++LylKX1DpA6qHffEv7SxxXMy9wDdyiapbfozuXy6WWC60+5B9murq6WLt2\nLXfffXewTa+fffZZAPbee28gnJcuNV0l2vzrQO0gjjnmGCBbZdv8dikyR0lFnDhxYkXflcWc/GJ+\nCtXLUI0B33QlM8zw4cOLfu8777wDhO/FLKG1Q6YpHeeoUaOCfVRiUOZEzTu486FzoDXIP0/d3d1l\n5+ybZGoYhpEAFYsPPT09IeeSnBha+VVZqJR050s0+rwcUHGtB+qBGo2Bawssg76ebr7RX5K4jPx+\n87msjEk0NjYyevToUHFrPY31V0/0F154IdhHT2g5pXzn1H777QdkSyLt6elhw4YNoRYyuk5LNdAb\nSDQ0NIS0QB9FXUhCVUNIgPnz5wOw2267AeEGiNJK5JTUX2kmWUFrhjQPtbb2i5brHMg59corrwTv\n6b6Wg0njk3YNvZqISaaGYRgpUrEYkcvlAlsFOBuUaguWY2/0401VE9MPhaonPT09bNq0KQjxAtfe\nVxLOsmXLAFiyZEmwj+yICg1SHUVwLaHVXmHq1KlAOLQkzTa6+XyelpaWUGtcvf7mN7/Z5+d1bm67\n7bZg27nnngvAFVdcAbi6n/Wkp6eHLVu2hGxguj41X5pLX1tS3VnZDBctWhS8p1q2pRrx1YN169YF\nr6VNSKLU/ebft08++STgJFN/jApn07WQNYlUSHvS+NQU0rfx6n7UNSD7KsCLL74IbNtM0Lcjb926\ntWw7uUmmhmEYCVCxZFooFIL2sODsFr79rC/U+hicbfJzn/tc0f03bNiQWstgRQ7oiQ2wzz77hPaR\nDeXll18Otvk2VnBRDuC8hLLb6Enqt8neYYcdQhJUlpE9+Otf/3qw7bjjjgMISbtRZNNKi1wuF9iH\nhaQZ2b/9ORSS2ubOnQvAc889F7yndt9ZYevWraxZsyaQtMFpOYpUUBC7pHBw7crVJPHOO+8M3jvz\nzDMBOO+880K/5dsiof5tvKUpSiKVdqw1yUfnxE9U0XmRphhHY2OjSaaGYRhpYoupYRhGAlSs5ufz\n+aCfNjhRuZxe8I899hgAf//3fx9s+/KXvxy7rx9cu2nTplTV/L7GIrF/zz33rOq35CCAXpUpra4H\nhUKBzZs3B2aHJCim3j/yyCPB6wkTJiT2e+WQz+dpbm4OBXFr7nTdKnjdV+XmzZsHOJPAJZdcErx3\n9tln9/m7aXavaGxsZMcddywZfifzStRc5ePfbyeddFLsPp2dnaH/FWaWJn49CDmKZGKMU++FEob8\nMDk5hpNKxDDJ1DAMIwH6FWHtV2WJOlX0tPKfzjL2X3fddUDY6K+Qmihr164NXo8cOTKz7WarQYZz\n6D2PaYVHFQoFenp6QsHJChmShKKwkdWrVwf7yJGmuSgV/qSUYX8e0w6Uz+VyRaVvOV7013cYKtxv\nxowZANxwww0V/26a9HVv+Om0Ue6//34g3Jq8WAtl/54eMmRIXe5Jv/KVNJ1SEqm48cYbAfjVr34V\nbIu2J6+WD98KZRiGUQf6JZmWsrXp6eUHCOvpp6fC7Nmz+z4wLy2xXk/BKLKhxSUmlHovikKgfEm0\nubk5NYkmn88XPU5Jq7Ix+WFw2lZMm/D57W9/C7gAeCC25mZWeOCBB4LXCmC/8sor63U4iaDiQ3H2\nVGkOsoN+9rOfLfo9upd9u+ouu+xSl9ThSosQLV26FICf/vSngNM2AE4//fTkDgyTTA3DMBLBFlPD\nMIwESFxOl6rqV6i56aabgN7WAwDXXntt0c8rk8MPv6q3ii+1PKqG+3VZpVIpd7sUMgmk1Q6lHOQ4\nlMNJqp2vVpXjQFImmMJxFH4C2RqveP755wG44447gm1TpkwB4KijjqrLMVWLzn2pEK1vfetbAJxy\nyil9fp9qMfj1M5qamjJZ2zXKv/zLvwDOnOGvPUmbKUwyNQzDSIDEJVNVr/nZz34WbFMolL8tiozb\n0eZWWUCSsRxGcsr4ucrlSKRy7khiqLfE7aMnt+oFSOrwQ2Z8430xFixYALhKQ1nSMHw0F48++igQ\nbgB5wQUX1OOQEkPaQbTa08KFC4PXd911FwCXXnpp0e+RhKvrPqsV96P41czkeDrnnHOA8iTx/pKd\nq9swDGMAU7VkqjRPPcVUmeapp54K9lF1oWJpauAkI0kyWbLH6FgkkS5fvhwIV0FS8Hc5gfdZtB0K\nVcuSXbdURR0fSXYav6q7Z22ssg2r15Uqr5911lnBPpMnT07/wBKkWP1RP0lDgfmS3C6//PJt9peW\nonTcLHVRKIWfZKGEhYsvvrjmv2uSqWEYRgJU/aiR1Cbvr570fhEQv7BJFBUuUGpl1iQZcNKMbL/q\nHeP33pFErqe3HyitwikaWxZsh1HJXzVK/d5VlRCt3K5z4/9OFrSNaJqr+v/MmjWr6GckpUPlQeNZ\nQB0DfIn1C1/4AuB6PykhA1xkiu7JLCdb+LzxxhuAGy+4BJNyNaxqqP9dbRiG8SHAFlPDMIwESEzN\nV2iTjNxHHnlkWZ+XSF6sUk0WkKNphx12AFx4RZxJQuNX5SFwRnBVvEmz3mUtkEroG/oV6D59+nQg\nvn5DFsYtNV3mDIXg+aqhHC8y2fh1WFWhKAsmi2LIGfjMM88A7rqbNm1asI//OsqKFSsAN+40mz1W\ng8Lc/EaV5TSITAqTTA3DMBIgV4m0kMvl1gEra3c4JWkrFArld+3rJzbGVPgojNPGmCADYZwVLaaG\nYRhGPKbmG4ZhJIAtpoZhGAlgi6lhGEYC2GJqGIaRALaYGoZhJIAtpoZhGAlgi6lhGEYC2GJqGIaR\nALaYGoZhJMD/A/8aY2yG4odpAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_conv_layer(layer=layer_conv2, image=image1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And these are the results of applying the filter-weights to the second image." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_conv_layer(layer=layer_conv2, image=image2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Close TensorFlow Session" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now done using TensorFlow, so we close the session to release its resources." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "# This has been commented out in case you want to modify and experiment\n", + "# with the Notebook without having to restart it.\n", + "# session.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed how to use the so-called *Layers API* for easily building Convolutional Neural Networks in TensorFlow. The syntax is different and more verbose than that of PrettyTensor. Both builder API's have advantages and disadvantages, but since PrettyTensor is only developed by one person and the Layers API is now an official part of TensorFlow Core, it is possible that PrettyTensor will become deprecated in the future. If this happens, we might hope that some of its unique and elegant features will become integrated into TensorFlow Core as well.\n", + "\n", + "I have been trying to get a clear answer from the TensorFlow developers for almost a year, on which of their APIs will be the main builder API for TensorFlow. They still seem to be undecided and very slow to implement it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Change the activation function to sigmoid for some of the layers.\n", + "* Can you find a simple way of changing the activation function for all the layers?\n", + "* Add a dropout-layer after the fully-connected layer. If you want a different probability during training and testing then you will need a placeholder variable and set it in the feed-dict.\n", + "* Plot the output of the max-pooling layers instead of the conv-layers.\n", + "* Replace the 2x2 max-pooling layers with stride=2 in the convolutional layers. Is there a difference in classification accuracy? What if you optimize it again and again? The difference is random, so how would you measure if there really is a difference? What are the pros and cons of using max-pooling vs. stride in the conv-layer?\n", + "* Change the parameters for the layers, e.g. the kernel, depth, size, etc. What is the difference in time usage and classification accuracy?\n", + "* Add and remove some convolutional and fully-connected layers.\n", + "* What is the simplest network you can design that still performs well?\n", + "* Retrieve the bias-values for the convolutional layers and print them. See `get_weights_variable()` for inspiration.\n", + "* Remake the program yourself without looking too much at this source-code.\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "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:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "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." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/03C_Keras_API.ipynb b/03C_Keras_API.ipynb new file mode 100644 index 0000000..b439d13 --- /dev/null +++ b/03C_Keras_API.ipynb @@ -0,0 +1,1706 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #03-C\n", + "# Keras API\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Tutorial #02 showed how to implement a Convolutional Neural Network in TensorFlow. We made a few helper-functions for creating the layers in the network. It is essential to have a good high-level API because it makes it much easier to implement complex models, and it lowers the risk of errors.\n", + "\n", + "There are several of these builder API's available for TensorFlow: PrettyTensor (Tutorial #03), Layers API (Tutorial #03-B), and several others. But they were never really finished and now they seem to be more or less abandoned by their developers.\n", + "\n", + "This tutorial is about the Keras API which is already highly developed with very good documentation - and the development continues. It seems likely that Keras will be the standard API for TensorFlow in the future so it is recommended that you use it instead of the other APIs.\n", + "\n", + "The author of Keras has written a [blog-post](https://blog.keras.io/user-experience-design-for-apis.html) on his API design philosophy which you should read." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flowchart" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. See Tutorial #02 for a more detailed description of convolution.\n", + "\n", + "There are two convolutional layers, each followed by a down-sampling using max-pooling (not shown in this flowchart). Then there are two fully-connected layers ending in a softmax-classifier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Flowchart](images/02_network_flowchart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import math" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to import several things from Keras." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import InputLayer, Input\n", + "from tensorflow.keras.layers import Reshape, MaxPooling2D\n", + "from tensorflow.keras.layers import Conv2D, Dense, Flatten" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Size of:\n", + "- Training-set:\t\t55000\n", + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" + ] + } + ], + "source": [ + "print(\"Size of:\")\n", + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copy some of the data-dimensions for convenience." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# The number of pixels in each dimension of an image.\n", + "img_size = data.img_size\n", + "\n", + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", + "\n", + "# Tuple with height and width of images used to reshape arrays.\n", + "img_shape = data.img_shape\n", + "\n", + "# Tuple with height, width and depth used to reshape arrays.\n", + "# This is used for reshaping in Keras.\n", + "img_shape_full = data.img_shape_full\n", + "\n", + "# Number of classes, one class for each of 10 digits.\n", + "num_classes = data.num_classes\n", + "\n", + "# Number of colour channels for the images: 1 channel for gray-scale.\n", + "num_channels = data.num_channels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for plotting images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_images(images, cls_true, cls_pred=None):\n", + " assert len(images) == len(cls_true) == 9\n", + " \n", + " # Create figure with 3x3 sub-plots.\n", + " fig, axes = plt.subplots(3, 3)\n", + " fig.subplots_adjust(hspace=0.3, wspace=0.3)\n", + "\n", + " for i, ax in enumerate(axes.flat):\n", + " # Plot image.\n", + " ax.imshow(images[i].reshape(img_shape), cmap='binary')\n", + "\n", + " # Show true and predicted classes.\n", + " if cls_pred is None:\n", + " xlabel = \"True: {0}\".format(cls_true[i])\n", + " else:\n", + " xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n", + "\n", + " # Show the classes as the label on the x-axis.\n", + " ax.set_xlabel(xlabel)\n", + " \n", + " # Remove ticks from the plot.\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " \n", + " # Ensure the plot is shown correctly with multiple plots\n", + " # in a single Notebook cell.\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot a few images to see if data is correct" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the first images from the test-set.\n", + "images = data.x_test[0:9]\n", + "\n", + "# Get the true classes for those images.\n", + "cls_true = data.y_test_cls[0:9]\n", + "\n", + "# Plot the images and labels using our helper-function above.\n", + "plot_images(images=images, cls_true=cls_true)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function to plot example errors\n", + "\n", + "Function for plotting examples of images from the test-set that have been mis-classified." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_example_errors(cls_pred):\n", + " # cls_pred is an array of the predicted class-number for\n", + " # all images in the test-set.\n", + "\n", + " # Boolean array whether the predicted class is incorrect.\n", + " incorrect = (cls_pred != data.y_test_cls)\n", + "\n", + " # Get the images from the test-set that have been\n", + " # incorrectly classified.\n", + " images = data.x_test[incorrect]\n", + " \n", + " # Get the predicted classes for those images.\n", + " cls_pred = cls_pred[incorrect]\n", + "\n", + " # Get the true classes for those images.\n", + " cls_true = data.y_test_cls[incorrect]\n", + " \n", + " # Plot the first 9 images.\n", + " plot_images(images=images[0:9],\n", + " cls_true=cls_true[0:9],\n", + " cls_pred=cls_pred[0:9])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PrettyTensor API\n", + "\n", + "This is how the Convolutional Neural Network was implemented in Tutorial #03 using the PrettyTensor API. It is shown here for easy comparison to the Keras implementation below." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "if False:\n", + " x_pretty = pt.wrap(x_image)\n", + "\n", + " with pt.defaults_scope(activation_fn=tf.nn.relu):\n", + " y_pred, loss = x_pretty.\\\n", + " conv2d(kernel=5, depth=16, name='layer_conv1').\\\n", + " max_pool(kernel=2, stride=2).\\\n", + " conv2d(kernel=5, depth=36, name='layer_conv2').\\\n", + " max_pool(kernel=2, stride=2).\\\n", + " flatten().\\\n", + " fully_connected(size=128, name='layer_fc1').\\\n", + " softmax_classifier(num_classes=num_classes, labels=y_true)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sequential Model\n", + "\n", + "The Keras API has two modes of constructing Neural Networks. The simplest is the Sequential Model which only allows for the layers to be added in sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Start construction of the Keras Sequential model.\n", + "model = Sequential()\n", + "\n", + "# Add an input layer which is similar to a feed_dict in TensorFlow.\n", + "# Note that the input-shape must be a tuple containing the image-size.\n", + "model.add(InputLayer(input_shape=(img_size_flat,)))\n", + "\n", + "# The input is a flattened array with 784 elements,\n", + "# but the convolutional layers expect images with shape (28, 28, 1)\n", + "model.add(Reshape(img_shape_full))\n", + "\n", + "# First convolutional layer with ReLU-activation and max-pooling.\n", + "model.add(Conv2D(kernel_size=5, strides=1, filters=16, padding='same',\n", + " activation='relu', name='layer_conv1'))\n", + "model.add(MaxPooling2D(pool_size=2, strides=2))\n", + "\n", + "# Second convolutional layer with ReLU-activation and max-pooling.\n", + "model.add(Conv2D(kernel_size=5, strides=1, filters=36, padding='same',\n", + " activation='relu', name='layer_conv2'))\n", + "model.add(MaxPooling2D(pool_size=2, strides=2))\n", + "\n", + "# Flatten the 4-rank output of the convolutional layers\n", + "# to 2-rank that can be input to a fully-connected / dense layer.\n", + "model.add(Flatten())\n", + "\n", + "# First fully-connected / dense layer with ReLU-activation.\n", + "model.add(Dense(128, activation='relu'))\n", + "\n", + "# Last fully-connected / dense layer with softmax-activation\n", + "# for use in classification.\n", + "model.add(Dense(num_classes, activation='softmax'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Compilation\n", + "\n", + "The Neural Network has now been defined and must be finalized by adding a loss-function, optimizer and performance metrics. This is called model \"compilation\" in Keras.\n", + "\n", + "We can either define the optimizer using a string, or if we want more control of its parameters then we need to instantiate an object. For example, we can set the learning-rate." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.optimizers import Adam\n", + "\n", + "optimizer = Adam(lr=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a classification-problem such as MNIST which has 10 possible classes, we need to use the loss-function called `categorical_crossentropy`. The performance metric we are interested in is the classification accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(optimizer=optimizer,\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training\n", + "\n", + "Now that the model has been fully defined with loss-function and optimizer, we can train it. This function takes numpy-arrays and performs the given number of training epochs using the given batch-size. An epoch is one full use of the entire training-set. So for 10 epochs we would iterate randomly over the entire training-set 10 times." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 55000 samples\n", + "55000/55000 [==============================] - 21s 375us/sample - loss: 0.2251 - accuracy: 0.9335\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(x=data.x_train,\n", + " y=data.y_train,\n", + " epochs=1, batch_size=128)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation\n", + "\n", + "Now that the model has been trained we can test its performance on the test-set. This also uses numpy-arrays as input." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10000/10000 [==============================] - 2s 187us/sample - loss: 0.0771 - accuracy: 0.9756\n" + ] + } + ], + "source": [ + "result = model.evaluate(x=data.x_test,\n", + " y=data.y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can print all the performance metrics for the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss 0.07707656768076122\n", + "accuracy 0.9756\n" + ] + } + ], + "source": [ + "for name, value in zip(model.metrics_names, result):\n", + " print(name, value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or we can just print the classification accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 97.56%\n" + ] + } + ], + "source": [ + "print(\"{0}: {1:.2%}\".format(model.metrics_names[1], result[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prediction\n", + "\n", + "We can also predict the classification for new images. We will just use some images from the test-set but you could load your own images into numpy arrays and use those instead." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "images = data.x_test[0:9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the true class-number for those images. This is only used when plotting the images." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "cls_true = data.y_test_cls[0:9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the predicted classes as One-Hot encoded arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model.predict(x=images)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the predicted classes as integers." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "cls_pred = np.argmax(y_pred, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_images(images=images,\n", + " cls_true=cls_true,\n", + " cls_pred=cls_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examples of Mis-Classified Images\n", + "\n", + "We can plot some examples of mis-classified images from the test-set.\n", + "\n", + "First we get the predicted classes for all the images in the test-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model.predict(x=data.x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we convert the predicted class-numbers from One-Hot encoded arrays to integers." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "cls_pred = np.argmax(y_pred, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot some of the mis-classified images." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_example_errors(cls_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Functional Model\n", + "\n", + "The Keras API can also be used to construct more complicated networks using the Functional Model. This may look a little confusing at first, because each call to the Keras API will create and return an instance that is itself callable. It is not clear whether it is a function or an object - but we can call it as if it is a function. This allows us to build computational graphs that are more complex than the Sequential Model allows." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an input layer which is similar to a feed_dict in TensorFlow.\n", + "# Note that the input-shape must be a tuple containing the image-size.\n", + "inputs = Input(shape=(img_size_flat,))\n", + "\n", + "# Variable used for building the Neural Network.\n", + "net = inputs\n", + "\n", + "# The input is an image as a flattened array with 784 elements.\n", + "# But the convolutional layers expect images with shape (28, 28, 1)\n", + "net = Reshape(img_shape_full)(net)\n", + "\n", + "# First convolutional layer with ReLU-activation and max-pooling.\n", + "net = Conv2D(kernel_size=5, strides=1, filters=16, padding='same',\n", + " activation='relu', name='layer_conv1')(net)\n", + "net = MaxPooling2D(pool_size=2, strides=2)(net)\n", + "\n", + "# Second convolutional layer with ReLU-activation and max-pooling.\n", + "net = Conv2D(kernel_size=5, strides=1, filters=36, padding='same',\n", + " activation='relu', name='layer_conv2')(net)\n", + "net = MaxPooling2D(pool_size=2, strides=2)(net)\n", + "\n", + "# Flatten the output of the conv-layer from 4-dim to 2-dim.\n", + "net = Flatten()(net)\n", + "\n", + "# First fully-connected / dense layer with ReLU-activation.\n", + "net = Dense(128, activation='relu')(net)\n", + "\n", + "# Last fully-connected / dense layer with softmax-activation\n", + "# so it can be used for classification.\n", + "net = Dense(num_classes, activation='softmax')(net)\n", + "\n", + "# Output of the Neural Network.\n", + "outputs = net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Compilation\n", + "\n", + "We have now defined the architecture of the model with its input and output. We now have to create a Keras model and compile it with a loss-function and optimizer, so it is ready for training." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.python.keras.models import Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a new instance of the Keras Functional Model. We give it the inputs and outputs of the Convolutional Neural Network that we constructed above." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "model2 = Model(inputs=inputs, outputs=outputs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compile the Keras model using the RMSprop optimizer and with a loss-function for multiple categories. The only performance metric we are interested in is the classification accuracy, but you could use a list of metrics here." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "model2.compile(optimizer='rmsprop',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training\n", + "\n", + "The model has now been defined and compiled so it can be trained using the same `fit()` function as used in the Sequential Model above. This also takes numpy-arrays as input." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 55000 samples\n", + "55000/55000 [==============================] - 16s 298us/sample - loss: 0.1977 - accuracy: 0.9389\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model2.fit(x=data.x_train,\n", + " y=data.y_train,\n", + " epochs=1, batch_size=128)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation\n", + "\n", + "Once the model has been trained we can evaluate its performance on the test-set. This is the same syntax as for the Sequential Model." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10000/10000 [==============================] - 2s 169us/sample - loss: 0.0563 - accuracy: 0.9809\n" + ] + } + ], + "source": [ + "result = model2.evaluate(x=data.x_test,\n", + " y=data.y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a list of values, containing the loss-value and all the metrics we defined when we compiled the model. Note that 'accuracy' is now called 'acc' which is a small inconsistency." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss 0.05628199413705152\n", + "accuracy 0.9809\n" + ] + } + ], + "source": [ + "for name, value in zip(model2.metrics_names, result):\n", + " print(name, value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also print the classification accuracy as a percentage:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 98.09%\n" + ] + } + ], + "source": [ + "print(\"{0}: {1:.2%}\".format(model2.metrics_names[1], result[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examples of Mis-Classified Images\n", + "\n", + "We can plot some examples of mis-classified images from the test-set.\n", + "\n", + "First we get the predicted classes for all the images in the test-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model2.predict(x=data.x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we convert the predicted class-numbers from One-Hot encoded arrays to integers." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "cls_pred = np.argmax(y_pred, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot some of the mis-classified images." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_example_errors(cls_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save & Load Model\n", + "\n", + "NOTE: You need to install `h5py` for this to work!\n", + "\n", + "Tutorial #04 was about saving and restoring the weights of a model using native TensorFlow code. It was an absolutely horrible API! Fortunately, Keras makes this very easy.\n", + "\n", + "This is the file-path where we want to save the Keras model." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "path_model = 'model.keras'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Saving a Keras model with the trained weights is then just a single function call, as it should be." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "model2.save(path_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Delete the model from memory so we are sure it is no longer used." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "del model2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to import this Keras function for loading the model." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.python.keras.models import load_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loading the model is then just a single function-call, as it should be." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "model3 = load_model(path_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then use the model again e.g. to make predictions. We get the first 9 images from the test-set and their true class-numbers." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "images = data.x_test[0:9]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "cls_true = data.y_test_cls[0:9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then use the restored model to predict the class-numbers for those images." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model3.predict(x=images)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the class-numbers as integers." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "cls_pred = np.argmax(y_pred, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the images with their true and predicted class-numbers." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_images(images=images,\n", + " cls_pred=cls_pred,\n", + " cls_true=cls_true)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization of Layer Weights and Outputs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for plotting convolutional weights" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_conv_weights(weights, input_channel=0):\n", + " # Get the lowest and highest values for the weights.\n", + " # This is used to correct the colour intensity across\n", + " # the images so they can be compared with each other.\n", + " w_min = np.min(weights)\n", + " w_max = np.max(weights)\n", + "\n", + " # Number of filters used in the conv. layer.\n", + " num_filters = weights.shape[3]\n", + "\n", + " # Number of grids to plot.\n", + " # Rounded-up, square-root of the number of filters.\n", + " num_grids = math.ceil(math.sqrt(num_filters))\n", + " \n", + " # Create figure with a grid of sub-plots.\n", + " fig, axes = plt.subplots(num_grids, num_grids)\n", + "\n", + " # Plot all the filter-weights.\n", + " for i, ax in enumerate(axes.flat):\n", + " # Only plot the valid filter-weights.\n", + " if i" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "layer_conv1 = model3.layers[2]\n", + "layer_conv1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second convolutional layer has index 4." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "layer_conv2 = model3.layers[4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convolutional Weights\n", + "\n", + "Now that we have the layers we can easily get their weights." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "weights_conv1 = layer_conv1.get_weights()[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This gives us a 4-rank tensor." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(5, 5, 1, 16)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights_conv1.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the weights using the helper-function from above." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_conv_weights(weights=weights_conv1, input_channel=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also get the weights for the second convolutional layer and plot them." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "weights_conv2 = layer_conv2.get_weights()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_conv_weights(weights=weights_conv2, input_channel=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for plotting the output of a convolutional layer" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_conv_output(values):\n", + " # Number of filters used in the conv. layer.\n", + " num_filters = values.shape[3]\n", + "\n", + " # Number of grids to plot.\n", + " # Rounded-up, square-root of the number of filters.\n", + " num_grids = math.ceil(math.sqrt(num_filters))\n", + " \n", + " # Create figure with a grid of sub-plots.\n", + " fig, axes = plt.subplots(num_grids, num_grids)\n", + "\n", + " # Plot the output images of all the filters.\n", + " for i, ax in enumerate(axes.flat):\n", + " # Only plot the images for valid filters.\n", + " if i" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "image1 = data.x_test[0]\n", + "plot_image(image1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Output of Convolutional Layer\n", + "\n", + "In order to show the output of a convolutional layer, we can create another Functional Model using the same input as the original model, but the output is now taken from the convolutional layer that we are interested in." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "output_conv2 = Model(inputs=layer_input.input,\n", + " outputs=layer_conv2.output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This creates a new model-object where we can call the typical Keras functions. To get the output of the convoloutional layer we call the `predict()` function with the input image." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 14, 14, 36)" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "layer_output2 = output_conv2.predict(np.array([image1]))\n", + "layer_output2.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then plot the images for all 36 channels." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_conv_output(values=layer_output2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed how to use the so-called *Keras API* for easily building Convolutional Neural Networks in TensorFlow. Keras is by far the most complete and best designed API for TensorFlow.\n", + "\n", + "This tutorial also showed how to use Keras to save and load a model, as well as getting the weights and outputs of convolutional layers.\n", + "\n", + "It seems likely that Keras will be the standard API for TensorFlow in the future, for the simple reason that is already very good and it is constantly being improved. So it is recommended that you use Keras." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Train for more epochs. Does it improve the classification accuracy?\n", + "* Change the activation function to sigmoid for some of the layers.\n", + "* Can you find a simple way of changing the activation function for all the layers?\n", + "* Plot the output of the max-pooling layers instead of the conv-layers.\n", + "* Replace the 2x2 max-pooling layers with stride=2 in the convolutional layers. Is there a difference in classification accuracy? What if you optimize it again and again? The difference is random, so how would you measure if there really is a difference? What are the pros and cons of using max-pooling vs. stride in the conv-layer?\n", + "* Change the parameters for the layers, e.g. the kernel, depth, size, etc. What is the difference in time usage and classification accuracy?\n", + "* Add and remove some convolutional and fully-connected layers.\n", + "* What is the simplest network you can design that still performs well?\n", + "* Change the Functional Model so it has another convolutional layer that connects in parallel to the existing conv-layers before going into the dense layers.\n", + "* Change the Functional Model so it outputs the predicted class both as a One-Hot encoded array and as an integer, so we don't have to use `numpy.argmax()` afterwards.\n", + "* Remake the program yourself without looking too much at this source-code.\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "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:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "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." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/03_PrettyTensor.ipynb b/03_PrettyTensor.ipynb index 238cc7c..a23fe03 100644 --- a/03_PrettyTensor.ipynb +++ b/03_PrettyTensor.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It is recommended that you use the _Keras API_ instead, see Tutorial #03-C.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -44,7 +53,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -120,14 +128,12 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'0.12.0-rc0'" + "'1.1.0'" ] }, "execution_count": 3, @@ -149,14 +155,12 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'0.7.1'" + "'0.7.4'" ] }, "execution_count": 4, @@ -185,9 +189,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -215,9 +217,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -354,15 +354,13 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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ANzuvq96Vd4EErN/vzxz4UwG4NbuSQkvW2jq6z6u7OhK5eDyOSCSCQCDA7vIE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mmFrFKfF6+eWXgaBLVJgfTrvvvvvGGlNllBmKiBBjZvjUU09l9Xy+m0VRURFQ+XAe31VH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\nRK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -463,7 +461,7 @@ }, "outputs": [], "source": [ - "y_true = tf.placeholder(tf.float32, shape=[None, 10], name='y_true')" + "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')" ] }, { @@ -795,7 +793,6 @@ "cell_type": "code", "execution_count": 22, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [], @@ -900,9 +897,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" @@ -1005,9 +1000,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" @@ -1050,9 +1043,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Counter for total number of iterations performed so far.\n", @@ -1225,9 +1216,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Split the test-set into smaller batches of this size.\n", @@ -1312,15 +1301,13 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 9.1% (909 / 10000)\n" + "Accuracy on Test-Set: 9.8% (977 / 10000)\n" ] } ], @@ -1340,9 +1327,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1361,7 +1346,6 @@ "cell_type": "code", "execution_count": 38, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1369,7 +1353,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 8.9% (892 / 10000)\n" + "Accuracy on Test-Set: 9.0% (905 / 10000)\n" ] } ], @@ -1390,7 +1374,6 @@ "cell_type": "code", "execution_count": 39, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1409,23 +1392,21 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 83.9% (8393 / 10000)\n", + "Accuracy on Test-Set: 87.5% (8748 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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22Wf4/PPPeYRTuVyG3W5HOBx+o9GG3W7nTRlt3iKRCDRNm2noYR41Ri5gn883kw15VVG6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\nA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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1449,7 +1430,6 @@ "cell_type": "code", "execution_count": 41, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1457,15 +1437,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimization Iteration: 101, Training Accuracy: 93.8%\n", - "Optimization Iteration: 201, Training Accuracy: 89.1%\n", - "Optimization Iteration: 301, Training Accuracy: 85.9%\n", - "Optimization Iteration: 401, Training Accuracy: 87.5%\n", - "Optimization Iteration: 501, Training Accuracy: 92.2%\n", + "Optimization Iteration: 101, Training Accuracy: 84.4%\n", + "Optimization Iteration: 201, Training Accuracy: 93.8%\n", + "Optimization Iteration: 301, Training Accuracy: 93.8%\n", + "Optimization Iteration: 401, Training Accuracy: 96.9%\n", + "Optimization Iteration: 501, Training Accuracy: 95.3%\n", "Optimization Iteration: 601, Training Accuracy: 95.3%\n", - "Optimization Iteration: 701, Training Accuracy: 95.3%\n", - "Optimization Iteration: 801, Training Accuracy: 90.6%\n", - "Optimization Iteration: 901, Training Accuracy: 98.4%\n", + "Optimization Iteration: 701, Training Accuracy: 100.0%\n", + "Optimization Iteration: 801, Training Accuracy: 96.9%\n", + "Optimization Iteration: 901, Training Accuracy: 95.3%\n", "Time usage: 0:00:03\n" ] } @@ -1478,7 +1458,6 @@ "cell_type": "code", "execution_count": 42, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1486,15 +1465,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 96.3% (9634 / 10000)\n", + "Accuracy on Test-Set: 96.6% (9656 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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Op1k8X3aqp8/DysoKQqEQ9xkVlgtqmtFut9Htdi9135vNZrhcLoRCISSTSS6LIo8H3cgz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\ne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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1518,7 +1497,6 @@ "cell_type": "code", "execution_count": 43, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1526,97 +1504,97 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimization Iteration: 1001, Training Accuracy: 98.4%\n", - "Optimization Iteration: 1101, Training Accuracy: 95.3%\n", + "Optimization Iteration: 1001, Training Accuracy: 96.9%\n", + "Optimization Iteration: 1101, Training Accuracy: 98.4%\n", "Optimization Iteration: 1201, Training Accuracy: 98.4%\n", - "Optimization Iteration: 1301, Training Accuracy: 96.9%\n", - "Optimization Iteration: 1401, Training Accuracy: 100.0%\n", - "Optimization Iteration: 1501, Training Accuracy: 95.3%\n", - "Optimization Iteration: 1601, Training Accuracy: 96.9%\n", - "Optimization Iteration: 1701, Training Accuracy: 96.9%\n", - "Optimization Iteration: 1801, Training Accuracy: 98.4%\n", - "Optimization Iteration: 1901, Training Accuracy: 96.9%\n", - "Optimization Iteration: 2001, Training Accuracy: 98.4%\n", - "Optimization Iteration: 2101, Training Accuracy: 95.3%\n", - "Optimization Iteration: 2201, Training Accuracy: 98.4%\n", + "Optimization Iteration: 1301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 1401, Training Accuracy: 96.9%\n", + "Optimization Iteration: 1501, Training Accuracy: 96.9%\n", + "Optimization Iteration: 1601, Training Accuracy: 98.4%\n", + "Optimization Iteration: 1701, Training Accuracy: 100.0%\n", + "Optimization Iteration: 1801, Training Accuracy: 96.9%\n", + "Optimization Iteration: 1901, Training Accuracy: 100.0%\n", + "Optimization Iteration: 2001, Training Accuracy: 100.0%\n", + "Optimization Iteration: 2101, Training Accuracy: 98.4%\n", + "Optimization Iteration: 2201, Training Accuracy: 95.3%\n", "Optimization Iteration: 2301, Training Accuracy: 98.4%\n", "Optimization Iteration: 2401, Training Accuracy: 98.4%\n", - "Optimization Iteration: 2501, Training Accuracy: 93.8%\n", 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"Optimization Iteration: 3701, Training Accuracy: 96.9%\n", + "Optimization Iteration: 3501, Training Accuracy: 98.4%\n", + "Optimization Iteration: 3601, Training Accuracy: 100.0%\n", + "Optimization Iteration: 3701, Training Accuracy: 98.4%\n", "Optimization Iteration: 3801, Training Accuracy: 100.0%\n", - "Optimization Iteration: 3901, Training Accuracy: 98.4%\n", + "Optimization Iteration: 3901, Training Accuracy: 100.0%\n", "Optimization Iteration: 4001, Training Accuracy: 96.9%\n", "Optimization Iteration: 4101, Training Accuracy: 98.4%\n", - "Optimization Iteration: 4201, Training Accuracy: 100.0%\n", + "Optimization Iteration: 4201, Training Accuracy: 96.9%\n", "Optimization Iteration: 4301, Training Accuracy: 100.0%\n", - "Optimization Iteration: 4401, Training Accuracy: 100.0%\n", - "Optimization Iteration: 4501, Training Accuracy: 100.0%\n", + "Optimization Iteration: 4401, Training Accuracy: 98.4%\n", + "Optimization Iteration: 4501, Training Accuracy: 98.4%\n", 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"Optimization Iteration: 5501, Training Accuracy: 98.4%\n", - "Optimization Iteration: 5601, Training Accuracy: 96.9%\n", - "Optimization Iteration: 5701, Training Accuracy: 100.0%\n", - "Optimization Iteration: 5801, Training Accuracy: 96.9%\n", + "Optimization Iteration: 5501, Training Accuracy: 95.3%\n", + "Optimization Iteration: 5601, Training Accuracy: 98.4%\n", + "Optimization Iteration: 5701, Training Accuracy: 98.4%\n", + "Optimization Iteration: 5801, Training Accuracy: 98.4%\n", "Optimization Iteration: 5901, Training Accuracy: 100.0%\n", "Optimization Iteration: 6001, Training Accuracy: 98.4%\n", "Optimization Iteration: 6101, Training Accuracy: 98.4%\n", - "Optimization Iteration: 6201, Training Accuracy: 98.4%\n", - "Optimization Iteration: 6301, Training Accuracy: 98.4%\n", - "Optimization Iteration: 6401, Training Accuracy: 100.0%\n", - "Optimization Iteration: 6501, Training Accuracy: 100.0%\n", + "Optimization Iteration: 6201, Training Accuracy: 100.0%\n", + 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"Optimization Iteration: 7601, Training Accuracy: 96.9%\n", + "Optimization Iteration: 7601, Training Accuracy: 100.0%\n", "Optimization Iteration: 7701, Training Accuracy: 100.0%\n", "Optimization Iteration: 7801, Training Accuracy: 100.0%\n", "Optimization Iteration: 7901, Training Accuracy: 100.0%\n", - "Optimization Iteration: 8001, Training Accuracy: 98.4%\n", - "Optimization Iteration: 8101, Training Accuracy: 100.0%\n", - "Optimization Iteration: 8201, Training Accuracy: 100.0%\n", - "Optimization Iteration: 8301, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8001, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8101, Training Accuracy: 98.4%\n", + "Optimization Iteration: 8201, Training Accuracy: 98.4%\n", + "Optimization Iteration: 8301, Training Accuracy: 98.4%\n", "Optimization Iteration: 8401, Training Accuracy: 100.0%\n", - "Optimization Iteration: 8501, Training Accuracy: 98.4%\n", + "Optimization Iteration: 8501, Training Accuracy: 100.0%\n", "Optimization Iteration: 8601, Training Accuracy: 100.0%\n", "Optimization Iteration: 8701, Training Accuracy: 100.0%\n", - "Optimization Iteration: 8801, Training Accuracy: 100.0%\n", - "Optimization Iteration: 8901, Training Accuracy: 100.0%\n", - "Optimization Iteration: 9001, Training Accuracy: 98.4%\n", - "Optimization Iteration: 9101, Training Accuracy: 98.4%\n", + "Optimization Iteration: 8801, Training Accuracy: 96.9%\n", + "Optimization Iteration: 8901, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9001, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9101, Training Accuracy: 100.0%\n", "Optimization Iteration: 9201, Training Accuracy: 100.0%\n", "Optimization Iteration: 9301, Training Accuracy: 100.0%\n", - "Optimization Iteration: 9401, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9401, Training Accuracy: 96.9%\n", "Optimization Iteration: 9501, Training Accuracy: 100.0%\n", - "Optimization Iteration: 9601, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9601, Training Accuracy: 96.9%\n", "Optimization Iteration: 9701, Training Accuracy: 100.0%\n", "Optimization Iteration: 9801, Training Accuracy: 98.4%\n", - "Optimization Iteration: 9901, Training Accuracy: 100.0%\n", - "Time usage: 0:00:27\n" + "Optimization Iteration: 9901, Training Accuracy: 98.4%\n", + "Time usage: 0:00:26\n" ] } ], @@ -1628,7 +1606,6 @@ "cell_type": "code", "execution_count": 44, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1636,15 +1613,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 98.8% (9881 / 10000)\n", + "Accuracy on Test-Set: 98.9% (9887 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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LhjORSLydP5Tk1lEUBQ6HAx6P51aNJ8XljSGO16l/ljwMjNKTxrZ6mUwGlUoF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\nL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fAT4lkamjgeQawmnqNI8AnnbO/ZxFmspJrvK4NTDNzOYAM4BJzrnng/dKKY+P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\nTRGRGFRoiojEoEJTRCQGFZoiIjH8PzBnVOmSu4nHAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1655,23 +1632,23 @@ "output_type": "stream", "text": [ "Confusion Matrix:\n", - "[[ 975 0 0 0 0 0 1 1 3 0]\n", - " [ 0 1127 2 0 0 0 1 2 3 0]\n", - " [ 2 2 1019 1 1 0 1 2 4 0]\n", - " [ 0 0 0 1005 0 1 0 1 3 0]\n", - " [ 0 0 0 0 977 0 1 0 1 3]\n", - " [ 2 0 0 13 0 870 1 0 6 0]\n", - " [ 5 2 0 0 1 3 943 0 4 0]\n", - " [ 0 2 8 2 1 0 0 1007 1 7]\n", - " [ 2 0 2 3 1 1 0 0 964 1]\n", - " [ 0 2 0 4 5 1 0 1 2 994]]\n" + "[[ 979 0 0 0 0 0 0 1 0 0]\n", + " [ 1 1128 1 0 0 1 2 0 2 0]\n", + " [ 5 1 1008 2 2 0 0 6 8 0]\n", + " [ 1 0 0 1001 0 3 0 0 5 0]\n", + " [ 0 0 0 0 975 0 1 1 1 4]\n", + " [ 2 0 0 3 0 883 1 1 2 0]\n", + " [ 5 2 0 0 1 1 948 0 1 0]\n", + " [ 1 1 1 2 0 0 0 1022 1 0]\n", + " [ 5 0 1 0 0 1 0 1 964 2]\n", + " [ 4 3 0 2 8 3 0 6 4 979]]\n" ] }, { "data": { - "image/png": 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uZWbWkZyWaY/gDuwD/IL0hlYA38v2Xw6c0KpGmVmHcnBvj+AeEb/CUyGYWVEc3NsjuJuZ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\n5ndJQflR0oidHSJiAWlUzTD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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1773,15 +1750,14 @@ "cell_type": "code", "execution_count": 46, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1814,15 +1790,14 @@ "cell_type": "code", "execution_count": 47, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ { "data": { - "image/png": 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J6Q74mOnz833eD/r3hewd/evgvaN/9M/1yGr54SJlZWX1lXSxpCpJzeYT2l+O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\nZ9cBeyfRv7aITe8k+tdWJ2v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16+K1o37Uz/UoaH9xkQoKCkolnScpIyn6n67xUCipQtKz2Wx2RycfC/XrgC5Y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\n", 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jWRb7l78EHJ8Rly8THT3K4pofOdRs3y7G+ZGRakn2H/8oH7jlFqfRGRNtN8ivSObrrzLP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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1879,9 +1852,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -1945,9 +1916,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1959,9 +1930,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/04_Save_Restore.ipynb b/04_Save_Restore.ipynb index 523cdba..4491afd 100644 --- a/04_Save_Restore.ipynb +++ b/04_Save_Restore.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It is recommended that you use the _Keras API_ instead, which also makes it much easier to save and load a model, see Tutorial #03-C.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -44,7 +53,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -104,9 +112,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -133,9 +139,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -169,9 +173,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -199,9 +201,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -231,9 +231,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "data.test.cls = np.argmax(data.test.labels, axis=1)\n", @@ -339,9 +337,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -572,9 +568,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "weights_conv1 = get_weights_variable(layer_name='layer_conv1')\n", @@ -602,9 +596,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" @@ -737,9 +729,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "save_path = os.path.join(save_dir, 'best_validation')" @@ -803,9 +793,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "init_variables()" @@ -873,9 +861,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Counter for total number of iterations performed so far.\n", @@ -1234,9 +1220,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def print_test_accuracy(show_example_errors=False,\n", @@ -1344,7 +1328,6 @@ "cell_type": "code", "execution_count": 42, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1371,7 +1354,6 @@ "cell_type": "code", "execution_count": 43, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1412,7 +1394,6 @@ "cell_type": "code", "execution_count": 44, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1491,7 +1472,6 @@ "cell_type": "code", "execution_count": 45, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1560,9 +1540,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1598,9 +1576,7 @@ { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "init_variables()" @@ -1616,9 +1592,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1643,7 +1617,6 @@ "cell_type": "code", "execution_count": 49, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1702,7 +1675,6 @@ "cell_type": "code", "execution_count": 51, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1768,7 +1740,6 @@ "cell_type": "code", "execution_count": 52, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1811,9 +1782,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -1875,9 +1844,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1889,9 +1858,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/05_Ensemble_Learning.ipynb b/05_Ensemble_Learning.ipynb index 60d1ccf..6afc6cf 100644 --- a/05_Ensemble_Learning.ipynb +++ b/05_Ensemble_Learning.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It is recommended that you use the _Keras API_ instead, which also makes it much easier to train or load multiple models to create an ensemble, see e.g. Tutorial #10 for inspiration on how to load and use pre-trained models using Keras.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -44,7 +53,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -105,7 +113,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -134,9 +141,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -170,9 +175,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -200,9 +203,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -234,9 +235,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "data.test.cls = np.argmax(data.test.labels, axis=1)\n", @@ -274,9 +273,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -303,7 +300,6 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -333,9 +329,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -363,9 +357,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -534,9 +526,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -740,9 +730,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" @@ -1008,9 +996,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def optimize(num_iterations, x_train, y_train):\n", @@ -1111,9 +1097,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1928,7 +1912,6 @@ "cell_type": "code", "execution_count": 47, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1958,9 +1941,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1989,7 +1970,6 @@ "cell_type": "code", "execution_count": 49, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2027,9 +2007,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2057,9 +2035,7 @@ { "cell_type": "code", "execution_count": 51, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2105,9 +2081,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "ensemble_incorrect = np.logical_not(ensemble_correct)" @@ -2127,9 +2101,7 @@ { "cell_type": "code", "execution_count": 54, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2156,9 +2128,7 @@ { "cell_type": "code", "execution_count": 55, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2186,9 +2156,7 @@ { "cell_type": "code", "execution_count": 56, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2215,9 +2183,7 @@ { "cell_type": "code", "execution_count": 57, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "best_net_pred_labels = pred_labels[best_net, :, :]" @@ -2294,9 +2260,7 @@ { "cell_type": "code", "execution_count": 61, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2323,9 +2287,7 @@ { "cell_type": "code", "execution_count": 62, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2352,9 +2314,7 @@ { "cell_type": "code", "execution_count": 63, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "ensemble_better = np.logical_and(best_net_incorrect,\n", @@ -2372,7 +2332,6 @@ "cell_type": "code", "execution_count": 64, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2421,7 +2380,6 @@ "cell_type": "code", "execution_count": 66, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2457,9 +2415,7 @@ { "cell_type": "code", "execution_count": 67, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def plot_images_comparison(idx):\n", @@ -2569,7 +2525,6 @@ "cell_type": "code", "execution_count": 72, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2598,9 +2553,7 @@ { "cell_type": "code", "execution_count": 73, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2624,9 +2577,7 @@ { "cell_type": "code", "execution_count": 74, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2651,7 +2602,6 @@ "cell_type": "code", "execution_count": 75, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2684,7 +2634,6 @@ "cell_type": "code", "execution_count": 76, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2714,7 +2663,6 @@ "cell_type": "code", "execution_count": 77, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2741,7 +2689,6 @@ "cell_type": "code", "execution_count": 78, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2767,9 +2714,7 @@ { "cell_type": "code", "execution_count": 79, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2804,9 +2749,7 @@ { "cell_type": "code", "execution_count": 80, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -2867,9 +2810,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2881,9 +2824,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/06_CIFAR-10.ipynb b/06_CIFAR-10.ipynb index 8a145e4..9e45d12 100644 --- a/06_CIFAR-10.ipynb +++ b/06_CIFAR-10.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It would take too much effort to update this tutorial to use e.g. the Keras API, especially because there is already a short [Keras tutorial on CIFAR-10](https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py) which does the same.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -42,7 +51,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -103,7 +111,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -132,9 +139,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -198,7 +203,6 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -224,9 +228,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -270,9 +272,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -300,9 +300,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -327,7 +325,6 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -472,7 +469,6 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -508,9 +504,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -712,9 +706,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "distorted_images = pre_process(images=x, training=True)" @@ -1021,9 +1013,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "weights_conv1 = get_weights_variable(layer_name='layer_conv1')\n", @@ -1049,9 +1039,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def get_layer_output(layer_name):\n", @@ -1075,9 +1063,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "output_conv1 = get_layer_output(layer_name='layer_conv1')\n", @@ -1182,9 +1168,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1559,9 +1543,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def print_test_accuracy(show_example_errors=False,\n", @@ -1808,7 +1790,6 @@ "cell_type": "code", "execution_count": 56, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1847,7 +1828,6 @@ "cell_type": "code", "execution_count": 57, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [], @@ -1869,7 +1849,6 @@ "cell_type": "code", "execution_count": 58, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1930,7 +1909,6 @@ "cell_type": "code", "execution_count": 59, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1968,7 +1946,6 @@ "cell_type": "code", "execution_count": 60, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -2048,9 +2025,7 @@ { "cell_type": "code", "execution_count": 62, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2079,7 +2054,6 @@ "cell_type": "code", "execution_count": 63, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2109,7 +2083,6 @@ "cell_type": "code", "execution_count": 64, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2145,9 +2118,7 @@ { "cell_type": "code", "execution_count": 65, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "label_pred, cls_pred = session.run([y_pred, y_pred_cls],\n", @@ -2165,7 +2136,6 @@ "cell_type": "code", "execution_count": 66, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2197,9 +2167,7 @@ { "cell_type": "code", "execution_count": 67, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2219,9 +2187,7 @@ { "cell_type": "code", "execution_count": 68, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2255,9 +2221,7 @@ { "cell_type": "code", "execution_count": 69, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -2315,9 +2279,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2329,9 +2293,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/07_Inception_Model.ipynb b/07_Inception_Model.ipynb index e00ebb4..da11720 100644 --- a/07_Inception_Model.ipynb +++ b/07_Inception_Model.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v.2. It would take too much effort to update this tutorial to use e.g. the Keras API, especially because Tutorial #10 is somewhat similar.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -48,7 +57,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -104,14 +112,12 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'0.10.0rc0'" + "'1.1.0'" ] }, "execution_count": 3, @@ -158,9 +164,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -186,27 +190,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Load the Inception model so it is ready for classifying images.\n", - "\n", - "Note the deprecation warning, which might cause the program to fail in the future." + "Load the Inception model so it is ready for classifying images." ] }, { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/magnus/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py:1811: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n", - " result_shape.insert(dim, 1)\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "model = inception.Inception()" ] @@ -255,14 +246,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This image of a panda is included in the Inception data-file. The Inception model is quite confident that this image shows a panda, with a classification score of 89.23% and the next highest score being only 0.86% for an indri, which is another exotic animal." + "This image of a panda is included in the Inception data-file. The Inception model is quite confident that this image shows a panda, with a classification score of about 89% and the next highest score being only about 0.8% for an indri, which is another exotic animal." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -280,16 +270,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "89.23% : giant panda\n", - " 0.86% : indri\n", - " 0.26% : lesser panda\n", - " 0.14% : custard apple\n", - " 0.11% : earthstar\n", - " 0.08% : sea urchin\n", + "89.11% : giant panda\n", + " 0.78% : indri\n", + " 0.30% : lesser panda\n", + " 0.15% : custard apple\n", + " 0.12% : earthstar\n", + " 0.09% : sea urchin\n", " 0.05% : forklift\n", - " 0.05% : soccer ball\n", - " 0.05% : go-kart\n", - " 0.05% : digital watch\n" + " 0.05% : digital watch\n", + " 0.05% : gibbon\n", + " 0.05% : go-kart\n" ] } ], @@ -310,7 +300,7 @@ "\n", "It is perhaps better to call the output values of a neural network for classification scores or ranks, because they indicate how strongly the network believes that the input image is of each possible class.\n", "\n", - "In the above example with the image of a panda, the Inception model gave a very high score of 89.23% for the panda-class, and the scores for the remaining 999 possible classes were all below 1%. This means the Inception model was quite certain that the image showed a panda and the remaining scores below 1% should be regarded as noise. For example, the 10th highest score was 0.05% for a digital watch, but this is probably more due to the imprecise nature of neural networks rather than an indication that the image looked slightly like a digital watch.\n", + "In the above example with the image of a panda, the Inception model gave a very high score of about 89% for the panda-class, and the scores for the remaining 999 possible classes were all below 1%. This means the Inception model was quite certain that the image showed a panda and the remaining scores below 1% should be regarded as noise. For example, the 8th highest score was 0.05% for a digital watch, but this is probably more due to the imprecise nature of neural networks rather than an indication that the image looked slightly like a digital watch.\n", "\n", "Sometimes the Inception model is confused about which class an image belongs to, so none of the scores are really high. Examples of this are shown below." ] @@ -326,14 +316,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The Inception model is very confident (score 97.30%) that this image shows a kind of parrot called a macaw." + "The Inception model is very confident (score about 97%) that this image shows a kind of parrot called a macaw." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -351,16 +340,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "97.30% : macaw\n", + "97.18% : macaw\n", " 0.07% : African grey\n", - " 0.07% : toucan\n", + " 0.06% : toucan\n", " 0.05% : jacamar\n", " 0.04% : bee eater\n", - " 0.04% : lorikeet\n", + " 0.03% : lorikeet\n", " 0.02% : sulphur-crested cockatoo\n", " 0.02% : jay\n", " 0.01% : kite\n", - " 0.01% : sandbar\n" + " 0.01% : indigo bunting\n" ] } ], @@ -409,7 +398,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now plot the resized image of the parrot. This is the image that is actually input to the neural network of the Inception model. We can see that it has been squeezed so it is rectangular, and the resolution has been reduced so the image has become more pixelated and grainy.\n", + "Now plot the resized image of the parrot. This is the image that is actually input to the neural network of the Inception model. We can see that it has been squeezed so it is square, and the resolution has been reduced so the image has become more pixelated and grainy.\n", "\n", "In this case the image still clearly shows a parrot, but some images may become so distorted from this naive resizing that you may want to resize the images yourself before inputting them to the Inception model." ] @@ -418,15 +407,14 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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LVxH6lix0ONTCRU4DaSocdsJmG3m7F+73xm72LLzw4trzaqiw8PlCeD0qr/o9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\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wD5QSh7UQVkJJEjwQFSFKaCdtmm0U1JKEGVg3vwXIubKEyLIEYsigRIknLiWgkHQZmkmU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\nxBFHHPH/M4561EccccQRNxzHQH3EEUccc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DuPWUOaDqyKmmYaEpNC20q8CTJw2Nzyz9zFq3kAuWYdrBuINfbqvMGYUfRni0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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -448,14 +436,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This image of the parrot has been cropped manually to 299 x 299 pixels and then input to the Inception model, which is still very confident (score 97.38%) that it shows a parrot (macaw)." + "This image of the parrot has been cropped manually to 299 x 299 pixels and then input to the Inception model, which is still very confident (score about 97%) that it shows a parrot (macaw)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -473,7 +460,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "97.38% : macaw\n", + "97.52% : macaw\n", " 0.09% : African grey\n", " 0.03% : sulphur-crested cockatoo\n", " 0.02% : toucan\n", @@ -501,14 +488,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This is another crop of the parrot image, this time showing its body without the head or tail. The Inception model is still very confident (score 93.94%) that it shows a macaw parrot." + "This is another crop of the parrot image, this time showing its body without the head or tail. The Inception model is still very confident (score about 94%) that it shows a macaw parrot." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -526,16 +512,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "93.94% : macaw\n", - " 0.77% : toucan\n", - " 0.55% : African grey\n", - " 0.13% : jacamar\n", - " 0.12% : bee eater\n", - " 0.11% : sulphur-crested cockatoo\n", - " 0.10% : magpie\n", - " 0.09% : jay\n", - " 0.07% : lorikeet\n", - " 0.05% : hornbill\n" + "94.21% : macaw\n", + " 0.76% : toucan\n", + " 0.58% : African grey\n", + " 0.11% : jacamar\n", + " 0.10% : sulphur-crested cockatoo\n", + " 0.10% : bee eater\n", + " 0.09% : magpie\n", + " 0.08% : jay\n", + " 0.06% : lorikeet\n", + " 0.04% : hornbill\n" ] } ], @@ -554,16 +540,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This image has been cropped so it only shows the tail of the parrot. Now the Inception model is quite confused and thinks the image might show a jacamar (score 26.11%) which is another exotic bird, or perhaps the image shows a grass-hopper (score 10.61%).\n", + "This image has been cropped so it only shows the tail of the parrot. Now the Inception model is quite confused and thinks the image might show a jacamar (score about 26%) which is another exotic bird, or perhaps the image shows a grass-hopper (score about 10%).\n", "\n", - "The Inception model also thinks the image might show a fountain-pen (score 2.00%). But this is a very low score and should be interpreted as unreliable noise." + "The Inception model also thinks the image might show a fountain-pen (score about 2%). But this is a very low score and should be interpreted as unreliable noise." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -581,16 +566,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "26.11% : jacamar\n", - "10.61% : grasshopper\n", - " 4.05% : chime\n", - " 2.24% : bulbul\n", - " 2.00% : fountain pen\n", - " 1.60% : leafhopper\n", - " 1.26% : cricket\n", - " 1.25% : kite\n", - " 1.13% : macaw\n", - " 0.80% : torch\n" + "26.51% : jacamar\n", + "10.56% : grasshopper\n", + " 3.58% : chime\n", + " 2.15% : bulbul\n", + " 1.93% : fountain pen\n", + " 1.64% : leafhopper\n", + " 1.31% : kite\n", + " 1.22% : cricket\n", + " 1.09% : macaw\n", + " 0.81% : bee eater\n" ] } ], @@ -609,14 +594,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The best way to input images to this Inception model, is to pad the image so it is rectangular and then resize the image to 299 x 299 pixels, like this example of the parrot which is classified correctly with a score of 96.78%." + "The best way to input images to this Inception model, is to pad the image so it is square and then resize the image to 299 x 299 pixels, like this example of the parrot which is classified correctly with a score of about 97%." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -634,9 +618,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "96.78% : macaw\n", - " 0.06% : toucan\n", + "96.87% : macaw\n", " 0.06% : African grey\n", + " 0.06% : toucan\n", " 0.05% : bee eater\n", " 0.04% : sulphur-crested cockatoo\n", " 0.03% : king penguin\n", @@ -662,14 +646,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This image shows the living legend and super-nerd-hero Elon Musk. But the Inception model is very confused about what the image shows, predicting that it maybe shows a sweatshirt (score 19.73%) or an abaya (score 16.82%). It also thinks the image might show a ping-pong ball (score 3.05%) or a baseball (score 1.86%). So the Inception model is confused and the classification scores are unreliable." + "This image shows the living legend and super-nerd-hero Elon Musk. But the Inception model is very confused about what the image shows, predicting that it maybe shows a sweatshirt (score about 17%) or an abaya (score about 16%). It also thinks the image might show a ping-pong ball (score about 3%) or a baseball (score about 2%). So the Inception model is confused and the classification scores are unreliable." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -687,16 +670,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "19.73% : sweatshirt\n", - "16.82% : abaya\n", - " 4.17% : suit\n", - " 3.46% : trench coat\n", - " 3.05% : ping-pong ball\n", - " 1.92% : cellular telephone\n", - " 1.86% : baseball\n", - " 1.77% : jersey\n", - " 1.54% : kimono\n", - " 1.43% : water bottle\n" + "16.63% : sweatshirt\n", + "16.45% : abaya\n", + " 4.62% : suit\n", + " 3.40% : ping-pong ball\n", + " 2.89% : trench coat\n", + " 2.37% : baseball\n", + " 2.31% : cellular telephone\n", + " 1.99% : jersey\n", + " 1.42% : water bottle\n", + " 1.34% : dumbbell\n" ] } ], @@ -715,14 +698,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If we instead use a 100 x 100 pixels image of Elon Musk, then the Inception model thinks it might show a sweatshirt (score 17.85%) or a cowboy boot (score 16.36%). So now the Inception model has somewhat different predictions but it is still very confused." + "If we instead use a 100 x 100 pixels image of Elon Musk, then the Inception model thinks it might show a sweatshirt (score about 22%) or a cowboy boot (score about 14%). So now the Inception model has somewhat different predictions but it is still very confused." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -740,16 +722,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "17.85% : sweatshirt\n", - "16.36% : cowboy boot\n", - "10.68% : balance beam\n", - " 8.87% : abaya\n", - " 5.36% : suit\n", - " 4.57% : Loafer\n", - " 2.94% : trench coat\n", - " 2.65% : maillot\n", - " 1.87% : jersey\n", - " 1.42% : unicycle\n" + "21.65% : sweatshirt\n", + "14.51% : cowboy boot\n", + " 9.11% : abaya\n", + " 8.70% : balance beam\n", + " 5.92% : suit\n", + " 5.65% : Loafer\n", + " 3.66% : trench coat\n", + " 1.75% : maillot\n", + " 1.44% : jersey\n", + " 1.44% : unicycle\n" ] } ], @@ -768,15 +750,14 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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RLgvc9xSptgAANrBeuy7I3mk9fNGzamVZDftRNwp6kPYTKduMOuf6vc0JgC10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zXKr/yuQAqwa5T7ssMlZdUwzkk50pqbyPo0BY6HA0+PD1Aj+544nSKnY1QOOUYK2obtvK08v7xw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\nt+kluKI7p+7buuWNbdtYttUdlS45avGV+rYV8lbIRbv7eq312xtRl1pYt42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9Hpf7A568/z3cXF/j5voKN5dX4qjU5xpsNUTKGed5wnk644//5Bf45NPPsKQX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\nFZBpbnhrx3C1SUFU5mnU+qBSBmouwhiLLzon0rSIdVrBDgljc9HDjGAsG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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -798,7 +779,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This image shows the actor Gene Wilder portraying Willy Wonka in the 1971 version of the movie. The Inception model is very confident that the image shows a bow tie (score 97.22%), which is true but a human would probably say this image shows a person.\n", + "This image shows the actor Gene Wilder portraying Willy Wonka in the 1971 version of the movie. The Inception model is very confident that the image shows a bow tie (score about 98%), which is true but a human would probably say this image shows a person.\n", "\n", "The reason might be that the Inception model was trained on images of people with bow-ties that were classified as a bow-tie rather than a person. So maybe the problem is that the class-name should be \"person with bow-tie\" instead of just \"bow-tie\"." ] @@ -807,7 +788,6 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -825,16 +805,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "97.22% : bow tie\n", - " 0.92% : cowboy hat\n", - " 0.21% : sombrero\n", - " 0.09% : suit\n", - " 0.06% : bolo tie\n", - " 0.05% : Windsor tie\n", - " 0.04% : cornet\n", - " 0.03% : flute\n", + "97.98% : bow tie\n", + " 0.63% : cowboy hat\n", + " 0.14% : sombrero\n", + " 0.07% : suit\n", + " 0.05% : bolo tie\n", + " 0.04% : Windsor tie\n", + " 0.03% : cornet\n", + " 0.02% : flute\n", " 0.02% : banjo\n", - " 0.02% : revolver\n" + " 0.01% : revolver\n" ] } ], @@ -853,14 +833,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This image shows the actor Johnny Depp portraying Willy Wonka in the 2005 version of the movie. The Inception model thinks that this image shows \"sunglasses\" (score 31.48%) or \"sunglass\" (score 18.77%). Actually, the full name of the first class is \"sunglasses, dark glasses, shades\". For some reason the Inception model has been trained to recognize two very similar classes for sunglasses. Once again, it is correct that the image shows sunglasses, but a human would probably have said that this image shows a person." + "This image shows the actor Johnny Depp portraying Willy Wonka in the 2005 version of the movie. The Inception model thinks that this image shows \"sunglasses\" (score about 34%) or \"sunglass\" (score about 18%). Actually, the full name of the first class is \"sunglasses, dark glasses, shades\". For some reason the Inception model has been trained to recognize two very similar classes for sunglasses. Once again, it is correct that the image shows sunglasses, but a human would probably have said that this image shows a person." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -878,16 +857,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "31.48% : sunglasses\n", - "18.77% : sunglass\n", - " 1.55% : velvet\n", - " 1.02% : wig\n", - " 0.77% : cowboy hat\n", - " 0.69% : seat belt\n", - " 0.67% : sombrero\n", - " 0.62% : jean\n", - " 0.46% : poncho\n", - " 0.43% : jersey\n" + "34.47% : sunglasses\n", + "18.10% : sunglass\n", + " 1.29% : velvet\n", + " 0.95% : wig\n", + " 0.85% : cowboy hat\n", + " 0.72% : sombrero\n", + " 0.64% : seat belt\n", + " 0.50% : jean\n", + " 0.44% : stole\n", + " 0.41% : poncho\n" ] } ], @@ -971,7 +950,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -985,9 +964,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/08_Transfer_Learning.ipynb b/08_Transfer_Learning.ipynb index e90922e..af5aede 100644 --- a/08_Transfer_Learning.ipynb +++ b/08_Transfer_Learning.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It would take too much effort to update this tutorial to use e.g. the Keras API, especially because Tutorial #10 is a similar but more advanced version of Transfer Learning using the Keras builder API. However, you may still want to watch the video for this Tutorial #08 as it explains more details about Transfer Learning than Tutorial #10 does.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -52,7 +61,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -114,7 +122,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -143,9 +150,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -226,9 +231,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -252,9 +255,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -298,9 +299,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -328,9 +327,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -354,9 +351,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -457,9 +452,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -521,7 +514,6 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -558,7 +550,6 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [], @@ -585,9 +576,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from inception import transfer_values_cache" @@ -603,9 +592,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "file_path_cache_train = os.path.join(cifar10.data_path, 'inception_cifar10_train.pkl')\n", @@ -615,9 +602,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -645,9 +630,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -682,9 +665,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -711,9 +692,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -740,9 +719,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def plot_transfer_values(i):\n", @@ -767,7 +744,6 @@ "cell_type": "code", "execution_count": 25, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -814,7 +790,6 @@ "cell_type": "code", "execution_count": 26, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -892,9 +867,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "pca = PCA(n_components=2)" @@ -910,9 +883,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "transfer_values = transfer_values_train[0:3000]" @@ -947,7 +918,6 @@ "cell_type": "code", "execution_count": 31, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -995,7 +965,6 @@ "cell_type": "code", "execution_count": 33, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1024,9 +993,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def plot_scatter(values, cls):\n", @@ -1057,7 +1024,6 @@ "cell_type": "code", "execution_count": 35, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1141,9 +1107,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "transfer_values_reduced = tsne.fit_transform(transfer_values_50d) " @@ -1160,7 +1124,6 @@ "cell_type": "code", "execution_count": 40, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1191,9 +1154,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1324,9 +1285,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Wrap the transfer-values as a Pretty Tensor object.\n", @@ -1417,9 +1376,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" @@ -1492,9 +1449,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" @@ -1905,7 +1860,6 @@ "cell_type": "code", "execution_count": 63, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1939,9 +1893,7 @@ { "cell_type": "code", "execution_count": 64, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2058,9 +2010,7 @@ { "cell_type": "code", "execution_count": 65, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2185,9 +2135,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2199,9 +2149,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/09_Video_Data.ipynb b/09_Video_Data.ipynb index f0e39d7..44117d1 100644 --- a/09_Video_Data.ipynb +++ b/09_Video_Data.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It would take too much effort to update this tutorial to use e.g. the Keras API, especially because Tutorial #10 is a similar but more advanced version of Transfer Learning using the Keras builder API. However, you may still want to watch the video for Tutorials #08 and #09 as they explain more details about Transfer Learning than Tutorial #10 does.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -50,7 +59,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -112,7 +120,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -141,9 +148,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -243,7 +248,6 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -272,7 +276,6 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -303,9 +306,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# This is the code you would run to load your own image-files.\n", @@ -333,9 +334,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -363,9 +362,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "image_paths_train, cls_train, labels_train = dataset.get_training_set()" @@ -381,9 +378,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -410,9 +405,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "image_paths_test, cls_test, labels_test = dataset.get_test_set()" @@ -428,9 +421,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -458,7 +449,6 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -593,9 +583,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -657,7 +645,6 @@ "cell_type": "code", "execution_count": 22, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -694,7 +681,6 @@ "cell_type": "code", "execution_count": 23, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [], @@ -721,9 +707,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from inception import transfer_values_cache" @@ -739,9 +723,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "file_path_cache_train = os.path.join(data_dir, 'inception-knifey-train.pkl')\n", @@ -751,9 +733,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -777,9 +757,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -810,9 +788,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -839,9 +815,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -868,9 +842,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def plot_transfer_values(i):\n", @@ -896,7 +868,6 @@ "cell_type": "code", "execution_count": 31, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -943,7 +914,6 @@ "cell_type": "code", "execution_count": 32, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1021,9 +991,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "pca = PCA(n_components=2)" @@ -1039,9 +1007,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# transfer_values = transfer_values_train[0:3000]\n", @@ -1078,7 +1044,6 @@ "cell_type": "code", "execution_count": 37, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1126,7 +1091,6 @@ "cell_type": "code", "execution_count": 39, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1155,9 +1119,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def plot_scatter(values, cls):\n", @@ -1191,7 +1153,6 @@ "cell_type": "code", "execution_count": 41, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1275,9 +1236,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "transfer_values_reduced = tsne.fit_transform(transfer_values_50d) " @@ -1294,7 +1253,6 @@ "cell_type": "code", "execution_count": 46, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1326,7 +1284,6 @@ "cell_type": "code", "execution_count": 47, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1459,9 +1416,7 @@ { "cell_type": "code", "execution_count": 52, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Wrap the transfer-values as a Pretty Tensor object.\n", @@ -1552,9 +1507,7 @@ { "cell_type": "code", "execution_count": 56, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" @@ -1627,9 +1580,7 @@ { "cell_type": "code", "execution_count": 59, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" @@ -1782,9 +1733,7 @@ { "cell_type": "code", "execution_count": 63, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def plot_example_errors(cls_pred, correct):\n", @@ -2050,7 +1999,6 @@ "cell_type": "code", "execution_count": 69, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -2090,7 +2038,6 @@ "cell_type": "code", "execution_count": 70, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2120,7 +2067,6 @@ "cell_type": "code", "execution_count": 71, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -2239,9 +2185,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2253,9 +2199,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/10_Fine-Tuning.ipynb b/10_Fine-Tuning.ipynb new file mode 100644 index 0000000..01bc5c9 --- /dev/null +++ b/10_Fine-Tuning.ipynb @@ -0,0 +1,2081 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #10\n", + "# Fine-Tuning\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "We have previously seen in Tutorials #08 and #09 how to use a pre-trained Neural Network on a new dataset using so-called Transfer Learning, by re-routing the output of the original model just prior to its classification layers and instead use a new classifier that we had created. Because the original model was 'frozen' its weights could not be further optimized, so whatever had been learned by all the previous layers in the model, could not be fine-tuned to the new data-set.\n", + "\n", + "This tutorial shows how to do both Transfer Learning and Fine-Tuning using the Keras API for Tensorflow. We will once again use the Knifey-Spoony dataset introduced in Tutorial #09. We previously used the Inception v3 model but we will use the VGG16 model in this tutorial because its architecture is easier to work with.\n", + "\n", + "NOTE: It takes around 15 minutes to execute this Notebook on a laptop PC with a 2.6 GHz CPU and a GTX 1070 GPU. Running it on the CPU alone is estimated to take around 10 hours!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flowchart\n", + "\n", + "The idea is to re-use a pre-trained model, in this case the VGG16 model, which consists of several convolutional layers (actually blocks of multiple convolutional layers), followed by some fully-connected / dense layers and then a softmax output layer for the classification. \n", + "\n", + "The dense layers are responsible for combining features from the convolutional layers and this helps in the final classification. So when the VGG16 model is used on another dataset we may have to replace all the dense layers. In this case we add another dense-layer and a dropout-layer to avoid overfitting.\n", + "\n", + "The difference between Transfer Learning and Fine-Tuning is that in Transfer Learning we only optimize the weights of the new classification layers we have added, while we keep the weights of the original VGG16 model. In Fine-Tuning we optimize both the weights of the new classification layers we have added, as well as some or all of the layers from the VGG16 model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Flowchart of Transfer Learning & Fine-Tuning](images/10_transfer_learning_flowchart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import PIL\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the imports from the Keras API." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.models import Model, Sequential\n", + "from tensorflow.keras.layers import Dense, Flatten, Dropout\n", + "from tensorflow.keras.applications import VGG16\n", + "from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.optimizers import Adam, RMSprop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 and TensorFlow version:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Helper Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for joining a directory and list of filenames." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def path_join(dirname, filenames):\n", + " return [os.path.join(dirname, filename) for filename in filenames]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for plotting images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function used to plot at most 9 images in a 3x3 grid, and writing the true and predicted classes below each image." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_images(images, cls_true, cls_pred=None, smooth=True):\n", + "\n", + " assert len(images) == len(cls_true)\n", + "\n", + " # Create figure with sub-plots.\n", + " fig, axes = plt.subplots(3, 3)\n", + "\n", + " # Adjust vertical spacing.\n", + " if cls_pred is None:\n", + " hspace = 0.3\n", + " else:\n", + " hspace = 0.6\n", + " fig.subplots_adjust(hspace=hspace, wspace=0.3)\n", + "\n", + " # Interpolation type.\n", + " if smooth:\n", + " interpolation = 'spline16'\n", + " else:\n", + " interpolation = 'nearest'\n", + "\n", + " for i, ax in enumerate(axes.flat):\n", + " # There may be less than 9 images, ensure it doesn't crash.\n", + " if i < len(images):\n", + " # Plot image.\n", + " ax.imshow(images[i],\n", + " interpolation=interpolation)\n", + "\n", + " # Name of the true class.\n", + " cls_true_name = class_names[cls_true[i]]\n", + "\n", + " # Show true and predicted classes.\n", + " if cls_pred is None:\n", + " xlabel = \"True: {0}\".format(cls_true_name)\n", + " else:\n", + " # Name of the predicted class.\n", + " cls_pred_name = class_names[cls_pred[i]]\n", + "\n", + " xlabel = \"True: {0}\\nPred: {1}\".format(cls_true_name, cls_pred_name)\n", + "\n", + " # Show the classes as the label on the x-axis.\n", + " ax.set_xlabel(xlabel)\n", + " \n", + " # Remove ticks from the plot.\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " \n", + " # Ensure the plot is shown correctly with multiple plots\n", + " # in a single Notebook cell.\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for printing confusion matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Import a function from sklearn to calculate the confusion-matrix.\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "def print_confusion_matrix(cls_pred):\n", + " # cls_pred is an array of the predicted class-number for\n", + " # all images in the test-set.\n", + "\n", + " # Get the confusion matrix using sklearn.\n", + " cm = confusion_matrix(y_true=cls_test, # True class for test-set.\n", + " y_pred=cls_pred) # Predicted class.\n", + "\n", + " print(\"Confusion matrix:\")\n", + " \n", + " # Print the confusion matrix as text.\n", + " print(cm)\n", + " \n", + " # Print the class-names for easy reference.\n", + " for i, class_name in enumerate(class_names):\n", + " print(\"({0}) {1}\".format(i, class_name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for plotting example errors\n", + "\n", + "Function for plotting examples of images from the test-set that have been mis-classified." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_example_errors(cls_pred):\n", + " # cls_pred is an array of the predicted class-number for\n", + " # all images in the test-set.\n", + "\n", + " # Boolean array whether the predicted class is incorrect.\n", + " incorrect = (cls_pred != cls_test)\n", + "\n", + " # Get the file-paths for images that were incorrectly classified.\n", + " image_paths = np.array(image_paths_test)[incorrect]\n", + "\n", + " # Load the first 9 images.\n", + " images = load_images(image_paths=image_paths[0:9])\n", + " \n", + " # Get the predicted classes for those images.\n", + " cls_pred = cls_pred[incorrect]\n", + "\n", + " # Get the true classes for those images.\n", + " cls_true = cls_test[incorrect]\n", + " \n", + " # Plot the 9 images we have loaded and their corresponding classes.\n", + " # We have only loaded 9 images so there is no need to slice those again.\n", + " plot_images(images=images,\n", + " cls_true=cls_true[0:9],\n", + " cls_pred=cls_pred[0:9])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function for calculating the predicted classes of the entire test-set and calling the above function to plot a few examples of mis-classified images." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def example_errors():\n", + " # The Keras data-generator for the test-set must be reset\n", + " # before processing. This is because the generator will loop\n", + " # infinitely and keep an internal index into the dataset.\n", + " # So it might start in the middle of the test-set if we do\n", + " # not reset it first. This makes it impossible to match the\n", + " # predicted classes with the input images.\n", + " # If we reset the generator, then it always starts at the\n", + " # beginning so we know exactly which input-images were used.\n", + " generator_test.reset()\n", + " \n", + " # Predict the classes for all images in the test-set.\n", + " y_pred = new_model.predict(generator_test, steps=steps_test)\n", + "\n", + " # Convert the predicted classes from arrays to integers.\n", + " cls_pred = np.argmax(y_pred,axis=1)\n", + "\n", + " # Plot examples of mis-classified images.\n", + " plot_example_errors(cls_pred)\n", + " \n", + " # Print the confusion matrix.\n", + " print_confusion_matrix(cls_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for loading images\n", + "\n", + "The data-set is not loaded into memory, instead it has a list of the files for the images in the training-set and another list of the files for the images in the test-set. This helper-function loads some image-files." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def load_images(image_paths):\n", + " # Load the images from disk.\n", + " images = [plt.imread(path) for path in image_paths]\n", + "\n", + " # Convert to a numpy array and return it.\n", + " return np.asarray(images)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for plotting training history\n", + "\n", + "This plots the classification accuracy and loss-values recorded during training with the Keras API." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_training_history(history):\n", + " # Get the classification accuracy and loss-value\n", + " # for the training-set.\n", + " acc = history.history['categorical_accuracy']\n", + " loss = history.history['loss']\n", + "\n", + " # Get it for the validation-set (we only use the test-set).\n", + " val_acc = history.history['val_categorical_accuracy']\n", + " val_loss = history.history['val_loss']\n", + "\n", + " # Plot the accuracy and loss-values for the training-set.\n", + " plt.plot(acc, linestyle='-', color='b', label='Training Acc.')\n", + " plt.plot(loss, 'o', color='b', label='Training Loss')\n", + " \n", + " # Plot it for the test-set.\n", + " plt.plot(val_acc, linestyle='--', color='r', label='Test Acc.')\n", + " plt.plot(val_loss, 'o', color='r', label='Test Loss')\n", + "\n", + " # Plot title and legend.\n", + " plt.title('Training and Test Accuracy')\n", + " plt.legend()\n", + "\n", + " # Ensure the plot shows correctly.\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset: Knifey-Spoony\n", + "\n", + "The Knifey-Spoony dataset was introduced in Tutorial #09. It was generated from video-files by taking individual frames and converting them to images." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import knifey" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download and extract the dataset if it hasn't already been done. It is about 22 MB." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has apparently already been downloaded and unpacked.\n" + ] + } + ], + "source": [ + "knifey.maybe_download_and_extract()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This dataset has another directory structure than the Keras API requires, so copy the files into separate directories for the training- and test-sets." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating dataset from the files in: data/knifey-spoony/\n", + "- Data loaded from cache-file: data/knifey-spoony/knifey-spoony.pkl\n", + "- Copied training-set to: data/knifey-spoony/train/\n", + "- Copied test-set to: data/knifey-spoony/test/\n" + ] + } + ], + "source": [ + "knifey.copy_files()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The directories where the images are now stored." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "train_dir = knifey.train_dir\n", + "test_dir = knifey.test_dir" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre-Trained Model: VGG16\n", + "\n", + "The following creates an instance of the pre-trained VGG16 model using the Keras API. This automatically downloads the required files if you don't have them already. Note how simple this is in Keras compared to Tutorial #08.\n", + "\n", + "The VGG16 model contains a convolutional part and a fully-connected (or dense) part which is used for classification. If `include_top=True` then the whole VGG16 model is downloaded which is about 528 MB. If `include_top=False` then only the convolutional part of the VGG16 model is downloaded which is just 57 MB.\n", + "\n", + "We will try and use the pre-trained model for predicting the class of some images in our new dataset, so we have to download the full model, but if you have a slow internet connection, then you can modify the code below to use the smaller pre-trained model without the classification layers." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "model = VGG16(include_top=True, weights='imagenet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Input Pipeline\n", + "\n", + "The Keras API has its own way of creating the input pipeline for training a model using files.\n", + "\n", + "First we need to know the shape of the tensors expected as input by the pre-trained VGG16 model. In this case it is images of shape 224 x 224 x 3." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(224, 224)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_shape = model.layers[0].output_shape[0][1:3]\n", + "input_shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Keras uses a so-called data-generator for inputting data into the neural network, which will loop over the data for eternity.\n", + "\n", + "We have a small training-set so it helps to artificially inflate its size by making various transformations to the images. We use a built-in data-generator that can make these random transformations. This is also called an augmented dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "datagen_train = ImageDataGenerator(\n", + " rescale=1./255,\n", + " rotation_range=180,\n", + " width_shift_range=0.1,\n", + " height_shift_range=0.1,\n", + " shear_range=0.1,\n", + " zoom_range=[0.9, 1.5],\n", + " horizontal_flip=True,\n", + " vertical_flip=True,\n", + " fill_mode='nearest')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also need a data-generator for the test-set, but this should not do any transformations to the images because we want to know the exact classification accuracy on those specific images. So we just rescale the pixel-values so they are between 0.0 and 1.0 because this is expected by the VGG16 model." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "datagen_test = ImageDataGenerator(rescale=1./255)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data-generators will return batches of images. Because the VGG16 model is so large, the batch-size cannot be too large, otherwise you will run out of RAM on the GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can save the randomly transformed images during training, so as to inspect whether they have been overly distorted, so we have to adjust the parameters for the data-generator above." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "if True:\n", + " save_to_dir = None\n", + "else:\n", + " save_to_dir='augmented_images/'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we create the actual data-generator that will read files from disk, resize the images and return a random batch.\n", + "\n", + "It is somewhat awkward that the construction of the data-generator is split into these two steps, but it is probably because there are different kinds of data-generators available for different data-types (images, text, etc.) and sources (memory or disk)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 4170 images belonging to 3 classes.\n" + ] + } + ], + "source": [ + "generator_train = datagen_train.flow_from_directory(directory=train_dir,\n", + " target_size=input_shape,\n", + " batch_size=batch_size,\n", + " shuffle=True,\n", + " save_to_dir=save_to_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data-generator for the test-set should not transform and shuffle the images." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 530 images belonging to 3 classes.\n" + ] + } + ], + "source": [ + "generator_test = datagen_test.flow_from_directory(directory=test_dir,\n", + " target_size=input_shape,\n", + " batch_size=batch_size,\n", + " shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because the data-generators will loop for eternity, we need to specify the number of steps to perform during evaluation and prediction on the test-set. Because our test-set contains 530 images and the batch-size is set to 20, the number of steps is 26.5 for one full processing of the test-set. This is why we need to reset the data-generator's counter in the `example_errors()` function above, so it always starts processing from the beginning of the test-set.\n", + "\n", + "This is another slightly awkward aspect of the Keras API which could perhaps be improved." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "26.5" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "steps_test = generator_test.n / batch_size\n", + "steps_test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the file-paths for all the images in the training- and test-sets." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "image_paths_train = path_join(train_dir, generator_train.filenames)\n", + "image_paths_test = path_join(test_dir, generator_test.filenames)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the class-numbers for all the images in the training- and test-sets." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "cls_train = generator_train.classes\n", + "cls_test = generator_test.classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the class-names for the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['forky', 'knifey', 'spoony']" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class_names = list(generator_train.class_indices.keys())\n", + "class_names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the number of classes for the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_classes = generator_train.num_classes\n", + "num_classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot a few images to see if data is correct" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the first images from the train-set.\n", + "images = load_images(image_paths=image_paths_train[0:9])\n", + "\n", + "# Get the true classes for those images.\n", + "cls_true = cls_train[0:9]\n", + "\n", + "# Plot the images and labels using our helper-function above.\n", + "plot_images(images=images, cls_true=cls_true, smooth=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Class Weights\n", + "\n", + "The Knifey-Spoony dataset is quite imbalanced because it has few images of forks, more images of knives, and many more images of spoons. This can cause a problem during training because the neural network will be shown many more examples of spoons than forks, so it might become better at recognizing spoons.\n", + "\n", + "Here we use scikit-learn to calculate weights that will properly balance the dataset. These weights are applied to the gradient for each image in the batch during training, so as to scale their influence on the overall gradient for the batch." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.utils.class_weight import compute_class_weight" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "class_weight = compute_class_weight(class_weight='balanced',\n", + " classes=np.unique(cls_train),\n", + " y=cls_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note how the weight is about 1.398 for the forky-class and only 0.707 for the spoony-class. This is because there are fewer images for the forky-class so the gradient should be amplified for those images, while the gradient should be lowered for spoony-images." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.39839034, 1.14876033, 0.70701933])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class_weight" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['forky', 'knifey', 'spoony']" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class_names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example Predictions\n", + "\n", + "Here we will show a few examples of using the pre-trained VGG16 model for prediction.\n", + "\n", + "We need a helper-function for loading and resizing an image so it can be input to the VGG16 model, as well as doing the actual prediction and showing the result." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(image_path):\n", + " # Load and resize the image using PIL.\n", + " img = PIL.Image.open(image_path)\n", + " img_resized = img.resize(input_shape, PIL.Image.LANCZOS)\n", + "\n", + " # Plot the image.\n", + " plt.imshow(img_resized)\n", + " plt.show()\n", + "\n", + " # Convert the PIL image to a numpy-array with the proper shape.\n", + " img_array = np.expand_dims(np.array(img_resized), axis=0)\n", + "\n", + " # Use the VGG16 model to make a prediction.\n", + " # This outputs an array with 1000 numbers corresponding to\n", + " # the classes of the ImageNet-dataset.\n", + " pred = model.predict(img_array)\n", + " \n", + " # Decode the output of the VGG16 model.\n", + " pred_decoded = decode_predictions(pred)[0]\n", + "\n", + " # Print the predictions.\n", + " for code, name, score in pred_decoded:\n", + " print(\"{0:>6.2%} : {1}\".format(score, name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then use the VGG16 model on a picture of a parrot which is classified as a macaw (a parrot species) with a fairly high score of 79%." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "79.02% : macaw\n", + " 6.61% : bubble\n", + " 3.64% : vine_snake\n", + " 1.90% : pinwheel\n", + " 1.22% : knot\n" + ] + } + ], + "source": [ + "predict(image_path='images/parrot_cropped1.jpg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then use the VGG16 model to predict the class of one of the images in our new training-set. The VGG16 model is very confused about this image and cannot make a good classification." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50.31% : shower_curtain\n", + "17.08% : handkerchief\n", + "12.75% : mosquito_net\n", + " 2.87% : window_shade\n", + " 1.32% : toilet_tissue\n" + ] + } + ], + "source": [ + "predict(image_path=image_paths_train[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can try it for another image in our new training-set and the VGG16 model is still confused." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "45.08% : shower_curtain\n", + "21.84% : mosquito_net\n", + "11.55% : handkerchief\n", + " 2.02% : window_shade\n", + " 0.91% : Windsor_tie\n" + ] + } + ], + "source": [ + "predict(image_path=image_paths_train[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also try an image from our new test-set, and again the VGG16 model is very confused." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "26.75% : spoonbill\n", + " 7.06% : black_stork\n", + " 7.04% : wooden_spoon\n", + " 4.21% : limpkin\n", + " 3.72% : paddle\n" + ] + } + ], + "source": [ + "predict(image_path=image_paths_test[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transfer Learning\n", + "\n", + "The pre-trained VGG16 model was unable to classify images from the Knifey-Spoony dataset. The reason is perhaps that the VGG16 model was trained on the so-called ImageNet dataset which may not have contained many images of cutlery.\n", + "\n", + "The lower layers of a Convolutional Neural Network can recognize many different shapes or features in an image. It is the last few fully-connected layers that combine these featuers into classification of a whole image. So we can try and re-route the output of the last convolutional layer of the VGG16 model to a new fully-connected neural network that we create for doing classification on the Knifey-Spoony dataset.\n", + "\n", + "First we print a summary of the VGG16 model so we can see the names and types of its layers, as well as the shapes of the tensors flowing between the layers. This is one of the major reasons we are using the VGG16 model in this tutorial, because the Inception v3 model has so many layers that it is confusing when printed out." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"vgg16\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_1 (InputLayer) [(None, 224, 224, 3)] 0 \n", + "_________________________________________________________________\n", + "block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 \n", + "_________________________________________________________________\n", + "block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 \n", + "_________________________________________________________________\n", + "block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 \n", + "_________________________________________________________________\n", + "block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 \n", + "_________________________________________________________________\n", + "block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 \n", + "_________________________________________________________________\n", + "block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 \n", + "_________________________________________________________________\n", + "block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 \n", + "_________________________________________________________________\n", + "block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 \n", + "_________________________________________________________________\n", + "block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 \n", + "_________________________________________________________________\n", + "block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 \n", + "_________________________________________________________________\n", + "block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 \n", + "_________________________________________________________________\n", + "block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 \n", + "_________________________________________________________________\n", + "block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 \n", + "_________________________________________________________________\n", + "block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 \n", + "_________________________________________________________________\n", + "block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 \n", + "_________________________________________________________________\n", + "block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 \n", + "_________________________________________________________________\n", + "block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 \n", + "_________________________________________________________________\n", + "block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 \n", + "_________________________________________________________________\n", + "flatten (Flatten) (None, 25088) 0 \n", + "_________________________________________________________________\n", + "fc1 (Dense) (None, 4096) 102764544 \n", + "_________________________________________________________________\n", + "fc2 (Dense) (None, 4096) 16781312 \n", + "_________________________________________________________________\n", + "predictions (Dense) (None, 1000) 4097000 \n", + "=================================================================\n", + "Total params: 138,357,544\n", + "Trainable params: 138,357,544\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the last convolutional layer is called 'block5_pool' so we use Keras to get a reference to that layer." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "transfer_layer = model.get_layer('block5_pool')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We refer to this layer as the Transfer Layer because its output will be re-routed to our new fully-connected neural network which will do the classification for the Knifey-Spoony dataset.\n", + "\n", + "The output of the transfer layer has the following shape:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transfer_layer.output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the Keras API it is very simple to create a new model. First we take the part of the VGG16 model from its input-layer to the output of the transfer-layer. We may call this the convolutional model, because it consists of all the convolutional layers from the VGG16 model." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "conv_model = Model(inputs=model.input,\n", + " outputs=transfer_layer.output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then use Keras to build a new model on top of this." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Start a new Keras Sequential model.\n", + "new_model = Sequential()\n", + "\n", + "# Add the convolutional part of the VGG16 model from above.\n", + "new_model.add(conv_model)\n", + "\n", + "# Flatten the output of the VGG16 model because it is from a\n", + "# convolutional layer.\n", + "new_model.add(Flatten())\n", + "\n", + "# Add a dense (aka. fully-connected) layer.\n", + "# This is for combining features that the VGG16 model has\n", + "# recognized in the image.\n", + "new_model.add(Dense(1024, activation='relu'))\n", + "\n", + "# Add a dropout-layer which may prevent overfitting and\n", + "# improve generalization ability to unseen data e.g. the test-set.\n", + "new_model.add(Dropout(0.5))\n", + "\n", + "# Add the final layer for the actual classification.\n", + "new_model.add(Dense(num_classes, activation='softmax'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the Adam optimizer with a fairly low learning-rate. The learning-rate could perhaps be larger. But if you try and train more layers of the original VGG16 model, then the learning-rate should be quite low otherwise the pre-trained weights of the VGG16 model will be distorted and it will be unable to learn." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = Adam(lr=1e-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have 3 classes in the Knifey-Spoony dataset so Keras needs to use this loss-function." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "loss = 'categorical_crossentropy'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The only performance metric we are interested in is the classification accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "metrics = ['categorical_accuracy']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper-function for printing whether a layer in the VGG16 model should be trained." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def print_layer_trainable():\n", + " for layer in conv_model.layers:\n", + " print(\"{0}:\\t{1}\".format(layer.trainable, layer.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default all the layers of the VGG16 model are trainable." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True:\tinput_1\n", + "True:\tblock1_conv1\n", + "True:\tblock1_conv2\n", + "True:\tblock1_pool\n", + "True:\tblock2_conv1\n", + "True:\tblock2_conv2\n", + "True:\tblock2_pool\n", + "True:\tblock3_conv1\n", + "True:\tblock3_conv2\n", + "True:\tblock3_conv3\n", + "True:\tblock3_pool\n", + "True:\tblock4_conv1\n", + "True:\tblock4_conv2\n", + "True:\tblock4_conv3\n", + "True:\tblock4_pool\n", + "True:\tblock5_conv1\n", + "True:\tblock5_conv2\n", + "True:\tblock5_conv3\n", + "True:\tblock5_pool\n" + ] + } + ], + "source": [ + "print_layer_trainable()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Transfer Learning we are initially only interested in reusing the pre-trained VGG16 model as it is, so we will disable training for all its layers." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "conv_model.trainable = False" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "for layer in conv_model.layers:\n", + " layer.trainable = False" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False:\tinput_1\n", + "False:\tblock1_conv1\n", + "False:\tblock1_conv2\n", + "False:\tblock1_pool\n", + "False:\tblock2_conv1\n", + "False:\tblock2_conv2\n", + "False:\tblock2_pool\n", + "False:\tblock3_conv1\n", + "False:\tblock3_conv2\n", + "False:\tblock3_conv3\n", + "False:\tblock3_pool\n", + "False:\tblock4_conv1\n", + "False:\tblock4_conv2\n", + "False:\tblock4_conv3\n", + "False:\tblock4_pool\n", + "False:\tblock5_conv1\n", + "False:\tblock5_conv2\n", + "False:\tblock5_conv3\n", + "False:\tblock5_pool\n" + ] + } + ], + "source": [ + "print_layer_trainable()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have changed whether the model's layers are trainable, we need to compile the model for the changes to take effect." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "new_model.compile(optimizer=optimizer, loss=loss, metrics=metrics)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An epoch normally means one full processing of the training-set. But the data-generator that we created above, will produce batches of training-data for eternity. So we need to define the number of steps we want to run for each \"epoch\" and this number gets multiplied by the batch-size defined above. In this case we have 100 steps per epoch and a batch-size of 20, so the \"epoch\" consists of 2000 random images from the training-set. We run 20 such \"epochs\".\n", + "\n", + "The reason these particular numbers were chosen, was because they seemed to be sufficient for training with this particular model and dataset, and it didn't take too much time, and resulted in 20 data-points (one for each \"epoch\") which can be plotted afterwards." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "epochs = 20\n", + "steps_per_epoch = 100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Training the new model is just a single function call in the Keras API. This takes about 6-7 minutes on a GTX 1070 GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "Train for 100 steps, validate for 26.5 steps\n", + "Epoch 1/20\n", + "100/100 [==============================] - 32s 322ms/step - loss: 1.1149 - categorical_accuracy: 0.4585 - val_loss: 0.8945 - val_categorical_accuracy: 0.5604\n", + "Epoch 2/20\n", + "100/100 [==============================] - 31s 309ms/step - loss: 0.9385 - categorical_accuracy: 0.5432 - val_loss: 0.7380 - val_categorical_accuracy: 0.7717\n", + "Epoch 3/20\n", + "100/100 [==============================] - 29s 285ms/step - loss: 0.8408 - categorical_accuracy: 0.6186 - val_loss: 0.6924 - val_categorical_accuracy: 0.7321\n", + "Epoch 4/20\n", + "100/100 [==============================] - 30s 299ms/step - loss: 0.7855 - categorical_accuracy: 0.6432 - val_loss: 0.6994 - val_categorical_accuracy: 0.7057\n", + "Epoch 5/20\n", + "100/100 [==============================] - 29s 294ms/step - loss: 0.7128 - categorical_accuracy: 0.6879 - val_loss: 0.7109 - val_categorical_accuracy: 0.6698\n", + "Epoch 6/20\n", + "100/100 [==============================] - 29s 286ms/step - loss: 0.6945 - categorical_accuracy: 0.7015 - val_loss: 0.6150 - val_categorical_accuracy: 0.7604\n", + "Epoch 7/20\n", + "100/100 [==============================] - 28s 282ms/step - loss: 0.6910 - categorical_accuracy: 0.7060 - val_loss: 0.6316 - val_categorical_accuracy: 0.7321\n", + "Epoch 8/20\n", + "100/100 [==============================] - 28s 284ms/step - loss: 0.6269 - categorical_accuracy: 0.7382 - val_loss: 0.5828 - val_categorical_accuracy: 0.7868\n", + "Epoch 9/20\n", + "100/100 [==============================] - 30s 300ms/step - loss: 0.6180 - categorical_accuracy: 0.7362 - val_loss: 0.6337 - val_categorical_accuracy: 0.7377\n", + "Epoch 10/20\n", + "100/100 [==============================] - 30s 297ms/step - loss: 0.5823 - categorical_accuracy: 0.7568 - val_loss: 0.5569 - val_categorical_accuracy: 0.7868\n", + "Epoch 11/20\n", + "100/100 [==============================] - 30s 295ms/step - loss: 0.5969 - categorical_accuracy: 0.7455 - val_loss: 0.6298 - val_categorical_accuracy: 0.7340\n", + "Epoch 12/20\n", + "100/100 [==============================] - 29s 289ms/step - loss: 0.5516 - categorical_accuracy: 0.7704 - val_loss: 0.5804 - val_categorical_accuracy: 0.7566\n", + "Epoch 13/20\n", + "100/100 [==============================] - 30s 297ms/step - loss: 0.5514 - categorical_accuracy: 0.7770 - val_loss: 0.5879 - val_categorical_accuracy: 0.7453\n", + "Epoch 14/20\n", + "100/100 [==============================] - 33s 326ms/step - loss: 0.5239 - categorical_accuracy: 0.7830 - val_loss: 0.5448 - val_categorical_accuracy: 0.7849\n", + "Epoch 15/20\n", + "100/100 [==============================] - 32s 325ms/step - loss: 0.5367 - categorical_accuracy: 0.7760 - val_loss: 0.6596 - val_categorical_accuracy: 0.7226\n", + "Epoch 16/20\n", + "100/100 [==============================] - 28s 282ms/step - loss: 0.5155 - categorical_accuracy: 0.7860 - val_loss: 0.5385 - val_categorical_accuracy: 0.7755\n", + "Epoch 17/20\n", + "100/100 [==============================] - 29s 289ms/step - loss: 0.5058 - categorical_accuracy: 0.7889 - val_loss: 0.6200 - val_categorical_accuracy: 0.7340\n", + "Epoch 18/20\n", + "100/100 [==============================] - 28s 283ms/step - loss: 0.4925 - categorical_accuracy: 0.8030 - val_loss: 0.6469 - val_categorical_accuracy: 0.7151\n", + "Epoch 19/20\n", + "100/100 [==============================] - 31s 312ms/step - loss: 0.4681 - categorical_accuracy: 0.8145 - val_loss: 0.7350 - val_categorical_accuracy: 0.6906\n", + "Epoch 20/20\n", + "100/100 [==============================] - 31s 307ms/step - loss: 0.4743 - categorical_accuracy: 0.8045 - val_loss: 0.5995 - val_categorical_accuracy: 0.7377\n" + ] + } + ], + "source": [ + "history = new_model.fit(x=generator_train,\n", + " epochs=epochs,\n", + " steps_per_epoch=steps_per_epoch,\n", + " class_weight=class_weight,\n", + " validation_data=generator_test,\n", + " validation_steps=steps_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Keras records the performance metrics at the end of each \"epoch\" so they can be plotted later. This shows that the loss-value for the training-set generally decreased during training, but the loss-values for the test-set were a bit more erratic. Similarly, the classification accuracy generally improved on the training-set while it was a bit more erratic on the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_training_history(history)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After training we can also evaluate the new model's performance on the test-set using a single function call in the Keras API." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "27/26 [==============================] - 4s 156ms/step - loss: 0.5888 - categorical_accuracy: 0.7377\n" + ] + } + ], + "source": [ + "result = new_model.evaluate(generator_test, steps=steps_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test-set classification accuracy: 73.77%\n" + ] + } + ], + "source": [ + "print(\"Test-set classification accuracy: {0:.2%}\".format(result[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot some examples of mis-classified images from the test-set. Some of these images are also difficult for a human to classify.\n", + "\n", + "The confusion matrix shows that the new model is especially having problems classifying the forky-class." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix:\n", + "[[137 9 5]\n", + " [ 56 81 0]\n", + " [ 56 13 173]]\n", + "(0) forky\n", + "(1) knifey\n", + "(2) spoony\n" + ] + } + ], + "source": [ + "example_errors()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fine-Tuning\n", + "\n", + "In Transfer Learning the original pre-trained model is locked or frozen during training of the new classifier. This ensures that the weights of the original VGG16 model will not change. One advantage of this, is that the training of the new classifier will not propagate large gradients back through the VGG16 model that may either distort its weights or cause overfitting to the new dataset.\n", + "\n", + "But once the new classifier has been trained we can try and gently fine-tune some of the deeper layers in the VGG16 model as well. We call this Fine-Tuning.\n", + "\n", + "It is a bit unclear whether Keras uses the `trainable` boolean in each layer of the original VGG16 model or if it is overrided by the `trainable` boolean in the \"meta-layer\" we call `conv_layer`. So we will enable the `trainable` boolean for both `conv_layer` and all the relevant layers in the original VGG16 model." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "conv_model.trainable = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to train the last two convolutional layers whose names contain 'block5' or 'block4'." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "for layer in conv_model.layers:\n", + " # Boolean whether this layer is trainable.\n", + " trainable = ('block5' in layer.name or 'block4' in layer.name)\n", + " \n", + " # Set the layer's bool.\n", + " layer.trainable = trainable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can check that this has updated the `trainable` boolean for the relevant layers." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False:\tinput_1\n", + "False:\tblock1_conv1\n", + "False:\tblock1_conv2\n", + "False:\tblock1_pool\n", + "False:\tblock2_conv1\n", + "False:\tblock2_conv2\n", + "False:\tblock2_pool\n", + "False:\tblock3_conv1\n", + "False:\tblock3_conv2\n", + "False:\tblock3_conv3\n", + "False:\tblock3_pool\n", + "True:\tblock4_conv1\n", + "True:\tblock4_conv2\n", + "True:\tblock4_conv3\n", + "True:\tblock4_pool\n", + "True:\tblock5_conv1\n", + "True:\tblock5_conv2\n", + "True:\tblock5_conv3\n", + "True:\tblock5_pool\n" + ] + } + ], + "source": [ + "print_layer_trainable()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use a lower learning-rate for the fine-tuning so the weights of the original VGG16 model only get changed slowly." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer_fine = Adam(lr=1e-7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because we have defined a new optimizer and have changed the `trainable` boolean for many of the layers in the model, we need to recompile the model so the changes can take effect before we continue training." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "new_model.compile(optimizer=optimizer_fine, loss=loss, metrics=metrics)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The training can then be continued so as to fine-tune the VGG16 model along with the new classifier." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "Train for 100 steps, validate for 26.5 steps\n", + "Epoch 1/20\n", + "100/100 [==============================] - 34s 339ms/step - loss: 0.4605 - categorical_accuracy: 0.8211 - val_loss: 0.5776 - val_categorical_accuracy: 0.7566\n", + "Epoch 2/20\n", + "100/100 [==============================] - 38s 384ms/step - loss: 0.4683 - categorical_accuracy: 0.8175 - val_loss: 0.5600 - val_categorical_accuracy: 0.7604\n", + "Epoch 3/20\n", + "100/100 [==============================] - 36s 357ms/step - loss: 0.4643 - categorical_accuracy: 0.8095 - val_loss: 0.5748 - val_categorical_accuracy: 0.7528\n", + "Epoch 4/20\n", + "100/100 [==============================] - 37s 371ms/step - loss: 0.4368 - categorical_accuracy: 0.8236 - val_loss: 0.5613 - val_categorical_accuracy: 0.7604\n", + "Epoch 5/20\n", + "100/100 [==============================] - 37s 367ms/step - loss: 0.4140 - categorical_accuracy: 0.8317 - val_loss: 0.5490 - val_categorical_accuracy: 0.7642\n", + "Epoch 6/20\n", + "100/100 [==============================] - 44s 439ms/step - loss: 0.4456 - categorical_accuracy: 0.8155 - val_loss: 0.5488 - val_categorical_accuracy: 0.7660\n", + "Epoch 7/20\n", + "100/100 [==============================] - 45s 454ms/step - loss: 0.4318 - categorical_accuracy: 0.8352 - val_loss: 0.5505 - val_categorical_accuracy: 0.7660\n", + "Epoch 8/20\n", + "100/100 [==============================] - 41s 409ms/step - loss: 0.4283 - categorical_accuracy: 0.8265 - val_loss: 0.5580 - val_categorical_accuracy: 0.7604\n", + "Epoch 9/20\n", + "100/100 [==============================] - 43s 427ms/step - loss: 0.4197 - categorical_accuracy: 0.8392 - val_loss: 0.5496 - val_categorical_accuracy: 0.7679\n", + "Epoch 10/20\n", + "100/100 [==============================] - 43s 431ms/step - loss: 0.4138 - categorical_accuracy: 0.8312 - val_loss: 0.5535 - val_categorical_accuracy: 0.7679\n", + "Epoch 11/20\n", + "100/100 [==============================] - 38s 378ms/step - loss: 0.4373 - categorical_accuracy: 0.8332 - val_loss: 0.5449 - val_categorical_accuracy: 0.7679\n", + "Epoch 12/20\n", + "100/100 [==============================] - 25s 252ms/step - loss: 0.4046 - categorical_accuracy: 0.8470 - val_loss: 0.5396 - val_categorical_accuracy: 0.7698\n", + "Epoch 13/20\n", + "100/100 [==============================] - 23s 234ms/step - loss: 0.4014 - categorical_accuracy: 0.8442 - val_loss: 0.5400 - val_categorical_accuracy: 0.7717\n", + "Epoch 14/20\n", + "100/100 [==============================] - 24s 237ms/step - loss: 0.4141 - categorical_accuracy: 0.8365 - val_loss: 0.5473 - val_categorical_accuracy: 0.7679\n", + "Epoch 15/20\n", + "100/100 [==============================] - 23s 231ms/step - loss: 0.4117 - categorical_accuracy: 0.8307 - val_loss: 0.5436 - val_categorical_accuracy: 0.7698\n", + "Epoch 16/20\n", + "100/100 [==============================] - 23s 229ms/step - loss: 0.3826 - categorical_accuracy: 0.8472 - val_loss: 0.5549 - val_categorical_accuracy: 0.7623\n", + "Epoch 17/20\n", + "100/100 [==============================] - 23s 228ms/step - loss: 0.3979 - categorical_accuracy: 0.8442 - val_loss: 0.5402 - val_categorical_accuracy: 0.7698\n", + "Epoch 18/20\n", + "100/100 [==============================] - 24s 239ms/step - loss: 0.3941 - categorical_accuracy: 0.8447 - val_loss: 0.5313 - val_categorical_accuracy: 0.7774\n", + "Epoch 19/20\n", + "100/100 [==============================] - 24s 244ms/step - loss: 0.3956 - categorical_accuracy: 0.8385 - val_loss: 0.5407 - val_categorical_accuracy: 0.7698\n", + "Epoch 20/20\n", + "100/100 [==============================] - 24s 240ms/step - loss: 0.4037 - categorical_accuracy: 0.8281 - val_loss: 0.5352 - val_categorical_accuracy: 0.7755\n" + ] + } + ], + "source": [ + "history = new_model.fit(x=generator_train,\n", + " epochs=epochs,\n", + " steps_per_epoch=steps_per_epoch,\n", + " class_weight=class_weight,\n", + " validation_data=generator_test,\n", + " validation_steps=steps_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then plot the loss-values and classification accuracy from the training. Depending on the dataset, the original model, the new classifier, and hyper-parameters such as the learning-rate, this may improve the classification accuracies on both training- and test-set, or it may improve on the training-set but worsen it for the test-set in case of overfitting. It may require some experimentation with the parameters to get this right." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_training_history(history)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "27/26 [==============================] - 3s 118ms/step - loss: 0.5256 - categorical_accuracy: 0.7755\n" + ] + } + ], + "source": [ + "result = new_model.evaluate(generator_test, steps=steps_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test-set classification accuracy: 77.55%\n" + ] + } + ], + "source": [ + "print(\"Test-set classification accuracy: {0:.2%}\".format(result[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot some examples of mis-classified images again, and we can also see from the confusion matrix that the model is still having problems classifying forks correctly.\n", + "\n", + "A part of the reason might be that the training-set contains only 994 images of forks, while it contains 1210 images of knives and 1966 images of spoons. Even though we have weighted the classes to compensate for this imbalance, and we have also augmented the training-set by randomly transforming the images in different ways during training, it may not be enough for the model to properly learn to recognize forks." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion matrix:\n", + "[[133 7 11]\n", + " [ 48 88 1]\n", + " [ 40 12 190]]\n", + "(0) forky\n", + "(1) knifey\n", + "(2) spoony\n" + ] + } + ], + "source": [ + "example_errors()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed how to use the Keras API for TensorFlow to do both Transfer Learning and Fine-Tuning of the pre-trained VGG16 model on a new dataset. It is much easier to implement this using the Keras API rather than directly in TensorFlow.\n", + "\n", + "Whether Fine-Tuning improves the classification accuracy over just using Transfer Learning depends on the pre-trained model, the transfer-layer you choose, your dataset, and how you train the new model. You may experience improved performance from the fine-tuning, or you may experience worse performance if the fine-tuned model is overfitting your training-data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook and the other files before making any changes.\n", + "\n", + "* Try using other layers in the VGG16 model as the transfer layer. How does it affect the training and classification accuracy?\n", + "* Change the new classification layers we added. Can you improve the classification accuracy by either increasing or decreasing the number of nodes in the fully-connected / dense layer?\n", + "* What happens if you remove the Dropout-layer in the new classifier?\n", + "* Change the learning-rates for both Transfer Learning and Fine-Tuning.\n", + "* Try fine-tuning on the whole VGG16 model instead of just the last few layers. How does it affect the classification accuracy on the training- and test-sets? Why?\n", + "* Try doing the fine-tuning from the beginning so the new classification layers are trained from scratch along with all the convolutional layers of the VGG16 model. You may need to lower the learning-rate for the optimizer.\n", + "* Add a few images from the test-set to the training-set. Does that improve performance?\n", + "* Try deleting some of the knifey and spoony images from the training-set so the classes all have the same number of images. Does that improve the numbers in the confusion-matrix?\n", + "* Use another dataset.\n", + "* Use another pre-trained model available from Keras.\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "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:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "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." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/11_Adversarial_Examples.ipynb b/11_Adversarial_Examples.ipynb index 8dc1bbb..2c63ec2 100644 --- a/11_Adversarial_Examples.ipynb +++ b/11_Adversarial_Examples.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -50,7 +59,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -107,7 +115,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -168,9 +175,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -204,9 +209,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "model = inception.Inception()" @@ -689,7 +692,6 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -885,7 +887,6 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -975,7 +976,6 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1152,7 +1152,6 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1360,7 +1359,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1374,9 +1373,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/12_Adversarial_Noise_MNIST.ipynb b/12_Adversarial_Noise_MNIST.ipynb index 6a11e12..0688cfd 100644 --- a/12_Adversarial_Noise_MNIST.ipynb +++ b/12_Adversarial_Noise_MNIST.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -43,6 +52,8 @@ "source": [ "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below.\n", "\n", + "![Flowchart](images/12_adversarial_noise_flowchart.png)\n", + "\n", "This example shows an input image with a hand-written 7-digit. The adversarial noise is then added to the image. Red noise-pixels are positive and make the input image darker in those pixels, while blue noise-pixels are negative and make the input lighter in those pixels.\n", "\n", "The noisy image is then fed to the neural network which results in a predicted class-number. In this case the adversarial noise fools the network into believing that the 7-digit shows a 3-digit. The noise is clearly visible to humans, but the 7-digit is still easily identified by a human.\n", @@ -54,31 +65,6 @@ "The two optimization procedures are completely separate. The first procedure only modifies the variables of the neural network, while the second procedure only modifies the adversarial noise." ] }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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OrWrYsh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\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pOIiIjEDfvn3xwQcfAAA0Gg2GDh2KrKwswamIiIiIDEtCQgLy8vJExyAiolJIrVZj4sSJ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\ndOv5sFgsHj9+SZCZmen2F+dBQUHO9yA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ARc2ox02S9u/f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\nzm0ZTjBIRI5y/vx5+Pr6OjoMIiJygOTkZEeHQEQCnDdJvF49YNQooFEjwNw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qKiqyKLNq1SoNGjRIP/30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- "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image('images/12_adversarial_noise_flowchart.png')" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -88,11 +74,18 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" + ] + } + ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", @@ -101,68 +94,34 @@ "from sklearn.metrics import confusion_matrix\n", "import time\n", "from datetime import timedelta\n", - "import math\n", - "\n", - "# We also need PrettyTensor.\n", - "import prettytensor as pt" + "import math" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:" + "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.12.0-rc0'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tf.__version__" - ] - }, - { - "cell_type": "markdown", + "execution_count": 2, "metadata": {}, - "source": [ - "PrettyTensor version:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, "outputs": [ { "data": { "text/plain": [ - "'0.7.1'" + "'1.9.0'" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pt.__version__" + "tf.__version__" ] }, { @@ -181,40 +140,25 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting data/MNIST/train-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n", - "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "execution_count": 3, + "metadata": {}, + "outputs": [], "source": [ - "from tensorflow.examples.tutorials.mnist import input_data\n", - "data = input_data.read_data_sets('data/MNIST/', one_hot=True)" + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -222,72 +166,45 @@ "text": [ "Size of:\n", "- Training-set:\t\t55000\n", - "- Test-set:\t\t10000\n", - "- Validation-set:\t5000\n" + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" ] } ], "source": [ "print(\"Size of:\")\n", - "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n", - "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n", - "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))" + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now." + "Copy some of the data-dimensions for convenience." ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "data.test.cls = np.argmax(data.test.labels, axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Dimensions" - ] - }, - { - "cell_type": "markdown", + "execution_count": 5, "metadata": {}, - "source": [ - "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, "outputs": [], "source": [ - "# We know that MNIST images are 28 pixels in each dimension.\n", - "img_size = 28\n", + "# The number of pixels in each dimension of an image.\n", + "img_size = data.img_size\n", "\n", - "# Images are stored in one-dimensional arrays of this length.\n", - "img_size_flat = img_size * img_size\n", + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", "\n", "# Tuple with height and width of images used to reshape arrays.\n", - "img_shape = (img_size, img_size)\n", - "\n", - "# Number of colour channels for the images: 1 channel for gray-scale.\n", - "num_channels = 1\n", + "img_shape = data.img_shape\n", "\n", "# Number of classes, one class for each of 10 digits.\n", - "num_classes = 10" + "num_classes = data.num_classes\n", + "\n", + "# Number of colour channels for the images: 1 channel for gray-scale.\n", + "num_channels = data.num_channels" ] }, { @@ -306,10 +223,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "def plot_images(images, cls_true, cls_pred=None, noise=0.0):\n", @@ -360,16 +275,14 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -378,10 +291,10 @@ ], "source": [ "# Get the first images from the test-set.\n", - "images = data.test.images[0:9]\n", + "images = data.x_test[0:9]\n", "\n", "# Get the true classes for those images.\n", - "cls_true = data.test.cls[0:9]\n", + "cls_true = data.y_test_cls[0:9]\n", "\n", "# Plot the images and labels using our helper-function above.\n", "plot_images(images=images, cls_true=cls_true)" @@ -421,10 +334,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')" @@ -439,10 +350,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])" @@ -457,10 +366,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')" @@ -475,13 +382,11 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ - "y_true_cls = tf.argmax(y_true, dimension=1)" + "y_true_cls = tf.argmax(y_true, axis=1)" ] }, { @@ -502,10 +407,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "noise_limit = 0.35" @@ -522,10 +425,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "noise_l2_weight = 0.02" @@ -542,10 +443,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "ADVERSARY_VARIABLES = 'adversary_variables'" @@ -560,13 +459,11 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ - "collections = [tf.GraphKeys.VARIABLES, ADVERSARY_VARIABLES]" + "collections = [tf.GraphKeys.GLOBAL_VARIABLES, ADVERSARY_VARIABLES]" ] }, { @@ -578,10 +475,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [], "source": [ "x_noise = tf.Variable(tf.zeros([img_size, img_size, num_channels]),\n", @@ -599,10 +494,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, + "execution_count": 17, + "metadata": {}, "outputs": [], "source": [ "x_noise_clip = tf.assign(x_noise, tf.clip_by_value(x_noise,\n", @@ -619,10 +512,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [], "source": [ "x_noisy_image = x_image + x_noise" @@ -637,10 +528,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ "x_noisy_image = tf.clip_by_value(x_noisy_image, 0.0, 1.0)" @@ -657,51 +546,50 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will use PrettyTensor to construct the convolutional neural network. First we need to wrap the tensor for the noisy image in a PrettyTensor-object, which provides functions that construct the neural network." + "We will use the Layers API to construct the convolutional neural network, see Tutorial #03-B." ] }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "x_pretty = pt.wrap(x_noisy_image)" - ] - }, - { - "cell_type": "markdown", + "execution_count": 20, "metadata": {}, - "source": [ - "Now that we have wrapped the input image in a PrettyTensor object, we can add the convolutional and fully-connected layers in just a few lines of source-code." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": true - }, "outputs": [], "source": [ - "with pt.defaults_scope(activation_fn=tf.nn.relu):\n", - " y_pred, loss = x_pretty.\\\n", - " conv2d(kernel=5, depth=16, name='layer_conv1').\\\n", - " max_pool(kernel=2, stride=2).\\\n", - " conv2d(kernel=5, depth=36, name='layer_conv2').\\\n", - " max_pool(kernel=2, stride=2).\\\n", - " flatten().\\\n", - " fully_connected(size=128, name='layer_fc1').\\\n", - " softmax_classifier(num_classes=num_classes, labels=y_true)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that `pt.defaults_scope(activation_fn=tf.nn.relu)` makes `activation_fn=tf.nn.relu` an argument for each of the layers constructed inside the `with`-block, so that Rectified Linear Units (ReLU) are used for each of these layers. The `defaults_scope` makes it easy to change arguments for all of the layers." + "# Start the network with the noisy input image.\n", + "net = x_noisy_image\n", + "\n", + "# 1st convolutional layer.\n", + "net = tf.layers.conv2d(inputs=net, name='layer_conv1', padding='same',\n", + " filters=16, kernel_size=5, activation=tf.nn.relu)\n", + "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)\n", + "\n", + "# 2nd convolutional layer.\n", + "net = tf.layers.conv2d(inputs=net, name='layer_conv2', padding='same',\n", + " filters=36, kernel_size=5, activation=tf.nn.relu)\n", + "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)\n", + "\n", + "# Flatten layer.This should eventually be replaced by:\n", + "# net = tf.layers.flatten(net)\n", + "net = tf.contrib.layers.flatten(net)\n", + "\n", + "# 1st fully-connected / dense layer.\n", + "net = tf.layers.dense(inputs=net, name='layer_fc1',\n", + " units=128, activation=tf.nn.relu)\n", + "\n", + "# 2nd fully-connected / dense layer.\n", + "net = tf.layers.dense(inputs=net, name='layer_fc_out',\n", + " units=num_classes, activation=None)\n", + "\n", + "# Unscaled output of the network.\n", + "logits = net\n", + "\n", + "# Softmax output of the network.\n", + "y_pred = tf.nn.softmax(logits=logits)\n", + "\n", + "# Loss measure to be optimized.\n", + "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_true,\n", + " logits=logits)\n", + "loss = tf.reduce_mean(cross_entropy)" ] }, { @@ -720,26 +608,25 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 21, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "['layer_conv1/weights:0',\n", + "['layer_conv1/kernel:0',\n", " 'layer_conv1/bias:0',\n", - " 'layer_conv2/weights:0',\n", + " 'layer_conv2/kernel:0',\n", " 'layer_conv2/bias:0',\n", - " 'layer_fc1/weights:0',\n", + " 'layer_fc1/kernel:0',\n", " 'layer_fc1/bias:0',\n", - " 'fully_connected/weights:0',\n", - " 'fully_connected/bias:0']" + " 'layer_fc_out/kernel:0',\n", + " 'layer_fc_out/bias:0']" ] }, - "execution_count": 25, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -759,10 +646,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" @@ -784,10 +669,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [], "source": [ "adversary_variables = tf.get_collection(ADVERSARY_VARIABLES)" @@ -802,9 +685,8 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 24, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -814,7 +696,7 @@ "['x_noise:0']" ] }, - "execution_count": 28, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -834,10 +716,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [], "source": [ "l2_loss_noise = noise_l2_weight * tf.nn.l2_loss(x_noise)" @@ -852,10 +732,8 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": true - }, + "execution_count": 26, + "metadata": {}, "outputs": [], "source": [ "loss_adversary = loss + l2_loss_noise" @@ -870,10 +748,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, + "execution_count": 27, + "metadata": {}, "outputs": [], "source": [ "optimizer_adversary = tf.train.AdamOptimizer(learning_rate=1e-2).minimize(loss_adversary, var_list=adversary_variables)" @@ -899,13 +775,11 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": true - }, + "execution_count": 28, + "metadata": {}, "outputs": [], "source": [ - "y_pred_cls = tf.argmax(y_pred, dimension=1)" + "y_pred_cls = tf.argmax(y_pred, axis=1)" ] }, { @@ -917,10 +791,8 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": true - }, + "execution_count": 29, + "metadata": {}, "outputs": [], "source": [ "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" @@ -935,10 +807,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": true - }, + "execution_count": 30, + "metadata": {}, "outputs": [], "source": [ "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" @@ -962,10 +832,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": true - }, + "execution_count": 31, + "metadata": {}, "outputs": [], "source": [ "session = tf.Session()" @@ -982,10 +850,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, + "execution_count": 32, + "metadata": {}, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" @@ -1000,10 +866,8 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": true - }, + "execution_count": 33, + "metadata": {}, "outputs": [], "source": [ "def init_noise():\n", @@ -1019,10 +883,8 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, + "execution_count": 34, + "metadata": {}, "outputs": [], "source": [ "init_noise()" @@ -1046,10 +908,8 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": true - }, + "execution_count": 35, + "metadata": {}, "outputs": [], "source": [ "train_batch_size = 64" @@ -1066,10 +926,8 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false - }, + "execution_count": 36, + "metadata": {}, "outputs": [], "source": [ "def optimize(num_iterations, adversary_target_cls=None):\n", @@ -1081,7 +939,7 @@ " # Get a batch of training examples.\n", " # x_batch now holds a batch of images and\n", " # y_true_batch are the true labels for those images.\n", - " x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n", + " x_batch, y_true_batch, _ = data.random_batch(batch_size=train_batch_size)\n", "\n", " # If we are searching for the adversarial noise, then\n", " # use the adversarial target-class instead.\n", @@ -1155,10 +1013,8 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": true - }, + "execution_count": 37, + "metadata": {}, "outputs": [], "source": [ "def get_noise():\n", @@ -1178,10 +1034,8 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false - }, + "execution_count": 38, + "metadata": {}, "outputs": [], "source": [ "def plot_noise():\n", @@ -1215,10 +1069,8 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": true - }, + "execution_count": 39, + "metadata": {}, "outputs": [], "source": [ "def plot_example_errors(cls_pred, correct):\n", @@ -1235,13 +1087,13 @@ " \n", " # Get the images from the test-set that have been\n", " # incorrectly classified.\n", - " images = data.test.images[incorrect]\n", + " images = data.x_test[incorrect]\n", " \n", " # Get the predicted classes for those images.\n", " cls_pred = cls_pred[incorrect]\n", "\n", " # Get the true classes for those images.\n", - " cls_true = data.test.cls[incorrect]\n", + " cls_true = data.y_test_cls[incorrect]\n", "\n", " # Get the adversarial noise from inside the TensorFlow graph.\n", " noise = get_noise()\n", @@ -1262,10 +1114,8 @@ }, { "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": true - }, + "execution_count": 40, + "metadata": {}, "outputs": [], "source": [ "def plot_confusion_matrix(cls_pred):\n", @@ -1275,7 +1125,7 @@ " # all images in the test-set.\n", "\n", " # Get the true classifications for the test-set.\n", - " cls_true = data.test.cls\n", + " cls_true = data.y_test_cls\n", " \n", " # Get the confusion matrix using sklearn.\n", " cm = confusion_matrix(y_true=cls_true,\n", @@ -1305,10 +1155,8 @@ }, { "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": false - }, + "execution_count": 41, + "metadata": {}, "outputs": [], "source": [ "# Split the test-set into smaller batches of this size.\n", @@ -1318,7 +1166,7 @@ " show_confusion_matrix=False):\n", "\n", " # Number of images in the test-set.\n", - " num_test = len(data.test.images)\n", + " num_test = data.num_test\n", "\n", " # Allocate an array for the predicted classes which\n", " # will be calculated in batches and filled into this array.\n", @@ -1336,10 +1184,10 @@ " j = min(i + test_batch_size, num_test)\n", "\n", " # Get the images from the test-set between index i and j.\n", - " images = data.test.images[i:j, :]\n", + " images = data.x_test[i:j, :]\n", "\n", " # Get the associated labels.\n", - " labels = data.test.labels[i:j, :]\n", + " labels = data.y_test[i:j, :]\n", "\n", " # Create a feed-dict with these images and labels.\n", " feed_dict = {x: images,\n", @@ -1353,7 +1201,7 @@ " i = j\n", "\n", " # Convenience variable for the true class-numbers of the test-set.\n", - " cls_true = data.test.cls\n", + " cls_true = data.y_test_cls\n", "\n", " # Create a boolean array whether each image is correctly classified.\n", " correct = (cls_true == cls_pred)\n", @@ -1394,9 +1242,8 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 42, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1404,17 +1251,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimization Iteration: 0, Training Accuracy: 12.5%\n", - "Optimization Iteration: 100, Training Accuracy: 90.6%\n", - "Optimization Iteration: 200, Training Accuracy: 84.4%\n", + "Optimization Iteration: 0, Training Accuracy: 20.3%\n", + "Optimization Iteration: 100, Training Accuracy: 71.9%\n", + "Optimization Iteration: 200, Training Accuracy: 90.6%\n", "Optimization Iteration: 300, Training Accuracy: 84.4%\n", - "Optimization Iteration: 400, Training Accuracy: 89.1%\n", - "Optimization Iteration: 500, Training Accuracy: 87.5%\n", - "Optimization Iteration: 600, Training Accuracy: 93.8%\n", + "Optimization Iteration: 400, Training Accuracy: 87.5%\n", + "Optimization Iteration: 500, Training Accuracy: 93.8%\n", + "Optimization Iteration: 600, Training Accuracy: 89.1%\n", "Optimization Iteration: 700, Training Accuracy: 93.8%\n", - "Optimization Iteration: 800, Training Accuracy: 93.8%\n", - "Optimization Iteration: 900, Training Accuracy: 96.9%\n", - "Optimization Iteration: 999, Training Accuracy: 92.2%\n", + "Optimization Iteration: 800, Training Accuracy: 92.2%\n", + "Optimization Iteration: 900, Training Accuracy: 90.6%\n", + "Optimization Iteration: 999, Training Accuracy: 93.8%\n", "Time usage: 0:00:03\n" ] } @@ -1432,9 +1279,8 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 43, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1442,15 +1288,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 96.3% (9633 / 10000)\n", + "Accuracy on Test-Set: 94.2% (9417 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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JkyZNiqaIRABCx/m0r1qdNm2ayQ8++KDJ9mkIu1vMfkyTPfBGtOzH5NmPhmrX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\nadYEcnxEigbhF5F73gfcG/ok/f+gK30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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1477,10 +1323,8 @@ }, { "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": false - }, + "execution_count": 44, + "metadata": {}, "outputs": [], "source": [ "init_noise()" @@ -1495,9 +1339,8 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 45, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1505,18 +1348,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 200, Training Accuracy: 96.9%\n", + "Optimization Iteration: 0, Training Accuracy: 7.8%\n", + "Optimization Iteration: 100, Training Accuracy: 92.2%\n", + "Optimization Iteration: 200, Training Accuracy: 93.8%\n", "Optimization Iteration: 300, Training Accuracy: 98.4%\n", - "Optimization Iteration: 400, Training Accuracy: 95.3%\n", - "Optimization Iteration: 500, Training Accuracy: 96.9%\n", - "Optimization Iteration: 600, Training Accuracy: 100.0%\n", - "Optimization Iteration: 700, Training Accuracy: 98.4%\n", + "Optimization Iteration: 400, Training Accuracy: 92.2%\n", + "Optimization Iteration: 500, Training Accuracy: 95.3%\n", + "Optimization Iteration: 600, Training Accuracy: 96.9%\n", + "Optimization Iteration: 700, Training Accuracy: 96.9%\n", "Optimization Iteration: 800, Training Accuracy: 95.3%\n", - "Optimization Iteration: 900, Training Accuracy: 93.8%\n", - "Optimization Iteration: 999, Training Accuracy: 100.0%\n", - "Time usage: 0:00:03\n" + "Optimization Iteration: 900, Training Accuracy: 96.9%\n", + "Optimization Iteration: 999, Training Accuracy: 92.2%\n", + "Time usage: 0:00:02\n" ] } ], @@ -1533,9 +1376,8 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 46, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1546,14 +1388,14 @@ "Noise:\n", "- Min: -0.35\n", "- Max: 0.35\n", - "- Std: 0.195455\n" + "- Std: 0.19540551\n" ] }, { "data": { - "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1577,9 +1419,8 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 47, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1587,15 +1428,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 13.2% (1323 / 10000)\n", + "Accuracy on Test-Set: 13.8% (1378 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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GOwF448aNofPZicL2OuyE5ebNm4fKkeJTam8CYX9++umnqpcsWaLaTla3oyHn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\nTkSuQmCC\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1606,16 +1447,16 @@ "output_type": "stream", "text": [ "Confusion Matrix:\n", - "[[ 85 0 0 895 0 0 0 0 0 0]\n", + "[[ 115 0 0 863 0 0 0 2 0 0]\n", " [ 0 0 0 1135 0 0 0 0 0 0]\n", - " [ 0 0 46 986 0 0 0 0 0 0]\n", + " [ 0 0 146 886 0 0 0 0 0 0]\n", " [ 0 0 0 1010 0 0 0 0 0 0]\n", - " [ 0 0 0 959 20 0 0 0 3 0]\n", - " [ 0 0 0 847 0 45 0 0 0 0]\n", - " [ 0 0 0 914 0 1 42 0 1 0]\n", - " [ 0 0 0 977 0 0 0 51 0 0]\n", - " [ 0 0 0 952 0 0 0 0 22 0]\n", - " [ 0 0 1 1006 0 0 0 0 0 2]]\n" + " [ 0 0 0 966 16 0 0 0 0 0]\n", + " [ 0 0 0 865 0 27 0 0 0 0]\n", + " [ 0 0 0 946 0 1 11 0 0 0]\n", + " [ 0 0 0 981 0 0 0 47 0 0]\n", + " [ 0 0 0 968 0 0 0 0 6 0]\n", + " [ 0 0 1 1008 0 0 0 0 0 0]]\n" ] } ], @@ -1640,10 +1481,8 @@ }, { "cell_type": "code", - "execution_count": 52, - "metadata": { - "collapsed": false - }, + "execution_count": 48, + "metadata": {}, "outputs": [], "source": [ "def find_all_noise(num_iterations=1000):\n", @@ -1675,83 +1514,81 @@ }, { "cell_type": "code", - "execution_count": 53, - "metadata": { - "collapsed": false - }, + "execution_count": 49, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Finding adversarial noise for target-class: 0\n", - "Optimization Iteration: 0, Training Accuracy: 9.4%\n", - "Optimization Iteration: 100, Training Accuracy: 90.6%\n", - "Optimization Iteration: 200, Training Accuracy: 92.2%\n", - "Optimization Iteration: 299, Training Accuracy: 93.8%\n", + "Optimization Iteration: 0, Training Accuracy: 4.7%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 200, Training Accuracy: 93.8%\n", + "Optimization Iteration: 299, Training Accuracy: 89.1%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 1\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 62.5%\n", - "Optimization Iteration: 200, Training Accuracy: 62.5%\n", - "Optimization Iteration: 299, Training Accuracy: 75.0%\n", + "Optimization Iteration: 0, Training Accuracy: 14.1%\n", + "Optimization Iteration: 100, Training Accuracy: 68.8%\n", + "Optimization Iteration: 200, Training Accuracy: 67.2%\n", + "Optimization Iteration: 299, Training Accuracy: 60.9%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 2\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 200, Training Accuracy: 95.3%\n", - "Optimization Iteration: 299, Training Accuracy: 96.9%\n", + "Optimization Iteration: 100, Training Accuracy: 96.9%\n", + "Optimization Iteration: 200, Training Accuracy: 93.8%\n", + "Optimization Iteration: 299, Training Accuracy: 98.4%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 3\n", - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 98.4%\n", - "Optimization Iteration: 200, Training Accuracy: 96.9%\n", - "Optimization Iteration: 299, Training Accuracy: 98.4%\n", + "Optimization Iteration: 0, Training Accuracy: 12.5%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 200, Training Accuracy: 100.0%\n", + "Optimization Iteration: 299, Training Accuracy: 100.0%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 4\n", "Optimization Iteration: 0, Training Accuracy: 12.5%\n", - "Optimization Iteration: 100, Training Accuracy: 81.2%\n", - "Optimization Iteration: 200, Training Accuracy: 82.8%\n", - "Optimization Iteration: 299, Training Accuracy: 82.8%\n", + "Optimization Iteration: 100, Training Accuracy: 87.5%\n", + "Optimization Iteration: 200, Training Accuracy: 81.2%\n", + "Optimization Iteration: 299, Training Accuracy: 92.2%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 5\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 96.9%\n", - "Optimization Iteration: 200, Training Accuracy: 96.9%\n", - "Optimization Iteration: 299, Training Accuracy: 98.4%\n", + "Optimization Iteration: 0, Training Accuracy: 15.6%\n", + "Optimization Iteration: 100, Training Accuracy: 92.2%\n", + "Optimization Iteration: 200, Training Accuracy: 92.2%\n", + "Optimization Iteration: 299, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 6\n", - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 200, Training Accuracy: 92.2%\n", - "Optimization Iteration: 299, Training Accuracy: 96.9%\n", + "Optimization Iteration: 0, Training Accuracy: 9.4%\n", + "Optimization Iteration: 100, Training Accuracy: 98.4%\n", + "Optimization Iteration: 200, Training Accuracy: 95.3%\n", + "Optimization Iteration: 299, Training Accuracy: 98.4%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 7\n", - "Optimization Iteration: 0, Training Accuracy: 12.5%\n", - "Optimization Iteration: 100, Training Accuracy: 98.4%\n", - "Optimization Iteration: 200, Training Accuracy: 93.8%\n", - "Optimization Iteration: 299, Training Accuracy: 92.2%\n", + "Optimization Iteration: 0, Training Accuracy: 14.1%\n", + "Optimization Iteration: 100, Training Accuracy: 85.9%\n", + "Optimization Iteration: 200, Training Accuracy: 85.9%\n", + "Optimization Iteration: 299, Training Accuracy: 87.5%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 8\n", - "Optimization Iteration: 0, Training Accuracy: 4.7%\n", - "Optimization Iteration: 100, Training Accuracy: 96.9%\n", - "Optimization Iteration: 200, Training Accuracy: 93.8%\n", - "Optimization Iteration: 299, Training Accuracy: 96.9%\n", + "Optimization Iteration: 0, Training Accuracy: 15.6%\n", + "Optimization Iteration: 100, Training Accuracy: 95.3%\n", + "Optimization Iteration: 200, Training Accuracy: 92.2%\n", + "Optimization Iteration: 299, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 9\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 84.4%\n", - "Optimization Iteration: 200, Training Accuracy: 87.5%\n", - "Optimization Iteration: 299, Training Accuracy: 90.6%\n", + "Optimization Iteration: 0, Training Accuracy: 9.4%\n", + "Optimization Iteration: 100, Training Accuracy: 87.5%\n", + "Optimization Iteration: 200, Training Accuracy: 81.2%\n", + "Optimization Iteration: 299, Training Accuracy: 85.9%\n", "Time usage: 0:00:01\n", "\n" ] @@ -1777,10 +1614,8 @@ }, { "cell_type": "code", - "execution_count": 54, - "metadata": { - "collapsed": false - }, + "execution_count": 50, + "metadata": {}, "outputs": [], "source": [ "def plot_all_noise(all_noise): \n", @@ -1812,17 +1647,16 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 51, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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7FMWpBPqFLIQQQmQATchCCCFEBmi3ZM3BLqrIDXHLgEudekM5IgHncysX8jxk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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1867,10 +1701,8 @@ }, { "cell_type": "code", - "execution_count": 56, - "metadata": { - "collapsed": true - }, + "execution_count": 52, + "metadata": {}, "outputs": [], "source": [ "def make_immune(target_cls, num_iterations_adversary=500,\n", @@ -1922,10 +1754,8 @@ }, { "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": false - }, + "execution_count": 53, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1933,23 +1763,23 @@ "text": [ "Target-class: 3\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 3.1%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 200, Training Accuracy: 93.8%\n", - "Optimization Iteration: 300, Training Accuracy: 96.9%\n", - "Optimization Iteration: 400, Training Accuracy: 96.9%\n", + "Optimization Iteration: 0, Training Accuracy: 1.6%\n", + "Optimization Iteration: 100, Training Accuracy: 95.3%\n", + "Optimization Iteration: 200, Training Accuracy: 96.9%\n", + "Optimization Iteration: 300, Training Accuracy: 92.2%\n", + "Optimization Iteration: 400, Training Accuracy: 93.8%\n", "Optimization Iteration: 499, Training Accuracy: 96.9%\n", - "Time usage: 0:00:02\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 14.4% (1443 / 10000)\n", + "Accuracy on Test-Set: 13.3% (1326 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 42.2%\n", - "Optimization Iteration: 100, Training Accuracy: 90.6%\n", - "Optimization Iteration: 199, Training Accuracy: 89.1%\n", + "Optimization Iteration: 0, Training Accuracy: 12.5%\n", + "Optimization Iteration: 100, Training Accuracy: 89.1%\n", + "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.3% (9529 / 10000)\n" + "Accuracy on Test-Set: 93.3% (9327 / 10000)\n" ] } ], @@ -1966,10 +1796,8 @@ }, { "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": false - }, + "execution_count": 54, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1977,23 +1805,23 @@ "text": [ "Target-class: 3\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 32.8%\n", + "Optimization Iteration: 0, Training Accuracy: 9.4%\n", + "Optimization Iteration: 100, Training Accuracy: 17.2%\n", "Optimization Iteration: 200, Training Accuracy: 32.8%\n", - "Optimization Iteration: 300, Training Accuracy: 29.7%\n", - "Optimization Iteration: 400, Training Accuracy: 34.4%\n", - "Optimization Iteration: 499, Training Accuracy: 26.6%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 300, Training Accuracy: 28.1%\n", + "Optimization Iteration: 400, Training Accuracy: 21.9%\n", + "Optimization Iteration: 499, Training Accuracy: 18.8%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 72.1% (7207 / 10000)\n", + "Accuracy on Test-Set: 80.0% (8002 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 75.0%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 0, Training Accuracy: 78.1%\n", + "Optimization Iteration: 100, Training Accuracy: 90.6%\n", "Optimization Iteration: 199, Training Accuracy: 92.2%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.2% (9519 / 10000)\n" + "Accuracy on Test-Set: 92.3% (9235 / 10000)\n" ] } ], @@ -2012,10 +1840,8 @@ }, { "cell_type": "code", - "execution_count": 59, - "metadata": { - "collapsed": false - }, + "execution_count": 55, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2023,203 +1849,203 @@ "text": [ "Target-class: 0\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 4.7%\n", - "Optimization Iteration: 100, Training Accuracy: 73.4%\n", - "Optimization Iteration: 200, Training Accuracy: 75.0%\n", - "Optimization Iteration: 300, Training Accuracy: 85.9%\n", - "Optimization Iteration: 400, Training Accuracy: 81.2%\n", - "Optimization Iteration: 499, Training Accuracy: 90.6%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 9.4%\n", + "Optimization Iteration: 100, Training Accuracy: 75.0%\n", + "Optimization Iteration: 200, Training Accuracy: 76.6%\n", + "Optimization Iteration: 300, Training Accuracy: 82.8%\n", + "Optimization Iteration: 400, Training Accuracy: 85.9%\n", + "Optimization Iteration: 499, Training Accuracy: 85.9%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 23.3% (2326 / 10000)\n", + "Accuracy on Test-Set: 24.6% (2464 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 34.4%\n", - "Optimization Iteration: 100, Training Accuracy: 95.3%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", + "Optimization Iteration: 0, Training Accuracy: 37.5%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 199, Training Accuracy: 98.4%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.6% (9559 / 10000)\n", + "Accuracy on Test-Set: 93.9% (9387 / 10000)\n", "\n", "Target-class: 1\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 12.5%\n", - "Optimization Iteration: 100, Training Accuracy: 57.8%\n", - "Optimization Iteration: 200, Training Accuracy: 62.5%\n", - "Optimization Iteration: 300, Training Accuracy: 62.5%\n", - "Optimization Iteration: 400, Training Accuracy: 67.2%\n", - "Optimization Iteration: 499, Training Accuracy: 67.2%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 7.8%\n", + "Optimization Iteration: 100, Training Accuracy: 62.5%\n", + "Optimization Iteration: 200, Training Accuracy: 78.1%\n", + "Optimization Iteration: 300, Training Accuracy: 65.6%\n", + "Optimization Iteration: 400, Training Accuracy: 78.1%\n", + "Optimization Iteration: 499, Training Accuracy: 71.9%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 42.2% (4218 / 10000)\n", + "Accuracy on Test-Set: 32.6% (3260 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 59.4%\n", + "Optimization Iteration: 0, Training Accuracy: 45.3%\n", "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", + "Optimization Iteration: 199, Training Accuracy: 96.9%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.5% (9555 / 10000)\n", + "Accuracy on Test-Set: 94.0% (9401 / 10000)\n", "\n", "Target-class: 2\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 43.8%\n", - "Optimization Iteration: 200, Training Accuracy: 57.8%\n", - "Optimization Iteration: 300, Training Accuracy: 70.3%\n", - "Optimization Iteration: 400, Training Accuracy: 68.8%\n", - "Optimization Iteration: 499, Training Accuracy: 71.9%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 9.4%\n", + "Optimization Iteration: 100, Training Accuracy: 57.8%\n", + "Optimization Iteration: 200, Training Accuracy: 81.2%\n", + "Optimization Iteration: 300, Training Accuracy: 73.4%\n", + "Optimization Iteration: 400, Training Accuracy: 87.5%\n", + "Optimization Iteration: 499, Training Accuracy: 79.7%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 46.4% (4639 / 10000)\n", + "Accuracy on Test-Set: 26.2% (2620 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 59.4%\n", - "Optimization Iteration: 100, Training Accuracy: 96.9%\n", - "Optimization Iteration: 199, Training Accuracy: 92.2%\n", + "Optimization Iteration: 0, Training Accuracy: 37.5%\n", + "Optimization Iteration: 100, Training Accuracy: 95.3%\n", + "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.5% (9545 / 10000)\n", + "Accuracy on Test-Set: 93.8% (9380 / 10000)\n", "\n", "Target-class: 3\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 48.4%\n", - "Optimization Iteration: 200, Training Accuracy: 46.9%\n", - "Optimization Iteration: 300, Training Accuracy: 53.1%\n", - "Optimization Iteration: 400, Training Accuracy: 50.0%\n", - "Optimization Iteration: 499, Training Accuracy: 48.4%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 14.1%\n", + "Optimization Iteration: 100, Training Accuracy: 50.0%\n", + "Optimization Iteration: 200, Training Accuracy: 57.8%\n", + "Optimization Iteration: 300, Training Accuracy: 59.4%\n", + "Optimization Iteration: 400, Training Accuracy: 64.1%\n", + "Optimization Iteration: 499, Training Accuracy: 59.4%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 56.5% (5648 / 10000)\n", + "Accuracy on Test-Set: 46.3% (4631 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 54.7%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 199, Training Accuracy: 96.9%\n", + "Optimization Iteration: 0, Training Accuracy: 46.9%\n", + "Optimization Iteration: 100, Training Accuracy: 95.3%\n", + "Optimization Iteration: 199, Training Accuracy: 90.6%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.8% (9581 / 10000)\n", + "Accuracy on Test-Set: 93.6% (9358 / 10000)\n", "\n", "Target-class: 4\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 9.4%\n", - "Optimization Iteration: 100, Training Accuracy: 85.9%\n", - "Optimization Iteration: 200, Training Accuracy: 85.9%\n", - "Optimization Iteration: 300, Training Accuracy: 87.5%\n", - "Optimization Iteration: 400, Training Accuracy: 95.3%\n", - "Optimization Iteration: 499, Training Accuracy: 92.2%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 100, Training Accuracy: 82.8%\n", + "Optimization Iteration: 200, Training Accuracy: 92.2%\n", + "Optimization Iteration: 300, Training Accuracy: 90.6%\n", + "Optimization Iteration: 400, Training Accuracy: 93.8%\n", + "Optimization Iteration: 499, Training Accuracy: 95.3%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 15.6% (1557 / 10000)\n", + "Accuracy on Test-Set: 16.9% (1689 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 18.8%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", - "Optimization Iteration: 199, Training Accuracy: 96.9%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 199, Training Accuracy: 92.2%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 95.6% (9557 / 10000)\n", + "Accuracy on Test-Set: 93.3% (9332 / 10000)\n", "\n", "Target-class: 5\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 18.8%\n", - "Optimization Iteration: 100, Training Accuracy: 71.9%\n", - "Optimization Iteration: 200, Training Accuracy: 90.6%\n", - "Optimization Iteration: 300, Training Accuracy: 95.3%\n", - "Optimization Iteration: 400, Training Accuracy: 89.1%\n", - "Optimization Iteration: 499, Training Accuracy: 92.2%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 10.9%\n", + "Optimization Iteration: 100, Training Accuracy: 65.6%\n", + "Optimization Iteration: 200, Training Accuracy: 71.9%\n", + "Optimization Iteration: 300, Training Accuracy: 78.1%\n", + "Optimization Iteration: 400, Training Accuracy: 75.0%\n", + "Optimization Iteration: 499, Training Accuracy: 76.6%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 17.4% (1745 / 10000)\n", + "Accuracy on Test-Set: 26.4% (2638 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 15.6%\n", - "Optimization Iteration: 100, Training Accuracy: 96.9%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 29.7%\n", + "Optimization Iteration: 100, Training Accuracy: 92.2%\n", + "Optimization Iteration: 199, Training Accuracy: 93.8%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 96.0% (9601 / 10000)\n", + "Accuracy on Test-Set: 94.1% (9407 / 10000)\n", "\n", "Target-class: 6\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 10.9%\n", - "Optimization Iteration: 100, Training Accuracy: 81.2%\n", - "Optimization Iteration: 200, Training Accuracy: 93.8%\n", - "Optimization Iteration: 300, Training Accuracy: 92.2%\n", + "Optimization Iteration: 0, Training Accuracy: 6.2%\n", + "Optimization Iteration: 100, Training Accuracy: 85.9%\n", + "Optimization Iteration: 200, Training Accuracy: 90.6%\n", + "Optimization Iteration: 300, Training Accuracy: 93.8%\n", "Optimization Iteration: 400, Training Accuracy: 89.1%\n", - "Optimization Iteration: 499, Training Accuracy: 92.2%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 499, Training Accuracy: 89.1%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 17.6% (1762 / 10000)\n", + "Accuracy on Test-Set: 15.8% (1584 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 20.3%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", + "Optimization Iteration: 0, Training Accuracy: 17.2%\n", + "Optimization Iteration: 100, Training Accuracy: 90.6%\n", + "Optimization Iteration: 199, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.7% (9570 / 10000)\n", + "Accuracy on Test-Set: 93.8% (9385 / 10000)\n", "\n", "Target-class: 7\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 14.1%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 0, Training Accuracy: 10.9%\n", + "Optimization Iteration: 100, Training Accuracy: 96.9%\n", "Optimization Iteration: 200, Training Accuracy: 98.4%\n", - "Optimization Iteration: 300, Training Accuracy: 100.0%\n", + "Optimization Iteration: 300, Training Accuracy: 96.9%\n", "Optimization Iteration: 400, Training Accuracy: 96.9%\n", - "Optimization Iteration: 499, Training Accuracy: 100.0%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 499, Training Accuracy: 98.4%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 12.8% (1281 / 10000)\n", + "Accuracy on Test-Set: 13.2% (1319 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 12.5%\n", - "Optimization Iteration: 100, Training Accuracy: 98.4%\n", - "Optimization Iteration: 199, Training Accuracy: 98.4%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 23.4%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 199, Training Accuracy: 90.6%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 95.9% (9587 / 10000)\n", + "Accuracy on Test-Set: 93.6% (9357 / 10000)\n", "\n", "Target-class: 8\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 4.7%\n", - "Optimization Iteration: 100, Training Accuracy: 64.1%\n", - "Optimization Iteration: 200, Training Accuracy: 81.2%\n", - "Optimization Iteration: 300, Training Accuracy: 71.9%\n", - "Optimization Iteration: 400, Training Accuracy: 78.1%\n", - "Optimization Iteration: 499, Training Accuracy: 84.4%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 9.4%\n", + "Optimization Iteration: 100, Training Accuracy: 68.8%\n", + "Optimization Iteration: 200, Training Accuracy: 73.4%\n", + "Optimization Iteration: 300, Training Accuracy: 89.1%\n", + "Optimization Iteration: 400, Training Accuracy: 89.1%\n", + "Optimization Iteration: 499, Training Accuracy: 76.6%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 24.9% (2493 / 10000)\n", + "Accuracy on Test-Set: 26.9% (2694 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 25.0%\n", + "Optimization Iteration: 0, Training Accuracy: 23.4%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", - "Optimization Iteration: 199, Training Accuracy: 96.9%\n", + "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 96.0% (9601 / 10000)\n", + "Accuracy on Test-Set: 94.5% (9452 / 10000)\n", "\n", "Target-class: 9\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 9.4%\n", "Optimization Iteration: 100, Training Accuracy: 48.4%\n", - "Optimization Iteration: 200, Training Accuracy: 50.0%\n", - "Optimization Iteration: 300, Training Accuracy: 53.1%\n", - "Optimization Iteration: 400, Training Accuracy: 64.1%\n", - "Optimization Iteration: 499, Training Accuracy: 65.6%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 200, Training Accuracy: 51.6%\n", + "Optimization Iteration: 300, Training Accuracy: 51.6%\n", + "Optimization Iteration: 400, Training Accuracy: 53.1%\n", + "Optimization Iteration: 499, Training Accuracy: 50.0%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 45.5% (4546 / 10000)\n", + "Accuracy on Test-Set: 46.6% (4657 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 51.6%\n", - "Optimization Iteration: 100, Training Accuracy: 95.3%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 50.0%\n", + "Optimization Iteration: 100, Training Accuracy: 89.1%\n", + "Optimization Iteration: 199, Training Accuracy: 93.8%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 96.2% (9615 / 10000)\n", + "Accuracy on Test-Set: 94.6% (9462 / 10000)\n", "\n" ] } @@ -2245,10 +2071,8 @@ }, { "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": false - }, + "execution_count": 56, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2256,355 +2080,361 @@ "text": [ "Target-class: 0\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 53.1%\n", - "Optimization Iteration: 200, Training Accuracy: 73.4%\n", - "Optimization Iteration: 300, Training Accuracy: 79.7%\n", - "Optimization Iteration: 400, Training Accuracy: 84.4%\n", - "Optimization Iteration: 499, Training Accuracy: 95.3%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 10.9%\n", + "Optimization Iteration: 100, Training Accuracy: 60.9%\n", + "Optimization Iteration: 200, Training Accuracy: 76.6%\n", + "Optimization Iteration: 300, Training Accuracy: 73.4%\n", + "Optimization Iteration: 400, Training Accuracy: 57.8%\n", + "Optimization Iteration: 499, Training Accuracy: 71.9%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 29.2% (2921 / 10000)\n", + "Accuracy on Test-Set: 36.0% (3601 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 29.7%\n", - "Optimization Iteration: 100, Training Accuracy: 96.9%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 45.3%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 199, Training Accuracy: 93.8%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 96.2% (9619 / 10000)\n", + "Accuracy on Test-Set: 94.7% (9474 / 10000)\n", "\n", "Target-class: 0\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 1.6%\n", - "Optimization Iteration: 100, Training Accuracy: 12.5%\n", - "Optimization Iteration: 200, Training Accuracy: 7.8%\n", - "Optimization Iteration: 300, Training Accuracy: 18.8%\n", - "Optimization Iteration: 400, Training Accuracy: 9.4%\n", - "Optimization Iteration: 499, Training Accuracy: 9.4%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 14.1%\n", + "Optimization Iteration: 100, Training Accuracy: 9.4%\n", + "Optimization Iteration: 200, Training Accuracy: 12.5%\n", + "Optimization Iteration: 300, Training Accuracy: 9.4%\n", + "Optimization Iteration: 400, Training Accuracy: 6.2%\n", + "Optimization Iteration: 499, Training Accuracy: 6.2%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 94.4% (9437 / 10000)\n", + "Accuracy on Test-Set: 93.3% (9334 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 89.1%\n", - "Optimization Iteration: 100, Training Accuracy: 98.4%\n", - "Optimization Iteration: 199, Training Accuracy: 93.8%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 87.5%\n", + "Optimization Iteration: 100, Training Accuracy: 96.9%\n", + "Optimization Iteration: 199, Training Accuracy: 98.4%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 96.4% (9635 / 10000)\n", + "Accuracy on Test-Set: 95.2% (9524 / 10000)\n", "\n", "Target-class: 1\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 42.2%\n", - "Optimization Iteration: 200, Training Accuracy: 60.9%\n", - "Optimization Iteration: 300, Training Accuracy: 75.0%\n", - "Optimization Iteration: 400, Training Accuracy: 70.3%\n", - "Optimization Iteration: 499, Training Accuracy: 85.9%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 6.2%\n", + "Optimization Iteration: 100, Training Accuracy: 53.1%\n", + "Optimization Iteration: 200, Training Accuracy: 73.4%\n", + "Optimization Iteration: 300, Training Accuracy: 73.4%\n", + "Optimization Iteration: 400, Training Accuracy: 82.8%\n", + "Optimization Iteration: 499, Training Accuracy: 81.2%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 28.7% (2875 / 10000)\n", + "Accuracy on Test-Set: 25.4% (2543 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 39.1%\n", - "Optimization Iteration: 100, Training Accuracy: 98.4%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 21.9%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 199, Training Accuracy: 93.8%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 96.4% (9643 / 10000)\n", + "Accuracy on Test-Set: 94.9% (9492 / 10000)\n", "\n", "Target-class: 1\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 15.6%\n", - "Optimization Iteration: 200, Training Accuracy: 18.8%\n", - "Optimization Iteration: 300, Training Accuracy: 12.5%\n", - "Optimization Iteration: 400, Training Accuracy: 9.4%\n", - "Optimization Iteration: 499, Training Accuracy: 12.5%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 12.5%\n", + "Optimization Iteration: 100, Training Accuracy: 23.4%\n", + "Optimization Iteration: 200, Training Accuracy: 10.9%\n", + "Optimization Iteration: 300, Training Accuracy: 10.9%\n", + "Optimization Iteration: 400, Training Accuracy: 10.9%\n", + "Optimization Iteration: 499, Training Accuracy: 9.4%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 94.3% (9428 / 10000)\n", + "Accuracy on Test-Set: 91.9% (9188 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 95.3%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", - "Optimization Iteration: 199, Training Accuracy: 92.2%\n", + "Optimization Iteration: 199, Training Accuracy: 89.1%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 96.9% (9685 / 10000)\n", + "Accuracy on Test-Set: 95.5% (9545 / 10000)\n", "\n", "Target-class: 2\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 60.9%\n", - "Optimization Iteration: 200, Training Accuracy: 64.1%\n", - "Optimization Iteration: 300, Training Accuracy: 71.9%\n", - "Optimization Iteration: 400, Training Accuracy: 75.0%\n", - "Optimization Iteration: 499, Training Accuracy: 82.8%\n", + "Optimization Iteration: 0, Training Accuracy: 7.8%\n", + "Optimization Iteration: 100, Training Accuracy: 62.5%\n", + "Optimization Iteration: 200, Training Accuracy: 70.3%\n", + "Optimization Iteration: 300, Training Accuracy: 78.1%\n", + "Optimization Iteration: 400, Training Accuracy: 73.4%\n", + "Optimization Iteration: 499, Training Accuracy: 71.9%\n", "Time usage: 0:00:02\n", "\n", - "Accuracy on Test-Set: 34.3% (3427 / 10000)\n", + "Accuracy on Test-Set: 34.7% (3474 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 31.2%\n", - "Optimization Iteration: 100, Training Accuracy: 100.0%\n", - "Optimization Iteration: 199, Training Accuracy: 98.4%\n", + "Optimization Iteration: 0, Training Accuracy: 51.6%\n", + "Optimization Iteration: 100, Training Accuracy: 87.5%\n", + "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 96.6% (9657 / 10000)\n", + "Accuracy on Test-Set: 95.2% (9520 / 10000)\n", "\n", "Target-class: 2\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 9.4%\n", - "Optimization Iteration: 200, Training Accuracy: 14.1%\n", - "Optimization Iteration: 300, Training Accuracy: 10.9%\n", - "Optimization Iteration: 400, Training Accuracy: 7.8%\n", - "Optimization Iteration: 499, Training Accuracy: 17.2%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 12.5%\n", + "Optimization Iteration: 100, Training Accuracy: 15.6%\n", + "Optimization Iteration: 200, Training Accuracy: 12.5%\n", + "Optimization Iteration: 300, Training Accuracy: 18.8%\n", + "Optimization Iteration: 400, Training Accuracy: 10.9%\n", + "Optimization Iteration: 499, Training Accuracy: 14.1%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 94.3% (9435 / 10000)\n", + "Accuracy on Test-Set: 90.5% (9051 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 96.9%\n", - "Optimization Iteration: 100, Training Accuracy: 98.4%\n", - "Optimization Iteration: 199, Training Accuracy: 96.9%\n", + "Optimization Iteration: 0, Training Accuracy: 87.5%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 199, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 96.6% (9664 / 10000)\n", + "Accuracy on Test-Set: 95.3% (9535 / 10000)\n", "\n", "Target-class: 3\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 14.1%\n", - "Optimization Iteration: 100, Training Accuracy: 20.3%\n", - "Optimization Iteration: 200, Training Accuracy: 40.6%\n", - "Optimization Iteration: 300, Training Accuracy: 57.8%\n", - "Optimization Iteration: 400, Training Accuracy: 54.7%\n", - "Optimization Iteration: 499, Training Accuracy: 64.1%\n", - "Time 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(9650 / 10000)\n", + "Accuracy on Test-Set: 95.4% (9537 / 10000)\n", "\n", "Target-class: 3\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 4.7%\n", + "Optimization Iteration: 0, Training Accuracy: 12.5%\n", "Optimization Iteration: 100, Training Accuracy: 10.9%\n", - "Optimization Iteration: 200, Training Accuracy: 17.2%\n", - "Optimization Iteration: 300, Training Accuracy: 15.6%\n", - "Optimization Iteration: 400, Training Accuracy: 1.6%\n", + "Optimization Iteration: 200, Training Accuracy: 18.8%\n", + "Optimization Iteration: 300, Training Accuracy: 10.9%\n", + "Optimization Iteration: 400, Training Accuracy: 9.4%\n", "Optimization Iteration: 499, Training Accuracy: 9.4%\n", - "Time usage: 0:00:02\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.7% (9570 / 10000)\n", + "Accuracy on Test-Set: 94.6% (9464 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 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"Accuracy on Test-Set: 97.0% (9699 / 10000)\n", + "Accuracy on Test-Set: 95.5% (9550 / 10000)\n", "\n", "Target-class: 8\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 48.4%\n", - "Optimization Iteration: 200, Training Accuracy: 46.9%\n", - "Optimization Iteration: 300, Training Accuracy: 71.9%\n", - "Optimization Iteration: 400, Training Accuracy: 70.3%\n", - "Optimization Iteration: 499, Training Accuracy: 75.0%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 12.5%\n", + "Optimization Iteration: 100, Training Accuracy: 26.6%\n", + "Optimization Iteration: 200, Training Accuracy: 43.8%\n", + "Optimization Iteration: 300, Training Accuracy: 60.9%\n", + "Optimization Iteration: 400, Training Accuracy: 62.5%\n", + "Optimization Iteration: 499, Training Accuracy: 64.1%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 36.8% (3678 / 10000)\n", + 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"Optimization Iteration: 100, Training Accuracy: 12.5%\n", + "Optimization Iteration: 200, Training Accuracy: 15.6%\n", + "Optimization Iteration: 300, Training Accuracy: 15.6%\n", + "Optimization Iteration: 400, Training Accuracy: 7.8%\n", "Optimization Iteration: 499, Training Accuracy: 9.4%\n", - "Time usage: 0:00:02\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 96.2% (9625 / 10000)\n", + "Accuracy on Test-Set: 95.5% (9555 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 96.9%\n", @@ -2612,47 +2442,47 @@ "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 97.2% (9720 / 10000)\n", + "Accuracy on Test-Set: 96.5% (9650 / 10000)\n", "\n", "Target-class: 9\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 9.4%\n", - "Optimization Iteration: 100, Training Accuracy: 23.4%\n", - "Optimization Iteration: 200, Training Accuracy: 43.8%\n", - "Optimization Iteration: 300, Training Accuracy: 37.5%\n", - "Optimization Iteration: 400, Training Accuracy: 45.3%\n", - "Optimization Iteration: 499, Training Accuracy: 39.1%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 7.8%\n", + "Optimization Iteration: 100, Training Accuracy: 28.1%\n", + "Optimization Iteration: 200, Training Accuracy: 39.1%\n", + "Optimization Iteration: 300, Training Accuracy: 42.2%\n", + "Optimization Iteration: 400, Training Accuracy: 46.9%\n", + "Optimization Iteration: 499, Training Accuracy: 48.4%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 64.9% (6494 / 10000)\n", + "Accuracy on Test-Set: 64.1% (6415 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 67.2%\n", - "Optimization Iteration: 100, Training Accuracy: 95.3%\n", - "Optimization Iteration: 199, Training Accuracy: 98.4%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 68.8%\n", + "Optimization Iteration: 100, Training Accuracy: 98.4%\n", + "Optimization Iteration: 199, Training Accuracy: 96.9%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 97.5% (9746 / 10000)\n", + "Accuracy on Test-Set: 96.3% (9629 / 10000)\n", "\n", "Target-class: 9\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 9.4%\n", - "Optimization Iteration: 100, Training Accuracy: 7.8%\n", - "Optimization Iteration: 200, Training Accuracy: 10.9%\n", - "Optimization Iteration: 300, Training Accuracy: 15.6%\n", - "Optimization Iteration: 400, Training Accuracy: 12.5%\n", + "Optimization Iteration: 100, Training Accuracy: 3.1%\n", + "Optimization Iteration: 200, Training Accuracy: 3.1%\n", + "Optimization Iteration: 300, Training Accuracy: 10.9%\n", + "Optimization Iteration: 400, Training Accuracy: 9.4%\n", "Optimization Iteration: 499, Training Accuracy: 4.7%\n", - "Time usage: 0:00:02\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 97.1% (9709 / 10000)\n", + "Accuracy on Test-Set: 96.1% (9614 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 98.4%\n", + "Optimization Iteration: 0, Training Accuracy: 96.9%\n", "Optimization Iteration: 100, Training Accuracy: 100.0%\n", "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 97.7% (9768 / 10000)\n", + "Accuracy on Test-Set: 96.7% (9666 / 10000)\n", "\n" ] } @@ -2686,9 +2516,8 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 57, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2699,14 +2528,14 @@ "Noise:\n", "- Min: -0.35\n", "- Max: 0.35\n", - "- Std: 0.270488\n" + "- Std: 0.27831247\n" ] }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2726,24 +2555,22 @@ }, { "cell_type": "code", - "execution_count": 62, - "metadata": { - "collapsed": false - }, + "execution_count": 58, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 97.7% (9768 / 10000)\n", + "Accuracy on Test-Set: 96.7% (9666 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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3bq1js70333yzjs1S7y233KLj8847L1yDQ/jwww913KdPH1Srxs856ZTJ/Awj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\nd+ZJRESUqqp45klERJQSdp5ERESW2HkSERFZYud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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2754,16 +2581,16 @@ "output_type": "stream", "text": [ "Confusion Matrix:\n", - "[[ 972 0 1 0 0 0 2 1 3 1]\n", - " [ 0 1119 4 0 0 2 2 0 8 0]\n", - " [ 3 0 1006 9 1 1 1 5 4 2]\n", - " [ 1 0 1 997 0 5 0 4 2 0]\n", - " [ 0 1 3 0 955 0 3 1 2 17]\n", - " [ 1 0 0 9 0 876 3 0 2 1]\n", - " [ 6 4 0 0 3 6 934 0 5 0]\n", - " [ 2 4 18 3 1 0 0 985 2 13]\n", - " [ 4 0 4 3 4 1 1 3 950 4]\n", - " [ 6 6 0 7 4 5 0 4 3 974]]\n" + "[[ 966 0 0 0 0 1 5 1 5 2]\n", + " [ 0 1113 4 2 0 0 3 1 11 1]\n", + " [ 2 1 991 8 6 0 0 7 16 1]\n", + " [ 0 0 4 978 0 11 0 7 9 1]\n", + " [ 0 0 4 0 946 0 6 0 3 23]\n", + " [ 2 1 1 6 0 870 3 2 2 5]\n", + " [ 3 3 2 0 6 11 926 1 6 0]\n", + " [ 0 3 24 6 1 0 0 972 3 19]\n", + " [ 4 0 3 8 5 4 3 4 939 4]\n", + " [ 3 6 1 11 8 3 0 5 7 965]]\n" ] } ], @@ -2783,10 +2610,8 @@ }, { "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": false - }, + "execution_count": 59, + "metadata": {}, "outputs": [], "source": [ "init_noise()" @@ -2801,24 +2626,22 @@ }, { "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": false - }, + "execution_count": 60, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 92.2% (9222 / 10000)\n", + "Accuracy on Test-Set: 87.6% (8764 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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RFrkziMgGEbmtgGWUx4HDWu0EcCjst4ui2AH7zQTI68Zyt3OFiJwL4AkA9wfT+sMeHQwO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\nAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2829,16 +2652,16 @@ "output_type": "stream", "text": [ "Confusion Matrix:\n", - "[[ 970 0 1 0 0 1 8 0 0 0]\n", - " [ 0 1121 5 0 0 0 9 0 0 0]\n", - " [ 2 1 1028 0 0 0 1 0 0 0]\n", - " [ 1 0 27 964 0 13 2 2 1 0]\n", - " [ 0 2 3 0 957 0 20 0 0 0]\n", - " [ 3 0 2 2 0 875 10 0 0 0]\n", - " [ 4 1 0 0 1 1 951 0 0 0]\n", - " [ 10 21 61 3 14 3 0 913 3 0]\n", - " [ 29 2 91 7 7 26 70 1 741 0]\n", - " [ 20 18 10 12 150 65 11 12 9 702]]\n" + "[[ 948 0 3 0 0 1 27 1 0 0]\n", + " [ 0 1110 5 1 0 0 19 0 0 0]\n", + " [ 5 1 1022 0 0 0 0 0 4 0]\n", + " [ 0 1 22 955 1 23 1 5 2 0]\n", + " [ 0 3 2 0 941 0 36 0 0 0]\n", + " [ 1 0 1 1 0 869 20 0 0 0]\n", + " [ 1 1 1 0 1 1 953 0 0 0]\n", + " [ 7 32 77 2 43 4 1 855 7 0]\n", + " [ 13 7 45 8 7 39 107 1 747 0]\n", + " [ 11 18 6 10 465 79 32 8 16 364]]\n" ] } ], @@ -2863,10 +2686,8 @@ }, { "cell_type": "code", - "execution_count": 65, - "metadata": { - "collapsed": false - }, + "execution_count": 61, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -2948,9 +2769,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2962,9 +2783,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/13B_Visual_Analysis_MNIST.ipynb b/13B_Visual_Analysis_MNIST.ipynb new file mode 100644 index 0000000..6441ae1 --- /dev/null +++ b/13B_Visual_Analysis_MNIST.ipynb @@ -0,0 +1,1927 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #13-B\n", + "# Visual Analysis (MNIST)\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Tutorial #13 showed how to find input images that maximized the response of individual neurons inside the Inception model, so as to find the images that the neuron *liked to see*. But because the Inception model is so large and complex the images were just complex wavy patterns.\n", + "\n", + "This tutorial uses a much simpler Convolutional Neural Network with the MNIST data-set for recognizing hand-written digits. The code is spliced together from Tutorial #03-B for constructing the neural network and Tutorial #13 for finding input images that maximize individual neuron responses inside the neural network, so a lot of this code may look familiar to you." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flowchart" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. Note that there are two separate optimization loops here:\n", + "\n", + "First the weights of the neural network are optimized by inputting images and their true classes to the network so as to improve the classification accuracy.\n", + "\n", + "Afterwards a second optimization is performed which finds the input image that maximizes a given feature or neuron inside the network. This finds an image that the network *likes to see*." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Flowchart](images/13b_visual_analysis_flowchart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TensorFlow 2\n", + "\n", + "This tutorial was developed using TensorFlow v.1 back in the year 2016. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\", which is still useful for learning how TensorFlow works, but you would have to implement it slightly differently in TF2 (see Tutorial 03C on the Keras API). It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.metrics import confusion_matrix\n", + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n" + ] + } + ], + "source": [ + "# Use TensorFlow v.2 with this old v.1 code.\n", + "# E.g. placeholder variables and sessions have changed in TF2.\n", + "import tensorflow.compat.v1 as tf\n", + "tf.disable_v2_behavior()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Size of:\n", + "- Training-set:\t\t55000\n", + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" + ] + } + ], + "source": [ + "print(\"Size of:\")\n", + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copy some of the data-dimensions for convenience." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# The number of pixels in each dimension of an image.\n", + "img_size = data.img_size\n", + "\n", + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", + "\n", + "# Tuple with height and width of images used to reshape arrays.\n", + "img_shape = data.img_shape\n", + "\n", + "# Number of classes, one class for each of 10 digits.\n", + "num_classes = data.num_classes\n", + "\n", + "# Number of colour channels for the images: 1 channel for gray-scale.\n", + "num_channels = data.num_channels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-functions for plotting images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_images(images, cls_true, cls_pred=None):\n", + " assert len(images) == len(cls_true) == 9\n", + " \n", + " # Create figure with 3x3 sub-plots.\n", + " fig, axes = plt.subplots(3, 3)\n", + " fig.subplots_adjust(hspace=0.3, wspace=0.3)\n", + "\n", + " for i, ax in enumerate(axes.flat):\n", + " # Plot image.\n", + " ax.imshow(images[i].reshape(img_shape), cmap='binary')\n", + "\n", + " # Show true and predicted classes.\n", + " if cls_pred is None:\n", + " xlabel = \"True: {0}\".format(cls_true[i])\n", + " else:\n", + " xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n", + "\n", + " # Show the classes as the label on the x-axis.\n", + " ax.set_xlabel(xlabel)\n", + " \n", + " # Remove ticks from the plot.\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " \n", + " # Ensure the plot is shown correctly with multiple plots\n", + " # in a single Notebook cell.\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function used to plot 10 images in a 2x5 grid." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_images10(images, smooth=True):\n", + " # Interpolation type.\n", + " if smooth:\n", + " interpolation = 'spline16'\n", + " else:\n", + " interpolation = 'nearest'\n", + "\n", + " # Create figure with sub-plots.\n", + " fig, axes = plt.subplots(2, 5)\n", + "\n", + " # Adjust vertical spacing.\n", + " fig.subplots_adjust(hspace=0.1, wspace=0.1)\n", + "\n", + " # For each entry in the grid.\n", + " for i, ax in enumerate(axes.flat):\n", + " # Get the i'th image and only use the desired pixels.\n", + " img = images[i, :, :]\n", + " \n", + " # Plot the image.\n", + " ax.imshow(img, interpolation=interpolation, cmap='binary')\n", + "\n", + " # Remove ticks.\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + "\n", + " # Ensure the plot is shown correctly with multiple plots\n", + " # in a single Notebook cell.\n", + " plt.show() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function used to plot a single image." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_image(image):\n", + " plt.imshow(image, interpolation='nearest', cmap='binary')\n", + " plt.xticks([])\n", + " plt.yticks([])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot a few images to see if data is correct" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the first images from the test-set.\n", + "images = data.x_test[0:9]\n", + "\n", + "# Get the true classes for those images.\n", + "cls_true = data.y_test_cls[0:9]\n", + "\n", + "# Plot the images and labels using our helper-function above.\n", + "plot_images(images=images, cls_true=cls_true)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TensorFlow Graph\n", + "\n", + "The neural network is constructed as a computational graph in TensorFlow using the `tf.layers` API, which is described in detail in Tutorial #03-B." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Placeholder variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph.\n", + "\n", + "First we define the placeholder variable for the input images. This allows us to change the images that are input to the TensorFlow graph. This is a so-called tensor, which just means that it is a multi-dimensional array. The data-type is set to `float32` and the shape is set to `[None, img_size_flat]`, where `None` means that the tensor may hold an arbitrary number of images with each image being a vector of length `img_size_flat`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The convolutional layers expect `x` to be encoded as a 4-rank tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "y_true_cls = tf.argmax(y_true, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Network\n", + "\n", + "We now implement the Convolutional Neural Network using the Layers API. We use the `net`-variable to refer to the last layer while building the neural network. This makes it easy to add or remove layers in the code if you want to experiment. First we set the `net`-variable to the reshaped input image." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "net = x_image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The input image is then input to the first convolutional layer, which has 16 filters each of size 5x5 pixels. The activation-function is the Rectified Linear Unit (ReLU) described in more detail in Tutorial #02." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :2: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.keras.layers.Conv2D` instead.\n", + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `layer.__call__` method instead.\n" + ] + } + ], + "source": [ + "net = tf.layers.conv2d(inputs=net, name='layer_conv1', padding='same',\n", + " filters=16, kernel_size=5, activation=tf.nn.relu)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After the convolution we do a max-pooling which is also described in Tutorial #02." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :1: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use keras.layers.MaxPooling2D instead.\n" + ] + } + ], + "source": [ + "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we make a second convolutional layer, also with max-pooling." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "net = tf.layers.conv2d(inputs=net, name='layer_conv2', padding='same',\n", + " filters=36, kernel_size=5, activation=tf.nn.relu)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output then needs to be flattened so it can be used in fully-connected (aka. dense) layers." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :1: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use keras.layers.Flatten instead.\n" + ] + } + ], + "source": [ + "net = tf.layers.flatten(net)\n", + "\n", + "# This should eventually be replaced by:\n", + "# net = tf.layers.flatten(net)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now add fully-connected (or dense) layers to the neural network." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :2: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use keras.layers.Dense instead.\n" + ] + } + ], + "source": [ + "net = tf.layers.dense(inputs=net, name='layer_fc1',\n", + " units=128, activation=tf.nn.relu)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need the neural network to classify the input images into 10 different classes. So the final fully-connected layer has `num_classes=10` output neurons." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "net = tf.layers.dense(inputs=net, name='layer_fc_out',\n", + " units=num_classes, activation=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The outputs of the final fully-connected layer are sometimes called logits, so we have a convenience variable with that name which we will also use further below." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "logits = net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the softmax function to 'squash' the outputs so they are between zero and one, and so they sum to one." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = tf.nn.softmax(logits=logits)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us how likely the neural network thinks the input image is of each possible class. The one that has the highest value is considered the most likely so its index is taken to be the class-number." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_cls = tf.argmax(y_pred, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loss-Function to be Optimized" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make the model better at classifying the input images, we must somehow change the variables of the neural network.\n", + "\n", + "The cross-entropy is a performance measure used in classification. The cross-entropy is a continuous function that is always positive and if the predicted output of the model exactly matches the desired output then the cross-entropy equals zero. The goal of optimization is therefore to minimize the cross-entropy so it gets as close to zero as possible by changing the variables of the model.\n", + "\n", + "TensorFlow has a function for calculating the cross-entropy, which uses the values of the `logits`-layer because it also calculates the softmax internally, so as to to improve numerical stability." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_true, logits=logits)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now calculated the cross-entropy for each of the image classifications so we have a measure of how well the model performs on each image individually. But in order to use the cross-entropy to guide the optimization of the model's variables we need a single scalar value, so we simply take the average of the cross-entropy for all the image classifications." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "loss = tf.reduce_mean(cross_entropy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimization Method\n", + "\n", + "Now that we have a cost measure that must be minimized, we can then create an optimizer. In this case it is the Adam optimizer with a learning-rate of 1e-4.\n", + "\n", + "Note that optimization is not performed at this point. In fact, nothing is calculated at all, we just add the optimizer-object to the TensorFlow graph for later execution." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classification Accuracy\n", + "\n", + "We need to calculate the classification accuracy so we can report progress to the user.\n", + "\n", + "First we create a vector of booleans telling us whether the predicted class equals the true class of each image." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimize the Neural Network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create TensorFlow session\n", + "\n", + "Once the TensorFlow graph has been created, we have to create a TensorFlow session which is used to execute the graph." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "session = tf.Session()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize variables\n", + "\n", + "The variables for the TensorFlow graph must be initialized before we start optimizing them." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "session.run(tf.global_variables_initializer())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function to perform optimization iterations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n", + "\n", + "If your computer crashes or becomes very slow because you run out of RAM, then you may try and lower this number, but you may then need to do more optimization iterations." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "train_batch_size = 64" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function performs a number of optimization iterations so as to gradually improve the variables of the neural network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Counter for total number of iterations performed so far.\n", + "total_iterations = 0\n", + "\n", + "def optimize(num_iterations):\n", + " # Ensure we update the global variable rather than a local copy.\n", + " global total_iterations\n", + "\n", + " for i in range(total_iterations,\n", + " total_iterations + num_iterations):\n", + "\n", + " # Get a batch of training examples.\n", + " # x_batch now holds a batch of images and\n", + " # y_true_batch are the true labels for those images.\n", + " x_batch, y_true_batch, _ = data.random_batch(batch_size=train_batch_size)\n", + "\n", + " # Put the batch into a dict with the proper names\n", + " # for placeholder variables in the TensorFlow graph.\n", + " feed_dict_train = {x: x_batch,\n", + " y_true: y_true_batch}\n", + "\n", + " # Run the optimizer using this batch of training data.\n", + " # TensorFlow assigns the variables in feed_dict_train\n", + " # to the placeholder variables and then runs the optimizer.\n", + " session.run(optimizer, feed_dict=feed_dict_train)\n", + "\n", + " # Print status every 100 iterations.\n", + " if i % 100 == 0:\n", + " # Calculate the accuracy on the training-set.\n", + " acc = session.run(accuracy, feed_dict=feed_dict_train)\n", + "\n", + " # Message for printing.\n", + " msg = \"Optimization Iteration: {0:>6}, Training Accuracy: {1:>6.1%}\"\n", + "\n", + " # Print it.\n", + " print(msg.format(i + 1, acc))\n", + "\n", + " # Update the total number of iterations performed.\n", + " total_iterations += num_iterations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function to plot example errors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function for plotting examples of images from the test-set that have been mis-classified." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_example_errors(cls_pred, correct):\n", + " # This function is called from print_test_accuracy() below.\n", + "\n", + " # cls_pred is an array of the predicted class-number for\n", + " # all images in the test-set.\n", + "\n", + " # correct is a boolean array whether the predicted class\n", + " # is equal to the true class for each image in the test-set.\n", + "\n", + " # Negate the boolean array.\n", + " incorrect = (correct == False)\n", + " \n", + " # Get the images from the test-set that have been\n", + " # incorrectly classified.\n", + " images = data.x_test[incorrect]\n", + " \n", + " # Get the predicted classes for those images.\n", + " cls_pred = cls_pred[incorrect]\n", + "\n", + " # Get the true classes for those images.\n", + " cls_true = data.y_test_cls[incorrect]\n", + " \n", + " # Plot the first 9 images.\n", + " plot_images(images=images[0:9],\n", + " cls_true=cls_true[0:9],\n", + " cls_pred=cls_pred[0:9])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function to plot confusion matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_confusion_matrix(cls_pred):\n", + " # This is called from print_test_accuracy() below.\n", + "\n", + " # cls_pred is an array of the predicted class-number for\n", + " # all images in the test-set.\n", + "\n", + " # Get the true classifications for the test-set.\n", + " cls_true = data.y_test_cls\n", + " \n", + " # Get the confusion matrix using sklearn.\n", + " cm = confusion_matrix(y_true=cls_true,\n", + " y_pred=cls_pred)\n", + "\n", + " # Print the confusion matrix as text.\n", + " print(cm)\n", + "\n", + " # Plot the confusion matrix as an image.\n", + " plt.matshow(cm)\n", + "\n", + " # Make various adjustments to the plot.\n", + " plt.colorbar()\n", + " tick_marks = np.arange(num_classes)\n", + " plt.xticks(tick_marks, range(num_classes))\n", + " plt.yticks(tick_marks, range(num_classes))\n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('True')\n", + "\n", + " # Ensure the plot is shown correctly with multiple plots\n", + " # in a single Notebook cell.\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for showing the performance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is a function for printing the classification accuracy on the test-set.\n", + "\n", + "It takes a while to compute the classification for all the images in the test-set, that's why the results are re-used by calling the above functions directly from this function, so the classifications don't have to be recalculated by each function.\n", + "\n", + "Note that this function can use a lot of computer memory, which is why the test-set is split into smaller batches. If you have little RAM in your computer and it crashes, then you can try and lower the batch-size." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the test-set into smaller batches of this size.\n", + "test_batch_size = 256\n", + "\n", + "def print_test_accuracy(show_example_errors=False,\n", + " show_confusion_matrix=False):\n", + "\n", + " # Number of images in the test-set.\n", + " num_test = data.num_test\n", + "\n", + " # Allocate an array for the predicted classes which\n", + " # will be calculated in batches and filled into this array.\n", + " cls_pred = np.zeros(shape=num_test, dtype=np.int)\n", + "\n", + " # Now calculate the predicted classes for the batches.\n", + " # We will just iterate through all the batches.\n", + " # There might be a more clever and Pythonic way of doing this.\n", + "\n", + " # The starting index for the next batch is denoted i.\n", + " i = 0\n", + "\n", + " while i < num_test:\n", + " # The ending index for the next batch is denoted j.\n", + " j = min(i + test_batch_size, num_test)\n", + "\n", + " # Get the images from the test-set between index i and j.\n", + " images = data.x_test[i:j, :]\n", + "\n", + " # Get the associated labels.\n", + " labels = data.y_test[i:j, :]\n", + "\n", + " # Create a feed-dict with these images and labels.\n", + " feed_dict = {x: images,\n", + " y_true: labels}\n", + "\n", + " # Calculate the predicted class using TensorFlow.\n", + " cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)\n", + "\n", + " # Set the start-index for the next batch to the\n", + " # end-index of the current batch.\n", + " i = j\n", + "\n", + " # Convenience variable for the true class-numbers of the test-set.\n", + " cls_true = data.y_test_cls\n", + "\n", + " # Create a boolean array whether each image is correctly classified.\n", + " correct = (cls_true == cls_pred)\n", + "\n", + " # Calculate the number of correctly classified images.\n", + " # When summing a boolean array, False means 0 and True means 1.\n", + " correct_sum = correct.sum()\n", + "\n", + " # Classification accuracy is the number of correctly classified\n", + " # images divided by the total number of images in the test-set.\n", + " acc = float(correct_sum) / num_test\n", + "\n", + " # Print the accuracy.\n", + " msg = \"Accuracy on Test-Set: {0:.1%} ({1} / {2})\"\n", + " print(msg.format(acc, correct_sum, num_test))\n", + "\n", + " # Plot some examples of mis-classifications, if desired.\n", + " if show_example_errors:\n", + " print(\"Example errors:\")\n", + " plot_example_errors(cls_pred=cls_pred, correct=correct)\n", + "\n", + " # Plot the confusion matrix, if desired.\n", + " if show_confusion_matrix:\n", + " print(\"Confusion Matrix:\")\n", + " plot_confusion_matrix(cls_pred=cls_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Performance before any optimization\n", + "\n", + "The accuracy on the test-set is very low because the variables for the neural network have only been initialized and not optimized at all, so it just classifies the images randomly." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on Test-Set: 8.7% (871 / 10000)\n" + ] + } + ], + "source": [ + "print_test_accuracy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Performance after 10,000 optimization iterations\n", + "\n", + "After 10,000 optimization iterations, the model has a classification accuracy on the test-set of about 99%." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization Iteration: 1, Training Accuracy: 10.9%\n", + "Optimization Iteration: 101, Training Accuracy: 82.8%\n", + "Optimization Iteration: 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Accuracy: 100.0%\n", + "Optimization Iteration: 8301, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8401, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8501, Training Accuracy: 98.4%\n", + "Optimization Iteration: 8601, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8701, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8901, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9001, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9101, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9201, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9301, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9401, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9501, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9601, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9701, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9801, Training Accuracy: 96.9%\n", + "Optimization Iteration: 9901, Training Accuracy: 98.4%\n", + "CPU times: user 25.6 s, sys: 2.81 s, total: 28.4 s\n", + "Wall time: 24.7 s\n" + ] + } + ], + "source": [ + "%%time\n", + "optimize(num_iterations=10000)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on Test-Set: 98.8% (9881 / 10000)\n", + "Example errors:\n" + ] + }, + { + "data": { + "image/png": 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wq9XK7OysjN4xGAx0d3cTCATo6emhv78fi8WC0+nccuFsGbFU89eNj49z/fp1XnrpJebn50mlUtJwrIrl0NAQsViMsbExZmdnpVgCcpSi0mw2ZeJZbcfHzUUVy6tXr5JMJqnX64RCIfbv38+uXbvo6Oigq6tLdnYbieoRsbCwwOjoqJyJaGK5uQgh5J486ihwenqaarVKJpOhWCzeVSzVGchq3nnnHflar9dz5MgRBgYGOHbsGA6HA7fbjcPh+GyJper2MTU1xZUrV1hcXOTSpUtSJEul0hqjsaIonD9/nr/+67+WKaOmpqbWOBkrikKlUkEIwUcffUS9Xsdut+NwOGhvb2doaAir1YrH49kxLgutjrpPy9zcHFevXiUajWIwGAgGg/T19TE8PEwoFNrwH7eiKLKzjEajjI+PMzk5SX9/P2azmb6+PlwuF3v27MHn82lJhjcJs9mM0+mks7OTRqNBV1cXgUBARvao4aqrF/VUL4ZSqUQ2m71j5I/qaqYOgtLpNIFAgF27dmE2m6XfrBpPvplsq1jWajWq1SqffPIJf/VXf8XCwgJXrlyhWq1Sr9dv+wBfffVVfvGLXwC/8s+7WSxzuRz5fJ6//du/5aWXXpIG/iNHjvCd73yHUCiEw+HQxHKDSCaTvPHGG0SjUU6cOEE8HicSidDV1cXRo0d55plnNiVcTVEULl68yMmTJ/nkk09455136O3t5dixY/T19fEP/sE/wOv1YjAYpF1MY+Ox2+3YbDY8Hg/Dw8NrcjQoikK5XOb06dPE43Gi0SiJRILp6WlmZ2dZWFi4bbYoFUVRiEajTE1NcerUKXQ6HV1dXTz66KO43W76+/vx+/0899xzD6dYqlPjxcVF4vE4U1NTLCwsyF5IDWkzmUxyIUBtbGqPpEYP3AlVRGu1GiaTSTrQZrNZGTGisTGUy2WuX79ONBqV0y7VPqz6T26kUKq/nVwux/T0NPPz8zSbTYLBIKFQiM7OToLBIE6nU+4cqNmrN5e7rVTrdDra29vlIo7P58PtdhMMBkkmk3R1dVEsFonH49TrdemKVCgUqFarUnTVNpvL5ZidnZXuZKqNVN2DfrNEc1vEslKpUK1W+eijj/jggw84d+4co6OjaxIlmEwmXC4XTz/9NF1dXTKr9uXLl7l8+TLJZPK24W23Qx3Cl8tlpqamEEJovpcbSCKR4Gc/+xkzMzNymw+Vm23IG0GlUuGdd97h6tWrjI6OMjU1RWdnJ1/5ylfo7OzkwIEDeL1e7Ha75gnRAphMJoaGhuRia7PZpFqtysFMpVJhZmaG1157jXQ6LUOWR0dH5YaEq0mlUpw8eRKDwSBz2ppMJgYGBjh+/Dh9fX2b8j22XCwbjYZMCDs7O8v09LQcUarCZzKZ8Pl8eDweent76e7ulqPL1am7VqMKrBACs9ksk4eqMciqS4Lql9dqDq87mUajQaFQkDsAGo3GNRmDPq0nghoap94nn88zPT0tfzuFQgGTyURXVxft7e14PB5cLpdmZmkhbu60Vgun+m8kEiGdTmMwGKRt0+12S5NcPp+X9k11wFWr1dDpdExNTW36hmZbKpaNRoNyucybb77J2NgYJ06c4Ny5c2uEEsDj8fDcc8/R29vLl7/8Zbq7u+VDvXHjhvSpXH2OyWSSoXMDAwN4PB65Atve3i7/T+2FtCiOzUU1e+TzedLpNBaL5YGfubplQSqV4oMPPmBhYYE333yTqakpud90IBDg7/29v4fb7aajo0NO/zVak9U+k4qi0NPTwwsvvEC9XqdQKMjAlHK5TC6Xo1Qq8dZbb/Gzn/2MQqEgRbNWq5FOp3nllVfwer0cOHBgTfTXRrJlYqmmc6pWq9J/cm5u7pYwKKPRiM1mo6enh3A4TEdHB8FgkFKpRLValdNp1V6p7v5mtVplYtGenh78fj+7du2iu7tbiqUae6yxuag/4kqlQj6fJ5VK3deIXrVPqS5fuVyOhYUFEomEzLqeSqXkfj6q353f78fhcGC327WZww5ArSMhhAwuUH87jUaDYDAoRTOfzzM2NobZbL5lQbdWqxGPx+VuCpvFlollo9GQmYNOnz7NO++8I30jVXw+H3v27GFgYIAvfvGLdHV14fV6aTabTExMEIvFuHHjBoVCQTqgO51O+vv76erq4rvf/S6dnZ0y2avT6cRqtWIymbBYLJqBf4toNBrE43HS6TSvvfYas7OzPPbYY/zar/3autLlZTIZ0uk0CwsLXLt2jVgsxltvvUWlUqGtrQ2j0cjnPvc5DAYDPT09BAIB9u/fT3t7+4YvJmlsLUKINSGOzWZTOrar4a3NZhOdTreuvX42ki0Ty2azSaFQIJfLMTc3x/T09C3HWK1Wuru7CYfD9PX1EQwG5UNR/SozmYzsWdQeKRgM0tvby2OPPaZllNkmVo8SAJnM4vr16zKSR+31Vduyurp58yKdmvl8bm5O+k6ePHkSgMOHD+PxeOjq6sLj8TAwMEBnZ6eMDtLYOtRVavVv9W/gQQYm6u9g9cq6+jtRZ5Kq4/vt2OzY8S0Ty1KpxCeffMLk5CSJRGLN/1ksFhwOBwMDA3zpS1+iq6trjYe+6quVz+flQwsEAkQiEfr7+3nhhRcIhUKa0/E2YTQa8Xq90tVDjQduNBosLCxQKpWwWq00Gg3C4TDHjx+nWCzyySefkMlkyOVya9zAcrkc2WxW+u7t2rWLf/JP/glms5menh4cDgd+vx+bzUZbWxt2u10Tyi1GTZyRSCSYnJwkFovJ+ujp6eHYsWP35ddaKpVYWFiQv5tarcaVK1dIJBKMj4/LxeDJyUnK5fIa1z+9Xo/D4ZDJpDeLLRPLSqXC5cuXuXr16i3Tb7PZjMPhoLu7m0ceeQS/34/Val3TS6ibVakuP21tbQwODrJv3z6+9KUv4Xa7tenXNqFm9nG5XGSzWZncVd0OJJPJIISgVCpx4MABhoeHSaVSvPnmm8zPz0tBValUKlQqFYaHh3nmmWcIBoNyZ0a1QWhuQdtLo9GQybY/+OADLl26hNfrJRQKceTIER577LH7Eq5qtcrCwgKVSkW29RMnTnDjxg2uXLlCLBaTv4ubUbfPbWtr29SMUpsulqrbR6FQYG5ubs2WAeqwua+vj+PHjzMyMoLP58Nut68Zxq8eWarZlT0eD4ODg/T29mp2qm0mFArx4osvMj8/z4cffkgqlZIRHGo0FSBDU9X6vHDhAtlsVtarWofqDoy7d+8mHA7j9/sJBAJYLBasVusdd4fU2HzUDOmFQoHR0VHOnj3LtWvXmJ+fl2Y2u93OxYsXZUo8ta6azSZLS0vSBSiTyQDLOpBOp7l8+bKcmVQqFSYmJkgmkzI/xM02StW+6XK5ePbZZ+nr6yMcDm/ad98SsVQjZ6amppicnJSNR01sMTw8zDe/+U0CgQCBQOC2vYN6DVUs/X4/+/fvp7OzUxthbDPd3d18//vfZ3FxEb1eTzQalb6u0WiU+fl54vE44+PjjI+Pc+LECeBX4aoqalji7t27OXjwIP39/ezatQu32y33WtLYXtSOLpvNcvbsWd566y1SqRSZTEYu3ul0Ok6fPi19m9W0eI1GQyaEjkajRKNR4FdiefXqVcrlMo1G45ZtrW+Huq2yx+Ph+eef58iRI/j9/k377lsyDVfdQFYncRVC0N/fT3d3NyMjIwQCAVwu120Nw0IIQqEQQ0NDALhcLh555BG5Wq6NMrYfnU6HxWKhr68Pm82Gw+HAaDTS3t5OIpFgbGxMbveg/gZW15sQgu7ubrxeLwcPHuTgwYMEg0EZiaN5MrQG6mLr0tKS9H9UTWPqLDIej3Pq1CkcDgc+n08OfprNJjMzMzKxt7p2IYSQGdfVKL7VrA6dNZvN8r3D4SAcDtPV1UUwGMThcOzsabjqX6k2ErXXEELw5JNP8vzzzxOJRBgYGLhjaJxer+fAgQMyTVM2myUQCNDX16dNyVoIm83GE088QaVSkQsw6qjj1VdfZW5ujmKxKDeUW43JZOL48eMcOHCARx99lKNHj66pW00sW4NarcbU1JTcf2d1Hlk1fHFiYkJmyb95hVqNxlF9aNVjVJ24HeqMo62tDbfbLf2lOzs7+epXv0pnZyeDg4N4vd6dvRquOo3b7XaGh4cxm80y2cLg4CAdHR243e679giqi5Dao1gsFulvp9kqWwe1t1d337RYLDKPaG9vL4cOHaJUKlEsFm+ZXhkMBoaGhgiHw/h8Pi14oEVR92x3uVwy21C1WpUuPurI8G65F1bXvdpprk52oibNUXeDVDPce71egsGgjM4KBoMyAEUdcW4mmy6WqgHWZrPxR3/0R1SrVfmAnE7nujNlq9sCqE6pm5HyS+PTodqS1Sm2EAKXy4XD4eC5557j+PHja7LHrEYIgc1mw2QyaVs/tDBms5mRkRG6u7sZHh4mmUzKAIRKpUKxWHyg6+r1eimCVqsVm83G5z//eXp6egiFQng8HsLhMLt3714jqna7XeaB2Gy2xGapplsLhUKf6hoarc/NJhHVtOJyuXC5XNtUKo2NQp3lNZtNOjo6CIfDWK1WHA6HnJKrU2wVNYxZjcxRt3xZfU1VJNUoHbvdTiQSoaenh2AwuCapznbREttKaGho7CwsFgvf+MY3+PVf/3Vpl37vvff4yU9+QrFYJJVKSUE0Go0MDQ3h8Xik2Q3WRuxYrVba29txOBzs3r0bh8NBW1sbZrMZk8mE0Wjc9hmHJpYaGhr3jbrFMSzH8ufzeebn5wkGgzIjkGpuMZvNdHZ2EggEpN/szVitVjo6OnC5XIyMjLRkRJYmlhoaGp8K1db89NNPEw6HZcYpdeSo+kKuXvi7Gb1eL6fh2z2CvBOaWGpoaHwqjEYjRqORXbt2sWvXru0uzqahrZpoaGhorANNLDU0NDTWgSaWGhoaGutAE0sNDQ2NdaCJpYaGhsY60MRSQ0NDYx1oYqmhoaGxDsTdkmve9UQh4kB0Y4vT8oQVRQlsdyG2Cq2OH360Ol4/DyyWGhoaGp8ltGm4hoaGxjrQxFJDQ0NjHWhiqaGhobEO7iqWQgifEOLsyt+8EGJ21ftNS00shPiaEEIRQhxdx7GNlfJcFEL8UAhh+xT3/UshxNfvccxvCSHOCyEuCCHeF0IcfND7tQLbUcdCiG8IIUaFEJeEED9Yx/GTK8/7vBDiF0KIB84iLYT4YyHEH67juH8phBgXQlwRQnzpQe/XCmx1HQshvr9SX2eFEO8JIUbWcc5Wt+OnhRCZVc/hj+55YUVR1vUH/DHwhzd9Zljv+fdxHyfwDvAhcHQdx+dXvf4b4A8etIzAXwJfv8cxxwDPyuvngI82+hls199W1DGwGziz6hm2r+OcScC/8vrfAf/xpv8XgO5Bv+NtjhkBzgFmoA+YAPTbXT87qI5dq14/D/x8HedsdTt+Gvj/7ud73fc0fEW1/0wI8RHwJzf31Cs9Q2Tl9XeEEB+vKPefCyHWsw3j/wr870D5fssGvAvsWuk13hVC/BQYFULohRD/hxDik5XRyT9aKZ8QQvynldHDL4H2e91AUZT3FUVJrbz9ENi+PPebxCbX8e8B/1l9hoqiLN5n8d5huY4jK/X2V8BFoEcI8S9W1fG/WVXefyWEuCqEeA8YWsc9/gfgvyqKUlEU5QYwDjx2n+VsaTazjhVFya56awfu1+Vm09vxg/CgNstu4JiiKH9wpwOEEMPAN4EnFEU5BDSA31r5v78Qt5liCyGOAD2KorxyvwUSQhhYHuldWPnoCPDPFUUZBL4HZBRFeRR4FPg9IUQf8JssN54R4LssjxrV6/1bIcTz97jt94BX77esO4RNqWNgEBgUQpwQQnwohPj1+yzXb/CrOt4N/KmiKHtZrsfdLIvaIeARIcSTQohHgG+tfPZllutfLf/3hRDfv809uoDpVe9nVj572NisOkYI8U+FEBPAnwD/83oLtMXt+PNCiHNCiFeFEHvvVbYHTf77Q0VRbr/J7694FngE+EQs78JoBRYBFEX5n24+WAihA/5P4H+8z7JYhRBnV16/C/w/LD+sj1dGBQB/Hziwyo7RxnLDehL4LyvfJSaEeEO9qKIod7VhCCGeYbnyjt9neXcKG17HKxhYfvZPs9xY3xFC7FcUJRojXQIAACAASURBVH2Pe70phGgA54F/DbiBqKIoH678/99f+Tuz8t6xch8n8CNFUYoAK6MUVsr4Z/e458POZtUxiqL8Z+A/CyG+zXJ9/c497rPV7fg0y87peSHEl4Efr1zrjjyoWBZWva6zdoSq5owXwP+rKMq/XOc1ncA+4K2VSgkBPxVCPK8oysm7nFda6fEkK+evLqMAfl9RlNduOu7L6yzbGoQQB4C/AJ5TFGXpQa6xA9iMOoblUdpHiqLUgBtCiKss/0g/ucd5zyiKklDfCCHc3FrH/5uiKH+++iQhxP9yH2VTmQV6Vr3vXvnsYWOz6ng1/xX4v9Zx3Ja249WmAkVRfiaE+FMhhH/1b+xmNsJ1aJLlobI6je5b+fx14OtCiPaV//MKIcJ3KXxGURS/oigRRVEiLNsDn1cU5aQQoksI8fqnKONrwD8WQhhXyjIohLCzbP/65ootpAN45l4XEkL0Ai8Dv60oytVPUaadxCQbUMcr/JjlUSVCCD/L0/LrK+8vf4oyvgb8rhDCsXKtrpVyvQO8IISwCiGcwFfXca2fAt8SQphXpnm7gY8/Rdl2ApNsUB0LIVaP0L4CXFv5vJXacUisqLEQ4jGWtfCuA5+N2IPnJeC7QohLwEfAVQBFUUaFEP8a+MXKFLsG/FMgKoT4C+DP7jFiXE0Hyz3fg/IXQAQ4vfKA4sALwI+ALwCjwBTwgXqCEOLfAicVRfnpTdf6I8AH/OnKs64rinJPF6cdzkbW8WvA3xdCjLJs//oXiqIsrQineNACKoryixX72gcr9ZIHvqMoymkhxH9jeXV7kVUjWNVeefN0XFGUS0KIv2X5d1EH/uk6pqs7nY2s438mhPjiyrEpfjUFb6V2/HWWhbcOlIBvKSvL5HdiR8SGCyH+GTB1my+s8ZAghPgNoF9RlP+43WXR2Bx2ejveEWKpoaGhsd1o4Y4aGhoa60ATSw0NDY11oImlhoaGxjp44NVwv9+vRCKRDSxK63Pq1KmE8hnKoq3V8cOPVsfr54HFMhKJcPLkej1/Hg6EEJ+p9PtaHT/8aHW8frRpuIaGhsY60MRSQ0NDYx1oYqmhoaGxDjYi3HFbKJVKZLNZ5ubmeP/996lUKhiNRiwWC48//jhdXV3YbDbMZvN2F1VDQ+MhYMeKZbFYJBaLcfr0af79v//3ZDIZbDYbHo8Hi8WC2Wymvb1dE0sNDY0NYceIZbPZRFEUZmZmiMViLCwsMDExwcTEBPl8nnK5jKIoCCE4f/489Xqdz33uc7S1tW130TXukwsXLnDhwgXsdjsej0dN1YUQArvdjtFoJBwO43Q6t7mkGp8ldoxY1ut16vU6J0+e5Oc//znT09NcunSJYrFILpej0WhQKpXI5/P89Kc/5b333sPpdDI8PLzdRde4DxRF4Sc/+Ql/8id/Qnd3NwcPHpRiaTab6erqwu1288ILL2hiqbGltLRYKopCpVKhXq8zOztLKpXi6tWrTE9Ps7CwQC6Xo1qtqhsQydGnarvU69ez5Y9Gq1Gv18nn86RSKWKxmBRLo9FIpVLB7XYzPz+Px+PBbrdjsVjucUWNrabZbNJsNqlWq9TrddLpNNVqdc3/NxoNKpUK+Xwek8mE2+1Gp1tec9bpdDidTsxmMxaLBZNp0zaTXTctLZaNRoPFxUUymQx/93d/xyeffMLU1BRTU1PUajXq9TrNZnPNOTqdjp6eHiKRCB6PZ5tKrrERLC0tcerUKfleCIFer8flctHT00OhUGB4eJje3t5tLKXGzTQaDarVKpVKhfn5ebLZLO+//z4LCwvymFKpRKlUYnZ2lkuXLtHe3s6xY8dkx2cymXjkkUfo6OggHA7T3r4pe5DdFy0rlo1Gg1qtRjqdJpFIEIvFmJmZIZFIUCgUuDm1nDr6UBSFer1OrVajWCzKXqsVeiaN9WG329c0jmq1SqFQkKMVRVFYWFhgbm5OE8oWoFQqUS6XqdfrciRZqVQol8vMzs6SyWSYnJxkcXFxzTmlUkm263K5TFdX1xqxDAaDNJtNvF4vfr8fIYRs59tBS4plvV4nl8uRyWQ4ceIEk5OTnDt3jsnJSWq12l3PbTQaXLhwgRs3bhAIBBBC0NfXx9DQenZA1dhuhBAcOXKE3/3d35WfxWIxfvnLX5LL5SgWi1QqFT788EOmpqZob2/X7NLbiKIoXLhwgUuXLjEzM8PExMQtYlkul8nlcmvabrPZlIOaZrNJOp3mvffek9Nwo9FINBrF7/fz7W9/G7/fL6fk20VLimWz2aRQKJDJZJibm2N6eppUKkWhULjnuY1Gg3Q6LXut6elpHA4Hvb296HQ6hBDodDoMhpb86hqAz+djaGhIzh4MBgN2u51arUa5XKbZbJJMJhFCUCwWt7m0nz1U81etVqNWqxGLxbh+/To3btxgdHRUTsPL5TILCwvU63U5ItTr9ej1elm3Op0Ol8sFIIWzXC6j0+mwWCwUCgVSqRTVanXb22xLKkYmk+Gtt95iZmaGt99+m6mpKZLJ5LrPbzQalMtl3nnnHcbGxjh06BDXrl3D4XDg9/tpa2tjZGREWxhoUSKRCG63W77v7OxkfHyc+fl5zpw5ownkNlIulxkdHSWZTPLee+8RjUaJRqPMz89TLBbJZrMoikKj0UCv1+P1ejEYDDidTiwWC319ffj9fnm9QCBAX18fOp0ORVGIx+P89V//NXNzcywtLZHJZJifn2dpaQlFUbDb7dv23VtSLMvlsuypbty4wczMzD3PWW3DrNeX90SanJwkGo2iKApWqxWPx0NPTw+hUIjBwcFNK7/Gp8Ptdq8Ry0qlQnd3N81mE6PRCLCttqvPMvV6nYWFBWZmZnjnnXe4ePEi+XyeUql0i03RarXicDiwWq14vV4cDgd79uxZY2fu7e3l0KFDUiyj0SivvfYayWSSXC4nTXLFYnHbXcVaSizL5TLJZJLJyUnOnj3L9PQ0uVzutseuFse7NRxFUZienuadd94hFApJB/bPf/7zG15+jc1HURR0Oh3BYJDu7m45hdPYGprNJqVSiUKhQLFYpFQq0d3djc/nk+3Q6/UyMDCA3W6nu7sbi8WCw+HAZDLR3t6+RvRcLhcOh0O6FxUKBfmnerq0SsfYUmJZqVSIx+PMzs5y7do1pqen1zXlUiN37sT8/Dzz8/Oy4sxmsxx9auw8hBD4fD4Z/6+xdai+z+oKeLlcpr29nf3798tjwuEwTz31FG1tbXR1dWE2mzGZTHLx5naoC3fqKnmlUqHRaNz1nK2mpcQyk8lw6dIlxsfHyefzaxzOVex2Ozabje7ubvbv30+j0ZDTgMnJSQqFAktLS5TL5VuuXygUmJ6exmKxMDY2RiaToaurC6vVulVfUeMBKJVKRKNRpqenpaG/o6OD/v7+NdN1jc3HbDYzODiI3+9Hp9MxNzdHJBKhs7NTHuP1euno6JDO5Hq9/p6jQ9Xlb/WfwWDAZDJhsViwWq3SBLNdtJRYJpNJ6RKSTqepVCpr/l8IgdPpJBQK8YUvfIF/+A//IZVKhenpaeLxOK+++ipzc3PSbWH1eYqikM/nuXz5MpVKhZMnT9Lb20tbW5smli1OPp9nbGyMubk5yuUyNpuNSCTCvn378Pl82128zxQWi4UjR46gKApf+MIX5KzuZjG8X59IdXW9Wq3KVXan04nVasVut2O327fdV7qlxLJYLDI9Pc38/PyaafLqBAqDg4OMjIwwNDSEy+WSvZDa4zkcDqLRKEtLS/L81RWq0+nQ6XQYjUaMRmPL2EM07ozJZMLv90vndCEELpcLv9+veTRsA5vhHF6tVllcXCQej68Ji1Tv1wq0lFiqI8tUKrXGgVWv1xMIBPD7/Xz5y1/mN3/zN2VjEUIQDAYpFos4HA7m5ua4ePEiU1NTt1xfp9PJYb3T6cTpdGrx4zsAl8vFwYMH8Xg8xONxdDodXV1d7N69W6u/h4RcLsf58+eZnJykVCpJn+hWEUpoMbFUA+/VnmX1g3K5XASDQbxeLy6XS44oFEVBURS5SlcsFm+JF1cxGAw4HA5cLhdutxuXy7Xtjq4at6KGNZbLZbLZLAsLCxQKBSqVCiaTSdqytLp7eFBdhPL5/JpgBHVxSLV9rka1c6rtX1EU9Ho9Op1uXXbS+2VH/Nr0ej179+7lkUceYWBgQNouyuWyTM0Wj8f54IMPmJqaIpFI3PY6bW1tDA8Ps2fPHg4dOkQwGNSSA7cgqs358uXLvPvuu0xPT/P+++/L2YOa4Fnj4aFcLkuvlUqlItcnfD4ffr9fLiitpl6vk0wmqdVqFAoFGo0Gbrcbq9WK1Wrd8N/IjhDL1ZRKJZLJpLQ91mo1stksS0tLzM3NsbCwcIvNQ8VoNOL1eqWDrLaw0xqoowK1MVSrVXK5HIlEgsnJSebn58lkMiiKQmdnp5YB/yGk0WiQy+VkkhwhBBaLBZvNhhBiTZtWk+xUKhUWFxepVCoyp22xWJRJo91uNwaDYcNmIDtCLOv1OidOnGBsbIyOjg7a29sxGo2YTCZyuRyTk5MUi0Xi8Tjlcpl0On3b63g8Hg4dOkQ4HN72lTWNZZrNJplMhlqthtVqxWQycf36dcbGxmSiZ9X3LhgM8u1vf5tdu3YRiUS2u+gaG0gul+PcuXPMzs5Km2VPTw+9vb0sLCzwxhtvyGPj8TjXrl0jl8sxNTUlzTVqhiKbzcbx48c5evQoHR0dDAwMbMiUvCXEUk0EqtofVFYn9V1YWCCVSpFMJonFYhiNRsxmM5lMhqtXr97iZrQag8GAXq/H6XQSCATwer3awkCL0Gw2ZVSVSiqVYnZ2Vv6p4apWq5Vdu3YxPDy87aFvGhuDGkdeKpVIJBJyoGOxWOTIMpvNMjk5Kc+JxWKcP3+eTCZDNBqlXC5LW6fP58PpdNLb28vAwMCGbivTEmI5Pj7O6dOn+eSTT6Qj+uqeQDXkNptNlpaWyOfz0g1IzZ93Nw4ePMgTTzzB4OAgn//852UGZo3tJ5/P84Mf/IDx8XGOHTvGwMAA7733Hm+99Rbz8/M0m038fj+PP/44kUiE/v5+bRr+EDEzM8O5c+c4e/YslUpFtv16vc7Zs2e5du2anHGoqAs69Xodh8OBw+Ggv78fq9XKo48+Sm9vLyMjI+zevRuHw7FhCz0tIZYLCwucOnWK8fFxKXw3C6b6ebVaXbNipnK3B9LT08NTTz1FT08P/f392ipqC1Eqlfjggw84efKk9KW9du0aly5dkunY1L2UIpEIfr9fG1U+RCSTSS5dusT169fXtP1arbbG/W91cu+2tjY6Ojrk9jFms5nu7m7cbjePP/44IyMjBAKBDQ9Y2DbVUBSFS5cuMTk5yenTpzl16hTxePyBgufvdazP52PPnj20tbW1VKzpZxk1MezMzAzJZJJ8Ps/o6CilUonx8XEKhQIGgwG/309PTw/Hjh2jo6NjW1N0aWwcY2NjjI2NceXKFd59910SiYT0rVa3D/H5fGsyF4VCIbq6unC5XHR0dKxxIVNTwO3evRu/378pOQO2VSwvX77M66+/zpUrVzhz5ozcV2f11qfr5W7Her1eBgcHNTtlC1Eul5mYmJC5SrPZLKOjo2u2DnG73Xi9XsLhMI8//viaPIgaO5uxsTF+/OMfE41GOXfuHJVKRboMwbK7YHt7O16vl1AohMfj4cCBAzz66KNyZKn6YW6V4/qWi6WiKBSLRcrlMrFYjMnJSdmr3M72qC7OhMNhfD6fjB9NJpPMzMzcMh2/HQsLC5w+fRqv10tvby8Gg6GlIgM+C1SrVRk0sLS0RDwe58SJE8RiMTKZDHq9Hr/fT2dnp4wFbjab5HI55ufnuXTpEqFQiHA4rPlY7hCq1SqJRIJiscjk5CS5XI5SqUS1WpVmt2QyKW2VBoMBm83G8PAwHo+HvXv3ymTdNpuNnp4eAoGATKqxGY7nd2NbxDKZTJJKpbh8+TJnzpyhXC5TKpVuET41PNFqtXLs2DEOHz5MNpslk8lw4cIF5ubm1pVq7erVq/zkJz9h7969BINBhBCa3XKLKRQKLC4uMjc3x5kzZ5ienubHP/4xyWSSYrGITqcjHA6zd+9eEokE8XicqakpLl68yMTEBL/85S8Jh8N4vV5NLHcIpVKJsbExYrEYL7/8MtevX5e7taoeMKsjbwwGAz6fj69//ev09/dz9OhR2V4B6VsN2xMvvuWK0Ww2SSQSzM3NycxCNzuRqz2M2Wymq6sLt9vN4OAgkUhEJgYtFotcvnyZYrFIKpW6q2im02kmJiawWCxMTk7idrsJhUKaYG4BaqKTRCLBxMQEsViMq1evkkgk0Ov1WCwWms0mOp0On89HZ2cnNpsNh8OBwWAgl8vhcDjkZnWzs7PAcjSW5ivbWqhJgVUn8WQyyYULF1hcXGR+fp50Oi2ToKjHwbLwGY1G/H4/7e3tdHR00NnZKRMGtwpbrhb1ep1z585x/vx5mbfy5lhus9lMJBLB5/Px/PPP09fXx8jICF1dXbInCofD1Ot1pqeneffddymVSjQajdve8/r168zMzDA5OYnJZCIcDvPcc89pWba3gEKhQC6X4+zZs7z66qvMzMxw6tQpDAaDzLAdj8cB2LdvH8ePH5f2q2g0Sm9vL7Ozs/ziF7/A7XZLJ+OjR48SCoW2+dtprGZhYYGxsTHm5+c5f/488XickydPkslkZITN8PAwXV1dXL16lWvXrslz29raOHToEH19fdL9Z7vzV97Mloml6itZKpVIpVLE4/HbCiUsjyy9Xi/BYJDOzk46OztlzKdKIBAgEonI9Gyqf+btrqcm51B7O4/Hc8dkGxobSzqdZm5ujtnZWebm5ojH4zLGOxgMygxQQgja29tpa2uTOQ3L5TLhcBhFUQgEApjNZrLZLPF4nEwmg8PhwGw2t1yj+qygDlwKhQKlUomZmRmi0ajco2d1TtpAIIDJZKKvr4/u7m7ZQaoYDAa595LVam1JP9otE8t6vU4sFmNpaYkLFy5w8uTJOya8cLlcHD9+nEgkwpEjRwiFQrc8vF27dvHd736Xjz/+mI8++gghhLSF3IlCocDMzIzc80Njc2k2m7z77rv8/Oc/Z3JyktHRURmN09PTw9e+9jU6Ojqk6PX29uL1emXy10AgwNDQEJlMhuPHj5NOpzl//jyxWEyOTAcHB+nq6trur/qZQ1EUuf3DiRMnOHfuHBMTE5w/f55SqUQ2m8VqtTI4OIjX6+Wpp56is7OTnp4ePB4P/+E//Ac++ugjYHkabrPZ2LVrV0sv4G2ZWKop1PL5POl0WvY6N288ptqx2tvbCYVCtLW1Sd86NTRKjeZZvbeHavi90+q4+nm9XqfRaKxrFV3jwVFH+UtLS0xMTLC4uEipVMJiseD3+wmFQnR3d9PV1YXP58Nischs2Ore0haLBZfLhdPpxGAwsLi4yNjYmFxRF0IQCoUIhUIyoktj81EXZ7LZLLlcjpmZGcbHx4lGo8RiMdm29Ho9wWBQ7qba29tLIBDAbrffYgLT6/W0tbXhcrla1sVvy8Sy0WiQSCRYWFggmUySTqdvsTHabDYCgQD9/f3s37+fcDgshVJ1LRofH2diYoKrV69y4sQJ4vG4jA9dnTB4NWrlORwOdu/e3ZL2kIcNdUOrmZkZrly5gtPpZPfu3ezbt48XX3wRv9/PwMDAGjcQtZGoOQlVDAYDRqOR9vZ2/H4/6XSaH/zgB7zyyisyx6Xb7da2mNgC6vU6S0tLZLNZfvjDH3Lx4kVu3LjB3NwcQggcDgfd3d08/vjjBINBjh49KhdULRYLxWJR2jBXr2irNsvOzs6WzQa2pTbLcrks7Ru3S3xhMBhwuVy0tbXh9Xpxu90IIWQ6JjX1/Pj4OKdOneJnP/uZnE7fzZVAHXlarVZ8Ph9ut7tle6+HATVcTc0Gk0qlsNvteL1e+vv7eeaZZ3A4HHc8/+YM2eo2IE6nE7/fTyqVIpfLcfnyZWKxGOl0umWnbg8bauKTpaUlzp8/z4cffkgmk6FQKMh1hmAwyMGDB+no6ODw4cNrZoaZTIZsNrtmJVyv12O1WmlvbycQCLTsQGZbfGfuJGyVSkXmMHzjjTdoa2sjFotRLBbl9HlmZobZ2VkWFhbWHRp5/Phxfu3Xfo3u7m4OHDhAW1ub1rg2kUajIfNQqnZpv9/P/v37iUQin7qjMpvNPPPMM3R2dhIKhVhYWNDCILeIXC7HT3/6UyYmJhgdHSWdTjM8PExfX5/M9NPe3s7w8DB2u33NWkOj0eDixYuMjo5y/fp1FEWhq6uLvXv3cujQITweD1artWXNKVsulncLZVTtIAsLC5w5cwaj0cjJkydZWlqS9spCobAmkcZ6QiP37t3L7/zO72Cz2WR8eKtWyMOAmlLvxo0bZLNZhBC0tbXR19dHMBj81M/eaDRy6NAhurq6yOfzJJNJQqHQPfeP1/j0FItF3n//fc6fP8/8/DzFYpFwOMzTTz/Nrl272L9/PxaL5bbZfprNJpOTk5w6dYq5uTlgOW/D4cOH5WaDrbgKrtJSXtnqdpiZTIbR0VH0ej2JRIJ8Pi+PuVMW9NWoNq++vj5CoRB79uzB6XTKxSCtQW0uQgjMZvOa9P5q1FWlUiESiazJHLNearWaDGZ47bXXuH79unQz0ul0HDp0SKvbTUYd0KRSKdkWjUajrGvVBr26HhqNhqy3q1evyjBHWLZV7tq1i+7u7pYPEmmp0jUaDRqNBpVKhVOnTqHT6R7IH1K1cY2MjPDoo4+yf/9+2tratIa0hZjNZux2OzabDbvdTjKZ5KOPPiKbzRIOh+no6MDn892XWFYqFcbHx5mZmeHll1/m1KlTchW8t7eXb37zm5v4jTRgeUCTSqWkeUVdfFPj+e12+y3trFarcePGDWKxGKOjo1y6dIlarYYQArfbzcjICH6/XxNLFZ1Oh9vtplQq0dbWhtPpvG2oo8qdHMxV1H2/Ye0UXK/X09HRgcvlYmRkhOHh4TXxpRqbjxq6qCgK/f39xGIxmc06lUpx8uRJmW7N6/XKEMc7USwWmZ2dJZlMcvLkSWKxGMlkEkVRCAaD+Hw+AoGAVsdbgMVi4ZFHHsFms0n3v3K5zMWLF2UYs9VqlZvKtbe3U61WicViRKNRMpkM9Xodj8eDy+WSvrVqYEIrs2ViaTQa6e7uxmazyU2nEonEuqbVt8NkMkn74+qHbDabOXbsGJFIhC9+8Ys88sgj2sr3FqPX6+nv7yccDlMoFGhra+Py5cucPHmS6elpLl26RHt7O6lUinA4zFe+8pW7imUikeCXv/ylTL6hbo1rMBg4cOAAhw4dYnh4uOUb28OA2+3mt3/7t1laWuLcuXMyc9iPfvQjdDodBoOBYDDIoUOHCIVCPPnkkwCcO3eOa9euMTc3R7VaJRwOc+TIER577DEikQhGo7Hl1xG2TCxVO5bNZpMuBqVSiUwms67zXS6XDG0zGAx4PB66u7tvEUuTycTQ0BAdHR14vd6WCsT/LKHajX0+H+FwmGq1SiaTIR6Pk06nURQFl8t1173bc7kc8XicmZkZuXNnuVxGURQ6OjqwWCwMDAwwMDCg+VhuEWpCZpPJJENOTSaTjIqr1+tYrVYWFxep1WqMjY3RbDaZmZlhcXFR1p/b7aa/v59AIIDBYGh5oYQtFEvVQ99kMnH48GHMZrMMgbwXBoOBoaEhmdPS7/czODjI8ePH5ahxtWBarVYMBkPLOrd+VhBCMDw8zMDAAPF4nKeeeopTp07x53/+57S3t/PEE08QiURwu923Pf/y5cv83d/9HclkkuvXr1MsFhFCEAgE+I3f+A2GhoY4fPgwu3fv3tIksJ9ljEYjkUiEZrPJ7t275faz5XKZTCbD0tISo6OjvPzyy5RKJX7+85/TaDSYn5+XuSz1ej3Dw8O88MILuFyuHSGUsMULPOrCSyAQoLu7m56eHubn5+95ntFoJBwOEw6H5Ybr4XCYnp4ebYrd4qi79KmuXrFYjGAwSHt7Oy6XS6ZiW02z2ZRb5E5NTVEoFGg2mzKKx2q1EolEiEQihEKhO4qtxsYjhJCzNdVX2eVyyc3DzGYzyWRS1oka5prL5WS0ldlsxul0ytykO6WT2/LlJ6PRyKOPPsq+fft48sknyeVy9zxHDaNS3RKMRiM2m00Tyh2EGvd99OhRfu/3fg+bzXZHkVMTPF+7do2TJ0/idrv53Oc+J33y1Iz3LpdLc0ZvAVSTi7pQ097ezsjICHNzc7zyyivMzs5y4sQJFhcXCYVCclHP6XTuqDa8LU7pXq8XQMsW8xnCYDDITNiDg4Ny/5TbobqPqan1dDodgUCAzs5O9u3bh8/nw+FwtGxY3GcNNTzVZDJhMpmw2+20t7fj8/k4e/Ys1WpV1pXNZsPn88mdPHcSre3YpPHQYbfbGRgYkGm5dDrdLaMLVQi/9KUv0dvbi9VqpaurC6vVSjAYxGw276gRyWcVNft5oVCQuzBGIhH27dtHR0fHdhfvvtHEUmNLMZlM91y5NpvNmM1mXC4Xe/bs2aKSaWw0Op0Op9Mpo+dWb228E3cp0MRSQ0NjU7Db7TLVotvtJpPJsGfPHrn/905DE0sNDY1NwWq1Mjw8DMDnPve5bS7Np2dnODhpaGhobDOaWGpoaGisA00sNTQ0NNaBJpYaGhoa60ATSw0NDY11IB50S1ghRByIbmxxWp6woiiB7S7EVqHV8cOPVsfr54HFUkNDQ+OzhDYN19DQ0FgHmlhqaGhorANNLDU0NDTWwV3FUgjhE0KcXfmbF0LMrnq/4fs1CCF6hRBvCiHOCCHOCyG+vI5zGivluSiE+KEQ4s6budz7Wn8phPj6Oo57euWel4QQbz/o/VoBrY5ve8xvrZTtghDifSHEwQe9XyuwDXX8B0KI0ZVn+LoQIryOcyZXnvd5IcQvhBChT3H/PxZC/OE9jvmtVc/grBCiKYQ4dNcLK4qyrj/gj4E/vOkz4bpNaQAAIABJREFUw3rPX+c9/m/gH6+8HgEm13FOftXrvwH+4EHLCPwl8PV7HOMGRoHelfftG/kMtvNPq2N5zDHAs/L6OeCj7a6bHVbHzwC2ldf/GPhv6zhnEvCvvP53wH+86f8FoHvQ73iP4/cDE/c67r6n4Ss9858JIT4C/uRmFV/p/SMrr78jhPh4Rbn/XAhxrySECqDmbmoD7r1Bz1reBXatjPzeFUL8FBgVQuiFEP+HEOKTlZ7rH62UTwgh/pMQ4ooQ4pdA+zru8W3gZUVRpgAURVm8zzK2PJ/1OlYU5X1FUVIrbz8Euu+zjC3PZtaxoihvKopSXHn7IM/vHZbrOLJSb38FXAR6hBD/YlUd/5tV5f1XQoirQoj3gKH7vN+LwH+910EParPsBo4pivIHdzpACDEMfBN4QlGUQ0AD+K2V//sLIcTR25z2x8B3hBAzwM+A319vgYQQBpZHARdWPjoC/HNFUQaB7wEZRVEeBR4Ffk8I0Qf8JssPdgT4LssjCvV6/1YI8fxtbjUIeIQQbwkhTgkhvrveMu4wPst1vJrvAa+ut4w7jM2q49U8yPP7DX5Vx7uBP1UUZS/L9bgbeAw4BDwihHhSCPEI8K2Vz77Mcv2r5f++EOL797jfN4H/cq9CPWiKth8qitK4xzHPAo8An4jlDYms/P/tnXtwXOd12H9nse9dLBaL3cX7wQdI8CGRFB8iVY0iUR6NHSUaZ5I0Th17MnaaKq3dpk39R5qMJ/UfzUyTtDOZ1rU6ztRWplFTJ/GUndSWbckZiZYoUpJJgiBBgHgtiAexeC32gX1//WP3XoMkAIIv7IL8fjMYLLD3cfaeveee75zznQ9mAJRSv7XGPr8OfEsp9WcicgL4SxHZr5QqrnMel4icL79+F/gLSjfEWaXUSPn/LwFPys9iVXWULvpzwBvlzzIpIm8bB1VKfXWN81nLn+vF8md6X0TOKKUG1pFxK/I46xgAEXmB0s3+7HrbbWEelo6BkkcKHAF+boPy/FhECsBF4A8phbzGlFJnyu+/VP75aflvLyUd1wLfNbzZ8miDsozfuIOMTwMppdSlOwl3r8YyueJ1nps9VKchB/BtpdTv38Vxvwh8EkAp9b6IOIEgZeWswXL5iWdSVupKGQX4slLqzVu2u2NyYRWuA3NKqSSQFJF3gAPAo2YsH2cdIyJPAt8EPqWUmruXY2wBHpaOEZFPAH8A/JxSKrPB3V5QSs2uOIaf23X8x0qp12451+/ejWy38Bk24FXCgykdGqU0HEJEngK2lf//FvArIhIuvxeQO2fFIpSeZIb77wSiItIqIm/dh4xvAr8jIrbysXeJiIdSbOTXyvGuZkqB6Tvxf4BnRcQqpazs08CV+5BtKzDKY6RjEekA/g743CM4YliLUR6QjkXkEPAa8MqtMX0R6b8PGd8EviAi3vKxWstyvQN8WkRcIlIL/OJGDiYiFuAfs4F4JTwYY/m3QEBE+oAvUfawlFKXKbnSPxCRi8APgeaykGvFOn6PUqzpAiVr/5uqlK5qpvTku1e+SSmD/bGIXKKkSCvwXWCw/N7rwPvGDmvFs5RSV4DvUxoqnAW+uREXfovzWOkY+CrQAHy9nNT48D7k2io8SB3/CaUh8nfK1+9UefsgJe/wnlBK/QD4K0qhr17gb4BapdTHwF8DFyjFR88Z+9whZvkcMK6UGt7I+bfE3HAR+RIQUUqduuPGmi2J1vGjj4j8ArBdKfXnlZblXtgSxlKj0WgqjZ7uqNFoNBtAG0uNRqPZANpYajQazQa453XDg8Gg6urqeoCiVD8fffTRrHqMumhrHT/6aB1vnHs2ll1dXXz44eNQUfEzROSxar+vdfzoo3W8cfQwXKPRaDaANpYajUazAbSx1Gg0mg2gjaVGo9FsAG0sNRqNZgNoY6nRaDQb4J5Lhx42xroX2WyWfD5PNBolHo+zsLDA4uIiTqeT2tpaamtr6ejowG6343A4sFi0/a8m5ufnWVpaYmpqirGxMWPNEzweD7t27cLr9RIKhXA4HBWWVKNZn6o1loVCgUKhQCwWI5VK8f7773Pt2jUuXbrEpUuXaGxsZPv27ezYsYNPf/rT1NfXEwwGtbGsIpRSjI2NcfXqVd5++22+853vUCyWGqJ3dnby27/923R2dnL8+HFCocemDlyzRakaY6mUolgsks/nWV5eZmxsjFQqxcLCAqlUiqtXrzI+Ps7i4iIAyWSSiYkJLBYLvb29hEIhDhw4gM/nw2q1Gp20NRVmenqay5cvMz4+TjKZpFAorWIwPz/PwMAAy8vLtLa2UigUsNvtWK1WHA6H9jSrAGN0Zzgu2WyWYrFIoVCgWCySTqfN+zWdTpvb2+12vF6v6bhYLBYCgYA58hMR82crURXG0lBGJpMhHo8zPDzM17/+dSYmJpifnyedTpPJZMjlcjQ2NtLW1kY0GuWDDz7gwoULnD9/no6ODr7yla+wfft26urq9M1WBRSLRd577z1ee+01MpnSygLGDTQ3N8cbb7xBIBBgeXmZnTt30tjYiM/no7W1lZaWlkqK/thTLBYpFotks1lSqRSpVIrp6WkymQzJZJJMJmM6L9euXWN8fNw0qE1NTRw4cACbzQaA2+3m5MmTtLS04HQ6sdlsWK1WamrutBBodVFRY5nNZsnlciSTSeLxuGksp6enSaVSZLNZrFYrbrcbt9sNQHNzM83NzeTzeYaHh0mn00xPT+N0OlleXiaXy6F7dFYPyWSSubnSEjaGVwGYIZZCocD4+DhWq5VMJoPf78flchEIBKipqTFvOM3mUCwWUUqZBjKZTDI7O2say2w2e5uxjEQiXL9+nVwuZzo9dXV1pu48Ho95r3q9XjPf4PV6sVqtW0bHFTOWRjwrEolw6dIlPvjgA3K5HJlMBo/Hw6FDh/D7/ezfv5/6+npzP+Op9A//8A/EYjEzeeDxeFhcXGRpaQmfz4fT6Vzn7JpqYXl5mb//+7/H5XLR0tKC3+/nxRdfJJ/P09DQQFtb25Ybrm1lUqkU6XSavr4+ent7GRoa4r333iObzZLJZMxwWbFYNEd76XSaXC5nGtpYLMbk5KSpN6vVyg9/+EMcDgednZ00NDRw5MgRDh8+TDAYpLOzc0vouKKeZSqVYnFxkenpaYaHh81YSGNjI4FAgObmZnbt2kU4HMZqtWKxWMhms2SzWUKhEG63m1QqZRrZXC5HPp/XnmUVYbfb8Xg8ptdxK/l8nhs3bpi6raurY2pqilgshtvtRim1JW6kRwGlFOl0mng8zo0bNxgaGuLq1atcvHiRfD5/08jA2H4lK4fuiUTitu1qamqIx+MEg0GCwSDt7e3Y7XaKxeKWGJJX1Fg6nU58Ph+7d+/GbrcTCATYtWsXhUKBhYUFEokEp0+fxm63s2fPHpqbm7ly5Qr9/f18+OGHDA4OmkNvTfUhIpw4cYJsNsv58+f5yU9+suaDrFgssri4SDKZZHp6munpaVwu1yZL/HhTLBa5fPky/f39nDlzhvfff5/l5WWcTidOp5NwOHyTUTN0mc/nyefzLC4uMjOz9orGhUKBGzduEIvFsFgsTE9Pc+LECbZt24bD4cButz/0z3g/VMxYigg2m81UgtVqpb29nRMnThCPx3n77beZn59namqKQqFAIBCgtraW8fFxent7uXbtGnNzc+Tz97MgoOZhIiK0t7dz9OhRFhYWeO+998zSodUwEnlLS0ssLi6SSqUoFotbMnO6FVFKEY1GGRoaYmRkhOHhYTOz7fV6aWxsNA3aSj1mMhny+TyFQoH5+XkzK76aruPxOIlEArvdTiaToa2tjUwmY8anq1nPFfUsGxoacDgcZLNZ0uk0tbW1ZtmPoZQLFy5w48YN+vr68Pl8TE5OMjk5yfz8/KrDOk114fV6CQaDeDwegFVvIOPmglISqK+vj1QqxeHDh3G73dTX17Nt2zas1qoo3nhksVgsdHd343A46Orq4tixY6ZD4/V6aWlpuSkZY+jMmDgyMTHB0NAQ0WiUK1eukEwmiUajZLPZ2861tLREPp9nZGSE/v5+gsEgO3bsqOpkT0W/fXV1ddTV1d32f8PrVEpx9epV+vv7TVdfs3UQEVwuF/X19bhcrlWH4CsNpeGNDA0NMTQ0RDabZffu3bS3t9Pe3q6N5UPGYrHQ2dlJKBRi7969JJNJbDYbDodj1WG4gZErGB8fZ2BggMHBQdNQLiwsrGosE4kEiUSCyclJRkZGyOVydHV1aWN5tzidTrq6unC73Zw4cYJwOExfXx8TExO3bSsiZvlBTU0NNTU1Ve3KP24Ynkl9fT0tLS0kk0kWFxfNUYGhq9USOVNTU7z11ls88cQTHD58WFc4bAIOh8Mc2blcLiwWi3l/rXVfGQbU7/eb921NTQ2Tk5NAaQJCNBolnU7ftu/U1BQ//vGP2b59O6FQiEAgQCgUqkqjWZXG0uVysWfPHjo7O0mlUoyNjRGPx9c1lsbsD20sqwuHw0FtbS3hcJjt27ebc/xXeplGXPLWqaojIyOMjo4SjUb5/Oc/v+ooRPNgMZI5d4PFYsFisRAOhwmFQvT09PD0008zPDxMNBpldHTULEm6lZGRESYmJti3bx87d+6kra2N2tpabSzvBhGhpqYGn89HQ0MDLpcLq9VKoVC46Uaz2+00NDQQCATw+XxmoaumOrDZbLjdbnNWx9jYmJn1Xjn90eDW0pS1EgWa6sS4b+12O36/n0OHDhEKhVhYWKCmpoalpaWbjGahUCCXy7GwsMBHH33E7Ows27dvN73TaqKqrYrVaqWlpQWHw0EgEMDpdJp1lgZut5vu7m527NhBR0cHzc3NuplGFeHxeHC73Rw9epTW1lZ6e3tJJBJEo1GuXbvG8vJypUXUPGCMnENraytf+MIXmJ2dJZPJcOHCBfr7+5menja3NaY6Dw8P89prr7Fz506OHz9OfX09dru9qgxmVVsVEcHhcOByucwpj7e658VikVwuZ06d1EXp1YUxvDamMPr9frxeL263W4dLHnEsFgtutxufz0dHRwc7duzA5/Otum0+nyeRSLC4uMjY2BgjIyOkUqlNlnh9qtqztFgs+P1+8ynV2dnJ9evXSSaT5jbpdJrR0VGzyNXn85leqKZ68Hq9uFwuotEoLS0tW2bWhub+8Xq9vPLKKzzzzDPMzMwwMDCw5rYLCwt861vfoq2tjVdffZUDBw5soqTrU9XGErgtZrWyxZNxsxlTtBYXF5mfn8fr9ZpZPU11YCQB3G43oVCIZDKJw+FgeXnZLGiG26fQQamOLxqNYrVazVpcHWrZOlgsFhoaGkz9ORwOsx3jrRj1mkop4vE4+Xy+apK2VW0sc7kcw8PDzMzM0N/fz8DAAMVi0czY+Xw+MpkMi4uLTE1N8aMf/Yhr167x8ssvs3379i3ZBupRp7Ozk89+9rMMDg4yOjrK+Pg4U1NTZrJntZticHCQr33ta7S3t/O5z32O1tZW6urqqn56nKaEiOB0OikUCnR1dbF//36mpqaYmpq6bdtsNsvExATLy8uMj4/T3t5uzt6rNFX7eFZKmfNNjeLWWCxGPp/H5XJRW1tLfX29eREzmQzXr19ndHSUWCxGJpPRM3yqEI/Hw44dO9i2bRsNDQ14vd47PtBisRg//elPOX/+PLOzsySTST1BYQuxMkNeW1trVresRrFYNFvDJZNJc+RRDVSlZ2mUEhjziQcHB80C13379vHUU0/R1NREd3c34+PjnDp1ikQiQX9/P6Ojo4TDYSKRCE899RQ7d+6s8KfRrIbb7Wbv3r1mHDMej69aawml70M0GkVEeOedd4hEInziE5+go6OjApJr7gWjsL21tZXdu3cTi8UYHh5edVsjzGa1Wqsq5FKVxrJQKLC0tMTc3BwDAwP09/cTi8UAaGlp4ciRI3R2dnLo0CH6+/u5ePEiExMTXLhwgVwuR19fH9lslo6ODm0sqxS73U5LSwu5XM6ML9/aAsygUCgQj8epqalhYGCAdDrNsWPHKiC15l4xjJ/f76e5uRmv17vmdsZvI85dDfFKqFJjmUgkOHv2LNevX2dwcJDr16+TyWTMaXPNzc0Eg0Hcbjft7e28/PLLjI+Ps7CwQDQaJRKJEI/H6e7uprm5GZ/Pd1MDYU3l8Xq9HDlyhMbGRr7//e9XWhzNJmA06vD5fCwvL5PJZJiZmWFoaMhM4hpLUywtLXH69Gnm5uZ44YUXOHjwYIWlr1JjGY/HOXfuHMPDwwwMDDAzM4PL5bppjnFDQwNut5vW1lZefvllRkZGePfdd0mn04yNjTE2NsbBgwfZtm0b7e3t2lhWGR6Ph8OHD9PU1ITf76+0OJpNwGKxsHPnTrZt22ZOf+zt7WVkZMQ0lsby10opTp8+zcDAAF1dXdpY3srKzPbk5CQ3btwws989PT20tbWxe/duMzEAJQU4HA58Ph/79u3D5XJx6dIlFhYW6O/vx+FwcOzYMRobG8055Jrq4W6GWPl8nqmpKZRSRCIR6urqqK+vX3NIp6kejDZu8/PzJBIJbty4QTweN6c+riwZW7mqpNErsxr6mlaVsYzH4wwODppxysnJSfL5PF6vl+eff54TJ06wZ88eM7BvZNncbjfhcJiTJ0+ya9cuJicnmZqa4vTp05w5c4Z0Os3BgwfNGUDVEgPR3B3pdNqcLnfw4EGKxSL79+/XxrLKKRaLZmbbuK8HBweZm5sjmUzeVEdtGE3DUBoNoY16y0qWAlaFsTQKVOfm5rh06RIjIyMsLS2Ry+Ww2+243W6CwSDNzc3U1dWtauxsNhvhcJhCoUA4HGZycpJ0Om2WIRjZ1traWm0sqxBj8sFqmU9jISxjkSxjnWpdHlZdGE0xcrmcWfJjrNo6MTFBIpFgdHSU2dlZxsfHmZmZYWlpadVjGesBJZNJRkdHuXDhAoFAgHA4bLb922yqwlgaM3B6e3v5xje+wdzcHNFoFIBQKERDQwN79uzh6NGja3YUcjqdPPHEE3R2dnLu3DkKhQJ9fX1Eo1Ez6dPU1EQoFNrMj6bZACsbAK/XST2fz5NMJhER4vE48Xhcr79URaRSKRYWFsz53YuLi/T19TE3N8cHH3zAzMyMuQqk0dPBeG2w0pGZn58nFotx6tQpent7OXHiBCdPnsTv99PW1rbpJUVVYSzz+TypVIp4PG5eoGKxiNVqJRAI0NjYiM/nw+FwrHmMlQ1L/X4/DQ0NOJ1OlFIkEglmZmbMWQTVVI7wuFIsFkmn0yQSibvyDo0uNcvLyyQSCdO71DqtHMbDbGlpiUgkwsLCAsPDw8RiMSKRCPPz80xPTzM7OwusHqde2QTawGiKMzc3h91uJxKJMDIyQmtrK01NTZteg1kVxnJpaYnR0VEmJibMoK/dbqe+vp4XX3yRnp4eurq6NnQsq9VKT08PNpuNsbEx+vv7uXbtGqdOneLYsWPs378fj8dTlc1FHyeSySSXLl1ifHzcDJGsVZQOmAa1pqYGpRTXr1/HZrPR3d1NZ2fnPTWt1TwY8vk8uVyOc+fO8cYbbzA7O0skEiGbzZqrr95rB6FiscjU1BRzc3NMTU1x7tw5nn32Wdra2vD5fLhcrk17SFaFsUyn0+bSt8bUJmO96ebmZtrb23G73Rs6lojg8Xjw+/2mJ2osr7qwsGCuTa7Xo64suVyO2dlZc2i2XlH6rboyRgvz8/Mkk0my2axu+FxBCoUC+Xze9CTn5uaIRCI3TVOsqalZtVeDkfVer8GzEQetqanBYrGwuLhoNl/ZzPu4ot8w4yJfu3aN733vewwPD5PL5XC5XPT09NDe3s6BAwfYu3fvfU2kT6fTzM/Ps7S0RDab1UmBKiAWi/H2228zMjLC3NzcXe2by+W4cuUKkUiEgwcPsnfvXgCdFa8Q2WyWZDKJUgqv12vGlQ2sVivhcBi3200gELhJT9FolLGxMXK53JoJO5fLhcPhYO/evRw6dIj9+/djt9s3PfRSUWNpuO+zs7MMDQ0xMzNj9jkMhUI0NjYSDocJBoMbPqaRNV0ZOM7n82ZzYON97VlWFqO7zPj4+Kprs6yHUsqMbS8uLlZVs4XHEaOaxegu5HQ6bwpzGUtM1NbWmhUtBiLCjRs3gFKd9WpYrVZcLhfhcJidO3ea8crNvn8rZiyNZW4HBwc5e/Ys165dM4fhbrebXbt20dnZaa43fSeKxSLxeJylpSXOnDljxsMAOjo6OHbsGE8++aTZ67JaJuc/rgQCAV555RUikQivv/76miUkmurHWB/r6NGj+P1+4vG4OaEESkPw+vp6nE4ntbW1N4XUhoaGzKbe77777m2xTYvFQl1dHeFwmD179vDcc89RV1eH2+3edINZUWMZiUT48MMPuXLlChMTE+bFtdvttLW10dHRseGgfaFQMJdZvXLlCh999JH5xAqFQjz55JNs27YNl8ulkztVgM/n45lnnqGjo4NTp05VWhzNfeBwOHA4HOzevZudO3eaCR0js23MsjPatK2ML7e2tmKxWOjr6+PMmTOrHt/j8RAIBOjs7GT//v0Vc3QqHrM0qvOVUuZKgMFgkK6uLrq6utbse7e0tEQ0GjVjHfF43KyrHBgYYH5+HqvVSn19Pe3t7fT09NDU1KQ9yiqhpqYGv99PKpUy1+YxMqe3YoRUROS2wnW98mP1sFIvtyZkjTKfW+8/j8dDS0sL0WgUp9NJKpUyw2WGvpPJpBl2SSaT2O32dcsIHxYVj1mm02nTWBrGrbGxkd27d9PV1bXmXO7FxUUuX75MKpVicXGRubk53nzzTWZmZszZAuFwmIaGBrq6ujhw4IC5aLym8hg1tLlcjlAoRCgU4saNG6vGrQwP5dbCdb0wXXVhTD82PMiN4PV66ezsNOugbTab+cA09JtIJADMJK3H48Futz8+MctbMZ4iRnH6zMwMbreb+vp6bDabWSYSi8WIxWJMTExw5coVMpkMiUTC3CeRSFBfX2/O+mlvb6e7uxun01mRoLBmfRwOB3v27KFQKHD27Nk7xi6NG9Lr9eLxeMw1XfTyIVsTu91OXV0dPp8Pj8dDIpEwk7EGRiLYWL21UtUsVWMsoZQhNar9+/v7WV5eZufOnbjdbj7++GNGR0e5fPkyfX19zM/Pc/36dXM+KpQuqtVq5fjx47S2tvLJT36So0ePmuUK2lBWH263m5dffpkjR44wPz/PyMjIqtsZ3qTFYqGmpoa2tjaCwSBNTU1m0k6z9XC73TidTpqbmwmFQmSzWRKJhFl7KyLmaMPogelwOCpSzVIxYyki1NXV0d7ezvT0NF6v11z/O5VKMTQ0ZDbBcDqdZhIoEokwMzNjxi+MrkM2mw2Xy4XL5aK7u5uOjg5aW1upr6/f1Cp/zd1hsVjwer1kMhmCwSDBYJBUKsXy8rK5za3DbSO7akyDNUYNmq2JkQAKBoOk02mmp6dvCsfYbDYcDgf5fJ6lpaWKJWgraiyN5rxOp5OBgQFisZjZx/L111/HbrebxadG9xLDoBoT8evr6zl8+DB+v59du3YRCATM6VAejwen06kNZRVjlIbU1NTwxBNPkEwmuXr16rprS1utVvbs2cO+ffvYsWMHDQ0NWsdbHL/fz9NPP01jYyOjo6NmnBKgrq6OhoYGCoUC/f39dHV1PX6NNDweD1arlaamJjo6OpifnzdjEsVikeXlZbOphjFZ32j0a8SuQqEQXV1d+P1+tm3bht/vN7sL6Ruo+hER03Pw+XxmB/w77eN0OvF4PDpe+Yhgt9sJh8PmSNJms5lVEMa9b9Rv1tXVUSgUHp86SygF9202Gy+88AI9PT1MT0/T29trdglKJBL09fURi8VobGyktraWp556ymzkW1dXh8PhwO/3m2VHNpvNNKaa6kdEzALj9vZ24vE4kUik0mJpNhm/38/x48dpamrinXfeAWB2dpZUKsXs7CxLS0ssLi7S39/P888/z5EjR/D5fLjd7sdjbrhRd9XY2EgoFDKb98bjcWpra4nFYszOzpreZ319PT09PRw+fBiv10tDQ8NNK8BpA7k1uXVNab/fj9frXbM0yEjo2Gw2XQr2iGC32814dSAQYG5uzkz0KKXIZDLMz8+bxjOXyz1ejTQMjI4zgUCAQ4cOmTMAcrkcJ0+eJJPJ4Ha7cTgcplE11hTWRvLRwGKxsHv3blpaWuju7uaXf/mX1zSWVqvVDLk0NDRssqSah4HVasXr9dLY2MhLL73E/v37uXTpErOzs2bDHaPyJRgMYrPZqKmpeXyG4SsREVwuF62trZUWRVMBRIRwOEw4HGbHjh2VFkezyRjNu30+Hz09PQSDQTKZDLW1tWad5fLyMslkktra2k1v/AtVZCw1Go3GbrfT1dVFY2MjgUCAZDJpJnwNo9na2mo28H4sPUuNRqOx2Wy0t7cDsGvXrgpLczM6Oq7RaDQbQBtLjUaj2QDaWGo0Gs0G0MZSo9FoNoA2lhqNRrMB5F4bqIpIFBh7sOJUPZ1KqVClhdgstI4ffbSON849G0uNRqN5nNDDcI1Go9kA2lhqNBrNBtDGUqPRaDbAusZSRBpE5Hz5Z1pEJlb8vbHl2+4CEekQkR+LyE9F5KKI/PwG9imU5bkkIt8RkfU7x65/rG+JyK/cYZvPlmXrFZH3ROTAvZ6vGqiAjp8TkY9FJH+na71in9Hy9b4oIj8Qkab7OP8fici/vcM2NhH5dvmcV0Tk9+/1fNVABXT8n1ccf0BEFjewz2br+NgKGS+IyC/d6bjrzg1XSs0BBw0BgIRS6k9XnNCqlMpv6BNsjD8E/rdS6r+JyF7g/wFdd9hnWSllyPg/gVeB//QQZRwBfk4ptSAinwL+O/D0Azz+plIBHUeA3wTW/TKvwgtKqVkR+Q/AvwP+pfGGlLopiFLqQS0i/quAQyn1RPnhe1lE3lBKjT6g428qm61jpdS/XnHsLwOHNrjrZur4EnBEKZUXkWbggoj83/Wuw10Pw8ve1zdWdUYXAAAEe0lEQVRE5APgP95qxcseXlf59W+IyNmy9X5NRO7U/18BvvLrOmDyLsV7F9gpIs+LyLsicorSF71GRP5ERM6Vn1z/rCyfiMh/EZGrIvIjIHynEyil3lNKLZT/PAO03aWMVc/D1LFSalQpdRG41y/9O5R03FXW2+uUvvjtIvKVFTr+9yvk/YOyh3Ma2L2BcyjAIyJWwAVkgfXX6N1iPOT7eCW/Drxxl+I9dB0rpVIrDKOTks7X5V5jlm3AM0qpf7PWBiKyB/g14B+VPb8C8Nnye98UkSOr7PZHwG+IyHVKXuWXNypQ+Yv9KaC3/K+ngH+llNoFfBGIKaWOAkeBfyoi24BfonRh9wKfB55ZcbyvicgrdzjtF4HvbVTGLcbD0vH98gv8TMfdwNeVUvso6bEbOEbJizospSH/YeAz5f/9PCX9G/K/KiKvrnKOvwGSwBQlT/hPlVLzD+GzVJqHqmMR6QS2AW/fpVyboWNE5GkR6Suf69U7edf32qLtO0qpO610/iJwGDhX8qBxATMASqnfWmOfXwe+pZT6MxE5AfyliOy/g+vtEpHz5dfvAn9ByeidVUoZi1C/BDwpP4uR1VG66M8Bb5Q/y6SImEpVSn11vQ8nIi9QMpbPrrfdFuZh6fhe+bGIFICLlMI1fmBMKXWm/P5L5Z+flv/2UtJxLfBdpVQKoDzaoCzjN9Y41zFKRqEFqAfeFZEfKaWGH+xHqjgPW8efAf5mA+cw2Ewdo5T6ANhXfiB8W0S+p5RKr7X9vRrL5IrXeW72UJ3l3wJ8Wyl1N8HxLwKfBFBKvS8iTiBIWTlrYMYsDcpKXSmjAF9WSr15y3Z3TCCthog8CXwT+FQ5HvQo8rB0fK+8oJSaNf4QET+36/iPlVKvrdxJRH73Hs71T4DvK6VywIyI/AQ4AjxqxvJh6/gzwL+4i+03U8cmSqkrIpIA9gMfrrXdgygdGqU05EVEnqLkdgO8BfyKiITL7wXKbvl6RCg9yQz33wlERaRVRN66DxnfBH5HRGzlY+8SEQ+l2MivlWOazcALdzqQiHQAfwd8Tim19uLWjxajPDgdr4mI9N+HjG8CXxARb/lYrWW53gE+LSIuEakFfnEDx4oAJ8vH8QDHgfuRbSswygPUsYj0UPLK37/l/1WhYxHZVg7dGeGCHkrXYE0ehLH8WyBQHvt/CRgAUEpdpuRK/0BELgI/BJrLwq0V6/g9SvHEC5SCwr+pSvMxmyk9+e6VbwKXgY9F5BLwGiWv+rvAYPm911mh2HVill8FGoCvlwPeaz6JHiEemI5F5Gg5Jv2rwGvlYyIiQUqewz2hlPoB8FfA+yLSSynuWKuU+hj4a+ACpfjyuRWyrBXP+q+AtyzbOeB/lJNSjzIP8j6Gklf5v9SK+dRVpuNnKWXAz1OyA/98pVe7GltibriIfAmIKKVO3XFjzZZERH4B2K6U+vNKy6J5OGx1HW8JY6nRaDSVRk931Gg0mg2gjaVGo9FsAG0sNRqNZgNoY6nRaDQbQBtLjUaj2QDaWGo0Gs0G+P+yEraVMkZn9QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix:\n", + "[[ 977 0 1 0 0 0 0 1 1 0]\n", + " [ 0 1125 4 0 0 1 1 3 1 0]\n", + " [ 1 0 1029 0 0 0 0 2 0 0]\n", + " [ 0 0 3 1000 0 4 0 0 2 1]\n", + " [ 0 0 3 0 973 0 1 1 0 4]\n", + " [ 2 1 0 3 0 883 3 0 0 0]\n", + " [ 6 2 0 1 1 4 943 0 1 0]\n", + " [ 1 0 8 1 0 0 0 1016 1 1]\n", + " [ 5 0 11 2 1 4 1 2 945 3]\n", + " [ 3 3 1 0 4 4 0 3 1 990]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print_test_accuracy(show_example_errors=True,\n", + " show_confusion_matrix=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing the Input Images\n", + "\n", + "Now that the neural network has been optimized so it can recognize hand-written digits with about 99% accuracy, we will then find the input images that maximize certain features inside the neural network. This will show us what images the neural network *likes to see* the most.\n", + "\n", + "We will do this by creating another form of optimization for the neural network, and we need several helper functions for doing this." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for getting the names of convolutional layers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function for getting the names of all the convolutional layers in the neural network. We could have made this list manually, but for larger neural networks it is easier to do this with a function." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "def get_conv_layer_names():\n", + " graph = tf.get_default_graph()\n", + " \n", + " # Create a list of names for the operations in the graph\n", + " # for the Inception model where the operator-type is 'Conv2D'.\n", + " names = [op.name for op in graph.get_operations() if op.type=='Conv2D']\n", + "\n", + " return names" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "conv_names = get_conv_layer_names()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['layer_conv1/Conv2D', 'layer_conv2/Conv2D']" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conv_names" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(conv_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for finding the input image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function finds the input image that maximizes a given feature in the network. It essentially just performs optimization with gradient ascent. The image is initialized with small random values and is then iteratively updated using the gradient for the given feature with regard to the image." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "def optimize_image(conv_id=None, feature=0,\n", + " num_iterations=30, show_progress=True):\n", + " \"\"\"\n", + " Find an image that maximizes the feature\n", + " given by the conv_id and feature number.\n", + "\n", + " Parameters:\n", + " conv_id: Integer identifying the convolutional layer to\n", + " maximize. It is an index into conv_names.\n", + " If None then use the last fully-connected layer\n", + " before the softmax output.\n", + " feature: Index into the layer for the feature to maximize.\n", + " num_iteration: Number of optimization iterations to perform.\n", + " show_progress: Boolean whether to show the progress.\n", + " \"\"\"\n", + "\n", + " # Create the loss-function that must be maximized.\n", + " if conv_id is None:\n", + " # If we want to maximize a feature on the last layer,\n", + " # then we use the fully-connected layer prior to the\n", + " # softmax-classifier. The feature no. is the class-number\n", + " # and must be an integer between 1 and 1000.\n", + " # The loss-function is just the value of that feature.\n", + " loss = tf.reduce_mean(logits[:, feature])\n", + " else:\n", + " # If instead we want to maximize a feature of a\n", + " # convolutional layer inside the neural network.\n", + "\n", + " # Get the name of the convolutional operator.\n", + " conv_name = conv_names[conv_id]\n", + " \n", + " # Get the default TensorFlow graph.\n", + " graph = tf.get_default_graph()\n", + " \n", + " # Get a reference to the tensor that is output by the\n", + " # operator. Note that \":0\" is added to the name for this.\n", + " tensor = graph.get_tensor_by_name(conv_name + \":0\")\n", + "\n", + " # The loss-function is the average of all the\n", + " # tensor-values for the given feature. This\n", + " # ensures that we generate the whole input image.\n", + " # You can try and modify this so it only uses\n", + " # a part of the tensor.\n", + " loss = tf.reduce_mean(tensor[:,:,:,feature])\n", + "\n", + " # Get the gradient for the loss-function with regard to\n", + " # the input image. This creates a mathematical\n", + " # function for calculating the gradient.\n", + " gradient = tf.gradients(loss, x_image)\n", + "\n", + " # Generate a random image of the same size as the raw input.\n", + " # Each pixel is a small random value between 0.45 and 0.55,\n", + " # which is the middle of the valid range between 0 and 1.\n", + " image = 0.1 * np.random.uniform(size=img_shape) + 0.45\n", + "\n", + " # Perform a number of optimization iterations to find\n", + " # the image that maximizes the loss-function.\n", + " for i in range(num_iterations):\n", + " # Reshape the array so it is a 4-rank tensor.\n", + " img_reshaped = image[np.newaxis,:,:,np.newaxis]\n", + "\n", + " # Create a feed-dict for inputting the image to the graph.\n", + " feed_dict = {x_image: img_reshaped}\n", + "\n", + " # Calculate the predicted class-scores,\n", + " # as well as the gradient and the loss-value.\n", + " pred, grad, loss_value = session.run([y_pred, gradient, loss],\n", + " feed_dict=feed_dict)\n", + " \n", + " # Squeeze the dimensionality for the gradient-array.\n", + " grad = np.array(grad).squeeze()\n", + "\n", + " # The gradient now tells us how much we need to change the\n", + " # input image in order to maximize the given feature.\n", + "\n", + " # Calculate the step-size for updating the image.\n", + " # This step-size was found to give fast convergence.\n", + " # The addition of 1e-8 is to protect from div-by-zero.\n", + " step_size = 1.0 / (grad.std() + 1e-8)\n", + "\n", + " # Update the image by adding the scaled gradient\n", + " # This is called gradient ascent.\n", + " image += step_size * grad\n", + "\n", + " # Ensure all pixel-values in the image are between 0 and 1.\n", + " image = np.clip(image, 0.0, 1.0)\n", + "\n", + " if show_progress:\n", + " print(\"Iteration:\", i)\n", + "\n", + " # Convert the predicted class-scores to a one-dim array.\n", + " pred = np.squeeze(pred)\n", + "\n", + " # The predicted class for the Inception model.\n", + " pred_cls = np.argmax(pred)\n", + "\n", + " # The score (probability) for the predicted class.\n", + " cls_score = pred[pred_cls]\n", + "\n", + " # Print the predicted score etc.\n", + " msg = \"Predicted class: {0}, score: {1:>7.2%}\"\n", + " print(msg.format(pred_cls, cls_score))\n", + "\n", + " # Print statistics for the gradient.\n", + " msg = \"Gradient min: {0:>9.6f}, max: {1:>9.6f}, stepsize: {2:>9.2f}\"\n", + " print(msg.format(grad.min(), grad.max(), step_size))\n", + "\n", + " # Print the loss-value.\n", + " print(\"Loss:\", loss_value)\n", + "\n", + " # Newline.\n", + " print()\n", + "\n", + " return image.squeeze()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This next function finds the images that maximize the first 10 features of a layer, by calling the above function 10 times." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def optimize_images(conv_id=None, num_iterations=30):\n", + " \"\"\"\n", + " Find 10 images that maximize the 10 first features in the layer\n", + " given by the conv_id.\n", + " \n", + " Parameters:\n", + " conv_id: Integer identifying the convolutional layer to\n", + " maximize. It is an index into conv_names.\n", + " If None then use the last layer before the softmax output.\n", + " num_iterations: Number of optimization iterations to perform.\n", + " \"\"\"\n", + "\n", + " # Which layer are we using?\n", + " if conv_id is None:\n", + " print(\"Final fully-connected layer before softmax.\")\n", + " else:\n", + " print(\"Layer:\", conv_names[conv_id])\n", + "\n", + " # Initialize the array of images.\n", + " images = []\n", + "\n", + " # For each feature do the following.\n", + " for feature in range(0,10):\n", + " print(\"Optimizing image for feature no.\", feature)\n", + " \n", + " # Find the image that maximizes the given feature\n", + " # for the network layer identified by conv_id (or None).\n", + " image = optimize_image(conv_id=conv_id, feature=feature,\n", + " show_progress=False,\n", + " num_iterations=num_iterations)\n", + "\n", + " # Squeeze the dim of the array.\n", + " image = image.squeeze()\n", + "\n", + " # Append to the list of images.\n", + " images.append(image)\n", + "\n", + " # Convert to numpy-array so we can index all dimensions easily.\n", + " images = np.array(images)\n", + "\n", + " # Plot the images.\n", + " plot_images10(images=images)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### First Convolutional Layer\n", + "\n", + "These are the input images that maximize the features in the first convolutional layer, so these are the images that it *likes to see*." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Layer: layer_conv1/Conv2D\n", + "Optimizing image for feature no. 0\n", + "Optimizing image for feature no. 1\n", + "Optimizing image for feature no. 2\n", + "Optimizing image for feature no. 3\n", + "Optimizing image for feature no. 4\n", + "Optimizing image for feature no. 5\n", + "Optimizing image for feature no. 6\n", + "Optimizing image for feature no. 7\n", + "Optimizing image for feature no. 8\n", + "Optimizing image for feature no. 9\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "optimize_images(conv_id=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note how these are very simple shapes such as lines and angles. Some of these images may be completely white, which suggests that those features of the neural network are perhaps unused, so the number of features could be reduced in this layer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Second Convolutional Layer\n", + "\n", + "This shows the images that maximize the features or neurons in the second convolutional layer, so these are the input images it *likes to see*. Note how these are more complex lines and patterns compared to the first convolutional layer." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Layer: layer_conv2/Conv2D\n", + "Optimizing image for feature no. 0\n", + "Optimizing image for feature no. 1\n", + "Optimizing image for feature no. 2\n", + "Optimizing image for feature no. 3\n", + "Optimizing image for feature no. 4\n", + "Optimizing image for feature no. 5\n", + "Optimizing image for feature no. 6\n", + "Optimizing image for feature no. 7\n", + "Optimizing image for feature no. 8\n", + "Optimizing image for feature no. 9\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "optimize_images(conv_id=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Final output layer\n", + "\n", + "Now find the image for the 2nd feature of the final output of the neural network. That is, we want to find an image that makes the neural network classify that image as the digit 2. This is the image that the neural network *likes to see the most* for the digit 2." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration: 0\n", + "Predicted class: 8, score: 89.26%\n", + "Gradient min: -0.846559, max: 0.558144, stepsize: 5.45\n", + "Loss: 0.3713841\n", + "\n", + "Iteration: 1\n", + "Predicted class: 2, score: 100.00%\n", + "Gradient min: -0.535254, max: 0.621156, stepsize: 5.34\n", + "Loss: 34.539898\n", + "\n", + "Iteration: 2\n", + "Predicted class: 2, score: 100.00%\n", + "Gradient min: -0.664117, max: 0.673569, stepsize: 5.53\n", + "Loss: 45.18827\n", + "\n", + "Iteration: 3\n", + "Predicted class: 2, score: 100.00%\n", + "Gradient min: -0.549961, max: 0.498956, stepsize: 5.67\n", + "Loss: 48.934826\n", + "\n", + "Iteration: 4\n", + "Predicted class: 2, score: 100.00%\n", + "Gradient min: -0.540532, max: 0.564252, stepsize: 5.45\n", + "Loss: 50.952587\n", + "\n", + "Iteration: 5\n", + "Predicted class: 2, score: 100.00%\n", + "Gradient min: -0.581356, max: 0.486933, stepsize: 5.69\n", + "Loss: 51.000446\n", + "\n", + "Iteration: 6\n", + "Predicted class: 2, score: 100.00%\n", + "Gradient min: -0.578246, max: 0.520858, stepsize: 5.55\n", + "Loss: 51.367252\n", + "\n", + "Iteration: 7\n", + "Predicted class: 2, score: 100.00%\n", + "Gradient min: -0.592440, max: 0.511202, stepsize: 5.60\n", + "Loss: 51.47485\n", + "\n", + "Iteration: 8\n", + "Predicted class: 2, score: 100.00%\n", + "Gradient min: -0.589151, max: 0.507705, stepsize: 5.53\n", + "Loss: 51.796883\n", + "\n", + "Iteration: 9\n", + "Predicted class: 2, score: 100.00%\n", + "Gradient min: -0.614109, max: 0.527479, stepsize: 5.59\n", + "Loss: 51.947083\n", + "\n" + ] + } + ], + "source": [ + "image = optimize_image(conv_id=None, feature=2,\n", + " num_iterations=10, show_progress=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note how the predicted class indeed becomes 2 already within the first few iterations so the optimization is working as intended. Also note how the loss-measure is increasing rapidly until it apparently converges. This is because the loss-measure is actually just the value of the feature or neuron that we are trying to maximize. Because this is the logits-layer prior to the softmax, these values can potentially be infinitely high, but they are limited because we limit the image-values between 0 and 1.\n", + "\n", + "Now plot the image that was found. This is the image that the neural network believes looks most like the digit 2." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_image(image)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although some of the curves do hint somewhat at the digit 2, it is hard for a human to see why the neural network believes this is the *optimal* image for the digit 2. This can only be understood when the optimal images for the remaining digits are also shown." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final fully-connected layer before softmax.\n", + "Optimizing image for feature no. 0\n", + "Optimizing image for feature no. 1\n", + "Optimizing image for feature no. 2\n", + "Optimizing image for feature no. 3\n", + "Optimizing image for feature no. 4\n", + "Optimizing image for feature no. 5\n", + "Optimizing image for feature no. 6\n", + "Optimizing image for feature no. 7\n", + "Optimizing image for feature no. 8\n", + "Optimizing image for feature no. 9\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "optimize_images(conv_id=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These images may vary each time you run the optimization. Some of the images can be seen to somewhat resemble the hand-written digits. But the other images are often impossible to recognize and it is hard to understand why the neural network thinks these are the *optimal* input images for those digits.\n", + "\n", + "The reason is perhaps that the neural network tries to recognize all digits simultaneously, and it has found that certain pixels often determine whether the image shows one digit or another. So the neural network has learned to differentiate those pixels that it has found to be important, but not the underlying curves and shapes of the digits, in the same way that a human recognizes the digits.\n", + "\n", + "Another possibility is that the data-set contains mis-classified digits which may confuse the neural network during training. We have previously seen how some of the digits in the data-set are very hard to read even for humans, and this may cause the neural network to become distorted and trying to recognize strange artifacts in the images.\n", + "\n", + "Yet another possibility is that the optimization process has stagnated in a local optimum. One way to test this, would be to run the optimization 50 times for the digits that are unclear, and see if some of the resulting images become more clear." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Close TensorFlow Session" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now done using TensorFlow, so we close the session to release its resources." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "# This has been commented out in case you want to modify and experiment\n", + "# with the Notebook without having to restart it.\n", + "# session.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed how to find the input images that maximize certain features inside a neural network. These are the images that the neural network *likes to see the most* in order to activate a certain feature or neuron inside the network.\n", + "\n", + "This was tested on a simple convolutional neural network using the MNIST data-set. The neural network had clearly learned to recognize the general shape of some of the digits, while it was impossible to see how it recognized other digits." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Plot the images for all features in each convolutional layer instead of just the first 10 features. How many of them appear to be unused or redundant? What happens if you lower the number of features in that layer and train the network again, does it still perform just as well?\n", + "\n", + "* Try adding more convolutional layers and find the input images that maximize their features. What do the images show? Do you think it is useful to add more convolutional layers than two?\n", + "\n", + "* Try adding more fully-connected layers and modify the code so it can find input images that maximize the features of the fully-connected / dense layers as well. Currently the code can only maximize the features of the convolutional layers and the final fully-connected layer.\n", + "\n", + "* For the input images that are unclear, run the optimization e.g. 50 times for each of those digits, to see if it produces more clear input images. It is possible that the optimization has simply become stuck in a local optimum.\n", + "\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "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:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "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." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/13_Visual_Analysis.ipynb b/13_Visual_Analysis.ipynb index 49286db..bcefc5a 100644 --- a/13_Visual_Analysis.ipynb +++ b/13_Visual_Analysis.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -46,7 +55,6 @@ "cell_type": "code", "execution_count": 44, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -102,7 +110,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -163,9 +170,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -219,9 +224,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "conv_names = get_conv_layer_names()" @@ -237,9 +240,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -266,9 +267,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -299,9 +298,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -339,9 +336,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def optimize_image(conv_id=None, feature=0,\n", @@ -691,9 +686,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -860,9 +853,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -897,7 +888,6 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -932,9 +922,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -967,9 +955,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1002,9 +988,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1037,9 +1021,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1072,9 +1054,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1107,9 +1087,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1142,9 +1120,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1177,9 +1153,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1212,9 +1186,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1247,9 +1219,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1282,9 +1252,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1317,9 +1285,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1352,9 +1318,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1387,9 +1351,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1422,9 +1384,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1457,9 +1417,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1493,7 +1451,6 @@ "cell_type": "code", "execution_count": 35, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1545,7 +1502,6 @@ "cell_type": "code", "execution_count": 36, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1591,9 +1547,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2110,9 +2064,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2209,7 +2161,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -2223,9 +2175,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/14_DeepDream.ipynb b/14_DeepDream.ipynb index 3f8f0fb..6f031d4 100644 --- a/14_DeepDream.ipynb +++ b/14_DeepDream.ipynb @@ -41,7 +41,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -80,9 +79,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -140,9 +137,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -181,9 +176,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import inception5h" @@ -217,9 +210,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -263,7 +254,6 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -671,7 +661,8 @@ " # Calculate the value of the gradient.\n", " # This tells us how to change the image so as to\n", " # maximize the mean of the given layer-tensor.\n", - " grad = tiled_gradient(gradient=gradient, image=img)\n", + " grad = tiled_gradient(gradient=gradient, image=img,\n", + " tile_size=tile_size)\n", " \n", " # Blur the gradient with different amounts and add\n", " # them together. The blur amount is also increased\n", @@ -839,9 +830,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -870,7 +859,6 @@ "cell_type": "code", "execution_count": 22, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -900,9 +888,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1147,7 +1133,6 @@ "cell_type": "code", "execution_count": 25, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1350,9 +1335,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "layer_tensor = model.layer_tensors[6]\n", @@ -1371,9 +1354,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "layer_tensor = model.layer_tensors[7][:,:,:,0:3]\n", @@ -1392,9 +1373,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "layer_tensor = model.layer_tensors[11][:,:,:,0]\n", @@ -1413,9 +1392,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "image = load_image(filename='images/giger.jpg')\n", @@ -1425,9 +1402,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "layer_tensor = model.layer_tensors[3]\n", @@ -1439,9 +1414,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "layer_tensor = model.layer_tensors[5]\n", @@ -1461,7 +1434,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [], @@ -1473,9 +1445,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "layer_tensor = model.layer_tensors[6]\n", @@ -1561,7 +1531,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1575,9 +1545,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.6" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/16_Reinforcement_Learning.ipynb b/16_Reinforcement_Learning.ipynb index a3fbaae..fcffd32 100644 --- a/16_Reinforcement_Learning.ipynb +++ b/16_Reinforcement_Learning.ipynb @@ -2,10 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# TensorFlow Tutorial #16\n", "# Reinforcement Learning (Q-Learning)\n", @@ -16,10 +13,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Introduction\n", "\n", @@ -31,15 +25,12 @@ "\n", "The basic idea is to have the agent estimate so-called Q-values whenever it sees an image from the game-environment. The Q-values tell the agent which action is most likely to lead to the highest cumulative reward in the future. The problem is then reduced to finding these Q-values and storing them for later retrieval using a function approximator.\n", "\n", - "This builds on some of the previous tutorials. You should be familiar with TensorFlow and Convolutional Neural Networks from Tutorial #01 and #02. It will also be helpful if you are familiar with the PrettyTensor builder API in Tutorial #03." + "This builds on some of the previous tutorials. You should be familiar with TensorFlow and Convolutional Neural Networks from Tutorial #01 and #02. It will also be helpful if you are familiar with one of the builder APIs in Tutorials #03 or #03-B." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## The Problem\n", "\n", @@ -52,40 +43,28 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Illustration of the problem](images/16_problem.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The problem is that there are 10 states between the ball going downwards and the paddle hitting the ball, and there are an additional 18 states before the reward is obtained when the ball hits the wall and smashes some bricks. How can we teach an agent to connect these three situations and generalize to similar situations? The answer is to use so-called Reinforcement Learning with a Neural Network, as shown in this tutorial." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Q-Learning" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "One of the simplest ways of doing Reinforcement Learning is called Q-learning. Here we want to estimate so-called Q-values which are also called action-values, because they map a state of the game-environment to a numerical value for each possible action that the agent may take. The Q-values indicate which action is expected to result in the highest future reward, thus telling the agent which action to take.\n", "\n", @@ -108,10 +87,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Simple Example\n", "\n", @@ -122,20 +98,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Q-values Simple Example](images/16_q-values-simple.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Detailed Example\n", "\n", @@ -144,26 +114,20 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Q-values Detailed Example](images/16_q-values-details.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "The Q-values for the possible actions have been estimated by a Neural Network. For the action NOOP in state *t* the Q-value is estimated to be 2.900, which is the highest Q-value for that state so the agent takes that action, i.e. the agent does not do anything between state *t* and *t+1* because NOOP means \"No Operation\".\n", + "The Q-values for the possible actions have been estimated by a Neural Network. For the action NOOP in state $t$ the Q-value is estimated to be 2.900, which is the highest Q-value for that state so the agent takes that action, i.e. the agent does not do anything between state $t$ and $t+1$ because NOOP means \"No Operation\".\n", "\n", - "In state *t+1* the agent scores 4 points, but this is limited to 1 point in this implementation so as to stabilize the training. The maximum Q-value for state *t+1* is 1.830 for the action RIGHTFIRE. So if we select that action and continue to select the actions proposed by the Q-values estimated by the Neural Network, then the discounted sum of all the future rewards is expected to be 1.830.\n", + "In state $t+1$ the agent scores 4 points, but this is limited to 1 point in this implementation so as to stabilize the training. The maximum Q-value for state $t+1$ is 1.830 for the action RIGHTFIRE. So if we select that action and continue to select the actions proposed by the Q-values estimated by the Neural Network, then the discounted sum of all the future rewards is expected to be 1.830.\n", "\n", - "Now that we know the reward of taking the NOOP action from state *t* to *t+1*, we can update the Q-value to incorporate this new information. This uses the formula above:\n", + "Now that we know the reward of taking the NOOP action from state $t$ to $t+1$, we can update the Q-value to incorporate this new information. This uses the formula above:\n", "\n", "$$\n", " Q(state_{t},NOOP) \\leftarrow \\underbrace{r_{t}}_{\\rm reward} + \\underbrace{\\gamma}_{\\rm discount} \\cdot \\underbrace{\\max_{a}Q(state_{t+1}, a)}_{\\rm estimate~of~future~rewards} = 1.0 + 0.97 \\cdot 1.830 \\simeq 2.775\n", @@ -176,10 +140,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Motion Trace\n", "\n", @@ -192,20 +153,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Motion Trace](images/16_motion-trace.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Training Stability\n", "\n", @@ -216,20 +171,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Training Stability](images/16_training_stability.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "If we were to train a Neural Network to estimate the Q-values for the two states $t$ and $t+1$ with Q-values 0.97 and 1.0, respectively, then the Neural Network will most likely be unable to distinguish properly between the images of these two states. As a result the Neural Network will also estimate a Q-value near 1.0 for state $t+2$ because the images are so similar. But this is clearly wrong because the Q-values for state $t+2$ should be zero as we do not know anything about future rewards at this point, and that is what the Q-values are supposed to estimate.\n", "\n", @@ -238,10 +187,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Flowchart\n", "\n", @@ -256,20 +202,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Flowchart](images/16_flowchart.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Neural Network Architecture\n", "\n", @@ -286,30 +226,21 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Installation" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The [documentation](https://github.com/openai/gym) for OpenAI Gym currently suggests that you need to build it in order to install it. But if you just want to install the Atari games, then you only need to install a single pip-package by typing the following commands in a terminal." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "- conda create --name tf-gym --clone tf\n", "- source activate tf-gym\n", @@ -318,30 +249,31 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This assumes you already have an Anaconda environment named `tf` which has TensorFlow installed, it will then be cloned to another environment named `tf-gym` where OpenAI Gym is also installed. This allows you to easily switch between your normal TensorFlow environment and another one which also contains OpenAI Gym." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "You can also have two environments named `tf-gpu` and `tf-gpu-gym` for the GPU versions of TensorFlow." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TensorFlow 2\n", + "\n", + "This tutorial was developed using TensorFlow v.1 back in the year 2016-2017. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\". It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working." + ] + }, { "cell_type": "markdown", "metadata": { "colab_type": "text", - "deletable": true, - "editable": true, "id": "xu2SVpFJjmJr" }, "source": [ @@ -351,40 +283,49 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", - "import tensorflow as tf\n", - "import prettytensor as pt\n", "import gym\n", "import numpy as np\n", "import math" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n" + ] + } + ], + "source": [ + "# Use TensorFlow v.2 with this old v.1 code.\n", + "# E.g. placeholder variables and sessions have changed in TF2.\n", + "import tensorflow.compat.v1 as tf\n", + "tf.disable_v2_behavior()" + ] + }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The main source-code for Reinforcement Learning is located in the following module:" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ "import reinforcement_learning as rl" @@ -392,53 +333,20 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This was developed using Python 3.6.0 (Anaconda) with package versions:" ] }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.0.1'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# TensorFlow\n", - "tf.__version__" - ] - }, { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "scrolled": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'0.7.4'" + "'2.1.0'" ] }, "execution_count": 4, @@ -447,24 +355,21 @@ } ], "source": [ - "# PrettyTensor\n", - "pt.__version__" + "# TensorFlow\n", + "tf.__version__" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "'0.8.1'" + "'0.17.1'" ] }, "execution_count": 5, @@ -479,10 +384,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Game Environment\n", "\n", @@ -492,11 +394,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "env_name = 'Breakout-v0'\n", @@ -505,10 +403,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This is the base-directory for the TensorFlow checkpoints as well as various log-files." ] @@ -516,11 +411,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "rl.checkpoint_base_dir = 'checkpoints_tutorial16/'" @@ -528,10 +419,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Once the base-dir has been set, you need to call this function to set all the paths that will be used. This will also create the checkpoint-dir if it does not already exist." ] @@ -539,11 +427,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "rl.update_paths(env_name=env_name)" @@ -551,58 +435,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Download Pre-Trained Model\n", "\n", - "You can download a TensorFlow checkpoint which holds all the pre-trained variables for the Neural Network. Two checkpoints are provided, one for Breakout and one for Space Invaders. They were both trained for about 150 hours on a laptop with 2.6 GHz CPU and a GTX 1070 GPU.\n", - "\n", - "#### WARNING!\n", - "\n", - "These checkpoints are 280-360 MB each. They are currently hosted on the webserver I use for [www.hvass-labs.org](www.hvass-labs.org) because it is awkward to automatically download large files on Google Drive. To lower the traffic on my webserver, this line has been commented out, so you have to activate it manually. You are welcome to download it, I just don't want it to download automatically for everyone who only wants to run this Notebook briefly." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "scrolled": false - }, - "outputs": [], - "source": [ - "# rl.maybe_download_checkpoint(env_name=env_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "I believe the webserver is located in Denmark. If you are having problems downloading the files using the automatic function above, then you can try and download the files manually in a webbrowser or using `wget` or `curl`. Or you can download from Google Drive, where you will get an anti-virus warning that is awkward to bypass automatically:\n", - "\n", - "* [Download Breakout Checkpoint from Google Drive](https://drive.google.com/uc?export=download&id=0B2aDiIly76ZvUjZTcXRuRFY0RjQ)\n", - "\n", - "* [Download Space Invaders Checkpoint from Google Drive](https://drive.google.com/uc?export=download&id=0B2aDiIly76ZvWDR4TExwdmw1RVE)\n", - "\n", - "You can use the checksum to ensure the downloaded files are complete:\n", - "\n", - "* [SHA256 Checksum](http://www.hvass-labs.org/projects/tensorflow/tutorial16/sha256sum.txt)" + "The original version of this tutorial provided some TensorFlow checkpoints with pre-trained models for download. But due to changes in both TensorFlow and OpenAI Gym, these pre-trained models cannot be loaded anymore so they have been deleted from the web-server. You will therefore have to train your own model further below." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Create Agent\n", "\n", @@ -611,27 +453,33 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2017-04-19 11:19:43,800] Making new env: Breakout-v0\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ + "WARNING:tensorflow:From /home/magnus/development/TensorFlow-Tutorials/reinforcement_learning.py:1189: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.keras.layers.Conv2D` instead.\n", + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `layer.__call__` method instead.\n", + "WARNING:tensorflow:From /home/magnus/development/TensorFlow-Tutorials/reinforcement_learning.py:1205: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use keras.layers.Flatten instead.\n", + "WARNING:tensorflow:From /home/magnus/development/TensorFlow-Tutorials/reinforcement_learning.py:1209: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use keras.layers.Dense instead.\n", + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/rmsprop.py:119: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Call initializer instance with the dtype argument instead of passing it to the constructor\n", "Trying to restore last checkpoint ...\n", - "Restored checkpoint from: checkpoints_tutorial16/Breakout-v0/checkpoint-127639066\n" + "INFO:tensorflow:Restoring parameters from checkpoints_tutorial16/Breakout-v0/checkpoint-1175644\n", + "Restored checkpoint from: checkpoints_tutorial16/Breakout-v0/checkpoint-1175644\n" ] } ], @@ -644,22 +492,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The Neural Network is automatically instantiated by the Agent-class. We will create a direct reference for convenience." ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "model = agent.model" @@ -667,22 +508,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Similarly, the Agent-class also allocates the replay-memory when `training==True`. The replay-memory will require more than 3 GB of RAM, so it should only be allocated when needed. We will need the replay-memory in this Notebook to record the states and Q-values we observe, so they can be plotted further below." ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "replay_memory = agent.replay_memory" @@ -690,10 +524,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Training\n", "\n", @@ -702,11 +533,8 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -714,7 +542,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "87584:127640000\t Epsilon: 0.10\t Reward: 22.0\t Episode Mean: 22.0\n" + "2388:1176704\t Epsilon: 0.10\t Reward: 26.0\t Episode Mean: 26.0\n" ] } ], @@ -724,10 +552,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "In training-mode, this function will output a line for each episode. The first counter is for the number of episodes that have been processed. The second counter is for the number of states that have been processed. These two counters are stored in the TensorFlow checkpoint along with the weights of the Neural Network, so you can restart the training e.g. if you only have one computer and need to train during the night.\n", "\n", @@ -735,26 +560,20 @@ "\n", "```\n", "source activate tf-gpu-gym # Activate your Python environment with TF and Gym.\n", - "python reinforcement-learning.py --env Breakout-v0 --training\n", + "python reinforcement_learning.py --env Breakout-v0 --training\n", "```" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Training Progress" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Data is being logged during training so we can plot the progress afterwards. The reward for each episode and a running mean of the last 30 episodes are logged to file. Basic statistics for the Q-values in the replay-memory are also logged to file before each optimization run.\n", "\n", @@ -765,12 +584,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "log_q_values = rl.LogQValues()\n", @@ -779,21 +594,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can now read the logs from file:" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [], @@ -804,10 +613,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Training Progress: Reward\n", "\n", @@ -816,21 +622,19 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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A5L8TESYl7VxAhySuOZDGpDAkUwqnXR7+QEX0cPkFXszNRq4Po2kOu5iPUdp4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\n3SjZ08d/0AAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -844,10 +648,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Training Progress: Q-Values\n", "\n", @@ -860,22 +661,21 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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eDkUppRqVXyf3owWlAMS2DPVyJEop1bj8Orln5pUAmtyVUs2PXyf3rMrkHqnJ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\nF5F+xpjjXo3Kc1OB2caYZ0X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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -888,10 +688,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Testing\n", "\n", @@ -902,12 +699,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "data": { @@ -915,7 +708,7 @@ "0.01" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -926,22 +719,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We will now instruct the agent that it should no longer perform training by setting this boolean:" ] }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [], "source": [ "agent.training = False" @@ -949,22 +735,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We also reset the previous episode rewards." ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ "agent.reset_episode_rewards()" @@ -972,22 +751,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can render the game-environment to screen so we can see the agent playing the game, by setting this boolean:" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [], "source": [ "agent.render = True" @@ -995,70 +767,44 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can now run a single episode by calling the `run()` function again. This should open a new window that shows the game being played by the agent. At the time of this writing, it was not possible to resize this tiny window, and the developers at OpenAI did not seem to care about this feature which should obviously be there." ] }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "87586:127640046\tQ-min: 1.767\tQ-max: 1.787\tLives: 5\tReward: 1.0\tEpisode Mean: 0.0\n", - "87586:127640089\tQ-min: 1.798\tQ-max: 1.806\tLives: 5\tReward: 2.0\tEpisode Mean: 0.0\n", - "87586:127640129\tQ-min: 1.902\tQ-max: 1.919\tLives: 5\tReward: 3.0\tEpisode Mean: 0.0\n", - "87586:127640165\tQ-min: 1.995\tQ-max: 2.002\tLives: 5\tReward: 4.0\tEpisode Mean: 0.0\n", - "87586:127640198\tQ-min: 1.940\tQ-max: 1.965\tLives: 5\tReward: 5.0\tEpisode Mean: 0.0\n", - "87586:127640231\tQ-min: 1.830\tQ-max: 1.853\tLives: 5\tReward: 6.0\tEpisode Mean: 0.0\n", - "87586:127640265\tQ-min: 1.772\tQ-max: 1.811\tLives: 5\tReward: 10.0\tEpisode Mean: 0.0\n", - 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"metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Mean Reward" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The game-play is slightly random, both with regard to selecting actions using the epsilon-greedy policy, but also because the OpenAI Gym environment will repeat any action between 2-4 times, with the number chosen at random. So the reward of one episode is not an accurate estimate of the reward that can be expected in general from this agent.\n", "\n", @@ -1092,12 +832,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 22, + "metadata": {}, "outputs": [], "source": [ "agent.reset_episode_rewards()" @@ -1105,22 +841,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We disable the screen-rendering so the game-environment runs much faster." ] }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 23, + "metadata": {}, "outputs": [], "source": [ "agent.render = False" @@ -1128,1624 +857,718 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can now run 30 episodes. This records the rewards for each episode. It might have been a good idea to disable the output so it does not print all these lines - you can do this as an exercise." ] }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "87588:127641916\tQ-min: 1.755\tQ-max: 1.765\tLives: 5\tReward: 1.0\tEpisode Mean: 0.0\n", - "87588:127641957\tQ-min: 1.791\tQ-max: 1.807\tLives: 5\tReward: 2.0\tEpisode Mean: 0.0\n", - "87588:127642001\tQ-min: 1.871\tQ-max: 1.878\tLives: 5\tReward: 3.0\tEpisode Mean: 0.0\n", - "87588:127642038\tQ-min: 1.969\tQ-max: 1.993\tLives: 5\tReward: 4.0\tEpisode Mean: 0.0\n", - "87588:127642059\tQ-min: -0.183\tQ-max: 0.103\tLives: 4\tReward: 4.0\tEpisode Mean: 0.0\n", - "87588:127642100\tQ-min: 1.892\tQ-max: 1.924\tLives: 4\tReward: 5.0\tEpisode Mean: 0.0\n", - "87588:127642143\tQ-min: 1.910\tQ-max: 1.925\tLives: 4\tReward: 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0.0\n", - "87588:127643095\tQ-min: 2.078\tQ-max: 2.699\tLives: 3\tReward: 42.0\tEpisode Mean: 0.0\n", - "87588:127643115\tQ-min: 2.247\tQ-max: 2.389\tLives: 3\tReward: 43.0\tEpisode Mean: 0.0\n", - "87588:127643134\tQ-min: 2.333\tQ-max: 2.500\tLives: 3\tReward: 47.0\tEpisode Mean: 0.0\n", - "87588:127643148\tQ-min: 0.006\tQ-max: 0.249\tLives: 2\tReward: 47.0\tEpisode Mean: 0.0\n", - "87588:127643192\tQ-min: 2.114\tQ-max: 2.231\tLives: 2\tReward: 51.0\tEpisode Mean: 0.0\n", - "87588:127643239\tQ-min: 2.236\tQ-max: 2.627\tLives: 2\tReward: 55.0\tEpisode Mean: 0.0\n", - "87588:127643262\tQ-min: 2.376\tQ-max: 2.427\tLives: 2\tReward: 56.0\tEpisode Mean: 0.0\n", - "87588:127643281\tQ-min: 2.371\tQ-max: 2.501\tLives: 2\tReward: 57.0\tEpisode Mean: 0.0\n", - "87588:127643301\tQ-min: 2.364\tQ-max: 2.523\tLives: 2\tReward: 58.0\tEpisode Mean: 0.0\n", - "87588:127643315\tQ-min: 0.041\tQ-max: 0.210\tLives: 1\tReward: 58.0\tEpisode Mean: 0.0\n", - "87588:127643358\tQ-min: 2.213\tQ-max: 2.263\tLives: 1\tReward: 62.0\tEpisode Mean: 0.0\n", - "87588:127643403\tQ-min: 2.129\tQ-max: 2.290\tLives: 1\tReward: 63.0\tEpisode Mean: 0.0\n", - "87588:127643447\tQ-min: 2.044\tQ-max: 2.162\tLives: 1\tReward: 67.0\tEpisode Mean: 0.0\n", - "87588:127643486\tQ-min: 2.325\tQ-max: 2.404\tLives: 1\tReward: 68.0\tEpisode Mean: 0.0\n", - "87588:127643519\tQ-min: 2.200\tQ-max: 2.273\tLives: 1\tReward: 69.0\tEpisode Mean: 0.0\n", - "87588:127643552\tQ-min: 2.272\tQ-max: 2.318\tLives: 1\tReward: 70.0\tEpisode Mean: 0.0\n", - "87588:127643584\tQ-min: 2.202\tQ-max: 2.265\tLives: 1\tReward: 71.0\tEpisode Mean: 0.0\n", - "87588:127643638\tQ-min: 2.055\tQ-max: 2.470\tLives: 1\tReward: 75.0\tEpisode Mean: 0.0\n", - "87588:127643658\tQ-min: 2.335\tQ-max: 2.428\tLives: 1\tReward: 76.0\tEpisode Mean: 0.0\n", - "87588:127643679\tQ-min: 2.365\tQ-max: 2.520\tLives: 1\tReward: 80.0\tEpisode Mean: 0.0\n", - "87588:127643691\tQ-min: -0.361\tQ-max: -0.116\tLives: 0\tReward: 80.0\tEpisode Mean: 80.0\n", - 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5\tReward: 11.0\tEpisode Mean: 80.0\n", - "87589:127644240\tQ-min: 1.960\tQ-max: 1.983\tLives: 5\tReward: 12.0\tEpisode Mean: 80.0\n", - "87589:127644274\tQ-min: 1.907\tQ-max: 1.978\tLives: 5\tReward: 13.0\tEpisode Mean: 80.0\n", - "87589:127644306\tQ-min: 1.994\tQ-max: 2.017\tLives: 5\tReward: 14.0\tEpisode Mean: 80.0\n", - "87589:127644337\tQ-min: 2.081\tQ-max: 2.093\tLives: 5\tReward: 15.0\tEpisode Mean: 80.0\n", - "87589:127644358\tQ-min: -0.094\tQ-max: 0.319\tLives: 4\tReward: 15.0\tEpisode Mean: 80.0\n", - "87589:127644400\tQ-min: 1.875\tQ-max: 1.904\tLives: 4\tReward: 16.0\tEpisode Mean: 80.0\n", - "87589:127644443\tQ-min: 1.952\tQ-max: 1.981\tLives: 4\tReward: 17.0\tEpisode Mean: 80.0\n", - "87589:127644495\tQ-min: 1.737\tQ-max: 1.758\tLives: 4\tReward: 18.0\tEpisode Mean: 80.0\n", - "87589:127644547\tQ-min: 2.005\tQ-max: 2.084\tLives: 4\tReward: 22.0\tEpisode Mean: 80.0\n", - "87589:127644583\tQ-min: 2.197\tQ-max: 2.347\tLives: 4\tReward: 26.0\tEpisode Mean: 80.0\n", - 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0.276\tLives: 3\tReward: 60.0\tEpisode Mean: 80.0\n", - "87589:127644878\tQ-min: 1.634\tQ-max: 2.413\tLives: 3\tReward: 64.0\tEpisode Mean: 80.0\n", - "87589:127644935\tQ-min: 2.321\tQ-max: 2.362\tLives: 3\tReward: 65.0\tEpisode Mean: 80.0\n", - "87589:127644979\tQ-min: 2.174\tQ-max: 2.536\tLives: 3\tReward: 69.0\tEpisode Mean: 80.0\n", - "87589:127644994\tQ-min: -0.034\tQ-max: 0.382\tLives: 2\tReward: 69.0\tEpisode Mean: 80.0\n", - "87589:127645044\tQ-min: 1.599\tQ-max: 2.176\tLives: 2\tReward: 76.0\tEpisode Mean: 80.0\n", - "87589:127645066\tQ-min: 2.362\tQ-max: 2.540\tLives: 2\tReward: 77.0\tEpisode Mean: 80.0\n", - "87589:127645088\tQ-min: 1.918\tQ-max: 2.701\tLives: 2\tReward: 84.0\tEpisode Mean: 80.0\n", - "87589:127645102\tQ-min: -0.022\tQ-max: 0.068\tLives: 1\tReward: 84.0\tEpisode Mean: 80.0\n", - "87589:127645159\tQ-min: 1.981\tQ-max: 2.173\tLives: 1\tReward: 85.0\tEpisode Mean: 80.0\n", - "87589:127645231\tQ-min: 1.785\tQ-max: 2.009\tLives: 1\tReward: 86.0\tEpisode Mean: 80.0\n", - "87589:127645289\tQ-min: 2.147\tQ-max: 2.292\tLives: 1\tReward: 87.0\tEpisode Mean: 80.0\n", - "87589:127645329\tQ-min: 2.359\tQ-max: 2.482\tLives: 1\tReward: 88.0\tEpisode Mean: 80.0\n", - "87589:127645365\tQ-min: 1.856\tQ-max: 2.511\tLives: 1\tReward: 95.0\tEpisode Mean: 80.0\n", - "87589:127645388\tQ-min: 1.407\tQ-max: 3.038\tLives: 1\tReward: 99.0\tEpisode Mean: 80.0\n", - "87589:127645407\tQ-min: 2.163\tQ-max: 3.081\tLives: 1\tReward: 100.0\tEpisode Mean: 80.0\n", - "87589:127645419\tQ-min: -0.183\tQ-max: 0.050\tLives: 0\tReward: 100.0\tEpisode Mean: 90.0\n", - "87590:127645462\tQ-min: 1.782\tQ-max: 1.796\tLives: 5\tReward: 1.0\tEpisode Mean: 90.0\n", - "87590:127645508\tQ-min: 1.808\tQ-max: 1.828\tLives: 5\tReward: 2.0\tEpisode Mean: 90.0\n", - "87590:127645561\tQ-min: 1.690\tQ-max: 1.713\tLives: 5\tReward: 3.0\tEpisode Mean: 90.0\n", - "87590:127645608\tQ-min: 1.970\tQ-max: 2.003\tLives: 5\tReward: 4.0\tEpisode Mean: 90.0\n", - "87590:127645630\tQ-min: 0.005\tQ-max: 0.280\tLives: 4\tReward: 4.0\tEpisode Mean: 90.0\n", - "87590:127645670\tQ-min: 1.899\tQ-max: 1.914\tLives: 4\tReward: 5.0\tEpisode Mean: 90.0\n", - "87590:127645718\tQ-min: 1.676\tQ-max: 1.734\tLives: 4\tReward: 6.0\tEpisode Mean: 90.0\n", - "87590:127645776\tQ-min: 1.920\tQ-max: 1.942\tLives: 4\tReward: 7.0\tEpisode Mean: 90.0\n", - "87590:127645813\tQ-min: 1.961\tQ-max: 1.988\tLives: 4\tReward: 8.0\tEpisode Mean: 90.0\n", - "87590:127645846\tQ-min: 1.993\tQ-max: 2.033\tLives: 4\tReward: 9.0\tEpisode Mean: 90.0\n", - "87590:127645877\tQ-min: 1.902\tQ-max: 1.921\tLives: 4\tReward: 10.0\tEpisode Mean: 90.0\n", - "87590:127645910\tQ-min: 1.969\tQ-max: 2.000\tLives: 4\tReward: 11.0\tEpisode Mean: 90.0\n", - "87590:127645931\tQ-min: -0.106\tQ-max: 0.325\tLives: 3\tReward: 11.0\tEpisode Mean: 90.0\n", - "87590:127645975\tQ-min: 1.846\tQ-max: 1.880\tLives: 3\tReward: 12.0\tEpisode Mean: 90.0\n", - "87590:127646020\tQ-min: 1.888\tQ-max: 1.929\tLives: 3\tReward: 13.0\tEpisode Mean: 90.0\n", - 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2.537\tLives: 3\tReward: 39.0\tEpisode Mean: 90.0\n", - "87590:127646340\tQ-min: 2.395\tQ-max: 2.524\tLives: 3\tReward: 43.0\tEpisode Mean: 90.0\n", - "87590:127646359\tQ-min: 2.258\tQ-max: 2.519\tLives: 3\tReward: 44.0\tEpisode Mean: 90.0\n", - "87590:127646371\tQ-min: 0.218\tQ-max: 0.566\tLives: 2\tReward: 44.0\tEpisode Mean: 90.0\n", - "87590:127646436\tQ-min: 1.883\tQ-max: 1.924\tLives: 2\tReward: 45.0\tEpisode Mean: 90.0\n", - "87590:127646505\tQ-min: 1.933\tQ-max: 1.983\tLives: 2\tReward: 46.0\tEpisode Mean: 90.0\n", - "87590:127646578\tQ-min: 1.977\tQ-max: 2.124\tLives: 2\tReward: 50.0\tEpisode Mean: 90.0\n", - "87590:127646629\tQ-min: 2.229\tQ-max: 2.280\tLives: 2\tReward: 51.0\tEpisode Mean: 90.0\n", - "87590:127646661\tQ-min: 2.252\tQ-max: 2.288\tLives: 2\tReward: 52.0\tEpisode Mean: 90.0\n", - "87590:127646682\tQ-min: -0.102\tQ-max: 0.229\tLives: 1\tReward: 52.0\tEpisode Mean: 90.0\n", - "87590:127646723\tQ-min: 2.112\tQ-max: 2.132\tLives: 1\tReward: 53.0\tEpisode Mean: 90.0\n", - "87590:127646767\tQ-min: 2.276\tQ-max: 2.451\tLives: 1\tReward: 57.0\tEpisode Mean: 90.0\n", - "87590:127646782\tQ-min: -0.150\tQ-max: 0.203\tLives: 0\tReward: 57.0\tEpisode Mean: 79.0\n", - "87591:127646829\tQ-min: 1.771\tQ-max: 1.805\tLives: 5\tReward: 1.0\tEpisode Mean: 79.0\n", - "87591:127646870\tQ-min: 1.784\tQ-max: 1.796\tLives: 5\tReward: 2.0\tEpisode Mean: 79.0\n", - "87591:127646915\tQ-min: 1.882\tQ-max: 1.889\tLives: 5\tReward: 3.0\tEpisode Mean: 79.0\n", - "87591:127646950\tQ-min: 2.012\tQ-max: 2.057\tLives: 5\tReward: 4.0\tEpisode Mean: 79.0\n", - "87591:127646982\tQ-min: 1.971\tQ-max: 1.977\tLives: 5\tReward: 5.0\tEpisode Mean: 79.0\n", - "87591:127647013\tQ-min: 1.945\tQ-max: 1.953\tLives: 5\tReward: 6.0\tEpisode Mean: 79.0\n", - "87591:127647044\tQ-min: 1.875\tQ-max: 1.892\tLives: 5\tReward: 10.0\tEpisode Mean: 79.0\n", - "87591:127647068\tQ-min: -0.056\tQ-max: 0.195\tLives: 4\tReward: 10.0\tEpisode Mean: 79.0\n", - "87591:127647122\tQ-min: 1.693\tQ-max: 1.703\tLives: 4\tReward: 11.0\tEpisode Mean: 79.0\n", - "87591:127647177\tQ-min: 1.931\tQ-max: 1.940\tLives: 4\tReward: 12.0\tEpisode Mean: 79.0\n", - "87591:127647217\tQ-min: 1.986\tQ-max: 2.007\tLives: 4\tReward: 13.0\tEpisode Mean: 79.0\n", - "87591:127647255\tQ-min: 2.036\tQ-max: 2.085\tLives: 4\tReward: 14.0\tEpisode Mean: 79.0\n", - "87591:127647287\tQ-min: 2.034\tQ-max: 2.065\tLives: 4\tReward: 15.0\tEpisode Mean: 79.0\n", - "87591:127647323\tQ-min: 2.038\tQ-max: 2.059\tLives: 4\tReward: 16.0\tEpisode Mean: 79.0\n", - "87591:127647360\tQ-min: 2.017\tQ-max: 2.061\tLives: 4\tReward: 20.0\tEpisode Mean: 79.0\n", - "87591:127647410\tQ-min: 1.760\tQ-max: 1.781\tLives: 4\tReward: 21.0\tEpisode Mean: 79.0\n", - "87591:127647473\tQ-min: 1.782\tQ-max: 1.823\tLives: 4\tReward: 22.0\tEpisode Mean: 79.0\n", - "87591:127647536\tQ-min: 1.745\tQ-max: 1.767\tLives: 4\tReward: 23.0\tEpisode Mean: 79.0\n", - "87591:127647598\tQ-min: 1.660\tQ-max: 1.691\tLives: 4\tReward: 24.0\tEpisode Mean: 79.0\n", - "87591:127647645\tQ-min: 2.131\tQ-max: 2.185\tLives: 4\tReward: 25.0\tEpisode Mean: 79.0\n", - "87591:127647674\tQ-min: 2.064\tQ-max: 2.089\tLives: 4\tReward: 26.0\tEpisode Mean: 79.0\n", - "87591:127647706\tQ-min: 1.983\tQ-max: 2.032\tLives: 4\tReward: 27.0\tEpisode Mean: 79.0\n", - "87591:127647739\tQ-min: 2.011\tQ-max: 2.067\tLives: 4\tReward: 28.0\tEpisode Mean: 79.0\n", - "87591:127647773\tQ-min: 1.913\tQ-max: 2.158\tLives: 4\tReward: 32.0\tEpisode Mean: 79.0\n", - "87591:127647806\tQ-min: 1.973\tQ-max: 2.135\tLives: 4\tReward: 33.0\tEpisode Mean: 79.0\n", - "87591:127647827\tQ-min: -0.069\tQ-max: 0.166\tLives: 3\tReward: 33.0\tEpisode Mean: 79.0\n", - "87591:127647876\tQ-min: 1.890\tQ-max: 1.962\tLives: 3\tReward: 34.0\tEpisode Mean: 79.0\n", - "87591:127647919\tQ-min: 2.035\tQ-max: 2.102\tLives: 3\tReward: 35.0\tEpisode Mean: 79.0\n", - "87591:127647962\tQ-min: 2.088\tQ-max: 2.182\tLives: 3\tReward: 39.0\tEpisode Mean: 79.0\n", - "87591:127648001\tQ-min: 2.207\tQ-max: 2.255\tLives: 3\tReward: 43.0\tEpisode Mean: 79.0\n", - "87591:127648037\tQ-min: 2.193\tQ-max: 2.259\tLives: 3\tReward: 44.0\tEpisode Mean: 79.0\n", - "87591:127648073\tQ-min: 2.094\tQ-max: 2.231\tLives: 3\tReward: 48.0\tEpisode Mean: 79.0\n", - "87591:127648106\tQ-min: 2.185\tQ-max: 2.267\tLives: 3\tReward: 49.0\tEpisode Mean: 79.0\n", - "87591:127648160\tQ-min: 1.744\tQ-max: 1.795\tLives: 3\tReward: 50.0\tEpisode Mean: 79.0\n", - "87591:127648227\tQ-min: 1.767\tQ-max: 1.814\tLives: 3\tReward: 51.0\tEpisode Mean: 79.0\n", - "87591:127648299\tQ-min: 1.462\tQ-max: 1.818\tLives: 3\tReward: 52.0\tEpisode Mean: 79.0\n", - "87591:127648365\tQ-min: 2.078\tQ-max: 2.492\tLives: 3\tReward: 56.0\tEpisode Mean: 79.0\n", - "87591:127648386\tQ-min: 2.260\tQ-max: 2.447\tLives: 3\tReward: 57.0\tEpisode Mean: 79.0\n", - "87591:127648406\tQ-min: 2.177\tQ-max: 2.367\tLives: 3\tReward: 58.0\tEpisode Mean: 79.0\n", - "87591:127648426\tQ-min: 2.419\tQ-max: 2.528\tLives: 3\tReward: 59.0\tEpisode Mean: 79.0\n", - "87591:127648439\tQ-min: 0.049\tQ-max: 0.212\tLives: 2\tReward: 59.0\tEpisode Mean: 79.0\n", - "87591:127648484\tQ-min: 2.005\tQ-max: 2.081\tLives: 2\tReward: 63.0\tEpisode Mean: 79.0\n", - "87591:127648530\tQ-min: 2.056\tQ-max: 2.156\tLives: 2\tReward: 67.0\tEpisode Mean: 79.0\n", - "87591:127648589\tQ-min: 2.019\tQ-max: 2.510\tLives: 2\tReward: 71.0\tEpisode Mean: 79.0\n", - "87591:127648642\tQ-min: 2.222\tQ-max: 2.368\tLives: 2\tReward: 75.0\tEpisode Mean: 79.0\n", - "87591:127648677\tQ-min: 2.273\tQ-max: 2.310\tLives: 2\tReward: 76.0\tEpisode Mean: 79.0\n", - "87591:127648710\tQ-min: 2.219\tQ-max: 2.331\tLives: 2\tReward: 80.0\tEpisode Mean: 79.0\n", - "87591:127648742\tQ-min: 2.265\tQ-max: 2.504\tLives: 2\tReward: 84.0\tEpisode Mean: 79.0\n", - "87591:127648767\tQ-min: 2.339\tQ-max: 2.488\tLives: 2\tReward: 88.0\tEpisode Mean: 79.0\n", - "87591:127648788\tQ-min: 2.077\tQ-max: 2.568\tLives: 2\tReward: 92.0\tEpisode Mean: 79.0\n", - "87591:127648809\tQ-min: 2.260\tQ-max: 2.533\tLives: 2\tReward: 96.0\tEpisode Mean: 79.0\n", - "87591:127648830\tQ-min: 1.901\tQ-max: 2.720\tLives: 2\tReward: 103.0\tEpisode Mean: 79.0\n", - "87591:127648853\tQ-min: 2.081\tQ-max: 2.668\tLives: 2\tReward: 107.0\tEpisode Mean: 79.0\n", - "87591:127648873\tQ-min: 2.564\tQ-max: 2.679\tLives: 2\tReward: 108.0\tEpisode Mean: 79.0\n", - "87591:127648892\tQ-min: 2.543\tQ-max: 2.745\tLives: 2\tReward: 112.0\tEpisode Mean: 79.0\n", - "87591:127648905\tQ-min: 0.118\tQ-max: 0.498\tLives: 1\tReward: 112.0\tEpisode Mean: 79.0\n", - "87591:127648962\tQ-min: 2.145\tQ-max: 2.298\tLives: 1\tReward: 116.0\tEpisode Mean: 79.0\n", - "87591:127649025\tQ-min: 2.106\tQ-max: 2.738\tLives: 1\tReward: 123.0\tEpisode Mean: 79.0\n", - "87591:127649040\tQ-min: -0.075\tQ-max: 0.226\tLives: 0\tReward: 123.0\tEpisode Mean: 90.0\n", - "87592:127649096\tQ-min: 1.651\tQ-max: 1.673\tLives: 5\tReward: 1.0\tEpisode Mean: 90.0\n", - "87592:127649148\tQ-min: 1.824\tQ-max: 1.838\tLives: 5\tReward: 2.0\tEpisode Mean: 90.0\n", - "87592:127649192\tQ-min: 1.875\tQ-max: 1.903\tLives: 5\tReward: 3.0\tEpisode Mean: 90.0\n", - "87592:127649228\tQ-min: 2.067\tQ-max: 2.100\tLives: 5\tReward: 4.0\tEpisode Mean: 90.0\n", - "87592:127649260\tQ-min: 2.018\tQ-max: 2.133\tLives: 5\tReward: 5.0\tEpisode Mean: 90.0\n", - "87592:127649294\tQ-min: 1.890\tQ-max: 1.913\tLives: 5\tReward: 6.0\tEpisode Mean: 90.0\n", - "87592:127649325\tQ-min: 1.776\tQ-max: 1.824\tLives: 5\tReward: 7.0\tEpisode Mean: 90.0\n", - "87592:127649370\tQ-min: 1.625\tQ-max: 1.662\tLives: 5\tReward: 8.0\tEpisode Mean: 90.0\n", - "87592:127649438\tQ-min: 1.618\tQ-max: 1.713\tLives: 5\tReward: 9.0\tEpisode Mean: 90.0\n", - "87592:127649503\tQ-min: 1.632\tQ-max: 1.649\tLives: 5\tReward: 10.0\tEpisode Mean: 90.0\n", - "87592:127649569\tQ-min: 1.584\tQ-max: 1.635\tLives: 5\tReward: 11.0\tEpisode Mean: 90.0\n", - "87592:127649619\tQ-min: 2.002\tQ-max: 2.035\tLives: 5\tReward: 12.0\tEpisode Mean: 90.0\n", - "87592:127649651\tQ-min: 1.980\tQ-max: 2.026\tLives: 5\tReward: 13.0\tEpisode Mean: 90.0\n", - "87592:127649686\tQ-min: 1.987\tQ-max: 2.054\tLives: 5\tReward: 17.0\tEpisode Mean: 90.0\n", - "87592:127649719\tQ-min: 2.018\tQ-max: 2.074\tLives: 5\tReward: 18.0\tEpisode Mean: 90.0\n", - "87592:127649752\tQ-min: 2.034\tQ-max: 2.090\tLives: 5\tReward: 22.0\tEpisode Mean: 90.0\n", - "87592:127649785\tQ-min: 2.080\tQ-max: 2.135\tLives: 5\tReward: 23.0\tEpisode Mean: 90.0\n", - "87592:127649815\tQ-min: 1.984\tQ-max: 2.087\tLives: 5\tReward: 24.0\tEpisode Mean: 90.0\n", - "87592:127649848\tQ-min: 2.038\tQ-max: 2.075\tLives: 5\tReward: 25.0\tEpisode Mean: 90.0\n", - "87592:127649882\tQ-min: 1.931\tQ-max: 1.960\tLives: 5\tReward: 26.0\tEpisode Mean: 90.0\n", - "87592:127649915\tQ-min: 2.079\tQ-max: 2.117\tLives: 5\tReward: 27.0\tEpisode Mean: 90.0\n", - "87592:127649948\tQ-min: 2.094\tQ-max: 2.180\tLives: 5\tReward: 28.0\tEpisode Mean: 90.0\n", - "87592:127649980\tQ-min: 2.096\tQ-max: 2.118\tLives: 5\tReward: 29.0\tEpisode Mean: 90.0\n", - "87592:127650012\tQ-min: 2.029\tQ-max: 2.075\tLives: 5\tReward: 30.0\tEpisode Mean: 90.0\n", - "87592:127650045\tQ-min: 2.064\tQ-max: 2.097\tLives: 5\tReward: 34.0\tEpisode Mean: 90.0\n", - "87592:127650080\tQ-min: 2.138\tQ-max: 2.191\tLives: 5\tReward: 35.0\tEpisode Mean: 90.0\n", - "87592:127650115\tQ-min: 2.078\tQ-max: 2.117\tLives: 5\tReward: 36.0\tEpisode Mean: 90.0\n", - "87592:127650147\tQ-min: 2.042\tQ-max: 2.062\tLives: 5\tReward: 37.0\tEpisode Mean: 90.0\n", - "87592:127650181\tQ-min: 2.132\tQ-max: 2.282\tLives: 5\tReward: 38.0\tEpisode Mean: 90.0\n", - "87592:127650217\tQ-min: 2.311\tQ-max: 2.349\tLives: 5\tReward: 42.0\tEpisode Mean: 90.0\n", - "87592:127650255\tQ-min: 1.799\tQ-max: 2.295\tLives: 5\tReward: 46.0\tEpisode Mean: 90.0\n", - "87592:127650279\tQ-min: 2.092\tQ-max: 2.350\tLives: 5\tReward: 50.0\tEpisode Mean: 90.0\n", - "87592:127650300\tQ-min: 2.212\tQ-max: 2.416\tLives: 5\tReward: 54.0\tEpisode Mean: 90.0\n", - "87592:127650323\tQ-min: 2.300\tQ-max: 2.828\tLives: 5\tReward: 61.0\tEpisode Mean: 90.0\n", - "87592:127650345\tQ-min: 2.156\tQ-max: 2.609\tLives: 5\tReward: 68.0\tEpisode Mean: 90.0\n", - "87592:127650371\tQ-min: 1.411\tQ-max: 3.465\tLives: 5\tReward: 75.0\tEpisode Mean: 90.0\n", - "87592:127650394\tQ-min: 2.413\tQ-max: 3.230\tLives: 5\tReward: 79.0\tEpisode Mean: 90.0\n", - "87592:127650423\tQ-min: 0.923\tQ-max: 6.278\tLives: 5\tReward: 86.0\tEpisode Mean: 90.0\n", - "87592:127650428\tQ-min: 3.001\tQ-max: 6.274\tLives: 5\tReward: 93.0\tEpisode Mean: 90.0\n", - "87592:127650433\tQ-min: 3.775\tQ-max: 6.792\tLives: 5\tReward: 100.0\tEpisode Mean: 90.0\n", - "87592:127650438\tQ-min: 4.326\tQ-max: 6.050\tLives: 5\tReward: 107.0\tEpisode Mean: 90.0\n", - "87592:127650442\tQ-min: 4.084\tQ-max: 6.912\tLives: 5\tReward: 114.0\tEpisode Mean: 90.0\n", - "87592:127650446\tQ-min: 3.861\tQ-max: 6.375\tLives: 5\tReward: 121.0\tEpisode Mean: 90.0\n", - "87592:127650451\tQ-min: 3.899\tQ-max: 5.700\tLives: 5\tReward: 128.0\tEpisode Mean: 90.0\n", - "87592:127650456\tQ-min: 3.069\tQ-max: 5.369\tLives: 5\tReward: 135.0\tEpisode Mean: 90.0\n", - "87592:127650461\tQ-min: 3.135\tQ-max: 6.155\tLives: 5\tReward: 142.0\tEpisode Mean: 90.0\n", - "87592:127650467\tQ-min: 2.198\tQ-max: 5.513\tLives: 5\tReward: 149.0\tEpisode Mean: 90.0\n", - "87592:127650474\tQ-min: 3.512\tQ-max: 5.457\tLives: 5\tReward: 156.0\tEpisode Mean: 90.0\n", - "87592:127650479\tQ-min: 3.024\tQ-max: 5.236\tLives: 5\tReward: 163.0\tEpisode Mean: 90.0\n", - "87592:127650483\tQ-min: 1.336\tQ-max: 4.921\tLives: 5\tReward: 170.0\tEpisode Mean: 90.0\n", - "87592:127650488\tQ-min: 3.208\tQ-max: 4.958\tLives: 5\tReward: 177.0\tEpisode Mean: 90.0\n", - "87592:127650495\tQ-min: 3.234\tQ-max: 6.032\tLives: 5\tReward: 184.0\tEpisode Mean: 90.0\n", - "87592:127650501\tQ-min: 3.603\tQ-max: 5.808\tLives: 5\tReward: 191.0\tEpisode Mean: 90.0\n", - "87592:127650508\tQ-min: 3.807\tQ-max: 5.607\tLives: 5\tReward: 198.0\tEpisode Mean: 90.0\n", - "87592:127650516\tQ-min: 3.144\tQ-max: 4.399\tLives: 5\tReward: 202.0\tEpisode Mean: 90.0\n", - "87592:127650525\tQ-min: 3.145\tQ-max: 4.964\tLives: 5\tReward: 209.0\tEpisode Mean: 90.0\n", - "87592:127650531\tQ-min: 3.249\tQ-max: 4.350\tLives: 5\tReward: 216.0\tEpisode Mean: 90.0\n", - "87592:127650566\tQ-min: 1.383\tQ-max: 4.393\tLives: 5\tReward: 220.0\tEpisode Mean: 90.0\n", - "87592:127650574\tQ-min: 2.265\tQ-max: 4.179\tLives: 5\tReward: 224.0\tEpisode Mean: 90.0\n", - "87592:127650604\tQ-min: 1.203\tQ-max: 3.316\tLives: 5\tReward: 228.0\tEpisode Mean: 90.0\n", - "87592:127650616\tQ-min: -0.359\tQ-max: 0.338\tLives: 4\tReward: 228.0\tEpisode Mean: 90.0\n", - "87592:127650681\tQ-min: 1.850\tQ-max: 4.044\tLives: 4\tReward: 232.0\tEpisode Mean: 90.0\n", - "87592:127650689\tQ-min: 2.815\tQ-max: 5.003\tLives: 4\tReward: 239.0\tEpisode Mean: 90.0\n", - "87592:127650695\tQ-min: 2.727\tQ-max: 4.569\tLives: 4\tReward: 246.0\tEpisode Mean: 90.0\n", - "87592:127650702\tQ-min: 2.739\tQ-max: 4.073\tLives: 4\tReward: 253.0\tEpisode Mean: 90.0\n", - "87592:127650709\tQ-min: 2.039\tQ-max: 3.717\tLives: 4\tReward: 260.0\tEpisode Mean: 90.0\n", - "87592:127650716\tQ-min: 2.436\tQ-max: 4.143\tLives: 4\tReward: 267.0\tEpisode Mean: 90.0\n", - "87592:127650756\tQ-min: 2.232\tQ-max: 3.893\tLives: 4\tReward: 271.0\tEpisode Mean: 90.0\n", - "87592:127650767\tQ-min: 1.886\tQ-max: 2.999\tLives: 4\tReward: 272.0\tEpisode Mean: 90.0\n", - "87592:127650774\tQ-min: 2.496\tQ-max: 4.255\tLives: 4\tReward: 279.0\tEpisode Mean: 90.0\n", - "87592:127650779\tQ-min: 2.053\tQ-max: 4.533\tLives: 4\tReward: 286.0\tEpisode Mean: 90.0\n", - "87592:127650785\tQ-min: 1.964\tQ-max: 4.617\tLives: 4\tReward: 293.0\tEpisode Mean: 90.0\n", - "87592:127650792\tQ-min: 2.058\tQ-max: 4.554\tLives: 4\tReward: 300.0\tEpisode Mean: 90.0\n", - "87592:127650812\tQ-min: 0.003\tQ-max: 0.512\tLives: 3\tReward: 300.0\tEpisode Mean: 90.0\n", - "87592:127650865\tQ-min: 2.230\tQ-max: 2.494\tLives: 3\tReward: 301.0\tEpisode Mean: 90.0\n", - "87592:127650939\tQ-min: 0.922\tQ-max: 2.301\tLives: 3\tReward: 305.0\tEpisode Mean: 90.0\n", - "87592:127650948\tQ-min: 2.071\tQ-max: 3.568\tLives: 3\tReward: 309.0\tEpisode Mean: 90.0\n", - "87592:127650957\tQ-min: 2.014\tQ-max: 3.165\tLives: 3\tReward: 310.0\tEpisode Mean: 90.0\n", - "87592:127650966\tQ-min: 0.540\tQ-max: 3.734\tLives: 3\tReward: 314.0\tEpisode Mean: 90.0\n", - "87592:127650973\tQ-min: 1.862\tQ-max: 3.545\tLives: 3\tReward: 321.0\tEpisode Mean: 90.0\n", - "87592:127650980\tQ-min: 2.206\tQ-max: 3.411\tLives: 3\tReward: 325.0\tEpisode Mean: 90.0\n", - "87592:127650990\tQ-min: 1.696\tQ-max: 2.256\tLives: 3\tReward: 329.0\tEpisode Mean: 90.0\n", - "87592:127650998\tQ-min: 1.816\tQ-max: 2.470\tLives: 3\tReward: 333.0\tEpisode Mean: 90.0\n", - "87592:127651006\tQ-min: 2.148\tQ-max: 3.769\tLives: 3\tReward: 340.0\tEpisode Mean: 90.0\n", - "87592:127651014\tQ-min: 2.506\tQ-max: 5.066\tLives: 3\tReward: 347.0\tEpisode Mean: 90.0\n", - "87592:127651023\tQ-min: 1.857\tQ-max: 4.926\tLives: 3\tReward: 351.0\tEpisode Mean: 90.0\n", - "87592:127651075\tQ-min: -0.036\tQ-max: 0.720\tLives: 2\tReward: 351.0\tEpisode Mean: 90.0\n", - "87592:127651144\tQ-min: 1.646\tQ-max: 3.200\tLives: 2\tReward: 355.0\tEpisode Mean: 90.0\n", - "87592:127651164\tQ-min: -0.252\tQ-max: 0.717\tLives: 1\tReward: 355.0\tEpisode Mean: 90.0\n", - "87592:127651217\tQ-min: 2.423\tQ-max: 2.651\tLives: 1\tReward: 356.0\tEpisode Mean: 90.0\n", - "87592:127651294\tQ-min: 1.397\tQ-max: 2.044\tLives: 1\tReward: 360.0\tEpisode Mean: 90.0\n", - "87592:127651314\tQ-min: 1.209\tQ-max: 3.124\tLives: 1\tReward: 364.0\tEpisode Mean: 90.0\n", - "87592:127651322\tQ-min: 2.887\tQ-max: 4.312\tLives: 1\tReward: 368.0\tEpisode Mean: 90.0\n", - "87592:127651329\tQ-min: 1.640\tQ-max: 2.904\tLives: 1\tReward: 372.0\tEpisode Mean: 90.0\n", - "87592:127651339\tQ-min: 1.747\tQ-max: 3.081\tLives: 1\tReward: 376.0\tEpisode Mean: 90.0\n", - "87592:127651348\tQ-min: 1.704\tQ-max: 2.896\tLives: 1\tReward: 380.0\tEpisode Mean: 90.0\n", - "87592:127651359\tQ-min: -0.101\tQ-max: 2.532\tLives: 1\tReward: 381.0\tEpisode Mean: 90.0\n", - "87592:127651411\tQ-min: 0.099\tQ-max: 0.369\tLives: 0\tReward: 381.0\tEpisode Mean: 148.2\n", - "87593:127651462\tQ-min: 1.684\tQ-max: 1.691\tLives: 5\tReward: 1.0\tEpisode Mean: 148.2\n", - "87593:127651514\tQ-min: 1.818\tQ-max: 1.837\tLives: 5\tReward: 2.0\tEpisode Mean: 148.2\n", - "87593:127651552\tQ-min: 1.826\tQ-max: 1.848\tLives: 5\tReward: 3.0\tEpisode Mean: 148.2\n", - "87593:127651591\tQ-min: 1.961\tQ-max: 1.980\tLives: 5\tReward: 4.0\tEpisode Mean: 148.2\n", - "87593:127651625\tQ-min: 1.938\tQ-max: 1.948\tLives: 5\tReward: 5.0\tEpisode Mean: 148.2\n", - "87593:127651656\tQ-min: 1.941\tQ-max: 1.958\tLives: 5\tReward: 6.0\tEpisode Mean: 148.2\n", - "87593:127651689\tQ-min: 1.880\tQ-max: 1.912\tLives: 5\tReward: 7.0\tEpisode Mean: 148.2\n", - "87593:127651709\tQ-min: -0.284\tQ-max: 0.017\tLives: 4\tReward: 7.0\tEpisode Mean: 148.2\n", - "87593:127651753\tQ-min: 1.890\tQ-max: 1.929\tLives: 4\tReward: 8.0\tEpisode Mean: 148.2\n", - "87593:127651797\tQ-min: 1.898\tQ-max: 1.923\tLives: 4\tReward: 9.0\tEpisode Mean: 148.2\n", - "87593:127651839\tQ-min: 1.885\tQ-max: 1.902\tLives: 4\tReward: 10.0\tEpisode Mean: 148.2\n", - "87593:127651879\tQ-min: 1.970\tQ-max: 2.000\tLives: 4\tReward: 11.0\tEpisode Mean: 148.2\n", - "87593:127651916\tQ-min: 1.968\tQ-max: 2.019\tLives: 4\tReward: 15.0\tEpisode Mean: 148.2\n", - "87593:127651948\tQ-min: 1.997\tQ-max: 2.050\tLives: 4\tReward: 16.0\tEpisode Mean: 148.2\n", - "87593:127651968\tQ-min: 0.030\tQ-max: 0.242\tLives: 3\tReward: 16.0\tEpisode Mean: 148.2\n", - "87593:127652010\tQ-min: 1.830\tQ-max: 1.858\tLives: 3\tReward: 17.0\tEpisode Mean: 148.2\n", - "87593:127652066\tQ-min: 1.768\tQ-max: 1.781\tLives: 3\tReward: 18.0\tEpisode Mean: 148.2\n", - "87593:127652108\tQ-min: 0.038\tQ-max: 0.210\tLives: 2\tReward: 18.0\tEpisode Mean: 148.2\n", - "87593:127652153\tQ-min: 1.949\tQ-max: 1.984\tLives: 2\tReward: 22.0\tEpisode Mean: 148.2\n", - "87593:127652214\tQ-min: 1.702\tQ-max: 1.806\tLives: 2\tReward: 23.0\tEpisode Mean: 148.2\n", - "87593:127652272\tQ-min: 2.032\tQ-max: 2.045\tLives: 2\tReward: 24.0\tEpisode Mean: 148.2\n", - "87593:127652310\tQ-min: 1.992\tQ-max: 2.060\tLives: 2\tReward: 25.0\tEpisode Mean: 148.2\n", - "87593:127652332\tQ-min: -0.201\tQ-max: 0.293\tLives: 1\tReward: 25.0\tEpisode Mean: 148.2\n", - "87593:127652377\tQ-min: 1.861\tQ-max: 1.898\tLives: 1\tReward: 26.0\tEpisode Mean: 148.2\n", - "87593:127652422\tQ-min: 1.996\tQ-max: 2.069\tLives: 1\tReward: 30.0\tEpisode Mean: 148.2\n", - "87593:127652475\tQ-min: 1.781\tQ-max: 1.830\tLives: 1\tReward: 31.0\tEpisode Mean: 148.2\n", - "87593:127652522\tQ-min: 2.116\tQ-max: 2.247\tLives: 1\tReward: 32.0\tEpisode Mean: 148.2\n", - "87593:127652555\tQ-min: 2.430\tQ-max: 2.530\tLives: 1\tReward: 36.0\tEpisode Mean: 148.2\n", - "87593:127652577\tQ-min: 2.241\tQ-max: 2.319\tLives: 1\tReward: 37.0\tEpisode Mean: 148.2\n", - "87593:127652598\tQ-min: 2.436\tQ-max: 2.499\tLives: 1\tReward: 38.0\tEpisode Mean: 148.2\n", - "87593:127652618\tQ-min: 2.377\tQ-max: 2.517\tLives: 1\tReward: 39.0\tEpisode Mean: 148.2\n", - "87593:127652639\tQ-min: 2.454\tQ-max: 2.543\tLives: 1\tReward: 43.0\tEpisode Mean: 148.2\n", - "87593:127652652\tQ-min: -0.131\tQ-max: 0.119\tLives: 0\tReward: 43.0\tEpisode Mean: 130.7\n", - "87594:127652696\tQ-min: 1.707\tQ-max: 1.717\tLives: 5\tReward: 1.0\tEpisode Mean: 130.7\n", - "87594:127652737\tQ-min: 1.795\tQ-max: 1.846\tLives: 5\tReward: 2.0\tEpisode Mean: 130.7\n", - "87594:127652790\tQ-min: 1.687\tQ-max: 1.728\tLives: 5\tReward: 3.0\tEpisode Mean: 130.7\n", - "87594:127652831\tQ-min: -0.221\tQ-max: 0.168\tLives: 4\tReward: 3.0\tEpisode Mean: 130.7\n", - "87594:127652874\tQ-min: 1.868\tQ-max: 1.916\tLives: 4\tReward: 4.0\tEpisode Mean: 130.7\n", - "87594:127652916\tQ-min: 1.904\tQ-max: 1.913\tLives: 4\tReward: 5.0\tEpisode Mean: 130.7\n", - "87594:127652960\tQ-min: 1.957\tQ-max: 1.974\tLives: 4\tReward: 6.0\tEpisode Mean: 130.7\n", - "87594:127652996\tQ-min: 1.844\tQ-max: 1.900\tLives: 4\tReward: 7.0\tEpisode Mean: 130.7\n", - "87594:127653017\tQ-min: -0.185\tQ-max: 0.181\tLives: 3\tReward: 7.0\tEpisode Mean: 130.7\n", - "87594:127653062\tQ-min: 1.787\tQ-max: 1.841\tLives: 3\tReward: 8.0\tEpisode Mean: 130.7\n", - "87594:127653116\tQ-min: 1.664\tQ-max: 1.683\tLives: 3\tReward: 9.0\tEpisode Mean: 130.7\n", - "87594:127653180\tQ-min: 1.723\tQ-max: 1.744\tLives: 3\tReward: 10.0\tEpisode Mean: 130.7\n", - "87594:127653230\tQ-min: 1.957\tQ-max: 1.995\tLives: 3\tReward: 11.0\tEpisode Mean: 130.7\n", - "87594:127653266\tQ-min: 1.938\tQ-max: 2.011\tLives: 3\tReward: 15.0\tEpisode Mean: 130.7\n", - "87594:127653300\tQ-min: 1.982\tQ-max: 2.009\tLives: 3\tReward: 16.0\tEpisode Mean: 130.7\n", - "87594:127653334\tQ-min: 2.004\tQ-max: 2.086\tLives: 3\tReward: 20.0\tEpisode Mean: 130.7\n", - "87594:127653387\tQ-min: 1.704\tQ-max: 1.741\tLives: 3\tReward: 21.0\tEpisode Mean: 130.7\n", - "87594:127653449\tQ-min: 1.710\tQ-max: 1.728\tLives: 3\tReward: 22.0\tEpisode Mean: 130.7\n", - "87594:127653513\tQ-min: 1.768\tQ-max: 1.787\tLives: 3\tReward: 23.0\tEpisode Mean: 130.7\n", - "87594:127653578\tQ-min: 1.693\tQ-max: 1.714\tLives: 3\tReward: 24.0\tEpisode Mean: 130.7\n", - "87594:127653628\tQ-min: 1.992\tQ-max: 2.069\tLives: 3\tReward: 25.0\tEpisode Mean: 130.7\n", - "87594:127653657\tQ-min: 2.077\tQ-max: 2.119\tLives: 3\tReward: 26.0\tEpisode Mean: 130.7\n", - "87594:127653688\tQ-min: 2.012\tQ-max: 2.061\tLives: 3\tReward: 27.0\tEpisode Mean: 130.7\n", - "87594:127653718\tQ-min: 2.003\tQ-max: 2.043\tLives: 3\tReward: 28.0\tEpisode Mean: 130.7\n", - "87594:127653756\tQ-min: 2.014\tQ-max: 2.069\tLives: 3\tReward: 29.0\tEpisode Mean: 130.7\n", - "87594:127653794\tQ-min: 2.222\tQ-max: 2.428\tLives: 3\tReward: 33.0\tEpisode Mean: 130.7\n", - "87594:127653819\tQ-min: 2.252\tQ-max: 2.413\tLives: 3\tReward: 37.0\tEpisode Mean: 130.7\n", - "87594:127653838\tQ-min: 2.313\tQ-max: 2.521\tLives: 3\tReward: 41.0\tEpisode Mean: 130.7\n", - "87594:127653852\tQ-min: 0.152\tQ-max: 0.471\tLives: 2\tReward: 41.0\tEpisode Mean: 130.7\n", - "87594:127653897\tQ-min: 2.052\tQ-max: 2.153\tLives: 2\tReward: 45.0\tEpisode Mean: 130.7\n", - "87594:127653945\tQ-min: 2.222\tQ-max: 2.328\tLives: 2\tReward: 49.0\tEpisode Mean: 130.7\n", - "87594:127653966\tQ-min: 2.228\tQ-max: 2.403\tLives: 2\tReward: 50.0\tEpisode Mean: 130.7\n", - "87594:127653986\tQ-min: 2.333\tQ-max: 2.457\tLives: 2\tReward: 54.0\tEpisode Mean: 130.7\n", - "87594:127654007\tQ-min: 2.129\tQ-max: 2.511\tLives: 2\tReward: 55.0\tEpisode Mean: 130.7\n", - "87594:127654027\tQ-min: 2.277\tQ-max: 2.426\tLives: 2\tReward: 56.0\tEpisode Mean: 130.7\n", - "87594:127654041\tQ-min: 0.065\tQ-max: 0.366\tLives: 1\tReward: 56.0\tEpisode Mean: 130.7\n", - "87594:127654092\tQ-min: 2.146\tQ-max: 2.544\tLives: 1\tReward: 63.0\tEpisode Mean: 130.7\n", - "87594:127654114\tQ-min: 2.406\tQ-max: 2.592\tLives: 1\tReward: 67.0\tEpisode Mean: 130.7\n", - "87594:127654135\tQ-min: 2.224\tQ-max: 2.806\tLives: 1\tReward: 71.0\tEpisode Mean: 130.7\n", - "87594:127654157\tQ-min: 1.790\tQ-max: 2.776\tLives: 1\tReward: 78.0\tEpisode Mean: 130.7\n", - "87594:127654179\tQ-min: 2.527\tQ-max: 2.749\tLives: 1\tReward: 82.0\tEpisode Mean: 130.7\n", - "87594:127654203\tQ-min: 2.535\tQ-max: 2.629\tLives: 1\tReward: 86.0\tEpisode Mean: 130.7\n", - "87594:127654218\tQ-min: -0.078\tQ-max: 0.100\tLives: 0\tReward: 86.0\tEpisode Mean: 124.3\n", - "87595:127654272\tQ-min: 1.659\tQ-max: 1.667\tLives: 5\tReward: 1.0\tEpisode Mean: 124.3\n", - "87595:127654331\tQ-min: 1.636\tQ-max: 1.656\tLives: 5\tReward: 2.0\tEpisode Mean: 124.3\n", - "87595:127654395\tQ-min: 1.692\tQ-max: 1.739\tLives: 5\tReward: 3.0\tEpisode Mean: 124.3\n", - "87595:127654443\tQ-min: 1.930\tQ-max: 1.987\tLives: 5\tReward: 4.0\tEpisode Mean: 124.3\n", - "87595:127654478\tQ-min: 1.938\tQ-max: 1.958\tLives: 5\tReward: 5.0\tEpisode Mean: 124.3\n", - "87595:127654511\tQ-min: 1.917\tQ-max: 1.943\tLives: 5\tReward: 6.0\tEpisode Mean: 124.3\n", - "87595:127654542\tQ-min: 1.752\tQ-max: 1.808\tLives: 5\tReward: 7.0\tEpisode Mean: 124.3\n", - "87595:127654599\tQ-min: 1.662\tQ-max: 1.706\tLives: 5\tReward: 8.0\tEpisode Mean: 124.3\n", - "87595:127654660\tQ-min: 1.702\tQ-max: 1.728\tLives: 5\tReward: 9.0\tEpisode Mean: 124.3\n", - "87595:127654727\tQ-min: 1.678\tQ-max: 1.689\tLives: 5\tReward: 10.0\tEpisode Mean: 124.3\n", - "87595:127654772\tQ-min: -0.204\tQ-max: 0.125\tLives: 4\tReward: 10.0\tEpisode Mean: 124.3\n", - "87595:127654831\tQ-min: 1.650\tQ-max: 1.691\tLives: 4\tReward: 11.0\tEpisode Mean: 124.3\n", - "87595:127654884\tQ-min: 2.004\tQ-max: 2.018\tLives: 4\tReward: 12.0\tEpisode Mean: 124.3\n", - "87595:127654924\tQ-min: 1.964\tQ-max: 1.985\tLives: 4\tReward: 13.0\tEpisode Mean: 124.3\n", - "87595:127654952\tQ-min: -0.139\tQ-max: 0.220\tLives: 3\tReward: 13.0\tEpisode Mean: 124.3\n", - "87595:127654998\tQ-min: 1.834\tQ-max: 1.893\tLives: 3\tReward: 14.0\tEpisode Mean: 124.3\n", - "87595:127655044\tQ-min: 1.888\tQ-max: 1.926\tLives: 3\tReward: 18.0\tEpisode Mean: 124.3\n", - "87595:127655099\tQ-min: 1.700\tQ-max: 1.754\tLives: 3\tReward: 19.0\tEpisode Mean: 124.3\n", - "87595:127655148\tQ-min: 2.050\tQ-max: 2.075\tLives: 3\tReward: 20.0\tEpisode Mean: 124.3\n", - "87595:127655182\tQ-min: 2.036\tQ-max: 2.072\tLives: 3\tReward: 21.0\tEpisode Mean: 124.3\n", - "87595:127655216\tQ-min: 1.989\tQ-max: 2.022\tLives: 3\tReward: 22.0\tEpisode Mean: 124.3\n", - "87595:127655248\tQ-min: 2.048\tQ-max: 2.058\tLives: 3\tReward: 23.0\tEpisode Mean: 124.3\n", - "87595:127655298\tQ-min: 1.708\tQ-max: 1.734\tLives: 3\tReward: 24.0\tEpisode Mean: 124.3\n", - "87595:127655364\tQ-min: 1.719\tQ-max: 1.740\tLives: 3\tReward: 25.0\tEpisode Mean: 124.3\n", - "87595:127655431\tQ-min: 1.702\tQ-max: 1.761\tLives: 3\tReward: 26.0\tEpisode Mean: 124.3\n", - "87595:127655493\tQ-min: 1.721\tQ-max: 1.764\tLives: 3\tReward: 27.0\tEpisode Mean: 124.3\n", - "87595:127655549\tQ-min: 2.043\tQ-max: 2.128\tLives: 3\tReward: 31.0\tEpisode Mean: 124.3\n", - "87595:127655583\tQ-min: 1.993\tQ-max: 2.055\tLives: 3\tReward: 32.0\tEpisode Mean: 124.3\n", - "87595:127655608\tQ-min: -0.172\tQ-max: 0.110\tLives: 2\tReward: 32.0\tEpisode Mean: 124.3\n", - "87595:127655654\tQ-min: 1.930\tQ-max: 1.989\tLives: 2\tReward: 33.0\tEpisode Mean: 124.3\n", - "87595:127655697\tQ-min: 2.022\tQ-max: 2.086\tLives: 2\tReward: 37.0\tEpisode Mean: 124.3\n", - "87595:127655742\tQ-min: 2.050\tQ-max: 2.101\tLives: 2\tReward: 38.0\tEpisode Mean: 124.3\n", - "87595:127655780\tQ-min: 2.116\tQ-max: 2.258\tLives: 2\tReward: 39.0\tEpisode Mean: 124.3\n", - "87595:127655811\tQ-min: 2.057\tQ-max: 2.093\tLives: 2\tReward: 40.0\tEpisode Mean: 124.3\n", - "87595:127655844\tQ-min: 2.102\tQ-max: 2.217\tLives: 2\tReward: 41.0\tEpisode Mean: 124.3\n", - "87595:127655875\tQ-min: 2.214\tQ-max: 2.261\tLives: 2\tReward: 42.0\tEpisode Mean: 124.3\n", - "87595:127655925\tQ-min: 1.807\tQ-max: 2.008\tLives: 2\tReward: 46.0\tEpisode Mean: 124.3\n", - "87595:127655994\tQ-min: 1.750\tQ-max: 1.954\tLives: 2\tReward: 47.0\tEpisode Mean: 124.3\n", - "87595:127656063\tQ-min: 1.748\tQ-max: 1.911\tLives: 2\tReward: 48.0\tEpisode Mean: 124.3\n", - "87595:127656129\tQ-min: 1.629\tQ-max: 1.785\tLives: 2\tReward: 52.0\tEpisode Mean: 124.3\n", - "87595:127656185\tQ-min: 2.132\tQ-max: 2.151\tLives: 2\tReward: 56.0\tEpisode Mean: 124.3\n", - "87595:127656218\tQ-min: 2.150\tQ-max: 2.171\tLives: 2\tReward: 57.0\tEpisode Mean: 124.3\n", - "87595:127656250\tQ-min: 2.087\tQ-max: 2.169\tLives: 2\tReward: 61.0\tEpisode Mean: 124.3\n", - "87595:127656285\tQ-min: 2.148\tQ-max: 2.171\tLives: 2\tReward: 62.0\tEpisode Mean: 124.3\n", - "87595:127656318\tQ-min: 2.195\tQ-max: 2.320\tLives: 2\tReward: 66.0\tEpisode Mean: 124.3\n", - "87595:127656356\tQ-min: 2.194\tQ-max: 2.338\tLives: 2\tReward: 70.0\tEpisode Mean: 124.3\n", - "87595:127656389\tQ-min: 2.213\tQ-max: 2.269\tLives: 2\tReward: 71.0\tEpisode Mean: 124.3\n", - "87595:127656426\tQ-min: 2.358\tQ-max: 2.574\tLives: 2\tReward: 75.0\tEpisode Mean: 124.3\n", - "87595:127656441\tQ-min: -0.137\tQ-max: 0.415\tLives: 1\tReward: 75.0\tEpisode Mean: 124.3\n", - "87595:127656507\tQ-min: 1.903\tQ-max: 2.232\tLives: 1\tReward: 79.0\tEpisode Mean: 124.3\n", - "87595:127656578\tQ-min: 1.994\tQ-max: 2.153\tLives: 1\tReward: 83.0\tEpisode Mean: 124.3\n", - "87595:127656633\tQ-min: 2.128\tQ-max: 2.158\tLives: 1\tReward: 84.0\tEpisode Mean: 124.3\n", - "87595:127656674\tQ-min: 2.155\tQ-max: 2.412\tLives: 1\tReward: 88.0\tEpisode Mean: 124.3\n", - "87595:127656696\tQ-min: 2.417\tQ-max: 2.483\tLives: 1\tReward: 92.0\tEpisode Mean: 124.3\n", - "87595:127656718\tQ-min: 2.471\tQ-max: 2.509\tLives: 1\tReward: 96.0\tEpisode Mean: 124.3\n", - "87595:127656741\tQ-min: 2.288\tQ-max: 2.486\tLives: 1\tReward: 100.0\tEpisode Mean: 124.3\n", - "87595:127656756\tQ-min: -0.398\tQ-max: 0.151\tLives: 0\tReward: 100.0\tEpisode Mean: 121.2\n", - "87596:127656800\tQ-min: 1.752\tQ-max: 1.776\tLives: 5\tReward: 1.0\tEpisode Mean: 121.2\n", - "87596:127656843\tQ-min: 1.811\tQ-max: 1.845\tLives: 5\tReward: 2.0\tEpisode Mean: 121.2\n", - "87596:127656898\tQ-min: 1.726\tQ-max: 1.790\tLives: 5\tReward: 3.0\tEpisode Mean: 121.2\n", - "87596:127656947\tQ-min: 1.975\tQ-max: 2.010\tLives: 5\tReward: 4.0\tEpisode Mean: 121.2\n", - "87596:127656979\tQ-min: 1.966\tQ-max: 1.980\tLives: 5\tReward: 5.0\tEpisode Mean: 121.2\n", - "87596:127657009\tQ-min: 1.879\tQ-max: 1.901\tLives: 5\tReward: 6.0\tEpisode Mean: 121.2\n", - "87596:127657029\tQ-min: -0.026\tQ-max: 0.200\tLives: 4\tReward: 6.0\tEpisode Mean: 121.2\n", - "87596:127657071\tQ-min: 1.842\tQ-max: 1.867\tLives: 4\tReward: 7.0\tEpisode Mean: 121.2\n", - "87596:127657114\tQ-min: 1.897\tQ-max: 1.913\tLives: 4\tReward: 8.0\tEpisode Mean: 121.2\n", - "87596:127657168\tQ-min: 1.680\tQ-max: 1.711\tLives: 4\tReward: 9.0\tEpisode Mean: 121.2\n", - "87596:127657215\tQ-min: 1.969\tQ-max: 1.988\tLives: 4\tReward: 10.0\tEpisode Mean: 121.2\n", - "87596:127657252\tQ-min: 1.557\tQ-max: 1.687\tLives: 4\tReward: 14.0\tEpisode Mean: 121.2\n", - "87596:127657286\tQ-min: 1.904\tQ-max: 1.924\tLives: 4\tReward: 15.0\tEpisode Mean: 121.2\n", - "87596:127657322\tQ-min: 2.066\tQ-max: 2.095\tLives: 4\tReward: 16.0\tEpisode Mean: 121.2\n", - "87596:127657368\tQ-min: 1.692\tQ-max: 1.720\tLives: 4\tReward: 17.0\tEpisode Mean: 121.2\n", - "87596:127657431\tQ-min: 1.703\tQ-max: 1.725\tLives: 4\tReward: 18.0\tEpisode Mean: 121.2\n", - "87596:127657496\tQ-min: 1.739\tQ-max: 1.753\tLives: 4\tReward: 19.0\tEpisode Mean: 121.2\n", - "87596:127657537\tQ-min: -0.069\tQ-max: 0.212\tLives: 3\tReward: 19.0\tEpisode Mean: 121.2\n", - "87596:127657590\tQ-min: 1.665\tQ-max: 1.690\tLives: 3\tReward: 20.0\tEpisode Mean: 121.2\n", - "87596:127657642\tQ-min: 1.944\tQ-max: 1.986\tLives: 3\tReward: 21.0\tEpisode Mean: 121.2\n", - "87596:127657698\tQ-min: 1.724\tQ-max: 1.814\tLives: 3\tReward: 22.0\tEpisode Mean: 121.2\n", - "87596:127657748\tQ-min: 1.969\tQ-max: 1.993\tLives: 3\tReward: 23.0\tEpisode Mean: 121.2\n", - "87596:127657782\tQ-min: 1.926\tQ-max: 1.983\tLives: 3\tReward: 24.0\tEpisode Mean: 121.2\n", - "87596:127657814\tQ-min: 1.967\tQ-max: 2.028\tLives: 3\tReward: 25.0\tEpisode Mean: 121.2\n", - "87596:127657847\tQ-min: 2.010\tQ-max: 2.035\tLives: 3\tReward: 26.0\tEpisode Mean: 121.2\n", - "87596:127657897\tQ-min: 1.694\tQ-max: 1.734\tLives: 3\tReward: 27.0\tEpisode Mean: 121.2\n", - "87596:127657963\tQ-min: 1.684\tQ-max: 1.748\tLives: 3\tReward: 28.0\tEpisode Mean: 121.2\n", - "87596:127658030\tQ-min: 1.734\tQ-max: 1.755\tLives: 3\tReward: 29.0\tEpisode Mean: 121.2\n", - "87596:127658095\tQ-min: 1.687\tQ-max: 1.738\tLives: 3\tReward: 30.0\tEpisode Mean: 121.2\n", - "87596:127658143\tQ-min: 2.046\tQ-max: 2.068\tLives: 3\tReward: 31.0\tEpisode Mean: 121.2\n", - "87596:127658176\tQ-min: 2.011\tQ-max: 2.042\tLives: 3\tReward: 32.0\tEpisode Mean: 121.2\n", - "87596:127658213\tQ-min: 2.052\tQ-max: 2.119\tLives: 3\tReward: 36.0\tEpisode Mean: 121.2\n", - "87596:127658249\tQ-min: 2.020\tQ-max: 2.121\tLives: 3\tReward: 37.0\tEpisode Mean: 121.2\n", - "87596:127658281\tQ-min: 2.072\tQ-max: 2.136\tLives: 3\tReward: 38.0\tEpisode Mean: 121.2\n", - "87596:127658315\tQ-min: 2.020\tQ-max: 2.088\tLives: 3\tReward: 42.0\tEpisode Mean: 121.2\n", - "87596:127658353\tQ-min: 2.306\tQ-max: 2.636\tLives: 3\tReward: 46.0\tEpisode Mean: 121.2\n", - "87596:127658374\tQ-min: 2.334\tQ-max: 2.455\tLives: 3\tReward: 47.0\tEpisode Mean: 121.2\n", - "87596:127658395\tQ-min: 2.047\tQ-max: 2.537\tLives: 3\tReward: 51.0\tEpisode Mean: 121.2\n", - "87596:127658416\tQ-min: 2.345\tQ-max: 2.731\tLives: 3\tReward: 58.0\tEpisode Mean: 121.2\n", - "87596:127658439\tQ-min: 2.366\tQ-max: 2.544\tLives: 3\tReward: 62.0\tEpisode Mean: 121.2\n", - "87596:127658452\tQ-min: 0.005\tQ-max: 0.233\tLives: 2\tReward: 62.0\tEpisode Mean: 121.2\n", - "87596:127658504\tQ-min: 2.038\tQ-max: 2.065\tLives: 2\tReward: 63.0\tEpisode Mean: 121.2\n", - "87596:127658561\tQ-min: 1.930\tQ-max: 2.016\tLives: 2\tReward: 64.0\tEpisode Mean: 121.2\n", - "87596:127658620\tQ-min: 2.147\tQ-max: 2.177\tLives: 2\tReward: 68.0\tEpisode Mean: 121.2\n", - "87596:127658660\tQ-min: 2.240\tQ-max: 2.259\tLives: 2\tReward: 72.0\tEpisode Mean: 121.2\n", - "87596:127658694\tQ-min: 2.320\tQ-max: 2.563\tLives: 2\tReward: 76.0\tEpisode Mean: 121.2\n", - "87596:127658715\tQ-min: 2.466\tQ-max: 2.543\tLives: 2\tReward: 80.0\tEpisode Mean: 121.2\n", - "87596:127658737\tQ-min: 2.368\tQ-max: 2.592\tLives: 2\tReward: 84.0\tEpisode Mean: 121.2\n", - "87596:127658753\tQ-min: -0.032\tQ-max: 0.342\tLives: 1\tReward: 84.0\tEpisode Mean: 121.2\n", - "87596:127658800\tQ-min: 1.889\tQ-max: 2.049\tLives: 1\tReward: 88.0\tEpisode Mean: 121.2\n", - "87596:127658844\tQ-min: 2.164\tQ-max: 2.220\tLives: 1\tReward: 89.0\tEpisode Mean: 121.2\n", - "87596:127658886\tQ-min: 2.279\tQ-max: 2.364\tLives: 1\tReward: 93.0\tEpisode Mean: 121.2\n", - "87596:127658934\tQ-min: 1.056\tQ-max: 2.037\tLives: 1\tReward: 100.0\tEpisode Mean: 121.2\n", - "87596:127658959\tQ-min: 1.181\tQ-max: 2.665\tLives: 1\tReward: 107.0\tEpisode Mean: 121.2\n", - "87596:127658973\tQ-min: -0.243\tQ-max: 0.129\tLives: 0\tReward: 107.0\tEpisode Mean: 119.7\n", - "87597:127659016\tQ-min: 1.779\tQ-max: 1.814\tLives: 5\tReward: 1.0\tEpisode Mean: 119.7\n", - "87597:127659066\tQ-min: 1.621\tQ-max: 1.648\tLives: 5\tReward: 2.0\tEpisode Mean: 119.7\n", - "87597:127659131\tQ-min: 1.662\tQ-max: 1.681\tLives: 5\tReward: 3.0\tEpisode Mean: 119.7\n", - "87597:127659179\tQ-min: 1.917\tQ-max: 1.930\tLives: 5\tReward: 4.0\tEpisode Mean: 119.7\n", - "87597:127659212\tQ-min: 1.970\tQ-max: 1.983\tLives: 5\tReward: 5.0\tEpisode Mean: 119.7\n", - "87597:127659245\tQ-min: 1.925\tQ-max: 1.954\tLives: 5\tReward: 6.0\tEpisode Mean: 119.7\n", - "87597:127659278\tQ-min: 1.738\tQ-max: 1.780\tLives: 5\tReward: 7.0\tEpisode Mean: 119.7\n", - "87597:127659327\tQ-min: 1.629\tQ-max: 1.649\tLives: 5\tReward: 8.0\tEpisode Mean: 119.7\n", - "87597:127659391\tQ-min: 1.684\tQ-max: 1.701\tLives: 5\tReward: 9.0\tEpisode Mean: 119.7\n", - "87597:127659455\tQ-min: 1.630\tQ-max: 1.658\tLives: 5\tReward: 10.0\tEpisode Mean: 119.7\n", - "87597:127659517\tQ-min: 1.650\tQ-max: 1.719\tLives: 5\tReward: 11.0\tEpisode Mean: 119.7\n", - "87597:127659564\tQ-min: 1.988\tQ-max: 2.015\tLives: 5\tReward: 12.0\tEpisode Mean: 119.7\n", - "87597:127659596\tQ-min: 1.913\tQ-max: 1.943\tLives: 5\tReward: 16.0\tEpisode Mean: 119.7\n", - "87597:127659630\tQ-min: 1.926\tQ-max: 1.975\tLives: 5\tReward: 17.0\tEpisode Mean: 119.7\n", - "87597:127659665\tQ-min: 1.984\tQ-max: 2.018\tLives: 5\tReward: 21.0\tEpisode Mean: 119.7\n", - "87597:127659700\tQ-min: 2.028\tQ-max: 2.077\tLives: 5\tReward: 22.0\tEpisode Mean: 119.7\n", - "87597:127659734\tQ-min: 2.169\tQ-max: 2.327\tLives: 5\tReward: 26.0\tEpisode Mean: 119.7\n", - "87597:127659757\tQ-min: -0.114\tQ-max: 0.223\tLives: 4\tReward: 26.0\tEpisode Mean: 119.7\n", - "87597:127659803\tQ-min: 2.177\tQ-max: 2.223\tLives: 4\tReward: 27.0\tEpisode Mean: 119.7\n", - "87597:127659857\tQ-min: 1.806\tQ-max: 1.820\tLives: 4\tReward: 28.0\tEpisode Mean: 119.7\n", - "87597:127659925\tQ-min: 1.780\tQ-max: 1.814\tLives: 4\tReward: 29.0\tEpisode Mean: 119.7\n", - "87597:127659973\tQ-min: 2.143\tQ-max: 2.427\tLives: 4\tReward: 33.0\tEpisode Mean: 119.7\n", - "87597:127659996\tQ-min: 2.260\tQ-max: 2.368\tLives: 4\tReward: 34.0\tEpisode Mean: 119.7\n", - "87597:127660008\tQ-min: 0.231\tQ-max: 0.392\tLives: 3\tReward: 34.0\tEpisode Mean: 119.7\n", - "87597:127660050\tQ-min: 2.000\tQ-max: 2.056\tLives: 3\tReward: 35.0\tEpisode Mean: 119.7\n", - "87597:127660104\tQ-min: 1.788\tQ-max: 1.853\tLives: 3\tReward: 36.0\tEpisode Mean: 119.7\n", - "87597:127660162\tQ-min: 1.819\tQ-max: 2.164\tLives: 3\tReward: 40.0\tEpisode Mean: 119.7\n", - "87597:127660201\tQ-min: 2.244\tQ-max: 2.290\tLives: 3\tReward: 41.0\tEpisode Mean: 119.7\n", - "87597:127660236\tQ-min: 2.125\tQ-max: 2.217\tLives: 3\tReward: 42.0\tEpisode Mean: 119.7\n", - "87597:127660268\tQ-min: 2.130\tQ-max: 2.243\tLives: 3\tReward: 46.0\tEpisode Mean: 119.7\n", - "87597:127660302\tQ-min: 2.197\tQ-max: 2.219\tLives: 3\tReward: 47.0\tEpisode Mean: 119.7\n", - "87597:127660349\tQ-min: 1.765\tQ-max: 1.831\tLives: 3\tReward: 48.0\tEpisode Mean: 119.7\n", - "87597:127660415\tQ-min: 1.657\tQ-max: 1.840\tLives: 3\tReward: 49.0\tEpisode Mean: 119.7\n", - "87597:127660491\tQ-min: 2.188\tQ-max: 2.759\tLives: 3\tReward: 53.0\tEpisode Mean: 119.7\n", - "87597:127660511\tQ-min: 2.256\tQ-max: 2.470\tLives: 3\tReward: 57.0\tEpisode Mean: 119.7\n", - "87597:127660525\tQ-min: 0.033\tQ-max: 0.183\tLives: 2\tReward: 57.0\tEpisode Mean: 119.7\n", - "87597:127660588\tQ-min: 1.652\tQ-max: 2.244\tLives: 2\tReward: 61.0\tEpisode Mean: 119.7\n", - "87597:127660657\tQ-min: 1.894\tQ-max: 1.975\tLives: 2\tReward: 62.0\tEpisode Mean: 119.7\n", - "87597:127660720\tQ-min: 1.984\tQ-max: 2.009\tLives: 2\tReward: 63.0\tEpisode Mean: 119.7\n", - "87597:127660770\tQ-min: 2.436\tQ-max: 2.488\tLives: 2\tReward: 64.0\tEpisode Mean: 119.7\n", - "87597:127660804\tQ-min: 2.209\tQ-max: 2.258\tLives: 2\tReward: 68.0\tEpisode Mean: 119.7\n", - "87597:127660842\tQ-min: 2.111\tQ-max: 2.718\tLives: 2\tReward: 75.0\tEpisode Mean: 119.7\n", - "87597:127660865\tQ-min: 2.376\tQ-max: 2.468\tLives: 2\tReward: 76.0\tEpisode Mean: 119.7\n", - "87597:127660886\tQ-min: 2.358\tQ-max: 2.477\tLives: 2\tReward: 80.0\tEpisode Mean: 119.7\n", - "87597:127660910\tQ-min: 1.924\tQ-max: 2.500\tLives: 2\tReward: 87.0\tEpisode Mean: 119.7\n", - "87597:127660932\tQ-min: 2.473\tQ-max: 2.577\tLives: 2\tReward: 91.0\tEpisode Mean: 119.7\n", - "87597:127660954\tQ-min: 2.096\tQ-max: 2.563\tLives: 2\tReward: 95.0\tEpisode Mean: 119.7\n", - "87597:127660975\tQ-min: 1.886\tQ-max: 2.625\tLives: 2\tReward: 99.0\tEpisode Mean: 119.7\n", - "87597:127660993\tQ-min: 2.135\tQ-max: 2.649\tLives: 2\tReward: 103.0\tEpisode Mean: 119.7\n", - "87597:127661016\tQ-min: 1.828\tQ-max: 2.659\tLives: 2\tReward: 107.0\tEpisode Mean: 119.7\n", - "87597:127661037\tQ-min: 2.431\tQ-max: 2.550\tLives: 2\tReward: 108.0\tEpisode Mean: 119.7\n", - "87597:127661057\tQ-min: 2.465\tQ-max: 2.723\tLives: 2\tReward: 112.0\tEpisode Mean: 119.7\n", - "87597:127661073\tQ-min: -0.070\tQ-max: 0.399\tLives: 1\tReward: 112.0\tEpisode Mean: 119.7\n", - "87597:127661129\tQ-min: 2.122\tQ-max: 2.860\tLives: 1\tReward: 116.0\tEpisode Mean: 119.7\n", - "87597:127661143\tQ-min: -0.218\tQ-max: 0.260\tLives: 0\tReward: 116.0\tEpisode Mean: 119.3\n", - "87598:127661199\tQ-min: 1.651\tQ-max: 1.664\tLives: 5\tReward: 1.0\tEpisode Mean: 119.3\n", - "87598:127661263\tQ-min: 1.674\tQ-max: 1.685\tLives: 5\tReward: 2.0\tEpisode Mean: 119.3\n", - "87598:127661323\tQ-min: 1.664\tQ-max: 1.678\tLives: 5\tReward: 3.0\tEpisode Mean: 119.3\n", - "87598:127661369\tQ-min: 1.971\tQ-max: 2.008\tLives: 5\tReward: 4.0\tEpisode Mean: 119.3\n", - "87598:127661400\tQ-min: 1.955\tQ-max: 1.971\tLives: 5\tReward: 5.0\tEpisode Mean: 119.3\n", - "87598:127661436\tQ-min: 1.857\tQ-max: 1.902\tLives: 5\tReward: 6.0\tEpisode Mean: 119.3\n", - "87598:127661467\tQ-min: 1.752\tQ-max: 1.774\tLives: 5\tReward: 7.0\tEpisode Mean: 119.3\n", - "87598:127661514\tQ-min: 1.695\tQ-max: 1.717\tLives: 5\tReward: 8.0\tEpisode Mean: 119.3\n", - "87598:127661577\tQ-min: 1.667\tQ-max: 1.682\tLives: 5\tReward: 9.0\tEpisode Mean: 119.3\n", - "87598:127661643\tQ-min: 1.624\tQ-max: 1.745\tLives: 5\tReward: 10.0\tEpisode Mean: 119.3\n", - "87598:127661709\tQ-min: 1.667\tQ-max: 1.704\tLives: 5\tReward: 11.0\tEpisode Mean: 119.3\n", - "87598:127661758\tQ-min: 1.936\tQ-max: 1.955\tLives: 5\tReward: 12.0\tEpisode Mean: 119.3\n", - "87598:127661789\tQ-min: 1.967\tQ-max: 2.024\tLives: 5\tReward: 13.0\tEpisode Mean: 119.3\n", - "87598:127661810\tQ-min: -0.354\tQ-max: 0.091\tLives: 4\tReward: 13.0\tEpisode Mean: 119.3\n", - "87598:127661852\tQ-min: 1.892\tQ-max: 1.921\tLives: 4\tReward: 14.0\tEpisode Mean: 119.3\n", - "87598:127661896\tQ-min: 1.922\tQ-max: 1.965\tLives: 4\tReward: 15.0\tEpisode Mean: 119.3\n", - "87598:127661952\tQ-min: 1.670\tQ-max: 1.706\tLives: 4\tReward: 16.0\tEpisode Mean: 119.3\n", - "87598:127662001\tQ-min: 1.951\tQ-max: 1.978\tLives: 4\tReward: 17.0\tEpisode Mean: 119.3\n", - "87598:127662037\tQ-min: 2.090\tQ-max: 2.104\tLives: 4\tReward: 18.0\tEpisode Mean: 119.3\n", - "87598:127662074\tQ-min: 2.078\tQ-max: 2.103\tLives: 4\tReward: 22.0\tEpisode Mean: 119.3\n", - "87598:127662107\tQ-min: 2.099\tQ-max: 2.133\tLives: 4\tReward: 23.0\tEpisode Mean: 119.3\n", - "87598:127662157\tQ-min: 1.611\tQ-max: 1.704\tLives: 4\tReward: 24.0\tEpisode Mean: 119.3\n", - "87598:127662221\tQ-min: 1.697\tQ-max: 1.741\tLives: 4\tReward: 25.0\tEpisode Mean: 119.3\n", - "87598:127662264\tQ-min: -0.043\tQ-max: 0.257\tLives: 3\tReward: 25.0\tEpisode Mean: 119.3\n", - "87598:127662316\tQ-min: 1.898\tQ-max: 1.918\tLives: 3\tReward: 26.0\tEpisode Mean: 119.3\n", - "87598:127662361\tQ-min: 1.909\tQ-max: 1.937\tLives: 3\tReward: 27.0\tEpisode Mean: 119.3\n", - "87598:127662407\tQ-min: 2.013\tQ-max: 2.042\tLives: 3\tReward: 28.0\tEpisode Mean: 119.3\n", - "87598:127662444\tQ-min: 1.961\tQ-max: 1.991\tLives: 3\tReward: 29.0\tEpisode Mean: 119.3\n", - "87598:127662477\tQ-min: 2.115\tQ-max: 2.183\tLives: 3\tReward: 30.0\tEpisode Mean: 119.3\n", - "87598:127662509\tQ-min: 2.000\tQ-max: 2.069\tLives: 3\tReward: 31.0\tEpisode Mean: 119.3\n", - "87598:127662544\tQ-min: 2.006\tQ-max: 2.097\tLives: 3\tReward: 35.0\tEpisode Mean: 119.3\n", - "87598:127662593\tQ-min: 1.437\tQ-max: 1.659\tLives: 3\tReward: 36.0\tEpisode Mean: 119.3\n", - "87598:127662665\tQ-min: 1.732\tQ-max: 1.751\tLives: 3\tReward: 37.0\tEpisode Mean: 119.3\n", - "87598:127662729\tQ-min: 1.667\tQ-max: 1.811\tLives: 3\tReward: 38.0\tEpisode Mean: 119.3\n", - "87598:127662793\tQ-min: 1.746\tQ-max: 1.779\tLives: 3\tReward: 39.0\tEpisode Mean: 119.3\n", - "87598:127662843\tQ-min: 2.004\tQ-max: 2.053\tLives: 3\tReward: 40.0\tEpisode Mean: 119.3\n", - "87598:127662878\tQ-min: 2.104\tQ-max: 2.161\tLives: 3\tReward: 44.0\tEpisode Mean: 119.3\n", - "87598:127662912\tQ-min: 2.015\tQ-max: 2.115\tLives: 3\tReward: 48.0\tEpisode Mean: 119.3\n", - "87598:127662945\tQ-min: 2.035\tQ-max: 2.218\tLives: 3\tReward: 52.0\tEpisode Mean: 119.3\n", - "87598:127662981\tQ-min: 2.047\tQ-max: 2.229\tLives: 3\tReward: 56.0\tEpisode Mean: 119.3\n", - "87598:127663017\tQ-min: 2.274\tQ-max: 2.418\tLives: 3\tReward: 60.0\tEpisode Mean: 119.3\n", - "87598:127663057\tQ-min: 1.995\tQ-max: 2.390\tLives: 3\tReward: 64.0\tEpisode Mean: 119.3\n", - "87598:127663079\tQ-min: 2.243\tQ-max: 2.510\tLives: 3\tReward: 68.0\tEpisode Mean: 119.3\n", - "87598:127663101\tQ-min: 2.320\tQ-max: 2.541\tLives: 3\tReward: 72.0\tEpisode Mean: 119.3\n", - "87598:127663123\tQ-min: 2.323\tQ-max: 2.403\tLives: 3\tReward: 79.0\tEpisode Mean: 119.3\n", - "87598:127663144\tQ-min: 2.353\tQ-max: 2.557\tLives: 3\tReward: 83.0\tEpisode Mean: 119.3\n", - "87598:127663168\tQ-min: 2.320\tQ-max: 2.440\tLives: 3\tReward: 87.0\tEpisode Mean: 119.3\n", - "87598:127663188\tQ-min: 2.030\tQ-max: 2.488\tLives: 3\tReward: 91.0\tEpisode Mean: 119.3\n", - "87598:127663203\tQ-min: 0.163\tQ-max: 0.445\tLives: 2\tReward: 91.0\tEpisode Mean: 119.3\n", - "87598:127663249\tQ-min: 2.209\tQ-max: 2.274\tLives: 2\tReward: 92.0\tEpisode Mean: 119.3\n", - "87598:127663306\tQ-min: 2.173\tQ-max: 2.579\tLives: 2\tReward: 96.0\tEpisode Mean: 119.3\n", - "87598:127663321\tQ-min: -0.015\tQ-max: 0.340\tLives: 1\tReward: 96.0\tEpisode Mean: 119.3\n", - "87598:127663366\tQ-min: 2.047\tQ-max: 2.259\tLives: 1\tReward: 100.0\tEpisode Mean: 119.3\n", - "87598:127663423\tQ-min: 2.059\tQ-max: 2.137\tLives: 1\tReward: 101.0\tEpisode Mean: 119.3\n", - "87598:127663475\tQ-min: 2.365\tQ-max: 2.465\tLives: 1\tReward: 102.0\tEpisode Mean: 119.3\n", - "87598:127663516\tQ-min: 2.050\tQ-max: 2.623\tLives: 1\tReward: 109.0\tEpisode Mean: 119.3\n", - "87598:127663539\tQ-min: 2.045\tQ-max: 2.740\tLives: 1\tReward: 113.0\tEpisode Mean: 119.3\n", - "87598:127663560\tQ-min: 2.421\tQ-max: 2.652\tLives: 1\tReward: 117.0\tEpisode Mean: 119.3\n", - "87598:127663581\tQ-min: 1.980\tQ-max: 2.516\tLives: 1\tReward: 121.0\tEpisode Mean: 119.3\n", - "87598:127663603\tQ-min: 2.362\tQ-max: 2.860\tLives: 1\tReward: 125.0\tEpisode Mean: 119.3\n", - "87598:127663623\tQ-min: 2.004\tQ-max: 3.475\tLives: 1\tReward: 129.0\tEpisode Mean: 119.3\n", - "87598:127663644\tQ-min: 2.571\tQ-max: 2.768\tLives: 1\tReward: 133.0\tEpisode Mean: 119.3\n", - "87598:127663665\tQ-min: 2.195\tQ-max: 2.878\tLives: 1\tReward: 137.0\tEpisode Mean: 119.3\n", - "87598:127663679\tQ-min: -0.607\tQ-max: -0.022\tLives: 0\tReward: 137.0\tEpisode Mean: 120.9\n", - "87599:127663726\tQ-min: 1.737\tQ-max: 1.761\tLives: 5\tReward: 1.0\tEpisode Mean: 120.9\n", - "87599:127663778\tQ-min: 1.641\tQ-max: 1.655\tLives: 5\tReward: 2.0\tEpisode Mean: 120.9\n", - "87599:127663828\tQ-min: 1.883\tQ-max: 1.905\tLives: 5\tReward: 3.0\tEpisode Mean: 120.9\n", - "87599:127663863\tQ-min: 1.992\tQ-max: 2.028\tLives: 5\tReward: 4.0\tEpisode Mean: 120.9\n", - "87599:127663895\tQ-min: 1.917\tQ-max: 1.933\tLives: 5\tReward: 5.0\tEpisode Mean: 120.9\n", - "87599:127663928\tQ-min: 1.930\tQ-max: 1.946\tLives: 5\tReward: 6.0\tEpisode Mean: 120.9\n", - "87599:127663949\tQ-min: -0.241\tQ-max: 0.163\tLives: 4\tReward: 6.0\tEpisode Mean: 120.9\n", - "87599:127663992\tQ-min: 1.814\tQ-max: 1.839\tLives: 4\tReward: 7.0\tEpisode Mean: 120.9\n", - "87599:127664033\tQ-min: 1.945\tQ-max: 1.975\tLives: 4\tReward: 8.0\tEpisode Mean: 120.9\n", - "87599:127664072\tQ-min: 1.938\tQ-max: 1.957\tLives: 4\tReward: 9.0\tEpisode Mean: 120.9\n", - "87599:127664110\tQ-min: 1.907\tQ-max: 1.938\tLives: 4\tReward: 10.0\tEpisode Mean: 120.9\n", - "87599:127664142\tQ-min: 1.898\tQ-max: 1.916\tLives: 4\tReward: 11.0\tEpisode Mean: 120.9\n", - "87599:127664174\tQ-min: 1.963\tQ-max: 1.981\tLives: 4\tReward: 12.0\tEpisode Mean: 120.9\n", - "87599:127664204\tQ-min: 1.917\tQ-max: 1.956\tLives: 4\tReward: 13.0\tEpisode Mean: 120.9\n", - "87599:127664252\tQ-min: 1.700\tQ-max: 1.718\tLives: 4\tReward: 14.0\tEpisode Mean: 120.9\n", - "87599:127664312\tQ-min: 1.655\tQ-max: 1.706\tLives: 4\tReward: 15.0\tEpisode Mean: 120.9\n", - "87599:127664377\tQ-min: 1.675\tQ-max: 1.742\tLives: 4\tReward: 16.0\tEpisode Mean: 120.9\n", - "87599:127664422\tQ-min: -0.064\tQ-max: 0.073\tLives: 3\tReward: 16.0\tEpisode Mean: 120.9\n", - "87599:127664467\tQ-min: 1.871\tQ-max: 1.887\tLives: 3\tReward: 17.0\tEpisode Mean: 120.9\n", - "87599:127664511\tQ-min: 1.819\tQ-max: 1.858\tLives: 3\tReward: 18.0\tEpisode Mean: 120.9\n", - "87599:127664571\tQ-min: 1.732\tQ-max: 1.760\tLives: 3\tReward: 19.0\tEpisode Mean: 120.9\n", - "87599:127664620\tQ-min: 1.984\tQ-max: 2.086\tLives: 3\tReward: 23.0\tEpisode Mean: 120.9\n", - "87599:127664653\tQ-min: 2.014\tQ-max: 2.045\tLives: 3\tReward: 24.0\tEpisode Mean: 120.9\n", - "87599:127664685\tQ-min: 2.040\tQ-max: 2.059\tLives: 3\tReward: 25.0\tEpisode Mean: 120.9\n", - "87599:127664718\tQ-min: 2.036\tQ-max: 2.087\tLives: 3\tReward: 29.0\tEpisode Mean: 120.9\n", - "87599:127664742\tQ-min: 0.008\tQ-max: 0.189\tLives: 2\tReward: 29.0\tEpisode Mean: 120.9\n", - "87599:127664796\tQ-min: 1.716\tQ-max: 1.748\tLives: 2\tReward: 30.0\tEpisode Mean: 120.9\n", - "87599:127664859\tQ-min: 1.773\tQ-max: 1.831\tLives: 2\tReward: 31.0\tEpisode Mean: 120.9\n", - "87599:127664914\tQ-min: 1.817\tQ-max: 2.035\tLives: 2\tReward: 35.0\tEpisode Mean: 120.9\n", - "87599:127664958\tQ-min: 2.191\tQ-max: 2.295\tLives: 2\tReward: 39.0\tEpisode Mean: 120.9\n", - "87599:127664992\tQ-min: 2.152\tQ-max: 2.191\tLives: 2\tReward: 40.0\tEpisode Mean: 120.9\n", - "87599:127665029\tQ-min: 2.133\tQ-max: 2.221\tLives: 2\tReward: 44.0\tEpisode Mean: 120.9\n", - "87599:127665067\tQ-min: 2.250\tQ-max: 2.432\tLives: 2\tReward: 48.0\tEpisode Mean: 120.9\n", - "87599:127665090\tQ-min: 2.432\tQ-max: 2.499\tLives: 2\tReward: 55.0\tEpisode Mean: 120.9\n", - "87599:127665110\tQ-min: 2.427\tQ-max: 2.559\tLives: 2\tReward: 56.0\tEpisode Mean: 120.9\n", - "87599:127665124\tQ-min: 0.132\tQ-max: 0.313\tLives: 1\tReward: 56.0\tEpisode Mean: 120.9\n", - "87599:127665169\tQ-min: 2.424\tQ-max: 2.512\tLives: 1\tReward: 60.0\tEpisode Mean: 120.9\n", - "87599:127665225\tQ-min: 1.837\tQ-max: 1.946\tLives: 1\tReward: 61.0\tEpisode Mean: 120.9\n", - "87599:127665269\tQ-min: -0.199\tQ-max: 0.186\tLives: 0\tReward: 61.0\tEpisode Mean: 115.9\n", - "87600:127665310\tQ-min: 1.774\tQ-max: 1.799\tLives: 5\tReward: 1.0\tEpisode Mean: 115.9\n", - "87600:127665364\tQ-min: 1.675\tQ-max: 1.686\tLives: 5\tReward: 2.0\tEpisode Mean: 115.9\n", - "87600:127665418\tQ-min: 1.875\tQ-max: 1.901\tLives: 5\tReward: 3.0\tEpisode Mean: 115.9\n", - "87600:127665453\tQ-min: 1.970\tQ-max: 2.015\tLives: 5\tReward: 4.0\tEpisode Mean: 115.9\n", - "87600:127665484\tQ-min: 1.936\tQ-max: 1.994\tLives: 5\tReward: 5.0\tEpisode Mean: 115.9\n", - "87600:127665516\tQ-min: 1.885\tQ-max: 1.940\tLives: 5\tReward: 6.0\tEpisode Mean: 115.9\n", - "87600:127665551\tQ-min: 1.767\tQ-max: 1.806\tLives: 5\tReward: 7.0\tEpisode Mean: 115.9\n", - "87600:127665600\tQ-min: 1.615\tQ-max: 1.647\tLives: 5\tReward: 8.0\tEpisode Mean: 115.9\n", - "87600:127665664\tQ-min: 1.683\tQ-max: 1.699\tLives: 5\tReward: 9.0\tEpisode Mean: 115.9\n", - "87600:127665733\tQ-min: 1.655\tQ-max: 1.673\tLives: 5\tReward: 10.0\tEpisode Mean: 115.9\n", - "87600:127665796\tQ-min: 1.660\tQ-max: 1.680\tLives: 5\tReward: 11.0\tEpisode Mean: 115.9\n", - "87600:127665847\tQ-min: 2.012\tQ-max: 2.020\tLives: 5\tReward: 15.0\tEpisode Mean: 115.9\n", - "87600:127665879\tQ-min: 1.947\tQ-max: 1.995\tLives: 5\tReward: 16.0\tEpisode Mean: 115.9\n", - "87600:127665914\tQ-min: 1.703\tQ-max: 1.790\tLives: 5\tReward: 17.0\tEpisode Mean: 115.9\n", - "87600:127665949\tQ-min: 1.917\tQ-max: 2.071\tLives: 5\tReward: 21.0\tEpisode Mean: 115.9\n", - "87600:127665972\tQ-min: -0.073\tQ-max: 0.402\tLives: 4\tReward: 21.0\tEpisode Mean: 115.9\n", - "87600:127666015\tQ-min: 1.913\tQ-max: 1.929\tLives: 4\tReward: 22.0\tEpisode Mean: 115.9\n", - "87600:127666067\tQ-min: 1.786\tQ-max: 1.824\tLives: 4\tReward: 23.0\tEpisode Mean: 115.9\n", - "87600:127666118\tQ-min: 2.042\tQ-max: 2.115\tLives: 4\tReward: 24.0\tEpisode Mean: 115.9\n", - "87600:127666156\tQ-min: 2.017\tQ-max: 2.055\tLives: 4\tReward: 25.0\tEpisode Mean: 115.9\n", - "87600:127666187\tQ-min: 2.090\tQ-max: 2.105\tLives: 4\tReward: 26.0\tEpisode Mean: 115.9\n", - "87600:127666219\tQ-min: 2.035\tQ-max: 2.050\tLives: 4\tReward: 27.0\tEpisode Mean: 115.9\n", - "87600:127666252\tQ-min: 1.887\tQ-max: 1.992\tLives: 4\tReward: 28.0\tEpisode Mean: 115.9\n", - "87600:127666302\tQ-min: 1.616\tQ-max: 1.821\tLives: 4\tReward: 32.0\tEpisode Mean: 115.9\n", - "87600:127666364\tQ-min: 1.836\tQ-max: 1.865\tLives: 4\tReward: 33.0\tEpisode Mean: 115.9\n", - "87600:127666433\tQ-min: 1.642\tQ-max: 1.861\tLives: 4\tReward: 37.0\tEpisode Mean: 115.9\n", - "87600:127666501\tQ-min: 1.760\tQ-max: 1.850\tLives: 4\tReward: 38.0\tEpisode Mean: 115.9\n", - "87600:127666550\tQ-min: 2.020\tQ-max: 2.126\tLives: 4\tReward: 39.0\tEpisode Mean: 115.9\n", - "87600:127666585\tQ-min: 2.036\tQ-max: 2.090\tLives: 4\tReward: 40.0\tEpisode Mean: 115.9\n", - "87600:127666616\tQ-min: 2.141\tQ-max: 2.219\tLives: 4\tReward: 41.0\tEpisode Mean: 115.9\n", - "87600:127666649\tQ-min: 2.192\tQ-max: 2.298\tLives: 4\tReward: 45.0\tEpisode Mean: 115.9\n", - "87600:127666684\tQ-min: 2.144\tQ-max: 2.288\tLives: 4\tReward: 49.0\tEpisode Mean: 115.9\n", - "87600:127666707\tQ-min: -0.094\tQ-max: 0.164\tLives: 3\tReward: 49.0\tEpisode Mean: 115.9\n", - "87600:127666761\tQ-min: 1.728\tQ-max: 1.789\tLives: 3\tReward: 50.0\tEpisode Mean: 115.9\n", - "87600:127666801\tQ-min: -0.084\tQ-max: 0.261\tLives: 2\tReward: 50.0\tEpisode Mean: 115.9\n", - "87600:127666859\tQ-min: 1.661\tQ-max: 1.769\tLives: 2\tReward: 54.0\tEpisode Mean: 115.9\n", - "87600:127666917\tQ-min: 2.044\tQ-max: 2.237\tLives: 2\tReward: 58.0\tEpisode Mean: 115.9\n", - "87600:127666974\tQ-min: 1.945\tQ-max: 2.079\tLives: 2\tReward: 59.0\tEpisode Mean: 115.9\n", - "87600:127667026\tQ-min: 2.108\tQ-max: 2.221\tLives: 2\tReward: 63.0\tEpisode Mean: 115.9\n", - "87600:127667062\tQ-min: 2.305\tQ-max: 2.482\tLives: 2\tReward: 67.0\tEpisode Mean: 115.9\n", - "87600:127667087\tQ-min: 2.277\tQ-max: 2.414\tLives: 2\tReward: 68.0\tEpisode Mean: 115.9\n", - "87600:127667108\tQ-min: 2.357\tQ-max: 2.434\tLives: 2\tReward: 72.0\tEpisode Mean: 115.9\n", - "87600:127667134\tQ-min: 1.988\tQ-max: 2.563\tLives: 2\tReward: 79.0\tEpisode Mean: 115.9\n", - "87600:127667158\tQ-min: 2.256\tQ-max: 2.404\tLives: 2\tReward: 83.0\tEpisode Mean: 115.9\n", - "87600:127667180\tQ-min: 2.289\tQ-max: 2.489\tLives: 2\tReward: 87.0\tEpisode Mean: 115.9\n", - "87600:127667201\tQ-min: 2.392\tQ-max: 2.561\tLives: 2\tReward: 91.0\tEpisode Mean: 115.9\n", - "87600:127667223\tQ-min: 2.205\tQ-max: 2.615\tLives: 2\tReward: 95.0\tEpisode Mean: 115.9\n", - "87600:127667246\tQ-min: 2.355\tQ-max: 2.599\tLives: 2\tReward: 96.0\tEpisode Mean: 115.9\n", - "87600:127667259\tQ-min: 0.162\tQ-max: 0.229\tLives: 1\tReward: 96.0\tEpisode Mean: 115.9\n", - "87600:127667318\tQ-min: 2.092\tQ-max: 2.474\tLives: 1\tReward: 100.0\tEpisode Mean: 115.9\n", - "87600:127667338\tQ-min: 2.278\tQ-max: 2.649\tLives: 1\tReward: 101.0\tEpisode Mean: 115.9\n", - "87600:127667358\tQ-min: 1.856\tQ-max: 2.470\tLives: 1\tReward: 105.0\tEpisode Mean: 115.9\n", - "87600:127667381\tQ-min: 2.661\tQ-max: 2.842\tLives: 1\tReward: 112.0\tEpisode Mean: 115.9\n", - "87600:127667395\tQ-min: 0.031\tQ-max: 0.259\tLives: 0\tReward: 112.0\tEpisode Mean: 115.6\n", - "87601:127667437\tQ-min: 1.772\tQ-max: 1.784\tLives: 5\tReward: 1.0\tEpisode Mean: 115.6\n", - "87601:127667478\tQ-min: 1.801\tQ-max: 1.829\tLives: 5\tReward: 2.0\tEpisode Mean: 115.6\n", - "87601:127667534\tQ-min: 1.698\tQ-max: 1.733\tLives: 5\tReward: 3.0\tEpisode Mean: 115.6\n", - "87601:127667580\tQ-min: 2.026\tQ-max: 2.073\tLives: 5\tReward: 4.0\tEpisode Mean: 115.6\n", - "87601:127667613\tQ-min: 1.956\tQ-max: 1.969\tLives: 5\tReward: 5.0\tEpisode Mean: 115.6\n", - "87601:127667646\tQ-min: 1.951\tQ-max: 1.978\tLives: 5\tReward: 6.0\tEpisode Mean: 115.6\n", - "87601:127667676\tQ-min: 1.863\tQ-max: 1.884\tLives: 5\tReward: 7.0\tEpisode Mean: 115.6\n", - "87601:127667695\tQ-min: -0.098\tQ-max: 0.156\tLives: 4\tReward: 7.0\tEpisode Mean: 115.6\n", - "87601:127667751\tQ-min: 1.660\tQ-max: 1.695\tLives: 4\tReward: 8.0\tEpisode Mean: 115.6\n", - "87601:127667809\tQ-min: 1.827\tQ-max: 1.867\tLives: 4\tReward: 9.0\tEpisode Mean: 115.6\n", - "87601:127667849\tQ-min: 1.889\tQ-max: 1.934\tLives: 4\tReward: 10.0\tEpisode Mean: 115.6\n", - "87601:127667886\tQ-min: 1.946\tQ-max: 1.981\tLives: 4\tReward: 11.0\tEpisode Mean: 115.6\n", - "87601:127667920\tQ-min: 1.944\tQ-max: 2.004\tLives: 4\tReward: 12.0\tEpisode Mean: 115.6\n", - "87601:127667957\tQ-min: 2.018\tQ-max: 2.054\tLives: 4\tReward: 16.0\tEpisode Mean: 115.6\n", - "87601:127667990\tQ-min: 1.935\tQ-max: 1.957\tLives: 4\tReward: 17.0\tEpisode Mean: 115.6\n", - "87601:127668036\tQ-min: 1.710\tQ-max: 1.733\tLives: 4\tReward: 18.0\tEpisode Mean: 115.6\n", - "87601:127668100\tQ-min: 1.683\tQ-max: 1.779\tLives: 4\tReward: 19.0\tEpisode Mean: 115.6\n", - "87601:127668164\tQ-min: 1.713\tQ-max: 1.733\tLives: 4\tReward: 20.0\tEpisode Mean: 115.6\n", - "87601:127668229\tQ-min: 1.737\tQ-max: 1.761\tLives: 4\tReward: 21.0\tEpisode Mean: 115.6\n", - "87601:127668275\tQ-min: 2.023\tQ-max: 2.044\tLives: 4\tReward: 22.0\tEpisode Mean: 115.6\n", - "87601:127668307\tQ-min: 1.987\tQ-max: 2.020\tLives: 4\tReward: 23.0\tEpisode Mean: 115.6\n", - "87601:127668339\tQ-min: 1.988\tQ-max: 2.029\tLives: 4\tReward: 24.0\tEpisode Mean: 115.6\n", - "87601:127668373\tQ-min: 2.085\tQ-max: 2.160\tLives: 4\tReward: 28.0\tEpisode Mean: 115.6\n", - "87601:127668411\tQ-min: 2.098\tQ-max: 2.124\tLives: 4\tReward: 29.0\tEpisode Mean: 115.6\n", - "87601:127668445\tQ-min: 2.101\tQ-max: 2.198\tLives: 4\tReward: 30.0\tEpisode Mean: 115.6\n", - "87601:127668478\tQ-min: 1.852\tQ-max: 2.134\tLives: 4\tReward: 34.0\tEpisode Mean: 115.6\n", - "87601:127668513\tQ-min: 2.055\tQ-max: 2.151\tLives: 4\tReward: 35.0\tEpisode Mean: 115.6\n", - "87601:127668547\tQ-min: 2.125\tQ-max: 2.159\tLives: 4\tReward: 39.0\tEpisode Mean: 115.6\n", - "87601:127668581\tQ-min: 2.101\tQ-max: 2.164\tLives: 4\tReward: 40.0\tEpisode Mean: 115.6\n", - "87601:127668605\tQ-min: -0.338\tQ-max: 0.211\tLives: 3\tReward: 40.0\tEpisode Mean: 115.6\n", - "87601:127668650\tQ-min: 1.977\tQ-max: 2.007\tLives: 3\tReward: 41.0\tEpisode Mean: 115.6\n", - "87601:127668701\tQ-min: 1.801\tQ-max: 1.886\tLives: 3\tReward: 42.0\tEpisode Mean: 115.6\n", - "87601:127668755\tQ-min: 1.908\tQ-max: 2.023\tLives: 3\tReward: 46.0\tEpisode Mean: 115.6\n", - "87601:127668792\tQ-min: 2.100\tQ-max: 2.161\tLives: 3\tReward: 47.0\tEpisode Mean: 115.6\n", - "87601:127668826\tQ-min: 2.139\tQ-max: 2.307\tLives: 3\tReward: 51.0\tEpisode Mean: 115.6\n", - "87601:127668862\tQ-min: 2.257\tQ-max: 2.364\tLives: 3\tReward: 52.0\tEpisode Mean: 115.6\n", - "87601:127668896\tQ-min: 2.252\tQ-max: 2.286\tLives: 3\tReward: 56.0\tEpisode Mean: 115.6\n", - "87601:127668950\tQ-min: 2.458\tQ-max: 2.644\tLives: 3\tReward: 60.0\tEpisode Mean: 115.6\n", - "87601:127668969\tQ-min: 2.445\tQ-max: 2.567\tLives: 3\tReward: 64.0\tEpisode Mean: 115.6\n", - "87601:127668990\tQ-min: 2.207\tQ-max: 2.511\tLives: 3\tReward: 65.0\tEpisode Mean: 115.6\n", - "87601:127669016\tQ-min: 2.154\tQ-max: 2.517\tLives: 3\tReward: 69.0\tEpisode Mean: 115.6\n", - "87601:127669038\tQ-min: 2.318\tQ-max: 2.589\tLives: 3\tReward: 73.0\tEpisode Mean: 115.6\n", - "87601:127669062\tQ-min: 2.291\tQ-max: 2.456\tLives: 3\tReward: 77.0\tEpisode Mean: 115.6\n", - "87601:127669085\tQ-min: 2.295\tQ-max: 2.671\tLives: 3\tReward: 78.0\tEpisode Mean: 115.6\n", - "87601:127669106\tQ-min: 2.310\tQ-max: 2.570\tLives: 3\tReward: 82.0\tEpisode Mean: 115.6\n", - "87601:127669120\tQ-min: 0.417\tQ-max: 0.690\tLives: 2\tReward: 82.0\tEpisode Mean: 115.6\n", - "87601:127669179\tQ-min: 1.564\tQ-max: 2.235\tLives: 2\tReward: 86.0\tEpisode Mean: 115.6\n", - "87601:127669240\tQ-min: 2.395\tQ-max: 2.511\tLives: 2\tReward: 90.0\tEpisode Mean: 115.6\n", - "87601:127669287\tQ-min: 2.236\tQ-max: 2.443\tLives: 2\tReward: 91.0\tEpisode Mean: 115.6\n", - "87601:127669328\tQ-min: 2.062\tQ-max: 2.538\tLives: 2\tReward: 95.0\tEpisode Mean: 115.6\n", - "87601:127669341\tQ-min: -0.015\tQ-max: 0.200\tLives: 1\tReward: 95.0\tEpisode Mean: 115.6\n", - "87601:127669387\tQ-min: 2.552\tQ-max: 2.691\tLives: 1\tReward: 99.0\tEpisode Mean: 115.6\n", - "87601:127669411\tQ-min: 2.696\tQ-max: 3.138\tLives: 1\tReward: 106.0\tEpisode Mean: 115.6\n", - "87601:127669434\tQ-min: 2.198\tQ-max: 3.207\tLives: 1\tReward: 110.0\tEpisode Mean: 115.6\n", - "87601:127669448\tQ-min: -0.426\tQ-max: -0.075\tLives: 0\tReward: 110.0\tEpisode Mean: 115.2\n", - "87602:127669494\tQ-min: 1.846\tQ-max: 1.862\tLives: 5\tReward: 1.0\tEpisode Mean: 115.2\n", - "87602:127669536\tQ-min: 1.860\tQ-max: 1.868\tLives: 5\tReward: 2.0\tEpisode Mean: 115.2\n", - "87602:127669580\tQ-min: 1.858\tQ-max: 1.872\tLives: 5\tReward: 3.0\tEpisode Mean: 115.2\n", - "87602:127669620\tQ-min: 2.002\tQ-max: 2.026\tLives: 5\tReward: 4.0\tEpisode Mean: 115.2\n", - "87602:127669641\tQ-min: -0.373\tQ-max: 0.258\tLives: 4\tReward: 4.0\tEpisode Mean: 115.2\n", - "87602:127669685\tQ-min: 1.873\tQ-max: 1.892\tLives: 4\tReward: 5.0\tEpisode Mean: 115.2\n", - "87602:127669737\tQ-min: 1.650\tQ-max: 1.670\tLives: 4\tReward: 6.0\tEpisode Mean: 115.2\n", - "87602:127669801\tQ-min: 1.640\tQ-max: 1.661\tLives: 4\tReward: 7.0\tEpisode Mean: 115.2\n", - "87602:127669850\tQ-min: 1.912\tQ-max: 2.008\tLives: 4\tReward: 8.0\tEpisode Mean: 115.2\n", - "87602:127669881\tQ-min: 1.983\tQ-max: 2.014\tLives: 4\tReward: 9.0\tEpisode Mean: 115.2\n", - "87602:127669917\tQ-min: 1.920\tQ-max: 1.981\tLives: 4\tReward: 10.0\tEpisode Mean: 115.2\n", - "87602:127669938\tQ-min: -0.144\tQ-max: 0.120\tLives: 3\tReward: 10.0\tEpisode Mean: 115.2\n", - "87602:127669982\tQ-min: 1.895\tQ-max: 1.932\tLives: 3\tReward: 11.0\tEpisode Mean: 115.2\n", - "87602:127670025\tQ-min: 1.828\tQ-max: 1.842\tLives: 3\tReward: 12.0\tEpisode Mean: 115.2\n", - "87602:127670075\tQ-min: 1.724\tQ-max: 1.734\tLives: 3\tReward: 13.0\tEpisode Mean: 115.2\n", - "87602:127670123\tQ-min: 1.911\tQ-max: 2.000\tLives: 3\tReward: 14.0\tEpisode Mean: 115.2\n", - "87602:127670154\tQ-min: 1.969\tQ-max: 1.996\tLives: 3\tReward: 15.0\tEpisode Mean: 115.2\n", - "87602:127670185\tQ-min: 1.919\tQ-max: 1.964\tLives: 3\tReward: 16.0\tEpisode Mean: 115.2\n", - "87602:127670215\tQ-min: 1.985\tQ-max: 2.007\tLives: 3\tReward: 17.0\tEpisode Mean: 115.2\n", - "87602:127670265\tQ-min: 1.564\tQ-max: 1.657\tLives: 3\tReward: 18.0\tEpisode Mean: 115.2\n", - "87602:127670336\tQ-min: 1.535\tQ-max: 1.684\tLives: 3\tReward: 22.0\tEpisode Mean: 115.2\n", - "87602:127670405\tQ-min: 1.777\tQ-max: 1.811\tLives: 3\tReward: 23.0\tEpisode Mean: 115.2\n", - "87602:127670467\tQ-min: 1.716\tQ-max: 1.739\tLives: 3\tReward: 24.0\tEpisode Mean: 115.2\n", - "87602:127670515\tQ-min: 2.014\tQ-max: 2.067\tLives: 3\tReward: 25.0\tEpisode Mean: 115.2\n", - "87602:127670548\tQ-min: 1.974\tQ-max: 2.002\tLives: 3\tReward: 26.0\tEpisode Mean: 115.2\n", - "87602:127670580\tQ-min: 1.974\tQ-max: 2.119\tLives: 3\tReward: 27.0\tEpisode Mean: 115.2\n", - "87602:127670615\tQ-min: 1.962\tQ-max: 1.986\tLives: 3\tReward: 28.0\tEpisode Mean: 115.2\n", - "87602:127670647\tQ-min: 2.032\tQ-max: 2.077\tLives: 3\tReward: 32.0\tEpisode Mean: 115.2\n", - "87602:127670680\tQ-min: 2.066\tQ-max: 2.091\tLives: 3\tReward: 33.0\tEpisode Mean: 115.2\n", - "87602:127670713\tQ-min: 2.021\tQ-max: 2.056\tLives: 3\tReward: 34.0\tEpisode Mean: 115.2\n", - "87602:127670745\tQ-min: 2.046\tQ-max: 2.105\tLives: 3\tReward: 38.0\tEpisode Mean: 115.2\n", - "87602:127670783\tQ-min: 2.086\tQ-max: 2.412\tLives: 3\tReward: 42.0\tEpisode Mean: 115.2\n", - "87602:127670805\tQ-min: 2.380\tQ-max: 2.468\tLives: 3\tReward: 46.0\tEpisode Mean: 115.2\n", - "87602:127670818\tQ-min: 0.071\tQ-max: 0.488\tLives: 2\tReward: 46.0\tEpisode Mean: 115.2\n", - "87602:127670865\tQ-min: 1.958\tQ-max: 1.985\tLives: 2\tReward: 47.0\tEpisode Mean: 115.2\n", - "87602:127670919\tQ-min: 1.791\tQ-max: 1.872\tLives: 2\tReward: 48.0\tEpisode Mean: 115.2\n", - "87602:127670979\tQ-min: 2.222\tQ-max: 2.258\tLives: 2\tReward: 52.0\tEpisode Mean: 115.2\n", - "87602:127671021\tQ-min: 2.213\tQ-max: 2.297\tLives: 2\tReward: 56.0\tEpisode Mean: 115.2\n", - "87602:127671054\tQ-min: 2.162\tQ-max: 2.189\tLives: 2\tReward: 57.0\tEpisode Mean: 115.2\n", - "87602:127671075\tQ-min: -0.140\tQ-max: 0.236\tLives: 1\tReward: 57.0\tEpisode Mean: 115.2\n", - "87602:127671125\tQ-min: 1.914\tQ-max: 1.943\tLives: 1\tReward: 58.0\tEpisode Mean: 115.2\n", - "87602:127671178\tQ-min: 2.243\tQ-max: 2.269\tLives: 1\tReward: 62.0\tEpisode Mean: 115.2\n", - "87602:127671238\tQ-min: 1.732\tQ-max: 1.933\tLives: 1\tReward: 66.0\tEpisode Mean: 115.2\n", - "87602:127671291\tQ-min: 2.280\tQ-max: 2.326\tLives: 1\tReward: 67.0\tEpisode Mean: 115.2\n", - "87602:127671328\tQ-min: 2.262\tQ-max: 2.353\tLives: 1\tReward: 71.0\tEpisode Mean: 115.2\n", - "87602:127671364\tQ-min: 2.267\tQ-max: 2.402\tLives: 1\tReward: 72.0\tEpisode Mean: 115.2\n", - "87602:127671401\tQ-min: 2.009\tQ-max: 2.370\tLives: 1\tReward: 76.0\tEpisode Mean: 115.2\n", - "87602:127671456\tQ-min: 1.768\tQ-max: 2.448\tLives: 1\tReward: 80.0\tEpisode Mean: 115.2\n", - "87602:127671478\tQ-min: 2.276\tQ-max: 2.643\tLives: 1\tReward: 84.0\tEpisode Mean: 115.2\n", - "87602:127671501\tQ-min: 2.352\tQ-max: 2.703\tLives: 1\tReward: 88.0\tEpisode Mean: 115.2\n", - "87602:127671524\tQ-min: 2.497\tQ-max: 2.574\tLives: 1\tReward: 92.0\tEpisode Mean: 115.2\n", - "87602:127671546\tQ-min: 1.849\tQ-max: 2.531\tLives: 1\tReward: 99.0\tEpisode Mean: 115.2\n", - "87602:127671571\tQ-min: 1.826\tQ-max: 2.690\tLives: 1\tReward: 103.0\tEpisode Mean: 115.2\n", - "87602:127671592\tQ-min: 2.442\tQ-max: 2.700\tLives: 1\tReward: 110.0\tEpisode Mean: 115.2\n", - "87602:127671613\tQ-min: 2.514\tQ-max: 2.937\tLives: 1\tReward: 114.0\tEpisode Mean: 115.2\n", - "87602:127671634\tQ-min: 1.072\tQ-max: 1.905\tLives: 1\tReward: 121.0\tEpisode Mean: 115.2\n", - "87602:127671648\tQ-min: -0.075\tQ-max: 0.241\tLives: 0\tReward: 121.0\tEpisode Mean: 115.6\n", - "87603:127671690\tQ-min: 1.763\tQ-max: 1.773\tLives: 5\tReward: 1.0\tEpisode Mean: 115.6\n", - "87603:127671729\tQ-min: 1.778\tQ-max: 1.796\tLives: 5\tReward: 2.0\tEpisode Mean: 115.6\n", - "87603:127671769\tQ-min: 1.926\tQ-max: 1.961\tLives: 5\tReward: 3.0\tEpisode Mean: 115.6\n", - "87603:127671807\tQ-min: 1.948\tQ-max: 1.971\tLives: 5\tReward: 4.0\tEpisode Mean: 115.6\n", - "87603:127671838\tQ-min: 2.037\tQ-max: 2.049\tLives: 5\tReward: 5.0\tEpisode Mean: 115.6\n", - "87603:127671870\tQ-min: 1.952\tQ-max: 1.976\tLives: 5\tReward: 6.0\tEpisode Mean: 115.6\n", - "87603:127671903\tQ-min: 1.955\tQ-max: 1.988\tLives: 5\tReward: 10.0\tEpisode Mean: 115.6\n", - "87603:127671953\tQ-min: 1.723\tQ-max: 1.739\tLives: 5\tReward: 11.0\tEpisode Mean: 115.6\n", - "87603:127672020\tQ-min: 1.764\tQ-max: 1.810\tLives: 5\tReward: 12.0\tEpisode Mean: 115.6\n", - "87603:127672087\tQ-min: 1.706\tQ-max: 1.776\tLives: 5\tReward: 13.0\tEpisode Mean: 115.6\n", - "87603:127672151\tQ-min: 1.714\tQ-max: 1.727\tLives: 5\tReward: 14.0\tEpisode Mean: 115.6\n", - "87603:127672199\tQ-min: 2.007\tQ-max: 2.077\tLives: 5\tReward: 15.0\tEpisode Mean: 115.6\n", - "87603:127672229\tQ-min: 1.977\tQ-max: 1.998\tLives: 5\tReward: 16.0\tEpisode Mean: 115.6\n", - "87603:127672260\tQ-min: 1.934\tQ-max: 1.969\tLives: 5\tReward: 17.0\tEpisode Mean: 115.6\n", - "87603:127672291\tQ-min: 2.022\tQ-max: 2.062\tLives: 5\tReward: 18.0\tEpisode Mean: 115.6\n", - "87603:127672325\tQ-min: 1.969\tQ-max: 2.009\tLives: 5\tReward: 19.0\tEpisode Mean: 115.6\n", - "87603:127672358\tQ-min: 1.991\tQ-max: 2.011\tLives: 5\tReward: 20.0\tEpisode Mean: 115.6\n", - "87603:127672391\tQ-min: 1.996\tQ-max: 2.031\tLives: 5\tReward: 21.0\tEpisode Mean: 115.6\n", - "87603:127672413\tQ-min: -0.426\tQ-max: 0.296\tLives: 4\tReward: 21.0\tEpisode Mean: 115.6\n", - "87603:127672469\tQ-min: 1.668\tQ-max: 1.694\tLives: 4\tReward: 22.0\tEpisode Mean: 115.6\n", - "87603:127672530\tQ-min: 1.771\tQ-max: 1.785\tLives: 4\tReward: 23.0\tEpisode Mean: 115.6\n", - "87603:127672582\tQ-min: 1.946\tQ-max: 1.987\tLives: 4\tReward: 24.0\tEpisode Mean: 115.6\n", - "87603:127672620\tQ-min: 1.989\tQ-max: 2.026\tLives: 4\tReward: 25.0\tEpisode Mean: 115.6\n", - "87603:127672654\tQ-min: 1.998\tQ-max: 2.088\tLives: 4\tReward: 26.0\tEpisode Mean: 115.6\n", - "87603:127672690\tQ-min: 2.024\tQ-max: 2.072\tLives: 4\tReward: 30.0\tEpisode Mean: 115.6\n", - "87603:127672724\tQ-min: 2.083\tQ-max: 2.123\tLives: 4\tReward: 31.0\tEpisode Mean: 115.6\n", - "87603:127672771\tQ-min: 1.733\tQ-max: 1.756\tLives: 4\tReward: 32.0\tEpisode Mean: 115.6\n", - "87603:127672833\tQ-min: 1.771\tQ-max: 1.959\tLives: 4\tReward: 36.0\tEpisode Mean: 115.6\n", - "87603:127672899\tQ-min: 1.730\tQ-max: 1.757\tLives: 4\tReward: 37.0\tEpisode Mean: 115.6\n", - "87603:127672964\tQ-min: 1.844\tQ-max: 1.933\tLives: 4\tReward: 41.0\tEpisode Mean: 115.6\n", - "87603:127673018\tQ-min: 2.125\tQ-max: 2.475\tLives: 4\tReward: 45.0\tEpisode Mean: 115.6\n", - "87603:127673038\tQ-min: 2.124\tQ-max: 2.300\tLives: 4\tReward: 46.0\tEpisode Mean: 115.6\n", - "87603:127673057\tQ-min: 2.286\tQ-max: 2.395\tLives: 4\tReward: 50.0\tEpisode Mean: 115.6\n", - "87603:127673078\tQ-min: 2.265\tQ-max: 2.469\tLives: 4\tReward: 54.0\tEpisode Mean: 115.6\n", - "87603:127673099\tQ-min: 2.380\tQ-max: 2.475\tLives: 4\tReward: 55.0\tEpisode Mean: 115.6\n", - "87603:127673123\tQ-min: 2.106\tQ-max: 2.561\tLives: 4\tReward: 59.0\tEpisode Mean: 115.6\n", - "87603:127673145\tQ-min: 2.153\tQ-max: 2.521\tLives: 4\tReward: 63.0\tEpisode Mean: 115.6\n", - "87603:127673168\tQ-min: 2.169\tQ-max: 2.521\tLives: 4\tReward: 67.0\tEpisode Mean: 115.6\n", - "87603:127673192\tQ-min: 1.558\tQ-max: 2.663\tLives: 4\tReward: 74.0\tEpisode Mean: 115.6\n", - "87603:127673215\tQ-min: 1.016\tQ-max: 2.691\tLives: 4\tReward: 81.0\tEpisode Mean: 115.6\n", - "87603:127673229\tQ-min: -0.185\tQ-max: 0.329\tLives: 3\tReward: 81.0\tEpisode Mean: 115.6\n", - "87603:127673283\tQ-min: 1.876\tQ-max: 1.961\tLives: 3\tReward: 82.0\tEpisode Mean: 115.6\n", - "87603:127673353\tQ-min: 1.985\tQ-max: 2.072\tLives: 3\tReward: 86.0\tEpisode Mean: 115.6\n", - "87603:127673407\tQ-min: 2.380\tQ-max: 2.514\tLives: 3\tReward: 87.0\tEpisode Mean: 115.6\n", - "87603:127673447\tQ-min: 2.269\tQ-max: 2.633\tLives: 3\tReward: 91.0\tEpisode Mean: 115.6\n", - "87603:127673481\tQ-min: 2.455\tQ-max: 2.735\tLives: 3\tReward: 95.0\tEpisode Mean: 115.6\n", - "87603:127673518\tQ-min: 1.768\tQ-max: 3.135\tLives: 3\tReward: 99.0\tEpisode Mean: 115.6\n", - "87603:127673531\tQ-min: -0.043\tQ-max: 0.415\tLives: 2\tReward: 99.0\tEpisode Mean: 115.6\n", - "87603:127673585\tQ-min: 2.326\tQ-max: 2.456\tLives: 2\tReward: 100.0\tEpisode Mean: 115.6\n", - "87603:127673655\tQ-min: 1.971\tQ-max: 2.831\tLives: 2\tReward: 104.0\tEpisode Mean: 115.6\n", - "87603:127673715\tQ-min: 2.525\tQ-max: 2.808\tLives: 2\tReward: 105.0\tEpisode Mean: 115.6\n", - "87603:127673755\tQ-min: 2.514\tQ-max: 2.690\tLives: 2\tReward: 109.0\tEpisode Mean: 115.6\n", - "87603:127673778\tQ-min: 2.405\tQ-max: 2.939\tLives: 2\tReward: 116.0\tEpisode Mean: 115.6\n", - "87603:127673801\tQ-min: 2.501\tQ-max: 2.843\tLives: 2\tReward: 120.0\tEpisode Mean: 115.6\n", - "87603:127673816\tQ-min: 0.207\tQ-max: 0.486\tLives: 1\tReward: 120.0\tEpisode Mean: 115.6\n", - "87603:127673871\tQ-min: 2.058\tQ-max: 2.291\tLives: 1\tReward: 121.0\tEpisode Mean: 115.6\n", - "87603:127673931\tQ-min: 2.451\tQ-max: 2.619\tLives: 1\tReward: 125.0\tEpisode Mean: 115.6\n", - "87603:127673961\tQ-min: -0.120\tQ-max: 0.246\tLives: 0\tReward: 125.0\tEpisode Mean: 116.2\n", - "87604:127674004\tQ-min: 1.712\tQ-max: 1.727\tLives: 5\tReward: 1.0\tEpisode Mean: 116.2\n", - "87604:127674058\tQ-min: 1.601\tQ-max: 1.640\tLives: 5\tReward: 2.0\tEpisode Mean: 116.2\n", - "87604:127674121\tQ-min: 1.680\tQ-max: 1.701\tLives: 5\tReward: 3.0\tEpisode Mean: 116.2\n", - "87604:127674169\tQ-min: 1.970\tQ-max: 1.998\tLives: 5\tReward: 4.0\tEpisode Mean: 116.2\n", - "87604:127674203\tQ-min: 1.933\tQ-max: 1.956\tLives: 5\tReward: 5.0\tEpisode Mean: 116.2\n", - "87604:127674236\tQ-min: 1.899\tQ-max: 1.954\tLives: 5\tReward: 6.0\tEpisode Mean: 116.2\n", - "87604:127674270\tQ-min: 1.745\tQ-max: 1.791\tLives: 5\tReward: 7.0\tEpisode Mean: 116.2\n", - "87604:127674317\tQ-min: 1.633\tQ-max: 1.675\tLives: 5\tReward: 8.0\tEpisode Mean: 116.2\n", - "87604:127674384\tQ-min: 1.578\tQ-max: 1.663\tLives: 5\tReward: 9.0\tEpisode Mean: 116.2\n", - "87604:127674450\tQ-min: 1.639\tQ-max: 1.650\tLives: 5\tReward: 10.0\tEpisode Mean: 116.2\n", - "87604:127674516\tQ-min: 1.689\tQ-max: 1.715\tLives: 5\tReward: 11.0\tEpisode Mean: 116.2\n", - "87604:127674566\tQ-min: 1.901\tQ-max: 2.028\tLives: 5\tReward: 12.0\tEpisode Mean: 116.2\n", - "87604:127674590\tQ-min: 0.057\tQ-max: 0.177\tLives: 4\tReward: 12.0\tEpisode Mean: 116.2\n", - "87604:127674633\tQ-min: 1.909\tQ-max: 1.937\tLives: 4\tReward: 13.0\tEpisode Mean: 116.2\n", - "87604:127674664\tQ-min: -0.219\tQ-max: 0.060\tLives: 3\tReward: 13.0\tEpisode Mean: 116.2\n", - "87604:127674710\tQ-min: 1.899\tQ-max: 1.941\tLives: 3\tReward: 17.0\tEpisode Mean: 116.2\n", - "87604:127674757\tQ-min: 2.001\tQ-max: 2.061\tLives: 3\tReward: 18.0\tEpisode Mean: 116.2\n", - "87604:127674803\tQ-min: 1.963\tQ-max: 2.027\tLives: 3\tReward: 19.0\tEpisode Mean: 116.2\n", - "87604:127674846\tQ-min: 2.039\tQ-max: 2.065\tLives: 3\tReward: 20.0\tEpisode Mean: 116.2\n", - "87604:127674866\tQ-min: -0.039\tQ-max: 0.309\tLives: 2\tReward: 20.0\tEpisode Mean: 116.2\n", - "87604:127674974\tQ-min: 1.860\tQ-max: 1.882\tLives: 2\tReward: 21.0\tEpisode Mean: 116.2\n", - "87604:127675021\tQ-min: 1.838\tQ-max: 1.922\tLives: 2\tReward: 25.0\tEpisode Mean: 116.2\n", - "87604:127675076\tQ-min: 1.763\tQ-max: 1.800\tLives: 2\tReward: 26.0\tEpisode Mean: 116.2\n", - "87604:127675123\tQ-min: 2.048\tQ-max: 2.080\tLives: 2\tReward: 27.0\tEpisode Mean: 116.2\n", - "87604:127675155\tQ-min: 2.133\tQ-max: 2.219\tLives: 2\tReward: 31.0\tEpisode Mean: 116.2\n", - "87604:127675194\tQ-min: 2.141\tQ-max: 2.510\tLives: 2\tReward: 35.0\tEpisode Mean: 116.2\n", - "87604:127675215\tQ-min: 2.279\tQ-max: 2.456\tLives: 2\tReward: 36.0\tEpisode Mean: 116.2\n", - "87604:127675234\tQ-min: 2.268\tQ-max: 2.562\tLives: 2\tReward: 37.0\tEpisode Mean: 116.2\n", - "87604:127675256\tQ-min: 2.287\tQ-max: 2.381\tLives: 2\tReward: 41.0\tEpisode Mean: 116.2\n", - "87604:127675277\tQ-min: 2.396\tQ-max: 2.486\tLives: 2\tReward: 42.0\tEpisode Mean: 116.2\n", - "87604:127675297\tQ-min: 2.355\tQ-max: 2.560\tLives: 2\tReward: 46.0\tEpisode Mean: 116.2\n", - "87604:127675317\tQ-min: 2.484\tQ-max: 2.578\tLives: 2\tReward: 47.0\tEpisode Mean: 116.2\n", - "87604:127675335\tQ-min: 2.340\tQ-max: 2.505\tLives: 2\tReward: 48.0\tEpisode Mean: 116.2\n", - "87604:127675355\tQ-min: 2.345\tQ-max: 2.403\tLives: 2\tReward: 49.0\tEpisode Mean: 116.2\n", - "87604:127675369\tQ-min: 0.086\tQ-max: 0.281\tLives: 1\tReward: 49.0\tEpisode Mean: 116.2\n", - "87604:127675413\tQ-min: 2.118\tQ-max: 2.130\tLives: 1\tReward: 50.0\tEpisode Mean: 116.2\n", - "87604:127675459\tQ-min: 2.116\tQ-max: 2.176\tLives: 1\tReward: 54.0\tEpisode Mean: 116.2\n", - "87604:127675519\tQ-min: 1.917\tQ-max: 1.969\tLives: 1\tReward: 55.0\tEpisode Mean: 116.2\n", - "87604:127675566\tQ-min: 2.229\tQ-max: 2.280\tLives: 1\tReward: 56.0\tEpisode Mean: 116.2\n", - "87604:127675597\tQ-min: 2.174\tQ-max: 2.217\tLives: 1\tReward: 57.0\tEpisode Mean: 116.2\n", - "87604:127675629\tQ-min: 2.081\tQ-max: 2.246\tLives: 1\tReward: 58.0\tEpisode Mean: 116.2\n", - "87604:127675663\tQ-min: 2.156\tQ-max: 2.261\tLives: 1\tReward: 62.0\tEpisode Mean: 116.2\n", - "87604:127675712\tQ-min: 1.844\tQ-max: 1.940\tLives: 1\tReward: 63.0\tEpisode Mean: 116.2\n", - "87604:127675777\tQ-min: 1.842\tQ-max: 1.905\tLives: 1\tReward: 64.0\tEpisode Mean: 116.2\n", - "87604:127675843\tQ-min: 1.753\tQ-max: 1.884\tLives: 1\tReward: 68.0\tEpisode Mean: 116.2\n", - "87604:127675909\tQ-min: 1.849\tQ-max: 1.920\tLives: 1\tReward: 69.0\tEpisode Mean: 116.2\n", - "87604:127675960\tQ-min: 2.219\tQ-max: 2.309\tLives: 1\tReward: 73.0\tEpisode Mean: 116.2\n", - "87604:127675996\tQ-min: 2.236\tQ-max: 2.317\tLives: 1\tReward: 74.0\tEpisode Mean: 116.2\n", - "87604:127676031\tQ-min: 2.235\tQ-max: 2.344\tLives: 1\tReward: 75.0\tEpisode Mean: 116.2\n", - "87604:127676055\tQ-min: -0.184\tQ-max: 0.132\tLives: 0\tReward: 75.0\tEpisode Mean: 113.8\n", - "87605:127676100\tQ-min: 1.728\tQ-max: 1.741\tLives: 5\tReward: 1.0\tEpisode Mean: 113.8\n", - "87605:127676143\tQ-min: 1.782\tQ-max: 1.821\tLives: 5\tReward: 2.0\tEpisode Mean: 113.8\n", - "87605:127676184\tQ-min: 1.895\tQ-max: 1.924\tLives: 5\tReward: 3.0\tEpisode Mean: 113.8\n", - "87605:127676221\tQ-min: 1.982\tQ-max: 2.001\tLives: 5\tReward: 4.0\tEpisode Mean: 113.8\n", - "87605:127676254\tQ-min: 1.910\tQ-max: 1.944\tLives: 5\tReward: 5.0\tEpisode Mean: 113.8\n", - "87605:127676285\tQ-min: 1.929\tQ-max: 1.943\tLives: 5\tReward: 6.0\tEpisode Mean: 113.8\n", - "87605:127676319\tQ-min: 1.768\tQ-max: 1.815\tLives: 5\tReward: 7.0\tEpisode Mean: 113.8\n", - "87605:127676342\tQ-min: -0.167\tQ-max: 0.291\tLives: 4\tReward: 7.0\tEpisode Mean: 113.8\n", - "87605:127676395\tQ-min: 1.653\tQ-max: 1.662\tLives: 4\tReward: 8.0\tEpisode Mean: 113.8\n", - "87605:127676451\tQ-min: 1.953\tQ-max: 2.010\tLives: 4\tReward: 12.0\tEpisode Mean: 113.8\n", - "87605:127676499\tQ-min: 1.979\tQ-max: 2.121\tLives: 4\tReward: 13.0\tEpisode Mean: 113.8\n", - "87605:127676538\tQ-min: 2.028\tQ-max: 2.045\tLives: 4\tReward: 14.0\tEpisode Mean: 113.8\n", - "87605:127676570\tQ-min: 1.959\tQ-max: 2.040\tLives: 4\tReward: 15.0\tEpisode Mean: 113.8\n", - "87605:127676602\tQ-min: 1.958\tQ-max: 1.976\tLives: 4\tReward: 16.0\tEpisode Mean: 113.8\n", - "87605:127676634\tQ-min: 2.033\tQ-max: 2.050\tLives: 4\tReward: 17.0\tEpisode Mean: 113.8\n", - "87605:127676685\tQ-min: 1.723\tQ-max: 1.733\tLives: 4\tReward: 18.0\tEpisode Mean: 113.8\n", - "87605:127676746\tQ-min: 1.699\tQ-max: 1.722\tLives: 4\tReward: 19.0\tEpisode Mean: 113.8\n", - "87605:127676811\tQ-min: 1.658\tQ-max: 1.797\tLives: 4\tReward: 20.0\tEpisode Mean: 113.8\n", - "87605:127676873\tQ-min: 1.677\tQ-max: 1.725\tLives: 4\tReward: 21.0\tEpisode Mean: 113.8\n", - "87605:127676923\tQ-min: 2.103\tQ-max: 2.153\tLives: 4\tReward: 22.0\tEpisode Mean: 113.8\n", - "87605:127676959\tQ-min: 2.068\tQ-max: 2.133\tLives: 4\tReward: 26.0\tEpisode Mean: 113.8\n", - "87605:127676996\tQ-min: 2.054\tQ-max: 2.265\tLives: 4\tReward: 30.0\tEpisode Mean: 113.8\n", - "87605:127677010\tQ-min: 0.105\tQ-max: 0.230\tLives: 3\tReward: 30.0\tEpisode Mean: 113.8\n", - "87605:127677066\tQ-min: 1.419\tQ-max: 1.752\tLives: 3\tReward: 34.0\tEpisode Mean: 113.8\n", - "87605:127677128\tQ-min: 1.803\tQ-max: 1.830\tLives: 3\tReward: 35.0\tEpisode Mean: 113.8\n", - "87605:127677198\tQ-min: 1.737\tQ-max: 2.505\tLives: 3\tReward: 39.0\tEpisode Mean: 113.8\n", - "87605:127677220\tQ-min: 2.372\tQ-max: 2.562\tLives: 3\tReward: 43.0\tEpisode Mean: 113.8\n", - "87605:127677239\tQ-min: 2.331\tQ-max: 2.440\tLives: 3\tReward: 44.0\tEpisode Mean: 113.8\n", - "87605:127677251\tQ-min: 0.008\tQ-max: 0.249\tLives: 2\tReward: 44.0\tEpisode Mean: 113.8\n", - "87605:127677307\tQ-min: 1.818\tQ-max: 1.864\tLives: 2\tReward: 45.0\tEpisode Mean: 113.8\n", - "87605:127677359\tQ-min: 2.208\tQ-max: 2.258\tLives: 2\tReward: 46.0\tEpisode Mean: 113.8\n", - "87605:127677390\tQ-min: 0.039\tQ-max: 0.266\tLives: 1\tReward: 46.0\tEpisode Mean: 113.8\n", - "87605:127677436\tQ-min: 2.228\tQ-max: 2.285\tLives: 1\tReward: 47.0\tEpisode Mean: 113.8\n", - "87605:127677481\tQ-min: 2.194\tQ-max: 2.596\tLives: 1\tReward: 51.0\tEpisode Mean: 113.8\n", - "87605:127677503\tQ-min: 2.291\tQ-max: 2.988\tLives: 1\tReward: 58.0\tEpisode Mean: 113.8\n", - "87605:127677519\tQ-min: -0.284\tQ-max: 0.126\tLives: 0\tReward: 58.0\tEpisode Mean: 110.7\n", - "87606:127677562\tQ-min: 1.736\tQ-max: 1.756\tLives: 5\tReward: 1.0\tEpisode Mean: 110.7\n", - "87606:127677617\tQ-min: 1.675\tQ-max: 1.694\tLives: 5\tReward: 2.0\tEpisode Mean: 110.7\n", - "87606:127677672\tQ-min: 1.797\tQ-max: 1.816\tLives: 5\tReward: 3.0\tEpisode Mean: 110.7\n", - "87606:127677711\tQ-min: 1.939\tQ-max: 1.988\tLives: 5\tReward: 4.0\tEpisode Mean: 110.7\n", - "87606:127677746\tQ-min: 1.982\tQ-max: 1.997\tLives: 5\tReward: 5.0\tEpisode Mean: 110.7\n", - "87606:127677778\tQ-min: 1.881\tQ-max: 1.989\tLives: 5\tReward: 6.0\tEpisode Mean: 110.7\n", - "87606:127677810\tQ-min: 1.759\tQ-max: 1.842\tLives: 5\tReward: 7.0\tEpisode Mean: 110.7\n", - "87606:127677857\tQ-min: 1.647\tQ-max: 1.674\tLives: 5\tReward: 8.0\tEpisode Mean: 110.7\n", - "87606:127677916\tQ-min: 1.698\tQ-max: 1.744\tLives: 5\tReward: 9.0\tEpisode Mean: 110.7\n", - "87606:127677987\tQ-min: 1.660\tQ-max: 1.681\tLives: 5\tReward: 10.0\tEpisode Mean: 110.7\n", - "87606:127678054\tQ-min: 1.626\tQ-max: 1.679\tLives: 5\tReward: 11.0\tEpisode Mean: 110.7\n", - "87606:127678099\tQ-min: 1.958\tQ-max: 2.001\tLives: 5\tReward: 12.0\tEpisode Mean: 110.7\n", - "87606:127678134\tQ-min: 1.897\tQ-max: 1.940\tLives: 5\tReward: 13.0\tEpisode Mean: 110.7\n", - "87606:127678163\tQ-min: 1.971\tQ-max: 1.991\tLives: 5\tReward: 14.0\tEpisode Mean: 110.7\n", - "87606:127678194\tQ-min: 1.974\tQ-max: 2.030\tLives: 5\tReward: 15.0\tEpisode Mean: 110.7\n", - "87606:127678228\tQ-min: 1.994\tQ-max: 2.000\tLives: 5\tReward: 16.0\tEpisode Mean: 110.7\n", - "87606:127678258\tQ-min: 1.935\tQ-max: 1.957\tLives: 5\tReward: 17.0\tEpisode Mean: 110.7\n", - "87606:127678292\tQ-min: 1.935\tQ-max: 1.981\tLives: 5\tReward: 18.0\tEpisode Mean: 110.7\n", - "87606:127678326\tQ-min: 1.991\tQ-max: 2.016\tLives: 5\tReward: 19.0\tEpisode Mean: 110.7\n", - "87606:127678359\tQ-min: 1.986\tQ-max: 2.003\tLives: 5\tReward: 20.0\tEpisode Mean: 110.7\n", - "87606:127678392\tQ-min: 1.963\tQ-max: 2.003\tLives: 5\tReward: 21.0\tEpisode Mean: 110.7\n", - "87606:127678426\tQ-min: 2.020\tQ-max: 2.039\tLives: 5\tReward: 22.0\tEpisode Mean: 110.7\n", - "87606:127678459\tQ-min: 2.033\tQ-max: 2.074\tLives: 5\tReward: 23.0\tEpisode Mean: 110.7\n", - "87606:127678481\tQ-min: -0.098\tQ-max: 0.338\tLives: 4\tReward: 23.0\tEpisode Mean: 110.7\n", - "87606:127678527\tQ-min: 1.891\tQ-max: 1.938\tLives: 4\tReward: 24.0\tEpisode Mean: 110.7\n", - "87606:127678568\tQ-min: 2.071\tQ-max: 2.099\tLives: 4\tReward: 25.0\tEpisode Mean: 110.7\n", - "87606:127678625\tQ-min: 1.695\tQ-max: 1.800\tLives: 4\tReward: 29.0\tEpisode Mean: 110.7\n", - "87606:127678674\tQ-min: 2.135\tQ-max: 2.178\tLives: 4\tReward: 30.0\tEpisode Mean: 110.7\n", - "87606:127678709\tQ-min: 2.126\tQ-max: 2.153\tLives: 4\tReward: 34.0\tEpisode Mean: 110.7\n", - "87606:127678743\tQ-min: 2.057\tQ-max: 2.076\tLives: 4\tReward: 35.0\tEpisode Mean: 110.7\n", - "87606:127678775\tQ-min: 2.045\tQ-max: 2.198\tLives: 4\tReward: 39.0\tEpisode Mean: 110.7\n", - "87606:127678829\tQ-min: 1.787\tQ-max: 1.855\tLives: 4\tReward: 40.0\tEpisode Mean: 110.7\n", - "87606:127678898\tQ-min: 1.540\tQ-max: 1.817\tLives: 4\tReward: 44.0\tEpisode Mean: 110.7\n", - "87606:127678966\tQ-min: 1.773\tQ-max: 1.854\tLives: 4\tReward: 45.0\tEpisode Mean: 110.7\n", - "87606:127679032\tQ-min: 1.541\tQ-max: 2.350\tLives: 4\tReward: 49.0\tEpisode Mean: 110.7\n", - "87606:127679045\tQ-min: -0.339\tQ-max: 0.173\tLives: 3\tReward: 49.0\tEpisode Mean: 110.7\n", - "87606:127679088\tQ-min: 1.919\tQ-max: 2.063\tLives: 3\tReward: 53.0\tEpisode Mean: 110.7\n", - "87606:127679141\tQ-min: 1.894\tQ-max: 1.917\tLives: 3\tReward: 54.0\tEpisode Mean: 110.7\n", - "87606:127679200\tQ-min: 1.727\tQ-max: 2.131\tLives: 3\tReward: 58.0\tEpisode Mean: 110.7\n", - "87606:127679245\tQ-min: 2.150\tQ-max: 2.552\tLives: 3\tReward: 62.0\tEpisode Mean: 110.7\n", - "87606:127679268\tQ-min: 2.253\tQ-max: 2.441\tLives: 3\tReward: 69.0\tEpisode Mean: 110.7\n", - "87606:127679289\tQ-min: 2.212\tQ-max: 2.487\tLives: 3\tReward: 70.0\tEpisode Mean: 110.7\n", - "87606:127679309\tQ-min: 2.323\tQ-max: 2.647\tLives: 3\tReward: 74.0\tEpisode Mean: 110.7\n", - "87606:127679332\tQ-min: 2.343\tQ-max: 2.527\tLives: 3\tReward: 78.0\tEpisode Mean: 110.7\n", - "87606:127679352\tQ-min: 2.450\tQ-max: 2.544\tLives: 3\tReward: 82.0\tEpisode Mean: 110.7\n", - "87606:127679374\tQ-min: 2.394\tQ-max: 2.458\tLives: 3\tReward: 83.0\tEpisode Mean: 110.7\n", - "87606:127679394\tQ-min: 2.342\tQ-max: 2.522\tLives: 3\tReward: 87.0\tEpisode Mean: 110.7\n", - "87606:127679408\tQ-min: -0.282\tQ-max: -0.029\tLives: 2\tReward: 87.0\tEpisode Mean: 110.7\n", - "87606:127679469\tQ-min: 1.934\tQ-max: 2.336\tLives: 2\tReward: 91.0\tEpisode Mean: 110.7\n", - "87606:127679490\tQ-min: 2.275\tQ-max: 2.529\tLives: 2\tReward: 95.0\tEpisode Mean: 110.7\n", - "87606:127679511\tQ-min: 1.537\tQ-max: 2.859\tLives: 2\tReward: 99.0\tEpisode Mean: 110.7\n", - "87606:127679533\tQ-min: 2.336\tQ-max: 2.599\tLives: 2\tReward: 103.0\tEpisode Mean: 110.7\n", - "87606:127679555\tQ-min: 2.316\tQ-max: 2.783\tLives: 2\tReward: 104.0\tEpisode Mean: 110.7\n", - "87606:127679576\tQ-min: 1.995\tQ-max: 2.765\tLives: 2\tReward: 108.0\tEpisode Mean: 110.7\n", - "87606:127679597\tQ-min: 2.346\tQ-max: 2.770\tLives: 2\tReward: 115.0\tEpisode Mean: 110.7\n", - "87606:127679618\tQ-min: 2.309\tQ-max: 2.690\tLives: 2\tReward: 116.0\tEpisode Mean: 110.7\n", - "87606:127679630\tQ-min: -0.457\tQ-max: 0.076\tLives: 1\tReward: 116.0\tEpisode Mean: 110.7\n", - "87606:127679677\tQ-min: 2.395\tQ-max: 2.593\tLives: 1\tReward: 120.0\tEpisode Mean: 110.7\n", - "87606:127679735\tQ-min: 1.948\tQ-max: 2.202\tLives: 1\tReward: 124.0\tEpisode Mean: 110.7\n", - "87606:127679806\tQ-min: 2.060\tQ-max: 2.495\tLives: 1\tReward: 128.0\tEpisode Mean: 110.7\n", - "87606:127679823\tQ-min: -0.506\tQ-max: 0.177\tLives: 0\tReward: 128.0\tEpisode Mean: 111.6\n", - "87607:127679877\tQ-min: 1.689\tQ-max: 1.702\tLives: 5\tReward: 1.0\tEpisode Mean: 111.6\n", - "87607:127679929\tQ-min: 1.845\tQ-max: 1.866\tLives: 5\tReward: 2.0\tEpisode Mean: 111.6\n", - "87607:127679981\tQ-min: 1.693\tQ-max: 1.738\tLives: 5\tReward: 3.0\tEpisode Mean: 111.6\n", - "87607:127680027\tQ-min: 1.962\tQ-max: 2.002\tLives: 5\tReward: 4.0\tEpisode Mean: 111.6\n", - "87607:127680064\tQ-min: 1.922\tQ-max: 2.053\tLives: 5\tReward: 5.0\tEpisode Mean: 111.6\n", - "87607:127680096\tQ-min: 2.018\tQ-max: 2.050\tLives: 5\tReward: 6.0\tEpisode Mean: 111.6\n", - "87607:127680128\tQ-min: 1.803\tQ-max: 1.831\tLives: 5\tReward: 7.0\tEpisode Mean: 111.6\n", - "87607:127680177\tQ-min: 1.645\tQ-max: 1.662\tLives: 5\tReward: 8.0\tEpisode Mean: 111.6\n", - "87607:127680242\tQ-min: 1.739\tQ-max: 1.793\tLives: 5\tReward: 9.0\tEpisode Mean: 111.6\n", - "87607:127680308\tQ-min: 1.656\tQ-max: 1.699\tLives: 5\tReward: 10.0\tEpisode Mean: 111.6\n", - "87607:127680378\tQ-min: 1.677\tQ-max: 1.700\tLives: 5\tReward: 11.0\tEpisode Mean: 111.6\n", - "87607:127680425\tQ-min: 2.023\tQ-max: 2.058\tLives: 5\tReward: 12.0\tEpisode Mean: 111.6\n", - "87607:127680459\tQ-min: 1.954\tQ-max: 1.999\tLives: 5\tReward: 13.0\tEpisode Mean: 111.6\n", - "87607:127680493\tQ-min: 1.998\tQ-max: 2.057\tLives: 5\tReward: 14.0\tEpisode Mean: 111.6\n", - "87607:127680527\tQ-min: 2.008\tQ-max: 2.044\tLives: 5\tReward: 15.0\tEpisode Mean: 111.6\n", - "87607:127680559\tQ-min: 2.022\tQ-max: 2.067\tLives: 5\tReward: 16.0\tEpisode Mean: 111.6\n", - "87607:127680594\tQ-min: 1.967\tQ-max: 2.004\tLives: 5\tReward: 20.0\tEpisode Mean: 111.6\n", - "87607:127680624\tQ-min: 2.048\tQ-max: 2.151\tLives: 5\tReward: 21.0\tEpisode Mean: 111.6\n", - "87607:127680657\tQ-min: 2.051\tQ-max: 2.088\tLives: 5\tReward: 22.0\tEpisode Mean: 111.6\n", - "87607:127680678\tQ-min: -0.184\tQ-max: 0.397\tLives: 4\tReward: 22.0\tEpisode Mean: 111.6\n", - "87607:127680723\tQ-min: 1.909\tQ-max: 2.042\tLives: 4\tReward: 26.0\tEpisode Mean: 111.6\n", - "87607:127680768\tQ-min: 1.944\tQ-max: 1.974\tLives: 4\tReward: 27.0\tEpisode Mean: 111.6\n", - "87607:127680809\tQ-min: 2.074\tQ-max: 2.107\tLives: 4\tReward: 28.0\tEpisode Mean: 111.6\n", - "87607:127680848\tQ-min: 2.161\tQ-max: 2.240\tLives: 4\tReward: 32.0\tEpisode Mean: 111.6\n", - "87607:127680882\tQ-min: 2.087\tQ-max: 2.127\tLives: 4\tReward: 33.0\tEpisode Mean: 111.6\n", - "87607:127680915\tQ-min: 2.135\tQ-max: 2.224\tLives: 4\tReward: 37.0\tEpisode Mean: 111.6\n", - "87607:127680951\tQ-min: 1.990\tQ-max: 2.214\tLives: 4\tReward: 41.0\tEpisode Mean: 111.6\n", - "87607:127681007\tQ-min: 1.553\tQ-max: 1.975\tLives: 4\tReward: 45.0\tEpisode Mean: 111.6\n", - "87607:127681081\tQ-min: 1.772\tQ-max: 1.872\tLives: 4\tReward: 46.0\tEpisode Mean: 111.6\n", - "87607:127681147\tQ-min: 1.825\tQ-max: 1.870\tLives: 4\tReward: 47.0\tEpisode Mean: 111.6\n", - "87607:127681213\tQ-min: 1.874\tQ-max: 2.023\tLives: 4\tReward: 48.0\tEpisode Mean: 111.6\n", - "87607:127681262\tQ-min: 2.039\tQ-max: 2.453\tLives: 4\tReward: 52.0\tEpisode Mean: 111.6\n", - "87607:127681300\tQ-min: 1.558\tQ-max: 2.585\tLives: 4\tReward: 56.0\tEpisode Mean: 111.6\n", - "87607:127681324\tQ-min: 2.214\tQ-max: 2.458\tLives: 4\tReward: 63.0\tEpisode Mean: 111.6\n", - "87607:127681345\tQ-min: 2.441\tQ-max: 2.550\tLives: 4\tReward: 64.0\tEpisode Mean: 111.6\n", - "87607:127681365\tQ-min: 2.225\tQ-max: 2.242\tLives: 4\tReward: 65.0\tEpisode Mean: 111.6\n", - "87607:127681381\tQ-min: -0.027\tQ-max: 0.256\tLives: 3\tReward: 65.0\tEpisode Mean: 111.6\n", - "87607:127681429\tQ-min: 2.047\tQ-max: 2.081\tLives: 3\tReward: 66.0\tEpisode Mean: 111.6\n", - "87607:127681477\tQ-min: 2.280\tQ-max: 2.430\tLives: 3\tReward: 70.0\tEpisode Mean: 111.6\n", - "87607:127681498\tQ-min: 2.250\tQ-max: 2.408\tLives: 3\tReward: 71.0\tEpisode Mean: 111.6\n", - "87607:127681516\tQ-min: 2.347\tQ-max: 2.513\tLives: 3\tReward: 75.0\tEpisode Mean: 111.6\n", - "87607:127681529\tQ-min: -0.143\tQ-max: 0.359\tLives: 2\tReward: 75.0\tEpisode Mean: 111.6\n", - "87607:127681575\tQ-min: 2.035\tQ-max: 2.133\tLives: 2\tReward: 79.0\tEpisode Mean: 111.6\n", - "87607:127681625\tQ-min: 1.562\tQ-max: 2.504\tLives: 2\tReward: 86.0\tEpisode Mean: 111.6\n", - "87607:127681650\tQ-min: 2.111\tQ-max: 2.595\tLives: 2\tReward: 93.0\tEpisode Mean: 111.6\n", - "87607:127681673\tQ-min: 2.018\tQ-max: 2.358\tLives: 2\tReward: 97.0\tEpisode Mean: 111.6\n", - "87607:127681694\tQ-min: 2.262\tQ-max: 2.581\tLives: 2\tReward: 101.0\tEpisode Mean: 111.6\n", - "87607:127681716\tQ-min: 2.455\tQ-max: 2.569\tLives: 2\tReward: 102.0\tEpisode Mean: 111.6\n", - "87607:127681737\tQ-min: 2.342\tQ-max: 2.699\tLives: 2\tReward: 106.0\tEpisode Mean: 111.6\n", - "87607:127681760\tQ-min: 2.547\tQ-max: 2.743\tLives: 2\tReward: 107.0\tEpisode Mean: 111.6\n", - "87607:127681779\tQ-min: 2.343\tQ-max: 2.583\tLives: 2\tReward: 111.0\tEpisode Mean: 111.6\n", - "87607:127681794\tQ-min: 0.046\tQ-max: 0.261\tLives: 1\tReward: 111.0\tEpisode Mean: 111.6\n", - "87607:127681846\tQ-min: 2.121\tQ-max: 2.734\tLives: 1\tReward: 115.0\tEpisode Mean: 111.6\n", - "87607:127681867\tQ-min: 2.635\tQ-max: 2.878\tLives: 1\tReward: 122.0\tEpisode Mean: 111.6\n", - "87607:127681891\tQ-min: 2.746\tQ-max: 3.404\tLives: 1\tReward: 123.0\tEpisode Mean: 111.6\n", - "87607:127681903\tQ-min: -0.278\tQ-max: 0.238\tLives: 0\tReward: 123.0\tEpisode Mean: 112.2\n", - "87608:127681952\tQ-min: 1.705\tQ-max: 1.727\tLives: 5\tReward: 1.0\tEpisode Mean: 112.2\n", - "87608:127682017\tQ-min: 1.662\tQ-max: 1.675\tLives: 5\tReward: 2.0\tEpisode Mean: 112.2\n", - "87608:127682069\tQ-min: 1.885\tQ-max: 1.895\tLives: 5\tReward: 3.0\tEpisode Mean: 112.2\n", - "87608:127682107\tQ-min: 1.982\tQ-max: 2.009\tLives: 5\tReward: 4.0\tEpisode Mean: 112.2\n", - "87608:127682139\tQ-min: 1.951\tQ-max: 1.975\tLives: 5\tReward: 5.0\tEpisode Mean: 112.2\n", - "87608:127682172\tQ-min: 2.009\tQ-max: 2.028\tLives: 5\tReward: 9.0\tEpisode Mean: 112.2\n", - "87608:127682206\tQ-min: 1.868\tQ-max: 1.909\tLives: 5\tReward: 10.0\tEpisode Mean: 112.2\n", - "87608:127682254\tQ-min: 1.723\tQ-max: 1.741\tLives: 5\tReward: 11.0\tEpisode Mean: 112.2\n", - "87608:127682317\tQ-min: 1.762\tQ-max: 1.793\tLives: 5\tReward: 12.0\tEpisode Mean: 112.2\n", - "87608:127682376\tQ-min: 1.661\tQ-max: 1.687\tLives: 5\tReward: 13.0\tEpisode Mean: 112.2\n", - "87608:127682437\tQ-min: 1.724\tQ-max: 1.744\tLives: 5\tReward: 14.0\tEpisode Mean: 112.2\n", - "87608:127682485\tQ-min: 2.060\tQ-max: 2.081\tLives: 5\tReward: 15.0\tEpisode Mean: 112.2\n", - "87608:127682518\tQ-min: 1.960\tQ-max: 1.978\tLives: 5\tReward: 16.0\tEpisode Mean: 112.2\n", - "87608:127682548\tQ-min: 1.963\tQ-max: 1.994\tLives: 5\tReward: 17.0\tEpisode Mean: 112.2\n", - "87608:127682580\tQ-min: 1.937\tQ-max: 1.969\tLives: 5\tReward: 18.0\tEpisode Mean: 112.2\n", - "87608:127682612\tQ-min: 2.015\tQ-max: 2.045\tLives: 5\tReward: 19.0\tEpisode Mean: 112.2\n", - "87608:127682647\tQ-min: 1.952\tQ-max: 2.102\tLives: 5\tReward: 20.0\tEpisode Mean: 112.2\n", - "87608:127682680\tQ-min: 2.053\tQ-max: 2.100\tLives: 5\tReward: 21.0\tEpisode Mean: 112.2\n", - "87608:127682714\tQ-min: 2.073\tQ-max: 2.150\tLives: 5\tReward: 25.0\tEpisode Mean: 112.2\n", - "87608:127682751\tQ-min: 2.129\tQ-max: 2.155\tLives: 5\tReward: 26.0\tEpisode Mean: 112.2\n", - "87608:127682788\tQ-min: 2.132\tQ-max: 2.283\tLives: 5\tReward: 30.0\tEpisode Mean: 112.2\n", - "87608:127682821\tQ-min: 2.081\tQ-max: 2.142\tLives: 5\tReward: 34.0\tEpisode Mean: 112.2\n", - "87608:127682852\tQ-min: 2.203\tQ-max: 2.245\tLives: 5\tReward: 35.0\tEpisode Mean: 112.2\n", - "87608:127682883\tQ-min: 2.171\tQ-max: 2.215\tLives: 5\tReward: 36.0\tEpisode Mean: 112.2\n", - "87608:127682914\tQ-min: 2.053\tQ-max: 2.085\tLives: 5\tReward: 37.0\tEpisode Mean: 112.2\n", - "87608:127682935\tQ-min: 0.017\tQ-max: 0.323\tLives: 4\tReward: 37.0\tEpisode Mean: 112.2\n", - "87608:127682989\tQ-min: 1.815\tQ-max: 1.885\tLives: 4\tReward: 38.0\tEpisode Mean: 112.2\n", - "87608:127683045\tQ-min: 2.031\tQ-max: 2.077\tLives: 4\tReward: 39.0\tEpisode Mean: 112.2\n", - "87608:127683099\tQ-min: 1.778\tQ-max: 1.807\tLives: 4\tReward: 40.0\tEpisode Mean: 112.2\n", - "87608:127683140\tQ-min: -0.037\tQ-max: 0.215\tLives: 3\tReward: 40.0\tEpisode Mean: 112.2\n", - "87608:127683186\tQ-min: 1.998\tQ-max: 2.027\tLives: 3\tReward: 41.0\tEpisode Mean: 112.2\n", - "87608:127683230\tQ-min: 2.024\tQ-max: 2.086\tLives: 3\tReward: 42.0\tEpisode Mean: 112.2\n", - "87608:127683277\tQ-min: 2.062\tQ-max: 2.135\tLives: 3\tReward: 46.0\tEpisode Mean: 112.2\n", - "87608:127683321\tQ-min: 2.004\tQ-max: 2.147\tLives: 3\tReward: 50.0\tEpisode Mean: 112.2\n", - "87608:127683354\tQ-min: 2.193\tQ-max: 2.238\tLives: 3\tReward: 51.0\tEpisode Mean: 112.2\n", - "87608:127683389\tQ-min: 2.188\tQ-max: 2.450\tLives: 3\tReward: 55.0\tEpisode Mean: 112.2\n", - "87608:127683409\tQ-min: 2.289\tQ-max: 2.449\tLives: 3\tReward: 56.0\tEpisode Mean: 112.2\n", - "87608:127683429\tQ-min: 2.306\tQ-max: 2.389\tLives: 3\tReward: 60.0\tEpisode Mean: 112.2\n", - "87608:127683450\tQ-min: 2.301\tQ-max: 2.498\tLives: 3\tReward: 61.0\tEpisode Mean: 112.2\n", - "87608:127683464\tQ-min: -0.149\tQ-max: 0.277\tLives: 2\tReward: 61.0\tEpisode Mean: 112.2\n", - "87608:127683524\tQ-min: 2.171\tQ-max: 2.437\tLives: 2\tReward: 65.0\tEpisode Mean: 112.2\n", - "87608:127683546\tQ-min: 2.401\tQ-max: 2.473\tLives: 2\tReward: 69.0\tEpisode Mean: 112.2\n", - "87608:127683567\tQ-min: 2.388\tQ-max: 2.501\tLives: 2\tReward: 73.0\tEpisode Mean: 112.2\n", - "87608:127683589\tQ-min: 2.242\tQ-max: 2.397\tLives: 2\tReward: 77.0\tEpisode Mean: 112.2\n", - "87608:127683608\tQ-min: 2.429\tQ-max: 2.583\tLives: 2\tReward: 78.0\tEpisode Mean: 112.2\n", - "87608:127683627\tQ-min: 2.494\tQ-max: 2.581\tLives: 2\tReward: 82.0\tEpisode Mean: 112.2\n", - "87608:127683646\tQ-min: 2.393\tQ-max: 2.474\tLives: 2\tReward: 83.0\tEpisode Mean: 112.2\n", - "87608:127683666\tQ-min: 2.321\tQ-max: 2.410\tLives: 2\tReward: 84.0\tEpisode Mean: 112.2\n", - "87608:127683686\tQ-min: 2.301\tQ-max: 2.409\tLives: 2\tReward: 85.0\tEpisode Mean: 112.2\n", - "87608:127683705\tQ-min: 2.244\tQ-max: 2.517\tLives: 2\tReward: 89.0\tEpisode Mean: 112.2\n", - "87608:127683726\tQ-min: 2.359\tQ-max: 2.571\tLives: 2\tReward: 93.0\tEpisode Mean: 112.2\n", - "87608:127683746\tQ-min: 2.380\tQ-max: 2.589\tLives: 2\tReward: 97.0\tEpisode Mean: 112.2\n", - "87608:127683767\tQ-min: 2.436\tQ-max: 2.557\tLives: 2\tReward: 101.0\tEpisode Mean: 112.2\n", - "87608:127683782\tQ-min: 0.033\tQ-max: 0.460\tLives: 1\tReward: 101.0\tEpisode Mean: 112.2\n", - "87608:127683825\tQ-min: 2.161\tQ-max: 2.451\tLives: 1\tReward: 105.0\tEpisode Mean: 112.2\n", - "87608:127683850\tQ-min: 2.134\tQ-max: 2.880\tLives: 1\tReward: 112.0\tEpisode Mean: 112.2\n", - "87608:127683872\tQ-min: 2.234\tQ-max: 2.596\tLives: 1\tReward: 116.0\tEpisode Mean: 112.2\n", - "87608:127683894\tQ-min: 2.373\tQ-max: 2.713\tLives: 1\tReward: 120.0\tEpisode Mean: 112.2\n", - "87608:127683916\tQ-min: 2.372\tQ-max: 2.832\tLives: 1\tReward: 127.0\tEpisode Mean: 112.2\n", - "87608:127683940\tQ-min: 2.511\tQ-max: 3.344\tLives: 1\tReward: 134.0\tEpisode Mean: 112.2\n", - "87608:127683962\tQ-min: 2.660\tQ-max: 3.282\tLives: 1\tReward: 138.0\tEpisode Mean: 112.2\n", - "87608:127683987\tQ-min: 1.914\tQ-max: 4.166\tLives: 1\tReward: 145.0\tEpisode Mean: 112.2\n", - "87608:127684012\tQ-min: 2.029\tQ-max: 3.421\tLives: 1\tReward: 152.0\tEpisode Mean: 112.2\n", - "87608:127684040\tQ-min: 2.152\tQ-max: 3.147\tLives: 1\tReward: 159.0\tEpisode Mean: 112.2\n", - "87608:127684066\tQ-min: 2.143\tQ-max: 3.272\tLives: 1\tReward: 166.0\tEpisode Mean: 112.2\n", - "87608:127684091\tQ-min: 2.826\tQ-max: 5.456\tLives: 1\tReward: 173.0\tEpisode Mean: 112.2\n", - "87608:127684116\tQ-min: 2.854\tQ-max: 3.919\tLives: 1\tReward: 180.0\tEpisode Mean: 112.2\n", - "87608:127684144\tQ-min: 2.312\tQ-max: 6.596\tLives: 1\tReward: 187.0\tEpisode Mean: 112.2\n", - "87608:127684148\tQ-min: 1.792\tQ-max: 6.289\tLives: 1\tReward: 194.0\tEpisode Mean: 112.2\n", - "87608:127684153\tQ-min: 3.303\tQ-max: 7.197\tLives: 1\tReward: 201.0\tEpisode Mean: 112.2\n", - "87608:127684189\tQ-min: 2.544\tQ-max: 6.305\tLives: 1\tReward: 208.0\tEpisode Mean: 112.2\n", - "87608:127684195\tQ-min: 3.050\tQ-max: 3.946\tLives: 1\tReward: 215.0\tEpisode Mean: 112.2\n", - "87608:127684200\tQ-min: 3.800\tQ-max: 5.113\tLives: 1\tReward: 222.0\tEpisode Mean: 112.2\n", - "87608:127684206\tQ-min: 2.798\tQ-max: 5.010\tLives: 1\tReward: 229.0\tEpisode Mean: 112.2\n", - "87608:127684212\tQ-min: 1.488\tQ-max: 4.776\tLives: 1\tReward: 236.0\tEpisode Mean: 112.2\n", - "87608:127684217\tQ-min: 2.993\tQ-max: 4.443\tLives: 1\tReward: 243.0\tEpisode Mean: 112.2\n", - "87608:127684221\tQ-min: 2.288\tQ-max: 4.760\tLives: 1\tReward: 250.0\tEpisode Mean: 112.2\n", - "87608:127684226\tQ-min: 3.077\tQ-max: 4.747\tLives: 1\tReward: 257.0\tEpisode Mean: 112.2\n", - "87608:127684245\tQ-min: -0.034\tQ-max: 0.486\tLives: 0\tReward: 257.0\tEpisode Mean: 119.0\n", - "87609:127684288\tQ-min: 1.793\tQ-max: 1.815\tLives: 5\tReward: 1.0\tEpisode Mean: 119.0\n", - "87609:127684331\tQ-min: 1.806\tQ-max: 1.825\tLives: 5\tReward: 2.0\tEpisode Mean: 119.0\n", - "87609:127684374\tQ-min: 1.904\tQ-max: 1.916\tLives: 5\tReward: 3.0\tEpisode Mean: 119.0\n", - "87609:127684411\tQ-min: 1.956\tQ-max: 1.980\tLives: 5\tReward: 4.0\tEpisode Mean: 119.0\n", - "87609:127684443\tQ-min: 1.983\tQ-max: 2.007\tLives: 5\tReward: 5.0\tEpisode Mean: 119.0\n", - "87609:127684478\tQ-min: 1.911\tQ-max: 1.938\tLives: 5\tReward: 6.0\tEpisode Mean: 119.0\n", - "87609:127684512\tQ-min: 1.798\tQ-max: 1.811\tLives: 5\tReward: 7.0\tEpisode Mean: 119.0\n", - "87609:127684556\tQ-min: 1.644\tQ-max: 1.655\tLives: 5\tReward: 8.0\tEpisode Mean: 119.0\n", - "87609:127684619\tQ-min: 1.700\tQ-max: 1.718\tLives: 5\tReward: 9.0\tEpisode Mean: 119.0\n", - "87609:127684685\tQ-min: 1.713\tQ-max: 1.743\tLives: 5\tReward: 10.0\tEpisode Mean: 119.0\n", - "87609:127684751\tQ-min: 1.632\tQ-max: 1.655\tLives: 5\tReward: 11.0\tEpisode Mean: 119.0\n", - "87609:127684798\tQ-min: 1.963\tQ-max: 1.979\tLives: 5\tReward: 12.0\tEpisode Mean: 119.0\n", - "87609:127684830\tQ-min: 1.980\tQ-max: 1.994\tLives: 5\tReward: 13.0\tEpisode Mean: 119.0\n", - "87609:127684864\tQ-min: 1.991\tQ-max: 2.009\tLives: 5\tReward: 14.0\tEpisode Mean: 119.0\n", - "87609:127684898\tQ-min: 2.052\tQ-max: 2.070\tLives: 5\tReward: 15.0\tEpisode Mean: 119.0\n", - "87609:127684928\tQ-min: 2.022\tQ-max: 2.052\tLives: 5\tReward: 16.0\tEpisode Mean: 119.0\n", - "87609:127684961\tQ-min: 1.990\tQ-max: 2.025\tLives: 5\tReward: 17.0\tEpisode Mean: 119.0\n", - "87609:127684995\tQ-min: 2.058\tQ-max: 2.099\tLives: 5\tReward: 21.0\tEpisode Mean: 119.0\n", - "87609:127685030\tQ-min: 1.981\tQ-max: 2.051\tLives: 5\tReward: 22.0\tEpisode Mean: 119.0\n", - "87609:127685064\tQ-min: 2.072\tQ-max: 2.088\tLives: 5\tReward: 23.0\tEpisode Mean: 119.0\n", - "87609:127685096\tQ-min: 2.144\tQ-max: 2.192\tLives: 5\tReward: 24.0\tEpisode Mean: 119.0\n", - "87609:127685131\tQ-min: 2.037\tQ-max: 2.160\tLives: 5\tReward: 28.0\tEpisode Mean: 119.0\n", - "87609:127685155\tQ-min: -0.157\tQ-max: 0.391\tLives: 4\tReward: 28.0\tEpisode Mean: 119.0\n", - "87609:127685200\tQ-min: 1.942\tQ-max: 1.993\tLives: 4\tReward: 29.0\tEpisode Mean: 119.0\n", - "87609:127685256\tQ-min: 1.632\tQ-max: 1.856\tLives: 4\tReward: 33.0\tEpisode Mean: 119.0\n", - "87609:127685312\tQ-min: 2.047\tQ-max: 2.110\tLives: 4\tReward: 34.0\tEpisode Mean: 119.0\n", - "87609:127685347\tQ-min: 2.252\tQ-max: 2.447\tLives: 4\tReward: 38.0\tEpisode Mean: 119.0\n", - "87609:127685369\tQ-min: 2.322\tQ-max: 2.428\tLives: 4\tReward: 39.0\tEpisode Mean: 119.0\n", - "87609:127685389\tQ-min: 2.230\tQ-max: 2.453\tLives: 4\tReward: 40.0\tEpisode Mean: 119.0\n", - "87609:127685409\tQ-min: 2.276\tQ-max: 2.474\tLives: 4\tReward: 44.0\tEpisode Mean: 119.0\n", - "87609:127685428\tQ-min: 2.284\tQ-max: 2.461\tLives: 4\tReward: 45.0\tEpisode Mean: 119.0\n", - "87609:127685445\tQ-min: 1.991\tQ-max: 2.370\tLives: 4\tReward: 46.0\tEpisode Mean: 119.0\n", - "87609:127685465\tQ-min: 2.400\tQ-max: 2.440\tLives: 4\tReward: 47.0\tEpisode Mean: 119.0\n", - "87609:127685485\tQ-min: 2.303\tQ-max: 2.431\tLives: 4\tReward: 48.0\tEpisode Mean: 119.0\n", - "87609:127685506\tQ-min: 2.367\tQ-max: 2.503\tLives: 4\tReward: 52.0\tEpisode Mean: 119.0\n", - "87609:127685519\tQ-min: -0.025\tQ-max: 0.163\tLives: 3\tReward: 52.0\tEpisode Mean: 119.0\n", - "87609:127685566\tQ-min: 2.125\tQ-max: 2.173\tLives: 3\tReward: 56.0\tEpisode Mean: 119.0\n", - "87609:127685623\tQ-min: 1.802\tQ-max: 1.973\tLives: 3\tReward: 57.0\tEpisode Mean: 119.0\n", - "87609:127685693\tQ-min: 1.866\tQ-max: 1.932\tLives: 3\tReward: 58.0\tEpisode Mean: 119.0\n", - "87609:127685747\tQ-min: 1.513\tQ-max: 1.983\tLives: 3\tReward: 62.0\tEpisode Mean: 119.0\n", - "87609:127685759\tQ-min: -0.099\tQ-max: 0.154\tLives: 2\tReward: 62.0\tEpisode Mean: 119.0\n", - "87609:127685819\tQ-min: 1.546\tQ-max: 2.547\tLives: 2\tReward: 66.0\tEpisode Mean: 119.0\n", - "87609:127685837\tQ-min: 2.272\tQ-max: 2.381\tLives: 2\tReward: 67.0\tEpisode Mean: 119.0\n", - "87609:127685858\tQ-min: 2.128\tQ-max: 2.495\tLives: 2\tReward: 71.0\tEpisode Mean: 119.0\n", - "87609:127685872\tQ-min: -0.050\tQ-max: 0.239\tLives: 1\tReward: 71.0\tEpisode Mean: 119.0\n", - "87609:127685918\tQ-min: 2.039\tQ-max: 2.260\tLives: 1\tReward: 75.0\tEpisode Mean: 119.0\n", - "87609:127685964\tQ-min: 2.164\tQ-max: 2.339\tLives: 1\tReward: 79.0\tEpisode Mean: 119.0\n", - "87609:127686011\tQ-min: 2.306\tQ-max: 2.872\tLives: 1\tReward: 83.0\tEpisode Mean: 119.0\n", - "87609:127686035\tQ-min: 2.208\tQ-max: 2.769\tLives: 1\tReward: 90.0\tEpisode Mean: 119.0\n", - "87609:127686048\tQ-min: -0.010\tQ-max: 0.147\tLives: 0\tReward: 90.0\tEpisode Mean: 117.7\n", - "87610:127686091\tQ-min: 1.730\tQ-max: 1.747\tLives: 5\tReward: 1.0\tEpisode Mean: 117.7\n", - "87610:127686143\tQ-min: 1.629\tQ-max: 1.657\tLives: 5\tReward: 2.0\tEpisode Mean: 117.7\n", - "87610:127686208\tQ-min: 1.713\tQ-max: 1.742\tLives: 5\tReward: 3.0\tEpisode Mean: 117.7\n", - "87610:127686251\tQ-min: 1.949\tQ-max: 1.982\tLives: 5\tReward: 4.0\tEpisode Mean: 117.7\n", - "87610:127686284\tQ-min: 1.976\tQ-max: 1.991\tLives: 5\tReward: 5.0\tEpisode Mean: 117.7\n", - "87610:127686313\tQ-min: 1.894\tQ-max: 1.917\tLives: 5\tReward: 6.0\tEpisode Mean: 117.7\n", - "87610:127686344\tQ-min: 1.839\tQ-max: 1.871\tLives: 5\tReward: 7.0\tEpisode Mean: 117.7\n", - "87610:127686390\tQ-min: 1.618\tQ-max: 1.636\tLives: 5\tReward: 8.0\tEpisode Mean: 117.7\n", - "87610:127686432\tQ-min: -0.040\tQ-max: 0.307\tLives: 4\tReward: 8.0\tEpisode Mean: 117.7\n", - "87610:127686476\tQ-min: 1.875\tQ-max: 1.899\tLives: 4\tReward: 9.0\tEpisode Mean: 117.7\n", - "87610:127686517\tQ-min: 1.867\tQ-max: 1.885\tLives: 4\tReward: 10.0\tEpisode Mean: 117.7\n", - "87610:127686557\tQ-min: 1.946\tQ-max: 1.969\tLives: 4\tReward: 11.0\tEpisode Mean: 117.7\n", - "87610:127686597\tQ-min: 1.940\tQ-max: 1.959\tLives: 4\tReward: 12.0\tEpisode Mean: 117.7\n", - "87610:127686632\tQ-min: 1.907\tQ-max: 1.936\tLives: 4\tReward: 13.0\tEpisode Mean: 117.7\n", - "87610:127686667\tQ-min: 1.965\tQ-max: 1.985\tLives: 4\tReward: 17.0\tEpisode Mean: 117.7\n", - "87610:127686701\tQ-min: 2.265\tQ-max: 2.471\tLives: 4\tReward: 21.0\tEpisode Mean: 117.7\n", - "87610:127686725\tQ-min: 2.356\tQ-max: 2.428\tLives: 4\tReward: 25.0\tEpisode Mean: 117.7\n", - "87610:127686750\tQ-min: 2.179\tQ-max: 2.463\tLives: 4\tReward: 29.0\tEpisode Mean: 117.7\n", - "87610:127686770\tQ-min: 2.372\tQ-max: 2.468\tLives: 4\tReward: 30.0\tEpisode Mean: 117.7\n", - "87610:127686789\tQ-min: 2.280\tQ-max: 2.383\tLives: 4\tReward: 31.0\tEpisode Mean: 117.7\n", - "87610:127686808\tQ-min: 2.272\tQ-max: 2.440\tLives: 4\tReward: 32.0\tEpisode Mean: 117.7\n", - "87610:127686821\tQ-min: 0.033\tQ-max: 0.192\tLives: 3\tReward: 32.0\tEpisode Mean: 117.7\n", - "87610:127686891\tQ-min: 1.759\tQ-max: 1.832\tLives: 3\tReward: 33.0\tEpisode Mean: 117.7\n", - "87610:127686955\tQ-min: 1.827\tQ-max: 1.860\tLives: 3\tReward: 34.0\tEpisode Mean: 117.7\n", - "87610:127687020\tQ-min: 1.674\tQ-max: 1.740\tLives: 3\tReward: 35.0\tEpisode Mean: 117.7\n", - "87610:127687074\tQ-min: 2.207\tQ-max: 2.296\tLives: 3\tReward: 39.0\tEpisode Mean: 117.7\n", - "87610:127687089\tQ-min: -0.018\tQ-max: 0.184\tLives: 2\tReward: 39.0\tEpisode Mean: 117.7\n", - "87610:127687134\tQ-min: 2.019\tQ-max: 2.062\tLives: 2\tReward: 40.0\tEpisode Mean: 117.7\n", - "87610:127687188\tQ-min: 1.810\tQ-max: 1.915\tLives: 2\tReward: 41.0\tEpisode Mean: 117.7\n", - "87610:127687241\tQ-min: 2.169\tQ-max: 2.252\tLives: 2\tReward: 45.0\tEpisode Mean: 117.7\n", - "87610:127687281\tQ-min: 2.391\tQ-max: 2.477\tLives: 2\tReward: 46.0\tEpisode Mean: 117.7\n", - "87610:127687314\tQ-min: 2.391\tQ-max: 2.472\tLives: 2\tReward: 47.0\tEpisode Mean: 117.7\n", - "87610:127687349\tQ-min: 2.098\tQ-max: 2.484\tLives: 2\tReward: 54.0\tEpisode Mean: 117.7\n", - "87610:127687371\tQ-min: 2.337\tQ-max: 2.577\tLives: 2\tReward: 55.0\tEpisode Mean: 117.7\n", - "87610:127687392\tQ-min: 2.367\tQ-max: 2.465\tLives: 2\tReward: 59.0\tEpisode Mean: 117.7\n", - "87610:127687411\tQ-min: 2.380\tQ-max: 2.512\tLives: 2\tReward: 60.0\tEpisode Mean: 117.7\n", - "87610:127687424\tQ-min: -0.228\tQ-max: 0.172\tLives: 1\tReward: 60.0\tEpisode Mean: 117.7\n", - "87610:127687470\tQ-min: 2.006\tQ-max: 2.056\tLives: 1\tReward: 61.0\tEpisode Mean: 117.7\n", - "87610:127687514\tQ-min: 1.924\tQ-max: 2.011\tLives: 1\tReward: 62.0\tEpisode Mean: 117.7\n", - "87610:127687559\tQ-min: 2.072\tQ-max: 2.121\tLives: 1\tReward: 66.0\tEpisode Mean: 117.7\n", - "87610:127687600\tQ-min: 2.294\tQ-max: 2.366\tLives: 1\tReward: 67.0\tEpisode Mean: 117.7\n", - "87610:127687621\tQ-min: -0.196\tQ-max: 0.134\tLives: 0\tReward: 67.0\tEpisode Mean: 115.5\n", - "87611:127687664\tQ-min: 1.729\tQ-max: 1.744\tLives: 5\tReward: 1.0\tEpisode Mean: 115.5\n", - "87611:127687717\tQ-min: 1.652\tQ-max: 1.662\tLives: 5\tReward: 2.0\tEpisode Mean: 115.5\n", - "87611:127687778\tQ-min: 1.706\tQ-max: 1.731\tLives: 5\tReward: 3.0\tEpisode Mean: 115.5\n", - "87611:127687828\tQ-min: 1.978\tQ-max: 1.993\tLives: 5\tReward: 4.0\tEpisode Mean: 115.5\n", - "87611:127687860\tQ-min: 1.933\tQ-max: 1.955\tLives: 5\tReward: 5.0\tEpisode Mean: 115.5\n", - "87611:127687891\tQ-min: 1.960\tQ-max: 1.971\tLives: 5\tReward: 6.0\tEpisode Mean: 115.5\n", - "87611:127687924\tQ-min: 1.744\tQ-max: 1.775\tLives: 5\tReward: 7.0\tEpisode Mean: 115.5\n", - "87611:127687973\tQ-min: 1.639\tQ-max: 1.650\tLives: 5\tReward: 8.0\tEpisode Mean: 115.5\n", - "87611:127688035\tQ-min: 1.706\tQ-max: 1.740\tLives: 5\tReward: 9.0\tEpisode Mean: 115.5\n", - "87611:127688101\tQ-min: 1.666\tQ-max: 1.698\tLives: 5\tReward: 10.0\tEpisode Mean: 115.5\n", - "87611:127688169\tQ-min: 1.679\tQ-max: 1.750\tLives: 5\tReward: 11.0\tEpisode Mean: 115.5\n", - "87611:127688219\tQ-min: 1.997\tQ-max: 2.017\tLives: 5\tReward: 12.0\tEpisode Mean: 115.5\n", - "87611:127688250\tQ-min: 1.939\tQ-max: 1.967\tLives: 5\tReward: 13.0\tEpisode Mean: 115.5\n", - "87611:127688271\tQ-min: 0.062\tQ-max: 0.184\tLives: 4\tReward: 13.0\tEpisode Mean: 115.5\n", - "87611:127688324\tQ-min: 1.693\tQ-max: 1.712\tLives: 4\tReward: 14.0\tEpisode Mean: 115.5\n", - "87611:127688388\tQ-min: 1.675\tQ-max: 1.700\tLives: 4\tReward: 15.0\tEpisode Mean: 115.5\n", - "87611:127688455\tQ-min: 1.696\tQ-max: 1.726\tLives: 4\tReward: 16.0\tEpisode Mean: 115.5\n", - "87611:127688503\tQ-min: 1.935\tQ-max: 1.997\tLives: 4\tReward: 17.0\tEpisode Mean: 115.5\n", - "87611:127688537\tQ-min: 2.007\tQ-max: 2.051\tLives: 4\tReward: 21.0\tEpisode Mean: 115.5\n", - "87611:127688572\tQ-min: 2.072\tQ-max: 2.103\tLives: 4\tReward: 22.0\tEpisode Mean: 115.5\n", - "87611:127688606\tQ-min: 2.079\tQ-max: 2.111\tLives: 4\tReward: 26.0\tEpisode Mean: 115.5\n", - "87611:127688654\tQ-min: 1.744\tQ-max: 1.785\tLives: 4\tReward: 27.0\tEpisode Mean: 115.5\n", - "87611:127688720\tQ-min: 1.739\tQ-max: 1.834\tLives: 4\tReward: 28.0\tEpisode Mean: 115.5\n", - "87611:127688795\tQ-min: 1.728\tQ-max: 1.873\tLives: 4\tReward: 32.0\tEpisode Mean: 115.5\n", - "87611:127688862\tQ-min: 1.820\tQ-max: 1.842\tLives: 4\tReward: 33.0\tEpisode Mean: 115.5\n", - "87611:127688903\tQ-min: -0.170\tQ-max: 0.218\tLives: 3\tReward: 33.0\tEpisode Mean: 115.5\n", - "87611:127688946\tQ-min: 1.959\tQ-max: 2.032\tLives: 3\tReward: 34.0\tEpisode Mean: 115.5\n", - "87611:127688992\tQ-min: 2.069\tQ-max: 2.191\tLives: 3\tReward: 38.0\tEpisode Mean: 115.5\n", - "87611:127689038\tQ-min: 2.113\tQ-max: 2.156\tLives: 3\tReward: 39.0\tEpisode Mean: 115.5\n", - "87611:127689077\tQ-min: 2.040\tQ-max: 2.087\tLives: 3\tReward: 40.0\tEpisode Mean: 115.5\n", - "87611:127689110\tQ-min: 2.259\tQ-max: 2.468\tLives: 3\tReward: 44.0\tEpisode Mean: 115.5\n", - "87611:127689131\tQ-min: 2.239\tQ-max: 2.321\tLives: 3\tReward: 45.0\tEpisode Mean: 115.5\n", - "87611:127689151\tQ-min: 2.311\tQ-max: 2.448\tLives: 3\tReward: 49.0\tEpisode Mean: 115.5\n", - "87611:127689174\tQ-min: 2.184\tQ-max: 2.402\tLives: 3\tReward: 53.0\tEpisode Mean: 115.5\n", - "87611:127689197\tQ-min: 1.841\tQ-max: 2.174\tLives: 3\tReward: 60.0\tEpisode Mean: 115.5\n", - "87611:127689217\tQ-min: 2.384\tQ-max: 2.471\tLives: 3\tReward: 61.0\tEpisode Mean: 115.5\n", - "87611:127689230\tQ-min: 0.197\tQ-max: 0.306\tLives: 2\tReward: 61.0\tEpisode Mean: 115.5\n", - "87611:127689277\tQ-min: 2.105\tQ-max: 2.176\tLives: 2\tReward: 62.0\tEpisode Mean: 115.5\n", - "87611:127689322\tQ-min: 2.283\tQ-max: 2.353\tLives: 2\tReward: 66.0\tEpisode Mean: 115.5\n", - "87611:127689382\tQ-min: 2.052\tQ-max: 2.270\tLives: 2\tReward: 70.0\tEpisode Mean: 115.5\n", - "87611:127689429\tQ-min: -0.143\tQ-max: 0.227\tLives: 1\tReward: 70.0\tEpisode Mean: 115.5\n", - "87611:127689487\tQ-min: 1.924\tQ-max: 2.064\tLives: 1\tReward: 74.0\tEpisode Mean: 115.5\n", - "87611:127689539\tQ-min: 2.145\tQ-max: 2.299\tLives: 1\tReward: 75.0\tEpisode Mean: 115.5\n", - "87611:127689582\tQ-min: 2.220\tQ-max: 2.257\tLives: 1\tReward: 76.0\tEpisode Mean: 115.5\n", - "87611:127689626\tQ-min: 2.219\tQ-max: 2.519\tLives: 1\tReward: 83.0\tEpisode Mean: 115.5\n", - "87611:127689647\tQ-min: 2.457\tQ-max: 2.644\tLives: 1\tReward: 87.0\tEpisode Mean: 115.5\n", - "87611:127689668\tQ-min: 2.354\tQ-max: 2.502\tLives: 1\tReward: 91.0\tEpisode Mean: 115.5\n", - "87611:127689691\tQ-min: 2.458\tQ-max: 2.631\tLives: 1\tReward: 95.0\tEpisode Mean: 115.5\n", - "87611:127689706\tQ-min: -0.110\tQ-max: 0.026\tLives: 0\tReward: 95.0\tEpisode Mean: 114.7\n", - "87612:127689750\tQ-min: 1.751\tQ-max: 1.769\tLives: 5\tReward: 1.0\tEpisode Mean: 114.7\n", - "87612:127689790\tQ-min: 1.795\tQ-max: 1.821\tLives: 5\tReward: 2.0\tEpisode Mean: 114.7\n", - "87612:127689833\tQ-min: 1.881\tQ-max: 1.913\tLives: 5\tReward: 3.0\tEpisode Mean: 114.7\n", - "87612:127689870\tQ-min: 1.958\tQ-max: 1.973\tLives: 5\tReward: 4.0\tEpisode Mean: 114.7\n", - "87612:127689900\tQ-min: 1.974\tQ-max: 1.990\tLives: 5\tReward: 5.0\tEpisode Mean: 114.7\n", - "87612:127689935\tQ-min: 1.944\tQ-max: 1.996\tLives: 5\tReward: 6.0\tEpisode Mean: 114.7\n", - "87612:127689967\tQ-min: 1.818\tQ-max: 1.843\tLives: 5\tReward: 7.0\tEpisode Mean: 114.7\n", - "87612:127690014\tQ-min: 1.702\tQ-max: 1.738\tLives: 5\tReward: 8.0\tEpisode Mean: 114.7\n", - "87612:127690082\tQ-min: 1.677\tQ-max: 1.687\tLives: 5\tReward: 9.0\tEpisode Mean: 114.7\n", - "87612:127690148\tQ-min: 1.712\tQ-max: 1.742\tLives: 5\tReward: 10.0\tEpisode Mean: 114.7\n", - "87612:127690189\tQ-min: -0.052\tQ-max: 0.091\tLives: 4\tReward: 10.0\tEpisode Mean: 114.7\n", - "87612:127690240\tQ-min: 1.706\tQ-max: 1.735\tLives: 4\tReward: 11.0\tEpisode Mean: 114.7\n", - "87612:127690299\tQ-min: 1.686\tQ-max: 1.711\tLives: 4\tReward: 12.0\tEpisode Mean: 114.7\n", - "87612:127690372\tQ-min: 1.751\tQ-max: 1.860\tLives: 4\tReward: 13.0\tEpisode Mean: 114.7\n", - "87612:127690423\tQ-min: 1.986\tQ-max: 2.016\tLives: 4\tReward: 14.0\tEpisode Mean: 114.7\n", - "87612:127690455\tQ-min: 1.965\tQ-max: 1.998\tLives: 4\tReward: 15.0\tEpisode Mean: 114.7\n", - "87612:127690475\tQ-min: -0.006\tQ-max: 0.193\tLives: 3\tReward: 15.0\tEpisode Mean: 114.7\n", - "87612:127690519\tQ-min: 1.897\tQ-max: 1.910\tLives: 3\tReward: 16.0\tEpisode Mean: 114.7\n", - "87612:127690564\tQ-min: 1.922\tQ-max: 1.952\tLives: 3\tReward: 20.0\tEpisode Mean: 114.7\n", - "87612:127690608\tQ-min: 1.945\tQ-max: 1.976\tLives: 3\tReward: 21.0\tEpisode Mean: 114.7\n", - "87612:127690645\tQ-min: 2.070\tQ-max: 2.110\tLives: 3\tReward: 22.0\tEpisode Mean: 114.7\n", - "87612:127690676\tQ-min: 2.045\tQ-max: 2.079\tLives: 3\tReward: 23.0\tEpisode Mean: 114.7\n", - "87612:127690710\tQ-min: 2.001\tQ-max: 2.037\tLives: 3\tReward: 27.0\tEpisode Mean: 114.7\n", - "87612:127690745\tQ-min: 1.910\tQ-max: 2.610\tLives: 3\tReward: 31.0\tEpisode Mean: 114.7\n", - "87612:127690767\tQ-min: 2.287\tQ-max: 2.336\tLives: 3\tReward: 32.0\tEpisode Mean: 114.7\n", - "87612:127690789\tQ-min: 2.112\tQ-max: 2.379\tLives: 3\tReward: 36.0\tEpisode Mean: 114.7\n", - "87612:127690812\tQ-min: 2.176\tQ-max: 2.347\tLives: 3\tReward: 40.0\tEpisode Mean: 114.7\n", - "87612:127690830\tQ-min: 2.378\tQ-max: 2.467\tLives: 3\tReward: 41.0\tEpisode Mean: 114.7\n", - "87612:127690849\tQ-min: 2.432\tQ-max: 2.491\tLives: 3\tReward: 42.0\tEpisode Mean: 114.7\n", - "87612:127690874\tQ-min: 2.261\tQ-max: 2.397\tLives: 3\tReward: 43.0\tEpisode Mean: 114.7\n", - "87612:127690896\tQ-min: 2.270\tQ-max: 2.462\tLives: 3\tReward: 47.0\tEpisode Mean: 114.7\n", - "87612:127690911\tQ-min: -0.162\tQ-max: 0.121\tLives: 2\tReward: 47.0\tEpisode Mean: 114.7\n", - "87612:127690942\tQ-min: -0.062\tQ-max: 0.110\tLives: 1\tReward: 47.0\tEpisode Mean: 114.7\n", - "87612:127690988\tQ-min: 2.038\tQ-max: 2.127\tLives: 1\tReward: 48.0\tEpisode Mean: 114.7\n", - "87612:127691043\tQ-min: 1.831\tQ-max: 1.892\tLives: 1\tReward: 49.0\tEpisode Mean: 114.7\n", - "87612:127691094\tQ-min: 2.045\tQ-max: 2.178\tLives: 1\tReward: 53.0\tEpisode Mean: 114.7\n", - "87612:127691130\tQ-min: 2.256\tQ-max: 2.296\tLives: 1\tReward: 54.0\tEpisode Mean: 114.7\n", - "87612:127691163\tQ-min: 2.092\tQ-max: 2.152\tLives: 1\tReward: 55.0\tEpisode Mean: 114.7\n", - "87612:127691199\tQ-min: 2.212\tQ-max: 2.310\tLives: 1\tReward: 59.0\tEpisode Mean: 114.7\n", - "87612:127691235\tQ-min: 2.541\tQ-max: 2.658\tLives: 1\tReward: 63.0\tEpisode Mean: 114.7\n", - "87612:127691257\tQ-min: 2.391\tQ-max: 2.556\tLives: 1\tReward: 64.0\tEpisode Mean: 114.7\n", - "87612:127691279\tQ-min: 2.441\tQ-max: 2.611\tLives: 1\tReward: 68.0\tEpisode Mean: 114.7\n", - "87612:127691301\tQ-min: 2.411\tQ-max: 2.553\tLives: 1\tReward: 72.0\tEpisode Mean: 114.7\n", - "87612:127691314\tQ-min: -0.039\tQ-max: 0.191\tLives: 0\tReward: 72.0\tEpisode Mean: 113.0\n", - "87613:127691359\tQ-min: 1.745\tQ-max: 1.768\tLives: 5\tReward: 1.0\tEpisode Mean: 113.0\n", - "87613:127691401\tQ-min: 1.827\tQ-max: 1.859\tLives: 5\tReward: 2.0\tEpisode Mean: 113.0\n", - "87613:127691444\tQ-min: 1.878\tQ-max: 1.923\tLives: 5\tReward: 3.0\tEpisode Mean: 113.0\n", - "87613:127691480\tQ-min: 1.994\tQ-max: 2.023\tLives: 5\tReward: 4.0\tEpisode Mean: 113.0\n", - "87613:127691512\tQ-min: 1.955\tQ-max: 1.972\tLives: 5\tReward: 8.0\tEpisode Mean: 113.0\n", - "87613:127691549\tQ-min: 2.132\tQ-max: 2.209\tLives: 5\tReward: 12.0\tEpisode Mean: 113.0\n", - "87613:127691569\tQ-min: 2.072\tQ-max: 2.212\tLives: 5\tReward: 13.0\tEpisode Mean: 113.0\n", - "87613:127691588\tQ-min: 2.069\tQ-max: 2.352\tLives: 5\tReward: 14.0\tEpisode Mean: 113.0\n", - "87613:127691608\tQ-min: 2.225\tQ-max: 2.355\tLives: 5\tReward: 15.0\tEpisode Mean: 113.0\n", - "87613:127691627\tQ-min: 2.158\tQ-max: 2.320\tLives: 5\tReward: 16.0\tEpisode Mean: 113.0\n", - "87613:127691646\tQ-min: 2.036\tQ-max: 2.218\tLives: 5\tReward: 17.0\tEpisode Mean: 113.0\n", - "87613:127691659\tQ-min: -0.011\tQ-max: 0.188\tLives: 4\tReward: 17.0\tEpisode Mean: 113.0\n", - "87613:127691706\tQ-min: 2.005\tQ-max: 2.059\tLives: 4\tReward: 18.0\tEpisode Mean: 113.0\n", - "87613:127691759\tQ-min: 1.721\tQ-max: 1.749\tLives: 4\tReward: 19.0\tEpisode Mean: 113.0\n", - "87613:127691814\tQ-min: 2.004\tQ-max: 2.048\tLives: 4\tReward: 20.0\tEpisode Mean: 113.0\n", - "87613:127691854\tQ-min: 2.058\tQ-max: 2.087\tLives: 4\tReward: 21.0\tEpisode Mean: 113.0\n", - "87613:127691887\tQ-min: 2.067\tQ-max: 2.160\tLives: 4\tReward: 25.0\tEpisode Mean: 113.0\n", - "87613:127691922\tQ-min: 2.128\tQ-max: 2.171\tLives: 4\tReward: 26.0\tEpisode Mean: 113.0\n", - "87613:127691956\tQ-min: 2.042\tQ-max: 2.120\tLives: 4\tReward: 27.0\tEpisode Mean: 113.0\n", - "87613:127691977\tQ-min: -0.151\tQ-max: 0.272\tLives: 3\tReward: 27.0\tEpisode Mean: 113.0\n", - "87613:127692019\tQ-min: 2.068\tQ-max: 2.130\tLives: 3\tReward: 28.0\tEpisode Mean: 113.0\n", - "87613:127692063\tQ-min: 2.098\tQ-max: 2.317\tLives: 3\tReward: 29.0\tEpisode Mean: 113.0\n", - "87613:127692114\tQ-min: 1.773\tQ-max: 1.810\tLives: 3\tReward: 30.0\tEpisode Mean: 113.0\n", - "87613:127692165\tQ-min: 2.143\tQ-max: 2.306\tLives: 3\tReward: 31.0\tEpisode Mean: 113.0\n", - "87613:127692196\tQ-min: 2.128\tQ-max: 2.189\tLives: 3\tReward: 32.0\tEpisode Mean: 113.0\n", - "87613:127692227\tQ-min: 2.140\tQ-max: 2.178\tLives: 3\tReward: 33.0\tEpisode Mean: 113.0\n", - "87613:127692261\tQ-min: 2.074\tQ-max: 2.304\tLives: 3\tReward: 34.0\tEpisode Mean: 113.0\n", - "87613:127692307\tQ-min: 1.637\tQ-max: 1.851\tLives: 3\tReward: 38.0\tEpisode Mean: 113.0\n", - "87613:127692374\tQ-min: 1.796\tQ-max: 1.816\tLives: 3\tReward: 39.0\tEpisode Mean: 113.0\n", - "87613:127692442\tQ-min: 1.801\tQ-max: 1.872\tLives: 3\tReward: 40.0\tEpisode Mean: 113.0\n", - "87613:127692511\tQ-min: 1.829\tQ-max: 1.987\tLives: 3\tReward: 44.0\tEpisode Mean: 113.0\n", - "87613:127692557\tQ-min: -0.002\tQ-max: 0.216\tLives: 2\tReward: 44.0\tEpisode Mean: 113.0\n", - "87613:127692609\tQ-min: 1.668\tQ-max: 1.715\tLives: 2\tReward: 45.0\tEpisode Mean: 113.0\n", - "87613:127692674\tQ-min: 1.819\tQ-max: 1.864\tLives: 2\tReward: 46.0\tEpisode Mean: 113.0\n", - "87613:127692740\tQ-min: 1.784\tQ-max: 1.839\tLives: 2\tReward: 47.0\tEpisode Mean: 113.0\n", - "87613:127692787\tQ-min: 2.032\tQ-max: 2.136\tLives: 2\tReward: 48.0\tEpisode Mean: 113.0\n", - "87613:127692820\tQ-min: 2.179\tQ-max: 2.296\tLives: 2\tReward: 52.0\tEpisode Mean: 113.0\n", - "87613:127692856\tQ-min: 2.184\tQ-max: 2.645\tLives: 2\tReward: 56.0\tEpisode Mean: 113.0\n", - "87613:127692878\tQ-min: 2.029\tQ-max: 2.462\tLives: 2\tReward: 60.0\tEpisode Mean: 113.0\n", - "87613:127692895\tQ-min: 0.059\tQ-max: 0.351\tLives: 1\tReward: 60.0\tEpisode Mean: 113.0\n", - "87613:127692954\tQ-min: 1.990\tQ-max: 2.033\tLives: 1\tReward: 61.0\tEpisode Mean: 113.0\n", - "87613:127693009\tQ-min: 2.110\tQ-max: 2.242\tLives: 1\tReward: 65.0\tEpisode Mean: 113.0\n", - "87613:127693071\tQ-min: 1.809\tQ-max: 2.087\tLives: 1\tReward: 69.0\tEpisode Mean: 113.0\n", - "87613:127693123\tQ-min: 2.208\tQ-max: 2.296\tLives: 1\tReward: 70.0\tEpisode Mean: 113.0\n", - "87613:127693156\tQ-min: 2.282\tQ-max: 2.346\tLives: 1\tReward: 71.0\tEpisode Mean: 113.0\n", - "87613:127693178\tQ-min: -0.106\tQ-max: 0.113\tLives: 0\tReward: 71.0\tEpisode Mean: 111.3\n", - "87614:127693222\tQ-min: 1.750\tQ-max: 1.774\tLives: 5\tReward: 1.0\tEpisode Mean: 111.3\n", - "87614:127693264\tQ-min: 1.777\tQ-max: 1.796\tLives: 5\tReward: 2.0\tEpisode Mean: 111.3\n", - "87614:127693306\tQ-min: 1.857\tQ-max: 1.881\tLives: 5\tReward: 3.0\tEpisode Mean: 111.3\n", - "87614:127693342\tQ-min: 1.973\tQ-max: 1.983\tLives: 5\tReward: 4.0\tEpisode Mean: 111.3\n", - "87614:127693373\tQ-min: 1.980\tQ-max: 1.996\tLives: 5\tReward: 5.0\tEpisode Mean: 111.3\n", - "87614:127693407\tQ-min: 2.012\tQ-max: 2.063\tLives: 5\tReward: 9.0\tEpisode Mean: 111.3\n", - "87614:127693442\tQ-min: 1.857\tQ-max: 1.899\tLives: 5\tReward: 10.0\tEpisode Mean: 111.3\n", - "87614:127693488\tQ-min: 1.685\tQ-max: 1.719\tLives: 5\tReward: 11.0\tEpisode Mean: 111.3\n", - "87614:127693554\tQ-min: 1.658\tQ-max: 1.684\tLives: 5\tReward: 12.0\tEpisode Mean: 111.3\n", - "87614:127693595\tQ-min: -0.077\tQ-max: 0.110\tLives: 4\tReward: 12.0\tEpisode Mean: 111.3\n", - "87614:127693639\tQ-min: 1.935\tQ-max: 1.953\tLives: 4\tReward: 13.0\tEpisode Mean: 111.3\n", - "87614:127693692\tQ-min: 1.743\tQ-max: 1.785\tLives: 4\tReward: 14.0\tEpisode Mean: 111.3\n", - "87614:127693756\tQ-min: 1.742\tQ-max: 1.772\tLives: 4\tReward: 15.0\tEpisode Mean: 111.3\n", - "87614:127693808\tQ-min: 2.016\tQ-max: 2.040\tLives: 4\tReward: 16.0\tEpisode Mean: 111.3\n", - "87614:127693837\tQ-min: 2.046\tQ-max: 2.066\tLives: 4\tReward: 17.0\tEpisode Mean: 111.3\n", - "87614:127693870\tQ-min: 2.004\tQ-max: 2.048\tLives: 4\tReward: 18.0\tEpisode Mean: 111.3\n", - "87614:127693904\tQ-min: 2.007\tQ-max: 2.039\tLives: 4\tReward: 22.0\tEpisode Mean: 111.3\n", - "87614:127693955\tQ-min: 1.739\tQ-max: 1.798\tLives: 4\tReward: 23.0\tEpisode Mean: 111.3\n", - "87614:127694016\tQ-min: 1.755\tQ-max: 1.785\tLives: 4\tReward: 24.0\tEpisode Mean: 111.3\n", - "87614:127694083\tQ-min: 1.750\tQ-max: 1.790\tLives: 4\tReward: 25.0\tEpisode Mean: 111.3\n", - "87614:127694148\tQ-min: 1.707\tQ-max: 1.768\tLives: 4\tReward: 26.0\tEpisode Mean: 111.3\n", - "87614:127694196\tQ-min: 2.009\tQ-max: 2.071\tLives: 4\tReward: 27.0\tEpisode Mean: 111.3\n", - "87614:127694227\tQ-min: 2.116\tQ-max: 2.152\tLives: 4\tReward: 28.0\tEpisode Mean: 111.3\n", - "87614:127694259\tQ-min: 2.037\tQ-max: 2.085\tLives: 4\tReward: 29.0\tEpisode Mean: 111.3\n", - "87614:127694292\tQ-min: 2.018\tQ-max: 2.095\tLives: 4\tReward: 30.0\tEpisode Mean: 111.3\n", - "87614:127694314\tQ-min: -0.285\tQ-max: 0.053\tLives: 3\tReward: 30.0\tEpisode Mean: 111.3\n", - "87614:127694371\tQ-min: 1.691\tQ-max: 1.742\tLives: 3\tReward: 31.0\tEpisode Mean: 111.3\n", - "87614:127694434\tQ-min: 1.687\tQ-max: 1.788\tLives: 3\tReward: 32.0\tEpisode Mean: 111.3\n", - "87614:127694488\tQ-min: 1.859\tQ-max: 1.963\tLives: 3\tReward: 36.0\tEpisode Mean: 111.3\n", - "87614:127694528\tQ-min: 2.115\tQ-max: 2.230\tLives: 3\tReward: 40.0\tEpisode Mean: 111.3\n", - "87614:127694561\tQ-min: 2.051\tQ-max: 2.173\tLives: 3\tReward: 41.0\tEpisode Mean: 111.3\n", - "87614:127694597\tQ-min: 2.074\tQ-max: 2.582\tLives: 3\tReward: 45.0\tEpisode Mean: 111.3\n", - "87614:127694619\tQ-min: 2.325\tQ-max: 2.481\tLives: 3\tReward: 49.0\tEpisode Mean: 111.3\n", - "87614:127694642\tQ-min: 2.416\tQ-max: 2.450\tLives: 3\tReward: 53.0\tEpisode Mean: 111.3\n", - "87614:127694663\tQ-min: 2.358\tQ-max: 2.467\tLives: 3\tReward: 54.0\tEpisode Mean: 111.3\n", - "87614:127694683\tQ-min: 2.331\tQ-max: 2.552\tLives: 3\tReward: 55.0\tEpisode Mean: 111.3\n", - "87614:127694696\tQ-min: -0.492\tQ-max: 0.288\tLives: 2\tReward: 55.0\tEpisode Mean: 111.3\n", - "87614:127694739\tQ-min: 1.990\tQ-max: 2.128\tLives: 2\tReward: 56.0\tEpisode Mean: 111.3\n", - "87614:127694797\tQ-min: 1.893\tQ-max: 1.925\tLives: 2\tReward: 57.0\tEpisode Mean: 111.3\n", - "87614:127694854\tQ-min: 2.099\tQ-max: 2.424\tLives: 2\tReward: 61.0\tEpisode Mean: 111.3\n", - "87614:127694898\tQ-min: 2.109\tQ-max: 2.136\tLives: 2\tReward: 65.0\tEpisode Mean: 111.3\n", - "87614:127694935\tQ-min: 2.176\tQ-max: 2.392\tLives: 2\tReward: 69.0\tEpisode Mean: 111.3\n", - "87614:127694956\tQ-min: 2.303\tQ-max: 2.598\tLives: 2\tReward: 73.0\tEpisode Mean: 111.3\n", - "87614:127694976\tQ-min: 2.456\tQ-max: 2.828\tLives: 2\tReward: 77.0\tEpisode Mean: 111.3\n", - "87614:127695000\tQ-min: 2.335\tQ-max: 2.567\tLives: 2\tReward: 81.0\tEpisode Mean: 111.3\n", - "87614:127695019\tQ-min: 2.435\tQ-max: 2.554\tLives: 2\tReward: 82.0\tEpisode Mean: 111.3\n", - "87614:127695040\tQ-min: 2.345\tQ-max: 2.483\tLives: 2\tReward: 86.0\tEpisode Mean: 111.3\n", - "87614:127695053\tQ-min: -0.225\tQ-max: 0.476\tLives: 1\tReward: 86.0\tEpisode Mean: 111.3\n", - "87614:127695101\tQ-min: 2.318\tQ-max: 2.462\tLives: 1\tReward: 90.0\tEpisode Mean: 111.3\n", - "87614:127695163\tQ-min: 1.764\tQ-max: 2.016\tLives: 1\tReward: 94.0\tEpisode Mean: 111.3\n", - "87614:127695227\tQ-min: 2.137\tQ-max: 2.588\tLives: 1\tReward: 98.0\tEpisode Mean: 111.3\n", - "87614:127695248\tQ-min: 2.451\tQ-max: 2.519\tLives: 1\tReward: 99.0\tEpisode Mean: 111.3\n", - "87614:127695260\tQ-min: -0.245\tQ-max: 0.131\tLives: 0\tReward: 99.0\tEpisode Mean: 110.9\n", - "87615:127695313\tQ-min: 1.644\tQ-max: 1.656\tLives: 5\tReward: 1.0\tEpisode Mean: 110.9\n", - "87615:127695365\tQ-min: 1.792\tQ-max: 1.807\tLives: 5\tReward: 2.0\tEpisode Mean: 110.9\n", - "87615:127695403\tQ-min: 1.872\tQ-max: 1.891\tLives: 5\tReward: 3.0\tEpisode Mean: 110.9\n", - "87615:127695441\tQ-min: 2.002\tQ-max: 2.014\tLives: 5\tReward: 4.0\tEpisode Mean: 110.9\n", - "87615:127695474\tQ-min: 1.930\tQ-max: 1.958\tLives: 5\tReward: 5.0\tEpisode Mean: 110.9\n", - "87615:127695507\tQ-min: 1.958\tQ-max: 1.969\tLives: 5\tReward: 6.0\tEpisode Mean: 110.9\n", - "87615:127695540\tQ-min: 1.767\tQ-max: 1.786\tLives: 5\tReward: 7.0\tEpisode Mean: 110.9\n", - "87615:127695586\tQ-min: 1.630\tQ-max: 1.665\tLives: 5\tReward: 8.0\tEpisode Mean: 110.9\n", - "87615:127695646\tQ-min: 1.651\tQ-max: 1.676\tLives: 5\tReward: 9.0\tEpisode Mean: 110.9\n", - "87615:127695714\tQ-min: 1.686\tQ-max: 1.746\tLives: 5\tReward: 10.0\tEpisode Mean: 110.9\n", - "87615:127695781\tQ-min: 1.688\tQ-max: 1.709\tLives: 5\tReward: 11.0\tEpisode Mean: 110.9\n", - "87615:127695828\tQ-min: 2.002\tQ-max: 2.024\tLives: 5\tReward: 12.0\tEpisode Mean: 110.9\n", - "87615:127695859\tQ-min: 1.929\tQ-max: 2.010\tLives: 5\tReward: 13.0\tEpisode Mean: 110.9\n", - "87615:127695894\tQ-min: 1.987\tQ-max: 2.036\tLives: 5\tReward: 14.0\tEpisode Mean: 110.9\n", - "87615:127695928\tQ-min: 2.012\tQ-max: 2.038\tLives: 5\tReward: 15.0\tEpisode Mean: 110.9\n", - "87615:127695962\tQ-min: 1.989\tQ-max: 2.046\tLives: 5\tReward: 19.0\tEpisode Mean: 110.9\n", - "87615:127695998\tQ-min: 1.958\tQ-max: 2.017\tLives: 5\tReward: 20.0\tEpisode Mean: 110.9\n", - "87615:127696031\tQ-min: 2.078\tQ-max: 2.125\tLives: 5\tReward: 21.0\tEpisode Mean: 110.9\n", - "87615:127696060\tQ-min: 2.000\tQ-max: 2.057\tLives: 5\tReward: 22.0\tEpisode Mean: 110.9\n", - "87615:127696091\tQ-min: 1.998\tQ-max: 2.015\tLives: 5\tReward: 23.0\tEpisode Mean: 110.9\n", - "87615:127696124\tQ-min: 1.998\tQ-max: 2.097\tLives: 5\tReward: 24.0\tEpisode Mean: 110.9\n", - "87615:127696158\tQ-min: 2.085\tQ-max: 2.110\tLives: 5\tReward: 25.0\tEpisode Mean: 110.9\n", - "87615:127696191\tQ-min: 2.076\tQ-max: 2.096\tLives: 5\tReward: 26.0\tEpisode Mean: 110.9\n", - "87615:127696226\tQ-min: 1.985\tQ-max: 2.069\tLives: 5\tReward: 27.0\tEpisode Mean: 110.9\n", - "87615:127696257\tQ-min: 2.051\tQ-max: 2.086\tLives: 5\tReward: 28.0\tEpisode Mean: 110.9\n", - "87615:127696290\tQ-min: 2.114\tQ-max: 2.177\tLives: 5\tReward: 29.0\tEpisode Mean: 110.9\n", - "87615:127696320\tQ-min: 2.070\tQ-max: 2.181\tLives: 5\tReward: 33.0\tEpisode Mean: 110.9\n", - "87615:127696355\tQ-min: 2.082\tQ-max: 2.118\tLives: 5\tReward: 34.0\tEpisode Mean: 110.9\n", - "87615:127696393\tQ-min: 2.118\tQ-max: 2.194\tLives: 5\tReward: 38.0\tEpisode Mean: 110.9\n", - "87615:127696430\tQ-min: 2.048\tQ-max: 2.562\tLives: 5\tReward: 42.0\tEpisode Mean: 110.9\n", - "87615:127696452\tQ-min: 2.323\tQ-max: 2.461\tLives: 5\tReward: 46.0\tEpisode Mean: 110.9\n", - "87615:127696472\tQ-min: 2.380\tQ-max: 2.524\tLives: 5\tReward: 50.0\tEpisode Mean: 110.9\n", - "87615:127696487\tQ-min: -0.145\tQ-max: 0.130\tLives: 4\tReward: 50.0\tEpisode Mean: 110.9\n", - "87615:127696535\tQ-min: 2.262\tQ-max: 2.437\tLives: 4\tReward: 54.0\tEpisode Mean: 110.9\n", - "87615:127696557\tQ-min: 2.189\tQ-max: 2.453\tLives: 4\tReward: 58.0\tEpisode Mean: 110.9\n", - "87615:127696572\tQ-min: -0.210\tQ-max: 0.358\tLives: 3\tReward: 58.0\tEpisode Mean: 110.9\n", - "87615:127696618\tQ-min: 2.260\tQ-max: 2.367\tLives: 3\tReward: 59.0\tEpisode Mean: 110.9\n", - "87615:127696675\tQ-min: 1.786\tQ-max: 1.954\tLives: 3\tReward: 60.0\tEpisode Mean: 110.9\n", - "87615:127696735\tQ-min: 2.186\tQ-max: 2.563\tLives: 3\tReward: 64.0\tEpisode Mean: 110.9\n", - "87615:127696758\tQ-min: 1.883\tQ-max: 2.417\tLives: 3\tReward: 71.0\tEpisode Mean: 110.9\n", - "87615:127696781\tQ-min: 2.119\tQ-max: 2.631\tLives: 3\tReward: 75.0\tEpisode Mean: 110.9\n", - "87615:127696801\tQ-min: 2.197\tQ-max: 2.471\tLives: 3\tReward: 76.0\tEpisode Mean: 110.9\n", - "87615:127696820\tQ-min: 2.283\tQ-max: 2.550\tLives: 3\tReward: 77.0\tEpisode Mean: 110.9\n", - "87615:127696841\tQ-min: 2.156\tQ-max: 2.532\tLives: 3\tReward: 81.0\tEpisode Mean: 110.9\n", - "87615:127696855\tQ-min: -0.188\tQ-max: 0.150\tLives: 2\tReward: 81.0\tEpisode Mean: 110.9\n", - "87615:127696899\tQ-min: 2.084\tQ-max: 2.306\tLives: 2\tReward: 85.0\tEpisode Mean: 110.9\n", - "87615:127696943\tQ-min: 2.099\tQ-max: 2.307\tLives: 2\tReward: 89.0\tEpisode Mean: 110.9\n", - "87615:127696972\tQ-min: -0.173\tQ-max: 0.569\tLives: 1\tReward: 89.0\tEpisode Mean: 110.9\n", - "87615:127697032\tQ-min: 1.803\tQ-max: 1.997\tLives: 1\tReward: 90.0\tEpisode Mean: 110.9\n", - "87615:127697097\tQ-min: 2.094\tQ-max: 2.214\tLives: 1\tReward: 94.0\tEpisode Mean: 110.9\n", - "87615:127697156\tQ-min: 2.298\tQ-max: 2.593\tLives: 1\tReward: 98.0\tEpisode Mean: 110.9\n", - "87615:127697183\tQ-min: -0.134\tQ-max: 0.380\tLives: 0\tReward: 98.0\tEpisode Mean: 110.4\n", - "87616:127697225\tQ-min: 1.703\tQ-max: 1.717\tLives: 5\tReward: 1.0\tEpisode Mean: 110.4\n", - "87616:127697278\tQ-min: 1.614\tQ-max: 1.670\tLives: 5\tReward: 2.0\tEpisode Mean: 110.4\n", - "87616:127697329\tQ-min: 1.848\tQ-max: 1.875\tLives: 5\tReward: 3.0\tEpisode Mean: 110.4\n", - "87616:127697362\tQ-min: 1.979\tQ-max: 2.041\tLives: 5\tReward: 4.0\tEpisode Mean: 110.4\n", - "87616:127697394\tQ-min: 1.945\tQ-max: 1.984\tLives: 5\tReward: 5.0\tEpisode Mean: 110.4\n", - "87616:127697423\tQ-min: 1.952\tQ-max: 1.963\tLives: 5\tReward: 6.0\tEpisode Mean: 110.4\n", - "87616:127697452\tQ-min: 1.739\tQ-max: 1.772\tLives: 5\tReward: 7.0\tEpisode Mean: 110.4\n", - "87616:127697495\tQ-min: 1.631\tQ-max: 1.654\tLives: 5\tReward: 8.0\tEpisode Mean: 110.4\n", - "87616:127697555\tQ-min: 1.644\tQ-max: 1.668\tLives: 5\tReward: 9.0\tEpisode Mean: 110.4\n", - "87616:127697616\tQ-min: 1.641\tQ-max: 1.662\tLives: 5\tReward: 10.0\tEpisode Mean: 110.4\n", - "87616:127697682\tQ-min: 0.999\tQ-max: 1.069\tLives: 5\tReward: 11.0\tEpisode Mean: 110.4\n", - "87616:127697733\tQ-min: 2.047\tQ-max: 2.073\tLives: 5\tReward: 12.0\tEpisode Mean: 110.4\n", - "87616:127697765\tQ-min: 1.928\tQ-max: 1.949\tLives: 5\tReward: 13.0\tEpisode Mean: 110.4\n", - "87616:127697799\tQ-min: 1.990\tQ-max: 2.029\tLives: 5\tReward: 14.0\tEpisode Mean: 110.4\n", - "87616:127697834\tQ-min: 2.052\tQ-max: 2.094\tLives: 5\tReward: 15.0\tEpisode Mean: 110.4\n", - "87616:127697869\tQ-min: 1.954\tQ-max: 2.032\tLives: 5\tReward: 19.0\tEpisode Mean: 110.4\n", - "87616:127697902\tQ-min: 2.165\tQ-max: 2.206\tLives: 5\tReward: 20.0\tEpisode Mean: 110.4\n", - "87616:127697935\tQ-min: 1.969\tQ-max: 2.142\tLives: 5\tReward: 24.0\tEpisode Mean: 110.4\n", - "87616:127697968\tQ-min: 2.081\tQ-max: 2.098\tLives: 5\tReward: 25.0\tEpisode Mean: 110.4\n", - "87616:127698001\tQ-min: 2.022\tQ-max: 2.087\tLives: 5\tReward: 26.0\tEpisode Mean: 110.4\n", - "87616:127698030\tQ-min: 2.072\tQ-max: 2.114\tLives: 5\tReward: 27.0\tEpisode Mean: 110.4\n", - "87616:127698061\tQ-min: 2.032\tQ-max: 2.050\tLives: 5\tReward: 28.0\tEpisode Mean: 110.4\n", - "87616:127698095\tQ-min: 1.993\tQ-max: 2.009\tLives: 5\tReward: 29.0\tEpisode Mean: 110.4\n", - "87616:127698128\tQ-min: 1.861\tQ-max: 2.085\tLives: 5\tReward: 30.0\tEpisode Mean: 110.4\n", - "87616:127698161\tQ-min: 2.075\tQ-max: 2.110\tLives: 5\tReward: 31.0\tEpisode Mean: 110.4\n", - "87616:127698194\tQ-min: 2.077\tQ-max: 2.090\tLives: 5\tReward: 32.0\tEpisode Mean: 110.4\n", - "87616:127698225\tQ-min: 2.082\tQ-max: 2.113\tLives: 5\tReward: 33.0\tEpisode Mean: 110.4\n", - "87616:127698261\tQ-min: 1.975\tQ-max: 2.015\tLives: 5\tReward: 34.0\tEpisode Mean: 110.4\n", - "87616:127698294\tQ-min: 2.130\tQ-max: 2.170\tLives: 5\tReward: 35.0\tEpisode Mean: 110.4\n", - "87616:127698329\tQ-min: 2.118\tQ-max: 2.168\tLives: 5\tReward: 36.0\tEpisode Mean: 110.4\n", - "87616:127698363\tQ-min: 2.075\tQ-max: 2.104\tLives: 5\tReward: 37.0\tEpisode Mean: 110.4\n", - "87616:127698401\tQ-min: 2.063\tQ-max: 2.154\tLives: 5\tReward: 41.0\tEpisode Mean: 110.4\n", - "87616:127698437\tQ-min: 2.146\tQ-max: 2.295\tLives: 5\tReward: 45.0\tEpisode Mean: 110.4\n", - "87616:127698460\tQ-min: -0.165\tQ-max: 0.070\tLives: 4\tReward: 45.0\tEpisode Mean: 110.4\n", - "87616:127698501\tQ-min: 1.846\tQ-max: 2.047\tLives: 4\tReward: 49.0\tEpisode Mean: 110.4\n", - "87616:127698549\tQ-min: 1.692\tQ-max: 1.968\tLives: 4\tReward: 53.0\tEpisode Mean: 110.4\n", - "87616:127698609\tQ-min: 1.560\tQ-max: 2.071\tLives: 4\tReward: 57.0\tEpisode Mean: 110.4\n", - "87616:127698663\tQ-min: 2.200\tQ-max: 2.331\tLives: 4\tReward: 58.0\tEpisode Mean: 110.4\n", - "87616:127698697\tQ-min: 2.231\tQ-max: 2.292\tLives: 4\tReward: 62.0\tEpisode Mean: 110.4\n", - "87616:127698719\tQ-min: -0.132\tQ-max: 0.132\tLives: 3\tReward: 62.0\tEpisode Mean: 110.4\n", - "87616:127698766\tQ-min: 2.324\tQ-max: 2.472\tLives: 3\tReward: 66.0\tEpisode Mean: 110.4\n", - "87616:127698780\tQ-min: -0.485\tQ-max: 0.170\tLives: 2\tReward: 66.0\tEpisode Mean: 110.4\n", - "87616:127698845\tQ-min: 1.700\tQ-max: 2.194\tLives: 2\tReward: 70.0\tEpisode Mean: 110.4\n", - "87616:127698858\tQ-min: -0.183\tQ-max: 0.237\tLives: 1\tReward: 70.0\tEpisode Mean: 110.4\n", - "87616:127698912\tQ-min: 1.906\tQ-max: 1.984\tLives: 1\tReward: 71.0\tEpisode Mean: 110.4\n", - "87616:127698981\tQ-min: 1.634\tQ-max: 2.061\tLives: 1\tReward: 75.0\tEpisode Mean: 110.4\n", - "87616:127699002\tQ-min: 2.364\tQ-max: 2.565\tLives: 1\tReward: 79.0\tEpisode Mean: 110.4\n", - "87616:127699023\tQ-min: 2.297\tQ-max: 2.456\tLives: 1\tReward: 83.0\tEpisode Mean: 110.4\n", - "87616:127699047\tQ-min: 2.357\tQ-max: 2.603\tLives: 1\tReward: 90.0\tEpisode Mean: 110.4\n", - "87616:127699071\tQ-min: 2.331\tQ-max: 2.936\tLives: 1\tReward: 97.0\tEpisode Mean: 110.4\n", - "87616:127699094\tQ-min: 2.704\tQ-max: 2.889\tLives: 1\tReward: 101.0\tEpisode Mean: 110.4\n", - "87616:127699118\tQ-min: 2.564\tQ-max: 3.139\tLives: 1\tReward: 105.0\tEpisode Mean: 110.4\n", - "87616:127699140\tQ-min: 2.534\tQ-max: 3.215\tLives: 1\tReward: 109.0\tEpisode Mean: 110.4\n", - "87616:127699152\tQ-min: 0.192\tQ-max: 0.497\tLives: 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0.094\tQ-max: 0.119\tLives: 2\tReward: 24.0\tEpisode Mean: 20.5\n", + "2421:1206925\tQ-min: 0.921\tQ-max: 1.427\tLives: 2\tReward: 28.0\tEpisode Mean: 20.5\n", + "2421:1206940\tQ-min: 0.208\tQ-max: 0.244\tLives: 1\tReward: 28.0\tEpisode Mean: 20.5\n", + "2421:1206992\tQ-min: 0.711\tQ-max: 1.110\tLives: 1\tReward: 32.0\tEpisode Mean: 20.5\n", + "2421:1207005\tQ-min: 0.121\tQ-max: 0.141\tLives: 0\tReward: 32.0\tEpisode Mean: 20.9\n" ] } ], @@ -2755,32 +1578,25 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can now print some statistics for the episode rewards, which vary greatly from one episode to the next." ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rewards for 30 episodes:\n", - "- Min: 43.0\n", - "- Mean: 111.233333333\n", - "- Max: 381.0\n", - "- Stdev: 62.8424927011\n" + "- Min: 8.0\n", + "- Mean: 20.866666666666667\n", + "- Max: 40.0\n", + "- Stdev: 8.155706931686273\n" ] } ], @@ -2795,31 +1611,26 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can also plot a histogram with the episode rewards." ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -2829,10 +1640,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Example States\n", "\n", @@ -2843,12 +1651,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 27, + "metadata": {}, "outputs": [], "source": [ "def print_q_values(idx):\n", @@ -2881,22 +1685,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This helper-function plots a state from the replay-memory and optionally prints the Q-values." ] }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 28, + "metadata": {}, "outputs": [], "source": [ "def plot_state(idx, print_q=True):\n", @@ -2928,30 +1725,23 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The replay-memory has room for 200k states but it is only partially full from the above call to `agent.run(num_episodes=1)`. This is how many states are actually used." ] }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 29, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "935" + "1061" ] }, - "execution_count": 30, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2963,22 +1753,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Get the Q-values from the replay-memory that are actually used." ] }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 30, + "metadata": {}, "outputs": [], "source": [ "q_values = replay_memory.q_values[0:num_used, :]" @@ -2986,22 +1769,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "For each state, calculate the min / max Q-values and their difference. This will be used to lookup interesting states in the following sections." ] }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 31, + "metadata": {}, "outputs": [], "source": [ "q_values_min = q_values.min(axis=1)\n", @@ -3011,10 +1787,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Example States: Highest Reward\n", "\n", @@ -3025,21 +1798,18 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "40" + "42" ] }, - "execution_count": 33, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -3051,10 +1821,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This state is where the ball hits the wall so the agent scores a point. \n", "\n", @@ -3065,21 +1832,19 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 33, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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AF5VSt7apOC7wR1rrW6kuF/f7tbI8Abyitd5LdcKrdhy0XwNOhu5/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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3088,23 +1853,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.682 \n", - "FIRE 1.676 \n", - "RIGHT 1.685 \n", - "LEFT 1.691 \n", - "RIGHTFIRE 1.690 \n", - "LEFTFIRE 1.699 (Action Taken)\n", + "NOOP 1.188 \n", + "FIRE 1.169 \n", + "RIGHT 1.148 \n", + "LEFT 1.278 (Action Taken)\n", "\n" ] }, { "data": { - "image/png": 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rpV5USr2hlDqtlPpS7fHNSqkfKaXO1n4OtqFstlLq50qp79fu71NK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\nITqcBHohhOhw/x/PY5u5EMbGGgAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3113,23 +1878,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.731 \n", - "FIRE 1.727 \n", - "RIGHT 1.734 \n", - "LEFT 1.734 \n", - "RIGHTFIRE 1.730 \n", - "LEFTFIRE 1.741 (Action Taken)\n", + "NOOP 1.220 \n", + "FIRE 1.206 \n", + "RIGHT 1.163 \n", + "LEFT 1.310 (Action Taken)\n", "\n" ] }, { "data": { - "image/png": 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l+vDi1UBwFqghCV/AwqOCzNnEjTEkRLeLXKIH6qYdCE8UJsRizARswKpmuBSik0Uq0YcP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\neiGE6HCS6IUQosNFYvZKYBK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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3138,23 +1903,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.744 \n", - "FIRE 1.729 \n", - "RIGHT 1.753 \n", - "LEFT 1.751 \n", - "RIGHTFIRE 1.754 (Action Taken)\n", - "LEFTFIRE 1.752 \n", + "NOOP 1.360 (Action Taken)\n", + "FIRE 1.271 \n", + "RIGHT 1.217 \n", + "LEFT 1.274 \n", "\n" ] }, { "data": { - "image/png": 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Dg4NYlkWpVGrYrpfmGm5F6M8D57XWh+vL36V2cVxWSm0FqP8db62JgrDmiG2v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\nb4n4PQoAWgAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3163,23 +1928,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.782 \n", - "FIRE 1.776 \n", - "RIGHT 1.789 \n", - "LEFT 1.787 \n", - "RIGHTFIRE 1.796 (Action Taken)\n", - "LEFTFIRE 1.784 \n", + "NOOP 1.301 \n", + "FIRE 1.335 (Action Taken)\n", + "RIGHT 1.243 \n", + "LEFT 1.305 \n", "\n" ] }, { "data": { - "image/png": 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2G2UkeTsx+quBC8BfichNwCvAF4Htxphz9XXOA9tbbSwiDwMPA+zevbuNZijK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\noihdjgq9oihKl6NCryiK0uX8fz3QJsgkVBabAAAAAElFTkSuQmCC\n", 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\n", 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1j1Jqi1LqJ0qp09W/g2tVWCE2itTt+LMsC9d1sW27JvBHLzNaf1W2TtVsL8XX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\nDCPlWok4lmtfC9ctdXvlpFz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T07q/rkhC6oK7C9jWWjKZDKVSifPnz7OwsIDneRhjNp2CK8hPvo159eRB/siRI5RKpb7OdtUJOexSFdyTO6YL7rOzs5w9e5ZWq6UdVjax1lIoFDhy5AiZTCZu2Wvsuxx2qQru0N/iMsZQKBSYnZ2l0+kouMsm1lpyuRzFYnFT3RE5zFIX3LejVphsxbXQVT9E+qU+uLux7VEUqTUmm+jaB5GtpT64e55HNpuNO1FdR5ocbsl6kM1mNRRSZIPUBnfXEstms+TzebLZblFdZ5kcbsl6kMlkyGazqhsiCakN7nBvXhm34yotIxu5UVVquYv0S3Vwh3sB3o1xF0nSxWwiW0t9cE/SKbeIyM6MxbmshrrJdlQ3RLY2Fi13l5rR6bdsRfVCZLPUB/fkjTq0E8t2VDdE+qU+uCfp9FtEZGcU3GWsqcUusrWxCu7akUVEdib1wd1dxKRWu2xH/TEim6U+uCcvXkruwLp45XDa+H9XPRDZWqqDe/LKVO3Ash1N+yuy2cDB3RiTAV4GrltrP2CMuQB8EVgAXgF+0VrbHuDz++YOiaJI84hIXz1w91IddnDf77otsp+GESU/DryeeP5bwO9Ya98KrAIfHeTDN45zz2QyfRc16XE4H8l6kKwnQ7avdVtkPw3UcjfGnAX+LfDfgV813T3sJ4Gf773l88B/BX5vr+twp9thGA5SVJlg+5GSOYi6LbKfBk3L/C7wa8CR3vMFYM1aG/SeXwPODLKCMAwV2GVHhtx63/e6LbKf9hzcjTEfAG5ba18xxvzEHpZ/BngG4OjRo1u+x1pLEAQEQaC7L8m2PM/D9/04VTOoYdZtkVEZpOX+HuCnjTHvBwrALPAcUDLGZHstnLPA9a0WttY+DzwPsLi4uOU5tUvHtNttwjDcr7yqjDF3sw5jhjrn/9DqtjFGQ3hkJPYc3K21nwY+DdBr3fwna+0vGGP+FPgQ3VEFTwNfGaSA7gbIYRhqlIxs4m6cPsyc+0HVbZH9tB/j3D8FfNEY89+AfwA+N+gHDrlVJhPkgK+DGHrdFtkvQwnu1tqvA1/v/X4J+JFhfC7cG8McBIGCu2ziWuz71em+n3VbZD+l9gpVd6odBAH1ep1OpxO30HQlorh6YK3F93183+97XeSwS11wT84VYq2l1WpRrVZpNBrxKfg47bzJs41xKnfaJYN7oVCgUCgwNTVFJpMBNOfMQcjlcmQyGTqdDkEQPHgBOVCpC+5JruXebDbHNrjL/kgGd2MMQRCoXhwwNzxZ2z2dUh3ck8axFbbxyskD7PgT2Tee5xFFUV9r3Q160AWH6TEWwX1cA+LGco/r9xBJ2uqCQjdkWdIjlcHdnWpba2m321QqFarV6lilZdzFNfl8Ps5LttvteFy2DCaZlgnDkKNHj8b1YhzqxzjKZDJxy3x6epr3ve99nD9/nhdffJEXX3wRay3ZbDekKAc/eqkK7snAbYwhiiIqlQq3b99mdXUVz/PiU8K0d5hFUUSxWOT48eNMT09Tr9dZXl6m3W7rYqwBuP+7qwdRFHH06FHm5+c31YlxaQiMi2KxSL1eJ4oiGo0GP/uzP8tTTz3FZz7zGV588UUAfN+P+0BktFIV3OHezmuMIQxDarUaS0tL3L59O57X3bUe0hbcXWvd5SPn5uaYnZ3lyJEj1Ot1bty4Qb1ej4ftJa+uTNt3SSsXrN12dtNTnD17tu+sSEF9+KanpzHGUKlU4gNrLpejVCrF71G/UnqkLrhv1Gq1KJfLVCoV4F5nTloly2eModVqxSN+1tbWaLfbCkBDkNzOxWKRVquV6noxCer1OrVaDegeXHO5HNbaeN8EDUFNk9QH941zuad9B06Wr91uUy6XyeVyVKvV+FRVQX1wye3s0nSyP/L5PJ1OJw7ipVKJT3ziE3z4wx8GukHf0e0O0yP1wd3ddcdJduqkUTLPG0URKysrNJtNyuXyprsGaSfYu2Q9cH0xsj+SfV2e5/Gxj32MX//1Xwfg9u3bXLx4MX5v2htfh8lYBPdkUExOQZDW0z9XvjAMWVlZoVwu0+l0NgXztJY/zZJ9MmmvB+POBfRGowHA8ePH+bmf+zmeffZZoBvYP/nJT/JXf/VX8TIaMJAeqQ/uydM8N5Z2XIa8RVFEs9nc9Hray512W9UDbdPhy+VyRFFEu929B/gTTzzBhz70IYrFIlevXuVTn/oUX/jCF4DuKJlcLketVlPrPSVSH9xFZDQymQy+78fB/fjx42QyGf7yL/+Sz372s30tdjdqSdJDwV0mglIzw9fpdOh0OvHzubk5rLW88MILcWCfnp4ml8uxurqqFnvKKDl2ABR4ZBxt7Ceamppibm6OYrEYv6Zx7eml4H4AlA+WcbBxZNr8/DwPP/xw/PyNN97glVdeoV6vUygUAKhWq33j3CU9lJYREeBejt2Njnn44Yf5sR/7MS5fvszXvvY1vvrVr3Lx4kXCMOxrsCRTN5IeCu4iAty7OY5TKBR429vextTUFF//+tdptVq89tprIyyh7IbSMiICEM8X47Tbber1Os1ms+91jWMfD2q5iwgAMzMzLCwsUK1WCcOQxcVFCoUC7XY7TsPkcjkKhQLlcnnEpZUHUXAXOaQ23j1penqad77znczNzdFqtXjkkUeYmprqu2Cs0+nEc7ZLuum/JHJIGWPwfT8O7p1Oh1KpxKOPPkq1WqVarfKNb3yD7373u3GnqbV2y6uuJX0U3EUOsY2za7qUDNwb+njnzh2AvhukSPqpZ0TkEHJ3OnNTBpRKJRYXF7HW0mg08H2fIAjiwA7dfLuMD7XcRQ6hZK69WCzy2GOPceHCBWZmZuIrU33fp1gsxuPe0zzVtmym4C5yCEVRRCaTYWpqitOnT3P8+HHm5ubI5XIsLy9z48YNLl++3DfsUfdFHS8K7iKHSPIG89lsljNnznDmzBkAVlZWyGazvPnmm7z66qtUq1Xg3o1RNI3GeFFwFzlEkpN8BUFANpsll8vRbrdZXl6mWq1y8+bNOLCD5kYaVwN1qBpjSsaYLxtj/tkY87ox5keNMfPGmL81xnyv9/PosAorclAmtW4nA3U2m42n9TXGUK1WuXjxInfv3gW6LXbX8SrjZ9DRMs8BX7PW/gDww8DrwLPAC9baR4EXes9Fxs1E1m13H9QjR45w/PjxeJx7Npslm832dZr6vq/pfMfYnoO7MWYO+HHgcwDW2ra1dg34IPD53ts+D/zMoIUUOUiTWLeTHaPGGI4ePcrJkyeZm5sjDEOq1SpBEPQNd2y1Wmq1j7FBWu4XgDvAHxpj/sEY8/vGmGnghLX2Zu89S8CJQQspcsAmrm4nW+BhGOJ5HoVCAd/3KZfLXL58matXr9Jut+P3Ktc+3gYJ7lngCeD3rLWPAzU2nKbabu3YsoYYY54xxrxsjHm5VqsNUAyRoRta3d73ku5QsgWey+VotVqsrq6yurrKysoK169fZ319XXOzT5BBgvs14Jq19qXe8y/T3SFuGWNOAfR+3t5qYWvt89baJ621T05PTw9QDJGhG1rdPpDS3keyFW6MoVQqMT8/T7PZ5PLly1y6dInl5eW+MexqsU+GPQd3a+0ScNUY81jvpfcCrwF/ATzde+1p4CsDlVDkgE1S3U6mY4wxTE9PMz09TRAE1Go1KpUK9Xodz/PUeTphBh3n/svAHxtjcsAl4D/QPWD8iTHmo8AV4KkB1yEyChNZt5PT98pkGyi4W2v/Edjq1PO9g3yuyKhNSt3eLpAnb4TtrlqVyaJZIUUm2Mbg7nnelrfJU0pm8mj6AZEJ5satu1vl1et1oijqGxWjVvtkUnAXmSDGmL7WeqFQIJ/PU6/XqdVqrK6u4vu+AvohoOAuMsGMMeRyub6WusayHw7KuYtMMGutbo13SCm4i0wo3/fJ5XKbUjUa0344KLiLTBAXxH3fZ2Zmhnw+H7feHbXiDwfl3EUmkGu1B0FAo9Gg1Wr1/V0XMk0+BXeRCRUEAa1Wq++uSnJ4KLiLTADP8+I7J0E39VKr1Wi32yMumYyKgrvIBPA8j3w+H49hbzabm1IxcrioQ1VkTG2c8TGTyZDNZvvmjXF/0+iYw0ctd5ExlewUjaKIIAgwxhCGYd/f1Hl6OCm4i0yAMAxptVp0Oh2stX03upbDScFdZEIk76YkouAuMoZcjt3l14MgUGtd+ii4i4whYwzZbBbf9+Ocuu6yJEkK7iJjyN3wWqNgZDsK7iJjyI2KAeIOVLXaJUnBXWSMuHQMdPPsmptdtqPgLjJm3D1QgyBQa122pStURUQmkIK7SMolO05dq929LrIdpWVEUs7zPDzPw1ob30XJWquUjNyXgrtIyhlj4uAOxPdEVXCX+1FwF0k510p3wx817FF2Qjl3kTGQzK8rsMtOKLiLjBl1pMpOKC0jkmKu89TN+KhWu+zUQC13Y8wnjDHfNcb8kzHmC8aYgjHmgjHmJWPMRWPMl4wxuWEVVuSgpKFuJ0fJuFy7OlJlp/Yc3I0xZ4CPAU9aa38QyAAfBn4L+B1r7VuBVeCjwyioyEFJQ912gV0pGNmrQXPuWaBojMkCU8BN4CeBL/f+/nngZwZch8gojKxuJy9aUitd9mrPwd1aex34H8CbdCv+OvAKsGatdbeEuQacGbSQIgcpLXVb49llEIOkZY4CHwQuAKeBaeCndrH8M8aYl40xL9dqtb0WQ2Tohlm397j+eGy7Arvs1SBpmX8NfN9ae8da2wH+HHgPUOqdygKcBa5vtbC19nlr7ZPW2ienp6cHKIbI0A2tbu+1AMq1y6AGCe5vAu82xkyZbk18L/Aa8HfAh3rveRr4ymBFFDlwI63barHLMAySc3+JbufSt4Dv9D7reeBTwK8aYy4CC8DnhlBOkQMzirq98QpU5dplUANdxGSt/Q3gNza8fAn4kUE+V2TUDrJua3SM7AdNPyAiMoEU3EVEJpCCu4jIBFJwFxmhZK5d+XYZJs0KKTJCCuiyX9RyFxkhXawk+0XBXURkAim4i4yQ0jKyXxTcRUQmkIK7SAoo9y7DpuAuIjKBFNxFUkC5dxk2BXcRkQmk4C4iMoEU3EVSSB2sMigFd5GUUWCXYdDcMjIRJqlD0lrbdwOPceC2/8Yyu++y8X2y/yYiuO91J1BFm0xuhsVx/v9aa8lms2Qymfj5TpZJBtOd7BfGmL7ttZd9abfbejfvjaJo1+WRrtQG991UsnHeiWU4kgEt+fs4140gCAiCYNTFkDGVmuC+3WnoOJ2aymh5XrcLaWPLddzq0LgflCQdUhPcoyjqq9Tu7u8PquSZTAbP83a1Q7j3RlFEGIYDl11Gz/0v3cPVhXFKzyRTJMYY5ubmmJmZAYjrqed5fd/HHbjCMCQIgng/cimdB+0XxhiiKIqXTX7mg7bbVvvRdsu616MoivftrQ66yb+5cmkf3ZtUBHdrLUEQYIwhDMP4n+7+sVtVZgDf9+MdIJPJ9HXqbFV5kn8PgoBKpUK5XNap75hzQaDVauH7PkEQYK0lk8nEwWQc+L5Pu90GIJ/P8653vYsnnngCz/Mol8sAZLPdXdbVZZeTr9VqrK6u0mg08H2f2dlZ5ubmyGazhGFIFEXxmY07gHieRyaTodlssrq6Sr1ex/O8vvdtxe1bmUyGIAio1WrU63WiKNp0QHE/3We2223q9TpBEMRlh3v7bLPZjP/WbrdZWVmJv/tODzrSlZrg7iq1a3lEUUSn07lvcM/n85w8eZKzZ8+Sy+W2bd245dxne55Ho9Hg6tWrNBqNOLjrdHg8RVFEs9mkUqnErUgX3F3LMq2SDREXAKEbtE+fPs0P/dAP4Xked+/eBaBQKGCtjeu6C/br6+vcvn2bcrlMLpfjxIkTLCws4Ps+nU4nDuZwL7hnMhkymQy1Wo1bt25RLpcxxuD7/n3PeFx5s9ksnU6HtbU1yuVy/JnJfS0Z3I0xNBoNqtUq7XY7fs39PYoiarUa7XabbDZLu92mUqnsz4Y/BFIR3OFer3jy1PBBra5cLsf8/Dxnz56lWCzGQTq5k2y1jmw2S7VapVKpcOPGDRqNRrxOBffx4878Wq0WmUwmDu6uHqQ5uG+XPnINnlqtFjdG4F56xv10rd9Go0Gz2aTVasXP6/U62Ww2PpPZLrjX6/V4WSAR2O4AAAdSSURBVM/z4u13v9SJ53lxh2+73abT6cQt962+o3u90+nQ6XQIgmDTwcyl1Nx+vLFhJ7uTiuDudk7oD+4uD3i/FoQ7JU+mcO4X3JMpn3HKx8ruuA76tP9/k2XcGEhd2sT9dMMjkwcr13J379v4cKmp5Ge6fcoF962Whf79ZWOZ3cO1vt3v7nkYhvG6Nr7fvSe5nyY/K/ke2btUBHfYeoTDViNokjtru93mzp07ZLNZfN/f1CG0UfIUsdVqcffuXTqdzpafLeMlGTySrc5xCRBblTOTycQdo67lm81m40aNtRbf9+PXk+91+4Tv+/FZsAuYG4N7clnP8+J1PGjbuXW7zwnDMC5nMt2SbLknA/fGYasu2CcPMOPy/0ujVAR3l79Ldqi6Cne/I3ir1WJpaYlyuRy3bO5XKZM7fBAE1Ot1BfcxlfxfhWFIvV5nfX29r5/GBYg0j7ZIpmWS5QyCgIsXL5LP5zHGUKvVgHvBfWNHZaPRYH19nWazie/7HDlyhJmZmb6W+lZpGc/zaDablMtlms1mX5Ddqt8q+VoyT95sNvuC+MZUmNuPW60W7XZ7U1rGaTabfWkfl2Zy5VGw37lUBPcwDKlWq5uCe61Wo9Vq9VWUZOUKgoByubznThelZSZDp9Ph7t27ZLNZ8vn8pmF5yQCRZsmGRrvd5pvf/CavvvoqwI7OSpMpzGTr90E2LrvbAHq/1GnSTq6GTf5tq85w7a87l4rg3mg0ePXVVzeNaGk2m9y4cWPT0TtJAfpw2io9V61W+4bEOuMS3B3XIm61WmNXdkkPs4MLFf4A+ABw21r7g73X5oEvAeeBy8BT1tpV0z0UPwe8H6gD/95a+60HFSKbzdpSqbRxvYRhSKvVik/VRO7nfq1aa+2mPx5E3TbGqOUh+2qrug07C+4/DlSBP0rsAJ8BVqy1v2mMeRY4aq39lDHm/cAv090B3gU8Z61914MKN8gOMGgOTq3+w2Gb4J7qur3VxGEb897JC3u26kjeyRWqD0qVPMhezp53+n416h5su+De94/d7kG3FfNPiedvAKd6v58C3uj9/lngI1u97wGfb/XQYz8fqtt6TOpju7q315t1nLDW3uz9vgSc6P1+BriaeN+13msPtNUYXY11lZ1KDq3b+NiloddtkVEYuEPVWmv3cuppjHkGeMY91+mXDGI/0mvDqtsio7DXlvstY8wpgN7P273XrwOLifed7b22ibX2eWvtk9baJ/dYBpH9oLotE2Gvwf0vgKd7vz8NfCXx+r8zXe8G1hOnuCLjQHVbJsMOOoS+ANwEOnTzjB8FFoAXgO8B/xuY773XAP8T+H/Ad4And9hhO/JOCT0m+6G6rcekPrarew8cCnkQNBZY9tu2w8X2meq27Lft6vZe0zIiIpJiCu4iIhNIwV1EZAIpuIuITKBUzAoJLAO13s+0OYbKtRtpLNfDI1y36vbuqVw7t23dTsVoGQBjzMtpvOhD5dqdtJZrlNK6TVSu3UlrubajtIyIyARScBcRmUBpCu7Pj7oA21C5diet5RqltG4TlWt30lquLaUm5y4iIsOTppa7iIgMSSqCuzHmp4wxbxhjLvZubTaqciwaY/7OGPOaMea7xpiP916fN8b8rTHme72fR0dQtowx5h+MMV/tPb9gjHmpt82+ZIzJHXSZeuUoGWO+bIz5Z2PM68aYH03D9koD1esdly91dXsS6vXIg7sxJkN3tr1/A7wD+Igx5h0jKk4AfNJa+w7g3cAv9cryLPCCtfZRujMGjmJH/TjweuL5bwG/Y619K7BKd0bDUXgO+Jq19geAH6ZbxjRsr5FSvd6VNNbt8a/XO5m2dD8fwI8Cf5N4/mng06MuV68sXwHexzb31TzAcpylW5l+Evgq3elnl4HsVtvwAMs1B3yfXt9N4vWRbq80PFSvd1yW1NXtSanXI2+5k9J7UxpjzgOPAy+x/X01D8rvAr8GuHsRLgBr1tqg93xU2+wCcAf4w95p9e8bY6YZ/fZKA9XrnUlj3Z6Iep2G4J46xpgZ4M+AX7HWlpN/s93D9oENMTLGfAC4ba195aDWuQtZ4Ang96y1j9O9zL7vVPWgt5dsL031uleetNbtiajXaQjuO7435UEwxvh0d4A/ttb+ee/l7e6reRDeA/y0MeYy8EW6p6/PASVjjJsbaFTb7BpwzVr7Uu/5l+nuFKPcXmmhev1gaa3bE1Gv0xDcvwk82ushzwEfpnu/ygNnjDHA54DXrbW/nfjTdvfV3HfW2k9ba89aa8/T3Tb/x1r7C8DfAR8aRZkSZVsCrhpjHuu99F7gNUa4vVJE9foB0lq3J6Zejzrp3+uceD/wL3TvT/lfRliOf0X3VOvbwD/2Hu9nm/tqjqB8PwF8tff7W4BvABeBPwXyIyrTO4GXe9vsfwFH07K9Rv1Qvd5VGVNVtyehXusKVRGRCZSGtIyIiAyZgruIyARScBcRmUAK7iIiE0jBXURkAim4i4hMIAV3EZEJpOAuIjKB/j+IaVT/eWPCBgAAAABJRU5ErkJggg==\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3213,23 +1978,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.794 \n", - "FIRE 1.805 (Action Taken)\n", - "RIGHT 1.787 \n", - "LEFT 1.786 \n", - "RIGHTFIRE 1.790 \n", - "LEFTFIRE 1.794 \n", + "NOOP 1.362 \n", + "FIRE 1.359 \n", + "RIGHT 1.260 \n", + "LEFT 1.496 (Action Taken)\n", "\n" ] }, { "data": { - "image/png": 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8+uqrjI2N+XMymMJ9wtKI0AuCEDqCYZhyucxPfvITjhw5QqFQYPfu3fzyL/8y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\na8BVrfUd9c9GgO8BE8Ak8Ft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\n", 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ByOaBBx7gC1/4ArD8EJ+dnSWTyQQdJsPTiS4tLcn/u04iJ/SNf2C4YdZk4hhB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\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3263,12 +2028,10 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.817 \n", - "FIRE 0.828 \n", - "RIGHT 0.820 \n", - "LEFT 0.832 (Action Taken)\n", - "RIGHTFIRE 0.825 \n", - "LEFTFIRE 0.832 \n", + "NOOP 0.394 \n", + "FIRE 0.386 \n", + "RIGHT 0.369 \n", + "LEFT 0.436 (Action Taken)\n", "\n" ] } @@ -3280,10 +2043,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Example: Highest Q-Value\n", "\n", @@ -3292,20 +2052,16 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 34, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "908" + "517" ] }, - "execution_count": 35, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -3317,22 +2073,21 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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pSqVCIpGgVCpx5coVO2rW9PCjRLhHn0qlmJ6eJpfLXTeeIEzzMZvRwo7j4Hke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\n7xAJoVdK2UfDIAiIxWLE43E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\n", 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PWs/ziKKICxcukM/nmZycbLqUX0mrx3Vdm/d1HIdCoUAmkyGVStkOzSQGejM6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\nTE1N3fZ7sTxKKVKpFLlcjnw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\n", 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YEu72GnePCW0zLeOoCb1pjZsBSxNmGab12n3ft9fT2usJgoCRkRF27NhBqVQi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\nemnRr45W2yyXy+RyOYaGhpo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\n", 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y8803c//995NIJDhx4gQnTpygXC6jlCKRSAAfZN2YcIXp+ocV4436vm//9zyP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\nDd1EgaRSKgqkgGHgE8CP5t/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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3416,23 +2171,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.979 \n", - "FIRE 0.992 \n", - "RIGHT 1.000 \n", - "LEFT 0.997 \n", - "RIGHTFIRE 1.001 (Action Taken)\n", - "LEFTFIRE 0.995 \n", + "NOOP 0.506 \n", + "FIRE 0.514 \n", + "RIGHT 0.564 (Action Taken)\n", + "LEFT 0.548 \n", "\n" ] }, { "data": { - "image/png": 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P8vd///eMjY3ZY/jDPoLQSDMe/QPAP1NKfRqIMxfH/DrQo5SKzHs+g8BIMwVc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\nQi8IgtDm/P+4HHIR/f5GqwAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3441,12 +2196,10 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.024 \n", - "FIRE 1.026 \n", - "RIGHT 1.013 \n", - "LEFT 1.027 (Action Taken)\n", - "RIGHTFIRE 1.040 \n", - "LEFTFIRE 1.022 \n", + "NOOP 0.503 \n", + "FIRE 0.513 \n", + "RIGHT 0.559 (Action Taken)\n", + "LEFT 0.520 \n", "\n" ] } @@ -3458,10 +2211,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Example: Loss of Life\n", "\n", @@ -3470,20 +2220,16 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 36, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "201" + "115" ] }, - "execution_count": 37, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -3495,22 +2241,21 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ { "data": { - "image/png": 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zEuiFEKLPSY5eCLGhwmkarfWKna9i60igF0JsKBPolVL4vi+BPQIk0AshOmJZ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\nGZxRSu0CaPx7pfH+NLAv9Lm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\n", 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rpWokyHeHBHohREcFx8TLiJpwkNSNEKItSiksa+1QIkG++6RFL4Roi2nBK6XQ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\nXAR+AIw23quA/w28DZwFHpS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\n", 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1BPgukxa9EKIjSqkNO1slwMeDBHohRMfaA7204uNFAr0QQvQ5CfRCCNHnJNAL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\nQohdEItAr5T6JaXUa0qpC0q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\n", 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lNXql1KbRBB8PmuiVUh1rH1Gjtfh40USvlFJ9ThO9Ukr1OU30SqlV0xOfepMm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\njO3+iuvVzHy2mTfgZ4C/jTz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+ "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3594,23 +2339,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.047 \n", - "FIRE -0.058 \n", - "RIGHT -0.008 \n", - "LEFT -0.001 \n", - "RIGHTFIRE -0.027 \n", - "LEFTFIRE 0.052 (Action Taken)\n", + "NOOP 0.225 \n", + "FIRE 0.232 \n", + "RIGHT 0.232 \n", + "LEFT 0.236 (Action Taken)\n", "\n" ] }, { "data": { - "image/png": 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MxpgelUZ10kCvlFJ9TgO9Ukr1OQ30SinV5zTQK6XWbLWRNJqPTzav1wVQSiWX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\nXb1OQqBPHBEZBP4G+G1jTCH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+ "image/png": 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\n", 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olVKqx2nQK6VUj9OgV0pt2Hp98caYXS6J2gwNeqXUhuiAa/fyOl0ApVSyacB3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\nqO/ifhkYEhG7VlGnPrMpYMo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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3644,23 +2389,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.013 \n", - "FIRE 0.044 \n", - "RIGHT 0.089 \n", - "LEFT -0.120 \n", - "RIGHTFIRE 0.029 \n", - "LEFTFIRE 0.115 (Action Taken)\n", + "NOOP 0.184 (Action Taken)\n", + "FIRE 0.171 \n", + "RIGHT 0.188 \n", + "LEFT 0.183 \n", "\n" ] }, { "data": { - "image/png": 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RK6XUgNNEr5RSA04TvVJq3USk10VQm6CJXim1Lprk+5fX6wIopeJNE3z/0xq9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\nTkSuQmCC\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3669,23 +2414,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP -0.117 \n", - "FIRE 0.109 \n", - "RIGHT 0.040 \n", - "LEFT -0.123 \n", - "RIGHTFIRE -0.003 \n", - "LEFTFIRE 0.116 (Action Taken)\n", + "NOOP 0.187 \n", + "FIRE 0.177 \n", + "RIGHT 0.194 (Action Taken)\n", + "LEFT 0.191 \n", "\n" ] }, { "data": { - "image/png": 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NeqWU6nMa9Eop1ec06JVStyQi3S6C6pAGvVLqtjTse5sGvVJK9TkNeqWU6nMa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\nRK5CYII=\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3694,23 +2439,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.168 \n", - "FIRE 0.004 \n", - "RIGHT -0.061 \n", - "LEFT 0.089 \n", - "RIGHTFIRE -0.060 \n", - "LEFTFIRE 0.273 (Action Taken)\n", + "NOOP 0.149 (Action Taken)\n", + "FIRE 0.113 \n", + "RIGHT 0.141 \n", + "LEFT 0.126 \n", "\n" ] }, { "data": { - "image/png": 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Ar5RSKaeBXimlUk4DvVJKpZwGeqWUSjkN9EoplXIa6JVSKuU00CulVMppoFdK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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3719,23 +2464,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.096 \n", - "FIRE 0.034 \n", - "RIGHT -0.001 \n", - "LEFT -0.031 \n", - "RIGHTFIRE -0.153 \n", - "LEFTFIRE 0.247 (Action Taken)\n", + "NOOP 0.140 (Action Taken)\n", + "FIRE 0.108 \n", + "RIGHT 0.132 \n", + "LEFT 0.111 \n", "\n" ] }, { "data": { - "image/png": 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UwmmgV0qphNNAr5RSCaeBXimlEk4DvVJKJdz/B8tIn3rkMwIjAAAA\nAElFTkSuQmCC\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3744,12 +2489,10 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.018 \n", - "FIRE 0.080 \n", - "RIGHT -0.099 \n", - "LEFT 0.073 \n", - "RIGHTFIRE -0.089 \n", - "LEFTFIRE 0.395 (Action Taken)\n", + "NOOP 0.137 (Action Taken)\n", + "FIRE 0.109 \n", + "RIGHT 0.130 \n", + "LEFT 0.105 \n", "\n" ] } @@ -3761,10 +2504,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Example: Greatest Difference in Q-Values\n", "\n", @@ -3773,21 +2513,18 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "730" + "699" ] }, - "execution_count": 39, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -3799,21 +2536,19 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 39, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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mrPFE0+k01WqVubk5O2s2iYIS9+iz2SxLS0sUi8VbJpbFaf3OZlDThEKuXl1L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\nzzuld4kR+mq12iRu5XKZSqW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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3822,23 +2557,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.219 (Action Taken)\n", - "FIRE 0.195 \n", - "RIGHT -0.083 \n", - "LEFT 0.199 \n", - "RIGHTFIRE 0.455 \n", - "LEFTFIRE 0.990 \n", + "NOOP 0.428 \n", + "FIRE 0.428 \n", + "RIGHT 0.906 (Action Taken)\n", + "LEFT 0.358 \n", "\n" ] }, { "data": { - "image/png": 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SBSXu0WezWebn5ymVSrcMLIvTecymU9OEQq5cWU7/DoLA9lfkcjnm5uaoVCo2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\nIAhCnyNCLwiC0OeI0AuCIPQ5/x+5Swy7yR87GQAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3847,23 +2582,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.608 (Action Taken)\n", - "FIRE 0.085 \n", - "RIGHT -0.416 \n", - "LEFT 0.350 \n", - "RIGHTFIRE 0.517 \n", - "LEFTFIRE 0.358 \n", + "NOOP 0.391 \n", + "FIRE 0.426 \n", + "RIGHT 0.849 (Action Taken)\n", + "LEFT 0.311 \n", "\n" ] }, { "data": { - "image/png": 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jKChRjz6dTrOwsEChULhhYFmU9ms2nZomFDI5uZL+7fu+7a/IZDLMz89TKpVs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\nbDZrPT4j+P1sQ1GMAHqeZ0V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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3872,23 +2607,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.071 \n", - "FIRE -0.186 \n", - "RIGHT -0.509 \n", - "LEFT 0.225 (Action Taken)\n", - "RIGHTFIRE 0.152 \n", - "LEFTFIRE -0.182 \n", + "NOOP 0.413 \n", + "FIRE 0.486 \n", + "RIGHT 0.852 (Action Taken)\n", + "LEFT 0.306 \n", "\n" ] }, { "data": { - "image/png": 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qNoqCEvboU6kUCwsL5PP5WwaWhWm9ZtOpaUIhk5Mr6d/1et32V6TTaebn5ykW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\nIvSCIAg9jgi9IAhCj/P/AZzEAaq5lwplAAAAAElFTkSuQmCC\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3897,23 +2632,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.050 \n", - "FIRE -0.050 \n", - "RIGHT -0.196 \n", - "LEFT 0.108 \n", - "RIGHTFIRE 0.177 (Action Taken)\n", - "LEFTFIRE -0.285 \n", + "NOOP 0.409 \n", + "FIRE 0.434 \n", + "RIGHT 0.737 (Action Taken)\n", + "LEFT 0.332 \n", "\n" ] }, { "data": { - "image/png": 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aUbNJFJS4R5/NZllYWKBUKt0wsCxO+zWbTk0TCrl8eTX9OwgC21+Ry+WYn5+n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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3922,12 +2657,10 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.134 \n", - "FIRE -0.051 \n", - "RIGHT -0.145 \n", - "LEFT 0.155 \n", - "RIGHTFIRE 0.300 (Action Taken)\n", - "LEFTFIRE -0.286 \n", + "NOOP 0.443 \n", + "FIRE 0.692 (Action Taken)\n", + "RIGHT 0.585 \n", + "LEFT 0.308 \n", "\n" ] } @@ -3939,10 +2672,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Example: Smallest Difference in Q-Values\n", "\n", @@ -3953,20 +2683,16 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 40, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "373" + "134" ] }, - "execution_count": 41, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -3978,21 +2704,19 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 41, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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CXAIl9DDXO7158yZvv/02ly5dorOz045lYjxdf4/I+tb4TCbDhQsXeO+990JR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\nZqFqrfEKEokEABcuXGBiYsL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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -4001,23 +2725,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.788 \n", - "FIRE 0.785 \n", - "RIGHT 0.784 \n", - "LEFT 0.787 \n", - "RIGHTFIRE 0.789 (Action Taken)\n", - "LEFTFIRE 0.787 \n", + "NOOP 0.600 (Action Taken)\n", + "FIRE 0.595 \n", + "RIGHT 0.596 \n", + "LEFT 0.599 \n", "\n" ] }, { "data": { - "image/png": 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JP83OztZ4MEuJXv3/npiYwLZtL0Yf9HNiOsLNzc3VtDeI0AcX/xhD/kly6tcx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\nQsgRoRcEQQg5/x823QprZ9/p4QAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -4026,23 +2750,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.777 \n", - "FIRE 0.777 \n", - "RIGHT 0.784 \n", - "LEFT 0.783 \n", - "RIGHTFIRE 0.780 \n", - "LEFTFIRE 0.790 (Action Taken)\n", + "NOOP 0.572 \n", + "FIRE 0.566 \n", + "RIGHT 0.545 \n", + "LEFT 0.704 (Action Taken)\n", "\n" ] }, { "data": { - "image/png": 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ZY+/maa/5OMLNCbTQa62Zn59nbGyMfD5PPp8nkUg03PFvRqVSYWpqaq2a2pMU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\nAAAASUVORK5CYII=\n", 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\n", 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SiYY0u6WoVqtMTEysVVE3JKVSadnXL8gE/QW12WnlRDSHB02tzZ9Bs9h9Nf1g\nmvcnLI/ACX0r/De/OW9cEIRgk0gkSCQSaK0pFAqe125qa2Z02tW0nS2Why+OQCOBF3rTCOlfFrEX\nhODSPJ9AOp0mk8l4v8/MzDQMOGhi8quJszfXHEToWxM4oW81xoVpmDUxPf86N7uhG7Hx9Wa0G1vf\niNdkI7QnbFaaOzSZFF7AS4c2z7DpB2LGqWoH/8i2cMOuxU4WEjihb5VBYjz45jFOlrs/ufGNyDUR\nOom/cVTVJ4vZsmULSimKxSKlUonZ2VkvgcI4Gf7smtXSbMdi260JnNALgrBx8adIw3w4Zm5ujnK5\n3JDmuxYsJfKb3dMPvNBvxDCDIISVVjFw08vaDC1tat0mo6ZcLi+aNrke+DVks4p94IVeEITgYETT\nNJwqpejv7ycWizExMUGxWPTmPfD3ZhW6iwi9IAiL4veGW01BqZRiy5YtZDIZSqUSxWLR+5ihN5rX\nX2+v+mY5+psBEXpBEBbFZL2ZDJdWmCE6jPc+NzfnzWlsZkUzdEtsN7PIgwi9IAhNmJCL8eCNwNu2\nTTKZxHVdCoUCMC+g+XyeWq3W0OO63dTJ9WKzNNJKAE0QhJY0x9f7+/u59dZb2bZtm/ed1ppsNsvo\n6Cj5fH69iygsE/HoBUEAGgciM0MSDAwMeENG79q1ix07dizowWrmf/Dvx0+QPWZ/KqhZDiMi9IIg\nAAtTmXt6erjtttvo7e315n1wHGfBoHjN4Y+NJpbNYh9GROgFYRNjJutxHMeLxUciEQYGBti/fz97\n9+5lcHCQUqnE8PAwly5d4tq1aw37CEOO+kYu+3IQoReETYwJ0ZgJfQCGhob4yEc+wsDAAPl83hvq\ne3JykkuXLnmhGtu2G14QYSQMLzEQoReETYWZh9e2bUqlErVabcHMbD09PfzMz/wMjuPw5ptvcvXq\nVTKZDDMzMwvi8cLGQIReEDYRrut64874G1X9Qwu7rks2myWXy/Huu+8yNTUF0DBcOMj0fRsJEXpB\nCDGRSIR4PE4kEqFarVIoFBpCLXfccQc9PT1cvHjR6+SUzWZ55ZVXmJ2d9UQe5mcnM55/mMM1fjZ6\nyMYgQi8IIaZWq3k9U5VSDVNxDg0N8YlPfIKenh6efvppT+hzuRynTp1q6bFvFoEPGyL0grBJ0Fpz\n++23c8cddzA7O8uOHTu47777yOfzRCI3pMBxnIZBy6LRaMO8EMLGQ4ReEEJEc5ZIJpOhWq1SLpfp\n6+vjl39xsWYSAAAQnElEQVT5l/n85z9PLBZjZGSEq1ev8sYbbzSEaOBGRo3WuiMThAjdpS2hV0r1\nAX8B3Alo4D8A54Cngf3AJeCzWuvptkrZQfzTjYUl/iZ0no1m2yabJpPJUKlUmJmZAeDBBx/kE5/4\nBIlEgmKxyKFDhzh06BCJRIJKpcK3vvUtvvnNbzIyMuLtq1arEYlElhzITNhYtOvRfwV4Xmv9qFIq\nBqSAPwBe0lr/sVLqceBx4PfbPE7HEIEXlsmGsm3XdSkWi1Sr1YYQy7333stjjz3mLX//+9/nhRde\noK+vj3fffZeXX36Z4eFhYD6rxnEcqtVqVycKETrPqoVeKbUFOA78BoDWugJUlFKfAh6or/Yk8H0C\n8jAIwnLYSLbdnAXTHEf3e+Raa37wgx/w7LPPesMKX7hwwfvdzBAlhI92PPoDwDjwV0qpo8BrwJeA\nIa311fo614ChVhsrpR4DHgPYs2dPG8VYGZZlNYRuxLsXWtAx215LzABkALFYjK1btzI0NMTs7Kwn\n4Llczls/n89z+vRpTp486X2XSCS850EaW8NLO8MUR4B7gK9qre8G5pivynroeRVtqaRa6ye01vdp\nre8bHBxsoxjLJ5FIsG3bNvbs2cPOnTvJZDINv4d9YCNh2XTMtteqgNFolGg06i3fdddd/NEf/RHf\n+c53ePLJJ3nggQcAGgYg6+3tbdgGbrRZie2Hm3aEfhgY1lqfqC9/g/mH47pSaidA/e9Ye0VcPf5Z\nb2A+A+Hw4cPcd9993HXXXQ3japv1BYENYNu2bWPbtre8Z88ePvOZz7Bt2zbuv/9+fvZnfxaAn/70\np7z//vsAnDx5smFCkHQ63TCZtxBeVh260VpfU0pdUUod0VqfAx4CztY/XwD+uP732x0p6Spo9lIy\nmQy33norhw4dYmpqisnJSS5evOgZuXg1AmwM224eWtd1XUqlEr29vQAkk0ksy+I73/kOv/mbv8mO\nHTu4ePEi58+fx7IstNZUq1UZxmCT0G7WzX8Cvl7PSrgA/Hvmawl/q5T6IvA+8Nk2j9FRzByYJlYv\nCIuw4Wzb75Uboa/Varz44ost15f8+M1DW0KvtT4FtIpDPtTOfjtFc3W0UCh4w6zOzs4yOTm55PrC\n5iXott3spESjUXp6erzlWCy23kUSAkyoe8a6rtvwQORyOc6dO8eVK1eoVqtks9kGcZfUMmGjYKb3\nM4yOjvLcc8/x4IMPcunSJU6fPu3ZcyaTIZVKkc/nKZfLYuebkFALPTR66eVymbGxMe8BaY5Pikcv\nbBSaUyHfeOMNHn/8cXp7eymVSoyPj3uCXi6XvcHNxMY3J6EXej9aa2l8EkKBEWyTKTY3N9fQ+cmg\nlJKersLmEnpBCBs3C8OIBy/AJhR6f1xTHgIhLNi2TTwe94YULpfLklUjeGw6oRdxF8KI4zgUi0Wv\nJ6zYueBn0wm9IIQVGbtJWAzp8y8IghByROgFQRBCjgi9IAhCyBGhFwRBCDki9IIgCCFHhF4QBCHk\niNALgiCEHBF6QRCEkCNCLwiCEHJE6AVBEEKOCL0gCELIEaEXBEEIOSL0giAIIUeEXhAEIeSI0AuC\nIIQcEXpBEISQI0IvCIIQckToBUEQQo4IvSAIQsgRoRcEQQg5IvSCIAghR4ReEAQh5LQl9Eqp/6KU\nOqOUeksp9TdKqYRS6oBS6oRS6rxS6mmlVKxThRWE9UJsWwgTqxZ6pdQu4D8D92mt7wRs4NeAPwH+\nTGt9CJgGvtiJggrCeiG2LYSNdkM3ESCplIoAKeAq8CDwjfrvTwKfbvMYgtANxLaF0LBqoddajwD/\nC7jM/EMwA7wGZLXWtfpqw8CuVtsrpR5TSp1USp2cmJhYbTEEoeN00rbXo7yCcDPaCd1sBT4FHABu\nAdLAx5a7vdb6Ca31fVrr+wYHB1dbDEHoOJ207TUqoiCsiHZCN/8GuKi1HtdaV4FvAh8F+urVXYDd\nwEibZRSE9UZsWwgV7Qj9ZeAjSqmUUkoBDwFnge8Bj9bX+QLw7faKKAjrjti2ECraidGfYL5h6nXg\ndH1fTwC/D/yOUuo8MAB8rQPlFIR1Q2xbCBuRm6+yOFrrPwT+sOnrC8CxdvYrCN1GbFsIE9IzVhAE\nIeSI0AuCIIQcEXpBEISQI0IvCIIQckToBUEQQo4IvSAIQsgRoRcEQQg5IvSCIAghR4ReEAQh5IjQ\nC4IghBwRekEQhJAjQi8IghByROgFQRBCjgi9IAhCyAmU0CulmJ/n4QZaa1zXbVgWhGaa7Wax7wRh\nM9LWePSdRmu9QMiVUti27S1b1sJ302IPtLwUNg9+2zH/NzsJgrBZCYzQu67bIOgw/8D6hd6yLO9j\nflsKpZQ86JuUVk5DEDGOy2rKauw/COcZhDIIixMYobcsa0HoRimF4ziUy2Vg/mVQq9U88RbjEgx+\n2zHOgW3bgQ3fKKXarnGI/QvLJRAxevOQmo/xcozQl0olb13x0IVW+Gt+kUgEy7ICK/Z+GxeE9SAQ\nHr3WGsdxgHkhN/9rrYlEIqTTafL5PIlEgmQySbFYxHXdRR8W82A7jtNQAxDCi6ntAdRqNRzHoVqt\nBjKEY+xdKUUsFsOyLBzHWVY40pyL/2Xhuu6ytr3Zftu5Tp3YttU+gnbvNiqBEfpqtUqtVqNSqWDb\nNpVKhUqlwuDgIHfffTdXrlwhk8kQj8e9UI4fy7I8QY9E5k9ramqKK1eukM/ngRvVZSFcaK0plUrM\nzMxg2za5XI5arUY8Hm9wHLqNaVsyNphMJtmxYweWZVEoFHAcx7PdVkkJcEPUI5EIsVgMgEql4r04\nbmbj/t/9+zQvx+Xso3lf/mvsbzdo9eJpdWyzvf+FZdrXzPetyi8sn0AIveM4zM3NATcMxMTm9+3b\nxyOPPEI2myUWi2HbdsODa2683ytKJpMAnD59mrm5OU/ozbZiKBsf/z10HIeZmRmuXr1KoVBgZmYG\nx3GIxWK4rku1Wu1iSW9gBEspRTKZZNu2bfT29nrnslQt1Y9lWUSjUaLRKK7rUiqVPJFeKea5MS8L\nU76l8Iux1tp7SawGs32rfTcnUwQtBLeRCITQG4/e/A94IZehoSF6e3up1WoLvA2/92BZlrdNT0+P\nt+6JEye84xijFjY+fqF3XZdisUg2m8V1XXK5XIPQd/ueN2fH9PT0sGPHDlKpFK7rUi6XPc/WrO8/\nP7/nHIlEvI8RSXOOJqFhuY6McaiM1+w//s0wgmzKZWoqKxVj/7atjiF0hsAIfbFYXCDipkpeLBZv\n6rH4hT4SiaCUolKpSHw+pDRnZ9m2TSwW8z6u6xKNRtuOXa8FJlTS7L0aoYaFYtsqJu9f37/NcoW6\n1Tb+7KWlwi+rPa5/m+bzF9aOQAi9UopoNArMG7FppCqXy1y5coXTp097oRu/V95sHOYBSCQSAJw7\nd46ZmRnv9257dsLaYOwnmUySSqWoVqu4rusJfrczXJo909nZWUqlEtu3b2fbtm3E43Ev/GFi9K3w\nh0oA4vG49wLwZ6otF7OuP7XZX2tebF+LvRxW+lJdTnuC0BkCIfS2bbN161bPC7dtm97eXi/u+sIL\nL/D+++/T29vrNcYaj8aPMRqTZpfL5ZienvZ+Ny8CYePTHKPPZrMMDw8zMzNDPp9v8OgrlUoXS3oD\nf2NsrVZjcnLSSwM1mWTGdm/WGGtqMADVanXFDanN+/Q3eq6mMXa1NWf/9s2Nsc0hHXl2V08ghN5x\nHKanpz2hN17N9PQ0ly9f5syZM0xPT2PbNolEglKptCyPo9kAxVDCg/++lstl3nvvPRKJBIlEgmKx\n6Imh1tprjO82zWJYLpe5evVqy0bHVrbaKnZvWK1tdyKLpRPPlTyba0sghH5ycpKvf/3rKKWo1WpY\nlkUqlWJubo5XXnnFC7/4s3OEzY1fGEulEu+88w7Xr1/30mz9IZtcLtetYrbEhFtMQ6ggrDUqCG/S\naDSqBwYGgBsNPeaBnZubY25uTt74wpIs1aBXDwl0JeCrlBLDFdaU5dj2TYVeKfWXwCeBMa31nfXv\n+oGngf3AJeCzWutpNf+kfQV4BCgAv6G1fv2mhbjJw2DimMvpAej/XUYvFAytHoag2Ha9fCs4G2/f\nq9620wShDJuV5Qj9ctIR/hr4WNN3jwMvaa0PAy/VlwE+Dhyufx4Dvrrcwi6F6fRiGoxMlbfVx/+7\niLxwE/6aANi2P5d8JZ92tu30Rwg4y7yJ+4G3fMvngJ31/3cC5+r//z/gc63WW+qjlNKxWKzhE4/H\ndSwW07Zta0A+8lnyo5TStm23/AC6W7bd7esin/B/lqPhq22MHdJaX63/fw0Yqv+/C7jiW2+4/t1V\nmlBKPca8ZwQQmBQ4YWOite5Uw2bHbVsQuk3bWTdaa72aBiet9RPAEyANVkIwEdsWwsJquwxeV0rt\nBKj/Hat/PwLs8a23u/6dIGwUxLaF0LFaoX8G+EL9/y8A3/Z9/+/UPB8BZnzVYEHYCIhtC+FjGY1J\nf8N8HLLKfFzyi8AA8xkJ7wHfAfrr6yrg/wA/BU4D9y2zsbfrDRryCfdHbFs+Yf0sxw4D0WFK4pjC\nWqOlw5QQUpZj2zJxpSAIQsgRoRcEQQg5IvSCIAghJxCjVwITwFz9b9AYRMq1EoJYrn1dPLbY9sqR\nci2fZdl2IBpjAZRSJ7XW93W7HM1IuVZGUMvVTYJ6TaRcKyOo5VoOEroRBEEIOSL0giAIISdIQv9E\ntwuwCFKulRHUcnWToF4TKdfKCGq5bkpgYvSCIAjC2hAkj14QBEFYAwIh9EqpjymlzimlziulHr/5\nFmtWjj1Kqe8ppc4qpc4opb5U/75fKfWiUuq9+t+tXSibrZT6iVLq2fryAaXUifo1e1opFVvvMtXL\n0aeU+oZS6h2l1NtKqZ8LwvUKAmLXyy5f4Gw7bHbddaFXStnMDxb1ceB24HNKqdu7VJwa8Lta69uB\njwC/VS/LYtPLrSdfAt72Lf8J8Gda60PANPMDcnWDrwDPa61vA44yX8YgXK+uIna9IoJo2+Gy6+WM\nfLaWH+DngBd8y18GvtztctXL8m3gYRaZXm4dy7GbecN6EHiW+ZEUJ4BIq2u4juXaAlyk3tbj+76r\n1ysIH7HrZZclcLYdRrvuukfP4lO0dRWl1H7gbuAEi08vt178b+D3ADPb+QCQ1VrX6svdumYHgHHg\nr+pV779QSqXp/vUKAmLXyyOIth06uw6C0AcOpVQG+Hvgt7XWOf9vev51vm6pSkqpTwJjWuvX1uuY\nKyAC3AN8VWt9N/Nd/Ruqs+t9vYTFCZJd18sTVNsOnV0HQegDNUWbUirK/MPwda31N+tfLza93Hrw\nUeBXlVKXgKeYr+J+BehTSpmxirp1zYaBYa31ifryN5h/QLp5vYKC2PXNCapth86ugyD0rwKH6y3t\nMeDXmJ+2bd1RSinga8DbWus/9f202PRya47W+sta691a6/3MX5vvaq1/Hfge8Gg3yuQr2zXgilLq\nSP2rh4CzdPF6BQix65sQVNsOpV13u5Gg3rDxCPAu89O0/bculuN+5qtjbwKn6p9HWGR6uS6U7wHg\n2fr/B4FXgPPA3wHxLpXpQ8DJ+jX7FrA1KNer2x+x6xWVMVC2HTa7lp6xgiAIIScIoRtBEARhDRGh\nFwRBCDki9IIgCCFHhF4QBCHkiNALgiCEHBF6QRCEkCNCLwiCEHJE6AVBEELO/wek9ZFhDMbjcgAA\nAABJRU5ErkJggg==\n", 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\n", 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TOKFvhd8IFnviC4IQTGKxmNeCLJfLDVNnm9HPJjy5EjEPykC/zUDghd50Qvq3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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -4101,12 +2825,10 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.833 (Action Taken)\n", - "FIRE 0.831 \n", - "RIGHT 0.809 \n", - "LEFT 0.829 \n", - "RIGHTFIRE 0.824 \n", - "LEFTFIRE 0.827 \n", + "NOOP 0.686 (Action Taken)\n", + "FIRE 0.660 \n", + "RIGHT 0.675 \n", + "LEFT 0.675 \n", "\n" ] } @@ -4118,10 +2840,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Output of Convolutional Layers\n", "\n", @@ -4132,12 +2851,8 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 42, + "metadata": {}, "outputs": [], "source": [ "def plot_layer_output(model, layer_name, state_index, inverse_cmap=False):\n", @@ -4200,10 +2915,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Game State\n", "\n", @@ -4212,22 +2924,21 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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pSqVCIpGgVCpx5coVO2rW9PCjRLhHn0qlmJ6eJpfLXTeeIEzzMZvRwo7j4Hke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\n7xAJoVdK2UfDIAiIxWLE43E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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -4238,10 +2949,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Output of Convolutional Layer 1\n", "\n", @@ -4252,11 +2960,8 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 44, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ @@ -4269,9 +2974,9 @@ }, { "data": { - "image/png": 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RmhEAoKieNSMbN27Mxo0be7U7AGBMSEYAgKI0IwBAUT17a+8v/MIvJEluvfXW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\nxmnSWLcTWHfu3Jkkuffee7u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+ "image/png": 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\n", 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" ] }, "metadata": {}, @@ -4284,10 +2989,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Output of Convolutional Layer 2\n", "\n", @@ -4296,11 +2998,8 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 45, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -4313,9 +3012,9 @@ }, { "data": { - "image/png": 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AAJxDBwcAADiHDg4AAHAOHRwAAOAcOjgAAMA5dHAAAIBz6OAAAADn0MEBAADO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\nAM6hgwMAAJwTyTo4t912m8ZXXnmlxkG6ilRVtDZ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\n", 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" ] }, "metadata": {}, @@ -4328,10 +3027,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Output of Convolutional Layer 3\n", "\n", @@ -4346,11 +3042,8 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 46, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -4363,9 +3056,9 @@ }, { "data": { - "image/png": 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V7caURXBlbOPrX/864M5a/u3f/q1oeyX/kY6ODsBV64Lw72PLlhs2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\nVQ5EJQzSj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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -4380,8 +3073,6 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "deletable": true, - "editable": true, "id": "Nv2JqNLBhy1j" }, "source": [ @@ -4395,12 +3086,8 @@ }, { "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 47, + "metadata": {}, "outputs": [], "source": [ "def plot_conv_weights(model, layer_name, input_channel=0):\n", @@ -4471,10 +3158,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Weights for Convolutional Layer 1\n", "\n", @@ -4485,11 +3169,8 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 48, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -4497,15 +3178,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Min: -0.68262, Max: 0.14787\n", - "Mean: -0.05167, Stdev: 0.11923\n" + "Min: -0.24494, Max: 0.13658\n", + "Mean: -0.01729, Stdev: 0.06023\n" ] }, { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -4518,21 +3199,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can also plot the convolutional weights for the second input channel, that is, the motion-trace of the game-environment. Once again we see that the negative weights (blue) have a much greater magnitude than the positive weights (red)." ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 49, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -4540,15 +3215,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Min: -0.95588, Max: 0.09746\n", - "Mean: -0.03578, Stdev: 0.15025\n" + "Min: -0.56904, Max: 0.06957\n", + "Mean: -0.05132, Stdev: 0.12694\n" ] }, { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -4561,10 +3236,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Weights for Convolutional Layer 2\n", "\n", @@ -4575,26 +3247,22 @@ }, { "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 50, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Min: -0.30984, Max: 0.24492\n", - "Mean: -0.02332, Stdev: 0.09427\n" + "Min: -0.24590, Max: 0.14826\n", + "Mean: -0.00605, Stdev: 0.06365\n" ] }, { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -4607,10 +3275,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Weights for Convolutional Layer 3\n", "\n", @@ -4621,11 +3286,8 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 51, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -4633,15 +3295,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Min: -0.33228, Max: 0.24060\n", - "Mean: -0.02068, Stdev: 0.09566\n" + "Min: -0.25325, Max: 0.17733\n", + "Mean: -0.03257, Stdev: 0.07194\n" ] }, { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -4654,10 +3316,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Discussion\n", "\n", @@ -4676,10 +3335,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Exercises & Research Ideas\n", "\n", @@ -4696,8 +3352,7 @@ "You may find it helpful to add more command-line parameters to `reinforcement_learning.py` so you don't have to edit the source-code for testing other parameters.\n", "\n", "* Change the epsilon-probability during testing to e.g. 0.001 or 0.05. Which gives the best results? Could you use this value during training? Why/not?\n", - "* Continue training the agent for the Breakout game using the downloaded checkpoint. Does the agent get better or worse the more you train it? Why? (You should run it in a terminal window as described above.)\n", - "* Try and change the game-environment to Space Invaders and re-run this Notebook. The checkpoint can be downloaded automatically. It was trained for about 150 hours, which is roughly the same as for Breakout, but note that it has processed far fewer states. The reason is that the hyper-parameters such as the learning-rate were tuned for Breakout. Can you make some kind of adaptive learning-rate that would work better for both Breakout and Space Invaders? What about the other hyper-parameters? What about other games?\n", + "* Try and change the game-environment to Space Invaders and re-run this Notebook. The hyper-parameters such as the learning-rate were tuned for Breakout. Can you make some kind of adaptive learning-rate that would work better for both Breakout and Space Invaders? What about the other hyper-parameters? What about other games?\n", "* Try different architectures for the Neural Network. You will need to restart the training because the checkpoints cannot be reused for other architectures. You will need to train the agent for several days with each new architecture so as to properly assess its performance.\n", "* The replay-memory throws away all data after optimization of the Neural Network. Can you make it reuse the data somehow? The ReplayMemory-class has the function `estimate_all_q_values()` which may be helpful.\n", "* The reward is limited to -1 and 1 in the function `ReplayMemory.add()` so as to stabilize the training. This means the agent cannot distinguish between small and large rewards. Can you use batch normalization to fix this problem, so you can use the actual reward values?\n", @@ -4715,10 +3370,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## License (MIT)\n", "\n", @@ -4750,9 +3402,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/17_Estimator_API.ipynb b/17_Estimator_API.ipynb new file mode 100644 index 0000000..4d90650 --- /dev/null +++ b/17_Estimator_API.ipynb @@ -0,0 +1,1232 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #17\n", + "# Estimator API\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "High-level API's are extremely important in all software development because they provide simple abstractions for doing very complicated tasks. This makes it easier to write and understand your source-code, and it lowers the risk of errors.\n", + "\n", + "In Tutorial #03 we saw how to use various builder API's for creating Neural Networks in TensorFlow. However, there was a lot of additional code required for training the models and using them on new data. The Estimator is another high-level API that implements most of this, although it can be debated how simple it really is.\n", + "\n", + "Using the Estimator API consists of several steps:\n", + "\n", + "1. Define functions for inputting data to the Estimator.\n", + "2. Either use an existing Estimator (e.g. a Deep Neural Network), which is also called a pre-made or Canned Estimator. Or create your own Estimator, in which case you also need to define the optimizer, performance metrics, etc.\n", + "3. Train the Estimator using the training-set defined in step 1.\n", + "4. Evaluate the performance of the Estimator on the test-set defined in step 1.\n", + "5. Use the trained Estimator to make predictions on other data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.9.0'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given dir." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Size of:\n", + "- Training-set:\t\t55000\n", + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" + ] + } + ], + "source": [ + "print(\"Size of:\")\n", + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copy some of the data-dimensions for convenience." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# The number of pixels in each dimension of an image.\n", + "img_size = data.img_size\n", + "\n", + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", + "\n", + "# Tuple with height and width of images used to reshape arrays.\n", + "img_shape = data.img_shape\n", + "\n", + "# Number of classes, one class for each of 10 digits.\n", + "num_classes = data.num_classes\n", + "\n", + "# Number of colour channels for the images: 1 channel for gray-scale.\n", + "num_channels = data.num_channels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for plotting images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_images(images, cls_true, cls_pred=None):\n", + " assert len(images) == len(cls_true) == 9\n", + " \n", + " # Create figure with 3x3 sub-plots.\n", + " fig, axes = plt.subplots(3, 3)\n", + " fig.subplots_adjust(hspace=0.3, wspace=0.3)\n", + "\n", + " for i, ax in enumerate(axes.flat):\n", + " # Plot image.\n", + " ax.imshow(images[i].reshape(img_shape), cmap='binary')\n", + "\n", + " # Show true and predicted classes.\n", + " if cls_pred is None:\n", + " xlabel = \"True: {0}\".format(cls_true[i])\n", + " else:\n", + " xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n", + "\n", + " # Show the classes as the label on the x-axis.\n", + " ax.set_xlabel(xlabel)\n", + " \n", + " # Remove ticks from the plot.\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " \n", + " # Ensure the plot is shown correctly with multiple plots\n", + " # in a single Notebook cell.\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot a few images to see if data is correct" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the first images from the test-set.\n", + "images = data.x_test[0:9]\n", + "\n", + "# Get the true classes for those images.\n", + "cls_true = data.y_test_cls[0:9]\n", + "\n", + "# Plot the images and labels using our helper-function above.\n", + "plot_images(images=images, cls_true=cls_true)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Input Functions for the Estimator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rather than providing raw data directly to the Estimator, we must provide functions that return the data. This allows for more flexibility in data-sources and how the data is randomly shuffled and iterated.\n", + "\n", + "Note that we will create an Estimator using the `DNNClassifier` which assumes the class-numbers are integers so we use `data.y_train_cls` instead of `data.y_train` which are one-hot encoded arrays.\n", + "\n", + "The function also has parameters for `batch_size`, `queue_capacity` and `num_threads` for finer control of the data reading. In our case we take the data directly from a numpy array in memory, so it is not needed." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "train_input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={\"x\": np.array(data.x_train)},\n", + " y=np.array(data.y_train_cls),\n", + " num_epochs=None,\n", + " shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This actually returns a function:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + ".input_fn>" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_input_fn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calling this function returns a tuple with TensorFlow ops for returning the input and output data:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "({'x': },\n", + " )" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_input_fn()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly we need to create a function for reading the data for the test-set. Note that we only want to process these images once so `num_epochs=1` and we do not want the images shuffled so `shuffle=False`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "test_input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={\"x\": np.array(data.x_test)},\n", + " y=np.array(data.y_test_cls),\n", + " num_epochs=1,\n", + " shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An input-function is also needed for predicting the class of new data. As an example we just use a few images from the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "some_images = data.x_test[0:9]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "predict_input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={\"x\": some_images},\n", + " num_epochs=1,\n", + " shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The class-numbers are actually not used in the input-function as it is not needed for prediction. However, the true class-number is needed when we plot the images further below." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "some_images_cls = data.y_test_cls[0:9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre-Made / Canned Estimator\n", + "\n", + "When using a pre-made Estimator, we need to specify the input features for the data. In this case we want to input images from our data-set which are numeric arrays of the given shape." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "feature_x = tf.feature_column.numeric_column(\"x\", shape=img_shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can have several input features which would then be combined in a list:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "feature_columns = [feature_x]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we want to use a 3-layer DNN with 512, 256 and 128 units respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "num_hidden_units = [512, 256, 128]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `DNNClassifier` then constructs the neural network for us. We can also specify the activation function and various other parameters (see the docs). Here we just specify the number of classes and the directory where the checkpoints will be saved." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial17-1/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n" + ] + } + ], + "source": [ + "model = tf.estimator.DNNClassifier(feature_columns=feature_columns,\n", + " hidden_units=num_hidden_units,\n", + " activation_fn=tf.nn.relu,\n", + " n_classes=num_classes,\n", + " model_dir=\"./checkpoints_tutorial17-1/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training\n", + "\n", + "We can now train the model for a given number of iterations. This automatically loads and saves checkpoints so we can continue the training later.\n", + "\n", + "Note that the text `INFO:tensorflow:` is printed on every line and makes it harder to quickly read the actual progress. It should have been printed on a single line instead." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Saving checkpoints for 0 into ./checkpoints_tutorial17-1/model.ckpt.\n", + "INFO:tensorflow:loss = 300.61185, step = 0\n", + "INFO:tensorflow:global_step/sec: 453.729\n", + "INFO:tensorflow:loss = 33.910957, step = 100 (0.221 sec)\n", + "INFO:tensorflow:global_step/sec: 545.745\n", + "INFO:tensorflow:loss = 38.821697, step = 200 (0.183 sec)\n", + "INFO:tensorflow:global_step/sec: 510.96\n", + "INFO:tensorflow:loss = 36.428062, step = 300 (0.196 sec)\n", + "INFO:tensorflow:global_step/sec: 509.188\n", + "INFO:tensorflow:loss = 10.77646, step = 400 (0.196 sec)\n", + "INFO:tensorflow:global_step/sec: 525.229\n", + "INFO:tensorflow:loss = 20.211845, step = 500 (0.190 sec)\n", + "INFO:tensorflow:global_step/sec: 529.656\n", + "INFO:tensorflow:loss = 16.973766, step = 600 (0.189 sec)\n", + "INFO:tensorflow:global_step/sec: 518.829\n", + "INFO:tensorflow:loss = 9.104766, step = 700 (0.193 sec)\n", + "INFO:tensorflow:global_step/sec: 517.877\n", + "INFO:tensorflow:loss = 11.87432, step = 800 (0.194 sec)\n", + "INFO:tensorflow:global_step/sec: 513.369\n", + "INFO:tensorflow:loss = 7.3187075, step = 900 (0.194 sec)\n", + "INFO:tensorflow:global_step/sec: 531.02\n", + "INFO:tensorflow:loss = 5.238852, step = 1000 (0.188 sec)\n", + "INFO:tensorflow:global_step/sec: 493.925\n", + "INFO:tensorflow:loss = 6.4892335, step = 1100 (0.203 sec)\n", + "INFO:tensorflow:global_step/sec: 513.837\n", + "INFO:tensorflow:loss = 10.295633, step = 1200 (0.194 sec)\n", + "INFO:tensorflow:global_step/sec: 516.007\n", + "INFO:tensorflow:loss = 4.5178833, step = 1300 (0.194 sec)\n", + "INFO:tensorflow:global_step/sec: 501.485\n", + "INFO:tensorflow:loss = 2.4612594, step = 1400 (0.200 sec)\n", + "INFO:tensorflow:global_step/sec: 508.118\n", + "INFO:tensorflow:loss = 10.878417, step = 1500 (0.197 sec)\n", + "INFO:tensorflow:global_step/sec: 505.549\n", + "INFO:tensorflow:loss = 22.480297, step = 1600 (0.198 sec)\n", + "INFO:tensorflow:global_step/sec: 512.93\n", + "INFO:tensorflow:loss = 6.8385906, step = 1700 (0.195 sec)\n", + "INFO:tensorflow:global_step/sec: 520.968\n", + "INFO:tensorflow:loss = 1.8562572, step = 1800 (0.192 sec)\n", + "INFO:tensorflow:global_step/sec: 547.812\n", + "INFO:tensorflow:loss = 4.875979, step = 1900 (0.183 sec)\n", + "INFO:tensorflow:Saving checkpoints for 2000 into ./checkpoints_tutorial17-1/model.ckpt.\n", + "INFO:tensorflow:Loss for final step: 2.701511.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.train(input_fn=train_input_fn, steps=2000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation\n", + "\n", + "Once the model has been trained, we can evaluate its performance on the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Starting evaluation at 2018-07-16-11:23:09\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-1/model.ckpt-2000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Finished evaluation at 2018-07-16-11:23:09\n", + "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.972, average_loss = 0.09360652, global_step = 2000, loss = 11.848927\n", + "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: ./checkpoints_tutorial17-1/model.ckpt-2000\n" + ] + } + ], + "source": [ + "result = model.evaluate(input_fn=test_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'accuracy': 0.972,\n", + " 'average_loss': 0.09360652,\n", + " 'global_step': 2000,\n", + " 'loss': 11.848927}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification accuracy: 97.20%\n" + ] + } + ], + "source": [ + "print(\"Classification accuracy: {0:.2%}\".format(result[\"accuracy\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predictions\n", + "\n", + "The trained model can also be used to make predictions on new data.\n", + "\n", + "Note that the TensorFlow graph is recreated and the checkpoint is reloaded every time we make predictions on new data. If the model is very large then this could add a significant overhead.\n", + "\n", + "It is unclear why the Estimator is designed this way, possibly because it will always use the latest checkpoint and it can also be distributed easily for use on multiple computers." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = model.predict(input_fn=predict_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-1/model.ckpt-2000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n" + ] + } + ], + "source": [ + "cls = [p['classes'] for p in predictions]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([7, 2, 1, 0, 4, 1, 4, 9, 6])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cls_pred = np.array(cls, dtype='int').squeeze()\n", + "cls_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_images(images=some_images,\n", + " cls_true=some_images_cls,\n", + " cls_pred=cls_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# New Estimator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you cannot use one of the built-in Estimators, then you can create an arbitrary TensorFlow model yourself. To do this, you first need to create a function which defines the following:\n", + "\n", + "1. The TensorFlow model, e.g. a Convolutional Neural Network.\n", + "2. The output of the model.\n", + "3. The loss-function used to improve the model during optimization.\n", + "4. The optimization method.\n", + "5. Performance metrics.\n", + "\n", + "The Estimator can be run in three modes: Training, Evaluation, or Prediction. The code is mostly the same, but in Prediction-mode we do not need to setup the loss-function and optimizer.\n", + "\n", + "This is another aspect of the Estimator API that is poorly designed and resembles how we did ANSI C programming using structs in the old days. It would probably have been more elegant to split this into several functions and sub-classed the Estimator-class." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def model_fn(features, labels, mode, params):\n", + " # Args:\n", + " #\n", + " # features: This is the x-arg from the input_fn.\n", + " # labels: This is the y-arg from the input_fn,\n", + " # see e.g. train_input_fn for these two.\n", + " # mode: Either TRAIN, EVAL, or PREDICT\n", + " # params: User-defined hyper-parameters, e.g. learning-rate.\n", + " \n", + " # Reference to the tensor named \"x\" in the input-function.\n", + " x = features[\"x\"]\n", + "\n", + " # The convolutional layers expect 4-rank tensors\n", + " # but x is a 2-rank tensor, so reshape it.\n", + " net = tf.reshape(x, [-1, img_size, img_size, num_channels]) \n", + "\n", + " # First convolutional layer.\n", + " net = tf.layers.conv2d(inputs=net, name='layer_conv1',\n", + " filters=16, kernel_size=5,\n", + " padding='same', activation=tf.nn.relu)\n", + " net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)\n", + "\n", + " # Second convolutional layer.\n", + " net = tf.layers.conv2d(inputs=net, name='layer_conv2',\n", + " filters=36, kernel_size=5,\n", + " padding='same', activation=tf.nn.relu)\n", + " net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2) \n", + "\n", + " # Flatten to a 2-rank tensor.\n", + " net = tf.contrib.layers.flatten(net)\n", + " # Eventually this should be replaced with:\n", + " # net = tf.layers.flatten(net)\n", + "\n", + " # First fully-connected / dense layer.\n", + " # This uses the ReLU activation function.\n", + " net = tf.layers.dense(inputs=net, name='layer_fc1',\n", + " units=128, activation=tf.nn.relu) \n", + "\n", + " # Second fully-connected / dense layer.\n", + " # This is the last layer so it does not use an activation function.\n", + " net = tf.layers.dense(inputs=net, name='layer_fc2',\n", + " units=10)\n", + "\n", + " # Logits output of the neural network.\n", + " logits = net\n", + "\n", + " # Softmax output of the neural network.\n", + " y_pred = tf.nn.softmax(logits=logits)\n", + " \n", + " # Classification output of the neural network.\n", + " y_pred_cls = tf.argmax(y_pred, axis=1)\n", + "\n", + " if mode == tf.estimator.ModeKeys.PREDICT:\n", + " # If the estimator is supposed to be in prediction-mode\n", + " # then use the predicted class-number that is output by\n", + " # the neural network. Optimization etc. is not needed.\n", + " spec = tf.estimator.EstimatorSpec(mode=mode,\n", + " predictions=y_pred_cls)\n", + " else:\n", + " # Otherwise the estimator is supposed to be in either\n", + " # training or evaluation-mode. Note that the loss-function\n", + " # is also required in Evaluation mode.\n", + " \n", + " # Define the loss-function to be optimized, by first\n", + " # calculating the cross-entropy between the output of\n", + " # the neural network and the true labels for the input data.\n", + " # This gives the cross-entropy for each image in the batch.\n", + " cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels,\n", + " logits=logits)\n", + "\n", + " # Reduce the cross-entropy batch-tensor to a single number\n", + " # which can be used in optimization of the neural network.\n", + " loss = tf.reduce_mean(cross_entropy)\n", + "\n", + " # Define the optimizer for improving the neural network.\n", + " optimizer = tf.train.AdamOptimizer(learning_rate=params[\"learning_rate\"])\n", + "\n", + " # Get the TensorFlow op for doing a single optimization step.\n", + " train_op = optimizer.minimize(\n", + " loss=loss, global_step=tf.train.get_global_step())\n", + "\n", + " # Define the evaluation metrics,\n", + " # in this case the classification accuracy.\n", + " metrics = \\\n", + " {\n", + " \"accuracy\": tf.metrics.accuracy(labels, y_pred_cls)\n", + " }\n", + "\n", + " # Wrap all of this in an EstimatorSpec.\n", + " spec = tf.estimator.EstimatorSpec(\n", + " mode=mode,\n", + " loss=loss,\n", + " train_op=train_op,\n", + " eval_metric_ops=metrics)\n", + " \n", + " return spec" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create an Instance of the Estimator\n", + "\n", + "We can specify hyper-parameters e.g. for the learning-rate of the optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "params = {\"learning_rate\": 1e-4}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then create an instance of the new Estimator.\n", + "\n", + "Note that we don't provide feature-columns here as it is inferred automatically from the data-functions when `model_fn()` is called.\n", + "\n", + "It is unclear from the TensorFlow documentation why it is necessary to specify the feature-columns when using `DNNClassifier` in the example above, when it is not needed here." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial17-2/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n" + ] + } + ], + "source": [ + "model = tf.estimator.Estimator(model_fn=model_fn,\n", + " params=params,\n", + " model_dir=\"./checkpoints_tutorial17-2/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training\n", + "\n", + "Now that our new Estimator has been created, we can train it." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Saving checkpoints for 0 into ./checkpoints_tutorial17-2/model.ckpt.\n", + "INFO:tensorflow:loss = 2.328683303358867, step = 0\n", + "INFO:tensorflow:global_step/sec: 30.0746\n", + "INFO:tensorflow:loss = 1.0425889833487076, step = 100 (3.326 sec)\n", + "INFO:tensorflow:global_step/sec: 30.7697\n", + "INFO:tensorflow:loss = 0.4519329631053862, step = 200 (3.250 sec)\n", + "INFO:tensorflow:global_step/sec: 30.5945\n", + "INFO:tensorflow:loss = 0.28173916577119856, step = 300 (3.269 sec)\n", + "INFO:tensorflow:global_step/sec: 30.3772\n", + "INFO:tensorflow:loss = 0.41579200542133726, step = 400 (3.292 sec)\n", + "INFO:tensorflow:global_step/sec: 31.44\n", + "INFO:tensorflow:loss = 0.2537537261934676, step = 500 (3.181 sec)\n", + "INFO:tensorflow:global_step/sec: 32.2734\n", + "INFO:tensorflow:loss = 0.2306796091927107, step = 600 (3.103 sec)\n", + "INFO:tensorflow:global_step/sec: 32.4727\n", + "INFO:tensorflow:loss = 0.16169791614095563, step = 700 (3.075 sec)\n", + "INFO:tensorflow:global_step/sec: 32.9575\n", + "INFO:tensorflow:loss = 0.24491770370504626, step = 800 (3.034 sec)\n", + "INFO:tensorflow:global_step/sec: 31.4056\n", + "INFO:tensorflow:loss = 0.1723769961825516, step = 900 (3.185 sec)\n", + "INFO:tensorflow:global_step/sec: 31.8268\n", + "INFO:tensorflow:loss = 0.0865023047044578, step = 1000 (3.142 sec)\n", + "INFO:tensorflow:global_step/sec: 33.1043\n", + "INFO:tensorflow:loss = 0.08865380930537742, step = 1100 (3.021 sec)\n", + "INFO:tensorflow:global_step/sec: 33.0132\n", + "INFO:tensorflow:loss = 0.09500106271291871, step = 1200 (3.029 sec)\n", + "INFO:tensorflow:global_step/sec: 32.2879\n", + "INFO:tensorflow:loss = 0.048251991971276796, step = 1300 (3.097 sec)\n", + "INFO:tensorflow:global_step/sec: 32.4468\n", + "INFO:tensorflow:loss = 0.0965478484811222, step = 1400 (3.082 sec)\n", + "INFO:tensorflow:global_step/sec: 31.0871\n", + "INFO:tensorflow:loss = 0.06810141978839185, step = 1500 (3.217 sec)\n", + "INFO:tensorflow:global_step/sec: 31.6667\n", + "INFO:tensorflow:loss = 0.13537004696386645, step = 1600 (3.158 sec)\n", + "INFO:tensorflow:global_step/sec: 31.98\n", + "INFO:tensorflow:loss = 0.08716099232839157, step = 1700 (3.127 sec)\n", + "INFO:tensorflow:global_step/sec: 32.1884\n", + "INFO:tensorflow:loss = 0.06138957874514458, step = 1800 (3.107 sec)\n", + "INFO:tensorflow:global_step/sec: 32.1328\n", + "INFO:tensorflow:loss = 0.11381113679326431, step = 1900 (3.113 sec)\n", + "INFO:tensorflow:Saving checkpoints for 2000 into ./checkpoints_tutorial17-2/model.ckpt.\n", + "INFO:tensorflow:Loss for final step: 0.09910375161965862.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.train(input_fn=train_input_fn, steps=2000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation\n", + "\n", + "Once the model has been trained, we can evaluate its performance on the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Starting evaluation at 2018-07-16-11:24:18\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-2/model.ckpt-2000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Finished evaluation at 2018-07-16-11:24:20\n", + "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9769, global_step = 2000, loss = 0.0701695\n", + "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: ./checkpoints_tutorial17-2/model.ckpt-2000\n" + ] + } + ], + "source": [ + "result = model.evaluate(input_fn=test_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'accuracy': 0.9769, 'global_step': 2000, 'loss': 0.0701695}" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification accuracy: 97.69%\n" + ] + } + ], + "source": [ + "print(\"Classification accuracy: {0:.2%}\".format(result[\"accuracy\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predictions\n", + "\n", + "The model can also be used to make predictions on new data." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "predictions = model.predict(input_fn=predict_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-2/model.ckpt-2000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n" + ] + }, + { + "data": { + "text/plain": [ + "array([7, 2, 1, 0, 4, 1, 4, 9, 5])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cls_pred = np.array(list(predictions))\n", + "cls_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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BmU7fhm4EnXXWWentvn37AtC+fXsAnnzySQAuu+yyOsfdeOONQN0BtFJc8bIIN2jGjRsHwAYbbNDs84YbQPHfoUq6YSCNCzdt63PIIYeUMJJklGmKiCRQtkwzTNAQ+rgAVq5cWe++YfjQscceC8Af//jH9Gdt27bN2jdMDTdmzJg65xw6dCgAX375JZCZ3KN169bN+4+QRk2YMAHIHsge+jLjfc3NFYapxLPLAw88EIAOHTrkfX4prngLIVhvvfUAGDlyZKnDyZkyTRGRBMqWaX799ddAw9klZB6BvPfee4HMnfLGhEwz3JW98MIL05+FR+tCxhmmn9pmm20SxS65CRNDh//vUJj+5NBKueuuu4DMnVaAiy++GFDroZKFBxtmzJhR57PQcuzVq1dJY0pCmaaISAJlv3ten9DfNXbsWCC3DLO2kEXeeeed6fdmzZpVgOikKZ9++ikAM2fOrPPZ4MGD8z7/zTffDGSmDuzZs2f6s4MOOijv80txNTYBTzWMbFGmKSKSQNkzzfqe+nnxxRfzPm94qij+FFDtJ43CXfgwZlAKI0zEsGzZMiAzvVehLFmyJOv1TjvtVNDzS3HVl2mG0Q6FaIkUmzJNEZEEVGmKiCRQtuZ5WLu8WCvLhVXt5s6dm36v9uOZl156aVGuvbbbcMMNgcywkfiEHeHRx44dOyY+74oVK4DMUKZgn332aVacUlrPP/88kBkqFhceg+7cuXNJY2oOZZoiIgmULdOcMmVKQc8Xhp8sXLgQaPwxrDCESQOgiyOsFhgemQyPUwIcfvjhQPZDB/WZP39+ejvc+AnT/tWelKNVK/3trwZhxclwQzaukifoqE2/bSIiCZR9yFGhhOnHbrjhhgb3qampAeCOO+4AMhOBSHEMHz4cyM4sQgsjPlFLfTbddNP0dsgsG3rk9tRTT80nTCmR2n3R8UlVzjzzzFKH02zKNEVEEqj6TDMskREmMG5MeNxuv/32K2pMktKjRw8ge0XQMJqh9gD12sI0gHGDBg0C6j6MEPpQpTKFhxxq3zWP3ykvxFSBpaJMU0QkgbJlmo0tnvbII49kvT7jjDMAeP/99xs8Ty7LHBT6jr0kt+uuu2b9TOL73/9+ve/Hx4H+4Ac/aF5gUjRhKrjad8379+9fjnDypkxTRCQBVZoiIgmUrXke5s0Ls6jHhQHQtR+xrO+Ry9C8z2XlSqluoXlXu5mnJnllC4Pag/BwyQUXXFCOcPKmTFNEJIGyZZoDBgwAYNSoUen3GlsvqCnhr1cY5nLLLbcAsOWWWzb7nFJZws0+rW1eXaZOnZr1ukuXLkBmko5qo0xTRCSBsmWaYdXIsNIkwKRJkwAYPXp04vP94Q9/ADJrmUvLE9arDzSovbKFFWcXL16c9X6bNm2A6p0wR5mmiEgCZX+MMqxtHt/u27cvkFl1MEwofOSRRwLwy1/+Mn1MuJMaX5FQWqawOmmY6GHYsGHlDEeaEKbsC49ILliwAIBtt922bDEVgjJNEZEEyp5p1ufQQw/N+ikCmYzl17/+NaA1zitdGDsdpm0Mox5++MMfli2mQlCmKSKSQEVmmiL1CX3bUl222morAG677bYyR1IYyjRFRBJQpSkikoAqTRGRBFRpiogkoEpTRCQBVZoiIglY7QldEx1s9iHwTuHCqQrd3H3TpndrGVTGLZ/KOJm8Kk0RkbWNmuciIgmo0hQRSUCVpohIAo1Wmma2iZnNi/4tN7P3Yq/XK1ZQZtbPzF43s8VmNiSH/UfEYnvVzA7P8/rPm1mvJvZpY2YTohhnmFnXfK5ZLuUq4+ja65rZK2Y2KYd9S17GsX2PNzPPdf9KU8bv8R1m9qGZzctx/9PD/ma2yMx+kef1x5nZ0U3ss2P0/V1jZjktj9nohB3u/hHQKzr5cOBzd7+y1kWN1A2l73K5YFPMrDVwPfDfwHJgtpk96O5vNHHoFe4+2sx2Ap4ys808dpfLzNZ1928KEWPkTGC5u3c3s5OAvwAnFvD8JVGOMo65EJgPtM1x/1KXMWa2ETAYmF3I85ZSGcv4NuAG4OYEx9zp7heY2RbAfDOb7O7pFReLUMYrgXOBY3M9oFnNczPrbmYLzexOYAHQxcw+iX0+0MxujbY3N7OJZjbbzGaZ2Z5NnH5PYJG7v+Pua4D7gP65xubu8wEDNo7+0txoZrOAkWa2gZndHsUx18yOjGJsa2bjo79u9wNtcrhUf+COaPs+4Me5xlgNilzGmFk34BBgbNLYSljGACOjf2uSxlnpil3G7v4MsKo5sbn7cmAp0DVqZfzNzF4Abo9aKFdHcbxiZqdHMbYys/81s9fM7HGgUw7X+cDdZwM5V8T59GnuAFzj7j2B9xrZ71pglLv3Bo4DQiHsYWY31bP/1sC/Yq+XRe/lxMz2Br5091BYWwJ7uvtQYBjwqLvvDhwEXGVmbYBfAR+7ew9gBLBr7HxjG2iWpeN096+A1WbWIdc4q0SxyhhgNDAESDzmrVRlbGa7AZu5+9Tan7UgxSzjZjOz7kA34K1YnAe7+0mkWnkrojLeDTjHUt1jxwL/BfQETgX2jp3vMjPrV4jY8plPc0lUQzelD7C9Zdaq3tjM1nf3F4EX87h+bUPM7OfAZ8DxsffHx5ocfYHDzOx30es2QFdgf2AUgLvPNbMF4WB3P7WAMVabopRx1M/0L3efZ2Z9EsRTsjI2s1bAVVRhl0tClfY9PtHMDiSV2Z/u7p9E13zQ3cNypH2BHmY2MHrdHtiWVBnfHf0uLDOzp8NJ3f0PhQown0pzdWz7O1LNpSDe9DFg9ygby8V7QJfY6840/hcwuMLd61v7Nx6nAUe7+5L4DrFfhCRCnMst1Znezt0/aeKYalOsMt4bGGBmR0Xn2cjM7nD3QU0cV8oy7kAqY3kuOnYL4GEzO9zd5yY9WQUrVhk3153uXt8NmdplPNjdn4jvYGbHFDWySEGGHEU1+8dmtm30Fzoe/DTgnPCigaZu3Eygp5l1M7PvkWoKTI6OHRX6qJppKqlO3xBLaKI9C/wsem8XYMcczjUZCF/y44DH8oir4hWyjN19qLt3dvca4CTgsVBhVkoZu/sqd+/k7jVRnLOBfi2swsxS4O9xg8zsfDM7q/mRMhUYbGbrRufb3szWJ1XGx0d9m1sDB+RxjQYVcpzmb0n9x0wn1Q8ZnAPsE3XYLgTOgIb7Qtz9a+A84HFgITDO3V+PPt6Z1B315roUaGepISsLgOHR+9cDm5jZIuASIP3FaKRP82ZgSzNbTKq/7KI84qoWBSnjJlRSGa+NClbGZjYeeI5UErQs6loB6AF8lEeMY4A3gXlmNh+4kVSreQLwLql6YywwIxZLvX2aZtbZzJaRqnOGR3E2Opqjap49t1Qb6RF31xKVLZTKeO1gZv8A+hd6eFipVE2lKSJSCfQYpYhIAqo0RUQSUKUpIpJAPuM06dSpk9fU1BQolOowZ86clWvTrN4q45ZPZZxMXpVmTU0Ns2dX7TwGzWJma9WyACrjlk9lnIya5yIiCajSFBFJQJWmiEgCqjRFRBJQpSkikoAqTRGRBFRpiogkkNc4TRGRYvj4448BePfddxvcp1u3bgBcc801AOy0004AbLfddgDssssuRYlNmaaISAIVlWmuWLECgOOOOw6AvfdOrYt05plnAqknFwrh008/BeDZZ58F4NBDU9M3tm7duiDnF5FkpkyZAsBDDz0EwNNPPw3Am2++2eAx22+/PQBLly4FYM2a7AVDv/uu0CtOpyjTFBFJoOyZZui7ANhxx9SyLSET3HzzzYHCZ5g//OEPAVi5MrUGfXjudtttty3IdSR3//73vwH43e9Si0cuWJBaJHLatGnpfdQCaBmWLEmtdXfDDTcAcPPNN6c/++KLLwBIMin666+/3vRORaBMU0QkgbJlmiHLC/2XAB99lFpr6ZxzUoveXXfddQW95ogRIwB4++23gcxfOmWYpTdu3DgALr74YqDuXdKQgQJssskmpQtMimbZstQ6baNH17cKc+522GEHIHO3vNSUaYqIJFC2TPOf//wnkLlLFjds2LCCXWf+/Pnp7SuvvBKAY45JLed8/PHHF+w6kpuQbfz6178GMi2O1EKUGeeem166nOuvvx6Ajh07liJEaYZQjpDJJPfdd18gMzplvfXWA6B9+/YAbLDBBuljPv/8cwB+/OMfA5ksco899gBg1113Te+7/vrrA9CuXbsC/1fkRpmmiEgCqjRFRBIoefM8DGC///7763x22223AbDppvkvzxKa5YccckidzwYMGADAhhtumPd1JJnQRRJu+jXknnvuSW8/8sgjQOamUWi6h+aelM/q1auB7O/Zyy+/DMCkSZOy9t1rr70AmDt3LpA9lDDcCOzcuTMArVpVbj5XuZGJiFSgkmea//M//wNkhpyEgeYAP/3pTwt2neeffx6A5cuXp9879dRTATjppJMKdh1p2jvvZNawGjt2bNZnYVKF8CDD448/Xuf48FBCyFJPPPFEALbYYovCBys5+eqrrwD42c9+BmSyS4CLLroIgD59+tR7bH0Pq3Tt2rXAERaPMk0RkQRKnmmGoSXh59Zbb53+LJ8+qvAY1siRI4HMo1rxoSyhz1RKa968eentMGh9//33B+CZZ54B4MsvvwTgrrvuAuAvf/lL+pjFixcDmVZD//79gUxfp4YilU4YGhS+Z2GCjfh9iCFDhgDQtm3bEkdXGso0RUQSKPuEHWFKKIC+ffsC0KFDBwDOPvvsJo8Pg+PDz5kzZ2Z9Xsh+Umme+JRdIfMPg9uDNm3aAPCLX/wCgAkTJqQ/CxM9hMkcQgaju+elF+6IX3755UBmIuDnnnsuvU8YvN5SKdMUEUmg5Jnm+eefD8CTTz4JwPvvv5/+LPRvhYziwQcfbPJ8Yd/aj+Fts802QKbvRcrn7rvvrvPeP/7xDwCOPvroeo8J0/XVZ8899wSyH8OT0pg+fXrW6/B4YxhfuTZQpikikkDJM80f/ehHALz66qtA9p3VRx99FIBRo0YBsNlmmwEwaNCgBs938sknA7DzzjtnvR+WyggZp5TPCSeckN4OrYeXXnoJgNdeew3I/D488MADQPbk1KGPO7wXpvQLZd+zZ8+ixS7Z4n3NkBnBcOmll6bfO+qoo4DsSTZaEmWaIiIJqNIUEUnAkqzJUVvv3r29sQ77UnjrrbeATDO8V69eADz22GNAYSb/iDOzOe7eu6AnrWCFKONVq1alt0M5hUcjG7qRF58AIjyocMQRRwDwxhtvAJlVSm+66aa84qtNZdyw2g+n1GedddYB4KyzzgIyc2L+61//AqB79+5AZk2wuLBGVJjco1g3mPIpY2WaIiIJlH1we77+9Kc/AZm/fOEmUqEzTGm++GOO48ePB+DYY48F6mac5513HgB//etf08eEge9hSr/wiOXUqVOBzOB30I2/YvvNb34DwFVXXdXgPt9++y2QaSGEn0mEm8AHHnggkD1VYLkp0xQRSaAqM82QrQDccccdAGy00UaAVi6sdGG6sDB0JUzQEYYVhZZDyC7jLrnkEgAWLVoEZIYvhWMg8/sgxREenwyryIZp+r7++uv0PmEdqJBxNkeYrDx81+MrT4bJqMtFmaaISAJVmWmGAbVxhx9+OJA9qbFUrpBxNjRRbX3CKoRhFdGQaT711FPpfcKdek0XVxzhzvhuu+0GZEYyxD3xxBNAJvscPnw4ALNmzUp8vdDXPWfOnMTHFosyTRGRBKo+0wxrH4e7etLyhf60yZMnA9l3VsMa6cOGDSt9YALAwQcfnPU6PCodMs3WrVsDmeVnAM444wwArrnmGiDT112JlGmKiCSgSlNEJIGqap6Hx+XiK0yGVQx1A2jtEdbEHjp0KJC9vna46TBw4EAAtttuu9IGJ3WEFRnCKpXhBlGYrQrgzTffBDIrMNQWX0us3JRpiogkUJWZZnyygH79+mXt89lnnwGZuReraT1lSSZMzvLnP/85/V64Ifj73/8egHHjxgGZ4UpSej169AAyQ8XuvffeOvvEh40BrLtuqmoKQwnjj9XY76NRAAAG3klEQVSWmzJNEZEEqirTrE/4ixQyijBkITx2pcfqWr5TTjklvT1mzBgAJk6cCGT6ymrP7C+lE7L80aNHA5nWYHzA+gcffABATU0NkCnT0EddSZRpiogkUPWZ5i233ALArbfeCsDpp58OZCZ3kJYvPg3gtGnTgMx63GGCiUoeLL22CCNdpkyZAsDf//739GczZswAMpllmBquEinTFBFJoKoyzeuuuw6AP/7xj+n39t9/fwDOPvtsADbeeGMA1ltvvRJHJ5UgjJYIy2WERy0XLlwIaOXKShJWE629XemUaYqIJFBVmeZ+++0HwJNPPlnmSKTShUmOd9llFwAWL14MKNOU/CnTFBFJQJWmiEgCVdU8F8lVWDPq7bffLnMk0tIo0xQRSUCVpohIAqo0RUQSsLDaW7MONvsQeKdw4VSFbu6+adO7tQwq45ZPZZxMXpWmiMjaRs1zEZEEVGmKiCTQaKVpZpuY2bzo33Izey/2uqgzYpjZumb2iplNymHfEbHYXjWzw/O89vNm1ivHfY83M891/0pTrjI2swvNbEH079wc9j/dzD6M4lpkZr/I8/rjzOzoJvbpaGaTo9/DF82sKp/BLGMZL4u+j/PM7MUc9i9HGfcxs09j/z/+0NR5Gx3c7u4fAb2ikw8HPnf3K2td1Ej1jX7X1MUSuhCYD7TNcf8r3H20me0EPGVmm3msw9bM1nX3bwoZoJltBAwGZhfyvKVUjjKO/sAMAnoD3wCPmdkUd29qJPqd7n6BmW0BzDezye6+MnbeQpfxJcCL7n6Ume0I/D/gkAKevyTK/D3ez90/SbB/qcsY4Cl3b7RyjWtW89zMupvZQjO7E1gAdDGzT2KfDzSzW6Ptzc1sopnNNrNZZrZnDufvRuqXc2zS2Nx9PmDAxtFfmhvNbBYw0sw2MLPbozjmmtmR0fXamtn46K/b/UCbHC83Mvq3Jmmcla7IZdwDmOnuX7j718CzwDG5xubuy4GlQNeolfE3M3sBuD1qoVwdxfGKmZ0exdjKzP7XzF4zs8eBTjlcqifwZHTNBcB2ZrZJrnFWumJ/j/NRwjJOLJ8+zR2Aa9y9J/BeI/tdC4xy997AcUAohD3M7KYGjhkNDAES39o3s72BL919VfTWlsCe7j4UGAY86u67AwcBV5lZG+BXwMfu3gMYAewaO99Yq6fpbWa7AZu5+9SkMVaRYpXxq8ABlmr+tgMOA7rkGpSZdQe6AW/F4jzY3U8CzgRWRGW8G3COmXUFjgX+i1RFeCqwd+x8l5lZ9rKmKS8DA6J99gI6R/9akmJ+jx140szmmNlpSYIqYRkD7GtmL5vZw5ZDF0w+z54vcfdcmqV9gO0ts+zuxma2vru/CNTp54j6IP7l7vPMrE+CeIaY2c+Bz4DjY++PjzU5+gKHmdnvotdtgK7A/sAoAHefa2YLwsHufmo9MbYCrgJOTBBfNSpKGbv7fDO7GpgGfA7MBb7N4TonmtmBpDL70939k+iaD7r7l9E+fYEeZjYwet0e2JZUGd8d/S4sM7OnY/E01I91GXCtmc0jVYG+nGOc1aQoZRzZ093fi5raj5vZInef3sR1Sl3GLwE17v551PKcSKqCblA+lebq2PZ3pJrEQbx5a8Du7v5VjufdGxhgZkdF59nIzO5w90FNHHeFu49uIk4Djnb3JfEdYr8IuepA6q/Zc9GxWwAPm9nh7j436ckqWLHKGHe/GbgZwMxGAYtzOOxOd7+giTgNGOzuT8R3MLOcm/+xGD8l1fca/lAuBVraDCDFLOP3op/LzexBYHegqUqzHGUcth+KuvM6NNYPW5AhR1HN/rGZbRv9csWDnwacE17U19Stda6h7t7Z3WuAk4DHQoVpZqNCP2QzTQXSd2rNLDTDnwV+Fr23C7BjEzGucvdO7l4TxTkb6NfCKswshSzjaJ/Nop81wFHAPdHr883srDxCnQoMNrN1o/Ntb2brkyrj46N+r62BA3KIsYOZtY5e/hKY5u6rGzummhWyjC11/2CDaLsdqXsU86PXlVTGW8S29wS+aerGVSHHaf6W1H/MdGBZ7P1zgH2iDtuFwBlRgI31hTRkZ2B5HjFeCrSz1DCIBcDw6P3rgU3MbBGpO6bpyq+hPs21VCHLeFK07yTgLHf/d/R+D+CjPGIcA7wJzDOz+cCNpFpUE4B3gYWkbjDOCAc00t/1A2Chmb0OHExqREdLV6gy3hJ4wcxeBmYBD7j7tOizSirjgZYa9jYPuIbsrr16Vc1jlJZqBz/i7oeWOxYpHjP7B9C/CMNKpEJUexlXTaUpIlIJ9BiliEgCqjRFRBJQpSkikoAqTRGRBFRpiogkoEpTRCQBVZoiIgn8f6S1hmwDdPmXAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_images(images=some_images,\n", + " cls_true=some_images_cls,\n", + " cls_pred=cls_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed how to use the Estimator API in TensorFlow. It is supposed to make it easier to train and use a model, but it seems to have several design problems:\n", + "\n", + "* The Estimator API is complicated, inconsistent and confusing.\n", + "* Error-messages are extremely long and often impossible to understand.\n", + "* The TensorFlow graph is recreated and the checkpoint is reloaded EVERY time you want to use a trained model to make a prediction on new data. Some models are very big so this could add a very large overhead. A better way might be to only reload the model if the checkpoint has changed on disk.\n", + "* It is unclear how to gain access to the trained model, e.g. to plot the weights of a neural network.\n", + "\n", + "It seems that the Estimator API could have been much simpler and easier to use. For small projects you may find it too complicated and confusing to be worth the effort. But it is possible that the Estimator API is useful if you have a very large dataset and if you train on many machines." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Run another 10000 training iterations for each model.\n", + "* Print classification accuracy on the test-set before optimization and after 1000, 2000 and 10000 iterations.\n", + "* Change the structure of the neural network inside the Estimator. Do you have to delete the checkpoint-files? Why?\n", + "* Change the batch-size for the input-functions.\n", + "* In many of the previous tutorials we plotted examples of mis-classified images. Do that here as well.\n", + "* Change the Estimator to use one-hot encoded labels instead of integer class-numbers.\n", + "* Change the input-functions to load image-files instead of using numpy-arrays.\n", + "* Can you find a way to plot the weights of the neural network and the output of the individual layers?\n", + "* List 5 things you like and don't like about the Estimator API. Do you have any suggestions for improvements? Maybe you should suggest them to the developers?\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "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:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "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." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/18_TFRecords_Dataset_API.ipynb b/18_TFRecords_Dataset_API.ipynb new file mode 100644 index 0000000..99a8d54 --- /dev/null +++ b/18_TFRecords_Dataset_API.ipynb @@ -0,0 +1,1908 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #18\n", + "# TFRecords & Dataset API\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "In the previous tutorials we used a so-called feed-dict for inputting data to the TensorFlow graph. It is a fairly simple input method but it is also a performance bottleneck because the data is read sequentially between training steps. This makes it hard to use the GPU at 100% efficiency because the GPU has to wait for new data to work on.\n", + "\n", + "Instead we want to read data in a parallel thread so new training data is always available whenever the GPU is ready. This used to be done with so-called QueueRunners in TensorFlow which was a very complicated system. Now it can be done with the Dataset API and a binary file-format called TFRecords, as described in this tutorial.\n", + "\n", + "This builds on Tutorial #17 for the Estimator API." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", + " return f(*args, **kwds)\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.image import imread\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import sys\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.4.0'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import knifey" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data dimensions have already been defined in the `knifey` module, so we just need to import the ones we need." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from knifey import img_size, img_size_flat, img_shape, num_classes, num_channels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set the directory for storing the data-set on your computer." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# knifey.data_dir = \"data/knifey-spoony/\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Knifey-Spoony data-set is about 22 MB and will be downloaded automatically if it is not located in the given path." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has apparently already been downloaded and unpacked.\n" + ] + } + ], + "source": [ + "knifey.maybe_download_and_extract()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now load the data-set. This scans the sub-directories for all `*.jpg` images and puts the filenames into two lists for the training-set and test-set. This does not actually load the images." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating dataset from the files in: data/knifey-spoony/\n", + "- Data loaded from cache-file: data/knifey-spoony/knifey-spoony.pkl\n" + ] + } + ], + "source": [ + "dataset = knifey.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the class-names." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['forky', 'knifey', 'spoony']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class_names = dataset.class_names\n", + "class_names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training and Test-Sets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function returns the file-paths for the images, the class-numbers as integers, and the class-numbers as One-Hot encoded arrays called labels.\n", + "\n", + "In this tutorial we will actually use the integer class-numbers and call them labels. This may be a little confusing but you can always add print-statements to see what the data actually is." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "image_paths_train, cls_train, labels_train = dataset.get_training_set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print the first image-path to see if it looks OK." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/magnus/development/TensorFlow-Tutorials/data/knifey-spoony/forky/forky-05-0023.jpg'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "image_paths_train[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "image_paths_test, cls_test, labels_test = dataset.get_test_set()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print the first image-path to see if it looks OK." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/magnus/development/TensorFlow-Tutorials/data/knifey-spoony/forky/test/forky-test-01-0163.jpg'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "image_paths_test[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Knifey-Spoony data-set has now been loaded and consists of 4700 images and associated labels (i.e. classifications of the images). The data-set is split into 2 mutually exclusive sub-sets, the training-set and the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Size of:\n", + "- Training-set:\t\t4170\n", + "- Test-set:\t\t530\n" + ] + } + ], + "source": [ + "print(\"Size of:\")\n", + "print(\"- Training-set:\\t\\t{}\".format(len(image_paths_train)))\n", + "print(\"- Test-set:\\t\\t{}\".format(len(image_paths_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for plotting images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def plot_images(images, cls_true, cls_pred=None, smooth=True):\n", + "\n", + " assert len(images) == len(cls_true)\n", + "\n", + " # Create figure with sub-plots.\n", + " fig, axes = plt.subplots(3, 3)\n", + "\n", + " # Adjust vertical spacing.\n", + " if cls_pred is None:\n", + " hspace = 0.3\n", + " else:\n", + " hspace = 0.6\n", + " fig.subplots_adjust(hspace=hspace, wspace=0.3)\n", + "\n", + " # Interpolation type.\n", + " if smooth:\n", + " interpolation = 'spline16'\n", + " else:\n", + " interpolation = 'nearest'\n", + "\n", + " for i, ax in enumerate(axes.flat):\n", + " # There may be less than 9 images, ensure it doesn't crash.\n", + " if i < len(images):\n", + " # Plot image.\n", + " ax.imshow(images[i],\n", + " interpolation=interpolation)\n", + "\n", + " # Name of the true class.\n", + " cls_true_name = class_names[cls_true[i]]\n", + "\n", + " # Show true and predicted classes.\n", + " if cls_pred is None:\n", + " xlabel = \"True: {0}\".format(cls_true_name)\n", + " else:\n", + " # Name of the predicted class.\n", + " cls_pred_name = class_names[cls_pred[i]]\n", + "\n", + " xlabel = \"True: {0}\\nPred: {1}\".format(cls_true_name,\n", + " cls_pred_name)\n", + "\n", + " # Show the classes as the label on the x-axis.\n", + " ax.set_xlabel(xlabel)\n", + " \n", + " # Remove ticks from the plot.\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " \n", + " # Ensure the plot is shown correctly with multiple plots\n", + " # in a single Notebook cell.\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for loading images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This dataset does not load the actual images, instead it has a list of the images in the training-set and another list for the images in the test-set. This helper-function loads some image-files." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def load_images(image_paths):\n", + " # Load the images from disk.\n", + " images = [imread(path) for path in image_paths]\n", + "\n", + " # Convert to a numpy array and return it.\n", + " return np.asarray(images)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot a few images to see if data is correct" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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I2agQT1JqahM5g5PHu8w6fSpGDt/Is7m8yh3X+xtX+9kiyzJGoxFxHLG4uEg+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\nSQjDkBdeuEU6Ewz7h0ymNmWrTRD4ZKTMPIdhMkENDZbqyyxX1kg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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load the first images from the test-set.\n", + "images = load_images(image_paths=image_paths_test[0:9])\n", + "\n", + "# Get the true classes for those images.\n", + "cls_true = cls_test[0:9]\n", + "\n", + "# Plot the images and labels using our helper-function above.\n", + "plot_images(images=images, cls_true=cls_true, smooth=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create TFRecords" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TFRecords is the binary file-format used internally in TensorFlow which allows for high-performance reading and processing of datasets.\n", + "\n", + "For this small dataset we will just create one TFRecords file for the training-set and another for the test-set. But if your dataset is very large then you can split it into several TFRecords files called shards. This will also improve the random shuffling, because the Dataset API only shuffles from a smaller buffer of e.g. 1024 elements loaded into RAM. So if you have e.g. 100 TFRecords files, then the randomization will be much better than for a single TFRecords file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "File-path for the TFRecords file holding the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'data/knifey-spoony/train.tfrecords'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "path_tfrecords_train = os.path.join(knifey.data_dir, \"train.tfrecords\")\n", + "path_tfrecords_train" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "File-path for the TFRecords file holding the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'data/knifey-spoony/test.tfrecords'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "path_tfrecords_test = os.path.join(knifey.data_dir, \"test.tfrecords\")\n", + "path_tfrecords_test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper-function for printing the conversion progress." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def print_progress(count, total):\n", + " # Percentage completion.\n", + " pct_complete = float(count) / total\n", + "\n", + " # Status-message.\n", + " # Note the \\r which means the line should overwrite itself.\n", + " msg = \"\\r- Progress: {0:.1%}\".format(pct_complete)\n", + "\n", + " # Print it.\n", + " sys.stdout.write(msg)\n", + " sys.stdout.flush()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper-function for wrapping an integer so it can be saved to the TFRecords file." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def wrap_int64(value):\n", + " return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper-function for wrapping raw bytes so they can be saved to the TFRecords file." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def wrap_bytes(value):\n", + " return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the function for reading images from disk and writing them along with the class-labels to a TFRecords file. This loads and decodes the images to numpy-arrays and then stores the raw bytes in the TFRecords file. If the original image-files are compressed e.g. as jpeg-files, then the TFRecords file may be many times larger than the original image-files.\n", + "\n", + "It is also possible to save the compressed image files directly in the TFRecords file because it can hold any raw bytes. We would then have to decode the compressed images when the TFRecords file is being read later in the `parse()` function below." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def convert(image_paths, labels, out_path):\n", + " # Args:\n", + " # image_paths List of file-paths for the images.\n", + " # labels Class-labels for the images.\n", + " # out_path File-path for the TFRecords output file.\n", + " \n", + " print(\"Converting: \" + out_path)\n", + " \n", + " # Number of images. Used when printing the progress.\n", + " num_images = len(image_paths)\n", + " \n", + " # Open a TFRecordWriter for the output-file.\n", + " with tf.python_io.TFRecordWriter(out_path) as writer:\n", + " \n", + " # Iterate over all the image-paths and class-labels.\n", + " for i, (path, label) in enumerate(zip(image_paths, labels)):\n", + " # Print the percentage-progress.\n", + " print_progress(count=i, total=num_images-1)\n", + "\n", + " # Load the image-file using matplotlib's imread function.\n", + " img = imread(path)\n", + " \n", + " # Convert the image to raw bytes.\n", + " img_bytes = img.tostring()\n", + "\n", + " # Create a dict with the data we want to save in the\n", + " # TFRecords file. You can add more relevant data here.\n", + " data = \\\n", + " {\n", + " 'image': wrap_bytes(img_bytes),\n", + " 'label': wrap_int64(label)\n", + " }\n", + "\n", + " # Wrap the data as TensorFlow Features.\n", + " feature = tf.train.Features(feature=data)\n", + "\n", + " # Wrap again as a TensorFlow Example.\n", + " example = tf.train.Example(features=feature)\n", + "\n", + " # Serialize the data.\n", + " serialized = example.SerializeToString()\n", + " \n", + " # Write the serialized data to the TFRecords file.\n", + " writer.write(serialized)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note the 4 function calls required to write the data-dict to the TFRecords file. In the original code-example from the Google Developers, these 4 function calls were actually nested. The design-philosophy for TensorFlow generally seems to be: If one function call is good, then 4 function calls are 4 times as good, and if they are nested then it is exponential goodness!\n", + "\n", + "Of course, this is quite poor API design because the last function `writer.write()` should just be able to take the data-dict directly and then call the 3 other functions internally.\n", + "\n", + "Convert the training-set to a TFRecords-file. Note how we use the integer class-numbers as the labels instead of the One-Hot encoded arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converting: data/knifey-spoony/train.tfrecords\n", + "- Progress: 100.0%" + ] + } + ], + "source": [ + "convert(image_paths=image_paths_train,\n", + " labels=cls_train,\n", + " out_path=path_tfrecords_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert the test-set to a TFRecords-file:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converting: data/knifey-spoony/test.tfrecords\n", + "- Progress: 100.0%" + ] + } + ], + "source": [ + "convert(image_paths=image_paths_test,\n", + " labels=cls_test,\n", + " out_path=path_tfrecords_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Input Functions for the Estimator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The TFRecords files contain the data in a serialized binary format which needs to be converted back to images and labels of the correct data-type. We use a helper-function for this parsing:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def parse(serialized):\n", + " # Define a dict with the data-names and types we expect to\n", + " # find in the TFRecords file.\n", + " # It is a bit awkward that this needs to be specified again,\n", + " # because it could have been written in the header of the\n", + " # TFRecords file instead.\n", + " features = \\\n", + " {\n", + " 'image': tf.FixedLenFeature([], tf.string),\n", + " 'label': tf.FixedLenFeature([], tf.int64)\n", + " }\n", + "\n", + " # Parse the serialized data so we get a dict with our data.\n", + " parsed_example = tf.parse_single_example(serialized=serialized,\n", + " features=features)\n", + "\n", + " # Get the image as raw bytes.\n", + " image_raw = parsed_example['image']\n", + "\n", + " # Decode the raw bytes so it becomes a tensor with type.\n", + " image = tf.decode_raw(image_raw, tf.uint8)\n", + " \n", + " # The type is now uint8 but we need it to be float.\n", + " image = tf.cast(image, tf.float32)\n", + "\n", + " # Get the label associated with the image.\n", + " label = parsed_example['label']\n", + "\n", + " # The image and label are now correct TensorFlow types.\n", + " return image, label" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper-function for creating an input-function that reads from TFRecords files for use with the Estimator API." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def input_fn(filenames, train, batch_size=32, buffer_size=2048):\n", + " # Args:\n", + " # filenames: Filenames for the TFRecords files.\n", + " # train: Boolean whether training (True) or testing (False).\n", + " # batch_size: Return batches of this size.\n", + " # buffer_size: Read buffers of this size. The random shuffling\n", + " # is done on the buffer, so it must be big enough.\n", + "\n", + " # Create a TensorFlow Dataset-object which has functionality\n", + " # for reading and shuffling data from TFRecords files.\n", + " dataset = tf.data.TFRecordDataset(filenames=filenames)\n", + "\n", + " # Parse the serialized data in the TFRecords files.\n", + " # This returns TensorFlow tensors for the image and labels.\n", + " dataset = dataset.map(parse)\n", + "\n", + " if train:\n", + " # If training then read a buffer of the given size and\n", + " # randomly shuffle it.\n", + " dataset = dataset.shuffle(buffer_size=buffer_size)\n", + "\n", + " # Allow infinite reading of the data.\n", + " num_repeat = None\n", + " else:\n", + " # If testing then don't shuffle the data.\n", + " \n", + " # Only go through the data once.\n", + " num_repeat = 1\n", + "\n", + " # Repeat the dataset the given number of times.\n", + " dataset = dataset.repeat(num_repeat)\n", + " \n", + " # Get a batch of data with the given size.\n", + " dataset = dataset.batch(batch_size)\n", + "\n", + " # Create an iterator for the dataset and the above modifications.\n", + " iterator = dataset.make_one_shot_iterator()\n", + "\n", + " # Get the next batch of images and labels.\n", + " images_batch, labels_batch = iterator.get_next()\n", + "\n", + " # The input-function must return a dict wrapping the images.\n", + " x = {'image': images_batch}\n", + " y = labels_batch\n", + "\n", + " return x, y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the input-function for the training-set for use with the Estimator API:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def train_input_fn():\n", + " return input_fn(filenames=path_tfrecords_train, train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the input-function for the test-set for use with the Estimator API:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def test_input_fn():\n", + " return input_fn(filenames=path_tfrecords_test, train=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Input Function for Predicting on New Images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An input-function is also needed for predicting the class of new data. As an example we just use a few images from the test-set.\n", + "\n", + "You could load any images you want here. Make sure they are the same dimensions as expected by the TensorFlow model, otherwise you need to resize the images." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "some_images = load_images(image_paths=image_paths_test[0:9])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These images are now stored as numpy arrays in memory, so we can use the standard input-function for the Estimator API. Note that the images are loaded as uint8 data but it must be input to the TensorFlow graph as floats so we do a type-cast." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predict_input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={\"image\": some_images.astype(np.float32)},\n", + " num_epochs=1,\n", + " shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The class-numbers are actually not used in the input-function as it is not needed for prediction. However, the true class-number is needed when we plot the images further below." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "some_images_cls = cls_test[0:9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre-Made / Canned Estimator\n", + "\n", + "When using a pre-made Estimator, we need to specify the input features for the data. In this case we want to input images from our data-set which are numeric arrays of the given shape." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "feature_image = tf.feature_column.numeric_column(\"image\",\n", + " shape=img_shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can have several input features which would then be combined in a list:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "feature_columns = [feature_image]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we want to use a 3-layer DNN with 512, 256 and 128 units respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "num_hidden_units = [512, 256, 128]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `DNNClassifier` then constructs the neural network for us. We can also specify the activation function and various other parameters (see the docs). Here we just specify the number of classes and the directory where the checkpoints will be saved." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial18-1/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n" + ] + } + ], + "source": [ + "model = tf.estimator.DNNClassifier(feature_columns=feature_columns,\n", + " hidden_units=num_hidden_units,\n", + " activation_fn=tf.nn.relu,\n", + " n_classes=num_classes,\n", + " model_dir=\"./checkpoints_tutorial18-1/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training\n", + "\n", + "We can now train the model for a given number of iterations. This automatically loads and saves checkpoints so we can continue the training later." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "INFO:tensorflow:Saving checkpoints for 1 into ./checkpoints_tutorial18-1/model.ckpt.\n", + "INFO:tensorflow:loss = 943.377, step = 1\n", + "INFO:tensorflow:global_step/sec: 21.6937\n", + "INFO:tensorflow:loss = 31.8647, step = 101 (4.614 sec)\n", + "INFO:tensorflow:Saving checkpoints for 200 into ./checkpoints_tutorial18-1/model.ckpt.\n", + "INFO:tensorflow:Loss for final step: 31.5808.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.train(input_fn=train_input_fn, steps=200)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation\n", + "\n", + "Once the model has been trained, we can evaluate its performance on the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Starting evaluation at 2017-11-25-09:31:37\n", + "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-1/model.ckpt-200\n", + "INFO:tensorflow:Finished evaluation at 2017-11-25-09:31:38\n", + "INFO:tensorflow:Saving dict for global step 200: accuracy = 0.456604, average_loss = 1.07088, global_step = 200, loss = 33.3863\n" + ] + } + ], + "source": [ + "result = model.evaluate(input_fn=test_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'accuracy': 0.45660377,\n", + " 'average_loss': 1.0708818,\n", + " 'global_step': 200,\n", + " 'loss': 33.386318}" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification accuracy: 45.66%\n" + ] + } + ], + "source": [ + "print(\"Classification accuracy: {0:.2%}\".format(result[\"accuracy\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predictions\n", + "\n", + "The trained model can also be used to make predictions on new data.\n", + "\n", + "Note that the TensorFlow graph is recreated and the checkpoint is reloaded every time we make predictions on new data. If the model is very large then this could add a significant overhead.\n", + "\n", + "It is unclear why the Estimator is designed this way, possibly because it will always use the latest checkpoint and it can also be distributed easily for use on multiple computers." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = model.predict(input_fn=predict_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-1/model.ckpt-200\n" + ] + } + ], + "source": [ + "cls = [p['classes'] for p in predictions]" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 2, 2, 2, 2, 2, 2, 2, 2])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cls_pred = np.array(cls, dtype='int').squeeze()\n", + "cls_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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8dPJDVVWGoy690y5bW1us1FaJNJGaPf3pAQlQKk7TmF5jIhzihmMODp8Qqjql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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_images(images=some_images,\n", + " cls_true=some_images_cls,\n", + " cls_pred=cls_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predictions for the Entire Test-Set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It appears that the model maybe classifies all images as 'spoony'. So let us see the predictions for the entire test-set. We can do this simply by using its input-function:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predictions = model.predict(input_fn=test_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Input graph does not contain a QueueRunner. That means predict yields forever. This is probably a mistake.\n", + "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-1/model.ckpt-200\n" + ] + } + ], + "source": [ + "cls = [p['classes'] for p in predictions]" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "cls_pred = np.array(cls, dtype='int').squeeze()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The test-set contains 530 images in total and they have all been predicted as class 2 (spoony). So this model does not work at all for classifying the Knifey-Spoony dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "530" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(cls_pred == 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# New Estimator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you cannot use one of the built-in Estimators, then you can create an arbitrary TensorFlow model yourself. To do this, you first need to create a function which defines the following:\n", + "\n", + "1. The TensorFlow model, e.g. a Convolutional Neural Network.\n", + "2. The output of the model.\n", + "3. The loss-function used to improve the model during optimization.\n", + "4. The optimization method.\n", + "5. Performance metrics.\n", + "\n", + "The Estimator can be run in three modes: Training, Evaluation, or Prediction. The code is mostly the same, but in Prediction-mode we do not need to setup the loss-function and optimizer.\n", + "\n", + "This is another aspect of the Estimator API that is poorly designed and resembles how we did ANSI C programming using structs in the old days. It would probably have been more elegant to split this into several functions and sub-classed the Estimator-class." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "def model_fn(features, labels, mode, params):\n", + " # Args:\n", + " #\n", + " # features: This is the x-arg from the input_fn.\n", + " # labels: This is the y-arg from the input_fn.\n", + " # mode: Either TRAIN, EVAL, or PREDICT\n", + " # params: User-defined hyper-parameters, e.g. learning-rate.\n", + " \n", + " # Reference to the tensor named \"image\" in the input-function.\n", + " x = features[\"image\"]\n", + "\n", + " # The convolutional layers expect 4-rank tensors\n", + " # but x is a 2-rank tensor, so reshape it.\n", + " net = tf.reshape(x, [-1, img_size, img_size, num_channels]) \n", + "\n", + " # First convolutional layer.\n", + " net = tf.layers.conv2d(inputs=net, name='layer_conv1',\n", + " filters=32, kernel_size=3,\n", + " padding='same', activation=tf.nn.relu)\n", + " net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)\n", + "\n", + " # Second convolutional layer.\n", + " net = tf.layers.conv2d(inputs=net, name='layer_conv2',\n", + " filters=32, kernel_size=3,\n", + " padding='same', activation=tf.nn.relu)\n", + " net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2) \n", + "\n", + " # Flatten to a 2-rank tensor.\n", + " net = tf.contrib.layers.flatten(net)\n", + " # Eventually this should be replaced with:\n", + " # net = tf.layers.flatten(net)\n", + "\n", + " # First fully-connected / dense layer.\n", + " # This uses the ReLU activation function.\n", + " net = tf.layers.dense(inputs=net, name='layer_fc1',\n", + " units=128, activation=tf.nn.relu) \n", + "\n", + " # Second fully-connected / dense layer.\n", + " # This is the last layer so it does not use an activation function.\n", + " net = tf.layers.dense(inputs=net, name='layer_fc_2',\n", + " units=num_classes)\n", + "\n", + " # Logits output of the neural network.\n", + " logits = net\n", + "\n", + " # Softmax output of the neural network.\n", + " y_pred = tf.nn.softmax(logits=logits)\n", + " \n", + " # Classification output of the neural network.\n", + " y_pred_cls = tf.argmax(y_pred, axis=1)\n", + "\n", + " if mode == tf.estimator.ModeKeys.PREDICT:\n", + " # If the estimator is supposed to be in prediction-mode\n", + " # then use the predicted class-number that is output by\n", + " # the neural network. Optimization etc. is not needed.\n", + " spec = tf.estimator.EstimatorSpec(mode=mode,\n", + " predictions=y_pred_cls)\n", + " else:\n", + " # Otherwise the estimator is supposed to be in either\n", + " # training or evaluation-mode. Note that the loss-function\n", + " # is also required in Evaluation mode.\n", + " \n", + " # Define the loss-function to be optimized, by first\n", + " # calculating the cross-entropy between the output of\n", + " # the neural network and the true labels for the input data.\n", + " # This gives the cross-entropy for each image in the batch.\n", + " cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels,\n", + " logits=logits)\n", + "\n", + " # Reduce the cross-entropy batch-tensor to a single number\n", + " # which can be used in optimization of the neural network.\n", + " loss = tf.reduce_mean(cross_entropy)\n", + "\n", + " # Define the optimizer for improving the neural network.\n", + " optimizer = tf.train.AdamOptimizer(learning_rate=params[\"learning_rate\"])\n", + "\n", + " # Get the TensorFlow op for doing a single optimization step.\n", + " train_op = optimizer.minimize(\n", + " loss=loss, global_step=tf.train.get_global_step())\n", + "\n", + " # Define the evaluation metrics,\n", + " # in this case the classification accuracy.\n", + " metrics = \\\n", + " {\n", + " \"accuracy\": tf.metrics.accuracy(labels, y_pred_cls)\n", + " }\n", + "\n", + " # Wrap all of this in an EstimatorSpec.\n", + " spec = tf.estimator.EstimatorSpec(\n", + " mode=mode,\n", + " loss=loss,\n", + " train_op=train_op,\n", + " eval_metric_ops=metrics)\n", + " \n", + " return spec" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create an Instance of the Estimator\n", + "\n", + "We can specify hyper-parameters e.g. for the learning-rate of the optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "params = {\"learning_rate\": 1e-4}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then create an instance of the new Estimator.\n", + "\n", + "Note that we don't provide feature-columns here as it is inferred automatically from the data-functions when `model_fn()` is called.\n", + "\n", + "It is unclear from the TensorFlow documentation why it is necessary to specify the feature-columns when using `DNNClassifier` in the example above, when it is not needed here." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial18-2/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n" + ] + } + ], + "source": [ + "model = tf.estimator.Estimator(model_fn=model_fn,\n", + " params=params,\n", + " model_dir=\"./checkpoints_tutorial18-2/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training\n", + "\n", + "Now that our new Estimator has been created, we can train it." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "INFO:tensorflow:Saving checkpoints for 1 into ./checkpoints_tutorial18-2/model.ckpt.\n", + "INFO:tensorflow:loss = 29.6568, step = 1\n", + "INFO:tensorflow:global_step/sec: 15.1419\n", + "INFO:tensorflow:loss = 20.0903, step = 101 (6.605 sec)\n", + "INFO:tensorflow:Saving checkpoints for 200 into ./checkpoints_tutorial18-2/model.ckpt.\n", + "INFO:tensorflow:Loss for final step: 3.11824.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.train(input_fn=train_input_fn, steps=200)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation\n", + "\n", + "Once the model has been trained, we can evaluate its performance on the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Starting evaluation at 2017-11-25-09:32:03\n", + "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-2/model.ckpt-200\n", + "INFO:tensorflow:Finished evaluation at 2017-11-25-09:32:04\n", + "INFO:tensorflow:Saving dict for global step 200: accuracy = 0.390566, global_step = 200, loss = 6.8253\n" + ] + } + ], + "source": [ + "result = model.evaluate(input_fn=test_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'accuracy': 0.39056605, 'global_step': 200, 'loss': 6.8253026}" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification accuracy: 39.06%\n" + ] + } + ], + "source": [ + "print(\"Classification accuracy: {0:.2%}\".format(result[\"accuracy\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predictions\n", + "\n", + "The model can also be used to make predictions on new data." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "predictions = model.predict(input_fn=predict_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-2/model.ckpt-200\n" + ] + }, + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0, 0, 0])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cls_pred = np.array(list(predictions))\n", + "cls_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2Niu1VSJNpGZPf3bQH0rFaRrTa0yEI9xwzOHRY0JVp1QsUK6UmQyGfPfff49J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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_images(images=some_images,\n", + " cls_true=some_images_cls,\n", + " cls_pred=cls_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predictions for the Entire Test-Set\n", + "\n", + "To get the predicted classes for the entire test-set, we just use its input-function:" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "predictions = model.predict(input_fn=test_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Input graph does not contain a QueueRunner. That means predict yields forever. This is probably a mistake.\n", + "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-2/model.ckpt-200\n" + ] + }, + { + "data": { + "text/plain": [ + "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n", + " 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n", + " 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 2, 0, 0, 0, 2,\n", + " 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 0, 0,\n", + " 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 0, 0, 0, 2,\n", + " 2, 0, 0, 2, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 2, 0, 2, 0, 0, 0, 0, 0, 0,\n", + " 0, 1, 1, 2, 1, 0, 0, 1, 2, 0, 0, 1, 0, 1, 1, 0, 0, 0, 2, 0, 1, 0, 0,\n", + " 1, 0, 0, 1, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 2, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n", + " 1, 2, 1, 0, 1, 0, 0, 1, 1, 0, 2, 2, 0, 2, 1, 0, 1, 2, 0, 0, 1, 2, 1,\n", + " 1, 0, 1, 0, 0, 1, 1, 0, 0, 2, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 2, 2, 0,\n", + " 1, 0, 0, 1, 1, 0, 0, 2, 1, 0, 1, 0, 0, 1, 0, 0, 1, 2, 2, 0, 1, 1, 2,\n", + " 1, 0, 0, 2, 2, 2, 0, 1, 1, 2, 2, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1,\n", + " 2, 0, 1, 0, 1, 0, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n", + " 1, 0, 2, 1, 2, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 1, 0, 2,\n", + " 2, 2, 0, 0, 1, 1, 1, 0, 1, 2, 0, 0, 0, 2, 2, 0, 1, 0, 1, 1, 0, 0, 1,\n", + " 0, 0, 1, 2, 0, 0, 1, 1, 1, 0, 1, 0, 1, 2, 0, 0, 0, 1, 0, 0, 1, 2, 1,\n", + " 0, 2, 1, 1, 1, 1, 1, 0, 1, 2, 0, 0, 0, 1, 0, 2, 1, 0, 1, 1, 1, 1, 0,\n", + " 0, 2, 1, 0, 1, 0, 2, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n", + " 0])" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cls_pred = np.array(list(predictions))\n", + "cls_pred" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Convolutional Neural Network predicts different classes for the images, although most have just been classified as 0 (forky), so the accuracy is horrible." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "333" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(cls_pred == 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "144" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(cls_pred == 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "53" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(cls_pred == 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed how to use TensorFlow's binary file-format TFRecords with the Dataset and Estimator APIs. This should simplify the process of training models with very large datasets while getting high usage of the GPU. However, the API could have been simpler in many ways." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Train the Convolutional Neural Network for much longer. Does it get any better at classifying the Knifey-Spoony dataset?\n", + "* Save the One-Hot-encoded label instead of the class-integer in the TFRecord and modify the rest of the code to use it.\n", + "* Make shards so you save multiple TFRecord files instead of just one.\n", + "* Save jpeg-files in the TFRecord instead of the decoded image. You will then need to decode the jpeg-image in the `parse()` function. What are the pro's and con's of doing this?\n", + "* Try using another dataset.\n", + "* Use a dataset where the images are different sizes. Would you resize before or after converting to the TFRecords file? Why?\n", + "* Try and use numpy input-functions instead of TFRecords for the Estimator API. What is the performance difference?\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "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:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "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." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/19_Hyper-Parameters.ipynb b/19_Hyper-Parameters.ipynb new file mode 100644 index 0000000..d20ff3a --- /dev/null +++ b/19_Hyper-Parameters.ipynb @@ -0,0 +1,2193 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #19\n", + "# Hyper-Parameter Optimization\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsl1877BS8m3yt8t_wq2IWji)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "There are many parameters you can select when building and training a Neural Network in TensorFlow. These are often called Hyper-Parameters. For example, there is a hyper-parameter for how many layers the network should have, and another hyper-parameter for how many nodes per layer, and another hyper-parameter for the activation function to use, etc. The optimization method also has one or more hyper-parameters you can select, such as the learning-rate.\n", + "\n", + "One way of searching for good hyper-parameters is by hand-tuning, where you try one set of parameters and see how they perform, and then you try another set of parameters and see if they improve the performance. You try and build an intuition for what works well and guide your parameter-search accordingly. Not only is this extremely time-consuming for a human researcher, but the optimal parameters are often counter-intuitive to humans so you will not find them!\n", + "\n", + "Another way of searching for good hyper-parameters is to divide each parameter's valid range into evenly spaced values, and then simply have the computer try all combinations of parameter-values. This is called Grid Search. Although it is run entirely by the computer, it quickly becomes extremely time-consuming because the number of parameter-combinations increases exponentially as you add more hyper-parameters. This problem is known as the Curse of Dimensionality. For example, if you have just 4 hyper-parameters to tune and each of them is allowed 10 possible values, then there is a total of 10^4 parameter-combinations. If you add just one more hyper-parameter then there are 10^5 parameter-combinations, and so on.\n", + "\n", + "Yet another way of searching for good hyper-parameters is by random search. Instead of systematically trying every single parameter-combination as in Grid Search, we now try a number of parameter-combinations completely at random. This is like searching for \"a needle in a haystack\" and as the number of parameters increases, the probability of finding the optimal parameter-combinations by random sampling decreases to zero.\n", + "\n", + "This tutorial uses a clever method for finding good hyper-parameters known as Bayesian Optimization. You should be familiar with TensorFlow, Keras and Convolutional Neural Networks, see Tutorials #01, #02 and #03-C." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flowchart\n", + "\n", + "The problem with hyper-parameter optimization is that it is extremely costly to assess the performance of a set of parameters. This is because we first have to build the corresponding neural network, then we have to train it, and finally we have to measure its performance on a test-set. In this tutorial we will use the small MNIST problem so this training can be done very quickly, but on more realistic problems the training may take hours, days or even weeks on a very fast computer. So we need an optimization method that can search for hyper-parameters as efficiently as possible, by only evaluating the actual performance when absolutely necessary.\n", + "\n", + "The idea with Bayesian optimization is to construct another model of the search-space for hyper-parameters. One kind of model is known as a Gaussian Process. This gives us an estimate of how the performance varies with changes to the hyper-parameters. Whenever we evaluate the actual performance for a set of hyper-parameters, we know for a fact what the performance is - except perhaps for some noise. We can then ask the Bayesian optimizer to give us a new suggestion for hyper-parameters in a region of the search-space that we haven't explored yet, or hyper-parameters that the Bayesian optimizer thinks will bring us most improvement. We then repeat this process a number of times until the Bayesian optimizer has built a good model of how the performance varies with different hyper-parameters, so we can choose the best parameters.\n", + "\n", + "The flowchart of the algorithm is roughly:\n", + "\n", + "![Flowchart](images/19_flowchart_bayesian_optimization.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import math" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to import several things from Keras." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import backend as K\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import InputLayer, Input\n", + "from tensorflow.keras.layers import Reshape, MaxPooling2D\n", + "from tensorflow.keras.layers import Conv2D, Dense, Flatten\n", + "from tensorflow.keras.callbacks import TensorBoard\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.models import load_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NOTE:** We will save and load models using Keras so you need to have [h5py](http://docs.h5py.org/en/latest/build.html#install) installed. You also need to have [scikit-optimize](https://scikit-optimize.github.io/) installed for doing the hyper-parameter optimization.\n", + "\n", + "You should be able to run the following command in a terminal to install them both:\n", + "\n", + "`pip install h5py scikit-optimize`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**NOTE:** This Notebook requires plotting functions in `scikit-optimize` that have not been merged into the official release at the time of this writing. If this Notebook cannot run with the version of `scikit-optimize` installed by the command above, you may have to install `scikit-optimize` from a development branch by running the following command instead:\n", + "\n", + "`pip install git+git://github.com/Hvass-Labs/scikit-optimize.git@dd7433da068b5a2509ef4ea4e5195458393e6555`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n", + " warnings.warn(msg, category=FutureWarning)\n", + "/home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.scorer module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n", + " warnings.warn(message, FutureWarning)\n" + ] + } + ], + "source": [ + "import skopt\n", + "from skopt import gp_minimize, forest_minimize\n", + "from skopt.space import Real, Categorical, Integer\n", + "from skopt.plots import plot_convergence\n", + "from skopt.plots import plot_objective, plot_evaluations\n", + "from skopt.plots import plot_histogram, plot_objective_2D\n", + "from skopt.utils import use_named_args" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 (Anaconda) and package versions:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.0'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.2.4-tf'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.keras.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.4'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "skopt.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hyper-Parameters\n", + "\n", + "In this tutorial we want to find the hyper-parametes that makes a simple Convolutional Neural Network perform best at classifying the MNIST dataset for hand-written digits.\n", + "\n", + "For this demonstration we want to find the following hyper-parameters:\n", + "\n", + "* The learning-rate of the optimizer.\n", + "* The number of fully-connected / dense layers.\n", + "* The number of nodes for each of the dense layers.\n", + "* Whether to use 'sigmoid' or 'relu' activation in all the layers.\n", + "\n", + "We will use the Python package `scikit-optimize` (or `skopt`) for finding the best choices of these hyper-parameters. Before we begin with the actual search for hyper-parameters, we first need to define the valid search-ranges or search-dimensions for each of these parameters.\n", + "\n", + "This is the search-dimension for the learning-rate. It is a real number (floating-point) with a lower bound of `1e-6` and an upper bound of `1e-2`. But instead of searching between these bounds directly, we use a logarithmic transformation, so we will search for the number `k` in `1ek` which is only bounded between -6 and -2. This is better than searching the entire exponential range." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "dim_learning_rate = Real(low=1e-6, high=1e-2, prior='log-uniform',\n", + " name='learning_rate')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the search-dimension for the number of dense layers in the neural network. This is an integer and we want at least 1 dense layer and at most 5 dense layers in the neural network." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "dim_num_dense_layers = Integer(low=1, high=5, name='num_dense_layers')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the search-dimension for the number of nodes for each dense layer. This is also an integer and we want at least 5 and at most 512 nodes in each layer of the neural network." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "dim_num_dense_nodes = Integer(low=5, high=512, name='num_dense_nodes')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the search-dimension for the activation-function. This is a combinatorial or categorical parameter which can be either 'relu' or 'sigmoid'." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "dim_activation = Categorical(categories=['relu', 'sigmoid'],\n", + " name='activation')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then combine all these search-dimensions into a list." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "dimensions = [dim_learning_rate,\n", + " dim_num_dense_layers,\n", + " dim_num_dense_nodes,\n", + " dim_activation]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is helpful to start the search for hyper-parameters with a decent choice that we have found by hand-tuning. But we will use the following parameters that do not perform so well, so as to better demonstrate the usefulness of hyper-parameter optimization: A learning-rate of 1e-5, a single dense layer with 16 nodes, and relu activation-functions.\n", + "\n", + "Note that these hyper-parameters are packed in a single list. This is how `skopt` works internally on hyper-parameters. You therefore need to ensure that the order of the dimensions are consistent with the order given in `dimensions` above." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "default_parameters = [1e-5, 1, 16, 'relu']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for log-dir-name\n", + "\n", + "We will log the training-progress for all parameter-combinations so they can be viewed and compared using TensorBoard. This is done by setting a common parent-dir and then have a sub-dir for each parameter-combination with an appropriate name." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def log_dir_name(learning_rate, num_dense_layers,\n", + " num_dense_nodes, activation):\n", + "\n", + " # The dir-name for the TensorBoard log-dir.\n", + " s = \"./19_logs/lr_{0:.0e}_layers_{1}_nodes_{2}_{3}/\"\n", + "\n", + " # Insert all the hyper-parameters in the dir-name.\n", + " log_dir = s.format(learning_rate,\n", + " num_dense_layers,\n", + " num_dense_nodes,\n", + " activation)\n", + "\n", + " return log_dir" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given dir." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Size of:\n", + "- Training-set:\t\t55000\n", + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" + ] + } + ], + "source": [ + "print(\"Size of:\")\n", + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Copy some of the data-dimensions for convenience." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# The number of pixels in each dimension of an image.\n", + "img_size = data.img_size\n", + "\n", + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", + "\n", + "# Tuple with height and width of images used to reshape arrays.\n", + "img_shape = data.img_shape\n", + "\n", + "# Tuple with height, width and depth used to reshape arrays.\n", + "# This is used for reshaping in Keras.\n", + "img_shape_full = data.img_shape_full\n", + "\n", + "# Number of classes, one class for each of 10 digits.\n", + "num_classes = data.num_classes\n", + "\n", + "# Number of colour channels for the images: 1 channel for gray-scale.\n", + "num_channels = data.num_channels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the performance on the validation-set as an indication of which choice of hyper-parameters performs the best on previously unseen data. The Keras API needs the validation-set as a tuple." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "validation_data = (data.x_val, data.y_val)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function for plotting images" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_images(images, cls_true, cls_pred=None):\n", + " assert len(images) == len(cls_true) == 9\n", + " \n", + " # Create figure with 3x3 sub-plots.\n", + " fig, axes = plt.subplots(3, 3)\n", + " fig.subplots_adjust(hspace=0.3, wspace=0.3)\n", + "\n", + " for i, ax in enumerate(axes.flat):\n", + " # Plot image.\n", + " ax.imshow(images[i].reshape(img_shape), cmap='binary')\n", + "\n", + " # Show true and predicted classes.\n", + " if cls_pred is None:\n", + " xlabel = \"True: {0}\".format(cls_true[i])\n", + " else:\n", + " xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n", + "\n", + " # Show the classes as the label on the x-axis.\n", + " ax.set_xlabel(xlabel)\n", + " \n", + " # Remove ticks from the plot.\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " \n", + " # Ensure the plot is shown correctly with multiple plots\n", + " # in a single Notebook cell.\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot a few images to see if data is correct" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the first images from the test-set.\n", + "images = data.x_test[0:9]\n", + "\n", + "# Get the true classes for those images.\n", + "cls_true = data.y_test_cls[0:9]\n", + "\n", + "# Plot the images and labels using our helper-function above.\n", + "plot_images(images=images, cls_true=cls_true)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-function to plot example errors\n", + "\n", + "Function for plotting examples of images from the test-set that have been mis-classified." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_example_errors(cls_pred):\n", + " # cls_pred is an array of the predicted class-number for\n", + " # all images in the test-set.\n", + "\n", + " # Boolean array whether the predicted class is incorrect.\n", + " incorrect = (cls_pred != data.y_test_cls)\n", + "\n", + " # Get the images from the test-set that have been\n", + " # incorrectly classified.\n", + " images = data.x_test[incorrect]\n", + " \n", + " # Get the predicted classes for those images.\n", + " cls_pred = cls_pred[incorrect]\n", + "\n", + " # Get the true classes for those images.\n", + " cls_true = data.y_test_cls[incorrect]\n", + " \n", + " # Plot the first 9 images.\n", + " plot_images(images=images[0:9],\n", + " cls_true=cls_true[0:9],\n", + " cls_pred=cls_pred[0:9])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hyper-Parameter Optimization\n", + "\n", + "There are several steps required to do hyper-parameter optimization.\n", + "\n", + "### Create the Model\n", + "\n", + "We first need a function that takes a set of hyper-parameters and creates the Convolutional Neural Network corresponding to those parameters. We use Keras to build the neural network in TensorFlow, see Tutorial #03-C for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "def create_model(learning_rate, num_dense_layers,\n", + " num_dense_nodes, activation):\n", + " \"\"\"\n", + " Hyper-parameters:\n", + " learning_rate: Learning-rate for the optimizer.\n", + " num_dense_layers: Number of dense layers.\n", + " num_dense_nodes: Number of nodes in each dense layer.\n", + " activation: Activation function for all layers.\n", + " \"\"\"\n", + " \n", + " # Start construction of a Keras Sequential model.\n", + " model = Sequential()\n", + "\n", + " # Add an input layer which is similar to a feed_dict in TensorFlow.\n", + " # Note that the input-shape must be a tuple containing the image-size.\n", + " model.add(InputLayer(input_shape=(img_size_flat,)))\n", + "\n", + " # The input from MNIST is a flattened array with 784 elements,\n", + " # but the convolutional layers expect images with shape (28, 28, 1)\n", + " model.add(Reshape(img_shape_full))\n", + "\n", + " # First convolutional layer.\n", + " # There are many hyper-parameters in this layer, but we only\n", + " # want to optimize the activation-function in this example.\n", + " model.add(Conv2D(kernel_size=5, strides=1, filters=16, padding='same',\n", + " activation=activation, name='layer_conv1'))\n", + " model.add(MaxPooling2D(pool_size=2, strides=2))\n", + "\n", + " # Second convolutional layer.\n", + " # Again, we only want to optimize the activation-function here.\n", + " model.add(Conv2D(kernel_size=5, strides=1, filters=36, padding='same',\n", + " activation=activation, name='layer_conv2'))\n", + " model.add(MaxPooling2D(pool_size=2, strides=2))\n", + "\n", + " # Flatten the 4-rank output of the convolutional layers\n", + " # to 2-rank that can be input to a fully-connected / dense layer.\n", + " model.add(Flatten())\n", + "\n", + " # Add fully-connected / dense layers.\n", + " # The number of layers is a hyper-parameter we want to optimize.\n", + " for i in range(num_dense_layers):\n", + " # Name of the layer. This is not really necessary\n", + " # because Keras should give them unique names.\n", + " name = 'layer_dense_{0}'.format(i+1)\n", + "\n", + " # Add the dense / fully-connected layer to the model.\n", + " # This has two hyper-parameters we want to optimize:\n", + " # The number of nodes and the activation function.\n", + " model.add(Dense(num_dense_nodes,\n", + " activation=activation,\n", + " name=name))\n", + "\n", + " # Last fully-connected / dense layer with softmax-activation\n", + " # for use in classification.\n", + " model.add(Dense(num_classes, activation='softmax'))\n", + " \n", + " # Use the Adam method for training the network.\n", + " # We want to find the best learning-rate for the Adam method.\n", + " optimizer = Adam(lr=learning_rate)\n", + " \n", + " # In Keras we need to compile the model so it can be trained.\n", + " model.compile(optimizer=optimizer,\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train and Evaluate the Model\n", + "\n", + "The neural network with the best hyper-parameters is saved to disk so it can be reloaded later. This is the filename for the model." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "path_best_model = '19_best_model.h5'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the classification accuracy for the model saved to disk. It is a global variable which will be updated during optimization of the hyper-parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "best_accuracy = 0.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the function that creates and trains a neural network with the given hyper-parameters, and then evaluates its performance on the validation-set. The function then returns the so-called fitness value (aka. objective value), which is the negative classification accuracy on the validation-set. It is negative because `skopt` performs minimization instead of maximization.\n", + "\n", + "Note the function decorator `@use_named_args` which wraps the fitness function so that it can be called with all the parameters as a single list, for example: `fitness(x=[1e-4, 3, 256, 'relu'])`. This is the calling-style `skopt` uses internally." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "@use_named_args(dimensions=dimensions)\n", + "def fitness(learning_rate, num_dense_layers,\n", + " num_dense_nodes, activation):\n", + " \"\"\"\n", + " Hyper-parameters:\n", + " learning_rate: Learning-rate for the optimizer.\n", + " num_dense_layers: Number of dense layers.\n", + " num_dense_nodes: Number of nodes in each dense layer.\n", + " activation: Activation function for all layers.\n", + " \"\"\"\n", + "\n", + " # Print the hyper-parameters.\n", + " print('learning rate: {0:.1e}'.format(learning_rate))\n", + " print('num_dense_layers:', num_dense_layers)\n", + " print('num_dense_nodes:', num_dense_nodes)\n", + " print('activation:', activation)\n", + " print()\n", + " \n", + " # Create the neural network with these hyper-parameters.\n", + " model = create_model(learning_rate=learning_rate,\n", + " num_dense_layers=num_dense_layers,\n", + " num_dense_nodes=num_dense_nodes,\n", + " activation=activation)\n", + "\n", + " # Dir-name for the TensorBoard log-files.\n", + " log_dir = log_dir_name(learning_rate, num_dense_layers,\n", + " num_dense_nodes, activation)\n", + " \n", + " # Create a callback-function for Keras which will be\n", + " # run after each epoch has ended during training.\n", + " # This saves the log-files for TensorBoard.\n", + " # Note that there are complications when histogram_freq=1.\n", + " # It might give strange errors and it also does not properly\n", + " # support Keras data-generators for the validation-set.\n", + " callback_log = TensorBoard(\n", + " log_dir=log_dir,\n", + " histogram_freq=0,\n", + " write_graph=True,\n", + " write_grads=False,\n", + " write_images=False)\n", + " \n", + " # Use Keras to train the model.\n", + " history = model.fit(x=data.x_train,\n", + " y=data.y_train,\n", + " epochs=3,\n", + " batch_size=128,\n", + " validation_data=validation_data,\n", + " callbacks=[callback_log])\n", + "\n", + " # Get the classification accuracy on the validation-set\n", + " # after the last training-epoch.\n", + " accuracy = history.history['val_accuracy'][-1]\n", + "\n", + " # Print the classification accuracy.\n", + " print()\n", + " print(\"Accuracy: {0:.2%}\".format(accuracy))\n", + " print()\n", + "\n", + " # Save the model if it improves on the best-found performance.\n", + " # We use the global keyword so we update the variable outside\n", + " # of this function.\n", + " global best_accuracy\n", + "\n", + " # If the classification accuracy of the saved model is improved ...\n", + " if accuracy > best_accuracy:\n", + " # Save the new model to harddisk.\n", + " model.save(path_best_model)\n", + " \n", + " # Update the classification accuracy.\n", + " best_accuracy = accuracy\n", + "\n", + " # Delete the Keras model with these hyper-parameters from memory.\n", + " del model\n", + " \n", + " # Clear the Keras session, otherwise it will keep adding new\n", + " # models to the same TensorFlow graph each time we create\n", + " # a model with a different set of hyper-parameters.\n", + " K.clear_session()\n", + " \n", + " # NOTE: Scikit-optimize does minimization so it tries to\n", + " # find a set of hyper-parameters with the LOWEST fitness-value.\n", + " # Because we are interested in the HIGHEST classification\n", + " # accuracy, we need to negate this number so it can be minimized.\n", + " return -accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test Run\n", + "\n", + "Before we run the hyper-parameter optimization, let us first check that the various functions above actually work, when we pass the default hyper-parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "learning rate: 1.0e-05\n", + "num_dense_layers: 1\n", + "num_dense_nodes: 16\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 4s 64us/sample - loss: 2.2207 - accuracy: 0.2039 - val_loss: 2.0769 - val_accuracy: 0.3326\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 2s 39us/sample - loss: 1.8787 - accuracy: 0.4489 - val_loss: 1.5766 - val_accuracy: 0.6934\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 2s 39us/sample - loss: 1.3661 - accuracy: 0.7220 - val_loss: 1.0646 - val_accuracy: 0.8080\n", + "\n", + "Accuracy: 80.80%\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "-0.808" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fitness(x=default_parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the Hyper-Parameter Optimization\n", + "\n", + "Now we are ready to run the actual hyper-parameter optimization using Bayesian optimization from the scikit-optimize package. Note that it first calls `fitness()` with `default_parameters` as the starting point we have found by hand-tuning, which should help the optimizer locate better hyper-parameters faster.\n", + "\n", + "There are many more parameters you can experiment with here, including the number of calls to the `fitness()` function which we have set to 40. But `fitness()` is very expensive to evaluate so it should not be run too many times, especially for larger neural networks and datasets.\n", + "\n", + "You can also experiment with the so-called acquisition function which determines how to find a new set of hyper-parameters from the internal model of the Bayesian optimizer. You can also try using another Bayesian optimizer such as Random Forests." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "learning rate: 1.0e-05\n", + "num_dense_layers: 1\n", + "num_dense_nodes: 16\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 47us/sample - loss: 2.2126 - accuracy: 0.3096 - val_loss: 2.0888 - val_accuracy: 0.4494\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 2s 38us/sample - loss: 1.9393 - accuracy: 0.4852 - val_loss: 1.7300 - val_accuracy: 0.5342\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 2s 38us/sample - loss: 1.5558 - accuracy: 0.5634 - val_loss: 1.2548 - val_accuracy: 0.7250\n", + "\n", + "Accuracy: 72.50%\n", + "\n", + "learning rate: 7.0e-04\n", + "num_dense_layers: 1\n", + "num_dense_nodes: 365\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 46us/sample - loss: 0.2217 - accuracy: 0.9350 - val_loss: 0.0633 - val_accuracy: 0.9828\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 2s 39us/sample - loss: 0.0576 - accuracy: 0.9822 - val_loss: 0.0507 - val_accuracy: 0.9870\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 2s 39us/sample - loss: 0.0397 - accuracy: 0.9878 - val_loss: 0.0396 - val_accuracy: 0.9892\n", + "\n", + "Accuracy: 98.92%\n", + "\n", + "learning rate: 6.8e-03\n", + "num_dense_layers: 4\n", + "num_dense_nodes: 466\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 53us/sample - loss: 2.3098 - accuracy: 0.1117 - val_loss: 2.3022 - val_accuracy: 0.1060\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 2s 44us/sample - loss: 2.3015 - accuracy: 0.1123 - val_loss: 2.3016 - val_accuracy: 0.1060\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 3s 47us/sample - loss: 2.3017 - accuracy: 0.1123 - val_loss: 2.3019 - val_accuracy: 0.1060\n", + "\n", + "Accuracy: 10.60%\n", + "\n", + "learning rate: 9.8e-04\n", + "num_dense_layers: 5\n", + "num_dense_nodes: 122\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 54us/sample - loss: 2.3093 - accuracy: 0.1038 - val_loss: 2.3061 - val_accuracy: 0.1060\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 2s 42us/sample - loss: 2.3057 - accuracy: 0.1078 - val_loss: 2.3039 - val_accuracy: 0.1060\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 2s 41us/sample - loss: 2.3053 - accuracy: 0.1073 - val_loss: 2.3054 - val_accuracy: 0.0978\n", + "\n", + "Accuracy: 9.78%\n", + "\n", + "learning rate: 3.7e-04\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 72\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 48us/sample - loss: 2.3049 - accuracy: 0.1066 - val_loss: 2.3042 - val_accuracy: 0.1060\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 2s 40us/sample - loss: 2.3008 - accuracy: 0.1220 - val_loss: 2.2349 - val_accuracy: 0.2808\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 3s 55us/sample - loss: 1.1898 - accuracy: 0.7546 - val_loss: 0.5112 - val_accuracy: 0.9176\n", + "\n", + "Accuracy: 91.76%\n", + "\n", + "learning rate: 6.7e-04\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 230\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 57us/sample - loss: 1.9215 - accuracy: 0.2895 - val_loss: 0.5522 - val_accuracy: 0.8572\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 5s 90us/sample - loss: 0.3142 - accuracy: 0.9098 - val_loss: 0.1402 - val_accuracy: 0.9612\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 3s 54us/sample - loss: 0.1451 - accuracy: 0.9567 - val_loss: 0.0997 - val_accuracy: 0.9708\n", + "\n", + "Accuracy: 97.08%\n", + "\n", + "learning rate: 9.7e-03\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 132\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 53us/sample - loss: 0.2108 - accuracy: 0.9304 - val_loss: 0.0595 - val_accuracy: 0.9840\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 2s 45us/sample - loss: 0.0719 - accuracy: 0.9801 - val_loss: 0.0487 - val_accuracy: 0.9874\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 3s 50us/sample - loss: 0.0673 - accuracy: 0.9824 - val_loss: 0.0474 - val_accuracy: 0.9870\n", + "\n", + "Accuracy: 98.70%\n", + "\n", + "learning rate: 1.6e-04\n", + "num_dense_layers: 4\n", + "num_dense_nodes: 112\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 8s 149us/sample - loss: 2.3076 - accuracy: 0.1080 - val_loss: 2.3030 - val_accuracy: 0.1060\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 6s 118us/sample - loss: 2.2995 - accuracy: 0.1135 - val_loss: 2.2653 - val_accuracy: 0.1128\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 6s 115us/sample - loss: 1.7192 - accuracy: 0.4761 - val_loss: 1.1804 - val_accuracy: 0.6904\n", + "\n", + "Accuracy: 69.04%\n", + "\n", + "learning rate: 7.7e-05\n", + "num_dense_layers: 5\n", + "num_dense_nodes: 154\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 56us/sample - loss: 0.8023 - accuracy: 0.7727 - val_loss: 0.2123 - val_accuracy: 0.9404\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 49us/sample - loss: 0.2085 - accuracy: 0.9384 - val_loss: 0.1236 - val_accuracy: 0.9644\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 7s 125us/sample - loss: 0.1413 - accuracy: 0.9564 - val_loss: 0.0950 - val_accuracy: 0.9746\n", + "\n", + "Accuracy: 97.46%\n", + "\n", + "learning rate: 2.9e-03\n", + "num_dense_layers: 4\n", + "num_dense_nodes: 156\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 4s 75us/sample - loss: 2.3168 - accuracy: 0.1028 - val_loss: 2.3084 - val_accuracy: 0.1060\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 51us/sample - loss: 2.3055 - accuracy: 0.1059 - val_loss: 2.3023 - val_accuracy: 0.1060\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 6s 101us/sample - loss: 2.3016 - accuracy: 0.1119 - val_loss: 2.3027 - val_accuracy: 0.1060\n", + "\n", + "Accuracy: 10.60%\n", + "\n", + "learning rate: 1.1e-05\n", + "num_dense_layers: 1\n", + "num_dense_nodes: 496\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 10s 185us/sample - loss: 1.8064 - accuracy: 0.6548 - val_loss: 1.0878 - val_accuracy: 0.8440\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 7s 124us/sample - loss: 0.7844 - accuracy: 0.8393 - val_loss: 0.4786 - val_accuracy: 0.9046\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 7s 128us/sample - loss: 0.4776 - accuracy: 0.8791 - val_loss: 0.3288 - val_accuracy: 0.9226\n", + "\n", + "Accuracy: 92.26%\n", + "\n", + "learning rate: 3.4e-03\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 451\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 62us/sample - loss: 0.1423 - accuracy: 0.9556 - val_loss: 0.0378 - val_accuracy: 0.9904\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 52us/sample - loss: 0.0438 - accuracy: 0.9869 - val_loss: 0.0379 - val_accuracy: 0.9896\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 6s 106us/sample - loss: 0.0342 - accuracy: 0.9892 - val_loss: 0.0370 - val_accuracy: 0.9890\n", + "\n", + "Accuracy: 98.90%\n", + "\n", + "learning rate: 1.2e-05\n", + "num_dense_layers: 5\n", + "num_dense_nodes: 182\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 59us/sample - loss: 1.9464 - accuracy: 0.4581 - val_loss: 1.0907 - val_accuracy: 0.8004\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 48us/sample - loss: 0.6953 - accuracy: 0.8265 - val_loss: 0.3989 - val_accuracy: 0.8992\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 4s 78us/sample - loss: 0.4034 - accuracy: 0.8862 - val_loss: 0.2750 - val_accuracy: 0.9256\n", + "\n", + "Accuracy: 92.56%\n", + "\n", + "learning rate: 2.8e-04\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 512\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 56us/sample - loss: 1.8869 - accuracy: 0.3200 - val_loss: 0.5330 - val_accuracy: 0.8646\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 52us/sample - loss: 0.3947 - accuracy: 0.8847 - val_loss: 0.2235 - val_accuracy: 0.9346\n", + "Epoch 3/3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "55000/55000 [==============================] - 4s 78us/sample - loss: 0.2316 - accuracy: 0.9292 - val_loss: 0.1444 - val_accuracy: 0.9592\n", + "\n", + "Accuracy: 95.92%\n", + "\n", + "learning rate: 3.4e-03\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 5\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 4s 64us/sample - loss: 2.3031 - accuracy: 0.1100 - val_loss: 2.3020 - val_accuracy: 0.1060\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 46us/sample - loss: 2.3020 - accuracy: 0.1118 - val_loss: 2.3021 - val_accuracy: 0.1060\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 4s 70us/sample - loss: 2.3019 - accuracy: 0.1128 - val_loss: 2.3018 - val_accuracy: 0.1060\n", + "\n", + "Accuracy: 10.60%\n", + "\n", + "learning rate: 1.0e-02\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 104\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 52us/sample - loss: 0.1708 - accuracy: 0.9451 - val_loss: 0.0552 - val_accuracy: 0.9836\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 46us/sample - loss: 0.0630 - accuracy: 0.9813 - val_loss: 0.0551 - val_accuracy: 0.9836\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 3s 46us/sample - loss: 0.0514 - accuracy: 0.9851 - val_loss: 0.0415 - val_accuracy: 0.9904\n", + "\n", + "Accuracy: 99.04%\n", + "\n", + "learning rate: 5.5e-04\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 277\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 4s 78us/sample - loss: 2.0127 - accuracy: 0.2534 - val_loss: 0.6090 - val_accuracy: 0.8268\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 50us/sample - loss: 0.3406 - accuracy: 0.9004 - val_loss: 0.1451 - val_accuracy: 0.9582\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 4s 80us/sample - loss: 0.1563 - accuracy: 0.9533 - val_loss: 0.0969 - val_accuracy: 0.9700\n", + "\n", + "Accuracy: 97.00%\n", + "\n", + "learning rate: 2.8e-03\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 441\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 58us/sample - loss: 0.1397 - accuracy: 0.9561 - val_loss: 0.0428 - val_accuracy: 0.9864\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 49us/sample - loss: 0.0471 - accuracy: 0.9853 - val_loss: 0.0401 - val_accuracy: 0.9892\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 7s 120us/sample - loss: 0.0316 - accuracy: 0.9905 - val_loss: 0.0482 - val_accuracy: 0.9882\n", + "\n", + "Accuracy: 98.82%\n", + "\n", + "learning rate: 5.0e-04\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 309\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 56us/sample - loss: 0.2504 - accuracy: 0.9302 - val_loss: 0.0794 - val_accuracy: 0.9784\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 49us/sample - loss: 0.0642 - accuracy: 0.9801 - val_loss: 0.0631 - val_accuracy: 0.9814\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 5s 83us/sample - loss: 0.0439 - accuracy: 0.9861 - val_loss: 0.0465 - val_accuracy: 0.9878\n", + "\n", + "Accuracy: 98.78%\n", + "\n", + "learning rate: 1.1e-04\n", + "num_dense_layers: 1\n", + "num_dense_nodes: 512\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 4s 64us/sample - loss: 0.5362 - accuracy: 0.8733 - val_loss: 0.1483 - val_accuracy: 0.9572\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 50us/sample - loss: 0.1423 - accuracy: 0.9581 - val_loss: 0.0895 - val_accuracy: 0.9730\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 4s 81us/sample - loss: 0.0942 - accuracy: 0.9724 - val_loss: 0.0687 - val_accuracy: 0.9806\n", + "\n", + "Accuracy: 98.06%\n", + "\n", + "learning rate: 5.5e-05\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 512\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 58us/sample - loss: 0.7263 - accuracy: 0.8272 - val_loss: 0.1854 - val_accuracy: 0.9470\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 51us/sample - loss: 0.1967 - accuracy: 0.9415 - val_loss: 0.1209 - val_accuracy: 0.9666\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 6s 115us/sample - loss: 0.1333 - accuracy: 0.9604 - val_loss: 0.0909 - val_accuracy: 0.9748\n", + "\n", + "Accuracy: 97.48%\n", + "\n", + "learning rate: 3.5e-05\n", + "num_dense_layers: 4\n", + "num_dense_nodes: 512\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 4s 70us/sample - loss: 0.7778 - accuracy: 0.8057 - val_loss: 0.1853 - val_accuracy: 0.9496\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 54us/sample - loss: 0.1914 - accuracy: 0.9435 - val_loss: 0.1170 - val_accuracy: 0.9678\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 6s 107us/sample - loss: 0.1281 - accuracy: 0.9622 - val_loss: 0.0848 - val_accuracy: 0.9778\n", + "\n", + "Accuracy: 97.78%\n", + "\n", + "learning rate: 3.6e-05\n", + "num_dense_layers: 5\n", + "num_dense_nodes: 446\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 63us/sample - loss: 0.7959 - accuracy: 0.8041 - val_loss: 0.1977 - val_accuracy: 0.9454\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 53us/sample - loss: 0.1957 - accuracy: 0.9419 - val_loss: 0.1231 - val_accuracy: 0.9658\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 6s 110us/sample - loss: 0.1334 - accuracy: 0.9598 - val_loss: 0.0973 - val_accuracy: 0.9734\n", + "\n", + "Accuracy: 97.34%\n", + "\n", + "learning rate: 6.6e-05\n", + "num_dense_layers: 4\n", + "num_dense_nodes: 143\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 4s 67us/sample - loss: 0.8583 - accuracy: 0.7593 - val_loss: 0.2149 - val_accuracy: 0.9410\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 50us/sample - loss: 0.2304 - accuracy: 0.9302 - val_loss: 0.1499 - val_accuracy: 0.9538\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 5s 86us/sample - loss: 0.1615 - accuracy: 0.9511 - val_loss: 0.1022 - val_accuracy: 0.9700\n", + "\n", + "Accuracy: 97.00%\n", + "\n", + "learning rate: 2.2e-04\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 195\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 53us/sample - loss: 2.2477 - accuracy: 0.1821 - val_loss: 1.6547 - val_accuracy: 0.6054\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 49us/sample - loss: 0.8448 - accuracy: 0.7859 - val_loss: 0.4386 - val_accuracy: 0.8970\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 3s 61us/sample - loss: 0.4028 - accuracy: 0.8893 - val_loss: 0.2623 - val_accuracy: 0.9310\n", + "\n", + "Accuracy: 93.10%\n", + "\n", + "learning rate: 4.2e-05\n", + "num_dense_layers: 1\n", + "num_dense_nodes: 512\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 61us/sample - loss: 2.3073 - accuracy: 0.1076 - val_loss: 2.2982 - val_accuracy: 0.1126\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 49us/sample - loss: 2.2914 - accuracy: 0.1344 - val_loss: 2.2685 - val_accuracy: 0.1802\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 4s 79us/sample - loss: 2.1696 - accuracy: 0.3317 - val_loss: 1.9508 - val_accuracy: 0.5936\n", + "\n", + "Accuracy: 59.36%\n", + "\n", + "learning rate: 5.1e-06\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 512\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 59us/sample - loss: 2.0897 - accuracy: 0.5289 - val_loss: 1.6486 - val_accuracy: 0.7448\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 57us/sample - loss: 1.1361 - accuracy: 0.7831 - val_loss: 0.6805 - val_accuracy: 0.8716\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 6s 118us/sample - loss: 0.5963 - accuracy: 0.8549 - val_loss: 0.4089 - val_accuracy: 0.9120\n", + "\n", + "Accuracy: 91.20%\n", + "\n", + "learning rate: 1.4e-06\n", + "num_dense_layers: 4\n", + "num_dense_nodes: 512\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 4s 70us/sample - loss: 2.3907 - accuracy: 0.0994 - val_loss: 2.3302 - val_accuracy: 0.0986\n", + "Epoch 2/3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "55000/55000 [==============================] - 3s 52us/sample - loss: 2.3122 - accuracy: 0.0994 - val_loss: 2.3043 - val_accuracy: 0.0986\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 7s 121us/sample - loss: 2.3020 - accuracy: 0.1115 - val_loss: 2.3020 - val_accuracy: 0.1060\n", + "\n", + "Accuracy: 10.60%\n", + "\n", + "learning rate: 4.8e-04\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 225\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 59us/sample - loss: 1.5780 - accuracy: 0.4531 - val_loss: 0.3782 - val_accuracy: 0.9030\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 52us/sample - loss: 0.2985 - accuracy: 0.9146 - val_loss: 0.1567 - val_accuracy: 0.9564\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 7s 118us/sample - loss: 0.1712 - accuracy: 0.9491 - val_loss: 0.1077 - val_accuracy: 0.9704\n", + "\n", + "Accuracy: 97.04%\n", + "\n", + "learning rate: 5.5e-03\n", + "num_dense_layers: 1\n", + "num_dense_nodes: 512\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 4s 69us/sample - loss: 0.1434 - accuracy: 0.9557 - val_loss: 0.0516 - val_accuracy: 0.9834\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 49us/sample - loss: 0.0457 - accuracy: 0.9858 - val_loss: 0.0441 - val_accuracy: 0.9874\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 6s 110us/sample - loss: 0.0330 - accuracy: 0.9894 - val_loss: 0.0582 - val_accuracy: 0.9854\n", + "\n", + "Accuracy: 98.54%\n", + "\n", + "learning rate: 2.4e-03\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 5\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 63us/sample - loss: 1.1980 - accuracy: 0.5365 - val_loss: 0.3991 - val_accuracy: 0.8454\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 50us/sample - loss: 0.3059 - accuracy: 0.9074 - val_loss: 0.1823 - val_accuracy: 0.9488\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 4s 77us/sample - loss: 0.1815 - accuracy: 0.9497 - val_loss: 0.1492 - val_accuracy: 0.9606\n", + "\n", + "Accuracy: 96.06%\n", + "\n", + "learning rate: 6.6e-04\n", + "num_dense_layers: 1\n", + "num_dense_nodes: 512\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 61us/sample - loss: 0.2145 - accuracy: 0.9392 - val_loss: 0.0638 - val_accuracy: 0.9830\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 51us/sample - loss: 0.0591 - accuracy: 0.9817 - val_loss: 0.0539 - val_accuracy: 0.9856\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 3s 62us/sample - loss: 0.0395 - accuracy: 0.9879 - val_loss: 0.0454 - val_accuracy: 0.9890\n", + "\n", + "Accuracy: 98.90%\n", + "\n", + "learning rate: 1.0e-06\n", + "num_dense_layers: 5\n", + "num_dense_nodes: 512\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 64us/sample - loss: 2.2933 - accuracy: 0.2440 - val_loss: 2.2784 - val_accuracy: 0.4338\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 4s 68us/sample - loss: 2.2577 - accuracy: 0.5072 - val_loss: 2.2230 - val_accuracy: 0.5994\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 8s 154us/sample - loss: 2.1768 - accuracy: 0.6255 - val_loss: 2.0998 - val_accuracy: 0.7048\n", + "\n", + "Accuracy: 70.48%\n", + "\n", + "learning rate: 2.9e-03\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 299\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 4s 69us/sample - loss: 0.1429 - accuracy: 0.9546 - val_loss: 0.0407 - val_accuracy: 0.9882\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 59us/sample - loss: 0.0452 - accuracy: 0.9855 - val_loss: 0.0391 - val_accuracy: 0.9882\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 7s 132us/sample - loss: 0.0310 - accuracy: 0.9903 - val_loss: 0.0370 - val_accuracy: 0.9894\n", + "\n", + "Accuracy: 98.94%\n", + "\n", + "learning rate: 1.8e-04\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 512\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 62us/sample - loss: 0.3695 - accuracy: 0.8938 - val_loss: 0.0923 - val_accuracy: 0.9740\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 54us/sample - loss: 0.0903 - accuracy: 0.9729 - val_loss: 0.0664 - val_accuracy: 0.9808\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 7s 126us/sample - loss: 0.0613 - accuracy: 0.9809 - val_loss: 0.0611 - val_accuracy: 0.9830\n", + "\n", + "Accuracy: 98.30%\n", + "\n", + "learning rate: 1.0e-06\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 512\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 9s 168us/sample - loss: 2.2758 - accuracy: 0.2655 - val_loss: 2.2455 - val_accuracy: 0.4236\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 8s 138us/sample - loss: 2.2145 - accuracy: 0.5053 - val_loss: 2.1717 - val_accuracy: 0.6046\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 7s 128us/sample - loss: 2.1320 - accuracy: 0.6202 - val_loss: 2.0713 - val_accuracy: 0.6850\n", + "\n", + "Accuracy: 68.50%\n", + "\n", + "learning rate: 1.0e-02\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 5\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 60us/sample - loss: 1.5650 - accuracy: 0.3523 - val_loss: 1.2933 - val_accuracy: 0.4494\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 48us/sample - loss: 0.9217 - accuracy: 0.6472 - val_loss: 0.6613 - val_accuracy: 0.7856\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 5s 85us/sample - loss: 0.6513 - accuracy: 0.7897 - val_loss: 0.4982 - val_accuracy: 0.8736\n", + "\n", + "Accuracy: 87.36%\n", + "\n", + "learning rate: 1.4e-03\n", + "num_dense_layers: 1\n", + "num_dense_nodes: 512\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 62us/sample - loss: 1.3383 - accuracy: 0.5226 - val_loss: 0.2304 - val_accuracy: 0.9322\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 53us/sample - loss: 0.2098 - accuracy: 0.9346 - val_loss: 0.1193 - val_accuracy: 0.9642\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 7s 123us/sample - loss: 0.1266 - accuracy: 0.9606 - val_loss: 0.0846 - val_accuracy: 0.9750\n", + "\n", + "Accuracy: 97.50%\n", + "\n", + "learning rate: 4.6e-04\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 105\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 9s 168us/sample - loss: 0.3599 - accuracy: 0.8924 - val_loss: 0.0980 - val_accuracy: 0.9706\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 7s 124us/sample - loss: 0.0884 - accuracy: 0.9728 - val_loss: 0.0749 - val_accuracy: 0.9788\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 7s 124us/sample - loss: 0.0643 - accuracy: 0.9799 - val_loss: 0.0548 - val_accuracy: 0.9860\n", + "\n", + "Accuracy: 98.60%\n", + "\n", + "learning rate: 1.0e-03\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 512\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 62us/sample - loss: 1.1924 - accuracy: 0.5680 - val_loss: 0.1600 - val_accuracy: 0.9518\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 54us/sample - loss: 0.1456 - accuracy: 0.9542 - val_loss: 0.0808 - val_accuracy: 0.9764\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 6s 117us/sample - loss: 0.0892 - accuracy: 0.9727 - val_loss: 0.0612 - val_accuracy: 0.9806\n", + "\n", + "Accuracy: 98.06%\n", + "\n", + "CPU times: user 12min 22s, sys: 2min 47s, total: 15min 9s\n", + "Wall time: 9min 35s\n" + ] + } + ], + "source": [ + "%%time\n", + "search_result = gp_minimize(func=fitness,\n", + " dimensions=dimensions,\n", + " acq_func='EI', # Expected Improvement.\n", + " n_calls=40,\n", + " x0=default_parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimization Progress\n", + "\n", + "The progress of the hyper-parameter optimization can be easily plotted. The best fitness value found is plotted on the y-axis, remember that this is the negated classification accuracy on the validation-set.\n", + "\n", + "Note how few hyper-parameters had to be tried before substantial improvements were found." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_convergence(search_result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Best Hyper-Parameters\n", + "\n", + "The best hyper-parameters found by the Bayesian optimizer are packed as a list because that is what it uses internally." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.01, 2, 104, 'relu']" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "search_result.x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can convert these parameters to a dict with proper names for the search-space dimensions.\n", + "\n", + "First we need a reference to the search-space object." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "space = search_result.space" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we can use it to create a dict where the hyper-parameters have the proper names of the search-space dimensions. This is a bit awkward." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'learning_rate': 0.01,\n", + " 'num_dense_layers': 2,\n", + " 'num_dense_nodes': 104,\n", + " 'activation': 'relu'}" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "space.point_to_dict(search_result.x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the fitness value associated with these hyper-parameters. This is a negative number because the Bayesian optimizer performs minimization, so we had to negate the classification accuracy which is posed as a maximization problem." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.9904" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "search_result.fun" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also see all the hyper-parameters tried by the Bayesian optimizer and their associated fitness values (the negated classification accuracies). These are sorted so the highest classification accuracies are shown first.\n", + "\n", + "It appears that 'relu' activation was generally better than 'sigmoid'. Otherwise it can be difficult to see a pattern of which parameter choices are good. We really need to plot these results." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(-0.9904, [0.01, 2, 104, 'relu']),\n", + " (-0.9894, [0.0029398096826104927, 2, 299, 'relu']),\n", + " (-0.9892, [0.000695826471438557, 1, 365, 'relu']),\n", + " (-0.989, [0.000658408209046353, 1, 512, 'relu']),\n", + " (-0.989, [0.0034329866293724173, 2, 451, 'relu']),\n", + " (-0.9882, [0.0027527776962813647, 2, 441, 'relu']),\n", + " (-0.9878, [0.0004989932212151087, 2, 309, 'relu']),\n", + " (-0.987, [0.009700568764470742, 3, 132, 'relu']),\n", + " (-0.986, [0.00045720538478821585, 3, 105, 'relu']),\n", + " (-0.9854, [0.005470139269128146, 1, 512, 'relu']),\n", + " (-0.983, [0.0001756750312956145, 3, 512, 'relu']),\n", + " (-0.9806, [0.00010996497783044355, 1, 512, 'relu']),\n", + " (-0.9806, [0.0010101832379944083, 2, 512, 'sigmoid']),\n", + " (-0.9778, [3.542177009701199e-05, 4, 512, 'relu']),\n", + " (-0.975, [0.0014121597194479596, 1, 512, 'sigmoid']),\n", + " (-0.9748, [5.4968307266680435e-05, 2, 512, 'relu']),\n", + " (-0.9746, [7.662555005436298e-05, 5, 154, 'relu']),\n", + " (-0.9734, [3.623853563736315e-05, 5, 446, 'relu']),\n", + " (-0.9708, [0.0006699631867581338, 3, 230, 'sigmoid']),\n", + " (-0.9704, [0.0004806874905820532, 2, 225, 'sigmoid']),\n", + " (-0.97, [6.592244748528267e-05, 4, 143, 'relu']),\n", + " (-0.97, [0.0005519055319736135, 3, 277, 'sigmoid']),\n", + " (-0.9606, [0.0024078003512664754, 3, 5, 'relu']),\n", + " (-0.9592, [0.0002839701110319199, 2, 512, 'sigmoid']),\n", + " (-0.931, [0.00021682795230749897, 2, 195, 'sigmoid']),\n", + " (-0.9256, [1.152958307942762e-05, 5, 182, 'relu']),\n", + " (-0.9226, [1.068205878028229e-05, 1, 496, 'relu']),\n", + " (-0.9176, [0.00037183580449927443, 3, 72, 'sigmoid']),\n", + " (-0.912, [5.101545871674443e-06, 3, 512, 'relu']),\n", + " (-0.8736, [0.01, 3, 5, 'relu']),\n", + " (-0.725, [1e-05, 1, 16, 'relu']),\n", + " (-0.7048, [1e-06, 5, 512, 'relu']),\n", + " (-0.6904, [0.0001581796478320478, 4, 112, 'sigmoid']),\n", + " (-0.685, [1e-06, 2, 512, 'relu']),\n", + " (-0.5936, [4.158851683068185e-05, 1, 512, 'sigmoid']),\n", + " (-0.106, [1.42502793530235e-06, 4, 512, 'sigmoid']),\n", + " (-0.106, [0.0028996992551655475, 4, 156, 'sigmoid']),\n", + " (-0.106, [0.0034427213718442543, 3, 5, 'sigmoid']),\n", + " (-0.106, [0.006781678732829231, 4, 466, 'relu']),\n", + " (-0.0978, [0.0009844064941977042, 5, 122, 'sigmoid'])]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(zip(search_result.func_vals, search_result.x_iters))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plots\n", + "\n", + "There are several plotting functions available in the `skopt` library. For example, we can plot a histogram for the `activation` parameter, which shows the distribution of samples during the hyper-parameter optimization." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plot_histogram(result=search_result,\n", + " dimension_name='activation')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also make a landscape-plot of the estimated fitness values for two dimensions of the search-space, here taken to be `learning_rate` and `num_dense_layers`.\n", + "\n", + "The Bayesian optimizer works by building a surrogate model of the search-space and then searching this model instead of the real search-space, because it is much faster. The plot shows the last surrogate model built by the Bayesian optimizer where yellow regions are better and blue regions are worse. The black dots show where the optimizer has sampled the search-space and the red star shows the best parameters found.\n", + "\n", + "Several things should be noted here. Firstly, this surrogate model of the search-space may not be accurate. It is built from only 40 samples of calls to the `fitness()` function for training a neural network with a given choice of hyper-parameters. The modelled fitness landscape may differ significantly from its true values especially in regions of the search-space with few samples. Secondly, the plot may change each time the hyper-parameter optimization is run because of random noise in the training process of the neural network. Thirdly, this plot shows the effect of changing these two parameters `num_dense_layers` and `learning_rate` when averaged over all other dimensions in the search-space, this is also called a Partial Dependence plot and is a way of visualizing high-dimensional spaces in only 2-dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plot_objective_2D(result=search_result,\n", + " dimension_name1='learning_rate',\n", + " dimension_name2='num_dense_layers',\n", + " levels=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We cannot make a landscape plot for the `activation` hyper-parameter because it is a categorical variable that can be one of two strings `relu` or `sigmoid`. How this is encoded depends on the Bayesian optimizer, for example, whether it is using Gaussian Processes or Random Forests. But it cannot currently be plotted using the built-in functions of `skopt`.\n", + "\n", + "Instead we only want to use the real- and integer-valued dimensions of the search-space which we identify by their names." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "dim_names = ['learning_rate', 'num_dense_nodes', 'num_dense_layers']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then make a matrix-plot of all combinations of these dimensions.\n", + "\n", + "The diagonal shows the influence of a single dimension on the fitness. This is a so-called Partial Dependence plot for that dimension. It shows how the approximated fitness value changes with different values in that dimension.\n", + "\n", + "The plots below the diagonal show the Partial Dependence for two dimensions. This shows how the approximated fitness value changes when we are varying two dimensions simultaneously.\n", + "\n", + "These Partial Dependence plots are only approximations of the modelled fitness function - which in turn is only an approximation of the true fitness function in `fitness()`. This may be a bit difficult to understand. For example, the Partial Dependence is calculated by fixing one value for the `learning_rate` and then taking a large number of random samples for the remaining dimensions in the search-space. The estimated fitness for all these points is then averaged. This process is then repeated for other values of the `learning_rate` to show how it affects the fitness on average. A similar procedure is done for the plots that show the Partial Dependence plots for two dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plot_objective(result=search_result, dimension_names=dim_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also show another type of matrix-plot. Here the diagonal shows histograms of the sample distributions for each of the hyper-parameters during the Bayesian optimization. The plots below the diagonal show the location of samples in the search-space and the colour-coding shows the order in which the samples were taken. For larger numbers of samples you will likely see that the samples eventually become concentrated in a certain region of the search-space." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plot_evaluations(result=search_result, dimension_names=dim_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate Best Model on Test-Set\n", + "\n", + "We can now use the best model on the test-set. It is very easy to reload the model using Keras." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "model = load_model(path_best_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then evaluate its performance on the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10000/10000 [==============================] - 1s 64us/sample - loss: 0.0490 - accuracy: 0.9882\n" + ] + } + ], + "source": [ + "result = model.evaluate(x=data.x_test,\n", + " y=data.y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can print all the performance metrics for the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss 0.048988553384863916\n", + "accuracy 0.9882\n" + ] + } + ], + "source": [ + "for name, value in zip(model.metrics_names, result):\n", + " print(name, value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or we can just print the classification accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 98.82%\n" + ] + } + ], + "source": [ + "print(\"{0}: {1:.2%}\".format(model.metrics_names[1], result[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predict on New Data\n", + "\n", + "We can also predict the classification for new images. We will just use some images from the test-set but you could load your own images into numpy arrays and use those instead." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "images = data.x_test[0:9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the true class-number for those images. This is only used when plotting the images." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "cls_true = data.y_test_cls[0:9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the predicted classes as One-Hot encoded arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model.predict(x=images)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the predicted classes as integers." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "cls_pred = np.argmax(y_pred,axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_images(images=images,\n", + " cls_true=cls_true,\n", + " cls_pred=cls_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examples of Mis-Classified Images\n", + "\n", + "We can plot some examples of mis-classified images from the test-set.\n", + "\n", + "First we get the predicted classes for all the images in the test-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model.predict(x=data.x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we convert the predicted class-numbers from One-Hot encoded arrays to integers." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "cls_pred = np.argmax(y_pred, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot some of the mis-classified images." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_example_errors(cls_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed how to optimize the hyper-parameters of a neural network using Bayesian optimization. We used the scikit-optimize (`skopt`) library which is still under development, but it is already an extremely powerful tool. It was able to substantially improve on hand-tuned hyper-parameters in a small number of iterations. This is vastly superior to Grid Search and Random Search of the hyper-parameters, which would require far more computational time, and would most likely find inferior hyper-parameters, especially for more difficult problems." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Try and run 100 or 200 iterations of the optimization instead of just 40 iterations. What happens to the plotted landscapes?\n", + "* Try some of the other optimization methods from scikit-optimize such as `forest_minimize` instead of `gp_minimize`. How do they perform?\n", + "* Try using another acquisition function for the optimizer e.g. Probability of Improvement.\n", + "* Try optimizing more hyper-parameters with the Bayesian optimization. For example, the kernel-size and number of filters in the convolutional-layers, or the batch-size used in training.\n", + "* Add a hyper-parameter for the number of convolutional layers and implement it in `create_model()`. Note that if you have pooling-layers after the convolution then the images are downsampled, so there is a limit to the number of layers you can have before the images become too small.\n", + "* Look at the plots. Do you think that some of the hyper-parameters may be irrelevant? Try and remove these parameters and redo the optimization of the remaining hyper-parameters.\n", + "* Use another and more difficult dataset with image-files.\n", + "* Train for more epochs. Does it improve the classification accuracy on the validiation- and test-sets? How does it affect the time-usage?\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2016-2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "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:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "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." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/20_Natural_Language_Processing.ipynb b/20_Natural_Language_Processing.ipynb new file mode 100644 index 0000000..a24a12b --- /dev/null +++ b/20_Natural_Language_Processing.ipynb @@ -0,0 +1,3092 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #20\n", + "# Natural Language Processing\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This tutorial is about a basic form of Natural Language Processing (NLP) called Sentiment Analysis, in which we will try and classify a movie review as either positive or negative.\n", + "\n", + "Consider a simple example: \"This movie is not very good.\" This text ends with the words \"very good\" which indicates a very positive sentiment, but it is negated because it is preceded by the word \"not\", so the text should be classified as having a negative sentiment. How can we teach a Neural Network to do this classification?\n", + "\n", + "Another problem is that neural networks cannot work directly on text-data, so we need to convert text into numbers that are compatible with a neural network.\n", + "\n", + "Yet another problem is that a text may be arbitrarily long. The neural networks we have worked with in previous tutorials use fixed data-shapes - except for the first dimension of the data which varies with the batch-size. Now we need a type of neural network that can work on both short and long sequences of text.\n", + "\n", + "You should be familiar with TensorFlow and Keras in general, see Tutorials #01 and #03-C." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flowchart\n", + "\n", + "To solve this problem we need several processing steps. First we need to convert the raw text-words into so-called tokens which are integer values. These tokens are really just indices into a list of the entire vocabulary. Then we convert these integer-tokens into so-called embeddings which are real-valued vectors, whose mapping will be trained along with the neural network, so as to map words with similar meanings to similar embedding-vectors. Then we input these embedding-vectors to a Recurrent Neural Network which can take sequences of arbitrary length as input and output a kind of summary of what it has seen in the input. This output is then squashed using a Sigmoid-function to give us a value between 0.0 and 1.0, where 0.0 is taken to mean a negative sentiment and 1.0 means a positive sentiment. This whole process allows us to classify input-text as either having a negative or positive sentiment.\n", + "\n", + "The flowchart of the algorithm is roughly:\n", + "\n", + "\"Flowchart" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recurrent Neural Network\n", + "\n", + "The basic building block in a Recurrent Neural Network (RNN) is a Recurrent Unit (RU). There are many different variants of recurrent units such as the rather clunky LSTM (Long-Short-Term-Memory) and the somewhat simpler GRU (Gated Recurrent Unit) which we will use in this tutorial. Experiments in the literature suggest that the LSTM and GRU have roughly similar performance. Even simpler variants also exist and the literature suggests that they may perform even better than both LSTM and GRU, but they are not implemented in Keras which we will use in this tutorial.\n", + "\n", + "The following figure shows the abstract idea of a recurrent unit, which has an internal state that is being updated every time the unit receives a new input. This internal state serves as a kind of memory. However, it is not a traditional kind of computer memory which stores bits that are either on or off. Instead the recurrent unit stores floating-point values in its memory-state, which are read and written using matrix-operations so the operations are all differentiable. This means the memory-state can store arbitrary floating-point values (although typically limited between -1.0 and 1.0) and the network can be trained like a normal neural network using Gradient Descent.\n", + "\n", + "The new state-value depends on both the old state-value and the current input. For example, if the state-value has memorized that we have recently seen the word \"not\" and the current input is \"good\" then we need to store a new state-value that memorizes \"not good\" which indicates a negative sentiment.\n", + "\n", + "The part of the recurrent unit that is responsible for mapping old state-values and inputs to the new state-value is called a gate, but it is really just a type of matrix-operation. There is another gate for calculating the output-values of the recurrent unit. The implementation of these gates vary for different types of recurrent units. This figure merely shows the abstract idea of a recurrent unit. The LSTM has more gates than the GRU but some of them are apparently redundant so they can be omitted.\n", + "\n", + "In order to train the recurrent unit, we must gradually change the weight-matrices of the gates so the recurrent unit gives the desired output for an input sequence. This is done automatically in TensorFlow.\n", + "\n", + "![Recurrent unit](images/20_recurrent_unit.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unrolled Network\n", + "\n", + "Another way to visualize and understand a Recurrent Neural Network is to \"unroll\" the recursion. In this figure there is only a single recurrent unit denoted RU, which will receive a text-word from the input sequence in a series of time-steps.\n", + "\n", + "The initial memory-state of the RU is reset to zero internally by Keras / TensorFlow every time a new sequence begins.\n", + "\n", + "In the first time-step the word \"this\" is input to the RU which uses its internal state (initialized to zero) and its gate to calculate the new state. The RU also uses its other gate to calculate the output but it is ignored here because it is only needed at the end of the sequence to output a kind of summary.\n", + "\n", + "In the second time-step the word \"is\" is input to the RU which now uses the internal state that was just updated from seeing the previous word \"this\".\n", + "\n", + "There is not much meaning in the words \"this is\" so the RU probably doesn't save anything important in its internal state from seeing these words. But when it sees the third word \"not\" the RU has learned that it may be important for determining the overall sentiment of the input-text, so it needs to be stored in the memory-state of the RU, which can be used later when the RU sees the word \"good\" in time-step 6.\n", + "\n", + "Finally when the entire sequence has been processed, the RU outputs a vector of values that summarizes what it has seen in the input sequence. We then use a fully-connected layer with a Sigmoid activation to get a single value between 0.0 and 1.0 which we interpret as the sentiment either being negative (values close to 0.0) or positive (values close to 1.0).\n", + "\n", + "Note that for the sake of clarity, this figure doesn't show the mapping from text-words to integer-tokens and embedding-vectors, as well as the fully-connected Sigmoid layer on the output.\n", + "\n", + "![Unrolled network](images/20_unrolled_flowchart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3-Layer Unrolled Network\n", + "\n", + "In this tutorial we will use a Recurrent Neural Network with 3 recurrent units (or layers) denoted RU1, RU2 and RU3 in the \"unrolled\" figure below.\n", + "\n", + "The first layer is much like the unrolled figure above for a single-layer RNN. First the recurrent unit RU1 has its internal state initialized to zero by Keras / TensorFlow. Then the word \"this\" is input to RU1 and it updates its internal state. Then it processes the next word \"is\", and so forth. But instead of outputting a single summary value at the end of the sequence, we use the output of RU1 for every time-step. This creates a new sequence that can then be used as input for the next recurrent unit RU2. The same process is repeated for the second layer and this creates a new output sequence which is then input to the third layer's recurrent unit RU3, whose final output is passed to a fully-connected Sigmoid layer that outputs a value between 0.0 (negative sentiment) and 1.0 (positive sentiment).\n", + "\n", + "Note that for the sake of clarity, the mapping of text-words to integer-tokens and embedding-vectors has been omitted from this figure.\n", + "\n", + "![Unrolled 3-layer network](images/20_unrolled_3layers_flowchart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploding & Vanishing Gradients\n", + "\n", + "In order to train the weights for the gates inside the recurrent unit, we need to minimize some loss-function which measures the difference between the actual output of the network relative to the desired output.\n", + "\n", + "From the \"unrolled\" figures above we see that the reccurent units are applied recursively for each word in the input sequence. This means the recurrent gate is applied once for each time-step. The gradient-signals have to flow back from the loss-function all the way to the first time the recurrent gate is used. If the gradient of the recurrent gate is multiplicative, then we essentially have an exponential function.\n", + "\n", + "In this tutorial we will use texts that have more than 500 words. This means the RU's gate for updating its internal memory-state is applied recursively more than 500 times. If a gradient of just 1.01 is multiplied with itself 500 times then it gives a value of about 145. If a gradient of just 0.99 is multiplied with itself 500 times then it gives a value of about 0.007. These are called exploding and vanishing gradients. The only gradients that can survive recurrent multiplication are 0 and 1.\n", + "\n", + "To avoid these so-called exploding and vanishing gradients, care must be made when designing the recurrent unit and its gates. That is why the actual implementation of the GRU is more complicated, because it tries to send the gradient back through the gates without this distortion." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "from scipy.spatial.distance import cdist" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to import several things from Keras." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, GRU, Embedding\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.preprocessing.text import Tokenizer\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 (Anaconda) and package versions:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.2.4-tf'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.keras.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data\n", + "\n", + "We will use a data-set consisting of 50000 reviews of movies from IMDB. Keras has a built-in function for downloading a similar data-set (but apparently half the size). However, Keras' version has already converted the text in the data-set to integer-tokens, which is a crucial part of working with natural languages that will also be demonstrated in this tutorial, so we download the actual text-data.\n", + "\n", + "NOTE: The data-set is 84 MB and will be downloaded automatically." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import imdb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Change this if you want the files saved in another directory." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# imdb.data_dir = \"data/IMDB/\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Automatically download and extract the files." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has apparently already been downloaded and unpacked.\n" + ] + } + ], + "source": [ + "imdb.maybe_download_and_extract()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the training- and test-sets." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "x_train_text, y_train = imdb.load_data(train=True)\n", + "x_test_text, y_test = imdb.load_data(train=False)\n", + "\n", + "# Convert to numpy arrays.\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train-set size: 25000\n", + "Test-set size: 25000\n" + ] + } + ], + "source": [ + "print(\"Train-set size: \", len(x_train_text))\n", + "print(\"Test-set size: \", len(x_test_text))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Combine into one data-set for some uses below." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "data_text = x_train_text + x_test_text" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print an example from the training-set to see that the data looks correct." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Return To the Lost World was filmed back-to-back with the 1992 version of The Lost World.

In this sequel, the same five people, lead by Challenger return to the plateau where a group has started drilling for oil which is threatening to destroy the land. Gomez has something to do with this. They manage to defeat the drillers and the plateau is saved, much to the delight of the natives.

Like in The Lost World, what few dinosaurs we see are made of rubber and these include a T-Rex and Ankylosaurus.

John Ryhs-Davies and David Warner reprise their roles as Challenger and Summerlee and three of the other actors are also back.

Despite reading several bad reviews of this and those cheap looking rubber dinosaurs, I enjoyed Return to the Lost World.

Rating: 3 stars out of 5.'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train_text[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The true \"class\" is a sentiment of the movie-review. It is a value of 0.0 for a negative sentiment and 1.0 for a positive sentiment. In this case the review is positive." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_train[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tokenizer\n", + "\n", + "A neural network cannot work directly on text-strings so we must convert it somehow. There are two steps in this conversion, the first step is called the \"tokenizer\" which converts words to integers and is done on the data-set before it is input to the neural network. The second step is an integrated part of the neural network itself and is called the \"embedding\"-layer, which is described further below.\n", + "\n", + "We may instruct the tokenizer to only use e.g. the 10000 most popular words from the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "num_words = 10000" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "tokenizer = Tokenizer(num_words=num_words)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tokenizer can then be \"fitted\" to the data-set. This scans through all the text and strips it from unwanted characters such as punctuation, and also converts it to lower-case characters. The tokenizer then builds a vocabulary of all unique words along with various data-structures for accessing the data.\n", + "\n", + "Note that we fit the tokenizer on the entire data-set so it gathers words from both the training- and test-data. This is OK as we are merely building a vocabulary and want it to be as complete as possible. The actual neural network will of course only be trained on the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 8.21 s, sys: 33.2 ms, total: 8.25 s\n", + "Wall time: 8.25 s\n" + ] + } + ], + "source": [ + "%%time\n", + "tokenizer.fit_on_texts(data_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to use the entire vocabulary then set `num_words=None` above, and then it will automatically be set to the vocabulary-size here. (This is because of Keras' somewhat awkward implementation.)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "if num_words is None:\n", + " num_words = len(tokenizer.word_index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then inspect the vocabulary that has been gathered by the tokenizer. This is ordered by the number of occurrences of the words in the data-set. These integer-numbers are called word indices or \"tokens\" because they uniquely identify each word in the vocabulary." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'the': 1,\n", + " 'and': 2,\n", + " 'a': 3,\n", + " 'of': 4,\n", + " 'to': 5,\n", + " 'is': 6,\n", + " 'br': 7,\n", + " 'in': 8,\n", + " 'it': 9,\n", + " 'i': 10,\n", + " 'this': 11,\n", + " 'that': 12,\n", + " 'was': 13,\n", + " 'as': 14,\n", + " 'for': 15,\n", + " 'with': 16,\n", + " 'movie': 17,\n", + " 'but': 18,\n", + " 'film': 19,\n", + " 'on': 20,\n", + " 'not': 21,\n", + " 'you': 22,\n", + " 'are': 23,\n", + " 'his': 24,\n", + " 'have': 25,\n", + " 'be': 26,\n", + " 'one': 27,\n", + " 'he': 28,\n", + " 'all': 29,\n", + " 'at': 30,\n", + " 'by': 31,\n", + " 'an': 32,\n", + " 'they': 33,\n", + " 'so': 34,\n", + " 'who': 35,\n", + " 'from': 36,\n", + " 'like': 37,\n", + " 'or': 38,\n", + " 'just': 39,\n", + " 'her': 40,\n", + " 'out': 41,\n", + " 'about': 42,\n", + " 'if': 43,\n", + " \"it's\": 44,\n", + " 'has': 45,\n", + " 'there': 46,\n", + " 'some': 47,\n", + " 'what': 48,\n", + " 'good': 49,\n", + " 'when': 50,\n", + " 'more': 51,\n", + " 'very': 52,\n", + " 'up': 53,\n", + " 'no': 54,\n", + " 'time': 55,\n", + " 'my': 56,\n", + " 'even': 57,\n", + " 'would': 58,\n", + " 'she': 59,\n", + " 'which': 60,\n", + " 'only': 61,\n", + " 'really': 62,\n", + " 'see': 63,\n", + " 'story': 64,\n", + " 'their': 65,\n", + " 'had': 66,\n", + " 'can': 67,\n", + " 'me': 68,\n", + " 'well': 69,\n", + " 'were': 70,\n", + " 'than': 71,\n", + " 'much': 72,\n", + " 'we': 73,\n", + " 'bad': 74,\n", + " 'been': 75,\n", + " 'get': 76,\n", + " 'do': 77,\n", + " 'great': 78,\n", + " 'other': 79,\n", + " 'will': 80,\n", + " 'also': 81,\n", + " 'into': 82,\n", + " 'people': 83,\n", + " 'because': 84,\n", + " 'how': 85,\n", + " 'first': 86,\n", + " 'him': 87,\n", + " 'most': 88,\n", + " \"don't\": 89,\n", + " 'made': 90,\n", + " 'then': 91,\n", + " 'its': 92,\n", + " 'them': 93,\n", + " 'make': 94,\n", + " 'way': 95,\n", + " 'too': 96,\n", + " 'movies': 97,\n", + " 'could': 98,\n", + " 'any': 99,\n", + " 'after': 100,\n", + " 'think': 101,\n", + " 'characters': 102,\n", + " 'watch': 103,\n", + " 'films': 104,\n", + " 'two': 105,\n", + " 'many': 106,\n", + " 'seen': 107,\n", + " 'character': 108,\n", + " 'being': 109,\n", + " 'never': 110,\n", + " 'plot': 111,\n", + " 'love': 112,\n", + " 'acting': 113,\n", + " 'life': 114,\n", + " 'did': 115,\n", + " 'best': 116,\n", + " 'where': 117,\n", + " 'know': 118,\n", + " 'show': 119,\n", + " 'little': 120,\n", + " 'over': 121,\n", + " 'off': 122,\n", + " 'ever': 123,\n", + " 'does': 124,\n", + " 'your': 125,\n", + " 'better': 126,\n", + " 'end': 127,\n", + " 'man': 128,\n", + " 'scene': 129,\n", + " 'still': 130,\n", + " 'say': 131,\n", + " 'these': 132,\n", + " 'here': 133,\n", + " 'scenes': 134,\n", + " 'why': 135,\n", + " 'while': 136,\n", + " 'something': 137,\n", + " 'such': 138,\n", + " 'go': 139,\n", + " 'through': 140,\n", + " 'back': 141,\n", + " 'should': 142,\n", + " 'those': 143,\n", + " 'real': 144,\n", + " \"i'm\": 145,\n", + " 'now': 146,\n", + " 'watching': 147,\n", + " 'thing': 148,\n", + " \"doesn't\": 149,\n", + " 'actors': 150,\n", + " 'though': 151,\n", + " 'funny': 152,\n", + " 'years': 153,\n", + " \"didn't\": 154,\n", + " 'old': 155,\n", + " 'another': 156,\n", + " '10': 157,\n", + " 'work': 158,\n", + " 'before': 159,\n", + " 'actually': 160,\n", + " 'nothing': 161,\n", + " 'makes': 162,\n", + " 'look': 163,\n", + " 'director': 164,\n", + " 'find': 165,\n", + " 'going': 166,\n", + " 'same': 167,\n", + " 'new': 168,\n", + " 'lot': 169,\n", + " 'every': 170,\n", + " 'few': 171,\n", + " 'again': 172,\n", + " 'part': 173,\n", + " 'cast': 174,\n", + " 'down': 175,\n", + " 'us': 176,\n", + " 'things': 177,\n", + " 'want': 178,\n", + " 'quite': 179,\n", + " 'pretty': 180,\n", + " 'world': 181,\n", + " 'horror': 182,\n", + " 'around': 183,\n", + " 'seems': 184,\n", + " \"can't\": 185,\n", + " 'young': 186,\n", + " 'take': 187,\n", + " 'however': 188,\n", + " 'got': 189,\n", + " 'thought': 190,\n", + " 'big': 191,\n", + " 'fact': 192,\n", + " 'enough': 193,\n", + " 'long': 194,\n", + " 'both': 195,\n", + " \"that's\": 196,\n", + " 'give': 197,\n", + " \"i've\": 198,\n", + " 'own': 199,\n", + " 'may': 200,\n", + " 'between': 201,\n", + " 'comedy': 202,\n", + " 'right': 203,\n", + " 'series': 204,\n", + " 'action': 205,\n", + " 'must': 206,\n", + " 'music': 207,\n", + " 'without': 208,\n", + " 'times': 209,\n", + " 'saw': 210,\n", + " 'always': 211,\n", + " 'original': 212,\n", + " \"isn't\": 213,\n", + " 'role': 214,\n", + " 'come': 215,\n", + " 'almost': 216,\n", + " 'gets': 217,\n", + " 'interesting': 218,\n", + " 'guy': 219,\n", + " 'point': 220,\n", + " 'done': 221,\n", + " \"there's\": 222,\n", + " 'whole': 223,\n", + " 'least': 224,\n", + " 'far': 225,\n", + " 'bit': 226,\n", + " 'script': 227,\n", + " 'minutes': 228,\n", + " 'feel': 229,\n", + " '2': 230,\n", + " 'anything': 231,\n", + " 'making': 232,\n", + " 'might': 233,\n", + " 'since': 234,\n", + " 'am': 235,\n", + " 'family': 236,\n", + " \"he's\": 237,\n", + " 'last': 238,\n", + " 'probably': 239,\n", + " 'tv': 240,\n", + " 'performance': 241,\n", + " 'kind': 242,\n", + " 'away': 243,\n", + " 'yet': 244,\n", + " 'fun': 245,\n", + " 'worst': 246,\n", + " 'sure': 247,\n", + " 'rather': 248,\n", + " 'hard': 249,\n", + " 'anyone': 250,\n", + " 'girl': 251,\n", + " 'each': 252,\n", + " 'played': 253,\n", + " 'day': 254,\n", + " 'found': 255,\n", + " 'looking': 256,\n", + " 'woman': 257,\n", + " 'screen': 258,\n", + " 'although': 259,\n", + " 'our': 260,\n", + " 'especially': 261,\n", + " 'believe': 262,\n", + " 'having': 263,\n", + " 'trying': 264,\n", + " 'course': 265,\n", + " 'dvd': 266,\n", + " 'everything': 267,\n", + " 'set': 268,\n", + " 'goes': 269,\n", + " 'comes': 270,\n", + " 'put': 271,\n", + " 'ending': 272,\n", + " 'maybe': 273,\n", + " 'place': 274,\n", + " 'book': 275,\n", + " 'shows': 276,\n", + " 'three': 277,\n", + " 'worth': 278,\n", + " 'different': 279,\n", + " 'main': 280,\n", + " 'once': 281,\n", + " 'sense': 282,\n", + " 'american': 283,\n", + " 'reason': 284,\n", + " 'looks': 285,\n", + " 'effects': 286,\n", + " 'watched': 287,\n", + " 'play': 288,\n", + " 'true': 289,\n", + " 'money': 290,\n", + " 'actor': 291,\n", + " \"wasn't\": 292,\n", + " 'job': 293,\n", + " 'together': 294,\n", + " 'war': 295,\n", + " 'someone': 296,\n", + " 'plays': 297,\n", + " 'instead': 298,\n", + " 'high': 299,\n", + " 'during': 300,\n", + " 'said': 301,\n", + " 'year': 302,\n", + " 'half': 303,\n", + " 'everyone': 304,\n", + " 'later': 305,\n", + " 'takes': 306,\n", + " '1': 307,\n", + " 'seem': 308,\n", + " 'audience': 309,\n", + " 'special': 310,\n", + " 'beautiful': 311,\n", + " 'left': 312,\n", + " 'himself': 313,\n", + " 'seeing': 314,\n", + " 'john': 315,\n", + " 'night': 316,\n", + " 'black': 317,\n", + " 'version': 318,\n", + " 'shot': 319,\n", + " 'excellent': 320,\n", + " 'idea': 321,\n", + " 'house': 322,\n", + " 'mind': 323,\n", + " 'star': 324,\n", + " 'wife': 325,\n", + " 'fan': 326,\n", + " 'death': 327,\n", + " 'used': 328,\n", + " 'else': 329,\n", + " 'simply': 330,\n", + " 'nice': 331,\n", + " 'budget': 332,\n", + " 'poor': 333,\n", + " 'short': 334,\n", + " 'completely': 335,\n", + " 'second': 336,\n", + " \"you're\": 337,\n", + " '3': 338,\n", + " 'read': 339,\n", + " 'less': 340,\n", + " 'along': 341,\n", + " 'top': 342,\n", + " 'help': 343,\n", + " 'home': 344,\n", + " 'men': 345,\n", + " 'either': 346,\n", + " 'line': 347,\n", + " 'boring': 348,\n", + " 'dead': 349,\n", + " 'friends': 350,\n", + " 'kids': 351,\n", + " 'try': 352,\n", + " 'production': 353,\n", + " 'enjoy': 354,\n", + " 'camera': 355,\n", + " 'use': 356,\n", + " 'wrong': 357,\n", + " 'given': 358,\n", + " 'low': 359,\n", + " 'classic': 360,\n", + " 'father': 361,\n", + " 'need': 362,\n", + " 'full': 363,\n", + " 'stupid': 364,\n", + " 'until': 365,\n", + " 'next': 366,\n", + " 'performances': 367,\n", + " 'school': 368,\n", + " 'hollywood': 369,\n", + " 'rest': 370,\n", + " 'truly': 371,\n", + " 'awful': 372,\n", + " 'video': 373,\n", + " 'couple': 374,\n", + " 'start': 375,\n", + " 'sex': 376,\n", + " 'recommend': 377,\n", + " 'women': 378,\n", + " 'let': 379,\n", + " 'tell': 380,\n", + " 'terrible': 381,\n", + " 'remember': 382,\n", + " 'mean': 383,\n", + " 'came': 384,\n", + " 'getting': 385,\n", + " 'understand': 386,\n", + " 'perhaps': 387,\n", + " 'moments': 388,\n", + " 'name': 389,\n", + " 'keep': 390,\n", + " 'face': 391,\n", + " 'itself': 392,\n", + " 'wonderful': 393,\n", + " 'playing': 394,\n", + " 'human': 395,\n", + " 'style': 396,\n", + " 'small': 397,\n", + " 'episode': 398,\n", + " 'perfect': 399,\n", + " 'others': 400,\n", + " 'person': 401,\n", + " 'doing': 402,\n", + " 'often': 403,\n", + " 'early': 404,\n", + " 'stars': 405,\n", + " 'definitely': 406,\n", + " 'written': 407,\n", + " 'head': 408,\n", + " 'lines': 409,\n", + " 'dialogue': 410,\n", + " 'gives': 411,\n", + " 'piece': 412,\n", + " \"couldn't\": 413,\n", + " 'went': 414,\n", + " 'finally': 415,\n", + " 'mother': 416,\n", + " 'title': 417,\n", + " 'case': 418,\n", + " 'absolutely': 419,\n", + " 'boy': 420,\n", + " 'live': 421,\n", + " 'yes': 422,\n", + " 'laugh': 423,\n", + " 'certainly': 424,\n", + " 'liked': 425,\n", + " 'become': 426,\n", + " 'entertaining': 427,\n", + " 'worse': 428,\n", + " 'oh': 429,\n", + " 'sort': 430,\n", + " 'loved': 431,\n", + " 'lost': 432,\n", + " 'hope': 433,\n", + " 'called': 434,\n", + " 'picture': 435,\n", + " 'felt': 436,\n", + " 'overall': 437,\n", + " 'entire': 438,\n", + " 'several': 439,\n", + " 'mr': 440,\n", + " 'based': 441,\n", + " 'supposed': 442,\n", + " 'cinema': 443,\n", + " 'friend': 444,\n", + " 'guys': 445,\n", + " 'sound': 446,\n", + " '5': 447,\n", + " 'problem': 448,\n", + " 'drama': 449,\n", + " 'against': 450,\n", + " 'waste': 451,\n", + " 'white': 452,\n", + " 'beginning': 453,\n", + " '4': 454,\n", + " 'fans': 455,\n", + " 'totally': 456,\n", + " 'dark': 457,\n", + " 'care': 458,\n", + " 'direction': 459,\n", + " 'humor': 460,\n", + " 'wanted': 461,\n", + " \"she's\": 462,\n", + " 'seemed': 463,\n", + " 'under': 464,\n", + " 'game': 465,\n", + " 'children': 466,\n", + " 'despite': 467,\n", + " 'lives': 468,\n", + " 'lead': 469,\n", + " 'guess': 470,\n", + " 'example': 471,\n", + " 'already': 472,\n", + " 'final': 473,\n", + " 'throughout': 474,\n", + " \"you'll\": 475,\n", + " 'turn': 476,\n", + " 'evil': 477,\n", + " 'becomes': 478,\n", + " 'unfortunately': 479,\n", + " 'able': 480,\n", + " 'quality': 481,\n", + " \"i'd\": 482,\n", + " 'days': 483,\n", + " 'history': 484,\n", + " 'fine': 485,\n", + " 'side': 486,\n", + " 'wants': 487,\n", + " 'heart': 488,\n", + " 'horrible': 489,\n", + " 'writing': 490,\n", + " 'amazing': 491,\n", + " 'b': 492,\n", + " 'flick': 493,\n", + " 'killer': 494,\n", + " 'run': 495,\n", + " 'son': 496,\n", + " '\\x96': 497,\n", + " 'michael': 498,\n", + " 'works': 499,\n", + " 'close': 500,\n", + " \"they're\": 501,\n", + " 'act': 502,\n", + " 'art': 503,\n", + " 'kill': 504,\n", + " 'matter': 505,\n", + " 'etc': 506,\n", + " 'tries': 507,\n", + " \"won't\": 508,\n", + " 'past': 509,\n", + " 'town': 510,\n", + " 'enjoyed': 511,\n", + " 'turns': 512,\n", + " 'brilliant': 513,\n", + " 'gave': 514,\n", + " 'behind': 515,\n", + " 'parts': 516,\n", + " 'stuff': 517,\n", + " 'genre': 518,\n", + " 'eyes': 519,\n", + " 'car': 520,\n", + " 'favorite': 521,\n", + " 'directed': 522,\n", + " 'late': 523,\n", + " 'hand': 524,\n", + " 'expect': 525,\n", + " 'soon': 526,\n", + " 'hour': 527,\n", + " 'obviously': 528,\n", + " 'themselves': 529,\n", + " 'sometimes': 530,\n", + " 'killed': 531,\n", + " 'actress': 532,\n", + " 'thinking': 533,\n", + " 'child': 534,\n", + " 'girls': 535,\n", + " 'viewer': 536,\n", + " 'starts': 537,\n", + " 'city': 538,\n", + " 'myself': 539,\n", + " 'decent': 540,\n", + " 'highly': 541,\n", + " 'stop': 542,\n", + " 'type': 543,\n", + " 'self': 544,\n", + " 'god': 545,\n", + " 'says': 546,\n", + " 'group': 547,\n", + " 'anyway': 548,\n", + " 'voice': 549,\n", + " 'took': 550,\n", + " 'known': 551,\n", + " 'blood': 552,\n", + " 'kid': 553,\n", + " 'heard': 554,\n", + " 'happens': 555,\n", + " 'except': 556,\n", + " 'fight': 557,\n", + " 'feeling': 558,\n", + " 'experience': 559,\n", + " 'coming': 560,\n", + " 'slow': 561,\n", + " 'daughter': 562,\n", + " 'writer': 563,\n", + " 'stories': 564,\n", + " 'moment': 565,\n", + " 'leave': 566,\n", + " 'told': 567,\n", + " 'extremely': 568,\n", + " 'score': 569,\n", + " 'violence': 570,\n", + " 'police': 571,\n", + " 'involved': 572,\n", + " 'strong': 573,\n", + " 'lack': 574,\n", + " 'chance': 575,\n", + " 'cannot': 576,\n", + " 'hit': 577,\n", + " 'roles': 578,\n", + " 'hilarious': 579,\n", + " 's': 580,\n", + " 'wonder': 581,\n", + " 'happen': 582,\n", + " 'particularly': 583,\n", + " 'ok': 584,\n", + " 'including': 585,\n", + " 'living': 586,\n", + " 'save': 587,\n", + " 'looked': 588,\n", + " \"wouldn't\": 589,\n", + " 'crap': 590,\n", + " 'please': 591,\n", + " 'simple': 592,\n", + " 'cool': 593,\n", + " 'murder': 594,\n", + " 'obvious': 595,\n", + " 'happened': 596,\n", + " 'complete': 597,\n", + " 'cut': 598,\n", + " 'age': 599,\n", + " 'serious': 600,\n", + " 'gore': 601,\n", + " 'attempt': 602,\n", + " 'hell': 603,\n", + " 'ago': 604,\n", + " 'song': 605,\n", + " 'shown': 606,\n", + " 'taken': 607,\n", + " 'english': 608,\n", + " 'james': 609,\n", + " 'robert': 610,\n", + " 'david': 611,\n", + " 'seriously': 612,\n", + " 'released': 613,\n", + " 'reality': 614,\n", + " 'opening': 615,\n", + " 'interest': 616,\n", + " 'jokes': 617,\n", + " 'across': 618,\n", + " 'none': 619,\n", + " 'hero': 620,\n", + " 'exactly': 621,\n", + " 'today': 622,\n", + " 'possible': 623,\n", + " 'alone': 624,\n", + " 'sad': 625,\n", + " 'brother': 626,\n", + " 'number': 627,\n", + " 'career': 628,\n", + " 'saying': 629,\n", + " \"film's\": 630,\n", + " 'hours': 631,\n", + " 'usually': 632,\n", + " 'cinematography': 633,\n", + " 'talent': 634,\n", + " 'view': 635,\n", + " 'annoying': 636,\n", + " 'running': 637,\n", + " 'yourself': 638,\n", + " 'relationship': 639,\n", + " 'documentary': 640,\n", + " 'wish': 641,\n", + " 'huge': 642,\n", + " 'order': 643,\n", + " 'whose': 644,\n", + " 'shots': 645,\n", + " 'ridiculous': 646,\n", + " 'taking': 647,\n", + " 'important': 648,\n", + " 'light': 649,\n", + " 'body': 650,\n", + " 'middle': 651,\n", + " 'level': 652,\n", + " 'ends': 653,\n", + " 'started': 654,\n", + " 'female': 655,\n", + " 'call': 656,\n", + " \"i'll\": 657,\n", + " 'husband': 658,\n", + " 'four': 659,\n", + " 'power': 660,\n", + " 'word': 661,\n", + " 'turned': 662,\n", + " 'major': 663,\n", + " 'opinion': 664,\n", + " 'change': 665,\n", + " 'mostly': 666,\n", + " 'usual': 667,\n", + " 'scary': 668,\n", + " 'silly': 669,\n", + " 'rating': 670,\n", + " 'beyond': 671,\n", + " 'somewhat': 672,\n", + " 'happy': 673,\n", + " 'ones': 674,\n", + " 'words': 675,\n", + " 'room': 676,\n", + " 'knows': 677,\n", + " 'knew': 678,\n", + " 'country': 679,\n", + " 'disappointed': 680,\n", + " 'talking': 681,\n", + " 'novel': 682,\n", + " 'apparently': 683,\n", + " 'non': 684,\n", + " 'strange': 685,\n", + " 'attention': 686,\n", + " 'upon': 687,\n", + " 'finds': 688,\n", + " 'single': 689,\n", + " 'basically': 690,\n", + " 'cheap': 691,\n", + " 'modern': 692,\n", + " 'due': 693,\n", + " 'jack': 694,\n", + " 'musical': 695,\n", + " 'television': 696,\n", + " 'problems': 697,\n", + " 'miss': 698,\n", + " 'episodes': 699,\n", + " 'clearly': 700,\n", + " 'local': 701,\n", + " '7': 702,\n", + " 'british': 703,\n", + " 'thriller': 704,\n", + " 'talk': 705,\n", + " 'events': 706,\n", + " 'five': 707,\n", + " 'sequence': 708,\n", + " \"aren't\": 709,\n", + " 'class': 710,\n", + " 'french': 711,\n", + " 'moving': 712,\n", + " 'ten': 713,\n", + " 'fast': 714,\n", + " 'review': 715,\n", + " 'earth': 716,\n", + " 'tells': 717,\n", + " 'predictable': 718,\n", + " 'team': 719,\n", + " 'songs': 720,\n", + " 'comic': 721,\n", + " 'straight': 722,\n", + " '8': 723,\n", + " 'whether': 724,\n", + " 'die': 725,\n", + " 'add': 726,\n", + " 'dialog': 727,\n", + " 'entertainment': 728,\n", + " 'above': 729,\n", + " 'sets': 730,\n", + " 'future': 731,\n", + " 'enjoyable': 732,\n", + " 'appears': 733,\n", + " 'near': 734,\n", + " 'space': 735,\n", + " 'easily': 736,\n", + " 'hate': 737,\n", + " 'soundtrack': 738,\n", + " 'bring': 739,\n", + " 'giving': 740,\n", + " 'lots': 741,\n", + " 'similar': 742,\n", + " 'romantic': 743,\n", + " 'george': 744,\n", + " 'supporting': 745,\n", + " 'release': 746,\n", + " 'mention': 747,\n", + " 'filmed': 748,\n", + " 'within': 749,\n", + " 'message': 750,\n", + " 'sequel': 751,\n", + " 'clear': 752,\n", + " 'falls': 753,\n", + " \"haven't\": 754,\n", + " 'needs': 755,\n", + " 'dull': 756,\n", + " 'suspense': 757,\n", + " 'bunch': 758,\n", + " 'eye': 759,\n", + " 'surprised': 760,\n", + " 'showing': 761,\n", + " 'sorry': 762,\n", + " 'tried': 763,\n", + " 'certain': 764,\n", + " 'easy': 765,\n", + " 'working': 766,\n", + " 'ways': 767,\n", + " 'theme': 768,\n", + " 'theater': 769,\n", + " 'among': 770,\n", + " 'named': 771,\n", + " \"what's\": 772,\n", + " 'storyline': 773,\n", + " 'monster': 774,\n", + " 'king': 775,\n", + " 'stay': 776,\n", + " 'effort': 777,\n", + " 'stand': 778,\n", + " 'fall': 779,\n", + " 'minute': 780,\n", + " 'gone': 781,\n", + " 'rock': 782,\n", + " 'using': 783,\n", + " '9': 784,\n", + " 'feature': 785,\n", + " 'buy': 786,\n", + " 'comments': 787,\n", + " \"'\": 788,\n", + " 't': 789,\n", + " 'typical': 790,\n", + " 'sister': 791,\n", + " 'editing': 792,\n", + " 'tale': 793,\n", + " 'avoid': 794,\n", + " 'dr': 795,\n", + " 'deal': 796,\n", + " 'mystery': 797,\n", + " 'doubt': 798,\n", + " 'fantastic': 799,\n", + " 'nearly': 800,\n", + " 'kept': 801,\n", + " 'subject': 802,\n", + " 'feels': 803,\n", + " 'okay': 804,\n", + " 'viewing': 805,\n", + " 'elements': 806,\n", + " 'check': 807,\n", + " 'oscar': 808,\n", + " 'realistic': 809,\n", + " 'points': 810,\n", + " 'greatest': 811,\n", + " 'means': 812,\n", + " 'herself': 813,\n", + " 'parents': 814,\n", + " 'famous': 815,\n", + " 'imagine': 816,\n", + " 'rent': 817,\n", + " 'viewers': 818,\n", + " 'richard': 819,\n", + " 'crime': 820,\n", + " 'form': 821,\n", + " 'peter': 822,\n", + " 'actual': 823,\n", + " 'lady': 824,\n", + " 'general': 825,\n", + " 'dog': 826,\n", + " 'follow': 827,\n", + " 'believable': 828,\n", + " 'period': 829,\n", + " 'red': 830,\n", + " 'move': 831,\n", + " 'brought': 832,\n", + " 'material': 833,\n", + " 'forget': 834,\n", + " 'somehow': 835,\n", + " 'begins': 836,\n", + " 're': 837,\n", + " 'reviews': 838,\n", + " 'animation': 839,\n", + " 'paul': 840,\n", + " \"you've\": 841,\n", + " 'leads': 842,\n", + " 'weak': 843,\n", + " 'figure': 844,\n", + " 'surprise': 845,\n", + " 'sit': 846,\n", + " 'hear': 847,\n", + " 'average': 848,\n", + " 'open': 849,\n", + " 'sequences': 850,\n", + " 'atmosphere': 851,\n", + " 'killing': 852,\n", + " 'eventually': 853,\n", + " 'learn': 854,\n", + " 'tom': 855,\n", + " 'premise': 856,\n", + " '20': 857,\n", + " 'wait': 858,\n", + " 'sci': 859,\n", + " 'deep': 860,\n", + " 'fi': 861,\n", + " 'expected': 862,\n", + " 'whatever': 863,\n", + " 'indeed': 864,\n", + " 'note': 865,\n", + " 'poorly': 866,\n", + " 'particular': 867,\n", + " 'lame': 868,\n", + " 'dance': 869,\n", + " 'imdb': 870,\n", + " 'situation': 871,\n", + " 'shame': 872,\n", + " 'third': 873,\n", + " 'box': 874,\n", + " 'york': 875,\n", + " 'truth': 876,\n", + " 'decided': 877,\n", + " 'free': 878,\n", + " 'hot': 879,\n", + " \"who's\": 880,\n", + " 'difficult': 881,\n", + " 'needed': 882,\n", + " 'season': 883,\n", + " 'acted': 884,\n", + " 'leaves': 885,\n", + " 'unless': 886,\n", + " 'romance': 887,\n", + " 'possibly': 888,\n", + " 'emotional': 889,\n", + " 'sexual': 890,\n", + " 'gay': 891,\n", + " 'boys': 892,\n", + " 'footage': 893,\n", + " 'write': 894,\n", + " 'western': 895,\n", + " 'forced': 896,\n", + " 'credits': 897,\n", + " 'memorable': 898,\n", + " 'reading': 899,\n", + " 'became': 900,\n", + " 'doctor': 901,\n", + " 'otherwise': 902,\n", + " 'de': 903,\n", + " 'air': 904,\n", + " 'begin': 905,\n", + " 'crew': 906,\n", + " 'question': 907,\n", + " 'meet': 908,\n", + " 'society': 909,\n", + " 'male': 910,\n", + " 'meets': 911,\n", + " \"let's\": 912,\n", + " 'plus': 913,\n", + " 'cheesy': 914,\n", + " 'hands': 915,\n", + " 'superb': 916,\n", + " 'screenplay': 917,\n", + " 'interested': 918,\n", + " 'beauty': 919,\n", + " 'street': 920,\n", + " 'features': 921,\n", + " 'perfectly': 922,\n", + " 'masterpiece': 923,\n", + " 'whom': 924,\n", + " 'laughs': 925,\n", + " 'stage': 926,\n", + " 'nature': 927,\n", + " 'effect': 928,\n", + " 'forward': 929,\n", + " 'comment': 930,\n", + " 'nor': 931,\n", + " 'e': 932,\n", + " 'badly': 933,\n", + " 'previous': 934,\n", + " 'sounds': 935,\n", + " 'japanese': 936,\n", + " 'weird': 937,\n", + " 'island': 938,\n", + " 'personal': 939,\n", + " 'inside': 940,\n", + " 'quickly': 941,\n", + " 'total': 942,\n", + " 'keeps': 943,\n", + " 'towards': 944,\n", + " 'america': 945,\n", + " 'result': 946,\n", + " 'battle': 947,\n", + " 'crazy': 948,\n", + " 'worked': 949,\n", + " 'setting': 950,\n", + " 'incredibly': 951,\n", + " 'earlier': 952,\n", + " 'background': 953,\n", + " 'mess': 954,\n", + " 'cop': 955,\n", + " 'writers': 956,\n", + " 'fire': 957,\n", + " 'copy': 958,\n", + " 'dumb': 959,\n", + " 'unique': 960,\n", + " 'realize': 961,\n", + " 'powerful': 962,\n", + " 'lee': 963,\n", + " 'mark': 964,\n", + " 'business': 965,\n", + " 'rate': 966,\n", + " 'older': 967,\n", + " 'dramatic': 968,\n", + " 'pay': 969,\n", + " 'following': 970,\n", + " 'directors': 971,\n", + " 'girlfriend': 972,\n", + " 'joke': 973,\n", + " 'plenty': 974,\n", + " 'directing': 975,\n", + " 'various': 976,\n", + " 'creepy': 977,\n", + " 'baby': 978,\n", + " 'development': 979,\n", + " 'appear': 980,\n", + " 'brings': 981,\n", + " 'front': 982,\n", + " 'dream': 983,\n", + " 'ask': 984,\n", + " 'water': 985,\n", + " 'bill': 986,\n", + " 'admit': 987,\n", + " 'rich': 988,\n", + " 'apart': 989,\n", + " 'joe': 990,\n", + " 'political': 991,\n", + " 'fairly': 992,\n", + " 'leading': 993,\n", + " 'reasons': 994,\n", + " 'spent': 995,\n", + " 'portrayed': 996,\n", + " 'telling': 997,\n", + " 'cover': 998,\n", + " 'outside': 999,\n", + " 'present': 1000,\n", + " ...}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer.word_index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then use the tokenizer to convert all texts in the training-set to lists of these tokens." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "x_train_tokens = tokenizer.texts_to_sequences(x_train_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example, here is a text from the training-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Return To the Lost World was filmed back-to-back with the 1992 version of The Lost World.

In this sequel, the same five people, lead by Challenger return to the plateau where a group has started drilling for oil which is threatening to destroy the land. Gomez has something to do with this. They manage to defeat the drillers and the plateau is saved, much to the delight of the natives.

Like in The Lost World, what few dinosaurs we see are made of rubber and these include a T-Rex and Ankylosaurus.

John Ryhs-Davies and David Warner reprise their roles as Challenger and Summerlee and three of the other actors are also back.

Despite reading several bad reviews of this and those cheap looking rubber dinosaurs, I enjoyed Return to the Lost World.

Rating: 3 stars out of 5.'" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train_text[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This text corresponds to the following list of tokens:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1037, 5, 1, 432, 181, 13, 748, 141, 5, 141, 16,\n", + " 1, 7418, 318, 4, 1, 432, 181, 7, 7, 8, 11,\n", + " 751, 1, 167, 707, 83, 469, 31, 1037, 5, 1, 117,\n", + " 3, 547, 45, 654, 15, 3336, 60, 6, 3746, 5, 2222,\n", + " 1, 1379, 45, 137, 5, 77, 16, 11, 33, 1894, 5,\n", + " 4399, 1, 2, 1, 6, 2011, 72, 5, 1, 3029, 4,\n", + " 1, 5836, 7, 7, 37, 8, 1, 432, 181, 48, 171,\n", + " 3847, 73, 63, 23, 90, 4, 4118, 2, 132, 1469, 3,\n", + " 789, 4019, 2, 7, 7, 315, 4180, 2, 611, 2991, 65,\n", + " 578, 14, 2, 2, 277, 4, 1, 79, 150, 23, 81,\n", + " 141, 7, 7, 467, 899, 439, 74, 838, 4, 11, 2,\n", + " 143, 691, 256, 4118, 3847, 10, 511, 1037, 5, 1, 432,\n", + " 181, 7, 7, 670, 338, 405, 41, 4, 447])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(x_train_tokens[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also need to convert the texts in the test-set to tokens." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "x_test_tokens = tokenizer.texts_to_sequences(x_test_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Padding and Truncating Data\n", + "\n", + "The Recurrent Neural Network can take sequences of arbitrary length as input, but in order to use a whole batch of data, the sequences need to have the same length. There are two ways of achieving this: (A) Either we ensure that all sequences in the entire data-set have the same length, or (B) we write a custom data-generator that ensures the sequences have the same length within each batch.\n", + "\n", + "Solution (A) is simpler but if we use the length of the longest sequence in the data-set, then we are wasting a lot of memory. This is particularly important for larger data-sets.\n", + "\n", + "So in order to make a compromise, we will use a sequence-length that covers most sequences in the data-set, and we will then truncate longer sequences and pad shorter sequences.\n", + "\n", + "First we count the number of tokens in all the sequences in the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "num_tokens = [len(tokens) for tokens in x_train_tokens + x_test_tokens]\n", + "num_tokens = np.array(num_tokens)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The average number of tokens in a sequence is:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "221.27716" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(num_tokens)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The maximum number of tokens in a sequence is:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2209" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(num_tokens)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The max number of tokens we will allow is set to the average plus 2 standard deviations." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "544" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max_tokens = np.mean(num_tokens) + 2 * np.std(num_tokens)\n", + "max_tokens = int(max_tokens)\n", + "max_tokens" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This covers about 95% of the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.94528" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(num_tokens < max_tokens) / len(num_tokens)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When padding or truncating the sequences that have a different length, we need to determine if we want to do this padding or truncating 'pre' or 'post'. If a sequence is truncated, it means that a part of the sequence is simply thrown away. If a sequence is padded, it means that zeros are added to the sequence.\n", + "\n", + "So the choice of 'pre' or 'post' can be important because it determines whether we throw away the first or last part of a sequence when truncating, and it determines whether we add zeros to the beginning or end of the sequence when padding. This may confuse the Recurrent Neural Network." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "pad = 'pre'" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "x_train_pad = pad_sequences(x_train_tokens, maxlen=max_tokens,\n", + " padding=pad, truncating=pad)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "x_test_pad = pad_sequences(x_test_tokens, maxlen=max_tokens,\n", + " padding=pad, truncating=pad)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now transformed the training-set into one big matrix of integers (tokens) with this shape:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(25000, 544)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train_pad.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The matrix for the test-set has the same shape:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(25000, 544)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_test_pad.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example, we had the following sequence of tokens above:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1037, 5, 1, 432, 181, 13, 748, 141, 5, 141, 16,\n", + " 1, 7418, 318, 4, 1, 432, 181, 7, 7, 8, 11,\n", + " 751, 1, 167, 707, 83, 469, 31, 1037, 5, 1, 117,\n", + " 3, 547, 45, 654, 15, 3336, 60, 6, 3746, 5, 2222,\n", + " 1, 1379, 45, 137, 5, 77, 16, 11, 33, 1894, 5,\n", + " 4399, 1, 2, 1, 6, 2011, 72, 5, 1, 3029, 4,\n", + " 1, 5836, 7, 7, 37, 8, 1, 432, 181, 48, 171,\n", + " 3847, 73, 63, 23, 90, 4, 4118, 2, 132, 1469, 3,\n", + " 789, 4019, 2, 7, 7, 315, 4180, 2, 611, 2991, 65,\n", + " 578, 14, 2, 2, 277, 4, 1, 79, 150, 23, 81,\n", + " 141, 7, 7, 467, 899, 439, 74, 838, 4, 11, 2,\n", + " 143, 691, 256, 4118, 3847, 10, 511, 1037, 5, 1, 432,\n", + " 181, 7, 7, 670, 338, 405, 41, 4, 447])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array(x_train_tokens[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This has simply been padded to create the following sequence. Note that when this is input to the Recurrent Neural Network, then it first inputs a lot of zeros. If we had padded 'post' then it would input the integer-tokens first and then a lot of zeros. This may confuse the Recurrent Neural Network." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 1037, 5, 1, 432,\n", + " 181, 13, 748, 141, 5, 141, 16, 1, 7418, 318, 4,\n", + " 1, 432, 181, 7, 7, 8, 11, 751, 1, 167, 707,\n", + " 83, 469, 31, 1037, 5, 1, 117, 3, 547, 45, 654,\n", + " 15, 3336, 60, 6, 3746, 5, 2222, 1, 1379, 45, 137,\n", + " 5, 77, 16, 11, 33, 1894, 5, 4399, 1, 2, 1,\n", + " 6, 2011, 72, 5, 1, 3029, 4, 1, 5836, 7, 7,\n", + " 37, 8, 1, 432, 181, 48, 171, 3847, 73, 63, 23,\n", + " 90, 4, 4118, 2, 132, 1469, 3, 789, 4019, 2, 7,\n", + " 7, 315, 4180, 2, 611, 2991, 65, 578, 14, 2, 2,\n", + " 277, 4, 1, 79, 150, 23, 81, 141, 7, 7, 467,\n", + " 899, 439, 74, 838, 4, 11, 2, 143, 691, 256, 4118,\n", + " 3847, 10, 511, 1037, 5, 1, 432, 181, 7, 7, 670,\n", + " 338, 405, 41, 4, 447], dtype=int32)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train_pad[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tokenizer Inverse Map\n", + "\n", + "For some strange reason, the Keras implementation of a tokenizer does not seem to have the inverse mapping from integer-tokens back to words, which is needed to reconstruct text-strings from lists of tokens. So we make that mapping here." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "idx = tokenizer.word_index\n", + "inverse_map = dict(zip(idx.values(), idx.keys()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper-function for converting a list of tokens back to a string of words." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "def tokens_to_string(tokens):\n", + " # Map from tokens back to words.\n", + " words = [inverse_map[token] for token in tokens if token != 0]\n", + " \n", + " # Concatenate all words.\n", + " text = \" \".join(words)\n", + "\n", + " return text" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example, this is the original text from the data-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Return To the Lost World was filmed back-to-back with the 1992 version of The Lost World.

In this sequel, the same five people, lead by Challenger return to the plateau where a group has started drilling for oil which is threatening to destroy the land. Gomez has something to do with this. They manage to defeat the drillers and the plateau is saved, much to the delight of the natives.

Like in The Lost World, what few dinosaurs we see are made of rubber and these include a T-Rex and Ankylosaurus.

John Ryhs-Davies and David Warner reprise their roles as Challenger and Summerlee and three of the other actors are also back.

Despite reading several bad reviews of this and those cheap looking rubber dinosaurs, I enjoyed Return to the Lost World.

Rating: 3 stars out of 5.'" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train_text[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can recreate this text except for punctuation and other symbols, by converting the list of tokens back to words:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'return to the lost world was filmed back to back with the 1992 version of the lost world br br in this sequel the same five people lead by return to the where a group has started for oil which is threatening to destroy the land has something to do with this they manage to defeat the and the is saved much to the delight of the natives br br like in the lost world what few dinosaurs we see are made of rubber and these include a t rex and br br john davies and david warner their roles as and and three of the other actors are also back br br despite reading several bad reviews of this and those cheap looking rubber dinosaurs i enjoyed return to the lost world br br rating 3 stars out of 5'" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokens_to_string(x_train_tokens[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the Recurrent Neural Network\n", + "\n", + "We are now ready to create the Recurrent Neural Network (RNN). We will use the Keras API for this because of its simplicity. See Tutorial #03-C for a tutorial on Keras." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first layer in the RNN is a so-called Embedding-layer which converts each integer-token into a vector of values. This is necessary because the integer-tokens may take on values between 0 and 10000 for a vocabulary of 10000 words. The RNN cannot work on values in such a wide range. The embedding-layer is trained as a part of the RNN and will learn to map words with similar semantic meanings to similar embedding-vectors, as will be shown further below.\n", + "\n", + "First we define the size of the embedding-vector for each integer-token. In this case we have set it to 8, so that each integer-token will be converted to a vector of length 8. The values of the embedding-vector will generally fall roughly between -1.0 and 1.0, although they may exceed these values somewhat.\n", + "\n", + "The size of the embedding-vector is typically selected between 100-300, but it seems to work reasonably well with small values for Sentiment Analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "embedding_size = 8" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The embedding-layer also needs to know the number of words in the vocabulary (`num_words`) and the length of the padded token-sequences (`max_tokens`). We also give this layer a name because we need to retrieve its weights further below." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "model.add(Embedding(input_dim=num_words,\n", + " output_dim=embedding_size,\n", + " input_length=max_tokens,\n", + " name='layer_embedding'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now add the first Gated Recurrent Unit (GRU) to the network. This will have 16 outputs. Because we will add a second GRU after this one, we need to return sequences of data because the next GRU expects sequences as its input." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "model.add(GRU(units=16, return_sequences=True))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This adds the second GRU with 8 output units. This will be followed by another GRU so it must also return sequences." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "model.add(GRU(units=8, return_sequences=True))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This adds the third and final GRU with 4 output units. This will be followed by a dense-layer, so it should only give the final output of the GRU and not a whole sequence of outputs." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "model.add(GRU(units=4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add a fully-connected / dense layer which computes a value between 0.0 and 1.0 that will be used as the classification output." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "model.add(Dense(1, activation='sigmoid'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the Adam optimizer with the given learning-rate." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = Adam(lr=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compile the Keras model so it is ready for training." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(loss='binary_crossentropy',\n", + " optimizer=optimizer,\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "layer_embedding (Embedding) (None, 544, 8) 80000 \n", + "_________________________________________________________________\n", + "gru (GRU) (None, 544, 16) 1248 \n", + "_________________________________________________________________\n", + "gru_1 (GRU) (None, 544, 8) 624 \n", + "_________________________________________________________________\n", + "gru_2 (GRU) (None, 4) 168 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, 1) 5 \n", + "=================================================================\n", + "Total params: 82,045\n", + "Trainable params: 82,045\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train the Recurrent Neural Network\n", + "\n", + "We can now train the model. Note that we are using the data-set with the padded sequences. We use 5% of the training-set as a small validation-set, so we have a rough idea whether the model is generalizing well or if it is perhaps over-fitting to the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 23750 samples, validate on 1250 samples\n", + "Epoch 1/3\n", + "23750/23750 [==============================] - 16s 690us/sample - loss: 0.4935 - accuracy: 0.7452 - val_loss: 0.3798 - val_accuracy: 0.8328\n", + "Epoch 2/3\n", + "23750/23750 [==============================] - 12s 510us/sample - loss: 0.2919 - accuracy: 0.8887 - val_loss: 0.3111 - val_accuracy: 0.8736\n", + "Epoch 3/3\n", + "23750/23750 [==============================] - 12s 511us/sample - loss: 0.2210 - accuracy: 0.9211 - val_loss: 0.3090 - val_accuracy: 0.8760\n", + "CPU times: user 48.9 s, sys: 1.58 s, total: 50.5 s\n", + "Wall time: 40.7 s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "model.fit(x_train_pad, y_train,\n", + " validation_split=0.05, epochs=3, batch_size=64)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance on Test-Set\n", + "\n", + "Now that the model has been trained we can calculate its classification accuracy on the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25000/25000 [==============================] - 12s 493us/sample - loss: 0.3331 - accuracy: 0.8674\n", + "CPU times: user 14 s, sys: 404 ms, total: 14.4 s\n", + "Wall time: 12.4 s\n" + ] + } + ], + "source": [ + "%%time\n", + "result = model.evaluate(x_test_pad, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 86.74%\n" + ] + } + ], + "source": [ + "print(\"Accuracy: {0:.2%}\".format(result[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example of Mis-Classified Text\n", + "\n", + "In order to show an example of mis-classified text, we first calculate the predicted sentiment for the first 1000 texts in the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.08 s, sys: 23.5 ms, total: 1.1 s\n", + "Wall time: 1.03 s\n" + ] + } + ], + "source": [ + "%%time\n", + "y_pred = model.predict(x=x_test_pad[0:1000])\n", + "y_pred = y_pred.T[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These predicted numbers fall between 0.0 and 1.0. We use a cutoff / threshold and say that all values above 0.5 are taken to be 1.0 and all values below 0.5 are taken to be 0.0. This gives us a predicted \"class\" of either 0.0 or 1.0." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "cls_pred = np.array([1.0 if p>0.5 else 0.0 for p in y_pred])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The true \"class\" for the first 1000 texts in the test-set are needed for comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "cls_true = np.array(y_test[0:1000])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then get indices for all the texts that were incorrectly classified by comparing all the \"classes\" of these two arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "incorrect = np.where(cls_pred != cls_true)\n", + "incorrect = incorrect[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of the 1000 texts used, how many were mis-classified?" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "132" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(incorrect)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us look at the first mis-classified text. We will use its index several times." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx = incorrect[0]\n", + "idx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The mis-classified text is:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"This HAS to be my guilty pleasure. I am a HUGE fan of 80's movies that were designed to entertain and they didn't care if they offended anyone. This move has no meat, not substance, no deep thought provoking scenes. Just plain old college kids having fun and if a few breasts have to be shown, then so be it! This movie is for when you just want to relax and NOT think. Viva la nudity!\"" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "text = x_test_text[idx]\n", + "text" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the predicted and true classes for the text:" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.27373913" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred[idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cls_true[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## New Data\n", + "\n", + "Let us try and classify new texts that we make up. Some of these are obvious, while others use negation and sarcasm to try and confuse the model into mis-classifying the text." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "text1 = \"This movie is fantastic! I really like it because it is so good!\"\n", + "text2 = \"Good movie!\"\n", + "text3 = \"Maybe I like this movie.\"\n", + "text4 = \"Meh ...\"\n", + "text5 = \"If I were a drunk teenager then this movie might be good.\"\n", + "text6 = \"Bad movie!\"\n", + "text7 = \"Not a good movie!\"\n", + "text8 = \"This movie really sucks! Can I get my money back please?\"\n", + "texts = [text1, text2, text3, text4, text5, text6, text7, text8]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first convert these texts to arrays of integer-tokens because that is needed by the model." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "tokens = tokenizer.texts_to_sequences(texts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To input texts with different lengths into the model, we also need to pad and truncate them." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8, 544)" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokens_pad = pad_sequences(tokens, maxlen=max_tokens,\n", + " padding=pad, truncating=pad)\n", + "tokens_pad.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use the trained model to predict the sentiment for these texts." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.95301837],\n", + " [0.92733926],\n", + " [0.79257476],\n", + " [0.9019553 ],\n", + " [0.5875022 ],\n", + " [0.55110747],\n", + " [0.89896274],\n", + " [0.33616564]], dtype=float32)" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.predict(tokens_pad)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A value close to 0.0 means a negative sentiment and a value close to 1.0 means a positive sentiment. These numbers will vary every time you train the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Embeddings\n", + "\n", + "The model cannot work on integer-tokens directly, because they are integer values that may range between 0 and the number of words in our vocabulary, e.g. 10000. So we need to convert the integer-tokens into vectors of values that are roughly between -1.0 and 1.0 which can be used as input to a neural network.\n", + "\n", + "This mapping from integer-tokens to real-valued vectors is also called an \"embedding\". It is essentially just a matrix where each row contains the vector-mapping of a single token. This means we can quickly lookup the mapping of each integer-token by simply using the token as an index into the matrix. The embeddings are learned along with the rest of the model during training.\n", + "\n", + "Ideally the embedding would learn a mapping where words that are similar in meaning also have similar embedding-values. Let us investigate if that has happened here.\n", + "\n", + "First we need to get the embedding-layer from the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "layer_embedding = model.get_layer('layer_embedding')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then get the weights used for the mapping done by the embedding-layer." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "weights_embedding = layer_embedding.get_weights()[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the weights are actually just a matrix with the number of words in the vocabulary times the vector length for each embedding. That's because it is basically just a lookup-matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(10000, 8)" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights_embedding.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us get the integer-token for the word 'good', which is just an index into the vocabulary." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "49" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "token_good = tokenizer.word_index['good']\n", + "token_good" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also get the integer-token for the word 'great'." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "78" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "token_great = tokenizer.word_index['great']\n", + "token_great" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These integertokens may be far apart and will depend on the frequency of those words in the data-set.\n", + "\n", + "Now let us compare the vector-embeddings for the words 'good' and 'great'. Several of these values are similar, although some values are quite different. Note that these values will change every time you train the model." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.01839033, 0.05229224, 0.0848575 , 0.03222338, -0.03947427,\n", + " -0.03776564, -0.01149088, -0.07443853], dtype=float32)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights_embedding[token_good]" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.14307617, 0.08333486, 0.15650608, 0.08930028, -0.08659173,\n", + " -0.12289459, -0.14367667, -0.10402057], dtype=float32)" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights_embedding[token_great]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, we can compare the embeddings for the words 'bad' and 'horrible'." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "token_bad = tokenizer.word_index['bad']\n", + "token_horrible = tokenizer.word_index['horrible']" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.05553182, -0.09014519, -0.06248455, -0.11525143, 0.14601274,\n", + " 0.07451952, 0.10784499, 0.10799433], dtype=float32)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights_embedding[token_bad]" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.15100664, -0.13359004, -0.15154287, -0.12776676, 0.10830297,\n", + " 0.15224072, 0.13508266, 0.14284784], dtype=float32)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights_embedding[token_horrible]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sorted Words\n", + "\n", + "We can also sort all the words in the vocabulary according to their \"similarity\" in the embedding-space. We want to see if words that have similar embedding-vectors also have similar meanings.\n", + "\n", + "Similarity of embedding-vectors can be measured by different metrics, e.g. Euclidean distance or cosine distance.\n", + "\n", + "We have a helper-function for calculating these distances and printing the words in sorted order." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "def print_sorted_words(word, metric='cosine'):\n", + " \"\"\"\n", + " Print the words in the vocabulary sorted according to their\n", + " embedding-distance to the given word.\n", + " Different metrics can be used, e.g. 'cosine' or 'euclidean'.\n", + " \"\"\"\n", + "\n", + " # Get the token (i.e. integer ID) for the given word.\n", + " token = tokenizer.word_index[word]\n", + "\n", + " # Get the embedding for the given word. Note that the\n", + " # embedding-weight-matrix is indexed by the word-tokens\n", + " # which are integer IDs.\n", + " embedding = weights_embedding[token]\n", + "\n", + " # Calculate the distance between the embeddings for\n", + " # this word and all other words in the vocabulary.\n", + " distances = cdist(weights_embedding, [embedding],\n", + " metric=metric).T[0]\n", + " \n", + " # Get an index sorted according to the embedding-distances.\n", + " # These are the tokens (integer IDs) for words in the vocabulary.\n", + " sorted_index = np.argsort(distances)\n", + " \n", + " # Sort the embedding-distances.\n", + " sorted_distances = distances[sorted_index]\n", + " \n", + " # Sort all the words in the vocabulary according to their\n", + " # embedding-distance. This is a bit excessive because we\n", + " # will only print the top and bottom words.\n", + " sorted_words = [inverse_map[token] for token in sorted_index\n", + " if token != 0]\n", + "\n", + " # Helper-function for printing words and embedding-distances.\n", + " def _print_words(words, distances):\n", + " for word, distance in zip(words, distances):\n", + " print(\"{0:.3f} - {1}\".format(distance, word))\n", + "\n", + " # Number of words to print from the top and bottom of the list.\n", + " k = 10\n", + "\n", + " print(\"Distance from '{0}':\".format(word))\n", + "\n", + " # Print the words with smallest embedding-distance.\n", + " _print_words(sorted_words[0:k], sorted_distances[0:k])\n", + "\n", + " print(\"...\")\n", + "\n", + " # Print the words with highest embedding-distance.\n", + " _print_words(sorted_words[-k:], sorted_distances[-k:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then print the words that are near and far from the word 'great' in terms of their vector-embeddings. Note that these may change each time you train the model." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Distance from 'great':\n", + "0.000 - great\n", + "0.012 - spring\n", + "0.013 - 1980\n", + "0.013 - permanent\n", + "0.013 - robinson\n", + "0.015 - anime\n", + "0.015 - pleasantly\n", + "0.016 - inter\n", + "0.016 - profit\n", + "0.017 - ramones\n", + "...\n", + "1.988 - mst3k\n", + "1.988 - consist\n", + "1.988 - save\n", + "1.989 - unless\n", + "1.990 - ripoff\n", + "1.991 - insipid\n", + "1.994 - avoid\n", + "1.995 - drivel\n", + "1.995 - expand\n", + "1.995 - profile\n" + ] + } + ], + "source": [ + "print_sorted_words('great', metric='cosine')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, we can print the words that are near and far from the word 'worst' in terms of their vector-embeddings." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Distance from 'worst':\n", + "0.000 - worst\n", + "0.004 - horrible\n", + "0.004 - dull\n", + "0.005 - below\n", + "0.005 - boredom\n", + "0.006 - conceived\n", + "0.008 - salvage\n", + "0.009 - slapped\n", + "0.009 - fails\n", + "0.010 - virus\n", + "...\n", + "1.989 - cried\n", + "1.989 - compelling\n", + "1.990 - carell\n", + "1.990 - stadium\n", + "1.991 - deanna\n", + "1.992 - eddie\n", + "1.992 - resolved\n", + "1.992 - sirk\n", + "1.994 - sidney\n", + "1.997 - concentrates\n" + ] + } + ], + "source": [ + "print_sorted_words('worst', metric='cosine')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed the basic methods for doing Natural Language Processing (NLP) using a Recurrent Neural Network with integer-tokens and an embedding layer. This was used to do sentiment analysis of movie reviews from IMDB. It works reasonably well if the hyper-parameters are chosen properly. But it is important to understand that this is not human-like comprehension of text. The system does not have any real understanding of the text. It is just a clever way of doing pattern-recognition." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Run more training-epochs. Does it improve performance?\n", + "* If your model overfits the training-data, try using dropout-layers and dropout inside the GRU.\n", + "* Increase or decrease the number of words in the vocabulary. This is done when the `Tokenizer` is initialized. Does it affect performance?\n", + "* Increase the size of the embedding-vectors to e.g. 200. Does it affect performance?\n", + "* Try varying all the different hyper-parameters for the Recurrent Neural Network.\n", + "* Use Bayesian Optimization from Tutorial #19 to find the best choice of hyper-parameters.\n", + "* Use 'post' for padding and truncating in `pad_sequences()`. Does it affect the performance?\n", + "* Use individual characters instead of tokenized words as the vocabulary. You can then use one-hot encoded vectors for each character instead of using the embedding-layer.\n", + "* Use `model.fit_generator()` instead of `model.fit()` and make your own data-generator, which creates a batch of data using a random subset of `x_train_tokens`. The sequences must be padded so they all match the length of the longest sequence.\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "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:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "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." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/21_Machine_Translation.ipynb b/21_Machine_Translation.ipynb new file mode 100644 index 0000000..94146fe --- /dev/null +++ b/21_Machine_Translation.ipynb @@ -0,0 +1,2040 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #21\n", + "# Machine Translation\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Tutorial #20 showed how to use a Recurrent Neural Network (RNN) to do so-called sentiment analysis on texts of movie reviews. This tutorial will extend that idea to do Machine Translation of human languages by combining two RNN's.\n", + "\n", + "You should be familiar with TensorFlow, Keras and the basics of Natural Language Processing, see Tutorials #01, #03-C and #20." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flowchart\n", + "\n", + "The following flowchart shows roughly how the neural network is constructed. It is split into two parts: An encoder which maps the source-text to a \"thought vector\" that summarizes the text's contents, which is then input to the second part of the neural network that decodes the \"thought vector\" to the destination-text.\n", + "\n", + "The neural network cannot work directly on text so first we need to convert each word to an integer-token using a tokenizer. But the neural network cannot work on integers either, so we use a so-called Embedding Layer to convert each integer-token to a vector of floating-point values. The embedding is trained alongside the rest of the neural network to map words with similar semantic meaning to similar vectors of floating-point values.\n", + "\n", + "For example, consider the Danish text \"der var engang\" which is the beginning of any fairytale and literally means \"there was once\" but is commonly translated into English as \"once upon a time\". We first convert the entire data-set to integer-tokens so the text \"der var engang\" becomes [12, 54, 1097]. Each of these integer-tokens is then mapped to an embedding-vector with e.g. 128 elements, so the integer-token 12 could for example become [0.12, -0.56, ..., 1.19] and the integer-token 54 could for example become [0.39, 0.09, ..., -0.12]. These embedding-vectors can then be input to the Recurrent Neural Network, which has 3 GRU-layers. See Tutorial #20 for a more detailed explanation.\n", + "\n", + "The last GRU-layer outputs a single vector - the \"thought vector\" that summarizes the contents of the source-text - which is then used as the initial state of the GRU-units in the decoder-part.\n", + "\n", + "The destination-text \"once upon a time\" is padded with special markers \"ssss\" and \"eeee\" to indicate its beginning and end, so the sequence of integer-tokens becomes [2, 337, 640, 9, 79, 3]. During training, the decoder will be given this entire sequence as input and the desired output sequence is [337, 640, 9, 79, 3] which is the same sequence but time-shifted one step. We are trying to teach the decoder to map the \"thought vector\" and the start-token \"ssss\" (integer 2) to the next word \"once\" (integer 337), and then map the word \"once\" to the word \"upon\" (integer 640), and so forth.\n", + "\n", + "This flow-chart depicts the main idea but does not show all the necessary details e.g. regarding the loss function which is also somewhat complicated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Flowchart](images/21_machine_translation_flowchart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import math\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to import several things from Keras." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# from tf.keras.models import Model # This does not work!\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n", + "from tensorflow.keras.optimizers import RMSprop\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard\n", + "from tensorflow.keras.preprocessing.text import Tokenizer\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 (Anaconda) and package versions:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.2.4-tf'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.keras.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data\n", + "\n", + "We will use the Europarl data-set which has sentence-pairs in most European languages. The data was created by the European Union which translates a lot of their communications to the languages of the member-countries of the European Union.\n", + "\n", + "http://www.statmt.org/europarl/" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import europarl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial I have used the English-Danish data-set which contains about 2 million sentence-pairs. You can use another language by changing this language-code, see `europarl.py` for a list of available language-codes." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "language_code='da'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order for the decoder to know when to begin and end a sentence, we need to mark the start and end of each sentence with words that most likely don't occur in the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "mark_start = 'ssss '\n", + "mark_end = ' eeee'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can change the directory for the data-files if you like." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# data_dir = \"data/europarl/\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will automatically download and extract the data-files if you don't have them already.\n", + "\n", + "**WARNING: The file for the English-Danish data-set is about 587 MB!**" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has apparently already been downloaded and unpacked.\n" + ] + } + ], + "source": [ + "europarl.maybe_download_and_extract(language_code=language_code)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the texts for the source-language, here we use Danish." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "data_src = europarl.load_data(english=False,\n", + " language_code=language_code)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the texts for the destination-language, here we use English." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "data_dest = europarl.load_data(english=True,\n", + " language_code=language_code,\n", + " start=mark_start,\n", + " end=mark_end)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will build a model to translate from the source language (Danish) to the destination language (English). If you want to make the inverse translation you can merely exchange the source and destination data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example Data\n", + "\n", + "The data is just a list of texts that is ordered so the source and destination texts match. I can confirm that this example is an accurate translation." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "idx = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Som De kan se, indfandt det store \"år 2000-problem\" sig ikke. Til gengæld har borgerne i en del af medlemslandene været ramt af meget forfærdelige naturkatastrofer.'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_src[idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"ssss Although, as you will have seen, the dreaded 'millennium bug' failed to materialise, still the people in a number of countries suffered a series of natural disasters that truly were dreadful. eeee\"" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_dest[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Error in Data\n", + "\n", + "The data-set contains about 2 million sentence-pairs. Some of the data is incorrect. This example appears to be French (or some other weird language I don't understand), although the Danish text is also included." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "idx = 8002" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\"Car il savait ce que cette foule en joie ignorait, et qu\\'on peut lire dans les livres, que le bacille de la peste ne meurt ni ne disparaît jamais, qu\\'il peut rester pendant des dizaines d\\'années endormi dans les meubles et le linge, qu\\'il attend patiemment dans les chambres, les caves, les malles, les mouchoirs et les paperasses, et que, peut-être, le jour viendrait où, pour le malheur et l\\'enseignement des hommes, la peste réveillerait ses rats et les enverrait mourir dans une cité heureuse.\" (Thi han vidste det, som denne glade forsamling ikke vidste, og som man kan læse i bøger, at pestens bacille aldrig dør og aldrig forsvinder, at den kan sove i mange år i møbler og linned, at den venter tålmodigt i kamre, kældre, kufferter, lommetørklæder og papirer, og at den dag måske kommer, hvor pesten til menneskenes skade og oplysning vågner sine rotter og sender dem ud for at dø i en lykkelig by.)'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_src[idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'ssss \"He knew what those jubilant crowds did not know but could have learned from books: that the plague bacillus never dies or disappears for good; that it can lie dormant for years and years in furniture and linen-chests; that it bides its time in bedrooms, cellars, trunks, and bookshelves; and that perhaps the day would come when, for the bane and the enlightening of men, it would rouse up its rats again and send them forth to die in a happy city.\" eeee'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_dest[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tokenizer\n", + "\n", + "Neural Networks cannot work directly on text-data. We use a two-step process to convert text into numbers that can be used in a neural network. The first step is to convert text-words into so-called integer-tokens. The second step is to convert integer-tokens into vectors of floating-point numbers using a so-called embedding-layer. See Tutorial #20 for a more detailed explanation.\n", + "\n", + "Set the maximum number of words in our vocabulary. This means that we will only use e.g. the 10000 most frequent words in the data-set. We use the same number for both the source and destination languages, but these could be different." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "num_words = 10000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need a few more functions than provided by Keras' Tokenizer-class so we wrap it." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "class TokenizerWrap(Tokenizer):\n", + " \"\"\"Wrap the Tokenizer-class from Keras with more functionality.\"\"\"\n", + " \n", + " def __init__(self, texts, padding,\n", + " reverse=False, num_words=None):\n", + " \"\"\"\n", + " :param texts: List of strings. This is the data-set.\n", + " :param padding: Either 'post' or 'pre' padding.\n", + " :param reverse: Boolean whether to reverse token-lists.\n", + " :param num_words: Max number of words to use.\n", + " \"\"\"\n", + "\n", + " Tokenizer.__init__(self, num_words=num_words)\n", + "\n", + " # Create the vocabulary from the texts.\n", + " self.fit_on_texts(texts)\n", + "\n", + " # Create inverse lookup from integer-tokens to words.\n", + " self.index_to_word = dict(zip(self.word_index.values(),\n", + " self.word_index.keys()))\n", + "\n", + " # Convert all texts to lists of integer-tokens.\n", + " # Note that the sequences may have different lengths.\n", + " self.tokens = self.texts_to_sequences(texts)\n", + "\n", + " if reverse:\n", + " # Reverse the token-sequences.\n", + " self.tokens = [list(reversed(x)) for x in self.tokens]\n", + " \n", + " # Sequences that are too long should now be truncated\n", + " # at the beginning, which corresponds to the end of\n", + " # the original sequences.\n", + " truncating = 'pre'\n", + " else:\n", + " # Sequences that are too long should be truncated\n", + " # at the end.\n", + " truncating = 'post'\n", + "\n", + " # The number of integer-tokens in each sequence.\n", + " self.num_tokens = [len(x) for x in self.tokens]\n", + "\n", + " # Max number of tokens to use in all sequences.\n", + " # We will pad / truncate all sequences to this length.\n", + " # This is a compromise so we save a lot of memory and\n", + " # only have to truncate maybe 5% of all the sequences.\n", + " self.max_tokens = np.mean(self.num_tokens) \\\n", + " + 2 * np.std(self.num_tokens)\n", + " self.max_tokens = int(self.max_tokens)\n", + "\n", + " # Pad / truncate all token-sequences to the given length.\n", + " # This creates a 2-dim numpy matrix that is easier to use.\n", + " self.tokens_padded = pad_sequences(self.tokens,\n", + " maxlen=self.max_tokens,\n", + " padding=padding,\n", + " truncating=truncating)\n", + "\n", + " def token_to_word(self, token):\n", + " \"\"\"Lookup a single word from an integer-token.\"\"\"\n", + "\n", + " word = \" \" if token == 0 else self.index_to_word[token]\n", + " return word \n", + "\n", + " def tokens_to_string(self, tokens):\n", + " \"\"\"Convert a list of integer-tokens to a string.\"\"\"\n", + "\n", + " # Create a list of the individual words.\n", + " words = [self.index_to_word[token]\n", + " for token in tokens\n", + " if token != 0]\n", + " \n", + " # Concatenate the words to a single string\n", + " # with space between all the words.\n", + " text = \" \".join(words)\n", + "\n", + " return text\n", + " \n", + " def text_to_tokens(self, text, reverse=False, padding=False):\n", + " \"\"\"\n", + " Convert a single text-string to tokens with optional\n", + " reversal and padding.\n", + " \"\"\"\n", + "\n", + " # Convert to tokens. Note that we assume there is only\n", + " # a single text-string so we wrap it in a list.\n", + " tokens = self.texts_to_sequences([text])\n", + " tokens = np.array(tokens)\n", + "\n", + " if reverse:\n", + " # Reverse the tokens.\n", + " tokens = np.flip(tokens, axis=1)\n", + "\n", + " # Sequences that are too long should now be truncated\n", + " # at the beginning, which corresponds to the end of\n", + " # the original sequences.\n", + " truncating = 'pre'\n", + " else:\n", + " # Sequences that are too long should be truncated\n", + " # at the end.\n", + " truncating = 'post'\n", + "\n", + " if padding:\n", + " # Pad and truncate sequences to the given length.\n", + " tokens = pad_sequences(tokens,\n", + " maxlen=self.max_tokens,\n", + " padding='pre',\n", + " truncating=truncating)\n", + "\n", + " return tokens" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create a tokenizer for the source-language. Note that we pad zeros at the beginning ('pre') of the sequences. We also reverse the sequences of tokens because the research literature suggests that this might improve performance, because the last words seen by the encoder match the first words produced by the decoder, so short-term dependencies are supposedly modelled more accurately." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2min 18s, sys: 940 ms, total: 2min 19s\n", + "Wall time: 2min 19s\n" + ] + } + ], + "source": [ + "%%time\n", + "tokenizer_src = TokenizerWrap(texts=data_src,\n", + " padding='pre',\n", + " reverse=True,\n", + " num_words=num_words)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create the tokenizer for the destination language. We need a tokenizer for both the source- and destination-languages because their vocabularies are different. Note that this tokenizer does not reverse the sequences and it pads zeros at the end ('post') of the arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 49s, sys: 752 ms, total: 1min 50s\n", + "Wall time: 1min 50s\n" + ] + } + ], + "source": [ + "%%time\n", + "tokenizer_dest = TokenizerWrap(texts=data_dest,\n", + " padding='post',\n", + " reverse=False,\n", + " num_words=num_words)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define convenience variables for the padded token sequences. These are just 2-dimensional numpy arrays of integer-tokens.\n", + "\n", + "Note that the sequence-lengths are different for the source and destination languages. This is because texts with the same meaning may have different numbers of words in the two languages. \n", + "\n", + "Furthermore, we have made a compromise when tokenizing the original texts in order to save a lot of memory. This means we only truncate about 5% of the texts." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1968800, 47)\n", + "(1968800, 55)\n" + ] + } + ], + "source": [ + "tokens_src = tokenizer_src.tokens_padded\n", + "tokens_dest = tokenizer_dest.tokens_padded\n", + "print(tokens_src.shape)\n", + "print(tokens_dest.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the integer-token used to mark the beginning of a text in the destination-language." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "token_start = tokenizer_dest.word_index[mark_start.strip()]\n", + "token_start" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the integer-token used to mark the end of a text in the destination-language." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "token_end = tokenizer_dest.word_index[mark_end.strip()]\n", + "token_end" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example of Token Sequences" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the output of the tokenizer. Note how it is padded with zeros at the beginning (pre-padding)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "idx = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3069,\n", + " 3374, 43, 7, 1386, 108, 1995, 7, 178, 9, 3, 302,\n", + " 19, 2076, 8, 20, 39, 285, 499, 69, 136, 5, 166,\n", + " 24, 10, 13], dtype=int32)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokens_src[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can reconstruct the original text by converting each integer-token back to its corresponding word:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'naturkatastrofer forfærdelige meget af ramt været medlemslandene af del en i borgerne har gengæld til ikke sig problem 2000 år store det se kan de som'" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer_src.tokens_to_string(tokens_src[idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This text is actually reversed, as can be seen when compared to the original text from the data-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Som De kan se, indfandt det store \"år 2000-problem\" sig ikke. Til gengæld har borgerne i en del af medlemslandene været ramt af meget forfærdelige naturkatastrofer.'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_src[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the sequence of integer-tokens for the corresponding text in the destination-language. Note how it is padded with zeros at the end (post-padding)." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2, 404, 19, 43, 26, 20, 618, 1, 1451, 5, 9785,\n", + " 174, 1, 81, 7, 9, 214, 4, 67, 2200, 9, 1596,\n", + " 4, 892, 1762, 8, 1480, 107, 5494, 3, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " dtype=int32)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokens_dest[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can reconstruct the original text by converting each integer-token back to its corresponding word:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'ssss although as you will have seen the failed to materialise still the people in a number of countries suffered a series of natural disasters that truly were dreadful eeee'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer_dest.tokens_to_string(tokens_dest[idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare this to the original text from the data-set, which is almost identical except for punctuation marks and a few words such as \"dreaded millennium bug\". This is because we only use a vocabulary of the 10000 most frequent words in the data-set and those 3 words were apparently not used frequently enough to be included in the vocabulary, so they are merely skipped." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"ssss Although, as you will have seen, the dreaded 'millennium bug' failed to materialise, still the people in a number of countries suffered a series of natural disasters that truly were dreadful. eeee\"" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_dest[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training Data\n", + "\n", + "Now that the data-set has been converted to sequences of integer-tokens that are padded and truncated and saved in numpy arrays, we can easily prepare the data for use in training the neural network.\n", + "\n", + "The input to the encoder is merely the numpy array for the padded and truncated sequences of integer-tokens produced by the tokenizer:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "encoder_input_data = tokens_src" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The input and output data for the decoder is identical, except shifted one time-step. We can use the same numpy array to save memory by slicing it, which merely creates different 'views' of the same data in memory." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1968800, 54)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decoder_input_data = tokens_dest[:, :-1]\n", + "decoder_input_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1968800, 54)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decoder_output_data = tokens_dest[:, 1:]\n", + "decoder_output_data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example, these token-sequences are identical except they are shifted one time-step." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "idx = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2, 404, 19, 43, 26, 20, 618, 1, 1451, 5, 9785,\n", + " 174, 1, 81, 7, 9, 214, 4, 67, 2200, 9, 1596,\n", + " 4, 892, 1762, 8, 1480, 107, 5494, 3, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " dtype=int32)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decoder_input_data[idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 404, 19, 43, 26, 20, 618, 1, 1451, 5, 9785, 174,\n", + " 1, 81, 7, 9, 214, 4, 67, 2200, 9, 1596, 4,\n", + " 892, 1762, 8, 1480, 107, 5494, 3, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " dtype=int32)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decoder_output_data[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we use the tokenizer to convert these sequences back into text, we see that they are identical except for the first word which is 'ssss' that marks the beginning of a text." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'ssss although as you will have seen the failed to materialise still the people in a number of countries suffered a series of natural disasters that truly were dreadful eeee'" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer_dest.tokens_to_string(decoder_input_data[idx])" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'although as you will have seen the failed to materialise still the people in a number of countries suffered a series of natural disasters that truly were dreadful eeee'" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer_dest.tokens_to_string(decoder_output_data[idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the Neural Network\n", + "\n", + "### Create the Encoder\n", + "\n", + "First we create the encoder-part of the neural network which maps a sequence of integer-tokens to a \"thought vector\". We will use the so-called functional API of Keras for this, where we first create the objects for all the layers of the neural network and then we connect them later, this allows for more flexibility than the so-called sequential API in Keras, which is useful when experimenting with more complicated architectures and ways of connecting the encoder and decoder." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the input for the encoder which takes batches of integer-token sequences. The `None` indicates that the sequences can have arbitrary length." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "encoder_input = Input(shape=(None, ), name='encoder_input')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the length of the vectors output by the embedding-layer, which maps integer-tokens to vectors of values roughly between -1 and 1, so that words that have similar semantic meanings are mapped to vectors that are similar. See Tutorial #20 for a more detailed explanation of this." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "embedding_size = 128" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the embedding-layer." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "encoder_embedding = Embedding(input_dim=num_words,\n", + " output_dim=embedding_size,\n", + " name='encoder_embedding')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the size of the internal states of the Gated Recurrent Units (GRU). The same size is used in both the encoder and decoder." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "state_size = 512" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This creates the 3 GRU layers that will map from a sequence of embedding-vectors to a single \"thought vector\" which summarizes the contents of the input-text. Note that the last GRU-layer does not return a sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "encoder_gru1 = GRU(state_size, name='encoder_gru1',\n", + " return_sequences=True)\n", + "encoder_gru2 = GRU(state_size, name='encoder_gru2',\n", + " return_sequences=True)\n", + "encoder_gru3 = GRU(state_size, name='encoder_gru3',\n", + " return_sequences=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This helper-function connects all the layers of the encoder." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "def connect_encoder():\n", + " # Start the neural network with its input-layer.\n", + " net = encoder_input\n", + " \n", + " # Connect the embedding-layer.\n", + " net = encoder_embedding(net)\n", + "\n", + " # Connect all the GRU-layers.\n", + " net = encoder_gru1(net)\n", + " net = encoder_gru2(net)\n", + " net = encoder_gru3(net)\n", + "\n", + " # This is the output of the encoder.\n", + " encoder_output = net\n", + " \n", + " return encoder_output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note how the encoder uses the normal output from its last GRU-layer as the \"thought vector\". Research papers often use the internal state of the encoder's last recurrent layer as the \"thought vector\". But this makes the implementation more complicated and is not necessary when using the GRU. But if you were using the LSTM instead then it is necessary to use the LSTM's internal states as the \"thought vector\" because it actually has two internal vectors, which we would need to initialize the two internal states of the decoder's LSTM units.\n", + "\n", + "We can now use this function to connect all the layers in the encoder so it can be connected to the decoder further below." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "encoder_output = connect_encoder()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the Decoder\n", + "\n", + "Create the decoder-part which maps the \"thought vector\" to a sequence of integer-tokens.\n", + "\n", + "The decoder takes two inputs. First it needs the \"thought vector\" produced by the encoder which summarizes the contents of the input-text." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_initial_state = Input(shape=(state_size,),\n", + " name='decoder_initial_state')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The decoder also needs a sequence of integer-tokens as inputs. During training we will supply this with a full sequence of integer-tokens e.g. corresponding to the text \"ssss once upon a time eeee\". \n", + "\n", + "During inference when we are translating new input-texts, we will start by feeding a sequence with just one integer-token for \"ssss\" which marks the beginning of a text, and combined with the \"thought vector\" from the encoder, the decoder will hopefully be able to produce the correct next word e.g. \"once\"." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_input = Input(shape=(None, ), name='decoder_input')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the embedding-layer which converts integer-tokens to vectors of real-valued numbers roughly between -1 and 1. Note that we have different embedding-layers for the encoder and decoder because we have two different vocabularies and two different tokenizers for the source and destination languages." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_embedding = Embedding(input_dim=num_words,\n", + " output_dim=embedding_size,\n", + " name='decoder_embedding')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This creates the 3 GRU layers of the decoder. Note that they all return sequences because we ultimately want to output a sequence of integer-tokens that can be converted into a text-sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_gru1 = GRU(state_size, name='decoder_gru1',\n", + " return_sequences=True)\n", + "decoder_gru2 = GRU(state_size, name='decoder_gru2',\n", + " return_sequences=True)\n", + "decoder_gru3 = GRU(state_size, name='decoder_gru3',\n", + " return_sequences=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The GRU layers output a tensor with shape `[batch_size, sequence_length, state_size]`, where each \"word\" is encoded as a vector of length `state_size`. We need to convert this into sequences of integer-tokens that can be interpreted as words from our vocabulary.\n", + "\n", + "One way of doing this is to convert the GRU output to a one-hot encoded array. It works but it is extremely wasteful, because for a vocabulary of e.g. 10000 words we need a vector with 10000 elements, so we can select the index of the highest element to be the integer-token." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_dense = Dense(num_words,\n", + " activation='softmax',\n", + " name='decoder_output')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The decoder is built using the functional API of Keras, which allows more flexibility in connecting the layers e.g. to route different inputs to the decoder. This is useful because we have to connect the decoder directly to the encoder, but we will also connect the decoder to another input so we can run it separately.\n", + "\n", + "This function connects all the layers of the decoder to some input of the initial-state values for the GRU layers." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "def connect_decoder(initial_state):\n", + " # Start the decoder-network with its input-layer.\n", + " net = decoder_input\n", + "\n", + " # Connect the embedding-layer.\n", + " net = decoder_embedding(net)\n", + " \n", + " # Connect all the GRU-layers.\n", + " net = decoder_gru1(net, initial_state=initial_state)\n", + " net = decoder_gru2(net, initial_state=initial_state)\n", + " net = decoder_gru3(net, initial_state=initial_state)\n", + "\n", + " # Connect the final dense layer that converts to\n", + " # one-hot encoded arrays.\n", + " decoder_output = decoder_dense(net)\n", + " \n", + " return decoder_output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect and Create the Models\n", + "\n", + "We can now connect the encoder and decoder in different ways.\n", + "\n", + "First we connect the encoder directly to the decoder so it is one whole model that can be trained end-to-end. This means the initial-state of the decoder's GRU units are set to the output of the encoder." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_output = connect_decoder(initial_state=encoder_output)\n", + "\n", + "model_train = Model(inputs=[encoder_input, decoder_input],\n", + " outputs=[decoder_output])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we create a model for just the encoder alone. This is useful for mapping a sequence of integer-tokens to a \"thought-vector\" summarizing its contents." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "model_encoder = Model(inputs=[encoder_input],\n", + " outputs=[encoder_output])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we create a model for just the decoder alone. This allows us to directly input the initial state for the decoder's GRU units." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_output = connect_decoder(initial_state=decoder_initial_state)\n", + "\n", + "model_decoder = Model(inputs=[decoder_input, decoder_initial_state],\n", + " outputs=[decoder_output])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that all these models use the same weights and variables of the encoder and decoder. We are merely changing how they are connected. So once the entire model has been trained, we can run the encoder and decoder models separately with the trained weights." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile the Model\n", + "\n", + "The output of the decoder is a sequence of one-hot encoded arrays. In order to train the decoder we need to supply the one-hot encoded arrays that we desire to see on the decoder's output, and then use a loss-function like cross-entropy to train the decoder to produce this desired output.\n", + "\n", + "However, our data-set contains integer-tokens instead of one-hot encoded arrays. Each one-hot encoded array has 10000 elements so it would be extremely wasteful to convert the entire data-set to one-hot encoded arrays.\n", + "\n", + "A better way is to use a so-called sparse cross-entropy loss-function, which does the conversion internally from integers to one-hot encoded arrays.\n", + "\n", + "We have used the Adam optimizer in many of the previous tutorials, but it seems to diverge in some of these experiments with Recurrent Neural Networks. RMSprop seems to work much better for these." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "model_train.compile(optimizer=RMSprop(lr=1e-3),\n", + " loss='sparse_categorical_crossentropy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Callback Functions\n", + "\n", + "During training we want to save checkpoints and log the progress to TensorBoard so we create the appropriate callbacks for Keras.\n", + "\n", + "This is the callback for writing checkpoints during training." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "path_checkpoint = '21_checkpoint.keras'\n", + "callback_checkpoint = ModelCheckpoint(filepath=path_checkpoint,\n", + " monitor='val_loss',\n", + " verbose=1,\n", + " save_weights_only=True,\n", + " save_best_only=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the callback for stopping the optimization when performance worsens on the validation-set." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "callback_early_stopping = EarlyStopping(monitor='val_loss',\n", + " patience=3, verbose=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the callback for writing the TensorBoard log during training." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "callback_tensorboard = TensorBoard(log_dir='./21_logs/',\n", + " histogram_freq=0,\n", + " write_graph=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "callbacks = [callback_early_stopping,\n", + " callback_checkpoint,\n", + " callback_tensorboard]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Checkpoint\n", + "\n", + "You can reload the last saved checkpoint so you don't have to train the model every time you want to use it." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " model_train.load_weights(path_checkpoint)\n", + "except Exception as error:\n", + " print(\"Error trying to load checkpoint.\")\n", + " print(error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train the Model\n", + "\n", + "We wrap the data in named dicts so we are sure the data is assigned correctly to the inputs and outputs of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "x_data = \\\n", + "{\n", + " 'encoder_input': encoder_input_data,\n", + " 'decoder_input': decoder_input_data\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "y_data = \\\n", + "{\n", + " 'decoder_output': decoder_output_data\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want a validation-set of 10000 sequences but Keras needs this number as a fraction." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0050792360828931325" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "validation_split = 10000 / len(encoder_input_data)\n", + "validation_split" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can train the model. One epoch of training took about 1 hour on a GTX 1070 GPU. You probably need to run 10 epochs or more during training. After 10 epochs the loss was about 1.10 on the training-set and about 1.15 on the validation-set.\n", + "\n", + "The batch-size was chosen to keep the GPU running at nearly 100% while being within the memory limits of 8GB for this GPU." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "model_train.fit(x=x_data,\n", + " y=y_data,\n", + " batch_size=384,\n", + " epochs=10,\n", + " validation_split=validation_split,\n", + " callbacks=callbacks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Translate Texts\n", + "\n", + "This function translates a text from the source-language to the destination-language and optionally prints a true translation." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "def translate(input_text, true_output_text=None):\n", + " \"\"\"Translate a single text-string.\"\"\"\n", + "\n", + " # Convert the input-text to integer-tokens.\n", + " # Note the sequence of tokens has to be reversed.\n", + " # Padding is probably not necessary.\n", + " input_tokens = tokenizer_src.text_to_tokens(text=input_text,\n", + " reverse=True,\n", + " padding=True)\n", + " \n", + " # Get the output of the encoder's GRU which will be\n", + " # used as the initial state in the decoder's GRU.\n", + " # This could also have been the encoder's final state\n", + " # but that is really only necessary if the encoder\n", + " # and decoder use the LSTM instead of GRU because\n", + " # the LSTM has two internal states.\n", + " initial_state = model_encoder.predict(input_tokens)\n", + "\n", + " # Max number of tokens / words in the output sequence.\n", + " max_tokens = tokenizer_dest.max_tokens\n", + "\n", + " # Pre-allocate the 2-dim array used as input to the decoder.\n", + " # This holds just a single sequence of integer-tokens,\n", + " # but the decoder-model expects a batch of sequences.\n", + " shape = (1, max_tokens)\n", + " decoder_input_data = np.zeros(shape=shape, dtype=np.int)\n", + "\n", + " # The first input-token is the special start-token for 'ssss '.\n", + " token_int = token_start\n", + "\n", + " # Initialize an empty output-text.\n", + " output_text = ''\n", + "\n", + " # Initialize the number of tokens we have processed.\n", + " count_tokens = 0\n", + "\n", + " # While we haven't sampled the special end-token for ' eeee'\n", + " # and we haven't processed the max number of tokens.\n", + " while token_int != token_end and count_tokens < max_tokens:\n", + " # Update the input-sequence to the decoder\n", + " # with the last token that was sampled.\n", + " # In the first iteration this will set the\n", + " # first element to the start-token.\n", + " decoder_input_data[0, count_tokens] = token_int\n", + "\n", + " # Wrap the input-data in a dict for clarity and safety,\n", + " # so we are sure we input the data in the right order.\n", + " x_data = \\\n", + " {\n", + " 'decoder_initial_state': initial_state,\n", + " 'decoder_input': decoder_input_data\n", + " }\n", + "\n", + " # Note that we input the entire sequence of tokens\n", + " # to the decoder. This wastes a lot of computation\n", + " # because we are only interested in the last input\n", + " # and output. We could modify the code to return\n", + " # the GRU-states when calling predict() and then\n", + " # feeding these GRU-states as well the next time\n", + " # we call predict(), but it would make the code\n", + " # much more complicated.\n", + "\n", + " # Input this data to the decoder and get the predicted output.\n", + " decoder_output = model_decoder.predict(x_data)\n", + "\n", + " # Get the last predicted token as a one-hot encoded array.\n", + " token_onehot = decoder_output[0, count_tokens, :]\n", + " \n", + " # Convert to an integer-token.\n", + " token_int = np.argmax(token_onehot)\n", + "\n", + " # Lookup the word corresponding to this integer-token.\n", + " sampled_word = tokenizer_dest.token_to_word(token_int)\n", + "\n", + " # Append the word to the output-text.\n", + " output_text += \" \" + sampled_word\n", + "\n", + " # Increment the token-counter.\n", + " count_tokens += 1\n", + "\n", + " # Sequence of tokens output by the decoder.\n", + " output_tokens = decoder_input_data[0]\n", + " \n", + " # Print the input-text.\n", + " print(\"Input text:\")\n", + " print(input_text)\n", + " print()\n", + "\n", + " # Print the translated output-text.\n", + " print(\"Translated text:\")\n", + " print(output_text)\n", + " print()\n", + "\n", + " # Optionally print the true translated text.\n", + " if true_output_text is not None:\n", + " print(\"True output text:\")\n", + " print(true_output_text)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examples\n", + "\n", + "Translate a text from the training-data. This translation is quite good. Note how it is not identical to the translation from the training-data, but the actual meaning is similar." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "De har udtrykt ønske om en debat om dette emne i løbet af mødeperioden.\n", + "\n", + "Translated text:\n", + " you have expressed a desire to speak on this subject during the debate eeee\n", + "\n", + "True output text:\n", + "ssss You have requested a debate on this subject in the course of the next few days, during this part-session. eeee\n", + "\n" + ] + } + ], + "source": [ + "idx = 3\n", + "translate(input_text=data_src[idx],\n", + " true_output_text=data_dest[idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is another example which is also a reasonable translation, although it has incorrectly translated the natural disasters. Note \"countries of the European Union\" has instead been translated as \"member states\" which are synonyms in this context." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "I mellemtiden ønsker jeg - som også en del kolleger har anmodet om - at vi iagttager et minuts stilhed til minde om ofrene for bl.a. stormene i de medlemslande, der blev ramt.\n", + "\n", + "Translated text:\n", + " in the meantime i have a part of the house who have also asked for a member of the victims of the terrible victims of the tragedy of which we were affected eeee\n", + "\n", + "True output text:\n", + "ssss In the meantime, I should like to observe a minute' s silence, as a number of Members have requested, on behalf of all the victims concerned, particularly those of the terrible storms, in the various countries of the European Union. eeee\n", + "\n" + ] + } + ], + "source": [ + "idx = 4\n", + "translate(input_text=data_src[idx],\n", + " true_output_text=data_dest[idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we join two texts from the training-set. The model first sends this combined text through the encoder, which produces a \"thought-vector\" that seems to summarize both texts reasonably well so the decoder can produce a reasonable translation." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "De har udtrykt ønske om en debat om dette emne i løbet af mødeperioden.I mellemtiden ønsker jeg - som også en del kolleger har anmodet om - at vi iagttager et minuts stilhed til minde om ofrene for bl.a. stormene i de medlemslande, der blev ramt.\n", + "\n", + "Translated text:\n", + " you have a part of this debate in which i have had a request to speak during the debate in the portuguese presidency as a member of the victims of the tragedy of which we have been victims of this morning eeee\n", + "\n", + "True output text:\n", + "ssss You have requested a debate on this subject in the course of the next few days, during this part-session. eeeessss In the meantime, I should like to observe a minute' s silence, as a number of Members have requested, on behalf of all the victims concerned, particularly those of the terrible storms, in the various countries of the European Union. eeee\n", + "\n" + ] + } + ], + "source": [ + "idx = 3\n", + "translate(input_text=data_src[idx] + data_src[idx+1],\n", + " true_output_text=data_dest[idx] + data_dest[idx+1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we reverse the order of these two texts then the meaning is not quite so clear for the latter text." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "I mellemtiden ønsker jeg - som også en del kolleger har anmodet om - at vi iagttager et minuts stilhed til minde om ofrene for bl.a. stormene i de medlemslande, der blev ramt.De har udtrykt ønske om en debat om dette emne i løbet af mødeperioden.\n", + "\n", + "Translated text:\n", + " in the meantime i have also asked a member of the members who have asked for a debate on the victims of this type of attack and that you have expressed a part of this debate as part of the spanish presidency eeee\n", + "\n", + "True output text:\n", + "ssss In the meantime, I should like to observe a minute' s silence, as a number of Members have requested, on behalf of all the victims concerned, particularly those of the terrible storms, in the various countries of the European Union. eeeessss You have requested a debate on this subject in the course of the next few days, during this part-session. eeee\n", + "\n" + ] + } + ], + "source": [ + "idx = 3\n", + "translate(input_text=data_src[idx+1] + data_src[idx],\n", + " true_output_text=data_dest[idx+1] + data_dest[idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is an example I made up. It is a quite broken translation." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "der var engang et land der hed Danmark\n", + "\n", + "Translated text:\n", + " there were a member of the european commission eeee\n", + "\n", + "True output text:\n", + "Once there was a country named Denmark\n", + "\n" + ] + } + ], + "source": [ + "translate(input_text=\"der var engang et land der hed Danmark\",\n", + " true_output_text='Once there was a country named Denmark')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is another example I made up. This is a better translation even though it is perhaps a more complicated text." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "Idag kan man læse i avisen at Danmark er blevet fornuftigt\n", + "\n", + "Translated text:\n", + " read you read that the netherlands has been sensible eeee\n", + "\n", + "True output text:\n", + "Today you can read in the newspaper that Denmark has become sensible.\n", + "\n" + ] + } + ], + "source": [ + "translate(input_text=\"Idag kan man læse i avisen at Danmark er blevet fornuftigt\",\n", + " true_output_text=\"Today you can read in the newspaper that Denmark has become sensible.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a text from a Danish song. It doesn't even make much sense in Danish. However the translation is probably so broken because several of the words are not in the vocabulary." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "Hvem spæner ud af en butik og tygger de stærkeste bolcher?\n", + "\n", + "Translated text:\n", + " who is a of the and the eeee\n", + "\n", + "True output text:\n", + "Who runs out of a shop and chews the strongest bon-bons?\n", + "\n" + ] + } + ], + "source": [ + "translate(input_text=\"Hvem spæner ud af en butik og tygger de stærkeste bolcher?\",\n", + " true_output_text=\"Who runs out of a shop and chews the strongest bon-bons?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed the basic idea of using two Recurrent Neural Networks in a so-called encoder/decoder model to do Machine Translation of human languages. It was demonstrated on the very large Europarl data-set from the European Union.\n", + "\n", + "The model could produce reasonable translations for some texts but not for others. It is possible that a better architecture for the neural network and more training epochs could improve performance. There are also more advanced models that are known to improve quality of the translations.\n", + "\n", + "However, it is important to note that these models do not really understand human language. The models have no knowledge of the actual meaning of the words. The models are merely very advanced function approximators that can map between sequences of integer-tokens." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Train for more than 10 epochs. Does it improve the translations?\n", + "* Increase the size of the vocabulary. Does it improve the translations? Would it make sense to have different sizes for the vocabularies of the source and destination languages?\n", + "* Find another data-set and use it together with Europarl.\n", + "* Change the architectures of the neural network, for example change the state-size for the GRU layers, the number of GRU layers, the embedding-size, etc. Does it improve the translations?\n", + "* Use hyper-parameter optimization from Tutorial #19 to automatically find the best hyper-parameters.\n", + "* When translating texts, instead of using `np.argmax()` to sample the next integer-token, could you sample the decoder's output as if it was a probability distribution instead? Note that the decoder's output is not softmax-limited so you have to do that first to turn it into a probability-distribution.\n", + "* Can you generate multiple sequences by doing this sampling? Can you find a way to select the best of these different sequences?\n", + "* Disable the reversal of words for the source-language. Does it improve the translations?\n", + "* What is a Bi-Directional GRU and can you use it here?\n", + "* We use the **output** of the encoder's GRU as the initial state of the decoder's GRU. The research literature often uses an LSTM instead of the GRU, so they used the encoder's **state** instead of its output as the initial state of the decoder. Can you rewrite this code to use the encoder's state as the decoder's initial state? Is there a reason to do this, or is the encoder's output sufficient to use as the decoder's initial state?\n", + "* Is it possible to connect multiple encoders and decoders in a single neural network, so that you can train it on different languages and allow for direct translation e.g. from Danish to Polish, German and French?\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "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:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "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." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/22_Image_Captioning.ipynb b/22_Image_Captioning.ipynb new file mode 100644 index 0000000..d5782f5 --- /dev/null +++ b/22_Image_Captioning.ipynb @@ -0,0 +1,2353 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #22\n", + "# Image Captioning\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Tutorial #21 on Machine Translation showed how to translate text from one human language to another. It worked by having two Recurrent Neural Networks (RNN), the first called an encoder and the second called a decoder. The first RNN encodes the source-text as a single vector of numbers and the second RNN decodes this vector into the destination-text. The intermediate vector between the encoder and decoder is a kind of summary of the source-text, which is sometimes called a \"thought-vector\". The reason for using this intermediate summary-vector is to understand the whole source-text before it is being translated. This also allows for the source- and destination-texts to have different lengths.\n", + "\n", + "In this tutorial we will replace the encoder with an image-recognition model similar to Transfer Learning and Fine-Tuning in Tutorials #08 and #10. The image-model recognizes what the image contains and outputs that as a vector of numbers - the \"thought-vector\" or summary-vector, which is then input to a Recurrent Neural Network that decodes this vector into text.\n", + "\n", + "This is a somewhat advanced tutorial and you should be familiar with TensorFlow, Keras, Transfer Learning and Natural Language Processing, see Tutorials #01, #03-C, #08, #10, #20, and #21." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flowchart\n", + "\n", + "We will use the VGG16 model that has been pre-trained for classifying images. But instead of using the last classification layer, we will redirect the output of the previous layer. This gives us a vector with 4096 elements that summarizes the image-contents - similar to how a \"thought-vector\" summarized the contents of an input-text in Tutorial #21 on language translation. We will use this vector as the initial state of the Gated Recurrent Units (GRU). However, the internal state-size of the GRU is only 512, so we need an intermediate fully-connected (dense) layer to map the vector with 4096 elements down to a vector with only 512 elements.\n", + "\n", + "The decoder then uses this initial-state together with a start-marker \"ssss\" to begin producing output words. In the first iteration it will hopefully output the word \"big\". Then we input this word into the decoder and hopefully we get the word \"brown\" out, and so on. Finally we have generated the text \"big brown bear sitting eeee\" where \"eeee\" marks the end of the text.\n", + "\n", + "The flowchart of the algorithm is roughly:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Flowchart](images/22_image_captioning_flowchart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import sys\n", + "import os\n", + "from PIL import Image\n", + "from cache import cache" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to import several things from Keras." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import backend as K\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n", + "from tensorflow.keras.applications import VGG16\n", + "from tensorflow.keras.optimizers import RMSprop\n", + "from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard\n", + "from tensorflow.keras.preprocessing.text import Tokenizer\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 (Anaconda) and package versions:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.2.4-tf'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.keras.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data\n", + "\n", + "We will use the COCO data-set which contains many images with text-captions.\n", + "\n", + "http://cocodataset.org/" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import coco" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can change the data-directory if you want to save the data-files somewhere else." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# coco.set_data_dir(\"data/coco/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Automatically download and extract the data-files if you don't have them already.\n", + "\n", + "**WARNING! These data-files are VERY large! The file for the training-data is 19 GB and the file for the validation-data is 816 MB! **" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading http://images.cocodataset.org/zips/train2017.zip\n", + "Data has apparently already been downloaded and unpacked.\n", + "Downloading http://images.cocodataset.org/zips/val2017.zip\n", + "Data has apparently already been downloaded and unpacked.\n", + "Downloading http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n", + "Data has apparently already been downloaded and unpacked.\n" + ] + } + ], + "source": [ + "coco.maybe_download_and_extract()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the filenames and captions for the images in the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "- Data loaded from cache-file: data/coco/records_train.pkl\n" + ] + } + ], + "source": [ + "_, filenames_train, captions_train = coco.load_records(train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Number of images in the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "118287" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_images_train = len(filenames_train)\n", + "num_images_train" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the filenames and captions for the images in the validation-set." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "- Data loaded from cache-file: data/coco/records_val.pkl\n" + ] + } + ], + "source": [ + "_, filenames_val, captions_val = coco.load_records(train=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-Functions for Loading and Showing Images\n", + "\n", + "This is a helper-function for loading and resizing an image." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def load_image(path, size=None):\n", + " \"\"\"\n", + " Load the image from the given file-path and resize it\n", + " to the given size if not None.\n", + " \"\"\"\n", + "\n", + " # Load the image using PIL.\n", + " img = Image.open(path)\n", + "\n", + " # Resize image if desired.\n", + " if not size is None:\n", + " img = img.resize(size=size, resample=Image.LANCZOS)\n", + "\n", + " # Convert image to numpy array.\n", + " img = np.array(img)\n", + "\n", + " # Scale image-pixels so they fall between 0.0 and 1.0\n", + " img = img / 255.0\n", + "\n", + " # Convert 2-dim gray-scale array to 3-dim RGB array.\n", + " if (len(img.shape) == 2):\n", + " img = np.repeat(img[:, :, np.newaxis], 3, axis=2)\n", + "\n", + " return img" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a helper-function for showing an image from the data-set along with its captions." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def show_image(idx, train):\n", + " \"\"\"\n", + " Load and plot an image from the training- or validation-set\n", + " with the given index.\n", + " \"\"\"\n", + "\n", + " if train:\n", + " # Use an image from the training-set.\n", + " dir = coco.train_dir\n", + " filename = filenames_train[idx]\n", + " captions = captions_train[idx]\n", + " else:\n", + " # Use an image from the validation-set.\n", + " dir = coco.val_dir\n", + " filename = filenames_val[idx]\n", + " captions = captions_val[idx]\n", + "\n", + " # Path for the image-file.\n", + " path = os.path.join(dir, filename)\n", + "\n", + " # Print the captions for this image.\n", + " for caption in captions:\n", + " print(caption)\n", + " \n", + " # Load the image and plot it.\n", + " img = load_image(path)\n", + " plt.imshow(img)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example Image\n", + "\n", + "Show an example image and captions from the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A giraffe eating food from the top of the tree.\n", + "A giraffe standing up nearby a tree \n", + "A giraffe mother with its baby in the forest.\n", + "Two giraffes standing in a tree filled area.\n", + "A giraffe standing next to a forest filled with trees.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "show_image(idx=1, train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre-Trained Image Model (VGG16)\n", + "\n", + "The following creates an instance of the VGG16 model using the Keras API. This automatically downloads the required files if you don't have them already.\n", + "\n", + "The VGG16 model was pre-trained on the ImageNet data-set for classifying images. The VGG16 model contains a convolutional part and a fully-connected (or dense) part which is used for the image classification.\n", + "\n", + "If `include_top=True` then the whole VGG16 model is downloaded which is about 528 MB. If `include_top=False` then only the convolutional part of the VGG16 model is downloaded which is just 57 MB.\n", + "\n", + "We will use some of the fully-connected layers in this pre-trained model, so we have to download the full model, but if you have a slow internet connection, then you can try and modify the code below to use the smaller pre-trained model without the classification layers.\n", + "\n", + "Tutorials #08 and #10 explain more details about Transfer Learning." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "image_model = VGG16(include_top=True, weights='imagenet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print a list of all the layers in the VGG16 model." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"vgg16\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_1 (InputLayer) [(None, 224, 224, 3)] 0 \n", + "_________________________________________________________________\n", + "block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 \n", + "_________________________________________________________________\n", + "block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 \n", + "_________________________________________________________________\n", + "block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 \n", + "_________________________________________________________________\n", + "block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 \n", + "_________________________________________________________________\n", + "block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 \n", + "_________________________________________________________________\n", + "block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 \n", + "_________________________________________________________________\n", + "block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 \n", + "_________________________________________________________________\n", + "block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 \n", + "_________________________________________________________________\n", + "block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 \n", + "_________________________________________________________________\n", + "block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 \n", + "_________________________________________________________________\n", + "block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 \n", + "_________________________________________________________________\n", + "block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 \n", + "_________________________________________________________________\n", + "block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 \n", + "_________________________________________________________________\n", + "block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 \n", + "_________________________________________________________________\n", + "block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 \n", + "_________________________________________________________________\n", + "block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 \n", + "_________________________________________________________________\n", + "block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 \n", + "_________________________________________________________________\n", + "block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 \n", + "_________________________________________________________________\n", + "flatten (Flatten) (None, 25088) 0 \n", + "_________________________________________________________________\n", + "fc1 (Dense) (None, 4096) 102764544 \n", + "_________________________________________________________________\n", + "fc2 (Dense) (None, 4096) 16781312 \n", + "_________________________________________________________________\n", + "predictions (Dense) (None, 1000) 4097000 \n", + "=================================================================\n", + "Total params: 138,357,544\n", + "Trainable params: 138,357,544\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "image_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the output of the layer prior to the final classification-layer which is named `fc2`. This is a fully-connected (or dense) layer." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "transfer_layer = image_model.get_layer('fc2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We call it the \"transfer-layer\" because we will transfer its output to another model that creates the image captions.\n", + "\n", + "To do this, first we need to create a new model which has the same input as the original VGG16 model but outputs the transfer-values from the `fc2` layer." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "image_model_transfer = Model(inputs=image_model.input,\n", + " outputs=transfer_layer.output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model expects input images to be of this size:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(224, 224)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img_size = K.int_shape(image_model.input)[1:3]\n", + "img_size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each input image, the new model will output a vector of transfer-values with this length:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4096" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transfer_values_size = K.int_shape(transfer_layer.output)[1]\n", + "transfer_values_size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Process All Images\n", + "\n", + "We now make functions for processing all images in the data-set using the pre-trained image-model and saving the transfer-values in a cache-file so they can be reloaded quickly.\n", + "\n", + "We effectively create a new data-set of the transfer-values. This is because it takes a long time to process an image in the VGG16 model. We will not be changing all the parameters of the VGG16 model, so every time it processes an image, it gives the exact same result. We need the transfer-values to train the image-captioning model for many epochs, so we save a lot of time by calculating the transfer-values once and saving them in a cache-file.\n", + "\n", + "This is a helper-function for printing the progress." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def print_progress(count, max_count):\n", + " # Percentage completion.\n", + " pct_complete = count / max_count\n", + "\n", + " # Status-message. Note the \\r which means the line should\n", + " # overwrite itself.\n", + " msg = \"\\r- Progress: {0:.1%}\".format(pct_complete)\n", + "\n", + " # Print it.\n", + " sys.stdout.write(msg)\n", + " sys.stdout.flush()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the function for processing the given files using the VGG16-model and returning their transfer-values." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def process_images(data_dir, filenames, batch_size=32):\n", + " \"\"\"\n", + " Process all the given files in the given data_dir using the\n", + " pre-trained image-model and return their transfer-values.\n", + " \n", + " Note that we process the images in batches to save\n", + " memory and improve efficiency on the GPU.\n", + " \"\"\"\n", + " \n", + " # Number of images to process.\n", + " num_images = len(filenames)\n", + "\n", + " # Pre-allocate input-batch-array for images.\n", + " shape = (batch_size,) + img_size + (3,)\n", + " image_batch = np.zeros(shape=shape, dtype=np.float16)\n", + "\n", + " # Pre-allocate output-array for transfer-values.\n", + " # Note that we use 16-bit floating-points to save memory.\n", + " shape = (num_images, transfer_values_size)\n", + " transfer_values = np.zeros(shape=shape, dtype=np.float16)\n", + "\n", + " # Initialize index into the filenames.\n", + " start_index = 0\n", + "\n", + " # Process batches of image-files.\n", + " while start_index < num_images:\n", + " # Print the percentage-progress.\n", + " print_progress(count=start_index, max_count=num_images)\n", + "\n", + " # End-index for this batch.\n", + " end_index = start_index + batch_size\n", + "\n", + " # Ensure end-index is within bounds.\n", + " if end_index > num_images:\n", + " end_index = num_images\n", + "\n", + " # The last batch may have a different batch-size.\n", + " current_batch_size = end_index - start_index\n", + "\n", + " # Load all the images in the batch.\n", + " for i, filename in enumerate(filenames[start_index:end_index]):\n", + " # Path for the image-file.\n", + " path = os.path.join(data_dir, filename)\n", + "\n", + " # Load and resize the image.\n", + " # This returns the image as a numpy-array.\n", + " img = load_image(path, size=img_size)\n", + "\n", + " # Save the image for later use.\n", + " image_batch[i] = img\n", + "\n", + " # Use the pre-trained image-model to process the image.\n", + " # Note that the last batch may have a different size,\n", + " # so we only use the relevant images.\n", + " transfer_values_batch = \\\n", + " image_model_transfer.predict(image_batch[0:current_batch_size])\n", + "\n", + " # Save the transfer-values in the pre-allocated array.\n", + " transfer_values[start_index:end_index] = \\\n", + " transfer_values_batch[0:current_batch_size]\n", + "\n", + " # Increase the index for the next loop-iteration.\n", + " start_index = end_index\n", + "\n", + " # Print newline.\n", + " print()\n", + "\n", + " return transfer_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper-function for processing all images in the training-set. This saves the transfer-values in a cache-file for fast reloading." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def process_images_train():\n", + " print(\"Processing {0} images in training-set ...\".format(len(filenames_train)))\n", + "\n", + " # Path for the cache-file.\n", + " cache_path = os.path.join(coco.data_dir,\n", + " \"transfer_values_train.pkl\")\n", + "\n", + " # If the cache-file already exists then reload it,\n", + " # otherwise process all images and save their transfer-values\n", + " # to the cache-file so it can be reloaded quickly.\n", + " transfer_values = cache(cache_path=cache_path,\n", + " fn=process_images,\n", + " data_dir=coco.train_dir,\n", + " filenames=filenames_train)\n", + "\n", + " return transfer_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper-function for processing all images in the validation-set." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def process_images_val():\n", + " print(\"Processing {0} images in validation-set ...\".format(len(filenames_val)))\n", + "\n", + " # Path for the cache-file.\n", + " cache_path = os.path.join(coco.data_dir, \"transfer_values_val.pkl\")\n", + "\n", + " # If the cache-file already exists then reload it,\n", + " # otherwise process all images and save their transfer-values\n", + " # to the cache-file so it can be reloaded quickly.\n", + " transfer_values = cache(cache_path=cache_path,\n", + " fn=process_images,\n", + " data_dir=coco.val_dir,\n", + " filenames=filenames_val)\n", + "\n", + " return transfer_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Process all images in the training-set and save the transfer-values to a cache-file. This took about 30 minutes to process on a GTX 1070 GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 118287 images in training-set ...\n", + "- Data loaded from cache-file: data/coco/transfer_values_train.pkl\n", + "dtype: float16\n", + "shape: (118287, 4096)\n", + "CPU times: user 187 ms, sys: 621 ms, total: 807 ms\n", + "Wall time: 806 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "transfer_values_train = process_images_train()\n", + "print(\"dtype:\", transfer_values_train.dtype)\n", + "print(\"shape:\", transfer_values_train.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Process all images in the validation-set and save the transfer-values to a cache-file. This took about 90 seconds to process on a GTX 1070 GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 5000 images in validation-set ...\n", + "- Data loaded from cache-file: data/coco/transfer_values_val.pkl\n", + "dtype: float16\n", + "shape: (5000, 4096)\n", + "CPU times: user 7.16 ms, sys: 32.7 ms, total: 39.8 ms\n", + "Wall time: 37.8 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "transfer_values_val = process_images_val()\n", + "print(\"dtype:\", transfer_values_val.dtype)\n", + "print(\"shape:\", transfer_values_val.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tokenizer\n", + "\n", + "Neural Networks cannot work directly on text-data. We use a two-step process to convert text into numbers that can be used in a neural network. The first step is to convert text-words into so-called integer-tokens. The second step is to convert integer-tokens into vectors of floating-point numbers using a so-called embedding-layer. See Tutorial #20 for a more detailed explanation.\n", + "\n", + "Before we can start processing the text, we first need to mark the beginning and end of each text-sequence with unique words that most likely aren't present in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "mark_start = 'ssss '\n", + "mark_end = ' eeee'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This helper-function wraps all text-strings in the above markers. Note that the captions are a list of list, so we need a nested for-loop to process it. This can be done using so-called list-comprehension in Python." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def mark_captions(captions_listlist):\n", + " captions_marked = [[mark_start + caption + mark_end\n", + " for caption in captions_list]\n", + " for captions_list in captions_listlist]\n", + " \n", + " return captions_marked" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now process all the captions in the training-set and show an example." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ssss Closeup of bins of food that include broccoli and bread. eeee',\n", + " 'ssss A meal is presented in brightly colored plastic trays. eeee',\n", + " 'ssss there are containers filled with different kinds of foods eeee',\n", + " 'ssss Colorful dishes holding meat, vegetables, fruit, and bread. eeee',\n", + " 'ssss A bunch of trays that have different food. eeee']" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "captions_train_marked = mark_captions(captions_train)\n", + "captions_train_marked[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is how the captions look without the start- and end-markers." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Closeup of bins of food that include broccoli and bread.',\n", + " 'A meal is presented in brightly colored plastic trays.',\n", + " 'there are containers filled with different kinds of foods',\n", + " 'Colorful dishes holding meat, vegetables, fruit, and bread.',\n", + " 'A bunch of trays that have different food.']" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "captions_train[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This helper-function converts a list-of-list to a flattened list of captions." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def flatten(captions_listlist):\n", + " captions_list = [caption\n", + " for captions_list in captions_listlist\n", + " for caption in captions_list]\n", + " \n", + " return captions_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now use the function to convert all the marked captions from the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "captions_train_flat = flatten(captions_train_marked)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set the maximum number of words in our vocabulary. This means that we will only use e.g. the 10000 most frequent words in the captions from the training-data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "num_words = 10000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need a few more functions than provided by Keras' Tokenizer-class so we wrap it." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "class TokenizerWrap(Tokenizer):\n", + " \"\"\"Wrap the Tokenizer-class from Keras with more functionality.\"\"\"\n", + " \n", + " def __init__(self, texts, num_words=None):\n", + " \"\"\"\n", + " :param texts: List of strings with the data-set.\n", + " :param num_words: Max number of words to use.\n", + " \"\"\"\n", + "\n", + " Tokenizer.__init__(self, num_words=num_words)\n", + "\n", + " # Create the vocabulary from the texts.\n", + " self.fit_on_texts(texts)\n", + "\n", + " # Create inverse lookup from integer-tokens to words.\n", + " self.index_to_word = dict(zip(self.word_index.values(),\n", + " self.word_index.keys()))\n", + "\n", + " def token_to_word(self, token):\n", + " \"\"\"Lookup a single word from an integer-token.\"\"\"\n", + "\n", + " word = \" \" if token == 0 else self.index_to_word[token]\n", + " return word \n", + "\n", + " def tokens_to_string(self, tokens):\n", + " \"\"\"Convert a list of integer-tokens to a string.\"\"\"\n", + "\n", + " # Create a list of the individual words.\n", + " words = [self.index_to_word[token]\n", + " for token in tokens\n", + " if token != 0]\n", + " \n", + " # Concatenate the words to a single string\n", + " # with space between all the words.\n", + " text = \" \".join(words)\n", + "\n", + " return text\n", + " \n", + " def captions_to_tokens(self, captions_listlist):\n", + " \"\"\"\n", + " Convert a list-of-list with text-captions to\n", + " a list-of-list of integer-tokens.\n", + " \"\"\"\n", + " \n", + " # Note that text_to_sequences() takes a list of texts.\n", + " tokens = [self.texts_to_sequences(captions_list)\n", + " for captions_list in captions_listlist]\n", + " \n", + " return tokens" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create a tokenizer using all the captions in the training-data. Note that we use the flattened list of captions to create the tokenizer because it cannot take a list-of-lists." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 8.43 s, sys: 20.6 ms, total: 8.45 s\n", + "Wall time: 8.45 s\n" + ] + } + ], + "source": [ + "%%time\n", + "tokenizer = TokenizerWrap(texts=captions_train_flat,\n", + " num_words=num_words)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the integer-token for the start-marker (the word \"ssss\"). We will need this further below." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "token_start = tokenizer.word_index[mark_start.strip()]\n", + "token_start" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the integer-token for the end-marker (the word \"eeee\")." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "token_end = tokenizer.word_index[mark_end.strip()]\n", + "token_end" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert all the captions from the training-set to sequences of integer-tokens. We get a list-of-list as a result." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 7.8 s, sys: 27 ms, total: 7.83 s\n", + "Wall time: 7.83 s\n" + ] + } + ], + "source": [ + "%%time\n", + "tokens_train = tokenizer.captions_to_tokens(captions_train_marked)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example of the integer-tokens for the captions of the first image in the training-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[[2, 841, 5, 2864, 5, 61, 26, 1984, 238, 9, 433, 3],\n", + " [2, 1, 429, 10, 3310, 7, 1025, 390, 501, 1110, 3],\n", + " [2, 63, 19, 993, 143, 8, 190, 958, 5, 743, 3],\n", + " [2, 299, 725, 25, 343, 208, 264, 9, 433, 3],\n", + " [2, 1, 170, 5, 1110, 26, 446, 190, 61, 3]]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokens_train[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the corresponding text-captions:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['ssss Closeup of bins of food that include broccoli and bread. eeee',\n", + " 'ssss A meal is presented in brightly colored plastic trays. eeee',\n", + " 'ssss there are containers filled with different kinds of foods eeee',\n", + " 'ssss Colorful dishes holding meat, vegetables, fruit, and bread. eeee',\n", + " 'ssss A bunch of trays that have different food. eeee']" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "captions_train_marked[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Generator\n", + "\n", + "Each image in the training-set has at least 5 captions describing the contents of the image. The neural network will be trained with batches of transfer-values for the images and sequences of integer-tokens for the captions. If we were to have matching numpy arrays for the training-set, we would either have to only use a single caption for each image and ignore the rest of this valuable data, or we would have to repeat the image transfer-values for each of the captions, which would waste a lot of memory.\n", + "\n", + "A better solution is to create a custom data-generator for Keras that will create a batch of data with randomly selected transfer-values and token-sequences.\n", + "\n", + "This helper-function returns a list of random token-sequences for the images with the given indices in the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def get_random_caption_tokens(idx):\n", + " \"\"\"\n", + " Given a list of indices for images in the training-set,\n", + " select a token-sequence for a random caption,\n", + " and return a list of all these token-sequences.\n", + " \"\"\"\n", + " \n", + " # Initialize an empty list for the results.\n", + " result = []\n", + "\n", + " # For each of the indices.\n", + " for i in idx:\n", + " # The index i points to an image in the training-set.\n", + " # Each image in the training-set has at least 5 captions\n", + " # which have been converted to tokens in tokens_train.\n", + " # We want to select one of these token-sequences at random.\n", + "\n", + " # Get a random index for a token-sequence.\n", + " j = np.random.choice(len(tokens_train[i]))\n", + "\n", + " # Get the j'th token-sequence for image i.\n", + " tokens = tokens_train[i][j]\n", + "\n", + " # Add this token-sequence to the list of results.\n", + " result.append(tokens)\n", + "\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This generator function creates random batches of training-data for use in training the neural network." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "def batch_generator(batch_size):\n", + " \"\"\"\n", + " Generator function for creating random batches of training-data.\n", + " \n", + " Note that it selects the data completely randomly for each\n", + " batch, corresponding to sampling of the training-set with\n", + " replacement. This means it is possible to sample the same\n", + " data multiple times within a single epoch - and it is also\n", + " possible that some data is not sampled at all within an epoch.\n", + " However, all the data should be unique within a single batch.\n", + " \"\"\"\n", + "\n", + " # Infinite loop.\n", + " while True:\n", + " # Get a list of random indices for images in the training-set.\n", + " idx = np.random.randint(num_images_train,\n", + " size=batch_size)\n", + " \n", + " # Get the pre-computed transfer-values for those images.\n", + " # These are the outputs of the pre-trained image-model.\n", + " transfer_values = transfer_values_train[idx]\n", + "\n", + " # For each of the randomly chosen images there are\n", + " # at least 5 captions describing the contents of the image.\n", + " # Select one of those captions at random and get the\n", + " # associated sequence of integer-tokens.\n", + " tokens = get_random_caption_tokens(idx)\n", + "\n", + " # Count the number of tokens in all these token-sequences.\n", + " num_tokens = [len(t) for t in tokens]\n", + " \n", + " # Max number of tokens.\n", + " max_tokens = np.max(num_tokens)\n", + " \n", + " # Pad all the other token-sequences with zeros\n", + " # so they all have the same length and can be\n", + " # input to the neural network as a numpy array.\n", + " tokens_padded = pad_sequences(tokens,\n", + " maxlen=max_tokens,\n", + " padding='post',\n", + " truncating='post')\n", + " \n", + " # Further prepare the token-sequences.\n", + " # The decoder-part of the neural network\n", + " # will try to map the token-sequences to\n", + " # themselves shifted one time-step.\n", + " decoder_input_data = tokens_padded[:, 0:-1]\n", + " decoder_output_data = tokens_padded[:, 1:]\n", + "\n", + " # Dict for the input-data. Because we have\n", + " # several inputs, we use a named dict to\n", + " # ensure that the data is assigned correctly.\n", + " x_data = \\\n", + " {\n", + " 'decoder_input': decoder_input_data,\n", + " 'transfer_values_input': transfer_values\n", + " }\n", + "\n", + " # Dict for the output-data.\n", + " y_data = \\\n", + " {\n", + " 'decoder_output': decoder_output_data\n", + " }\n", + " \n", + " yield (x_data, y_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set the batch-size used during training. This is set very high so the GPU can be used maximally - but this also requires a lot of RAM on the GPU. You may have to lower this number if the training runs out of memory." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 384" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create an instance of the data-generator." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "generator = batch_generator(batch_size=batch_size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test the data-generator by creating a batch of data." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "batch = next(generator)\n", + "batch_x = batch[0]\n", + "batch_y = batch[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example of the transfer-values for the first image in the batch." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0. , 1.483, ..., 0. , 0. , 0.813], dtype=float16)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "batch_x['transfer_values_input'][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example of the token-sequence for the first image in the batch. This is the input to the decoder-part of the neural network." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2, 1, 21, 80, 13, 34, 315, 1, 69, 20, 12,\n", + " 1, 1083, 3, 0, 0, 0, 0, 0, 0, 0],\n", + " dtype=int32)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "batch_x['decoder_input'][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the token-sequence for the output of the decoder. Note how it is the same as the sequence above, except it is shifted one time-step." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 21, 80, 13, 34, 315, 1, 69, 20, 12, 1,\n", + " 1083, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " dtype=int32)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "batch_y['decoder_output'][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps Per Epoch\n", + "\n", + "One epoch is a complete processing of the training-set. We would like to process each image and caption pair only once per epoch. However, because each batch is chosen completely at random in the above batch-generator, it is possible that an image occurs in multiple batches within a single epoch, and it is possible that some images may not occur in any batch at all within a single epoch.\n", + "\n", + "Nevertheless, we still use the concept of an 'epoch' to measure approximately how many iterations of the training-data we have processed. But the data-generator will generate batches for eternity, so we need to manually calculate the approximate number of batches required per epoch.\n", + "\n", + "This is the number of captions for each image in the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "num_captions_train = [len(captions) for captions in captions_train]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the total number of captions in the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "total_num_captions_train = np.sum(num_captions_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the approximate number of batches required per epoch, if we want to process each caption and image pair once per epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1541" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "steps_per_epoch = int(total_num_captions_train / batch_size)\n", + "steps_per_epoch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the Recurrent Neural Network\n", + "\n", + "We will now create the Recurrent Neural Network (RNN) that will be trained to map the vectors with transfer-values from the image-recognition model into sequences of integer-tokens that can be converted into text. We call this neural network for the 'decoder' as it is almost identical to the decoder when doing Machine Translation in Tutorial #21.\n", + "\n", + "Note that we are using the functional model from Keras to build this neural network, because it allows more flexibility in how the neural network can be connected, in case you want to experiment and connect the image-model directly to the decoder (see the exercises). This means we have split the network construction into two parts: (1) Creation of all the layers that are not yet connected, and (2) a function that connects all these layers.\n", + "\n", + "The decoder consists of 3 GRU layers whose internal state-sizes are:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "state_size = 512" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The embedding-layer converts integer-tokens into vectors of this length:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "embedding_size = 128" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This inputs transfer-values to the decoder:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "transfer_values_input = Input(shape=(transfer_values_size,),\n", + " name='transfer_values_input')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to use the transfer-values to initialize the internal states of the GRU units. This informs the GRU units of the contents of the images. The transfer-values are vectors of length 4096 but the size of the internal states of the GRU units are only 512, so we use a fully-connected layer to map the vectors from 4096 to 512 elements.\n", + "\n", + "Note that we use a `tanh` activation function to limit the output of the mapping between -1 and 1, otherwise this does not seem to work." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_transfer_map = Dense(state_size,\n", + " activation='tanh',\n", + " name='decoder_transfer_map')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the input for token-sequences to the decoder. Using `None` in the shape means that the token-sequences can have arbitrary lengths." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_input = Input(shape=(None, ), name='decoder_input')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the embedding-layer which converts sequences of integer-tokens to sequences of vectors." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_embedding = Embedding(input_dim=num_words,\n", + " output_dim=embedding_size,\n", + " name='decoder_embedding')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This creates the 3 GRU layers of the decoder. Note that they all return sequences because we ultimately want to output a sequence of integer-tokens that can be converted into a text-sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_gru1 = GRU(state_size, name='decoder_gru1',\n", + " return_sequences=True)\n", + "decoder_gru2 = GRU(state_size, name='decoder_gru2',\n", + " return_sequences=True)\n", + "decoder_gru3 = GRU(state_size, name='decoder_gru3',\n", + " return_sequences=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The GRU layers output a tensor with shape `[batch_size, sequence_length, state_size]`, where each \"word\" is encoded as a vector of length `state_size`. We need to convert this into sequences of integer-tokens that can be interpreted as words from our vocabulary.\n", + "\n", + "One way of doing this is to convert the GRU output to a one-hot encoded array. It works but it is extremely wasteful, because for a vocabulary of e.g. 10000 words we need a vector with 10000 elements, so we can select the index of the highest element to be the integer-token." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_dense = Dense(num_words,\n", + " activation='softmax',\n", + " name='decoder_output')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect and Create the Training Model\n", + "\n", + "The decoder is built using the functional API of Keras, which allows more flexibility in connecting the layers e.g. to have multiple inputs. This is useful e.g. if you want to connect the image-model directly with the decoder instead of using pre-calculated transfer-values.\n", + "\n", + "This function connects all the layers of the decoder to some input of transfer-values." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "def connect_decoder(transfer_values):\n", + " # Map the transfer-values so the dimensionality matches\n", + " # the internal state of the GRU layers. This means\n", + " # we can use the mapped transfer-values as the initial state\n", + " # of the GRU layers.\n", + " initial_state = decoder_transfer_map(transfer_values)\n", + "\n", + " # Start the decoder-network with its input-layer.\n", + " net = decoder_input\n", + " \n", + " # Connect the embedding-layer.\n", + " net = decoder_embedding(net)\n", + " \n", + " # Connect all the GRU layers.\n", + " net = decoder_gru1(net, initial_state=initial_state)\n", + " net = decoder_gru2(net, initial_state=initial_state)\n", + " net = decoder_gru3(net, initial_state=initial_state)\n", + "\n", + " # Connect the final dense layer that converts to\n", + " # one-hot encoded arrays.\n", + " decoder_output = decoder_dense(net)\n", + " \n", + " return decoder_output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Connect and create the model used for training. This takes as input transfer-values and sequences of integer-tokens and outputs sequences of one-hot encoded arrays that can be converted into integer-tokens." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_output = connect_decoder(transfer_values=transfer_values_input)\n", + "\n", + "decoder_model = Model(inputs=[transfer_values_input, decoder_input],\n", + " outputs=[decoder_output])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile the Model\n", + "\n", + "The output of the decoder is a sequence of one-hot encoded arrays. In order to train the decoder we need to supply the one-hot encoded arrays that we desire to see on the decoder's output, and then use a loss-function like cross-entropy to train the decoder to produce this desired output.\n", + "\n", + "However, our data-set contains integer-tokens instead of one-hot encoded arrays. Each one-hot encoded array has 10000 elements so it would be extremely wasteful to convert the entire data-set to one-hot encoded arrays. We could do this conversion from integers to one-hot arrays in the `batch_generator()` above.\n", + "\n", + "A better way is to use a so-called sparse cross-entropy loss-function, which does the conversion internally from integers to one-hot encoded arrays.\n", + "\n", + "We have used the Adam optimizer in many of the previous tutorials, but it seems to diverge in some of these experiments with Recurrent Neural Networks. RMSprop seems to work much better for these." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_model.compile(optimizer=RMSprop(lr=1e-3),\n", + " loss='sparse_categorical_crossentropy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Callback Functions\n", + "\n", + "During training we want to save checkpoints and log the progress to TensorBoard so we create the appropriate callbacks for Keras.\n", + "\n", + "This is the callback for writing checkpoints during training." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "path_checkpoint = '22_checkpoint.keras'\n", + "callback_checkpoint = ModelCheckpoint(filepath=path_checkpoint,\n", + " verbose=1,\n", + " save_weights_only=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the callback for writing the TensorBoard log during training." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "callback_tensorboard = TensorBoard(log_dir='./22_logs/',\n", + " histogram_freq=0,\n", + " write_graph=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "callbacks = [callback_checkpoint, callback_tensorboard]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Checkpoint\n", + "\n", + "You can reload the last saved checkpoint so you don't have to train the model every time you want to use it." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " decoder_model.load_weights(path_checkpoint)\n", + "except Exception as error:\n", + " print(\"Error trying to load checkpoint.\")\n", + " print(error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train the Model\n", + "\n", + "Now we will train the decoder so it can map transfer-values from the image-model to sequences of integer-tokens for the captions of the images.\n", + "\n", + "One epoch of training took about 7 minutes on a GTX 1070 GPU. You probably need to run 20 epochs or more during training.\n", + "\n", + "Note that if we didn't use pre-computed transfer-values then each epoch would take maybe 40 minutes to run, because all the images would have to be processed by the VGG16 model as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "decoder_model.fit(x=generator,\n", + " steps_per_epoch=steps_per_epoch,\n", + " epochs=20,\n", + " callbacks=callbacks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Captions\n", + "\n", + "This function loads an image and generates a caption using the model we have trained." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_caption(image_path, max_tokens=30):\n", + " \"\"\"\n", + " Generate a caption for the image in the given path.\n", + " The caption is limited to the given number of tokens (words).\n", + " \"\"\"\n", + "\n", + " # Load and resize the image.\n", + " image = load_image(image_path, size=img_size)\n", + " \n", + " # Expand the 3-dim numpy array to 4-dim\n", + " # because the image-model expects a whole batch as input,\n", + " # so we give it a batch with just one image.\n", + " image_batch = np.expand_dims(image, axis=0)\n", + "\n", + " # Process the image with the pre-trained image-model\n", + " # to get the transfer-values.\n", + " transfer_values = image_model_transfer.predict(image_batch)\n", + "\n", + " # Pre-allocate the 2-dim array used as input to the decoder.\n", + " # This holds just a single sequence of integer-tokens,\n", + " # but the decoder-model expects a batch of sequences.\n", + " shape = (1, max_tokens)\n", + " decoder_input_data = np.zeros(shape=shape, dtype=np.int)\n", + "\n", + " # The first input-token is the special start-token for 'ssss '.\n", + " token_int = token_start\n", + "\n", + " # Initialize an empty output-text.\n", + " output_text = ''\n", + "\n", + " # Initialize the number of tokens we have processed.\n", + " count_tokens = 0\n", + "\n", + " # While we haven't sampled the special end-token for ' eeee'\n", + " # and we haven't processed the max number of tokens.\n", + " while token_int != token_end and count_tokens < max_tokens:\n", + " # Update the input-sequence to the decoder\n", + " # with the last token that was sampled.\n", + " # In the first iteration this will set the\n", + " # first element to the start-token.\n", + " decoder_input_data[0, count_tokens] = token_int\n", + "\n", + " # Wrap the input-data in a dict for clarity and safety,\n", + " # so we are sure we input the data in the right order.\n", + " x_data = \\\n", + " {\n", + " 'transfer_values_input': transfer_values,\n", + " 'decoder_input': decoder_input_data\n", + " }\n", + "\n", + " # Note that we input the entire sequence of tokens\n", + " # to the decoder. This wastes a lot of computation\n", + " # because we are only interested in the last input\n", + " # and output. We could modify the code to return\n", + " # the GRU-states when calling predict() and then\n", + " # feeding these GRU-states as well the next time\n", + " # we call predict(), but it would make the code\n", + " # much more complicated.\n", + " \n", + " # Input this data to the decoder and get the predicted output.\n", + " decoder_output = decoder_model.predict(x_data)\n", + "\n", + " # Get the last predicted token as a one-hot encoded array.\n", + " # Note that this is not limited by softmax, but we just\n", + " # need the index of the largest element so it doesn't matter.\n", + " token_onehot = decoder_output[0, count_tokens, :]\n", + "\n", + " # Convert to an integer-token.\n", + " token_int = np.argmax(token_onehot)\n", + "\n", + " # Lookup the word corresponding to this integer-token.\n", + " sampled_word = tokenizer.token_to_word(token_int)\n", + "\n", + " # Append the word to the output-text.\n", + " output_text += \" \" + sampled_word\n", + "\n", + " # Increment the token-counter.\n", + " count_tokens += 1\n", + "\n", + " # This is the sequence of tokens output by the decoder.\n", + " output_tokens = decoder_input_data[0]\n", + "\n", + " # Plot the image.\n", + " plt.imshow(image)\n", + " plt.show()\n", + " \n", + " # Print the predicted caption.\n", + " print(\"Predicted caption:\")\n", + " print(output_text)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examples\n", + "\n", + "Try this with a picture of a parrot." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted caption:\n", + " a bird is sitting on a tree branch eeee\n", + "\n" + ] + } + ], + "source": [ + "generate_caption(\"images/parrot_cropped1.jpg\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try it with a picture of a person (Elon Musk). In Tutorial #07 the Inception model mis-classified this picture as being either a sweatshirt or a cowboy boot." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted caption:\n", + " a man in a suit and tie is standing outside eeee\n", + "\n" + ] + } + ], + "source": [ + "generate_caption(\"images/elon_musk.jpg\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper-function for loading an image from the COCO data-set and printing the true captions as well as the predicted caption." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_caption_coco(idx, train=False):\n", + " \"\"\"\n", + " Generate a caption for an image in the COCO data-set.\n", + " Use the image with the given index in either the\n", + " training-set (train=True) or validation-set (train=False).\n", + " \"\"\"\n", + " \n", + " if train:\n", + " # Use image and captions from the training-set.\n", + " data_dir = coco.train_dir\n", + " filename = filenames_train[idx]\n", + " captions = captions_train[idx]\n", + " else:\n", + " # Use image and captions from the validation-set.\n", + " data_dir = coco.val_dir\n", + " filename = filenames_val[idx]\n", + " captions = captions_val[idx]\n", + "\n", + " # Path for the image-file.\n", + " path = os.path.join(data_dir, filename)\n", + "\n", + " # Use the model to generate a caption of the image.\n", + " generate_caption(image_path=path)\n", + "\n", + " # Print the true captions from the data-set.\n", + " print(\"True captions:\")\n", + " for caption in captions:\n", + " print(caption)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try this on a picture from the training-set that the model has been trained on. In some cases the generated caption is actually better than the human-generated captions." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted caption:\n", + " a giraffe standing in a field next to a tree eeee\n", + "\n", + "True captions:\n", + "A giraffe eating food from the top of the tree.\n", + "A giraffe standing up nearby a tree \n", + "A giraffe mother with its baby in the forest.\n", + "Two giraffes standing in a tree filled area.\n", + "A giraffe standing next to a forest filled with trees.\n" + ] + } + ], + "source": [ + "generate_caption_coco(idx=1, train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is another picture of giraffes from the training-set, so this image was also used during training of the model. But the model can't produce an accurate caption. Perhaps it needs more training, or perhaps another architecture for the Recurrent Neural Network?" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted caption:\n", + " a giraffe standing next to a tree in a forest eeee\n", + "\n", + "True captions:\n", + "A couple of giraffe snuggling each other in a forest.\n", + "A couple of giraffe standing next to some trees.\n", + "Two Zebras seem to be embracing in the wild. \n", + "Two giraffes hang out near trees and nuzzle up to each other.\n", + "The two giraffes appear to be hugging each other.\n" + ] + } + ], + "source": [ + "generate_caption_coco(idx=10, train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a picture from the validation-set which was not used during training of the model. Sometimes the model can produce good captions for images it hasn't seen during training and sometimes it can't. Can you make a better model?" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted caption:\n", + " a dog is standing on a grassy field eeee\n", + "\n", + "True captions:\n", + "A big burly grizzly bear is show with grass in the background.\n", + "The large brown bear has a black nose.\n", + "Closeup of a brown bear sitting in a grassy area.\n", + "A large bear that is sitting on grass. \n", + "A close up picture of a brown bear's face.\n" + ] + } + ], + "source": [ + "generate_caption_coco(idx=1, train=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed how to generate captions for images. We used a pre-trained image-model (VGG16) to generate a \"thought-vector\" of what the image contains, and then we trained a Recurrent Neural Network to map this \"thought-vector\" to a sequence of words.\n", + "\n", + "This works reasonably well, although it is easy to find examples both in the training- and validation-sets where the captions are incorrect.\n", + "\n", + "It is also important to understand that this model doesn't have a human-like understanding of what the images contain. If it sees an image of a giraffe and correctly produces a caption stating that, it doesn't mean that the model has a deep understanding of what a giraffe is; the model doesn't know that it's a tall animal that lives in Africa and Zoos.\n", + "\n", + "The model is merely a clever way of mapping pixels in an image to a vector of floating-point numbers that summarize the contents of the image, and then map these numbers to a sequence of integers-tokens representing words. So the model is basically just a very advanced function approximator rather than human-like intelligence." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Train the model for more epochs. Does it improve the quality of the generated captions?\n", + "* Try another architecture for the Recurrent Neural Network, e.g. change the number of GRU layers, their internal state-size, the embedding-size, etc. Can you improve the quality of the generated captions?\n", + "* Use another transfer-layer from the VGG16-model, for example the flattened output of the last convolutional layer.\n", + "* Try adding more dense-layers to the mapping between the transfer-values and the initial-state in the decoder.\n", + "* When generating captions, instead of using `np.argmax()` to sample the next integer-token, could you sample the decoder's output as if it was a probability distribution instead? Note that the decoder's output is not softmax-limited so you have to do that first to turn it into a probability-distribution.\n", + "* Can you generate multiple sequences by doing this sampling? Can you find a way to select the best of these different sequences?\n", + "* Connect the image-model directly to the decoder so you can fine-tune the weights of the image-model. See Tutorial #10 on Fine-Tuning.\n", + "* Can you train a Machine Translation model from Tutorial #21 and then connect its decoder to a pre-trained image-model to make an image captioning model? Perhaps you need an intermediate fully-connected layer that you will train.\n", + "* Can you measure the quality of the generated captions using some mathematical formula?\n", + "* Modify the decoder so it also returns the states of the GRU-units. Then change `generate_caption()` so it only inputs and outputs one integer-token in each iteration. You need to get the GRU-states out of `decoder_model.predict()` and feed them back in next time you call it. Now you compute less in each iteration, but there is still a lot of overhead, so it may not be much faster when using a GPU?\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "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:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "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." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/23_Time-Series-Prediction.ipynb b/23_Time-Series-Prediction.ipynb new file mode 100644 index 0000000..f8c5759 --- /dev/null +++ b/23_Time-Series-Prediction.ipynb @@ -0,0 +1,3067 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #23\n", + "# Time-Series Prediction\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This tutorial tries to predict the future weather of a city using weather-data from several other cities.\n", + "\n", + "Because we will be working with sequences of arbitrary length, we will use a Recurrent Neural Network (RNN).\n", + "\n", + "You should be familiar with TensorFlow and Keras in general, see Tutorials #01 and #03-C, and the basics of Recurrent Neural Networks as explained in Tutorial #20." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Location\n", + "\n", + "We will use weather-data from the period 1980-2018 for five cities in [Denmark](https://en.wikipedia.org/wiki/Denmark):\n", + "\n", + "* **[Aalborg](https://en.wikipedia.org/wiki/Aalborg)** The weather-data is actually from an airforce base which is also home to [The Hunter Corps (Jægerkorps)](https://en.wikipedia.org/wiki/Jaeger_Corps_(Denmark)).\n", + "* **[Aarhus](https://en.wikipedia.org/wiki/Aarhus)** is the city where [the inventor of C++](https://en.wikipedia.org/wiki/Bjarne_Stroustrup) studied and the [Google V8 JavaScript Engine](https://en.wikipedia.org/wiki/Chrome_V8) was developed.\n", + "* **[Esbjerg](https://en.wikipedia.org/wiki/Esbjerg)** has a large fishing-port.\n", + "* **[Odense](https://en.wikipedia.org/wiki/Odense)** is the birth-city of the fairytale author [H. C. Andersen](https://en.wikipedia.org/wiki/Hans_Christian_Andersen).\n", + "* **[Roskilde](https://en.wikipedia.org/wiki/Roskilde)** has an old cathedral housing the tombs of the Danish royal family." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following map shows the location of the cities in Denmark:\n", + "\n", + "![Map of Denmark](images/Denmark.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following map shows the location of Denmark within Europe:\n", + "\n", + "![Map of Europe](images/Europe.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flowchart\n", + "\n", + "In this tutorial, we are trying to predict the weather for the Danish city \"Odense\" 24 hours into the future, given the current and past weather-data from 5 cities (although the flowchart below only shows 2 cities).\n", + "\n", + "We use a Recurrent Neural Network (RNN) because it can work on sequences of arbitrary length. During training we will use sub-sequences of 1344 data-points (8 weeks) from the training-set, with each data-point or observation having 20 input-signals for the temperature, pressure, etc. for each of the 5 cities. We then want to train the neural network so it outputs the 3 signals for tomorrow's temperature, pressure and wind-speed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Flowchart](images/23_time_series_flowchart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "from sklearn.preprocessing import MinMaxScaler" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to import several things from Keras." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n", + "from tensorflow.keras.optimizers import RMSprop\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau\n", + "from tensorflow.keras.backend import square, mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 (Anaconda) and package versions:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.2.4-tf'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.keras.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.0.3'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data\n", + "\n", + "Weather-data for 5 cities in Denmark will be downloaded automatically below.\n", + "\n", + "The raw weather-data was originally obtained from the [National Climatic Data Center (NCDC), USA](https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd). Their web-site and database-access is very confusing and may change soon. Furthermore, the raw data-file had to be manually edited before it could be read. So you should expect some challenges if you want to download weather-data for another region. The following Python-module provides some functionality that may be helpful if you want to use new weather-data, but you will have to modify the source-code to fit your data-format." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import weather" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download the data-set if you don't have it already. It is about 35 MB." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has apparently already been downloaded and unpacked.\n" + ] + } + ], + "source": [ + "weather.maybe_download_and_extract()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "List of the cities used in the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Aalborg', 'Aarhus', 'Esbjerg', 'Odense', 'Roskilde']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cities = weather.cities\n", + "cities" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load and resample the data so it has observations at regular time-intervals for every 60 minutes. Missing data-points are linearly interpolated. This takes about 30 seconds to run the first time but uses a cache-file so it loads very quickly the next time." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 13.9 ms, sys: 55.4 ms, total: 69.3 ms\n", + "Wall time: 157 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "df = weather.load_resampled_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the top rows of the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AalborgAarhusEsbjergOdenseRoskilde
TempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDir
DateTime
1980-03-01 11:00:005.0000001007.76666710.2280.0000005.01008.30000015.4290.06.083333NaN12.383333310.0000006.1428571011.06666712.585714290.05.000000NaN11.466667280.000000
1980-03-01 12:00:005.0000001008.00000010.3290.0000005.01008.60000013.4280.06.583333NaN12.883333310.0000007.0000001011.20000011.300000290.05.000000NaN12.466667280.000000
1980-03-01 13:00:005.0000001008.0666679.7290.0000005.01008.43333315.4280.06.888889NaN13.244444309.4444447.0000001011.30000012.118182290.05.166667NaN13.133333278.333333
1980-03-01 14:00:004.3333331008.13333311.1283.3333335.01008.26666714.9300.06.222222NaN12.911111306.1111116.8571431011.40000012.742857290.05.833333NaN12.300000270.000000
1980-03-01 15:00:004.0000001008.20000011.3280.0000005.01008.10000017.0290.05.555556NaN12.577778302.7777786.0000001011.50000012.400000290.04.833333NaN12.300000270.000000
\n", + "
" + ], + "text/plain": [ + " Aalborg Aarhus \\\n", + " Temp Pressure WindSpeed WindDir Temp \n", + "DateTime \n", + "1980-03-01 11:00:00 5.000000 1007.766667 10.2 280.000000 5.0 \n", + "1980-03-01 12:00:00 5.000000 1008.000000 10.3 290.000000 5.0 \n", + "1980-03-01 13:00:00 5.000000 1008.066667 9.7 290.000000 5.0 \n", + "1980-03-01 14:00:00 4.333333 1008.133333 11.1 283.333333 5.0 \n", + "1980-03-01 15:00:00 4.000000 1008.200000 11.3 280.000000 5.0 \n", + "\n", + " Esbjerg \\\n", + " Pressure WindSpeed WindDir Temp Pressure \n", + "DateTime \n", + "1980-03-01 11:00:00 1008.300000 15.4 290.0 6.083333 NaN \n", + "1980-03-01 12:00:00 1008.600000 13.4 280.0 6.583333 NaN \n", + "1980-03-01 13:00:00 1008.433333 15.4 280.0 6.888889 NaN \n", + "1980-03-01 14:00:00 1008.266667 14.9 300.0 6.222222 NaN \n", + "1980-03-01 15:00:00 1008.100000 17.0 290.0 5.555556 NaN \n", + "\n", + " Odense \\\n", + " WindSpeed WindDir Temp Pressure WindSpeed \n", + "DateTime \n", + "1980-03-01 11:00:00 12.383333 310.000000 6.142857 1011.066667 12.585714 \n", + "1980-03-01 12:00:00 12.883333 310.000000 7.000000 1011.200000 11.300000 \n", + "1980-03-01 13:00:00 13.244444 309.444444 7.000000 1011.300000 12.118182 \n", + "1980-03-01 14:00:00 12.911111 306.111111 6.857143 1011.400000 12.742857 \n", + "1980-03-01 15:00:00 12.577778 302.777778 6.000000 1011.500000 12.400000 \n", + "\n", + " Roskilde \n", + " WindDir Temp Pressure WindSpeed WindDir \n", + "DateTime \n", + "1980-03-01 11:00:00 290.0 5.000000 NaN 11.466667 280.000000 \n", + "1980-03-01 12:00:00 290.0 5.000000 NaN 12.466667 280.000000 \n", + "1980-03-01 13:00:00 290.0 5.166667 NaN 13.133333 278.333333 \n", + "1980-03-01 14:00:00 290.0 5.833333 NaN 12.300000 270.000000 \n", + "1980-03-01 15:00:00 290.0 4.833333 NaN 12.300000 270.000000 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Missing Data\n", + "\n", + "The two cities Esbjerg and Roskilde have missing data for the atmospheric pressure, as can be seen in the following two plots. \n", + "\n", + "Because we are using resampled data, we have filled in the missing values with new values that are linearly interpolated from the neighbouring values, which appears as long straight lines in these plots.\n", + "\n", + "This may confuse the neural network. For simplicity, we will simply remove these two signals from the data.\n", + "\n", + "But it is only short periods of data that are missing, so you could actually generate this data by creating a predictive model that generates the missing data from all the other input signals. Then you could add these generated values back into the data-set to fill the gaps." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['Roskilde']['Pressure'].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before removing these two signals, there are 20 input-signals in the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(333109, 20)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.values.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we remove the two signals that have missing data." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df.drop(('Esbjerg', 'Pressure'), axis=1, inplace=True)\n", + "df.drop(('Roskilde', 'Pressure'), axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now there are only 18 input-signals in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(333109, 18)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.values.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can verify that these two data-columns have indeed been removed." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AalborgAarhusEsbjergOdenseRoskilde
TempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempWindSpeedWindDirTempPressureWindSpeedWindDirTempWindSpeedWindDir
DateTime
1980-03-01 11:00:005.01007.76666710.2280.05.01008.315.4290.06.08333312.383333310.06.1428571011.06666712.585714290.05.011.466667280.0
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" + ], + "text/plain": [ + " Aalborg Aarhus \\\n", + " Temp Pressure WindSpeed WindDir Temp Pressure \n", + "DateTime \n", + "1980-03-01 11:00:00 5.0 1007.766667 10.2 280.0 5.0 1008.3 \n", + "\n", + " Esbjerg Odense \\\n", + " WindSpeed WindDir Temp WindSpeed WindDir Temp \n", + "DateTime \n", + "1980-03-01 11:00:00 15.4 290.0 6.083333 12.383333 310.0 6.142857 \n", + "\n", + " Roskilde \\\n", + " Pressure WindSpeed WindDir Temp WindSpeed \n", + "DateTime \n", + "1980-03-01 11:00:00 1011.066667 12.585714 290.0 5.0 11.466667 \n", + "\n", + " \n", + " WindDir \n", + "DateTime \n", + "1980-03-01 11:00:00 280.0 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Errors\n", + "\n", + "There are some errors in this data. As shown in the plot below, the temperature in the city of Odense suddenly jumped to almost 50 degrees C. But the highest temperature ever measured in Denmark was only 36.4 degrees Celcius and the lowest was -31.2 C. So this is clearly a data error. However, we will not correct any data-errors in this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['Odense']['Temp']['2006-05':'2006-07'].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This can also be confirmed to be an error by considering the temperatures in some of the other cities in Denmark for that period, which was only around 10 degrees. Because the country is so small, it is not possible for one city in Denmark to have 50 degrees while another city only has 10 degrees." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['Roskilde']['Temp']['2006-05':'2006-07'].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add Data\n", + "\n", + "We can add some input-signals to the data that may help our model in making predictions.\n", + "\n", + "For example, given just a temperature of 10 degrees Celcius the model wouldn't know whether that temperature was measured during the day or the night, or during summer or winter. The model would have to infer this from the surrounding data-points which might not be very accurate for determining whether it's an abnormally warm winter, or an abnormally cold summer, or whether it's day or night. So having this information could make a big difference in how accurately the model can predict the next output.\n", + "\n", + "Although the data-set does contain the date and time information for each observation, it is only used in the index so as to order the data. We will therefore add separate input-signals to the data-set for the day-of-year (between 1 and 366) and the hour-of-day (between 0 and 23)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "df['Various', 'Day'] = df.index.dayofyear\n", + "df['Various', 'Hour'] = df.index.hour" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Target Data for Prediction\n", + "\n", + "We will try and predict the future weather-data for this city." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "target_city = 'Odense'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will try and predict these signals." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "target_names = ['Temp', 'WindSpeed', 'Pressure']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following is the number of time-steps that we will shift the target-data. Our data-set is resampled to have an observation for each hour, so there are 24 observations for 24 hours.\n", + "\n", + "If we want to predict the weather 24 hours into the future, we shift the data 24 time-steps. If we want to predict the weather 7 days into the future, we shift the data 7 * 24 time-steps." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "shift_days = 1\n", + "shift_steps = shift_days * 24 # Number of hours." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a new data-frame with the time-shifted data.\n", + "\n", + "**Note the negative time-shift!**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "df_targets = df[target_city][target_names].shift(-shift_steps)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**WARNING!** You should double-check that you have shifted the data in the right direction! We want to predict the future, not the past!\n", + "\n", + "The shifted data-frame is confusing because Pandas keeps the original time-stamps even though we have shifted the data. You can check the time-shift is correct by comparing the original and time-shifted data-frames.\n", + "\n", + "This is the first `shift_steps + 5` rows of the original data-frame:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TempWindSpeedPressure
DateTime
1980-03-01 11:00:006.14285712.5857141011.066667
1980-03-01 12:00:007.00000011.3000001011.200000
1980-03-01 13:00:007.00000012.1181821011.300000
1980-03-01 14:00:006.85714312.7428571011.400000
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" + ], + "text/plain": [ + " Temp WindSpeed Pressure\n", + "DateTime \n", + "2018-03-01 19:00:00 NaN NaN NaN\n", + "2018-03-01 20:00:00 NaN NaN NaN\n", + "2018-03-01 21:00:00 NaN NaN NaN\n", + "2018-03-01 22:00:00 NaN NaN NaN\n", + "2018-03-01 23:00:00 NaN NaN NaN" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_targets.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### NumPy Arrays\n", + "\n", + "We now convert the Pandas data-frames to NumPy arrays that can be input to the neural network. We also remove the last part of the numpy arrays, because the target-data has `NaN` for the shifted period, and we only want to have valid data and we need the same array-shapes for the input- and output-data.\n", + "\n", + "These are the input-signals:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "x_data = df.values[0:-shift_steps]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Shape: (333085, 20)\n" + ] + } + ], + "source": [ + "print(type(x_data))\n", + "print(\"Shape:\", x_data.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the output-signals (or target-signals):" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "y_data = df_targets.values[:-shift_steps]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Shape: (333085, 3)\n" + ] + } + ], + "source": [ + "print(type(y_data))\n", + "print(\"Shape:\", y_data.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the number of observations (aka. data-points or samples) in the data-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "333085" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_data = len(x_data)\n", + "num_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the fraction of the data-set that will be used for the training-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "train_split = 0.9" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the number of observations in the training-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "299776" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_train = int(train_split * num_data)\n", + "num_train" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the number of observations in the test-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "33309" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_test = num_data - num_train\n", + "num_test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the input-signals for the training- and test-sets:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "333085" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train = x_data[0:num_train]\n", + "x_test = x_data[num_train:]\n", + "len(x_train) + len(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the output-signals for the training- and test-sets:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "333085" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_train = y_data[0:num_train]\n", + "y_test = y_data[num_train:]\n", + "len(y_train) + len(y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the number of input-signals:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_x_signals = x_data.shape[1]\n", + "num_x_signals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the number of output-signals:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_y_signals = y_data.shape[1]\n", + "num_y_signals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scaled Data\n", + "\n", + "The data-set contains a wide range of values:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min: -27.0\n", + "Max: 1050.8\n" + ] + } + ], + "source": [ + "print(\"Min:\", np.min(x_train))\n", + "print(\"Max:\", np.max(x_train))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The neural network works best on values roughly between -1 and 1, so we need to scale the data before it is being input to the neural network. We can use `scikit-learn` for this.\n", + "\n", + "We first create a scaler-object for the input-signals." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "x_scaler = MinMaxScaler()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then detect the range of values from the training-data and scale the training-data." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "x_train_scaled = x_scaler.fit_transform(x_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apart from a small rounding-error, the data has been scaled to be between 0 and 1." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min: 0.0\n", + "Max: 1.0000000000000002\n" + ] + } + ], + "source": [ + "print(\"Min:\", np.min(x_train_scaled))\n", + "print(\"Max:\", np.max(x_train_scaled))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the same scaler-object for the input-signals in the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "x_test_scaled = x_scaler.transform(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The target-data comes from the same data-set as the input-signals, because it is the weather-data for one of the cities that is merely time-shifted. But the target-data could be from a different source with different value-ranges, so we create a separate scaler-object for the target-data." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "y_scaler = MinMaxScaler()\n", + "y_train_scaled = y_scaler.fit_transform(y_train)\n", + "y_test_scaled = y_scaler.transform(y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Generator\n", + "\n", + "The data-set has now been prepared as 2-dimensional numpy arrays. The training-data has almost 300k observations, consisting of 20 input-signals and 3 output-signals.\n", + "\n", + "These are the array-shapes of the input and output data:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(299776, 20)\n", + "(299776, 3)\n" + ] + } + ], + "source": [ + "print(x_train_scaled.shape)\n", + "print(y_train_scaled.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of training the Recurrent Neural Network on the complete sequences of almost 300k observations, we will use the following function to create a batch of shorter sub-sequences picked at random from the training-data." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "def batch_generator(batch_size, sequence_length):\n", + " \"\"\"\n", + " Generator function for creating random batches of training-data.\n", + " \"\"\"\n", + "\n", + " # Infinite loop.\n", + " while True:\n", + " # Allocate a new array for the batch of input-signals.\n", + " x_shape = (batch_size, sequence_length, num_x_signals)\n", + " x_batch = np.zeros(shape=x_shape, dtype=np.float16)\n", + "\n", + " # Allocate a new array for the batch of output-signals.\n", + " y_shape = (batch_size, sequence_length, num_y_signals)\n", + " y_batch = np.zeros(shape=y_shape, dtype=np.float16)\n", + "\n", + " # Fill the batch with random sequences of data.\n", + " for i in range(batch_size):\n", + " # Get a random start-index.\n", + " # This points somewhere into the training-data.\n", + " idx = np.random.randint(num_train - sequence_length)\n", + " \n", + " # Copy the sequences of data starting at this index.\n", + " x_batch[i] = x_train_scaled[idx:idx+sequence_length]\n", + " y_batch[i] = y_train_scaled[idx:idx+sequence_length]\n", + " \n", + " yield (x_batch, y_batch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use a large batch-size so as to keep the GPU near 100% work-load. You may have to adjust this number depending on your GPU, its RAM and your choice of `sequence_length` below." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 256" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use a sequence-length of 1344, which means that each random sequence contains observations for 8 weeks. One time-step corresponds to one hour, so 24 x 7 time-steps corresponds to a week, and 24 x 7 x 8 corresponds to 8 weeks." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1344" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sequence_length = 24 * 7 * 8\n", + "sequence_length" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then create the batch-generator." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "generator = batch_generator(batch_size=batch_size,\n", + " sequence_length=sequence_length)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then test the batch-generator to see if it works." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "x_batch, y_batch = next(generator)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This gives us a random batch of 256 sequences, each sequence having 1344 observations, and each observation having 20 input-signals and 3 output-signals." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(256, 1344, 20)\n", + "(256, 1344, 3)\n" + ] + } + ], + "source": [ + "print(x_batch.shape)\n", + "print(y_batch.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot one of the 20 input-signals as an example." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "batch = 0 # First sequence in the batch.\n", + "signal = 0 # First signal from the 20 input-signals.\n", + "seq = x_batch[batch, :, signal]\n", + "plt.plot(seq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also plot one of the output-signals that we want the model to learn how to predict given all those 20 input signals." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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iN/jd0kY89lWjoj72HunCaT9/HU9ed6pRap5xh5ISCNFYHN6gD7PGDcTzK3dioBK9ko7JaNX2Q4jGBWaMqQUA/GP+6Sjze7DyUy1WXbXxB7weTB5a43it08YNBAD85o3N8HkIg6uDmDxEMxndfflxuOzE4bjvlU9sTTz3fO44VAaSP5qxgyrh9RC8llpG97y0AQBw3xXH43tPr0Z1mbOgSgXp2ROrtx8yNap5p1HL8WgLRZMirnqCFGLnHzvEEEIATBnYW1raMKBSC9OVzkUiYO8R5/yQdJHfhbZQFA++tgm3XDg562syPSdqk4z2iytPwA+fXYOh/cpM527ccxRxAdz63Bq8c+u5OR1nqVNaAiEu4PcSBtdo5pL6gQmzT8DnSZmHsHyblpB2si4Qjh+pdTxbrWccq0nFXdEYpg53FghSq+iKxFEd9OHCqUON8gw1ZX6cd+wQ/OaNRnTaRFFcdeqYbsfpszTekUX0BlZq7zubGvJqhIddo5p9R7pQpeQN9BRp9rHmZahRJau3H8Ip9QNM4xpaU4ZD3SStpYuqLX7Q5G6vDCZzTAEb+nfgMxMGYczAiqS8ExmJ5tRZj+k5pSUQYprqf+rYgThnch1uv/RY41g6PoRlzQcxaUhVUtVN6SaQpSPC0TgiMYGKNO3pXdGYre29zO8x+RBqK/yYc9ywlNdzMm/IiKds+seoE7JdPSG3aiXJ920NBFCdyttbO5TzE7kWmRQEdEJ9nW5SH5gcofpy1OZTZT5vkgmJHc29R0lFGUXjcfg8HpQHvHjiazMxYXAiqieYwocQiwus+PQgTh4zIOmYXJHImkHS7l+RwnRy41nj4fUQIjGBMpuaR+V+ryV6Jo6qYGohY40yksgqq9bQzZ2HOlG/4KW0QklVDcEuI9it+vRSsFRYBILUdqrLfDiiJPbJcdWU+1L6gtIh3SRFJjeonfPk517m92qLJku2Mle77T1KSiBEYsKxj3EqH8KmvUdxtCuKU+prk47J/gNyHuqIaF9Y62SW9Jo+j+F3sNMQygNeQ7gIIRw1ieTx2L9Hq+CSfKybvJ5f4VwWQ6L+2A51Jjtv3dIQ5Psud3i/Q2rKTAJJjqumzI9wNJ51a0Wn5kVMflBLrsgaYkGfB8E8aAjbWztMvqy+REkJhEMdYfQrt3eopjIZyYS0GTYagjQZycm9PWS/urWilrUos0lgK/N7jckoHItDCHvBkTQeh1BXQyBYbEap6i6pqNE8hzuTY/Td6rcgGwZV2DjPAaCuKmgqRigngeoyH+LC3HyoJ6j9roup3EepomoIhzrC8JC28AnaaAi9Xd/ojPuX4jybKL++QMkIhEgsjiNdUSOJzEqqsNPlza0YXB3EqAHlScccTUYOk5nxmsrEbash+L2GbbwrHHc8z0pqDcG8XwrCdARCVyRumLf2HulKWsG7tZqW/gmrAL9wyhAAWtiurYagn5+tyUftd22NYmF6l18s+QQNzWZHvupDONwZQdCn9ciw0xB609wnNc9W1hCKGxl5MsBJIHg9SV8slWXNBzGjvtaIBFKRTlxDQ9BXt3alMlRU85W9UznhQ5CrHjtfg9N4AOD4Ef2MxzIa9fo/NWDdrsPoisTw+3e24n690J+TIAGAB/65EfULXsI7jfsNIdXaHsakIeaIomhM4Md//xhPL+u+73Eqbl64EkCyQHjoqulYfceFqCmzFwgyMzzbSSEci+OS47VcjfW7uFFOtvx95U7c8cLabs8JRWOoX/ASHlq6BVc+8j5WfJqomaW2mF26scX4HVgDLwBz+LdTZntP+UTP5QGAl9bshhAC859agTc37nP1dQqVkhEI/cr9eOGm2ZjjkJDVnYaw+7BWOM7OoQwkkrViFg3BrlSG+TUTx52cyp2RGA53RPDk+9uMfalQJ/Znv3laYpyKMPv+M6txzE9ewd0vrjdadjqZ3YUQ+J83Gm2PDagMmExj4Vgcf/7gU9z63Mcpx9kdsh1orSWiy+/1oF+FH/0q/KbM7bAhEDQB8vi7W02x65kSicUR8HpwxsRBONAexpaW9DvXMcl89+lV+NP720yZ+VZ2Heoybd/45HLjcbslY3z+uRMBaJ930/52k0lJ9ZG57f9RFwc3PbUCXZE4XlyzG9c+sczV1ylUSkYgBHwenDiqPwbX2Kv/3fkQGpql/yDZoQwAXott3tAQUkQZqSaaoINTORYXuOmpFfjtUm1CTsuHoAgEtdqqul9d6UiczO52jmJZ3C8SE3hvwbn4/TUz9G13VmQBrwdThtWgn0O2d79yP7oicSO6SL6uvOe/eaMRT36wrcevH4nGEfB5DBPVeb/qmzZjt+kuR6TNUg5eNcuoNaXOmVyH604fCyBRNub1DYmMf/Ur6Lb/50B7Iulx3imj+lzAQckIhFR0V/66obkVFQGvY6KZ1WTUkSJCRhIwOZXtTUYAsHZXotVmehqCk1O5++fJpjBWrH0brjx5JGbpmdbReBz9KwI4VW7HsnPmSoi05kVOyIlAFhGU2oBqpssmQS2s1Eli3KO7CfRoyPx51Sv1wlSTkboouuLkkQDM2m28lzoECiHws8WaeXVYvzJE46KoGk65QZ/5RUiTkV244rLmgzhpdH8ju9iKNMVYTUYpNQSTU9neZASYJ7buejRLnBLT7PwfKtboI4mq5h8ztBr3X3GCUZ5CCgDpD3EjB0AIgbBusnFCOo+lH0FGBaVy5KdLOKoJBHfEGyPpLixZ1RAmDK4y9RyRzztuRA2+f2Gizaz8jan5MaoPwa0waMCclCktCsua+1YWe58RCDIE1DqhHemKYMOeI0aJBDvkBCxLVyRCJjMIO7VZ+ds93ylsVsXJOexNIRCcmo2oCWB+rwceDxkCIaL/+Py6VuKGySgaFxDCudsckCwQtMqnlJbATIdITKQVdcVkRncr9r16T4+vza7HgMqAyS/QFopiUFUAL377DFNCqQzXVr+7qobg5gpe1VKCukD43tOrXbt+MdBnfhFyNWo1G63YdhBCIIVA0P6rGgJR9xMaYDEZOZSusDLUwQdiGo9D8l137ScB+/jteFxg1fZEo3k1UxhItPj0eAg+D7kiENIJg5WCcf1uzckX1U08QZfMPFJDyTK/jbHglDQWicXxkxfWAQDmnzMBlQGvaTLvCEVttT/pe1M1BFXTtWtT21PU0FdpUZg51nleKEXS+nUR0Rwi2khEjUS0wOb4jUT0MRGtIqJ3iGiKvr+eiDr1/auI6BG330C6yMnHKhAamg/C66Fu661bE76OdkVRGfClNNGYBULyrbZLwbdG3djhmIdgeYmkdp2W1dvW/e24+elVuPvF9ca+4XpMvizVrWos/hShu+mSiUD4yd/XIhrTakf5POTKqj4WF4jFBfxeT7clzJnMcfIhHGhLOJCrynyoDPpMkUVtoZitxmzVEMLROF7dkAgBPepigyOpsTx81XR0hGJ4e1MLxul+jnQWaqVAyl8XEXkBPATgYgBTAHxJTvgKTwkhjhdCTANwP4AHlGNbhBDT9L8b3Rp4psiJ5J3G/QhFY7jzH+twuCOCZc2tmDq8plt/gNWpvO1AO0Yp9k/H11R9CDZOZTv7pyeNuvxWH8Ki+bPx4rdPT9IQxgw0j/HDra2Y8+u3jVXcOb98E/9Yvcs4/sOLJuOBL0wDAPSvCOCxr5yM33z5JOO430umqpQ95VO9aF13k7vaI+HKR95HS1sIAb2UgeTB1zfjuwtXYrWi4TgRjwv8fPEG7DjYYWg5fh/h304YjsqAN2kyemHVTqP1ab7oimjf07Yi6urmlEGu9rkI+ryoCvqwdX87Gvdp0XCvbdhrW1bd5/XAQ8ADr27CV37/ISb9+GVs2J0IDf2vReuMgIN9R7twyzOrehxCvP2g9r0cVB1E0/52ROMCC5dtB6C1sFW1n+XbWvHoW1t69DqFTDrLrZkAGoUQTUKIMICFAOaqJwgh1MyeSqDwfHXTR2shpUs/2YcXVu3CE+824+cvb8DqHYeMctdOyNWqDEnbcySEEf2TM5qtpDIZXXpC6sqmdlijjE4Y2R/HjeiXJBDsVjWf7DmKdzbvt73uF2aMMoWBXjh1KIYo1wj4PIYJKRvkD3ZYNxnCAyoDmD1Bi2xatf0QljcfhN/rSRIif1+1Cz9/eUPK12za34ZH327ClQ+/bwiEgO4v+dY5E9ARjuE1pZnRzQtX4Vt/WZHxe3OTJ9/fhifebcbDb9rniBQisbi9Bil9QePqtBW3XICd/8DbxufhtBiSMuZflu/t4OogWtvD2Ki3fX19wz48v2In/qyEI3dFYiZh1B2y3PuUYTW4/oyxScdVQXTtE8vw85c/KbmaR+kIhBEAtivbO/R9JojoJiLaAk1D+I5yaCwRrSSit4joDLsXIKIbiKiBiBpaWloyGH76TB5ajVnjBmD7wU5j4vxoayu6IvFu/QcAMLK2Aj4PYdsBbQURisTSyihWhYCdv6GmzI/mey/FujsvyuStOEYZWU1GTjH+u4902e6vTFFp1e/1uBLVIc0KU4f3czzH6yH85T9m4R/zTwegxan7vGRbWiSd8FNp3ttzpCvJZCXbpf7HnxoAZNdPwk3kajvbuk25xCksWZorH/yipnGq4cN7DmvfR6ekUiee+NopAGD6XQLmwIcvPvYBZvy/17A0jUxj+b0I+jy4/dKEEeQYvRf6fsXsJa0FMumzVHDNqSyEeEgIMR7ArQB+rO/eDWC0EOIkALcAeIqIkoL9hRCPCSFmCCFm1NXVuTWkJPqXB3C0K2JMzk36isApIU3i9ZCpEF0oGjeZLpwYo5iVujMFydXS9NHp9Y1NVctI4hSxFHIw+6TKgfB7Pa6YjNLN9AYSk3Z7OAq/12PreIylMWGqvqOIEUqrXbvG0mGuUMorO+WNFDJOn0WH0f9Cu+eqiXa5XliyqiyzkGLZEEq2VJWmNTXaTpoTf/XPjSmvF47GtaJ6lsCFB+dpQkzVNOT7dLt0Rr5JRyDsBDBK2R6p73NiIYDLAUAIERJCHNAfLwewBcCkbp7bq2h1UeIms8PoARWO2c0qaqZzV5oaQjr+AMk7t56DJ687Na1zHTUEi0BQJ7raCj+WfPdMAM4TXionud9LJpNRT1fSqUpfq6hNf5zyFqz9H+wwC4SEyQhIFkyqFuSUu5FLZFvTQkX9HjhpM13GIkCb9FWBsPOQ1vCoOsPWrP0socky4sguVyadhMpwLJ5kkpw4uMowvZq+F6L4tLd0SEcgLAMwkYjGElEAwDwAi9QTiGiisnkpgM36/jrdKQ0iGgdgIoAmNwbeE4I+rZic+iNPpR1I1H4KmkBIr1tauoysrUiZ6CaxttCUWPMQasoT1wv4PEahulA0ntaq2orf6zHVF+rJNQBttej3UlqZwuoP1Ol9pzNpq0JQPvbr11Ydyk8v+9QUDulGIl6pE1EmW0cNQb+nchGgOpClychJQ/i8nq1spczvQcDrUZIXtc/KLhQ1Ha0vHDUnS35y9xy89J0zUBaQkU7JyXHZ1NMqRFL+IoUQUQDzASwBsAHAM0KIdUR0FxFdpp82n4jWEdEqaKaha/T9ZwJYo+9/FsCNQoi8pf6V+bW+yuqPfEYK/4Ek4PMYK0vNZJS/FA6niZQsuysV8wqBQESGptOTyBW/14M9iv9hxaeH8PlH3su4YUlnOH2Bqv5And53OnLJXkPQBIyqcd363MemlWChmI8KmajiSHZaMbcbJeO9pv8AsFsKBIcFkdN3hYhQU+43+mZILcDuu201k/5/bzclVWcNReOWgpReBHya0PGQWSDItxlxqZRLoZDWklQIsRjAYsu+O5THNzs87zkAz2UzQDeR5abVyWGGTYc0OwI+D5r2t6MzHEM0LlzXEDJBRjhZfQnCMnepNmi5+gr6PAhFY0a5ivuvOAE/em5NWq/r95LJgXvb82uwpaUd63cfMaK40qEzbB9zboeqITgJhHRWaeFY4sfc1NJuurbVdKUKhHy22iwQ3zYOd0QQjsVRV23Oa+kIR3G4M2Ly6zhFGe053IV+5X7jd6NO/jKDudpBQ6joJtihX7nP0BCkYJIlMlRTllWw37NYi0y7a+5xxr6ww0KPiLTKxDYBFVGH91usuFMYpkgI+r0IReOmL8eEuqpuntRegiEAACAASURBVJGgcZ8WKnmrPnmmm6jy5HUzbXsTZ0OZ34u75k5NSqarCHrRr9yPk8fUoqG5FZccPwzhaBw7D3Xhyuma2u33evDWphZ8YYbmFsrEkWedkLfoE+v21o6MBEJHJJZ2TaKgSSBoAvCPX5+JvzVsxwVThuB7T6/C4c4INu45isl6NEhrexh/X7kTXz51tDEBqQl1Nz21wvR+Rg2oQFXQZ6wsP2g6YJxbCCajFK4dV1jW3Io1Ow7j67PrQUQQQuCVtXuwbtcRoxLvmz84G/WDKvHK2j14e3MLnvpQ64mx/MfnG9eRGsL+thD+8G4zRg+owHnHDsYbn+yzjRADgI93asUdq4L2QRDfPnciCIS504bjjU/24RdLEg7i/hUBpbyJ9to7D3Xin+v2mEpcHGgPIxYX8HoIB2zCUF/fsBfPrdjh+LsO+r14c1OLES1jvN++qCGUCrKCpowW+NxJIzJy/AIwil2NqE2dhwAAZ0zsnaipr55Wn7TP7/Vg9U8vNO374imjTdse0lbIMhdArsrOP3Zwytd0SiT7cGsr5k5LikR2JFOTkd9Ler9s7fXPmlSHsyZp9/X9LQewcNl2XPTrt/HOredgZG0FzrjvDbSHY1i36wh+9YUTAdhP7KqAmzaqP95p1OLc7335E2N/PjWEXPL5R94HoMX2/9uJw7Fpbxu+acnDOPuXb6Lhx+fjxj8vN+1XQy9j+gR5z0sb8H8rtdiTS08Yhp2HOnHCyESYsV1ip1PWeFXQhwUXHwMAOHZYjUkgDKgMYNsBbWEiNcXdh7tww5PLk67zh/eacd3pY3H6fUtN+7siMVz3Ry3keI9DSHZnOIbG9jYc7oiYNJa+GGVUMozXtQGZYHL35cd1d7otst1iOiGThcgvP69NkPOf0jqWVZf50fSzS/DYV2akfK6cQK0RVpk61joj0bRNRh4PGXkCdiYjVdP4n9c34+8rdxr2anWlb+cLUK/nNPGf88s3MfveN9Iaq9vkKh9CndSk+cap0Y1M3lLZebDTeCw1hCYlW1hO2P8+PeEcHjWgApvvudjYfu2WM3tkhh1fV4WmlnbE4sIoxOjEp/o4rOU10imhfeucycZz1fvVF6OMSgZpA5VRDd2VX7bymy9pscgygsGuFEUxYO0fXBX0weOhtDQlafcdWBnEX6+fZexvzzBZrTMcSyvkVNJfr+/kt4kyUoVTud+L7z69ythWm53YCQTVHNVd6KoMi8wXvW0xUstSSy1QnTTl7ybo89j2GpZ5AEAi+kb2OvB6CHsOa5+DNfFRFch1VT2rFdS/wo9oXKCppQ0vrdmdVK4FAO674ngAMPqtn6oXrJPfJ7Xo4++umm77OlVliV7eqhBgDaGIkXH5MuPQboJx4t9OHI4yv8dYOblVhjnXWBPqajLwIci474FVAZMDMNMmJR3hWEYalhQedhqCmkQ0YUi16VhXJNH/QmoAV89KmNDU68ULxYObB9RQYnlPrJ/puccMxsQhVbYRZWqggZwsw9E4Jg6uwnHDawwTbWU3fqOeatxSqEuNt8Umc3jq8H6oKfMZ45TfY5nfoZbWPs/BdKr2A4kpfoNS8yEU56zWQ2Rc/v62EAJeT8pELCt+ryehIeQxyigbrOae6rL0q33KH1JtRcC0um7PMIS1M5KZhiDHbKfRBRShbmf2+eN7zQAS5ZN/eOExxjF1QSAXfblw4OaKrkjM1HrSCTVuX67wVQ3h8mnDEdAr3Urn/ANfOBF/1hMpzRpC3Hh+ecBr+n51ZybMZHH25g/Oxt9vmg0godHIgAC70io+L2FgVRAHdO1GvjfZMEsVck5WA6OfikVDKLUooz4lEKrL/PB5SI83zvytB32JWj75zEPIBquGkE7GtUQKhKDPYzI9Zdp3NpOwUyAhfAdUJpcGVzWEkE2/B1n4TAoL1WyhaghSk7hudnJRMwB4fsUOUwN2yea9RzH3t++YVtluIbIUUnf+Yz2u+2MD1iktWu1Qx/7wm1twuCNiTJKLv3MGFlx8rNEfQN7jMyfVYfxgzSykRtHJyVImb6rhpd0lXmayOKsfVGlE2Mnvc3f5In6vBwMqA0Z0kRpxFo2bBYLTOOR3JRIzJ3WWmMWobwkEr4cwUo8O6olAUCeQUtEQMvkhSoHg8xKq9cJ8lxw/NHMNIUOTkfwB2goExfexYffRpOOvrt+H/1q0Tm+ZSSYBopq95Od5+Un20VK3PLMaX//DMgBaiOIP/qZ10vr165uxesdhvLmxd4oyAj0rXRGNxfHXj7SwUKdOeRLZtxrQ/CU/eWGtsW9EbTm8HjI6iMlrlfm9xmSsfv7yszrSGUV10GfqQtYbvSfkwsxuMSAJeD0YWBkw/B+qz0B9T91h9FOJxU1+g3TKphQTfUogAMCYgdqqJtKDcEJVIGRi8igk0inK54Rc/anCsCLgw5aW9rSFQlsoiqOhaEb3T5okBtcEk46pn4na20Gyvy2EP7zXjPW7jySZA9RaTw/Om4bvnDcRU4cn1V402HOkCy+t2Y3r/tiAZ5fvwI6DHeivC8k9h/PreLbSrFcABYArHn4Pb21qwRuf7MXbm1qSIoisYaRL1u3B6h2HEPB5DB/TJ3uOYvfhLiOhq8znMRYXr6zbYzw3Ghc40hXB+t1HMLgmaFq598ZvRk7U6rwsQ5Ilfq8HA6sC+GTPUew42GHyj0RicWzWezL883tnOr6OoSFYyr4USmVct+hzAkHW8+lJpyV1ZeBUYK7QUccty/qmi8y9OHtywvEmbb9n//LNtK5x3E+XAHCu2GrHmXoux+QhyZO1U30jK29ubMH4weYkRDWyali/ctxywaSUGpNMagOA0+9bapiiXl2f2lafKdlMNbLZi+Saxz/C1//QgK8+/pGpz4MqyC8+Tis/HYrG8eKa3SjzJfxs1sqrPm+iWZG6wo7FBG577mMAWn2uE5XcA6d7m43fRmoI8rd5zuQ6PHy1OVLI7yVjEXP6fUuxeV8iJDYci+PF1VojpNHdNL2SgicUM/sQelrPq1DpU4lpAPDd8yfhf9/ZiooerFZmjh2AF1Ylr0KLjfcWnItYXBiN7NPlrEl1eOP7Z2GcKbtb+zW3HA2hPRRNu0DfhRnUvr/hzHE479ghRltPFbvIo6dvmIXRAytw2s/N+QPX2CTz2fHBbeehPRzFeb96y9j3rbPH43dvJnfIkj4la9MiN+nJhKm2rLSiNpqRWcKPXD0dp0+sw+UnjcA39KSuU8cNNM575hun4f0tBxCOxjFJX0jYLYpiQuvRPb6uEtedrvljTqkfgGOH2Wtey398flb3zpioo3GMG1SJh68+GWV+L/71o3Nwxv1aAprP68G5xwzGE+82G8/zegixuMCyrQfxUXMrxg6q7NYMHHDQEFggFDmVQR82/b+Le1QnRpa5+LqD47FYGJ5GtzcnxllKfajz8bLmVpP24NYYiMhWGADJ0Sn9K/w4ddxA2/BIGRufiqH9ypLi7a84eSR8Xg/+919NuPOyqfjhs1oJE1nFsxBKXKi0tnffuKXlaAiPvLUFu3VT14TB1agK+nDR1KE4eUwtlm87aLrnFQEfzjt2SMrXjcUFDrSH8NXT6o0JtjvhP7Aq2QyYCaoJdFxdYlIfNaACd142FT9dtA4VAW9Sf/GaMh8OdkQMjS/V4sjJh1Bq4cp9zmQEwLYVYzpIR2ipfQmyQS25fe0Ty1KeLyfwWpccjHJ1aeRIVMoktuTPN5OIKjWK7EszR2F8XRVuuWAS1t81xzR5SA2hO6dmT5HRWz0xT7a2J0c9TRpSZSRpnXLPa/j9O1ux+GPN/q+WSpcv51R9tDs6wlF0ReIZ5bdkg/o5Wf1j13ymHs33Xgq/15P0Xqzh1oEUpkenKKMSUxD6noaQDUFFPWU0Mq0FNWFwNUb0L884B8QJKWD6V/jx3fMn4nx9FWs3icrV4yNXT09aMVpRJxqrSUMNmf1kjzms1U126xnS1j4X6dDaHjIV7Pv+BZPwrXMmwOshfLi1Fc+v2InqMp+Rg6A62MN6spVT9VGVB+dNw80Ltezw0QMqcNCS/NXbmAWCs8C3vhfrdqreHAGHPAQ2GfVhgrJyZi+sBouVTJzDABCOxlzN4ZCrwmhM4GspTHlSIMw5bljK6/q8Hvg8hKheIVNFrZ8kY/A37W3D1v3tRt0lN5AlM3pSHqG1PYwR/cuNPIxvn5foYXXP5cdj9vhBGFFbjnmPfYByv9dkP5e1qdLREOZOG4FT6gdg496juE9pOp+pf6qnqFpBsBsfgNW3ZS3lnUogJDKVhVlDKDGB0CdNRj3F0BDSiFvuK1g1hB2W6BYrdm0Ks0FOZN2Z8eSEXpbh68qVoFXoWWvySM5JM9IqXXboReOO2HQAS0VrexiDqpPzNgDN9HnFySMxa9xA/PN7Z+KNH5xlOi7LMaRrMhrevxznTB4Mn5cM34u1T3VvoX6XVLOXFeuEby1znUogBL3aZx6Jxs15Fy6Yj7siMfz5g20FIVxYIGTAxMFadMVJo/unOLPvcJKlJ8NV//tht+db2xRmizTfWPtJA8BFUzXzkQwG8PdQEFl7RnRXk8ctQtGYUYq5J/00WtvDGKA3oT9+RD/H8yYNqcawfmYHf0Qvx5Bp03uvx4PWDqkh5N6HkEnBSet7/sz4gQ5navh9iVpGB5SAAzcm8f9+bRN+/Pe1WLx2d9bXyhY2GWXAlOE1ePuH5zg2+uiLzDluGN764dk46xdvAgC2HXDWEFZ+ehB7j4TQFs58xeuEFAh2PoMH552Egx1heIjw/pYDPV61WiOcMim70VP2Hg4ZkXCN+9pQv+AlAJqvZGRtOV646XRHZ/OBthCaD3TgzEl1+Oj28zJ2Dg/rV4amlnZUOzSs6Q455lz5EFRTVyYFJ48ZlsjBGVITxNdm13d7vhp2qkZwZSIPbnpqBV5ak5j0b7/kWFx/5ji06iHCmWb89wYsEDJktE153b6OzP6WHGwPG6WGVX7/zlYAPXOSOiEnRScnslwJOpWk6I57PnccljcfxBxL2OTAqiC+fOporNh20HAqu41aX0jmCgBaZdFDHREcaA9hcLV9yWiZeDVpSLXjOd1x52VT8dKaPTh2WGaJi2rf4nGD0utEmC3lAS++c95EfNh0wOgC6MQjV5+MLS1tqKsKmjLSB1UFUwY5eD0EIk1D2Hc0hJoyH7oi8YxMRu827jdt37N4A64/c5yR8teTEiVuwwKBcZ3vLFyJJ/VKmConj6nFi2t24z8vOda115L1jeZOG+7aNSVXnToGV506xvbYzz6nhW/KlbtEFnXLFhkd5CH7Vej+o2HHyV6WgJ6p1/3PlAmDq3Hz+ZkJA8Dsx8k0+iwbbrlgUlrnzTkuIdhVU886m6KFVoi0ek5dkRh2HurEyNoKNO1vy8hk1BFKDkYx5cvkXx6wD4FxH7uuWkAiNDOdcMZ06V8RwNo7L8J3zp2Y+uRewFru4PKH3nXlurJpjVPtKbXktJVPWzWznbUZUm8j5cHdc6fm9HV7Qk8EVlXQj7ZQFO2hKKrKfPAQpR12GorGEI7F8e1zJ2DeKaOU/fFEVduMR+Q+aQkEIppDRBuJqJGIFtgcv5GIPiaiVUT0DhFNUY7dpj9vIxFd5ObgmcLh2+dOMEIuz55s30daCgQ3o4yARNe3fPCP+aebMtfdMiHJKqFOpcW7a0q0ae9RjOhfnrNIH4kU9Jk6o4uFGj1vozOilW/3EqXtQ5ACflBVEEOUCKdQJGYUyHMrNycbUv4yicgL4CEAFwOYAuBL6oSv85QQ4nghxDQA9wN4QH/uFADzAEwFMAfA7/TrMSXG9y+cjKU/OBt+LznWMwrH4iDKPHehkOlX4Xcsq5ENR1OEmjoJivZQFC+s2pWXtp8DuskSLwWqynyGhlAZ0BYh6VYtkKHDVUGfyd+172gIHzW3AigeDWEmgEYhRJMQIgxgIYC56glCCNUIV4lEoca5ABYKIUJCiK0AGvXrMSVKJCbw6FtNtmWBZchpIayE3KQ3BJxTxImMcHMSCDsO5q8Mt1wIFFuf4SkOhfesVJf50NYVNfp5yAJ5qWgLRY0claoyn6msx2d/805ePzMr6QiEEQC2K9s79H0miOgmItoCTUP4TobPvYGIGoiooaWl9xqNMLlj7G2Lk4RCTzvVFTq9UQq9LRRNqnL63oJz8fw3tdaRdsX7gMy717mJDMe1a2NZyLz47dPTOk+WAmkLRVEZ8Go+hDQ0hO8uXGk8rg76MHGIvcO+ENZJrv06hRAPCSHGA7gVwI8zfO5jQogZQogZdXX29mem+FBrPh3uiOAP7zWnNIUUI9aeDH96vxm3PLMKy7e19uh6neEYfvNGY5LpZXj/cmPSveOFdfjFkk9MQrczHMOPntU6ubmZ/JcuMrqqO/9GIZKu/6kq6Mf21g4c6YpiZG2FFgEWF/jdm43Y1Y2J7rUN+4zH0bjA7AmDMGtccgTYY2834devbcr8DbhIOt+anQDUAN+R+j4nFgK4vIfPZYqcH1+aCClVS3y833QgH8PJCVYN4Y4X1uH5FTtN9fcluw93GmGhTvz+nSYAmont/itPAAA8983PADAnxT20dIvJ3LB6xyFs2qvlIHzwn+dl/kay5Ouzx2LykGpcdqL7IcC9wQ8unISbzhmf9vnVZT6068Ju6vAaDKkpw1ubWnD/KxuTus45MX1MLQAtadLKJ3uO4u1N+bWQpCMQlgGYSERjiSgAzUm8SD2BiNSYv0sBbNYfLwIwj4iCRDQWwEQAH2U/bKZQ+Y8zxuEEvUuW2rv2iF5+4VefPzEv4+pNnHwI21uTs7ZP+/kbOOWe17q9nmqW/sKMUWi+91KcrE8kVv+LDDHd3xbCvMc+AAAsmj/btv90bzNqQAWWfO9MDK7JbbhrT5l/7kT88KJj0j5fzfieMKQKEwdXYfdhrbzI/m6EfMDnwTfOGofmey81rjGkpgzN916adK4vzw75lK8uhIgCmA9gCYANAJ4RQqwjoruI6DL9tPlEtI6IVgG4BcA1+nPXAXgGwHoArwC4SQhRXPokkzGyM5lq55b1eC6YmrrJSrHhdej41ZM2rUDq0hiyDSwAoz/ypr2JcFdrrX/GHdR+GmV+LyqUIoeyQdKq7YeMpkOAZlIKR+OOdZZetfRxzoepTyWtgGEhxGIAiy377lAe39zNc+8BcE9PB8gUH7KRkNpr93BnBB4CqnJQGC7XOGkIPamK29oexot6vZvLHbKv//aNz+Cj5lZc/6cG0z2W9KSxDZMaNQO9zOc1hVdLV87lD72L/hV+rLrjQgAJP5pT9rrVwZxuj/DeovRCPpi8I1dSVg2hptyftwSy3sQpysgpEqg7bnlmFVZtPwQAuPVie3NGvwq/UYvH7jXczARnEqiVVf1eMlW9JUq0Uz3UkahDJT+fdLv19WZv7nRggcC4jvyhtCkmk8OdkZxVwMw1dqW3iXomEPboNmmge/OBXHHK11CjH91sQMQkUBvwEJkTMInIVMlUIjUEpxIkVqw9wnMNf3MY1+lfoTk0mw8kahqVskBoCyX3K6gM+NBlaauZTiG0vUcUgdDNxG5t56q28Cy1xL9CwSpoKxVfDwHY0qJ939WQUqk1lAfSm2rzneXNAoFxnf4V2sR/+/+txSG9CJus/1KK2DWwqQx6EYsLox0lALy3JRF6u78tOSpl1fZDRk9ioHuBIDUEaV7qiTbCZIbVD6BqCPuOhvDIW1sAAHFlHbB+t1bEYWRtemXz2YfAlBy1FYmQx5fX7gGglTPI9+qntzilPjnJSDp225WSx1f/PtFNbsm6PUnPWfyx2eTQnclI+i22HehALC7wzb+sAACM4X4dvYYUCENqtE50Tq1U1Wzx9/VFQLrlMfzsQ2BKjYDPg8Z7LgYA3Pb8xwDcb51ZSIyrq0LzvZdiRP9EJz3ZxP2A0l1LLZV9+/+tTVrVH+oIY0hNEE0/uwTN916a0vTzxRmjsPdIl1EZFQD+ev2srN4L44w0GUnHb4VDxJwqEDrCMYweUOFY8NEKawhMSWJNsCllDUGizt91evOaVqX/7uDqIGZPSPTuVTOWn1m2Hc807ECZ35t2JNbQfmU40B42CZZyF5rzMPZYTUZO4b3q5xGKxjJy8uf7N1Lav1Amr/z79BHGqjkSEz1ucl8sqNFG/fQm8+1hdXIwa0mq7+FHz60BkFkdIOmk3380IXQy6SvMZIZ1YnfyiZkEQiSzgo4cZcSULH6PBzsPdSISi5e0yUiiaggBrzZZxBQPo7Za9OIuvaPYERtndDiD0tFSILQoDmqnjFgme9LRECYPqTYJ9VA0npGGUPClKximp7ywWqtj+Pg7WxGJxRHwlXY4pKohyFVhNJYINQ1H4wj6PUZdIlVDkNnO4Wj6AkEmoKl1dEox8a9QsGakV1gEwtThNZg9YRDawzGjpEg4Gk87BwFgkxFTwsiyCq0dYYT7mA9BCoRYXOD8B97Ck+83G6tFubI/3BkxylfLVWQmAkGuWOXkw/Qu0uErAwYqLBrDqNoKPNOgtX/50/vbAOhaYQZmPH+eBTrnuDO9RsDnQTgax6NvNaHM7yl5gaBqCHKCj8YFGve14ScvrNP3ew2B8PzKnVigR2HZXSMV8n7KjPDaitJM/CsUhvUrx8///Xicd8xgAJo29t9fPBG7DnXhF0s2ggi494rjMf+plTiiC+m2UBRD+6Vf/ZVNRkzJcsGxicqmXRk614oRdXGXEAjmFf/4ukrD9vzRVnMDncqAF8/ceFraryfvZ5ue6/DI1SdnPGYmM740c7SpvPfnThqJ08ZrkWMnjOyPOVOHAgAefasJHzQdQNP+dkweml4OApB/pzJrCEyv8asvnIizJtUZETSlriEQkn0Ikai5XMWFU4ea8gt+NGcyBlUFURHw4oIpQzKyN0snfaeeh5DvGPa+yvTRtXjx26djyrAakw9n8ce7IUTCxJQO+f6NlPYvlMkrZX4vvnBKomFeoMQnLHOUkV7xNWoOI62yVCL9+uyx+MKMUfjsCcMzEgZAQujIRKhMzE2Muxw3ol+SQ/9AmxYOnCo35HdXTTce51uos0Bgep1rThsDIP+rn95muJKpHLQpAQ4kwkKfuv5UXHPaGMc6+emQEAiaWSrfpZMZM7JBUqoaXpOHJnoicOkKpuSRZYNT1/osbv77C9OMxzIPQW1g46GEjfgz4wfhzrnHZfV68loy7p3lQWEwblAlAKBVL1tSnkIgqDoBawhMyWOUau5BB7Fiop8S5RMwylMnNISgz+tqaWr5GlILcWrUw+SWZ7/5GQDA2p1apdNUJiPV1JdvLZoFAtPrBG0mx1JHRhKpGoLbUVbSvCDvq5d9CAXBgMqAaTuVyUj92PJdIp4FAtPrBCzNXPoCMvZc9SG43SBIriwjejY0awiFSSoNQY1OG1iVfkRSb8ACgel1ZPRMX9AQJg6uAqBVNgXMQlAtf+0K+jwicx1YIBQO6ko/pQ9B+dgGWrSLXJOWQCCiOUS0kYgaiWiBzfFbiGg9Ea0hoteJaIxyLEZEq/S/RW4OnikOxupOtnGDqvI8kt7nqetn4YlrTzEmBFVDcNscIOd/WS+Jw04Lh4eVJMGUGoLysaUSHr1NysQ0IvICeAjABQB2AFhGRIuEEOuV01YCmCGE6CCibwK4H8AX9WOdQohpYPosZ06qw/Pf+gymjeyf76H0OnXVQZyjlzbwesjkQ3B7BS8d1NE4m4wKjWOVUFKnRjoSNdCgGMJOZwJoFEI0CSHCABYCmKueIIRYKoTo0Dc/ADDS3WEyxc700bV9rhKn10MmM5nbE3ZCQ5B5CH3r/hYyak2ishTF7dSPrRjCTkcA2K5s79D3OXEdgJeV7TIiaiCiD4jocrsnENEN+jkNLS0taQyJYQofn4dMobZuT9hWp3JfE7iFjFqTKFWosepUzrdAcLWWERFdDWAGgLOU3WOEEDuJaByAN4joYyHEFvV5QojHADwGADNmzCj1/CWmj+D1kKl0RW9N2IZTmX0IBUMm+QTqx1YMJqOdAEYp2yP1fSaI6HwAtwO4TAhhdOwQQuzU/zcBeBPASVmMl2GKhlxpCFHWEAqOngqEfH+G6Yx6GYCJRDSWiAIA5gEwRQsR0UkAHoUmDPYp+2uJKKg/HgRgNgDVGc0wJYvX4zFpCF6XV39y7oiwD6HgyMRfRCiczy2lyUgIESWi+QCWAPACeFwIsY6I7gLQIIRYBOAXAKoA/E23l30qhLgMwLEAHiWiODThc68lOolhSpb9bSHsb0tsu12VgKOMSoNCsvSl5UMQQiwGsNiy7w7l8fkOz3sPwPHZDJBhSgW3q5FyHkJh85VZY1Cv5+B0R21FfpPRVLhBDsPkiN7LQ+BM5ULk7svTq2ZbSJ8bl65gmBzRGzZ+IkC3GKGA5hWmSGGBwDA5ojciSKSZyOshV0trM7nlsycMMyrk5pP8j4Bh+gi9kScgr8g5CMXNb788PfVJOYA1BIbJEb1hK5YaAndLY9yAv0YMkyN6y4egXZt/ykz28LeIYXqJ/7zkGNN2b/gQpEBghzLjBiwQGKaXuPi4Yabt3tAQVKcyw2QLCwSG6SWClh7K1m03MJzKLBAYF2CBwDC9hGwdCgDXfqYe82aOdv01WENg3ITDThmmlwgqjVF++m9TeiVPQF6Sw04ZN2ANgWF6CdVE1FtJY2SEnbJAYLKHBQLD9BK5yByWcoBNRowbsEBgmCKGfQiMm7BAYJgihn0IjJuwQGCYIoZYQ2BchAUCwxQxUgxwcxzGDVggMEwRwz4Exk04D4FhepE/fn0mtrd29Nr1pRw40Bbqtddg+g4sEBimFzlrUl2vXl/6EC45fliKMxkmNWwyYpgiRroO2IXAuEFaAoGI5hDRRiJqJKIFNsdvIaL1RLSGiF4nojHKsWuIaLP+d42bg2eYvo70IXD7TMYNUgoEIvICeAjAxQCmAPgSEU2xnLYSwAwhxAkAngVwv/7ca0CvZgAACeVJREFUAQB+CuBUADMB/JSIat0bPsP0bQwNIb/DYEqEdDSEmQAahRBNQogwgIUA5qonCCGWCiGk5+wDACP1xxcBeFUI0SqEOAjgVQBz3Bk6wzCsITBuko5AGAFgu7K9Q9/nxHUAXs7kuUR0AxE1EFFDS0tLGkNiGAZIaAYsDxg3cNWpTERXA5gB4BeZPE8I8ZgQYoYQYkZdXe9GZTBMKcEmI8ZN0hEIOwGMUrZH6vtMENH5AG4HcJkQIpTJcxmG6RkJk1GeB8KUBOkIhGUAJhLRWCIKAJgHYJF6AhGdBOBRaMJgn3JoCYALiahWdyZfqO9jGMYFpCDg0hWMG6RMTBNCRIloPrSJ3AvgcSHEOiK6C0CDEGIRNBNRFYC/6c6tT4UQlwkhWonobmhCBQDuEkK09so7YZg+iKEh5HkcTGmQVqayEGIxgMWWfXcoj8/v5rmPA3i8pwNkGMYZ4sw0xkU4U5lhihiy/GeYbGCBwDAlAPsQGDdggcAwJQDLA8YNWCAwTAnA8oBxAxYIDFPEsE+ZcRMWCAxTAnAtI8YNWCAwTAnA8oBxAxYIDFPEJGoZsURgsocFAsMUMVIQsIbAuAELBIYpATwsEBgXYIHAMCUAm4wYN2CBwDAlAJuMGDdggcAwRQwLAsZNWCAwTAnAeQiMG7BAYJgSgJ3KjBuwQGCYIobLXzNuwgKBYYoZo6cyiwQme1ggMEwJwPKAcQMWCAxTArCGwLgBCwSGKQFYHDBuwAKBYYqYtq4IADYZMe6QlkAgojlEtJGIGologc3xM4loBRFFiehKy7EYEa3S/xa5NXCGYYAtLe0AgM172/I8EqYU8KU6gYi8AB4CcAGAHQCWEdEiIcR65bRPAVwL4Ac2l+gUQkxzYawMwzgQjcfzPQSmBEgpEADMBNAohGgCACJaCGAuAEMgCCGa9WP8rWSYPODzsPWXyZ50vkUjAGxXtnfo+9KljIgaiOgDIrrc7gQiukE/p6GlpSWDSzMMAwBeTlVmXCAXy4oxQogZAL4M4NdENN56ghDiMSHEDCHEjLq6uhwMiWEYhrGSjkDYCWCUsj1S35cWQoid+v8mAG8COCmD8TEMkwZxIfI9BKYESEcgLAMwkYjGElEAwDwAaUULEVEtEQX1x4MAzIbie2AYxh3icRYITPakFAhCiCiA+QCWANgA4BkhxDoiuouILgMAIjqFiHYA+DyAR4lonf70YwE0ENFqAEsB3GuJTmIYxgVirCEwLpBOlBGEEIsBLLbsu0N5vAyaKcn6vPcAHJ/lGBmGSUGM4/sYF+BYNYYpYu68bCoANhkx7sACgWGKmHK/FwCbjBh3YIHAMEWMR88/YA2BcQMWCAxTxPh0gcAaAuMGLBAYpogJ+LSfMJeuYNwgrSgjhmEKkwunDMGNZ43HN84cl++hMCUACwSGKWJ8Xg8WXHxMvofBlAisZzIMwzAAWCAwDMMwOiwQGIZhGAAsEBiGYRgdFggMwzAMABYIDMMwjA4LBIZhGAYACwSGYRhGh0SB1UAhohYA27K4xCAA+10aTq7gMeeOYhw3jzl3FOO45ZjHCCGyakpfcAIhW4ioQQgxI9/jyAQec+4oxnHzmHNHMY7bzTGzyYhhGIYBwAKBYRiG0SlFgfBYvgfQA3jMuaMYx81jzh3FOG7XxlxyPgSGYRimZ5SihsAwDMP0ABYIDMMwDIASEghENIeINhJRIxEtyPd4JEQ0ioiWEtF6IlpHRDfr+wcQ0atEtFn/X6vvJyL6H/19rCGi6Xkcu5eIVhLRi/r2WCL6UB/b00QU0PcH9e1G/Xh9Hsfcn4ieJaJPiGgDEZ1W6PeaiL6nfzfWEtFfiaisEO81ET1ORPuIaK2yL+N7S0TX6OdvJqJr8jDmX+jfjzVE9H9E1F85dps+5o1EdJGyP6fzi924lWPfJyJBRIP0bffutRCi6P8AeAFsATAOQADAagBT8j0ufWzDAEzXH1cD2ARgCoD7ASzQ9y8AcJ/++BIALwMgALMAfJjHsd8C4CkAL+rbzwCYpz9+BMA39cffAvCI/ngegKfzOOY/AvgP/XEAQP9CvtcARgDYCqBcucfXFuK9BnAmgOkA1ir7Mrq3AAYAaNL/1+qPa3M85gsB+PTH9yljnqLPHUEAY/U5xZuP+cVu3Pr+UQCWQEveHeT2vc7pl78Xb95pAJYo27cBuC3f43IY6wsALgCwEcAwfd8wABv1x48C+JJyvnFejsc5EsDrAM4F8KL+Zduv/JCMe65/QU/TH/v08ygPY+6nT65k2V+w9xqaQNiu/2h9+r2+qFDvNYB6y+Sa0b0F8CUAjyr7TeflYsyWY58D8Bf9sWnekPc6X/OL3bgBPAvgRADNSAgE1+51qZiM5I9KskPfV1Do6v1JAD4EMEQIsVs/tAfAEP1xobyXXwP4EYC4vj0QwCEhRNRmXMaY9eOH9fNzzVgALQCe0E1d/0tElSjgey2E2AnglwA+BbAb2r1bjsK/15JM723e77mFr0NbXQMFPmYimgtgpxBiteWQa+MuFYFQ8BBRFYDnAHxXCHFEPSY08V0w8b9E9FkA+4QQy/M9lgzxQVOzHxZCnASgHZoZw6AA73UtgLnQhNlwAJUA5uR1UD2k0O5tKojodgBRAH/J91hSQUQVAP4TwB29+TqlIhB2QrOtSUbq+woCIvJDEwZ/EUI8r+/eS0TD9OPDAOzT9xfCe5kN4DIiagawEJrZ6EEA/YnIZzMuY8z68X4ADuRywDo7AOwQQnyobz8LTUAU8r0+H8BWIUSLECIC4Hlo97/Q77Uk03tbCPccRHQtgM8CuEoXZEBhj3k8tEXDav13ORLACiIa2s34Mh53qQiEZQAm6pEZAWjOtkV5HhMALQIAwO8BbBBCPKAcWgRAev2vgeZbkPu/qkcOzAJwWFHJc4IQ4jYhxEghRD20e/mGEOIqAEsBXOkwZvlertTPz/lKUQixB8B2Ipqs7zoPwHoU8L2GZiqaRUQV+ndFjrmg77VCpvd2CYALiahW144u1PflDCKaA80cepkQokM5tAjAPD2SayyAiQA+QgHML0KIj4UQg4UQ9frvcge0YJU9cPNe97ZjJFd/0Dztm6BFA9ye7/Eo4zodmhq9BsAq/e8SaHbf1wFsBvAagAH6+QTgIf19fAxgRp7HfzYSUUbjoP1AGgH8DUBQ31+mbzfqx8flcbzTADTo9/vv0KIrCvpeA7gTwCcA1gJ4ElqUS8HdawB/hebniOgT0nU9ubfQ7PaN+t/X8jDmRmi2dfl7fEQ5/3Z9zBsBXKzsz+n8Yjduy/FmJJzKrt1rLl3BMAzDACgdkxHDMAyTJSwQGIZhGAAsEBiGYRgdFggMwzAMABYIDMMwjA4LBIZhGAYACwSGYRhG5/8H566slvQFYKwAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "seq = y_batch[batch, :, signal]\n", + "plt.plot(seq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Validation Set\n", + "\n", + "The neural network trains quickly so we can easily run many training epochs. But then there is a risk of overfitting the model to the training-set so it does not generalize well to unseen data. We will therefore monitor the model's performance on the test-set after each epoch and only save the model's weights if the performance is improved on the test-set.\n", + "\n", + "The batch-generator randomly selects a batch of short sequences from the training-data and uses that during training. But for the validation-data we will instead run through the entire sequence from the test-set and measure the prediction accuracy on that entire sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "validation_data = (np.expand_dims(x_test_scaled, axis=0),\n", + " np.expand_dims(y_test_scaled, axis=0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the Recurrent Neural Network\n", + "\n", + "We are now ready to create the Recurrent Neural Network (RNN). We will use the Keras API for this because of its simplicity. See Tutorial #03-C for a tutorial on Keras and Tutorial #20 for more information on Recurrent Neural Networks." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now add a Gated Recurrent Unit (GRU) to the network. This will have 512 outputs for each time-step in the sequence.\n", + "\n", + "Note that because this is the first layer in the model, Keras needs to know the shape of its input, which is a batch of sequences of arbitrary length (indicated by `None`), where each observation has a number of input-signals (`num_x_signals`)." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "model.add(GRU(units=512,\n", + " return_sequences=True,\n", + " input_shape=(None, num_x_signals,)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The GRU outputs a batch of sequences of 512 values. We want to predict 3 output-signals, so we add a fully-connected (or dense) layer which maps 512 values down to only 3 values.\n", + "\n", + "The output-signals in the data-set have been limited to be between 0 and 1 using a scaler-object. So we also limit the output of the neural network using the Sigmoid activation function, which squashes the output to be between 0 and 1." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "model.add(Dense(num_y_signals, activation='sigmoid'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A problem with using the Sigmoid activation function, is that we can now only output values in the same range as the training-data.\n", + "\n", + "For example, if the training-data only has temperatures between -20 and +30 degrees, then the scaler-object will map -20 to 0 and +30 to 1. So if we limit the output of the neural network to be between 0 and 1 using the Sigmoid function, this can only be mapped back to temperature values between -20 and +30.\n", + "\n", + "We can use a linear activation function on the output instead. This allows for the output to take on arbitrary values. It might work with the standard initialization for a simple network architecture, but for more complicated network architectures e.g. with more layers, it might be necessary to initialize the weights with smaller values to avoid `NaN` values during training. You may need to experiment with this to get it working." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "if False:\n", + " from tensorflow.python.keras.initializers import RandomUniform\n", + "\n", + " # Maybe use lower init-ranges.\n", + " init = RandomUniform(minval=-0.05, maxval=0.05)\n", + "\n", + " model.add(Dense(num_y_signals,\n", + " activation='linear',\n", + " kernel_initializer=init))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loss Function\n", + "\n", + "We will use Mean Squared Error (MSE) as the loss-function that will be minimized. This measures how closely the model's output matches the true output signals.\n", + "\n", + "However, at the beginning of a sequence, the model has only seen input-signals for a few time-steps, so its generated output may be very inaccurate. Using the loss-value for the early time-steps may cause the model to distort its later output. We therefore give the model a \"warmup-period\" of 50 time-steps where we don't use its accuracy in the loss-function, in hope of improving the accuracy for later time-steps." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "warmup_steps = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "def loss_mse_warmup(y_true, y_pred):\n", + " \"\"\"\n", + " Calculate the Mean Squared Error between y_true and y_pred,\n", + " but ignore the beginning \"warmup\" part of the sequences.\n", + " \n", + " y_true is the desired output.\n", + " y_pred is the model's output.\n", + " \"\"\"\n", + "\n", + " # The shape of both input tensors are:\n", + " # [batch_size, sequence_length, num_y_signals].\n", + "\n", + " # Ignore the \"warmup\" parts of the sequences\n", + " # by taking slices of the tensors.\n", + " y_true_slice = y_true[:, warmup_steps:, :]\n", + " y_pred_slice = y_pred[:, warmup_steps:, :]\n", + "\n", + " # These sliced tensors both have this shape:\n", + " # [batch_size, sequence_length - warmup_steps, num_y_signals]\n", + "\n", + " # Calculat the Mean Squared Error and use it as loss.\n", + " mse = mean(square(y_true_slice - y_pred_slice))\n", + " \n", + " return mse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile Model\n", + "\n", + "This is the optimizer and the beginning learning-rate that we will use." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = RMSprop(lr=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then compile the Keras model so it is ready for training." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(loss=loss_mse_warmup, optimizer=optimizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a very small model with only two layers. The output shape of `(None, None, 3)` means that the model will output a batch with an arbitrary number of sequences, each of which has an arbitrary number of observations, and each observation has 3 signals. This corresponds to the 3 target signals we want to predict." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "gru (GRU) (None, None, 512) 820224 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, None, 3) 1539 \n", + "=================================================================\n", + "Total params: 821,763\n", + "Trainable params: 821,763\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Callback Functions\n", + "\n", + "During training we want to save checkpoints and log the progress to TensorBoard so we create the appropriate callbacks for Keras.\n", + "\n", + "This is the callback for writing checkpoints during training." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "path_checkpoint = '23_checkpoint.keras'\n", + "callback_checkpoint = ModelCheckpoint(filepath=path_checkpoint,\n", + " monitor='val_loss',\n", + " verbose=1,\n", + " save_weights_only=True,\n", + " save_best_only=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the callback for stopping the optimization when performance worsens on the validation-set." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "callback_early_stopping = EarlyStopping(monitor='val_loss',\n", + " patience=5, verbose=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the callback for writing the TensorBoard log during training." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "callback_tensorboard = TensorBoard(log_dir='./23_logs/',\n", + " histogram_freq=0,\n", + " write_graph=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This callback reduces the learning-rate for the optimizer if the validation-loss has not improved since the last epoch (as indicated by `patience=0`). The learning-rate will be reduced by multiplying it with the given factor. We set a start learning-rate of 1e-3 above, so multiplying it by 0.1 gives a learning-rate of 1e-4. We don't want the learning-rate to go any lower than this." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "callback_reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.1,\n", + " min_lr=1e-4,\n", + " patience=0,\n", + " verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "callbacks = [callback_early_stopping,\n", + " callback_checkpoint,\n", + " callback_tensorboard,\n", + " callback_reduce_lr]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train the Recurrent Neural Network\n", + "\n", + "We can now train the neural network.\n", + "\n", + "Note that a single \"epoch\" does not correspond to a single processing of the training-set, because of how the batch-generator randomly selects sub-sequences from the training-set. Instead we have selected `steps_per_epoch` so that one \"epoch\" is processed in a few minutes.\n", + "\n", + "With these settings, each \"epoch\" took about 2.5 minutes to process on a GTX 1070. After 14 \"epochs\" the optimization was stopped because the validation-loss had not decreased for 5 \"epochs\". This optimization took about 35 minutes to finish.\n", + "\n", + "Also note that the loss sometimes becomes `NaN` (not-a-number). This is often resolved by restarting and running the Notebook again. But it may also be caused by your neural network architecture, learning-rate, batch-size, sequence-length, etc. in which case you may have to modify those settings." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "Train for 100 steps, validate on 1 samples\n", + "Epoch 1/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0086\n", + "Epoch 00001: val_loss improved from inf to 0.00398, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 68s 684ms/step - loss: 0.0085 - val_loss: 0.0040\n", + "Epoch 2/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0048\n", + "Epoch 00002: val_loss did not improve from 0.00398\n", + "\n", + "Epoch 00002: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n", + "100/100 [==============================] - 71s 713ms/step - loss: 0.0048 - val_loss: 0.0043\n", + "Epoch 3/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0031\n", + "Epoch 00003: val_loss improved from 0.00398 to 0.00258, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 71s 712ms/step - loss: 0.0031 - val_loss: 0.0026\n", + "Epoch 4/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0029\n", + "Epoch 00004: val_loss improved from 0.00258 to 0.00250, saving model to 23_checkpoint.keras\n", + "\n", + "Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.0001.\n", + "100/100 [==============================] - 67s 670ms/step - loss: 0.0029 - val_loss: 0.0025\n", + "Epoch 5/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0028\n", + "Epoch 00005: val_loss improved from 0.00250 to 0.00248, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 71s 713ms/step - loss: 0.0028 - val_loss: 0.0025\n", + "Epoch 6/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0028\n", + "Epoch 00006: val_loss improved from 0.00248 to 0.00243, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 68s 678ms/step - loss: 0.0028 - val_loss: 0.0024\n", + "Epoch 7/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0027\n", + "Epoch 00007: val_loss did not improve from 0.00243\n", + "100/100 [==============================] - 65s 651ms/step - loss: 0.0027 - val_loss: 0.0024\n", + "Epoch 8/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0027\n", + "Epoch 00008: val_loss did not improve from 0.00243\n", + "100/100 [==============================] - 65s 650ms/step - loss: 0.0027 - val_loss: 0.0024\n", + "Epoch 9/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0027\n", + "Epoch 00009: val_loss improved from 0.00243 to 0.00239, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 65s 652ms/step - loss: 0.0027 - val_loss: 0.0024\n", + "Epoch 10/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n", + "Epoch 00010: val_loss improved from 0.00239 to 0.00239, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 65s 650ms/step - loss: 0.0026 - val_loss: 0.0024\n", + "Epoch 11/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n", + "Epoch 00011: val_loss improved from 0.00239 to 0.00231, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 65s 652ms/step - loss: 0.0026 - val_loss: 0.0023\n", + "Epoch 12/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n", + "Epoch 00012: val_loss improved from 0.00231 to 0.00229, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 65s 651ms/step - loss: 0.0026 - val_loss: 0.0023\n", + "Epoch 13/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n", + "Epoch 00013: val_loss improved from 0.00229 to 0.00228, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 65s 652ms/step - loss: 0.0026 - val_loss: 0.0023\n", + "Epoch 14/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n", + "Epoch 00014: val_loss did not improve from 0.00228\n", + "100/100 [==============================] - 65s 653ms/step - loss: 0.0026 - val_loss: 0.0023\n", + "Epoch 15/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n", + "Epoch 00015: val_loss did not improve from 0.00228\n", + "100/100 [==============================] - 65s 653ms/step - loss: 0.0026 - val_loss: 0.0023\n", + "Epoch 16/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0025\n", + "Epoch 00016: val_loss did not improve from 0.00228\n", + "100/100 [==============================] - 66s 657ms/step - loss: 0.0025 - val_loss: 0.0023\n", + "Epoch 17/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0025\n", + "Epoch 00017: val_loss did not improve from 0.00228\n", + "100/100 [==============================] - 67s 665ms/step - loss: 0.0025 - val_loss: 0.0023\n", + "Epoch 18/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0025\n", + "Epoch 00018: val_loss did not improve from 0.00228\n", + "100/100 [==============================] - 69s 685ms/step - loss: 0.0025 - val_loss: 0.0023\n", + "Epoch 00018: early stopping\n", + "CPU times: user 15min 17s, sys: 4min 8s, total: 19min 26s\n", + "Wall time: 20min 4s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "model.fit(x=generator,\n", + " epochs=20,\n", + " steps_per_epoch=100,\n", + " validation_data=validation_data,\n", + " callbacks=callbacks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Checkpoint\n", + "\n", + "Because we use early-stopping when training the model, it is possible that the model's performance has worsened on the test-set for several epochs before training was stopped. We therefore reload the last saved checkpoint, which should have the best performance on the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " model.load_weights(path_checkpoint)\n", + "except Exception as error:\n", + " print(\"Error trying to load checkpoint.\")\n", + " print(error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance on Test-Set\n", + "\n", + "We can now evaluate the model's performance on the test-set. This function expects a batch of data, but we will just use one long time-series for the test-set, so we just expand the array-dimensionality to create a batch with that one sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "1/1 [==============================] - 1s 729ms/sample - loss: 0.0023\n" + ] + } + ], + "source": [ + "result = model.evaluate(x=np.expand_dims(x_test_scaled, axis=0),\n", + " y=np.expand_dims(y_test_scaled, axis=0))" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss (test-set): 0.002279780339449644\n" + ] + } + ], + "source": [ + "print(\"loss (test-set):\", result)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "# If you have several metrics you can use this instead.\n", + "if False:\n", + " for res, metric in zip(result, model.metrics_names):\n", + " print(\"{0}: {1:.3e}\".format(metric, res))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Predictions\n", + "\n", + "This helper-function plots the predicted and true output-signals." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_comparison(start_idx, length=100, train=True):\n", + " \"\"\"\n", + " Plot the predicted and true output-signals.\n", + " \n", + " :param start_idx: Start-index for the time-series.\n", + " :param length: Sequence-length to process and plot.\n", + " :param train: Boolean whether to use training- or test-set.\n", + " \"\"\"\n", + " \n", + " if train:\n", + " # Use training-data.\n", + " x = x_train_scaled\n", + " y_true = y_train\n", + " else:\n", + " # Use test-data.\n", + " x = x_test_scaled\n", + " y_true = y_test\n", + " \n", + " # End-index for the sequences.\n", + " end_idx = start_idx + length\n", + " \n", + " # Select the sequences from the given start-index and\n", + " # of the given length.\n", + " x = x[start_idx:end_idx]\n", + " y_true = y_true[start_idx:end_idx]\n", + " \n", + " # Input-signals for the model.\n", + " x = np.expand_dims(x, axis=0)\n", + "\n", + " # Use the model to predict the output-signals.\n", + " y_pred = model.predict(x)\n", + " \n", + " # The output of the model is between 0 and 1.\n", + " # Do an inverse map to get it back to the scale\n", + " # of the original data-set.\n", + " y_pred_rescaled = y_scaler.inverse_transform(y_pred[0])\n", + " \n", + " # For each output-signal.\n", + " for signal in range(len(target_names)):\n", + " # Get the output-signal predicted by the model.\n", + " signal_pred = y_pred_rescaled[:, signal]\n", + " \n", + " # Get the true output-signal from the data-set.\n", + " signal_true = y_true[:, signal]\n", + "\n", + " # Make the plotting-canvas bigger.\n", + " plt.figure(figsize=(15,5))\n", + " \n", + " # Plot and compare the two signals.\n", + " plt.plot(signal_true, label='true')\n", + " plt.plot(signal_pred, label='pred')\n", + " \n", + " # Plot grey box for warmup-period.\n", + " p = plt.axvspan(0, warmup_steps, facecolor='black', alpha=0.15)\n", + " \n", + " # Plot labels etc.\n", + " plt.ylabel(target_names[signal])\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now plot an example of predicted output-signals. It is important to understand what these plots show, as they are actually a bit more complicated than you might think.\n", + "\n", + "These plots only show the output-signals and not the 20 input-signals used to predict the output-signals. The time-shift between the input-signals and the output-signals is held fixed in these plots. The model **always** predicts the output-signals e.g. 24 hours into the future (as defined in the `shift_steps` variable above). So the plot's x-axis merely shows how many time-steps of the input-signals have been seen by the predictive model so far.\n", + "\n", + "The prediction is not very accurate for the first 30-50 time-steps because the model has seen very little input-data at this point.\n", + "The model generates a single time-step of output data for each time-step of the input-data, so when the model has only run for a few time-steps, it knows very little of the history of the input-signals and cannot make an accurate prediction. The model needs to \"warm up\" by processing perhaps 30-50 time-steps before its predicted output-signals can be used.\n", + "\n", + "That is why we ignore this \"warmup-period\" of 50 time-steps when calculating the mean-squared-error in the loss-function. The \"warmup-period\" is shown as a grey box in these plots.\n", + "\n", + "Let us start with an example from the training-data. This is data that the model has seen during training so it should perform reasonably well on this data." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_comparison(start_idx=100000, length=1000, train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model was able to predict the overall oscillations of the temperature quite well but the peaks were sometimes inaccurate. For the wind-speed, the overall oscillations are predicted reasonably well but the peaks are quite inaccurate. For the atmospheric pressure, the overall curve-shape has been predicted although there seems to be a slight lag and the predicted curve has a lot of noise compared to the smoothness of the original signal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Strange Example\n", + "\n", + "The following is another example from the training-set.\n", + "\n", + "Note how the temperature does not oscillate very much within each day (this plot shows almost 42 days). The temperature normally oscillates within each day, see e.g. the plot above where the daily temperature-oscillation is very clear. It is unclear whether this period had unusually stable temperature, or if perhaps there's a data-error." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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RLXvqjDHZwCxgmXvou8aYdcaYB40xQ7pjDd3B4ARwFnW/FBERERGR7uF5UGeMGQg8B3zfWlsB3AtMAmbiZPJ+38HjrjfGrDTGrCwqKvJ6mfERCuCMDxvqfomCOhERERER8Y6nQZ0xJhEnoHvMWrsIwFpbYK0NWGuDwP3AvPYea61daK2dY62dk5WV5eUy4yciqCPc/VKNUkRERERExDtedr80wAPAp9baP0QcHxVxt0uBDV6tobuFmqJY44sov9RIAxERERER8Y6X3S/nA18G1htjctxjPwWuMsbMxOn1nwt8y8M1dK/I8kuj4eMiIiIiIuI9L7tffgiYdm561avX7GnN4wt8apQiIiIiIiLdolu6Xx4xQuWXPh/hb62COhERERER8ZCCuniKyNSFyy/V/VJERERERDykoC6OwnPq1P1SRERERES6iYK6eAoFcOp+KSIiIiIi3URBXTyFAjhj1P1SRERERES6hYK6OAoFcNb4UaMUERERERHpDp2ONDDG3NzZ7ZFDxYWIUkvTXH6JMnUiIiIiIuKdrubUpbt/TwPmAi+61y8Elnu1qD4rvKfOj3UbpRjtqRMREREREQ91GtRZa38BYIx5H5htra10r98GvOL56vqYUABnjQHjdw5qT52IiIiIiHgo2j11I4CGiOsN7jFpoTlT1zzSQHvqRERERETEO12VX4Y8Aiw3xvzbvX4J8LA3S+rDQgGcMVhC3S8V1ImIiIiIiHeiCuqstb8yxrwGnOYeus5au8a7ZfVNoQDORs6pQ0GdiIiIiIh451BGGgwAKqy1dwN5xpiJHq2p7wpn6iKHjyuoExERERER70QV1Bljfg78BPgf91Ai8KhXi+qrmkstfRHdLxXUiYiIiIiId6LN1F0KXARUA1hr99M87kBC2iu/VFAnIiIiIiIeijaoa7DWWtz2jsaYNO+W1Ie1W36pkQYiIiIiIuKdaIO6p40xfwcyjDHfBN4E7vduWX2ToTlTF+5+qUYpIiIiIiLioWi7X95ljDkXqACmAbdaa5d4urK+SI1SRERERESkm0U7pw5gK2CttW8aYwYYY9KttZVeLaxPUlAnIiIiIiLdLNrul98EngX+7h4aAzzv1aL6KuPun7PG5zRLiTgmIiIiIiLihWj31H0HmI9Tfom1dhsw3KtF9Vk24F4w7p/IYyIiIiIiIvEXbVBXb61tCF0xxiTgdsKUZuGsnPGHyy+VqRMRERERES9FG9S9Z4z5KZDqNkx5BnjJu2X1UW5WzhoDxmAxoO6XIiIiIiLioWiDuluAImA98C3gVeBnnT3AGDPOGPOOMWaTMWajMeZ77vGhxpglxpht7t9DDucL6F0iMnXgZOuUqRMREREREQ9FO9IgaIx5GFiGE7lscYeRd6YJ+IG1drUxJh1YZYxZAnwVeMtae6cx5hacgPEnMX8FvYiJzNQBGBM+JiIiIiIi4oVou1+eD+wA7gH+Amw3xny+s8dYaw9Ya1e7lyuBT3G6Zl4MPOze7WHgktiW3guF99Q531Zr/MrUiYiIiIiIp6KdU/d74Cxr7XYAY8wk4BXgtWgebIzJBmbhZPpGWGsPuDflAyMOYb29W+ScOucC2lMnIiIiIiJeinZPXWUooHPtBKIaPG6MGQg8B3zfWlsReZtbwtluKssYc70xZqUxZmVRUVGUy+xZxg3qbOjbanyYoII6ERERERHxTrRB3UpjzKvGmK8aY67F6Xy5whhzmTHmso4eZIxJxAnoHrPWLnIPFxhjRrm3jwIK23ustXahtXaOtXZOVlZW1F9Qj2qVqXMGkCuoExERERER70Qb1KUABcAZwJk4nTBTgQuBC9p7gDHGAA8An1pr/xBx04vAte7la4EXDnnVvcHuj0kpzGl5rHX5pbpfioiIiIiIx6LtfnldDM89H/gysN4YE4p+fgrcCTxtjPk6sBv4zxieu+e9dTvD6urJO+e+8CHjZuVsOKhT90sREREREfFWp0GdMeabwLvW2m0RmbfLcYKxa621azp6rLX2Q5xOIe35bIzr7T18/ubMXEib8kt1vxQREREREW91VX75PSDXvXwVcAJwFHAzzniDI5fxtcnCGXW/FBERERGRbtZVUNdkrW10L18APGKtLbHWvgmkebu0Xs7XThbOti6/9DUHeiIiIiIiIh7oKqgLGmNGGWNScEom34y4LdW7ZfUB7WTq2pZfmrYlmiIiIiIiInHUVaOUW4GVgB940Vq7EcAYcwbOrLojl2m7p67tnLp29t2JiIiIiIjEUadBnbX2ZWPMBCDdWnsw4qaVwBc9XVlv5/NDm0yde93nZuowGDVKERERERERD3XV/fKyiMvt3WVRewePCMaHoWXA1qZRivG1DfxERERERETiqKvyywvdv4cDpwJvu9fPApZyhAd1bTN1oUYp/ub7oEydiIiIiIh4p6vyy+sAjDFvAMdYaw+410cBD3m+ut7M52/b2TIU5IUbpaj7pYiIiIiIeKur7pch40IBnasAGO/BevoO44dgB41Swpk6db8UERERERFvdVV+GfKWMeZ14An3+hdpOd7gyNNuo5TWw8d9CupERERERMRTUQV11trvuk1TTnMPLbTW/tu7ZfUB7TZKaaf8UnvqRERERETEQ9Fm6rDWLuJIbozSmmk/UxcuvQS3mYoydSIiIiIi4p2o9tQZYy4zxmwzxpQbYyqMMZXGmAqvF9er+do2QTE2oKBORERERES6VbSZut8CF1prP/VyMX2K8bcN2IKBiP106n4pIiIiIiLei7b7ZYECulZ8bYM607r8Eh9Y7akTERERERHvRJupW2mMeQp4HqgPHXT32R2Z2svC2WCLTJ0z0qDVvjsREREREZE4ijaoGwTUAP8RccxyJDdOaadRirEB8EWWX/rV/VJERERERDwV7UiD67xeSJ/j87ebqWvZKEXDx0VERERExFudBnXGmB9ba39rjPkztE05WWtv8mxlvV07nS2NDbQqv/Q5zVNEREREREQ80lWmLtkYMw9YCzQAxvsl9RHtjSuwQWxk90sMPu2pExERERERD3UV1A0G/gQcDawDPgKWAkuttaUer61366D8khbll351vxQREREREU91GtRZa38IYIxJAuYApwLXAQuNMWXW2mO8X2IvZfxOuWXkIRtokanDGEB76kRERERExDvRdr9MxemAOdj9sx9Y79Wi+gSfm5Gz1g3ecIePN2fqNHxcRERERES81lWjlIXAsUAlsAyn9PIP1tqD3bC23i2UkbMBMM63sc3w8fb23YmIiIhIhwor6kjw+xialtTTSxHpM3xd3D4eSAbygX1AHlAWzRMbYx40xhQaYzZEHLvNGLPPGJPj/jkv1oX3ODeoa5GJa6/7pYI6ERERkajN+7+3mP3LJT29DJE+pdOgzlq7AJgL3OUe+gGwwhjzhjHmF10890PAgnaO/9FaO9P98+qhLrjXCJdfNgdtpp3ulyq/FBERERERL3W5p85aa4ENxpgyoNz9cwEwD/h5J4973xiTHZ9l9kKmbVDXbvdLNUoREREREREPdZqpM8bcZIx50hizB3gPJ5jbDFwGDI3xNb9rjFnnlmcO6eS1rzfGrDTGrCwqKorxpTzUTvll2+HjRiMNRERERETEU13tqcsGngFOstZOstZ+2Vp7r7V2rbUx1RXeC0wCZgIHgN93dEdr7UJr7Rxr7ZysrKwYXspj4fLLiLEGrRqlqPuliIiIiIh4ras5dTfH88WstQWhy8aY+4GX4/n83coN3tpk6nyRcbJRoxQRkSPQc6vyCAQt/zl3XE8vRUREjgDRzqmLC2PMKGvtAffqpcCGzu7fq/kiRhqEtGqUgvErqBMROQL94Jm1AArqRESkW3gW1BljngDOBIYZY/JwmqqcaYyZCVggF/iWV6/vufCcuog9czYIvuZvqTUGg/bUiYgcSeoaA13fSUREJI48C+qstVe1c/gBr16v24XLL5t/eZtggKA/OeI+vpaZPBER6fe2F1b19BJEROQI01WjFOlIu3PqWna/tMavRikiIkeYzfmVPb0EERE5wiioi1U7jVJad7/E54egMnUiIkeST3aWhC/XN+l3gIiIeE9BXazCe+oig7rWmbqEFuWZIiLSv9U1Bli8IT98vaquqQdXIyIiRwoFdbFqt/yyne6XytSJiPQJpdUNlNU0HNZzvPVpIVX1TXzhxLEAVCqoExGRbqCgLlZu8GZajTQgcvi4z4+x+oUuItIX3PjYKn7y3LrDeo7nc/YxPD2Zc44eASioEzlU1qpruEgsFNTFqoNGKbZNoxRl6kREertg0LIur5zdJTUxP0dZTQPvbinkohNGMzg1EYDKusZ4LVHkiBAIKqgTiUW3Dh/vV0KNUmjZKCVyT50apYiI9A37ymqpaQhQVFkf83O8sv4AjQHLJbPGhI9V1itTJ3IomhTUicREmbpYtdMoxbQuvzR+Z/i4xhqIiPRqWwucMQQl1Q00BoJYa8m+5RXuen1LVI8PBi1PLt/L5OEDOXb0IAalhDJ1CupEDoUydSKxUVAXK7f80gRbdr+0vshMnZsIVbZORKRX21rQPDC8pKqBshqnbPIv72yP6vHPrs5j/b5yvn3GJIwxDExxfv6r/FLk0ChTJxIblV/GKpSRi9gz5wwfb5mpCx23JHbr8kREJHqhTB1AUWU9xkT/2PLaRn7z2mZOnDCEy9zSy3Q3qNNIA5FDo0ydSGwU1MUqnJGL+OHTevh4uENmE/oRJSLSe20tqCRjQCJlNY0cKK+lpLrz0QbWWrYWVNHQFOSxZbsprWng4Yvm4fM50WCi38eAJD8Ha+KbqatrDNAYCJKeohOF0j8pqBOJjYK6WLUz0sC0GT7uBngqvxQR6bUCQcv2wio+e/RwXl2fz4+fWxcuv+zI6xvz+fajq8PXrzl5PDPGDG5xn+HpyRRW1sV1rRf8+UO2F1aRe+f5cX1ekd5CQZ1IbBTUxcq0HWlAsGWmzvqayy9FRKR32lNaQ31TkFMnDePV9fltArrKusY2mbGcveUk+g1/u/pEkhN8nDops83zDk9PofAwumm2Z3thVdd3EunDmoJqLicSCwV1sWovYGs90sAkNB8XEZFeaUu+s5/uuDGD3exay0Asv7yuTVC3Ob+CycPTOfeYER0+7/BByWzcXxH/BYv0Y5GZOmst5lA2uIocwdT9MlbhTF3zD582w8fDHTK1UV4k5OGluVzzj2U9vQw5wn28o4ST/+8tNudXsM1tkjJ5+ECyh6WF73PVvHEA3PDYam54dFWLx2/Jr2T6yPROX2N4egqFFfEtvwwJqkRN+qnI7petSzF//eqn3PxUTqeP//0bW5h/59uerE2kN1NQF6vwnLrWmbrIRiltO2SKHOne31rEh9uLqW3Q+0J6zqPLdpNfUcf3nshh3b5yxg1NJS05gQlDBwBw/elH8YuLZjB9ZDrbC6t4bUN++P9seU0jB8rrugzqRgxKprohQJUHA8jrmvT+kf4pMpBrPd5gyaYCXl5/gPpO/v//+e3t7Cur9Wx9Ir2VgrpY+UKNUiKHj7fK1IVGGqhRikhYbkk1ALtLq3t4JXKkqqxr5M1NBZwwdjBbCipZsqmAqcOdAG1QqlNmOTg1kaQEH/d/ZQ5nTM0C4OhbF7O1oJLN+U5J5fRRgzp9neGDkgHilq2L/LBbXa/fK9I/NQWa/58HI6qh6hoD5JZU09AUZH1eeZfPEwxaymsb2VmkfahyZFBQF6v2GqW02lOnRikiLQWClr2lzhnU3OKaHl6NHKle31hAfVOQWy88luvmZwMw1c26DUx29kL73dEE44YO4J6rZoUf+8bGfDa7e/CiKb8EKKiIT7OUitrmBi41DSrrl/4p0EH55fbCKkJXl+eWho9X1jVireUvb2/jhZx94eN7D9Zwwi/e4Ozfv4e1KleW/k+NUmIVDtjcoM4GMVisL+JbGm6UoqBOBGB/WS0NAec9E8rYicRTeW0jaUl+Evwdn7N8IWcf44cOYPb4DI4dPYimgOXimaMB+PppEymsrOdLJ40P339waiLfP2cKf3pzG3kHazGmliEDEhmentzpWkYODgV18cnUlUUEdV6UdIr0BpHdLyODulBDo4HJCazYVQpnws6iKhbc/QH3XDmLuyPp57oAACAASURBVN7Y2uJ5Pt5REr5c3RAIn7AR6a+UqYtVeE9dsOXf7TZKUVAnAi0Dud0K6iTOmgJBzvzdOzy+fE+H9ymsqOOj7cVcPHM0xhhSEv388pIZTB/plFIOSknk15cdx6BW3S6/f85U5mUPZUdRFZvzK5k2Mr3LrnxjMlIB4ra/p6ymeSB6jfakSj/VUaZua0ElSQk+zjtuJCt3HyQQtDyxfA8NTUEWrc5r8zzFVc0Z8sq6zudOivQHCupi1aoJSqjE0rYYaRC6j86oigDkljgll2MyUtlVrKBODl8gaLn//Z3sLa2hpLqBgzWNbCto3kOzv6yWBz/cFe4W+eLa/QQtXDxzzCG/1qThA9lWWOV2vux8Px1ASqKfzLSk+AV1EZm6amXqpJ/qqPvl5vxKJmcN5OSjMqmsa2LDvnKeXeUEc29sKmjzPMVVzSdBKuv0fpH+T0FdrNopv3QORAwfV6MUkRZyi6tJSfQxb+JQdpdoT50cvvs/2MmvXv2Uf32ymyJ3vlxRxJy5+97bwe0vb2LRGmevzfvbipk6YiCThw885NealJVGWU0jNQ2BLvfThYzOSGXfQWXqRKLVIlNnW2bqpo9MZ272UAB+9eqnHKxp5HS3kVFrkT8HlKmTI4GCuli1Kr8MN0NRoxSRDu0uqSY7M42Jw9I4UF6nsQZyWNbnlXPX61sAWL6rlMJKZ+9a6G+ARHdv3cNLc7HWsnZvGbPHD4np9eZNHBq+3FXny5AxGansj1OmLr+8+UOqMnXSX0Vm6kKdMENjRKaOTGfskFRGDkph+a5SxmSk8pvLj2v3eYoiyi8rlKmTI4BnQZ0x5kFjTKExZkPEsaHGmCXGmG3u37H9Zu0NTKuRBu7f7Zdf6oOrCMCu4momZA5gQqYzC2xPqbJ1Epuahia+9+Qahg1M5ksnjWfDvvJw9ndXcTVr9hwEoKzGOUO/v6yW3SU1lNc2csK4jJhec8boweHLU0dEl+kbnZHKvrLamLrvWWtZvCGfpduLAaf7X4LblbOiroml24vbDGcW6esCEY1SQiMNthY6TVJCe1nnuidYvjh3HKMGp3LJzNEkJ7ify9ytrpF76pZuL6a8Vtk66d+8zNQ9BCxodewW4C1r7RTgLfd63+RrNdIgqPJLkc6ExhmEMnWgDpgSu7c+LWRncTW/vvw4zjl6OE1ByztbigA4WNPIpX9byorcUvYedAK90poGVu12Ar0TxsYW1Pl8hjOmZpGW5GdAUnSd9EZnpFDTEIjpA+XqPQf59qOr+NI/llFZ18j2oqpwQPrPj3bxpX8s4xsPrzjk5xXpzQIRk6JCWbvQGJFpI5yy57OnO+/D/5wzDoA/fnEmm3+5gJ+dfzQPXjsXgOLKehL9ToR3/we7uP6Rld31JYj0CM/6u1pr3zfGZLc6fDFwpnv5YeBd4CdercFTpmVpZbuNUnzK1ImEHCh3xhlkD0tjQqYb1KlZisQoNCZg9rgh1DQ6pVUfbCtqcZ8r7vs4fNlaeHtLISmJvqizbO154No5HEpybOyQ5g6YGQOSDum1dhQ1vz8KKurZUVjFZbPHkLO3jDx3n947W4rIL68Lj08Q6Yve3VLIruJqrps/sWWmzn2zbc2vJD0lgVHu//NLZo7hc8eODJ9cCXWi/cZpR7G90GmUVFHXxLihqeHZqMt2Nc+2E+mPuntP3Qhr7QH3cj4woqM7GmOuN8asNMasLCoq6uhuPad1pq6zRikK6kTCw8YnZA5gcGoiQ9OSwt0wpW8qr+k6+xTNfWJRUt1Aot8wKDWBkYNSGJyaSEcVjqmJzs/iNzcVcNyYwZ3OsOtKgt9HUkL0jx8dGmsQQ7OUPRHvj437y6mqb2LK8IGkuK8favby/tZe+DtSJEq7iqv56j9X8IuXNhEI2pZ76tzLW/IrmTaieYyIMabDbHlqUvPnsKyBzbMku5hA4qmq+iaVSovneqxRinU2GHT4P9xau9BaO8daOycrq/3ORj3KtAzqOs3UqfxSJFxqGSq9nJA5QJm6Pshay8NLczn1129xwu1vdNoEZHthJbPvWOJJ0FFSVU9mWjLGGIwx4X2aIZHdKae6l+ubgjGXXsYqNKsulmYpuyP2nK7ZUwbA2KEDwhm/a0+ZwKjBKTy+fA9NkTVrIn3I0h3F4cv7Dta2mVNnrWVLQWX4fdyV0EkcgIER8yZD+1G7WzBomfHz1/nZ8xu6vnMfEwjacDZVel53B3UFxphRAO7fhd38+vHTQaOUcCCHMnUikXKLq0lO8DEi3SmfmZiZpgHkfUx9U4D/fiqHn7+4kf3loU6T9R3e/8NtTiOP1zfmx30txVUNZA5sLmfMdkt6H//GSTx3w6m89r3T+Nr8iQBMiRhfMCvGzpexGpqWRHKCL6ZZdXtKqjlujNOcZeP+cgBGpKfw4Ffn8vcvn8h/zh3HLZ+fTs7eMp7P2R/XdYt0l7KIbP62wspwx0twgoaCinrKaxujHiPSIqhLbr4ctPRItqym0fkM+MTyPd3+2l6b8fPX+d5TOeQWV4f3LEvP6e6g7kXgWvfytcAL3fz68eMLjTRovacuMqhza72VqRMht6SG7Mw0fO7Z0gmZaewvr6OuUe+PvqC8ppFrH1zO8zn7+cG5U8MBU2f/fitynV/yH2wr7vA+sSqpqiczorTq1guP4cnrT+bUycM4ccIQjDEMTnXO0g9Mbi7TOm3qsLivpTPGGHesQV3Xd25ld2kNx40dTKLfsHF/BQAjBiUzbWQ6nzt2JMkJfi48fjRJfh/bCirjvXSRbhHZRGh7YVWbOXVb3P/bU0dEF9QlR5RHR773A0HboiNmd6nqp+MUAkFLbWOAl9bu58y73uXye5f29JKOeF6ONHgC+BiYZozJM8Z8HbgTONcYsw04x73eN5nWw8fbzqlr3nfXP9/QIodid0k14yNK5LKHaaxBb9AYCHL8ba/zvDucu6P7fOkfn7B6dxl3XzmT//rsFC44YRTQcVBnrWVFbinJCT72lNbEPStbXNXAsIhM3bCByZx8VGaL+4xxm5QMTWu+36CIcqzuMmZIKq+sP8Bjy3a3ua2+KcCCP73PK+sOtDheXttIWU0j2ZkDyBqYTE1DgASfYUirZis+n2FURgp5cZqFJ0emfWW1rNrdM41EymoaGDkoheHpySzemN8iyAsELVvynRMa06IM6nw+Q0qi81ksLTmBs6Y1b+GJ18zIQ1HVz2ZK7i6pZubtb7Ait+3/l1hGt0j8eBbUWWuvstaOstYmWmvHWmsfsNaWWGs/a62dYq09x1rbd1sRhQM25z+waWdOXUfll8mlWxjy6b+6YZEivUd5bWOLD+HD3TLM4k7K98R7B2saqKhr4tYXOt7v8fDSXDbur+DuK2dy8cwxQHOJU0dB3d7SWgor67nm5AkAvB/HbJ21lpLqeoZFZOrac+msMdx+8bFcf/pR3HfNiSz579PjtoaoWAsr/sHfq27i/sS7yMlt+z3YW1rL5vxKfv7ihhYfZkNNUsYPTSMr3fk6h6cnhzPdkcZkpMbUiEUEnCz8/Dvf5vJ7P+6RvZllNY1kDEjkR5+bxpo9ZTzySW74Nieoq2J4ejJD0qLvHpuc4Px8Sk9O4J/XzePVm04DiCljfriq+1lQ9/iyPZTVNPLQR7ltbtOQ957VY41S+jzTsvyyve6XHTVKmbD4GrLW3INp0i9hOXLUNARadCsbkuZkTA561B1RolNT7/x8aupgr0lhZR1/enMbZ03LYsGMkeHjKW5QV9tBULfcPYt7xZyxjB2SygdxbJZS3RCgrjFIZhcf8vw+w1dOySYl0c+CGSOZEuWZ/rhZ8yi88gMG1BVyrn81p+Q/2uYuocxBcVUDf3pza/j47lInszkhc0B4XEHWoPbHFoxxB5yLxOLxiL1eoXlw3amstpHBqYlcMWccEzIHhEcQgBvUFVQwLcr9dCGhEyShLH2oYdGB8u5/n0QGdf2hA2aouqa6wfm6/vucqeHbSnqgvFWaKaiLVavyS9NO+WV7mTpffXn4cmJVx+VO0lZTIKgOb32UtZbqhiYGRLSaDpWRHaxp6KllCVDpnlmNbE4Q6c7XNtPQFOTWC48NtxOH5kxdbUP778mVuaUMTk1k6vB0TpuSxcc7SmiM0/s39MEhs4tMXY9qaoC3fwnjToIf72RFyimcV/YYrHoYNr0A7iyu0IfMs6Zl8cjHu9nslprtDmfqBnD29OEA5HfwgXTMkFSKKuu1P1VisnjDgXA2+L2tRVGPIalvis//t3I3Uwcwc1zL7rQNgSDbCqqiLr1s7ezpzuSsQakJDEjy90imLrL8sqS6dwU9TYHgIXevXJfnfI7d5O7z/dyMETzytXmAc3JKeo6Culi1GixuAs4PQetrPnPcXqOU1KK14csK6g7N1f9Yxs1Pr+36jtLr1DcFsZYWmbrQL/EyBXU9qrLe+dnVFGwbcO0oqmLR6n1847SJ4VEUIalRZOrmTBiCz2c4fcowKuubWLu3LC5rDn1wiOx+2evseAuqCuAzN4MxvDbsOqcc86Wb4OmvwJL/BZxyMGPgt184gUEpCfxu8RYA9hRXc+uAZ0lb+0/OG90c4LWnOQvR/R9YASrrGvn7ezviFrRL9ymsrGNtXjnXzc9mTEYqv3t9C7PvWELewc73Oj/00S6m/WwxB6sP/+d3WW0DGanOe3lWq6BuV1E19U3BQ87UhYT2cRtjGJ2R2uN76goreldQN/n/vcaPnl0X9f2LKuvDVQEl7r/90LSkcCn8bxZv7pFmNOJQUBer8EgDd09d0A3q/BHDMMMdMpvf0P665m2ECuqiFwxa1uaV8eLa/ery1geFyk/SItpLJyf4GZDkV/llDwtl6to7Wbt0u7MH7Mq549vclpLk/HxrLztUUlXPzqJq5mQPBeDUScPwmfjtqwtl6rJ6c6Zu3VMwIBMmfxaAqozpXJp4L3zjLTj2Ulj5T6grZ39ZLcPTk8lKT+bKeeN5d2sRxVX1jNy3mK8FF8GrPyT9gc/w6tcm87erT2z3pcYOcT649tS+uvs/2MWvX9vMc6vyeuT1JXahrqpzJgzl718+ka/Nn+iOEej8BMFtL20CYPWew29jXxaRqWs9i27TAbdJyiEGdW/efDof/PisFsdGDU6Jrvxy6+vwwnehNj4t+iPLL3siqOxIZZ3zu/e51dG/bze4o1XSWlXdDEt3gvJVuw9y24sbY19U0RbY+O9wvwo5NArqYmWMU17pBmwm6JyxaJmpa1t+mVDv/JAI+hJJqtQvwGgVVtZT1+icBb733R09vBo5VDUNznsgcn4QOL8MVH7Zszprt70i9yAjBiUzbmhqm9uS/D58pv2gbqU7r2hutjMTbvCARE4Yl8EH2+Kzr67XZ+rqymHzqzDjcvA7H1YHpyayqy4Nxs6B+d+DxmrYsIgD5XWMdjNtl8wcQyBoeWXdAT5f/iT5ydlw+QMQbOSYkrfCJXKtjXW7fO4ri7KT7I634dUfwX2fgdWH37QrlKHLiVMmVrrPFncP3bQR6cwYMzi8b7ajsmqAj3eUhC8f7r95bUOA+qYgg92gbtyQltnoTfsrMAamDD+0oG7y8HTGtcpsO3tPu8hmWwuv/RjW/Aue+SoEDr/xR1V988/Ivb2ooVGoxPtQbHBLL88+2ilrHZyaSKLfx9CIrry7imPsdFyeB3+d53zft78Z23Mc4RTUHQbrSwxn6Norvww3TYkov/TXlRJISKNh8FHK1B2C0A+JY0YN4oW1+9nbj9rg55fXceXCj3l5Xf8dHhzaUJ0WMTMInBLMkqoGrlz4MW9vLuiJpR3xOmq3HRpJMDd7aIu9dCHGGFIS/dQ2tA3qVuwqJSnBx3FjB4ePnTYli7V7y6Ler9OZUKZu6CF0w+tWm16EQD0cf2X4UMaAJOoag84+pFEzYfB42LaE/WW1jB7sBGXTRqYzfWQ6H3zyCdPtTraOvgSO+wKMOgHWPt7h2euRg1PwmeZM3eINB7hy4cfhM/F1jQG+9tAKHl6aC/VV8MSXYPlCOLjHKQct3n5YX26BW/a5NOLDPkDewRou+suHcfl5/a1/rWTRIWQUJDqbD1QwenBKOKjqqqw6GLT81xOrmZSVxpiMVNbsiS2oW7KpgCvuW8q/3VEqofLLUFOgkE0HKpgwdACpSf42z3GoRg1OpbiqvvO9gPnr4WAuZE6Gne/CLzNh7/LDet3q+iZ8xslu/fLlTfz61U87vf9TK/bwX0+sOazXjEZuB2Nmnli+h+892f7rr80rZ+KwNKYMHwjAEPf/TYK/OZzYVlDV4vdCQ1OQS/76EWfd9W7nmcqd7zVfXvb3aL8MiaCg7jBYX2I4mGuv/NL63D11tvmMl7/uIIGUITSlDsNf1/IXoHQsNOPq9ouPxWdg4fs7e3hF8VFYUceX7v+ET3aW8n+vfEpDU//ckxLK1A1Iapupe29rEZ/sLOWnizpuqS/eCX3wh5ZZt7yDtRwor2OuW0LZntREf7sf/lbsPsjMsRnhtuIAZ0wdRtDCRzsOvwSzpLqB9JSEFs/fq6x7CoZOgjGzw4cGuYPQy2sbcVIP52B3vUdReSWjQh9k68q5/qhS5pcuAqB2yoXO8dnXOh82O/hwmej3MWJQ86y6Rav38cnOUn7ulkHd+dpm3t5cyO0vb2LX0megqRa+/Dz81ypISIF/XQJ7Pon5y93r7r/aU1rDdx5fzfJdzjaDp1fsZV1e+WEPny+pquf1jQXc994OzcGKs00HWnaWTHXLqjsK6nYWV1Nc1cC3zpjEZ48ezuo9B6lrDISbZkRjX1ktNz+dw4rcg/z03+uZPjKd/zjWyfwk+tt+LD3k/XTBgHPyopVRGc77LL+zvae5Hzp/X/McTDrbubzk1kN7/Vaq6ptIS04IZw7//v7ODv8fW2v527s7eHndfs8bH+VGZNRq3BOvzutv56W1+9sEv5V1jXywrYjPTB7GJTPH8OWTJ/Cjz00P3/7nq2bx/XOm0BAIsjJi5uF7W4vI2VvGruLqcCOodu1eCqlD4OQbYdd7ENDWjEOloO4wWH9XmbpWYw9wM3UpQwgkZ+CvV6lKtHaVVJPk9zFr/BAunz2Wp1bupbCyZ5oCxEthZR1X3v8J+RV13HT2ZPaX1/FCTv/M3oba5kc2SoHmZilAi8Hk0n0qIzJ1kaWwocGynQV1Ke0EdTUNTWzcV87ciUNaHD9hbAbpyQlxKcEsrqr3Zj9dUz3s+gAeuRj+PAdqy5qzYzvedjpXNnSRdSrbA7kfwPFfdII312A3qAt/+J18DqahihmBT8PllzxxFZetvpbrEl5nUeAzZI2d5Bw/4UpIHgQ5bUcihIRm1VlrWbn7IOkpCSxavY+f/ns9Dy3N5cq54xg5KIWCDx4hOHgcTDwDBmbBVU+ALwGeugaqYwu+IlvQv7LuAF9+YBnWWp7PcaoPtnT2QS4KoRLBrQVVfHpAe6rjJbe4mq0FVZx0VGb4WGhUSV07GXiANe4eutnjMzh1UiY1DQHOu+cDzrvng6gysoGg5b+fyiEYtNx24THcfO5Unv/O/E5nTh5y58snroK7j28zTirUUKjTDpj562DgSBiSDV/+N5xzG+z5GCoOHNoaIlTXNzEwOaFFoj3yPRMpZ28Zu0tqsBbPK5K2FjQHvtsLqzhY3cDqPQfZW1pL0LZd4+sbC6hvCnLJrDGMzxzALy+ZwfnHjwrffuEJo/nmaUeR4DMtsvbPR3yuOVjdQaAWDDiZ0fGnwujZEGiA4q3t31c6pKDuMDjll+6HoHCmrvlDKsaHNT5MsPlDU0L9QQIpQwmkDHGCOp11jMru4hrGDU3F7zN864xJNAWCPPhhbk8vq43X1h/g6RV7u7xfMGj5ygPLyS+v46Hr5vHf505l+sh07n1vR7+YY9NaqPyydaYuLSLI62xvl3inMuL7HvkLd0VuKekpCZ2eJU9N8lPv7nVds+cgf1yylVueW09T0HLKUcNa3DfB7+PUyZm8v7X40LItlQXwwe9h3dNO0AWUVDXEfz9dTamzx+zhC5wPFyXb4O+nw50T4MHPw78udUoV3/9d58/z3m/Blwgzr2pxOBTUffWfK5yOrxNPJ+hL5AzfOkZnpDhnpfNWQuYU3kz9PL9qvJoJoT1BSWlw1Jmw/a0Of2eMGeLMqttRVEVpdQO3fH46s8dn8PiyPUwfmc5tFx3LXy4Zx5xADu8lnU5ZXZPz73DUmXDlY05At/TPh/xtq2sMkF9Rx1ER3VHrm4Ks3lPGntIafAY+PczZZ6HH+0zLD4gSux1FVVx+71IALjphdPh4V+WXK3OdEwZHDRvIyW4wuLPIyfjc9cYW3tlS2OnrPvDhTpbvKuX2i2fw1fkTuemzU8KBZEemjRwU3RcFsOMd2PY61JRA3ooWN4Uy4p02S8lfDyOPa74+8Qzn7z1Lo19DK6FMXWRVxNMr93L9Iyv5xsMr+O7jq9lZ5ARYL+Q0b8PIjWHPW7TW7DnIy+v2M3WEU0Z50V8+4vL7lvL8mubX392qPPOFnH2MHzqA2eNbdiiNlJacwMxxGby/tQhrLZV1jby5qYBLZ40BOhlh9OlLUJHnnMAKff/z1x/GV3hkUlB3GCLLL33hRimJLe9j/C0apfjrSmlKdjJ1vkC9BpBHKbekOtxSfeKwNM47bhSPfrI7PGC0t/jt61v4w5Kuzy4VV9WzOb+Sm8+dyryJzp6l75w1mZ1F1byxMb8bVtq9QvX1rffUXTxzNBfPHM1Z07I0PLmHRAbT2wqbP3wv3+WMJPD72u6nC4ksv7zs3qXc/dY2Xly7n+vmZzN/cmab+582xfl3PqSN9G//Et66HRZ9M1wGVVxVT2ZanDN17/wflO6C//gV/HgXnP2/ztnixhrnw+HZP4PJ58Ky+6Cug8xTwSbIeQzmfRMyWnYMTU9p/r9fXNUAyemUDTuRc3yrGD042en6FqiHM35CymV/4awTj225Z3DyOVCxD4o2t/vSYzJSyS+v4+OdTob1lKMyufvKWSw4diR/+dIsUhL9zKp4lwQT5M6845h5+xKeCp2AGnEszLgMVvwDGrr+twkGLfe8tY3iqvrwmIpTJrX8934hZx/JCT4uOH40W/Irow/k66tg+f3OvibXlvwKhg1M4qxpw3kxZ3+/PPHV3V5au5+S6gauPWVCc6YYwnvX2gvq1u4t47nVefzHMSPx+QwZA5L46qnZDHR/rr+Qs5/r/rmizeMivbo+n1njM7hs9pgO7/OPr8zh827DFoCjstI6vG8bKx8Av/u+2bq4xU2jw5m6Dn7XNNU776/IoG7k8ZA00CkNjFEoqLv7qllcPHM0SX4ff3lnO+9tLeJAeR3vbinixsdWU9PQxEtr94d/dubG2nAkCi/k7Cc5wc+jXz8p/Ho7i6p5fs2+8PXIn9OFlXV8tL2Yi2eObnePdaSLZ45m4/4Knlyxl8Ub8qlvCnLNyePx+0z7QV1TA7zzK6dkffr5MGwKJKQqqIuBgrrD4JRfut0v2yu/BKdZSiios0H89WVOpi7ZKU1SCWbXrLXkllQzIbP5B/uNZ06mqr6Jf32c22Pram1vaQ27iqvJr6jrshlEYaWTcRgb0enrvONGkZ05gL+92//2jYQbpbTK1J06eRh3XzmLeRMzKa9t7LBph3insq6Ro0cNIi3JHy65LKmqZ0fESIKOpCT6qG0I0ODOIQz5zznj2v3Ff/qULIDo91hVFToZutlfcTJKm14EaympjnOmrqkBNjwLx1wEp34XBgyF038IP9gM/1sEP94Jp/8ITvqWE+S192Fj1/tO17akdOe+rYQaC0Bzc5oNIy5ism8/E3c/AwdynBtHz+QzU4Zx1xUntPweuqMROsrWjRmSSlPQ8sq6/QwbmMTEYWmMGzqA+758IpNDnQPXP0P14KlssU7A+UzkCII5X4eGKtj8SpffrtV7DvKHJVv5f/9ez7tbi0jwGS44fnSL+7y4dj/nHDOCOdlDKK9tJL+LFvlhr/0YXv0h3DMLFv8UrGVzfiXTRqZz8awx5FfUsWyX9qMfrs0HKpk4LI1fXDyjxfEUd59q6wZI1fVNfP+pHLLSk7n1gmPCx2+76FhWLNjDQ4m/4VLfB8wwHe93t9ayo6iKGaMHdxoYnHPMCH5+4bHh62OGtO2+266qQtjymvM+nbrAOTlQ0twtOyXRz9C0JPaX1zknDyIbcwDsXQbBJhg7t/mYPwHGzYPdH0e3hnZU1DYyKCWBudlDufvKWZx33Mjw1/nKTadx95Uz2ZxfyTX/WEZJdQNfOSWbjAGJHTYyiYflu0qZPSGD4YNSeOwbJ/Pk9ScDTjn+dadOJD0loUV3zJfWHiBo4eKZHQfjIVefNIHPTB7G7S9t4oEPd7nZvSEMGZBIaevyy5pSp9y9eCssuNOZAe3zQ9Y0KNwU16/5SKCg7jC0W37ZOlPn84eHj/saKjE2SCB5MIFkJ33tr4/PHJS+ZH9ZLS+t3U8wyrOtBRXOOIPsiPKeY0YP4uzpw3nwo9zwBt+eFvlBdUsXs/SKQnO2IlqU+32Gb58xifX7Dr+xQE9Zl1fGn9/a1uYMY2hPXUcdzEa7G9h70wyfI0VVfRMZqYnMnjCEtz8t5J63tvHr15xs0LyJXQV1TqZubV7zyalBKQkd7oEZnzmACZkD2uyre3ZVHg9+uIum1sOr3/6lc1Ls1O/BcVdA5X6a9q/jYE1Dp3twDtmu95yZVMd/se1txkCKW/41wv0AXNBqDtMn98HDF0JTHVy20AkKW0lPSeSZb58CNDen+Sj1LD4KHkf6mz+GV34AaVnO2er2DB4LWdPhjf8Hd46HF74DEQPjQ/uFlu0qZc6EdjqWlu2FvctImtX8NW4tqGz+8D7+FKcj5/pnyTtYw+INHe8hKnNPWuVX1PP+1iJOnDCkzZ7YsppGLpk5hulu6dzmaPbCFW1xMp1zvwkzr4ZP/kpw3dNsLahk+shBnHv0CNKS/Dy/prkEDRDv7wAAIABJREFUMxi0LFqdF5ch2P3F3tIalmxq2U34tfUH2FdWy+b8ClbtPsiWgkqmj0yHN/7X2YN2YC0APp8hJdHXpknH7S9tIrekmj9+cWa4UyYAu5eS+voPOdO/lj8m3cvLyT8LZ7VqGwI8uXxPeORFUWU9lXVNTI44wdERX8Sn00EpiR3fMdLaJ5ygbNZX4PzfOyc/3vkVlDYHmqMzUpzfMx/+ER65CHKeaH789rec/aUTT2v5vBNOhcKNTgASg31ldeH3J8DN505jUlYaN5zhvNc/e/QIvnLKBFbvKWNQSgJnTssiOzPNs6Cuoq6RT/MrmDOh+efUsaMHYYyzz/30qVlMHNby9V/I2cdxYwZ3/W9nLb7Kffz+CzNITvSxOb8ynN0bMiCpzfvULrqeQN4KGi/4C0z9j+Ybhk2BksPrynskUlB3GKwvqblRSqj80t/qh49JCGfq/A3OL7Vg0qBwUJdQd+Rl6u54ZRP/9cQarv7Hsqg+xId+sGS3+tDw7TMmUVrdwMtrY9/AHE8fbCsi3S1D6aoxQLGbqRveau7UpbPHMGJQMn99p2/+MPvN4s38fslWfvTs2hbZxubulwntPi6Usdzj4R4CaV9FbRMDUxJYMGMk+8vr+MOSrTy7Ko/Rg1M4bszgTh+bmuinrjHA9kJnP8j8yZmcf/xofJ2UbM6fPIxPdpaGA7j88jp++Mxabn95E2siZ15texNWPwInfRuGTQ53oqvb/j7WwrB4Zur2LncaW2V/pvP7pY+E1KFQENGptbIAFt8CUz8PN34M0xZ0/HC3BDO0j3F/eT13DPwfmPM1Z6bdtS+3/DTb2rh5zZfXPOpkF12hWXU+G2Bue8F47gcAJB59Ho994yR+efGxVNY18dV/uh01fT6YtgCb+wHf/ufH3PDY6hZDkyOF9iRV1jYSLNjEdxJfYuSr32AkTgYtOcHH2CGpnDE1K7wnc3M0++rWPOp8qD7jJ3DhPTD0KOpWPUFdY5DpI9NJTfJz0czRLFq9j/XuvKx/Ls3l5qfXsmiN9tqB06jo2geX881HVvKmG9gt3nCAGx5bzd/f28GCP33A5fcuJbekms8lrYWl98CWV+Ffl4WDltZdbV9bf4CnVu7lhjMmhffRhS37O6Rk8MPGb/HvwHzK7QCCS/8CwG0vbuSWRet5aa2zT2u7u29sUlbXQV1CZ++DjuQ8DuNOhqypzkmQYy+FDc85WV+3rHhsxgD2lNbAvlXOYxbf4pRTN9bCphecxye3Oik1Yb7zdwwdYusaAxRX1Yffn+Cc3HrrB2cyI+Ln60/PO5oTxg7m6pMnkJzgJztzALnF3vw+XLazFGvhpKOaf06kpyRy2pQsvnzyBJISfEyICCrLaxpZl1cenmEYFgzC8zfC7ybDwjNh7VPw5NXwx2MZ8eTnuOvSo8lMS+ILJ44FYEhaq7m0BRsx25fwu/rLeTXh7JbPnTnZORHV2Lcb4nU3BXWHocWeukAUmbpG55daICmdQEqo/PLIytRV1zfx9uZCThg7mLV5ZXzuT+/z4trO57OFsj7ZmS3r6udmD/n/7J11eFN3+8Y/J0nTVFL30tJSKlB8uDNguAzmytzd7Z379s59zJiwDduAbQwY7u5eipW6Wxo5vz+eJE3bpALM3h/3dXF1a5qTk+Sc7/eR+7lvksL9+HFT08IkfzYsVhsrD+YzumM0RoOuyQDG0amr323w1mm5YUAb1h0uZNORU6sK/p1wUCs2ZBax+WjttV1ZY8HgpfE4n5Vkn5nIyG8oQ30Wfy6Kq2oI9vXi8l6tOfTCaOe/VQ+f26SAgY9ekrpjhZXoNApfXduLFyd1bPQ5vRJDKDdZ2JtdRlFFDT9vqw3GnVLjZTkw52aIaC+zbAABMWCMxnp8MwChZ7JTd2KjvJa+idkdRZH5M9ekLmsLoEK/O5t8vmP2yDHHeLK4isCgEBj7Bkx8HyLSGns69Lsb+twO9+8H/8g6M0MxQT6EU8Q679uYnPFEQ4rokVVgCILwNPq1DePKPgncPSyZdYcLybMXmWgzBMVciX/+ZlQVDuS6vx9PFFeTqhzlyuL3+Un3CAOPvod2/zw+1L8BqOx5ZiQrHhyCXqch0MeLmEBD41LmIAnF5i+FNucfLklm8nl4H1+NNzXOjt9DI9MIN3pz1/QtbDpSxMv2rvKJf5Cp89+JZ+ft4XBBBXEhPjw4czs7jpfw8Cy5Fly7paoKfYt+lu7sdQuhMl8SIOxJnb0Qd7Kkiodn7aBTq0DuHpZS98VKTojARbcrmWEdxD3m25huHYJyYAGLNu7m+43HUBScKqiH7NdTczp12ibmthqg8LDMw6VPrP1d71tq//vYOgCSI/05VlCOemKT+D9WF8Oa96SjV5QJA+5peOyYbuDlK++1hThuvy5bBTeu7mzw0vLT7f15aKSsAQlhfmSVVP0ptgZL9+Xip9fW6dQBfHVtT+47LxWAxFBfThRVUWOxcTBPrpt20fWS3Y1TpbMe003mEWffCPvmS4ErewfDSmay6YnhztGZYF+vuknd1m+xKTp+sA5uqEga2hZQoejwGX3v/+s4m9SdBlSNztmpw2ZGVbS1NgYOKFoUVTZwjbNTZ/zHzdQdKahwayJ8prF4by7VZhuPjm7Hr3cNoG2EP3d+t4W7pm+powzlisyCSry0Sp1hbhDz44u6x7Ehs8ipHPV3YdvxEsqqLQxMCSctysj++vTL4qMiGb7mPUBoKP7eOrd0xEt7xhPs68X7Sw41eOyfjpLKGidl77BLlbGixuKxSwdizBzqp3eqqJ3FX4eSKjNBvtL10moU57+mhuGhtqJ/rKiKmCCfRkVVmH0LbPjUaZGw/nAht36zmRd+qRX+yCmtFsuA7y6RmZcLPgMvl/s+piteOTJ7FnqmjMdtNqnax57TvL+P7QYnt9cKipzcCigiqNAEjHYaWal9rTtZUt1gXWsUoUkw4nn5TJLOFaU/OwXTV6/jQZ+fCFNKCcqYC58MrSt6cmS10MhcOiAD7DOO09ZkUm22stySiknVcWPodoCG65gd/idWMl//KJdp/+B3W3dyr9sI496miyaDnspeNPWun7TogMbpl+ZqoZOaymDIY7W/bzscrc1EX+1uku0qfUG+el6/qDOHCyq4+KM1BPpK0vj/mbq943gJS/flMnXlYb5bf5SbBibx+ZQeVJgsnP/+KkxmGwOSw9hQp1CoElq8Q7rTcT0hsqN0SlUVg17ua5tN5d7vt1FjsfHWJV3R6+rFN5s+B9UGPa53/uo3a08Um4Vf5/1I51aB3DigDSsP5JFXJnO6/t46IgOaLshotS1M6g78Lj+TXSh8UR3gkRPS/T0sneqUSCMjWYNiKoXet0H6JFj2kii/dpjM/Mp0Lv90LRszXT4rLwNl7S9F3fGjJLItwHG7h2Or5s4F2pEQ6oeq1j7/TMFBze3bNqzh9+mC1qF+YmtQVMmhXFlH6nRYTWWw9EVIHAiXfQ83LYeBD8KFX8Jl00XYac17TsVigBA/PUUOvYHKQtj0BUejhlFIgKz9rgi109DzD5yR9/3/BWeTutOAqq1Lv1S1DYMM1UUoxUG/tOoDsHn5YdN4oa0+tW6MYq7EN2s1hvwdp22LUG22MubtlTz5859v/jxvWxaRAd70SAihdagfP97Uh3uHpzB3WxYfLXM/YJ2ZX0FciK/bgPH8brFoNUrdgf+/ASsO5KEoQj9LjTKy11XtTVVh5g1S5VvwKOTuIa/MVGeezhV+3jqu6pPA4r25DRe6fziKKs3Ohb+gvHYxrzRZG9gZ1EdSmB9Hcv993cl/M6rNVqrNNqfcfksREWAgt8zE3G1ZxIU0ErSUnoRt38L8+4ixnCA2yIcNmYV1ujfeOo0ICK37ALI2wwVTIaJd3ePEdMO3NIMQSgnzcP+0GIWHoLoEWnVv3t8nDpIZaodwwolNMv/h3XT3wdmpM1mw2lSyS6uJCWxZsOdE0rlQVVgrsGIqZxzLWGkcKZ0/q0ksEgDKsmWuqHXfOodw0Gvf/uMg499dycNzM1mqH8iQ6sWE6arY745xYLNxUdbLZKpR9DK9y9OGBwlv1RY6Xkip6st13osbPCU1ysihvHJqLLaGxwNY9JRQAEe+BJG1Ihwk9KNG0TPOd1edrnHfpDBuHpSEVVV5/cLOtI00/r9Szy2urHEaQ+eWVTPu3ZVM+XwDz87bTadWgdw7PIW2EUaeGNsei03lqfHtOS89qk6okKQvRluVL0UKgO5T5FrKXOHs1P2+O4c1GQX8Z1x7p/q0ExYTbPoCUkdBcALf39ibC85pxU41ERN60q17efOSrlxwTitsKszbnsXB3HKSwv2aVTDysid1F9qpe43CXA1rP5CZ19B6M6ne/iJ8suNHMJXRzr+Sl7w+oTCkC7SfINecXu7d/LTLueO7zazLKOSGrzY65/4tVhsXbu0i8d7Wb5o+Hxc4OnXNFnuxw6EhcPgMUzCfnrubCpOFGwa0adbrZ+ZXcDCvHL1OU9ttVFVRIq4sgKFPCYNB6wXnPlbbKe1zG1Tkws5ZzmMG2WfqVFUV2m9NBcuipgC4Serays+zc3Utwtmk7nSgqTUf11jNDZUvwe5TVyuUAmDz8gdFweoThq6qhYIYVjPhm14naeYwWi29i/jfryVk56en9TZ27DuA0ZTD7C0nyP0Tk4iyajNL9+cxumO0c+ZGp9Vw59BkUiKN7Dnpnp6TWVBBYqh7WlOE0cCQ1HBmbjreUGThL8SKA/l0ahVEkK+e1KgAyqotnHRQyTKWwrG1MORx0HrDpi/JLWvcPHlQqlTQtxz999Bzq81WqsxWYoMMGLw05LsmdTXWOp507nCV5hem5ZwP+xecmRMqPQn5ZzeExuCwBHE1gW8JbhrYhv5txY8u0KAT9TlrvY67zVY3EFr7Hj0TQ9iQWegMEuJDfIkMMFBalAer3hIKXtqYhi/YbiwqCtfqfiWsMUuDrK3w0+1iGN4UHLM1sc1M6uL7iGT6gd/hj+fkZ9LQZj1Vq1Hw02spq7aQW1aN1aa2rFPnijZD5KfjPe77BYNqotuE26X7gOKknDnl2OsldXqdhkdHp3FZr3j255STVVJNVacpKOYKLg3cyX539MuszUTYclkbO4Wv7xjF/Dv6S5Cu98W7z/WcxxqxdnBBWpQRi02UDxug8LAk8j1uENVCV3j5sEnTkf5sbfC0B0eksu7RoQxMCSc2yIdjRZW1M4BNGcT/y9HlmYVc+anMQjooy4+NbsesW/vyw019nB2YK3q3Zv1jQ7m4Rzyd6s3Hnhdk7zjF2JO6LleAXwSs+8jZgZ+95Thh/t7uE6u986AiT+w7gF5tQnlgRCpmdGy1taG31wESw/xIjjTSPjqAOVuzOJRX3vQ8XclxqC7BW6dlw2PDmqRzAyKoVHQYznvW/ePDnhI7kM9G0Wb1g3hhYWbrJ8DLAMZIuGcXXPUzK8wp2FS4rFc8RZVmZ6GguMrMXlMoGf7dZC2zNT/WOFlShVajEGE0NPs5UKsh4OoVd6ywslk+uE2dT+82oU2KYDleP7OgkkO55bQJ86strB/6AzZ+Bn3vhFYeGA5thoiw07oPnI2HEF89FptKRVE2rPsYOkxij0XUNBuo43obxQS+4N/HWPo7cTapOw3YNF4o1lr1S1XjJmjV1iZ+rp06AIshTCplzYWqErXuOYL3TacscTTHh7xLWfwwwnZ8jHfRvlN7E6pK/C9XssZwB5P5g2lrj5zacZqBhbtzqLHYGkhfAyRF+DuHqOuensqRgso6dgb1cWH3OHLLTCyvp6j3V6GkyszWY8UMTJbg1qH8ty+7TBazpS9CQKzM3LQZjGXPfLYcLSQ91rOhanpMAHqtpq5wxCmiuSqjp4tSZ4KgJ9TPm4LyWu58RY3Fo/KlA/2L56DDhjrrRumcnA7MVfDpMHj3HEmqz8ItHCqGQT6nRmX089bx8Kg00pSjPHtsCryWDO/2qE2ms3fA1GESdPkEQ+dLYdv3DA0rJr+8hr3ZpQxKCWfObf2INSqce+Ij+e5dKXiuiGjH/pBBXKb9gwCDh+vJYhJPuy3TxGKgKd+14xulUh+e2rw3rfeVpGn9R2JE3vUKOO+55j0X8DeICbGDLhgd1LJgr/ZA4UL5PPSHBJmrRFjEt00/8AmSGcEjq+Rvj6wGLz+I6tzgMDcOTOL5ibWy9u26DwFjDMM1G8k6mS20yIO13Td11xzMqpa8aBF6iAioPX/vgfegePk2MDFvF21XwHQ3V2ef46LfXQ0eKqyoYX51JyLMJyB3T53HFKU2UI4JNFBcaabn84tQ138KL8TA1BGnrFb4b8D6zEJUVXWus+ckBNMtPrjBHKzjM+ocF8QzE9KZ2CUGo0FHT58sGReJtFsHeBmg3VjIWIq/l43s0mqW7M1jXOdodFo3oeKhJTKjmTjY+atQPz0aBbbY2pJsOyxWIcDErjFsO1bMyZJqklzn6eonR4UZ8FYXeKk1/PEc4WqB+9d2xcntsOZdoYAmnev+b+J7w8XfQEUumkOLmWqYwrpiFwNtnyBoM4j1h8VY3WHG7piNdyg2LvEfI3N3e+c1fk4uKKu24O+ta5ya7gZBvnqCfL3qeMWNf3clD87cflqjMsWV5mYxM0L89BgNOmenrs73tupNMEaLl6cnKIoUaU5ug0OL4cdrSK0U5oBt5TtiDTPoIWeHLrfU1PAYoW3PdupaiLNJ3elA4+JTZzO7pV/atD4oFrloNeYyVEWLqpPKrMW3ZZ06Q8FuAjJ/oSD9WnJ6PU5ldC9yej6KVR9A6LYPTu09HFhIZKWYZT/r9QWr1qz602br5m0/SWyQD93igxo8lhTuz7HCygZDwSdLqqkyW0kM8zxkfG5aBGH+en7Y8PdQMNccKsBqUxmYIt01R1K3N7sMTmyWann/e0DnDamj0JUeJYWjXNsv0eMxvXVa2sUEsOXo6SV12SXV9HxhEe8v/fMXRgdXPsjXizCjN/ku0sWVNVb8vBtJ6vL2E1R1jB8sg1Acg+ung3UfQan9eljywukd638Yxfah9VPt1AGkB9Twc8g7BHuZYfCjYCqVRG765fBhf5knHfyoBFUD7ge9LyM3XEu8koPNauEi7zWEbP2QV4rv57zKedDtaoj2PJ+2zdCTEKUMpdBDBXfV2+J5NPABSRC3/9D4Gzi2DmK6ijdSczHsKTEo7n0rjH1TvKyaCaPBi3KTpZaWdaqdOhDq25HVsPotyNkBgx+pnZlLGiKPmcpEUCWhv8fzVBSFZyak0zMxhJQoI6SNpl3FBsZWzZY5q68niW1DdSls/orFtm74BYU1PJBvCHS+WBI1l4QqMcwPvU7Dsn0NC2+W7TPYrKby/ta6QV1JpZnuzy1kgbUHqqKpQ+Oqj8hASVzCzcfh1wfE+D1rs1yDzTBT/zfBlcKa+MgvTuXZ5syYXtUngTcv6cqMm/vSz5grQbOXS1Gh7XCoKaejdQ8ZeRXUWG1M9ORLdnSNdK1dZjR1Wg1h/t7strXGC4vch8D4zrXHSI7wl6LbT7fBs6HiUeaYu9owVajNMV2kYPLlOGdi6BEbPhWjaoegkiekjYbb1sMNf7Az/goO5NalFleYLCzdl0v31sG1RQg7e6jAvpctVvpASBuhejYTFSark3bdUrQO9avjFefYY0s96A80ByVV5rqWFB6gKAoJoX7syynjWGFlbYe1KFM8OXveALomrrnOl4ExBr6eDLtmMXDtDVyi/QP/bZ9CxwsgPJUcezKXUyrMhWlrjzj3JUKToODsTF1LcDapOw2oLl04xVrTQPkSwOblg8YiN6W2phSr3igVDMBqaFlSF3hwJjadD0Xtr6w9vt5IYfur8c9ahU/u5ha/B8vy18lSQ/mw2zw0Oh2XW2Yxa8uZT45KKs2sOJDHmE7Rbvn0bSP8sak08GXZZu9UpTcire6l1XB+11gW7cmpQ/n7q7D8QB7+3jq6xEmyGujrRXSgQUQGds4UqlbHC+WP243HiparfNcSF9K4GlbXuCB2HC85LVrpkn255JfX8Mpv+/h81Z+rIuVMEHz0hPnp687U1VgbFUpxUMjetk6iKGG0JHXlp9h5tVklOEgYAMOfkaD9LA3TLYrt3dVTnalDVWHW9ehNBWgumw6DH4JLp0tXbt+v0qG+eaX8PqGfWBNc8xtaxcbX3i/zk/4Jxhz4Dyx8ghBrPrfb7kcd+2ajL7ms2j5rcbSeGbDNJpLmy16G9hOl2xeeBjtmNDyIAyUnIHu75wq/JwREy/sa+aLMkrQARoOOsmoLu7JK0es0DWeVWoJzpki3ZdFTEN5ObBEcSBkB1hpY9gqUHBN590ZwVZ8Efripj6zPvW9Fi5W7dbOweAdB2lj47SH4oC+KqYT3LBMI8UR/7XG9zPNt+dr5Ky+thmv7JTJnaxa/7cyu/duc3ejy9zDb0odXfttXR5jlo+WHhAo3tAdqwgDxIKtP7bVjfOcYrumXwI3a+dTYtCzq9zWc/yEcXQ0vJ8IbHeDpEJFd3zC1RfS5fxrq+7Iu3Z8LtEwNNjXKiL5gT8OZ1cSBoNXTxbQBgDZhfnRq5WbvLcuRDko9Oi9ARIA3e9TW8j92P8eoQAOfT+nBy5M7MjglXETDtnwDbQYLk2LaJPj2Eln3O14ENy6FyVPlNVa/7fmNmKtkj+0wWdacpuATBLHnkBzpz9HCyjoF7Kfn7iK7tJqbByXh560jPsSXvTl1O3UFlVZoNx6Ob5DXbgYqayyNFzQbQWKor7NT55rMO2jzLYXZarP7kjaPmZEQ5sf6w4XYVBfF0l2z5WeHC5o+gJcBRr3k/F9V0fGS16dU+bWCkS9js6lk2empFpvKgl3ZPDFnJ1+uPkJGXjll/gkyt+facc/aCnt/kX3eFaoKK/4r605ZDvzyAHw1saHB/P84ziZ1pwFX83HFZnab1KlaHzQWufk1NeXYvGolYS0+YWjNZc5OXmPQ1JRhPPI7pQkjZSbPBcUpF2HxCSds63stE03Z+wu642v52DKGTunt0JxzNRO0q1m8bNkZp+wt2J2N2aoypmO028c7xQaiUeDj5Rl1Xnvz0SL0Wg3pMZ6pigAX94jDYlP5bt3RM3reTUFVVZbvz6NPUiheLjSR1CgjB04WyYbTdphsJgB+oaz36sFI67I6qlDu0DU+iCqz1bORublKFrf9v3s8xvL9eUQHGhiRHsnTc3efNh+/MRS7zGeF+uvrzdRZ8GuMfpm5ghr/OI6r4exKvV0+m/n3nJoI0J6foeSoBJeOQLY5s1X/z1BWbeZnu9T4KXfq9i+QoGz4s9LtAlHSu3MLPHBQEh9jPW+jsLYol04nWimggyaT/R3vg5tWMKP/fObVdOOaLzey+pD7YpeqqiwvCKRCFwSZK+s++PvjMOcWETwZ95YUz9qNl8C+PNf9+e/7RX6mjT21938K8PfWUVptYeuxYtJjAuqsGy1GQIwIPUR3lgTTtdsY30eq5KvfBr1RunrNRWgSJQOeBuBQ1GgJsNtPgLKTHO/+CDvUNoR68gmMTIf4viJ37pI83Ts8hY6xgTw8a3utKMLOmdjQ8Ku1F0aDjmfnySxeucnCtLVHGNMxmnuGp6Dpc7skptumu31Jg5eWu3sFMFm7gpnWAby+ukQC/Sm/SEch9hwRbrBZYf69MGNKw6DwX4KKekyaVQcL8NZpGl9f66OmQjouEel1f+/tD/F9SK+Qeb0JXWLdi5o47pukIQ0eijQayFCjsSh66R7bMSQtgot7xKPP3gwHF8Hwp+HK2WISnr9fBIt63ST3LkgnJ/18YVocW+/+fRxYCDXl0OnC5r93hE2jqji7nL/vyuaHjce5dXASvew+fK1DfZ3d9EJ7wbKwskbuK5tZWDjNQLmpceXnxtA6VGwNTBZrnYLHqSZ1pS2coXb1BnbYDrHvV1nrg1s370XbT4D79sODhzk+ZQOTTU+ycMCPEgtlFlJcaWZcJ4kLp62R8Z+l+3M59/Vl3LHE/j4d1i2qKgWB6ZeKUIsDpjIpbC1+WjrAr6eIiM+x9fDdpS1WLP0342xSdxpQNXqwNkG/9PJFsSd12ppSbPq6SR2ArrpeAOMmkA08OAeN1URJ29pKbG65mXvnHyOrSktBx+vxyd+OX9aq5p38zpkw6wayfFOZqQyjW3wwysAHsOqN3F3+X5bvObPB/7ztJ4kP8XVf9UMqQrcPacuszSd4bE7tRrD5aDEdYgPw1jW+YbWNMDIkNZzXF+7n+fm7G/3bU4ab6u6RgkqOF1U55+kcSI0ykpS/BMqzhU7mgi/M5xJoKxI1rkbQNU4qj1vdzdUdXgFvdpTF7dsLpXpVDxarjVUH8xmYHM7bl3ZlYEo4D83azoBX/uDWbzY19W49YvaW49z7fe3rlVSaKa02U1LpmtTJTJ1DAbTCZMXH08ZmroYjq7DYDV6PaFoJlWbPXFj7PhQdaX5yV1MpQUB4GrQbB4Fx4BsmvP6zcKLGYmPo68uYv+MkgNPSoMVY/7H4XHW/puFjviHyzx3iezO9329cZHqCqh53QHQnosIlmFq6L4+bvnJ/fZ4sqabMZCU7YoAklI7OzaElcq10uxqmzK8toqSfL5Lr275zfx5750NospgV/0UIMHhRUlnDjuMldG7VkIreYvS6UeTE6wfYWi+4chakjoGr5tR+Js1E4KBbmWB7lRnB12PR6LndcjezR29kTxv5rhul+/W4TpKGQ7WzeHqdhjcv6YLJbOP+H7ehqiolW2ax1prGoG7pXNWnNasO5lNttjJ783HKqi1cP8BOUU8eDtFdYOV/nXtug/Pd/hleWPjYOoYAu8k7Cf3EAuKiL0VE46blUoDY/ZPYZnzQ33NwXp536myBJnDfD9vc+rM+P383U1c2zqhwZwgf6qdvlqKkE/n7AdW9L2LycKJMh4kljwldGs6/A7B7DoQkidpkPUQEGLCipSAwXbqi9Ysvm76QGdbu18r/97geHjgAt2+AUS+aNV5hAAAgAElEQVTLzKoD496CwFiYcR1U2ffBk9tqTcB3zZb1vXX/5r93INk+IrE/p4xyk4Unf95FWpSxjg9fdKCBk/a510L73GJRRQ1qXE/5g4OLmvValTVWjHqNnGtZTovOMyHMF1WVOCPPpUhaeopJXXH9pE5VPd5PQB0tgzZh/lJszdpSa8beXBgjwTeEgPA4NqmpFJg0LNmXyyUfr8VXr+W6/qLEuSajAMA5drLM3F7u+98eEWGV4xuluAOw7kPI3inFmRnXypxf2tha0apxb8PNK8BcATsbYWv8j+FsUncaULW6ep26hkGrTWeo06mzuknqtJW1SZ1P9nrazBlD9IoHnR08Q/4Owra/T3lMP0whtcP8iw6WsTOnmgX7SylpMx6zXzQhuz5rOgDOXCkS+5HpPKB7hE4JkTJc7ReGdsK7dNIcpvzXp0/tQ3GDwooaVh3M90i9dOCe4SmM7xzDvO0nUVWVGouNHSdK6BbfDFoF8OhooZL8ssNO71FV2PxV7QZwqijLlqH7D/pIAuKCFXZxFoffkwNdWxm5STOHamNrCUiAuduyeODHbSyoTiffPxWWvgRVRVL5mnEdrHyjTvU4LsSHUD99w7m6rK3w7UVCN7noKxFA+KOhUMP2EyWUVlsYkBKGt07LR1ecw40D2hAVYOCXHdkcKzw1hbiPlmUwa8sJp+ra7d9tZvRbK9htnz9weM5ZbCqlVbJhVDXWqVvzDlQV4dXlUgDyy2qg7x1CBVrwKLzVSWazHJt6TaUECw5FPwdUFebeJb42I56XroWiyHzGqSZ1J7fJa1UUnNrz/6HILqkW+wA7WlTld6CyULp0HSe3mIIIMKZPJ3oNGeekVse5mPMG+bk/nqNrbU0dK6bBe+fBtu/hh6skkR/xQt1uVWR7CfjWf9IweKkqhswV7lU2/0SE+uvJLKikymylfRMMhNNGRDu49Nvm2zW4QKMRo/VdeWa+XnuEedtP8vjP+9idJfd5SGNJXbvxoqS4+u06+1FSuD8PjUxlxYF8lq9ZTWB5Blv8+vPMhHTSogKwqZCRV8GvO7NJjvCnq2PtVxSZkSzMgPd7we/1BBqqS2HDVLJjz6PSP8G9yqbjOP3ulFmfA79LJ+nL8XJ9bPseZt0Ivz4k1f53zpG1Z+lLZ3Qur6TKzMzNx7nzuy11fm+22pi29ggfLD2EtRGmjCOp6xgbSL+2UghpcVGmKFN+hriRtU8dDcCbHY84Ze3rnkCBFBXTJzpHSVwRYbca2dHrNdAZYPO02gctJmFStJ8g6oZNwRAIkz+DsizpuGyYCp+Ngm8uFJXM/b9B+/EtmmkF6UDptRr255TxxsL9ZJdW88KkjnW65jFBPuSVm6ix2JydOotN5cmFWajtxok4SzNiiwqThevK3hfRpp9ua9F5drEXdtccKqC8unb9Kq2olLghp2UFbIcwVqCPlxRL3+sJX4zxGDOemxbBpT3jeWRUmgidndwulG5HYttCGA0iGHOssJKHZmzHaNDx9Ph0pwclQC8XVU4VDUz6WOaXf31AZrUBblohyfzXk+DToXIvj34NLvkGLp4Gl8+EzpfITF7sObViTP8P8LckdYqiZCqKskNRlK2Komz8O87hTEDVeKGxmUFVUTxYGth0vs6kTltT4lS+BDD7y/Cwvry2Kxa641N0VXkYjy0heuXDaKqLiV71OBafCLL7PlPn2CuPyMa1NEMEWArbXYlP/o6GlbH6WPQ0BMZSMPFbVuXq6ZtU22XSpY9jb9R4hpf9xL79exo5SPPx285srDaVsZ3cUy8dUBSF3m1CKasWEYFdWSXUWGx0a928pC450shNg9qQV2ZCrciHmdfBz3fAZyNEhe9UMf8+sSTI2yvVIRcsP5BPXIgPrX2qhRbwyVDYM49zi2bQXnOEn0OvcwaZL/yyx+6np3Cw+3+k4vRyglSM9/8m9IEt05wLrKIodIkLatip++1h2eymzJfNsf/dcHAh5O2v82cr9udLDGP/fn30Wh4Z3Y4XJ4kIxYoDLbTTQDxrHIpga+1VtcyCCo4XVfHF6kw6xAbgp9c6PfjyK0zYbCqVZiu+jmHxk9tgyYsyb1F8DJa/Du3G45U8mEAfLwoqTPKZXT4TLv1eKus5O2UgHkRJcf69MmC/8g2RRAfZZHf8IF45bYfVnnR0Z8jb0+wZCCeyd4qC5vx74aMB0tX5H0FxlQQp/duGMTI9qmVVfgf2zBUPziZmtTwhxE/PfeelOtXtWrn43IV6mNc6YE/qIrqMkhmyH6fA7Bslobv8R/decX1ulXtt79y6vz+4CGyWvzypi3RRi3RNZP+JSInwZ+eJEl5fuJ/OcUGYbapTdMnTdwSIgMKA+0RQ4fVUSf6XvABbv+WKXvG0iw6gfNGrWFQNlW1G4uetcwZ2P2w8xupDBQxtF1n3mGljJEBzzFkddwkdNn0BphJixjzM9QMSyS+vcc5BucXIF6DrlUL/C4qHX+6X6yhjqUi17/1FjKuTh8PSFzG93ZPNO07fxzV/32p2fnozkTRU5dxzspRqs438chObjni2sqkwSeHvibHtuaKXUOD2uFMVbQyOpC7IDYUuNAmiO9OjYqn75+6dJ/d9+wluH3Zc374RCZA4QOIRR9JwcLEIKaVPav65xvWA8e9Kh2j+vdJ5MZXCG+mioHgK649Oq6FNuB8Ld+fw+arDXNozvkHxOCbQB1UVAY9Cl2vpqzVHKBj8irBAvr7Ao8LquowCdp4owWKqoG+Z3abn4MIWFRgTw/xoE+bH4r25zmQ+iDL6r7hS4oaPBrSo+1dS5RDG0st+mr9fYpusLW7/PsRPz4uTOnLTILv333E7DbbVqSV1Go1CkI8X364/Sm6Zia+u7cmF3ePqKLZO6ZtQ90nhqXD1XLjsRxj1ipibR3cSw3ONl8zLj33Daa2B3g+Sh9UWHDpMls/8/8lc/d/ZqRuiqmoXVVVbXkL8h8CZxNksdvNxNzN1OkMt/dJUjNVQu3CY/WOxabzQl2Q6j2Mo3ENR6iXk9HwE/6xVtJ01HF1lDif7PovNJSE8WWrmYIGJNiF6ssrMHCgwUdpmHBZDCKx4zfNJ7/9dbsw+t7PquNzg/drWpQ7GTHwaDTYKf2m+TDeqCivfhNdSsP0wBYvV5vw3b3sWbcL8aB/dRFXaYuK8Q8/zldeLZBzczWZ7h6pZnTpTGRxdR8/KlYxTl2L7eKgEnT2uB58QmbcB6fIcWNT8eYoDC2UTG/qkeGeteB0qJBkyW22sOVTAgORwlD+ekdcrPQHfX47+jyfZ5teX5zPTKKyowWZTyXPpjASnDZKkbOCDMP4deChTqCxz75IqpB1d44M4mFtey6HPWCoCEf3vAf8I+d0514gYy/qP6pz6igN5dIoNJLheRT0p3I/oQIOT6tAS/GoXOfDx0rI2owBVlffVJS6I1qG+PDuhA4qiOAO+gvIaqi1WVBUxH68uge8ug2UvScL0hVSFGfE8AGH++tp5G50eUkdKZT1pqGxCqiodupAk8RVb9BS83QVeS5XvuP0EUVh0RUw3Cd7dUFQ9QlWlIGAIkm6oTzB8f6V8/3+3ml7REVj9rgyEn6Ifl+N6unNoMh9e6cFnqCnsmiWV/ijPSpUtQYChdv3Ue5gz25ddToTRm6DAQLjmFxHCGf8uXPsbBMW5P3DKSAhOgDXv1/19xhIpjsSe4vs/RUQH1iZ1MadqZ/AXISXSSGm1hWqzlf9e1JmbB7bBZLHhq9c2aVFCzxtkbbLWSAFm2csw5xZ0K17hv70rGGNbwofWcbROFMqbQzDmi9WZAIxIr5fUKQpcNA2G/ke+t6Uvyu/N1UK9TRwIsd2cyoUL9zQS7PoEw4R3RSDnllVwx2YJHO/dA49lw0OH5fq66CsOjP6emrICQmdMOq3iYOHmOXh/O4l+BTP4Vv88Rn3dQspmeyKn0yj8uvOkx+NU2IVSfPVazm0ne0Bz2SxOFGWCbygYPOzJHSaLemhhPSqoqopPW3Cix/u+c1wgUQEGEdZIGCAqxEX246z7APwj5btqCbpcCvfvh+sWwYVfQP975ffxfVtOBbQjJdJIRn4FIX56HhrRkIbq8I/MKq5qIMB21OQDkz+FmjKP9gYXf7yWse+spJNpM3rVJPuId6AIF7UAg1LDWZtRQGm1GR0WPtO/SnDpPqGv2iyyjjUTtRY2XjJvFpYq3dQt05p4ph2Hl8t3H9B4gb4xBPvpqbHYuKh7q9pOvAscKuIOVJutcu+nnCczlw5z85gucNc2md12UHndIf18QJH96v8BztIvTwMOYRTFZvYolGLT+aKxmVEs1aJ+6V0717Atp4ZD1ijM9gqCviQDjbWa6tAOlLSdRE6PR6gK7UBOj4epDq+7gDq6dPcPiESnsXfrdAaK0q6QwP+Em5mU3L3SvYpIh65XsvpgPkaDjg71KEABUW3YHHE+PYp+ofBYE926rK2iYvXtxbDoSawVBWh2z+aCx9+h7WO/0vaxX1l9qICxTVAvKTgEb3QgbP90Bmp30Hr1o2w+WkRMoIGowEYCn0NL4LOR8GIcfHYeQ3fcz+v6D7GiyJD8mNeFtpOxVKgPnwyBbybDj1c3TVO1WoSKE5YCfW6XjpGpTOTygXUZhZSbLAxqEyAUk3Ougbt3wMQPYcSL+Fz8GeU1VqauzKCwsgaLTeU/Y9sz747+pEYZRWL83Meg21VCX3N4cx1c6NxMHfSLbceKue2LFeR/fT3WoESpMjvgHy7qmlu/c1IUS6vNbDlW3IAWCtIBbBXsQ35Z40It7vDbzpN0ahVIv7ZhrM0ooMxkodpsY0zHaJY9MMS5SDtEFPLLTc7Ksp9eC789KlSaSZ8IfcJcDZd8LdVyINzozYJdObyxsG7XkQ6ToeykzL/k7hY/pWt/lUX93Cfke0kZBRPer0MJWr4/jxMBdm+uI82cNwWZGcnaLNL17SdI0hDZHhY/Axs/b/Hn5kR1aaMzDE3i8Ar4oB/8/phcz19NEApvC1FyuqqXFfmywaef75aCdbrwJARwILeMFPs8DL4h4m3W7crG7Qg0Wuh1ixSzDi6GzFVy7jtmStDZEiuDM4Aol05do2vbPwCpUfJZX9s/kaRwf24Z3JbYIB9nJ75RaLQw7k241YWilj4Jlr1Eu18vplgXzruWiU6/Tte56Z9v7+c24CMwVjqA/e+VTuuhJdK1Kzsp6zzCTOgSF8Trv+9zzvQ2CkWR7lTiQDnnetfDV1mtuLrmIQxKjRSilr3ScgGnPXMJ+nkKR4nkWfMVJGlOMti2rs75bT5aTFSAgcGpESzYme3x3B0dG39vHd46LSseHMLUq1tYGy88LMG5Jzi6X/UD4UN/iJpwn9s83vfpMYGsfXSo+OMlDJBfZq6Ue+7wcjGsbkoK3x0MAdK1Sz9fZq4v+Q6umHHK96/j2n58THu3Ev8O/8iFu3NYc6iASV1jiQyQ6/5YYaWIhYQkNUrtU7BxhXU2FbpgobX2vV2SwOej69JSG0FSuD81FhuH8yt4TPcN3TQHmZ34Hxj9uuyh+xc0+z07kzpvVbpzycNlf9sxo+kCodUs36MbcZyWINTuf/fgyLqJ9COj0hiRHomft67OpdWoorlWV9eSwx0CYkSldceMUxNe+5fh70rqVOB3RVE2KYpyo7s/UBTlRkVRNiqKsjEv7+8xlW4Kjs6cYqtphH4p1R5dpXQ4HEldqcnKy8ty2GeLRWOvYvnmCJ2kOlQUqUqSJ3FsxOeUtp3Y4LgrM8tpG+pN21AD3WP9WHq4HJuqUpw8SaqY9avSNZUw/TKpylz2Peh9WX2ogF6JoW7NPcNHP0oNXhTPf8rzB7D8Nfh4EPx0q2yw5z3HlPDvKcOXl+LWcN/wFO4bnsJDI9O4tn8jGwgINaemAq6cw3TjFBKK11GasZGujVEvd86EaRPFB2uQSKnvGjeXkaaXWDPqN9kAAHreKNU8R2ejdX/pqjmkeT1hy1eiyDXsKdmEwlNkQdv2HdhsfLwigzB/PYMjq4SOEtdLkrMul0KfW0mJjyY+xJejhVVOY82oQAMdPNkzpI2WpBAkmZx5PX1/Gc53+ucwLH6cfgf/S5gtj59aP1Z3mByg181CS9nwCQCrD4p33oB6Ai4OGA1e4nWTt18C3WZIfJ8ormLb8RJGdoiiT1IomQWVbD8mJuHhRm8wlctMyozrCDXI8QrKTU7Z6KjqQ7D1a+h3N3S6CO7ZIQPyLlTJR0e3o0dCMG8tPsCuLBcDcvtcInPvlMp/tD1RC06AgfdLp/Oy6U763bztWYx+awVXfbaeAe9ur2vE3BSsZkneItoLLx9k/uOmFRIIHfCsNtoo1n8idNvX2rZoI3YiZ7fcw4Gxoi55wedwcit8PkbmPluA007q9vwsAiQtoVE1Az/f3o+OsYEUVdaI6a2Ll5TNpnIgp7w2qWsJul4uXdevJ0l3+MtxYKmqS9P9i+CayDUlAPV3o1/bMF65oBP32AUkfPRapk7pzkuTWtCdNUZJJ+yWNTIfkzwC/KPwOv89XrioZx0Gx4yb+zDvjv50akpApucNUmybNhGWPC8WFvbuj0ajMLZTNDmlJmcQezrYmVXCZjWFi3lF1qElz7es6m8qQ517Nztsiazo/zWTb3mOXK9YrtP8TKFLwLr5aBHdWgcxqkMUWSXVbDte4vZwDvVLX7tMflyI76nN1AUneH48KB7iesOGz2SfNVdLQLzkBQhoJYXI5iA8FfzCJZn75UGhe/a4rmXn6g4areyX+lO3A7msZzxvXdLFoxhMbJAPOo3CpysP4+et46kJ6Sy5fzCAqGIqihQbDy9voK5bVWNllGYd3+ufpZtygFVt7pLYoP890h02V4rkfqnnjqwDrYIlhkzM+JZrdAuYrh3LOp+B4g+Yfr5cix8PEX/OJvbx4soaFAWMRXvEdiSulxSITaWw/9fGT+T4BlEabTO4yXNuDI+Pac8X1/QkrJ4Fx02DkvjoSilOLL1/MNfZY8b88iZ8CpuDDpMgf5/TYuN/GX9XUtdfVdVuwCjgNkVRGvTiVVX9WFXV7qqqdg8Pb9ht+CegtlPXGP1Sbkivcrl5rd5BqKrKm6tyKa62UODTmlBLNpSdJHjvN1SFdXLO2nlCXoWZPXnVDEiQAHZwG3/yKyzsyqlG9fKTrs3OGRKsgyzGvz4gCcrkTyEojmOFlRwtrHQOWtdHm8Qk5vqMJyF7gXs54WWvyGxTx4vg6nmiMtT3Do6Ua9kZOJi04pXcMTCOO4Ymc8vgpMY3nbx9snH0ugmShpA85m4KVCNP1rzBrZUfwYLHhAaZs1vey6Yv4bUUmHWTVMvu3AJDHoHUURgTurNXjSe33GUz1+rgqp8kqLhjE1z9s1AdZ1wjlL2qejNrVgvMuxfm3SPccfvgOCALYMkxMtfOZvn+PK7tn4h3qd1GIaRh4hpg0FFWbSa3TCiFEU1VuIPiRSTkwALY/TOayPYE61W6ZM/gMt0frLKm83u5m+H26E4SLK15D0xlrDiQh59e677aDQR4a5hc9rUMSn89CX64sskqlsNfalSHaPrYZZ+/33is9n0tfxW2fw87ZxC29iUURRZkB10o+fgs0HrL+/OATq2CeOUCSdj2ZbtYOfhHyAaasxMULbTqUed5J8qtTP5gNVnFVVhtKrd/u8Up3GJTwdp2uHRrXQfLs7bCW50b+pht+kIEGYY9VbcKrCgy13N0jSSwzYXVIh3KX+6X8w5oJUI3fzzf/GNUl0o3XO8Hl88Q2mOHSXDZDxKgzb6p+cfiDCR1O2aKamRketN/2wJ0ahVE36RQiqvMDH5tKcP+u9z52PGiKqrMVlIi3czNNQVvI1y/WGjUl34PV8ySdau5wekZxD+9O+cKrUbhonozL2lRAfRJcr9veERoknS6tV5SVLx3D37pI5jUrVUdBkf3hBDPRS9X6P3gyjny/fW+VTzpXOAwdD9R3MI52nqwWG3ssa8jmdV+WCd/IQWlBY83fw1Y8z5KZT7/MU8hJCiI9q2Cyet4A100GZTuFepcblk1x4uq6BYfzLB2keg0Sl0/PxdUmCx4YSFoxxdCSa8n3tUkrGYRGWksqQNJnEuPi7Lg5yOF5XNiIwx6AHTN9MRTFGGk7PhR5pqHPApePk0/7y9AsJ/es2UDYpNxjr2oPCglnACDF756HWH++lqRsQ6Tpbj1WrKIuNiRV1rF815T6anZx3/MV3Mszj5/qPWStef2TVJU2vFDk+fZKtiXwZotXFf2IcuUHnzlf30tk2HkizLCAbDwiSYVtQsqagj21aM9YY/p4npKF8s7sGk/t73zZcyjzel16jq2CnR+rp7QOtSP8Z0l2T4VRlEDtJ8IGp0I5/2P429J6lRVPWH/mQvMBk5t6vJvRvPol/akrkKSOrM+kGlbClmZWc6UbqEEdhiFBpWUuePxqswlv/OtTdKZVh+RmZ7+9qSuT7w/3lqFpRn2INhBzft6kizIH/aXLtXAB6HNIDmG3Qeq/jydK6p63EaZ6gNTh8N2l8Vi+WtSrex8qWymiQMgMh1VVcktq+ZwxHmNcs0bYOWbstD3EWWoc9IS+SL8AfyUalJzfxXZ9G8uEPXJNztJtyYgVjpwF3xeZ4OJsNMjcusvBFovCSp0egnUR9rnMVa/IxUzV+z4UTyWet0iCaDr92GXybctf41Ag4YrereunRdwQ2cxGrworTI7zyfC2IyAbvizcOs6uH8fXPIN83p8ybCaV7mt5k5eD/kPO064r+Ay6EGh4m2YyooD+fRJCkOvc3OLqypTcl/iWvN06Zj1vs2uIuhB9t2OBbuySYsykhjmR1qUkc5xQcy1y3JHGL3kc0seAZ0uRrNlGnE+ZgoqTHazXJXoEwvEK8uTzL0dYS7UzTqYPFUotXducdI1Hfh9VzabjhRx6SdrueXrhtTjra2uAu+A2tlKkDmCokwJVhzzdqYymf1p3R+Sz2t4csnDpVN4eHnDx9zBXCUJ3Nr3pJt6zS9w/UJR31v+StPdYgcWPCoB1kVf1Z0dSxoincqMpVIcaSZKqszotRoMXqewBRxdB0dWCu3xT6BeBvp61THadcDh05QSdQqdOhDj8wH3ypxm26Gybv3F1EvglD2r/megKNJlOF0Exso88sgXGyQKscFnJqk7mFdOtdlG5zjpHJbV2ERlryxLilhNoSwHVr9DUeuRbFPbEmaUtc3S8VLy1EBy5r+AyWJl8xEpLHaNDybQ14s+SaH85mGurrLazAten6L//SER8Zp2fstoZSXHhFnSVFLXfoIYTLcbL0yAXbMlge7awkJI50tr/7vduJY9929GeowUGFyTkLgQX44V2ZO6iDTodLH89/z7nAXCiiObCFHKubvmVr6yjsDP2+WeVxRZi6I6iiBPE2jlZ+VVr4/Zq8bzku99+PsYWLg7h/eWHMSm6GSE4/rFUuBe/EytzYsbFJTXiBXJsXWyhxqjZA1M6A+HG0nqbDZhNyUO8jyHeYYRZi+A5zVGv2wu/MKg0yWw+Usp2P4P4y9P6hRF8VMUxej4b+A84PRlpf4GOJM6azPol/ak7pUNNXy9tZAhbYxM7hBMcnJ7VqrSmSjocD1VkU0P7e/JqybMT0dcoLyej5eG3vF+LM8sFynkmC5i+ps2Vjpgqk3mvIY86jzG6kMFhPl7kxzhueo9sns7JpmfIc83WTp9y16BT4fXdugmvFcnKHLMV1W26i9qdEtfanSBAUT0Yfv3cM4UufHsmHjxdXzcfZ7Q6u7bJwH92DdFojtlJFzyrSiY1euOGby0hPjpOZzfhJhF4kBRU+p2lVTLHMG11SyKihHpboMFtF7kdLuHNtW7mRbxLQHlmTKf4OVXK1ziAqNBR1m1hVy7+Icj6WwUiiKbhY9sJBO7xnBUjWS+rTfDurblhJvBbUBky5POxbrqbXILixiY4iFh3/wVXYp+5x3rJNSJH8J5z4noyB/PeVSItNpUth0rdhYBNBqFty/p4nw8qniriMR0vFA6rjXlTNKvo6C8hsoaK1EU4l2d16yhdn9vHXqdpiHtQlHEd8qN6en6w6JAdqSgkt93NxRI2FemF4ruocUyh2O1wJ55ch34R4ooitUs9KKKPDHGdZewxPeR7/rgwibfByBKs4cWy7U76mW5X7x8xH+pVQ/46fbmVUi3TJP5MXdS0t2usgvlfNK8c0K8BQN9dChbv4Flr7YsKFzynFCqelzf/Oe0AMH1uvqO2SKHnUFja9a/Bc9OSOeb63v93afxPwuHyMXNX29i89GWz5w64FChHGSnsRdXmuUe7HyZiLPkH3D/xKPrxBz5m8lgs7C73V0ATspZWlw43+vG05vt5O5dw5ajRei1GjrY5wvPTYsgs6CSLDdJadusOVyoXS5F2pEvwdHVMuvWXDjtDJoYidB6wQVTRSL+nl0iIDPyxZYn5Ckj4LznJRk+Dbrk34Hbz23Llb1bc2H32kJaTKAPJ0tcuqOTPpbPpnU/GT/YMw+t3Z9xlU18/OokdQ6kjZXk6uT2Rs/BsO4dwpUSHjdfi9bgzxV9WtMlLohXF+xjwS57N1ejkf2t9LgkXx5QUGGSefdj64V66UDiQLkuPCU8W6ZB8ZHacYS/AJFGb3z1Wmen/LQx8H7Ze7+c0HIl7H8R/o5OXSSwUlGUbcB6YL6qqr/9DedxWnhj4X6WH5MbWzp1NR586mRzKcgWit6OEgMPDIjk4UGRaDUKXlqFGa2foqf5E461u6FZr3240ESb4LqBz6BEIyXVVraetFeQojqKZ8fjeXDLapnzsgepqqqy+lABfZNCGxUviQo0EJHQkXu5B9UnWLpzVYWykUz8oEGV25G4hAf6wrCnoeCABMmeYDULJU2jFSESFySF+/PkuHSZ9/MNgY4XiMHx5T8IfacR9aXebUJYeSC/6SH5lPNgzH9FHXHePVCaJQaW+fskAfbw2byWcw4f2ybQKfdneLe7KHoFt3b7986krsxEgEFXh8bUXAEdMq8AACAASURBVLSNMPLWJV2449y2tIuSTf9IgYeh5kEPoa0q4Grt725FUtg1G+bdzbHgXrxhnkS1RZUNYdhTkpR5SAwyCyowWWykuXRJWof60d+e5PntnwNevtKJi+kGYamMsC13CqV00tg3i5iuTb5fRVEI9/dufEDaBaqqsiHTvay0HE9kqelxvSi+bfpC1DfLs6VLOfpVyN4Oz0VKoNb1Ss++XjpvmSnYO1+UPB2w2STAc1VVzVgq10bPGxuac+v00nULiIGvxktyZ7PKPeGqrnloiQQK0V1g8CPuz8kvTGbbtk2XTmMzUFJl5ibtzyK2suS55vv4ZCyTLuWA+/60AC2oHiW03C4McSCnjNggH4yGU6SM/oNwZZ+ERlkSZ3F6cBijqyo8PLPxoLkxbMwsIszf29mpG/zaUjYdKYRhT8r1P/1yYUds/EyCYsd4wBejYdVbYl5+wVSOaloBtUmdt05Lzwvup0T1xXftG2w+WkR6bIBzxrJHgrAZGqxrm75g/JEX2ay0lyC++7XCWvlxikdZ+gZwJHVNdepcodE2n3LpDn1vr5Wc/xchxE/PsxM74O+SlEUEeDtn5J3w8pGZ7piu8P3lpOx+ixXWDuQh142ft5t9v+eNUgj+dJgUwN3Nw+Xtg5VvstR7CFvUZPy9dYzvHMPMW/oS6OPFoj0us3zJI4QttPYDj++noKKGeJ9qERZyVS9NHSk/3dn27PtN2Eyt+wvd9C+CTquhW3yws2B72ghJFOXUkqOw9Vuw1DRkt6iqfAa/PSrK2s1l0vyD8JcndaqqZqiq2tn+L11V1RYMlvwzoKoqRwsrWZghN3ZBaQUaSxU2r9ogJ7fczPtr8/h2lyQ6Zfkye/T8+HSGJwfUSaYGJYeSa/Vj4YGmKxJmq8qxkhoSQ+ousD1b+eLrpWFpRj2ev07fINk4kFtOXpnJ4zydKyZ2jWFFYRDbJyyE+w/C7Ruh9y1uzT4dC12E0SCLRLerYOV/YeGTDResgkPwfm8RnRj5olBpzhAGp0SQXVrNxka8fpzQesH5H8lcwhvp0q1KP9+jd9WJ4ipmb80iq/tDcONSUdfscIGIf7iB0eAlM3WlJiICmkG99IAJXWK577xUZ1BQ4Cnhie/NNt/ePOL1HQnrnpSFy4Gja2Hm9dCqJ6vOeQsbGu6fsY2pKw+jJvQX24CFT8DSlxsc1jHf1jGwWhZEu4Ljl9f2ZMujg1F2z5GEzttfrrdOF9KuZifepUeorLHQWXNIih5RHZr1fsP89c0ekN6VVUp+eQ3nd42ts/m+cH5H7h6WTKift8w06vTSSdw9R6hTXa+Q67TdeElqjVFSTR7/TuMv2P8eUX90GCDbrDBtgiT473STDmBFPsy5FULbSoHDHQJi4IY/ZMZwyzRY/LRQqV6Igf+mw/t9RQgisBVcOr3xoKrnDUJ53ja90VPPLzfx/tKDpOYvZIrpG6kWR3eW7ntTNh+qKoUdY4wovf5JqK9EV2C/DvbllNcxqT2Ls/AE1/3V312XBClCvrfkIBZrw2D6YG4Z09cfZf3hQnomBhPkck0u2JUja8WFX4pn3ssJUhR8uyt8cq6MByQOhIeOwH17IW2Mcy7IoQoMEBIaxseWsYQeX0TsiV/r2BK0iw7A31tXN6CtKoZFT3HApzMP+z4je7DOG6bMAxRY93HzPpyiTOnsG09dlv7/MyKMBspNFvtYgQu8jdLVjOxItdaf56xXO4ue7ujk+IbAFTOdXoisfF1Uy4uOyOOmMvj5TtD78Udr6fQ6rmWtRmFAchjL9udhcxjVazQSmx1fX9fD0QUF5TUka+zdvfDU2geCE6QRsGuOrPOmcnn9rC3ivxvRTt7bn0C3bww9EkLYl1NGceUZEEsBoY+26iF77dtdRFNg3r3yXg+voOjNviJGtvY9YWzt+9f1m85aGpwKFEXhjYu7cH5HuWE/XHYARbVh1UsXw2JTefaPk8zfW8LGHLmZ07xysXoZiQxsOCTcPsJAu3AD763N4901uVS7WwDsOF5Sg8UGifU6dXqdhn6t/Vh5pByTpfHgbNVBmadzNR33hJEdotHrNMzeliPS+Y3c1Dll9SiGY9+E7tdJ92v2TcL3XvAYLH5W5vyqikTo4QzTuIa2iyDMX881n29o3mIQngI3LpF5gaH/Ebl9D+/zk+XScbphYBupyvW4XigqnS92+/dGg46KGisnS6qccsing1qrAPfvy2K1cV/FVRz3SUPZ8IkYcoN0V767RHj0l32Pj790/OZvP8mz83YzfcMxSWbi+8oGU6+CtfdkKRFKCakzh8GcW5yzaVqNQnD2Kungdqz116PLFVjRMqpyDkrxUS7XLsbcqm+zh+TD/L2bPSC9aE8OigKPjWnHwntrNZcu6xXP3cNSiAzwJsdRWe1zu4jfxHQTI1OQ77r/PXDvbkmOmtq44nqIgtvWb2QW7+Ai+Xw7XwY6H/j+cng1SeZQzv+4oVKpK7yNQn895xqp7GeukK5bQj9J+oY9BdcvatoXKPYc6eY5vPw84NXf9rFowVxuLXqFDO92QqHuf68EevvdbGA2m3Q2V70t3/mxdSKU0JSM9Gkgsl7xo6CiBpPFysHcMtKi/pp5jrP49+OOc9sCntfK79Yf49UF+1hxIL/BYxd+uIaHZ+3gRHEVnVsF1REUctKD2wyS+0fnI8Wb5PNEHfC854Ta7xPkXEvyy00YDbo6aqfhRm8+tI7joHc7ntF8ysDIWkqfVqPQrXVw3U7dlq+hqogvjTfg6+uypoS0gfbjhHbXHFpZYYbsA3/DPOn/AhxiZw26dSBr9i0ruav1HKqDk/nPuPakRRnpmeihgB7VES7+Wtb8P56TosDU4TKb934fWW9HvUJkdEMPzl6JIeSXm5xxFwBdLpPZ8V8fbCBwZ7baKKkykyCSFqIe64quV4kQzrSJsn+9lipzgjpvuGpOk7PwfwaG2n0YX/xl75k5oKLABZ+JEnLpCRmn2DhVCjNfjqW8OI+3jffA3TvhxmUNBJj+Dfh/PrF9eujUKgj2Qbp/JVTB7EM2uiVambWriH35Jh4fEsW54b7wM/hZS6gK6uj2OIqi8OqoWKZuKmD2rmK2ZFXy8KAoksMaBk6Hi2Qhqd+pAxjcxsjCg2Us35/P8PaRDR53YPWhAuJDfIkLaSTYtCPQx4uhaRHM257F42PaubU/cMDBM3cGZRqtdLICYqQT4Ip242DECw0EL84EQv29efvSrlz2yTo2HSliaDvPn4UTEe2cBtiekF9uYvqGo5zfNdaprtYUHIbKGXkVDGvkO2kuQj2JiNix+2QpB01BbDp/Fq32Pigbxf7fpHIX2lYoIj5BBBhqA51OrQJ5Z/EBLu5+LpqLv4a3Osk8yNAn5XNRFPZkl3G//wIUU6lw8dd9aKf+RAtl0RAknT7nG49mX+RoLs+ZC8t/oxJvLCNeornC26H+enZmeRCEqYdFe3LoFh9MmL83qqqSFO7HNf1q50WiAgxkOwzNg+JEqOR00ec28Rn6WISHMATJnJyiwPYfZMNIHSWbdnMw8kV5bt4+oTa3NGlSFElIf7pNvIQSB7j9M4O5iHf1b5OthjAj5VUe9QmSbl1gnNigqKoIE8X1EtP3jGUw967aA3S5ArpNadm5tRBR9ZO6chN7TpZhtqp0iWuGMuJZnAVw33nSiXhvyUFqLLYGolGOhGnO1hMMSas7D13kYoWQGOZHoE/tylVtdimadrlUpPV1hkY76fnlNYTXk3APMOjQ6by4rvRGftE/Qv/tj8E585zJVs+EYF77PY+iihqCfbRSsInrzaqiONrH1Nt/Ol4oSd/+BbXmzJ6Qu0dm3s/ilOAoWueUVpMQ1pCCrqoqG48UMzg1gpRII7/d3YTRuqLA2DeEdl9yTPaOmdcJlfK63yGuJ23ts3M5LomkY240u6SaaEezwNsoDKk170pyeOVssVAAikrLmaxZzoAj00VBun7sdc4UucZy98gxTmyWJK/rlc75/r8aHWIDuXVwEu8tOcTg1HBGdTwD3eWgeGGc5eyQ4m7WZtj9M5tzrFy+sytVeQYmWENYecxGdGku56adftz2V+JsUncaULVyc1/WtgZ2wJYCHR/MOkKpycqI5AAGJhqxmrWoihZFtWIKSvJ4LL1Owy29wunVyo9XV2Rz59xjXN0tlIs7BdehkhwurEGnwSmS4oquMb4EeGuYuy3LY1JnsdpYm1HAmBbcHBO6xPLrzmxWHSpgUIpne4nM/ArCjd516S6KIgOqwQmyWPS6CSzVf0oy54quccHoNErzk7pmYOrKw5gsNm4e7Pl7rA+jQT6LMpOlaTuDZsBbpyXAoPOY1K3LkECld5tQaPehzGqV5wotY9CD4mHocl4A1/VP5K7pW1m0J4fiSjPDOlxLyOa3JRlMGAAVeVxT4E9v2yZRMhv+DLzZAb67uFYee8TzDQxlt3d5mrnz/Ogeo+fJ4+ewPLp9s99nmL83BeU12GwqGo3nztnJkip2nijlIbuRqaIoLL5vcJ2/iQgwePR8OmUEJ8Ctq+HAIjFDj+9T+/67Xt7y43n5yMZ+OugwWbppaz/wmNQNyfmccEqYaH6G/3S102+0OpnvWPiEqFpq9aKEtuZd8VPyDROqscX0f+3deXxU5bnA8d87k0wy2fcdskDYt7CLiCigIAhIraKtWpdrvbd6tdp67WZXl9tFLVZbrdatt2K1WrcqKmhlUQRk3yRkIQQI2ROyL+/948xMZpJJyGSbDHm+n48fnDMTcjQn55znvM9idG3rZ+2bCpTWNDoaRthrm4TojtToYFo1PLMph6ggCxePjSMuNJDmlla+PFaOScEH+4uoaWh2Oe6UalvwTosJdlmp65D9Edj5gwatNRuPlFBUVe/o5tf2PRSxoQHkl8fzWuzt3FDwW3j3bqMhUlSGo65uR345C/kcynPRC+6ncG1dx+t72gUQkmBkD4xd3nkzk8Zao/RhwpVn+T8nOmN/aN2hw7bN0eIaSmsamZnuQSBkjTD6BWhtlElsetSo9bY1BRsRa6SdOx5O0jYa5ZRz0xYw6p39rbD9OaPW8pq1UJZD6OY/8TvLXmjCOKe3X6n1sxj9F5TJOH6a6mHHc8Y4AC+6a+EoNh0p4b7X9zJleERbANsbfhYjuwWMP5On8csnNxMZXk9dZT0fHiji6U9zuCAz1ueCOkm/7IWWQOOXNrDaaIJy45wMwgPNJIf585+zjeBH+wfRFGzM22iMOPsN0dTkIJ66IpU5qSH8ZUcp733lWmeXW97A8HALfm5udP1MinnpoXx4oKhjvrfNvhNVVNc3M8eDIv2LxsQSFujHmzsLu/xcbkkN6W6eXAFGo5MFPzEKg/s5oANjSO7YxLBedT6zq2ts4adv7uOPnxxl2aQkxwm2O5ybOvSmps5ZTGjnTUS25paSFh1kXHgCQuGqF+Cm94ygy+nmw3m/Fo2Lx9+suOPlndz7jz0s25HFmQnXGU/r8jZC8SGmtOylOGQsLHnYSMNd+ogxONUaATetc4yjcBYZFswfW5bzjP83KPVL6DI4ay82NIDmVk35WdJn19sKxReN69h51C4+LIDSmgaa3NTO9EpUBsy6FS5/rNP02wHlbzXGcBx+1+iG176rWvUp5lS8w+stF5BtHuG4aQSMdNJAW8D07Y1wywaj5jA00bjJjBg2IAGdO6VnGth9vJK40IAOq3hCdGVicjhKwa/fP8x9r+/lgXcPAkZGQ21jC9+cnUpdUwsfHHCdCRdoS5NUCoZHBbms8lXUnaWjs5N39pzk+r98wfb8clLcZHfE2gK9kNk3GLNTdzwPL66ExlomD4vAYjaxLbfUuMmPTKdk2GIam1tJaj/r0GQ2VlqOfACPjDFSwt0pPgjoPp8vOZQ40i87CersK8DT03qQrqgUpJ5nBHhOXZ5To42sqmtmtt072YObk+2DuqAouPjH8B/rjdTg55bAm99B11XwvaZvk73oOaN7uDtmv7YHAv6BxsPgs6X+9zN/s4nHVmfR1NLKPX/f3VZD2IfySmrYeayC6+ekMSzKygf7izhd3eDSGM5XSFDXC82BRp60pSoPgPiYaJ66Yjh/WplKkNP8p/ooYxWhMTSlW39vWICZH12UQFailT9uLaagsu3GNqe8Y5MUZ/PTQ6hranHc7LZnn083x4PhsQF+Zi6bmMi6/aeoa+y8Xi+3pIaMzoI6L5iWGsnugkq3hfDdta+wkmWPb+SFz/K5eW46v7ly0tm/yEmY04pYX6zUgbGKdaToDNX1rjcXra2aL3LLmNVZ/r4T55W6IIsfwyKDaLDVclYRzI0l19KybA184x/sW/Ux4xqeY8+SN9oCw6xvwA9PGAHAcPet2e3z5grKa913/+qCPbXkREXXg3U/OlhEanRQl4F2fFggWneesjpQrnhyM4999FX/fpPzvmMMN3/pCnjqAtfB6pt/j4kWjo69jX0/v9Q1yLYEwx074Mb3jXEaKdOMJ8X3HDLSML2oqKqB3QUVTB4W0WW3XiHaG50Qys6fLOLzHyzgymkpjlU5ewOS/5w/guQIK//cecLxNU0trdTb6tITwwIdHYtzHryMSSnhxliDbnJ+uJrkJqiLCw3Az6RYMM7WeGXqDUbr+PfuJZBGVsfls3LXTXBiJzUzbqewyrgXSI50Uzox/z5j9I8ywV+/Bi9fC6fb1SKdsk2PkqCux8Kt/lj8TI5u3+1tyysjJsTSp/dC/mYTRx+8jLsXtdXBRQYZ+3Gqk/0gMg3u2A4rnoSvPcujY9fyFheRMnNFp9fswSo9JpifXT6eLUdL+fPGvp8z9+auEygFyycnMSMtii9sgfmYRAnqhhTtZ6XVz+oI6loDwjApY0yBs9PT76V0ws3UJnT/F8mkFN+fl4DFrHj4k1M0tWiqG1ooqWnuMqgbH28lPizAMRS6vS3ZpYxJCHV0UeyuFVOSqWls4cODHWeAgdEiveRMY+crdV6QNTyCuqYWDp3qXpt3Zy2tmic+zmblE5upaWjhrzfP4ifLxnk8ksBlpa6PgjqF0cH0iie3uATZh05VU1XfzKyMsz8htHdzWz3DKMC21wZckBnDL1eOZ1teOU9+nA2ZC9lTZ6zqjkls16TCZOqyqYj9GDtRUYfV4tn/N3vNYmFFHaVnGlzGUzQ2t3LgRBW7CyrYcrSUhWPju7zZtzeoOXSqmv0nKtl/onJAA7zT1fXUNjaz81gFj31kzLWqqm9yrc3pKwEhcPWLbUXw//wvoy3zlsfRn/+R15rnERQ/An93tbHBMcZT4kEkISyQPYWV5JTUMEVSL0UPRARZSAgP5OvTUqhrauHDA0VsyytjWJSVxHAry6cksSm7xHFOKK5ucKRejktqO+eZTIqIIAunKuvZf6KSI0XVZx2b43z+tw9Ed3bNzOHct2QMEUEWYyV8+RqjcdHOl+Dh4fyi/H8Y1pjLztaRTH0rms9zSgFIinCzYq2UkRFz1UtGPdbRDfDcYji5u+0zxz4Ha5TxvugRpRRxoQFdrtRNT43q8wdQ5naZLkopEsMDXVbq2l9XtCWE7OQV7I9exOaj5UweFt6jsUqDwdenp7BkQgK//eAw+wr7rpxCa82buwqZmRZFUoSV8zLaHoqPlpW6oac5MApTs1Hv0WJx35mtNTCC0km3gZs5dl2JCfbju+fHc6S0gRe/LCW33HhK177zpTOzSbF0YhKfHC6mqt1KTn1TC9vyyjjPg1U6u1npUSRHWHng3QNsye7YLSz7tDFKYTAFddNSjfTYnqRg/mPHcX6z7jCXTkjg/bsuYG5mz2ZKxYS2/azcXdR7YritwU326TO8su2YY/vWXOOCPyvj7D/fIIsfn/3gYn610hgxYE/vSIsOZuWUZJZPTuKx9UfYeaycQ6eqCAnwI8XD/Y+2BXWtGoItnh379qDuo4NFTPvVR7zhlPr72w8Oc9majax4YjONza1ccpYGNHGhxg3Qjc9tY+maTSxds4m5/7uBFz/LO/ssw17akV/OzAfW8+v3XbuJLn98EzMe+KhXq8h2La2aW1/cziZ7F7/kaXD7Nrg310gRffVb8MGPqU1byM+ab3C7YjDYLLTVwU5KCWd3QQUAk1MkqBM9NyPNuIa9vrOQ7XnljvTjK7KSaWnVjs7G9pWPR6+ezO+umuLyd0RY/TlcVM3SNZtY9OinvLfPNW2zPecbbHe/d/NHx3HLBRmuGxfcD9/4B8y8la+m3Me0hj9xReMvaMDCw+8ZK28p7lbq7IbNgDt3wX99ZswOffYSyF5v1Gvlfgppcz0fIC5cGEFdxxWyU5X1FJTVMSN9YDpFJoYHctJpQP2qJ7fwwzf2Ol5/dPA0Cx/5N0vXbOLAySqj1t5HKaV4aNVEooMD+O+1OzstMfLUBweKyCmp4YosY6zWiilt47XaNzfyBfKb3UstgcYvrzb5oc19X+8xNy2EJaPC+Pvect48YNzcZHSxUgewfEoSjS2trGt3wdl5rIKG5lbO78Yog/ZMJsVT100jOMCPa5/Zyq/eOeBywdqRbyxXTxk+eG68kiOsxIQEsLvA86c6m7JLiA8L4A/XZBlPUXsoMdzKa7edx6u3ndf1hdgDP7l8HB/dfSGhAX7klrQNqt6aU0ZKpLXbnTkTw62Obqb2YDwtJhilFL9cOYGEsEDuXLuLnccqGJ0Q6vGTx2CLmQBbLUqQhyt1EUH+WP3NvLbjOGAER3YHT1aRERvMU9dN42+3zGLmWS6gzi3yV05J4k/fnMbM9Gjuf3M/Nz6/ze3Fua+8+FkeAM9vyXNsq2loJq+0lur6ZpdgtadKzzTwwYEinvg42/WNoCj49r+NtK7r3+STrDXUE8C49iuug9Afrs3io7svdJlLNzFFOl+KnjOZFMunJPHpV8VGIwtbUDcqPpTVM4bx9MYcthwtcTSeGB0f5tIgBdrS1heMiSM0wI9Nbh5wOqtzukYmu1tdc0cpyFwIlz6Aac4dNLTrGfzQqokd9sutqHSjLXt0JrxyHex9FaqOGzP0RK/EhQa6dKK0s6ftzexJPV0PDI8KIqekBq01RVX1ZJ8+w7t7TjpKM17dXkBsaABPXTeNZ66fzm0Xdr/J22AUEWThkasnk1tSwy/fOdjrv6+oqp77/rGHCclhrJpqlEdZ/Ezsun8RG+650CfT/SWo6yV7XV2LJazfBjPeNiuW5DB/NuadISzARJS16xvkySnhDIuy8vaeky7btxwtwWxS3UrPc2dCcjjv3nEB181O5ZlNuax8YjOHThmNXLbllZMWHeRYFRkMlFKMiA0mv7Tm7B9uZ0d+eZ+lUExPi3JtStFLYYH+jIwLITnSSqGt5kxrzRd5ZWcNcDqTERNi+9MI7sKt/jx69RSOl9eyt7CyRwXDSilHCmb7jobd+VrnlU3nlJH80lrGJYZx6fgE5oyMOevPKDq47aZo/ug4Fk9I4IUbZ/Dz5eP57Ggpix/byLr9XT9x74m8khrebfc7CMbDFbuepAa3V2xLG/ssp5SCslrXN/0CjBbnGfPZd7IKf7PyiQHegf5mRsaFkBptHI/JEdbu3cgK0YWVTk/hnRtZ3H/5ONKjg7n7ld2O38nE9s1IgGO2368VWcnGHDnn4eBu1De1rcT3ZIXc3T5cNsGDxhUhscYYG5MZXv8PCEv2ejfDc0F8WIDbmrptuWUEW8yMHaBarAnJ4ZTVNHKisp4vbQ8+G5pbWbe/iMraJj45XMzlk5K4dHwCC8fFe3wdHozmjIjh2/NG8PIXx3p13W5t1Xzv1d3UNbXw+9VZLs2QIoIsZHjQEG8w8f2fsJe12IK6Vkv//RJb/U3cNz+BO98uID0y4Kw3sUopLp+UxJOfHGXkD//l2N7cqskaHuGS5+/xvljM/HLlBC4eE8f3X9vD4sc24mdSNLdqrpzWvUYwAyk1OoiPDxd79DUnK+sorKjj5rmDu+4gKcJKoS314sjpM5TVNDK7G01S3JkzIpo/XJvFPKeRFTPTo/jORSN5fEN2j7tARYdYKKyow9qDPP7kCKsjrdeeEtXU0kphRR2XT+7+jY1zQxB7mqlSihvmpHH+yGjuXLuLb7+0g999fTJfsx3Dr24v4OH3DvHZDxZ0mG/VHVprfvXuQfzNJtZ9dy7Pbsql9EwD6/YX8c1ntzo+l1fi+QOH9oqdajte/7KQOxdmuv3cvsJKRsWHugxAHuzsq6zzR3c+SkWI7hqdEMrYxDBOV9UzIratVCDI4sdjq6ew6sktrFl/hAA/k6Pu2Nm8zFg2HilhdkYUBWW1/GbdYUrONHRao27PZvnLt6YT5GEKOrh/GBZm9fDvCU8xhkcf3WDMswuR36XeigsLpKq+mfqmFpcHjltzS5maGtnlPN++NCHZyF7Ye7ySnQUVWMwmYkMDeHNXIU0trTS2tLIyK2lA9mUg3b1oFJuyi/n2SzvwMykC/EysvfW8DtkcuwsquOn5bWTGh7A9z7UMR2OULjx4xUSPOpoPdhLU9VKLrQ14feTofv0+o2IC+cWiJMIDu3dDdvPcdPzNJppbXWt2FvbRzLaLxsSx7q4LePmLY9Q1tWBSiq9NHYxBXTDF1cc7zCHqiv2Xf3qadwZudldyhJUd+eX8Zt0h/rbVqK3r6SqsyaRYNqnjyf+/F2QSHxbI5ZN7dmHo6UodwD2XjGJicjhrtxVQZEuJOlFRR0urJjWqZ7Wb7Ws+R8aF8sZ/nc/SNRt5ZVsBq6Ymc+Pz2/jE9iCgtKahR3NxXtiSx0cHi/jx0rGMiA3hwSsmorXm3tf28KotpTQ5wkpeD1aR27MHdanRQbz2ZQF3XDyyw/iI7XllbM0t4+uD8MFLVy4YGcMDV0xgVZZv7bcYvH5z5SQqaps6PBydlBLBvFGxbDh0moTwQLcPT2+em87qmcMIDfTnwlGx/GbdYdYfLOLqGe7H9NQ3taAUXDS685ErnupR9ohtFpfoG/ZRFKerGhhue1CYX1rDV0VnuGr6sAHbahTRawAAEu5JREFUj3GJYZhNin2FlXyZX86E5DDOHxnDEx9nU1zdQEZsMBOTz720dYufiWeun8HLXxyjsq6J57fkcfBkFRNTwsktqSE1KgiTSfHnjTmU1jRSmlPGnBHRZLUrDxoeFTSgP6+BIEFdL1VkXkmzNY6qjKX9/r1mpHT/RjY6JIDvOrW/7Q/RIQHcfrH7VYHBwr4yc6yslrHdrCXakV+O1d/c7c97S3Kklcq6Jp74+CgAIQF+jiYqfcXfbOKbs1PP/sFO2FMfPa2pA+Mma1JKBMfLa9luSy3JLzXSn+wXUk+5q4+0+Jm4bGIiazYc4WjxGUdAB1Be0+RxULf3eCUP/usQC8bEuaz2KqV4cNVER1B3/sho/rnzBC2tukNnM0+UnDEaKN06L4MfvbGPL/LKXAriD5+q5qbnt5ESYXVpie0LTCbFN2b1/PgTor0JXdzkZsaHsOHQ6U5X3kwm5ch0GZ8UxrAoK+/tO9VlUGf1N/tkbY7oXNusunrHtcjeNGfxhIQB249AfzPDo4I4XFTNnsJKrp+dyoopyTy+IZtDp6q5e9Goc/bYSwgP5LuLRlHT0MzzW/Ior20kt6SGi377CXcuyKSuqYV3nMof7lyQ2a0mcr5OgrpearHGUJm5ytu7ITqRZqvJuePlnS4z40YnhPHQqoluv2ZHvtH6123b90GkfY1GaKDfoDuB2ztg9iSos4sPD+R0lTHWIN9W05LqYVC39tbZjnoYd5ZMTOD364/w7KY8l+1nG37eXnV9E7e//CXRIRZ++/XJHX4ezsdU1vBI/r79OKue3IzFz8T3Lx3To5rI4uoGQgL8WJWVwkP/OsTdr+wiwakWJ6ekBqvFzIs3z3T8PIQQHY2ON9LMG5rPPm5EKcWSCYk8tzmXyromwq3+PL7+CPHhgY6n//VNrT7bQl50zp4W7jzW4L19p5iUEt5nDdG6KyXSyqYjJTQ2tzI1NZKRcSFMSA5jX2EVK6ace6mX7QVZzFjMJspqGx1jST49UuxSux4bGsDU1MGdedVXJKgT57RR8aGsnJJEaU3bzXnpmUZe/uIYN56fxqh411qxmoZmDpys4j99oEvUeRnRXDIunsTwQCx+JhZ7UkA/QOwDyHtST2KXEBZIY0srpTWNHCutweJnIt7DhjyzM6K7bOc8Oj6UtOggXv/SWEXzNyuaWjRlNd0P6rTW3Pf6Xo6X1/HKrbOJDHbfNfWFm2Zy4EQVF46KZeHYeBqaW9h1rII/b8zpNKg7UlRNyZlGt+NIis80EBNiwWox8z9LxvBBu+LxGWlRfP/S0QN+syGEr8mMM64HVXXda5d+6fgEnv40h48PnSYuNIDfffgVFrOJGWlRpMcEU9fUQmAPanKdfWtOGs9vyeP2i0Zy0Zi+S+MUPWdfqSuqaisL2F1Qwfcv7d8yHHeGRQWx0TbOZupwI3D53iWj+fJYhaPR1LlMKUVEkD8VNW0jvJzn975zx1xSIq2D/iF9X5GgTpzTLH4mHlud5bLtdHU9sx9czzu7T3D3Ja4n4d0FFbS0aqYN8no6MJ4+PX39dG/vRpeibUFdcEDPn1bbV1tzS2rIL6115Mv3JaUUiyck8qd/G6ms7905j4WP/Jv80hpufn4bF4+NO2sa4N++OMa7e05y7+LRLp312rtwVCwX2hrSPHOD8fP7xdsH+Ovn+VTVNxHmppHRokc/BeCLHy3o0GG2uLreUeNx3exUrutFuqwQQ9nIOKNhwl2dNBtqL2tYBPFhAby9+wTHympJjjBS4n/+9n6e+9YMo5FGL7IUAO5fNo57F4/u1YMx0bcigyz4mZRjpe59W+rlkgFMvbQbZntYlxQe6MjQmD86jvl9WMc52EUFW/gsp9QxcsTeWO0XK8Z3mW59LhoaoasQTuJCA5mVHs07e052GD69Pb8cpdqeeInesdemWHtxQ2K/0co+fYZjZbUep152l/2CHBro5/geL39RwPpDp/nRG/s6jgtwcuBEFT9/+wDzRsVy2zzPV3mXTU6ksaWVD/cXdfm5mQ+s5ylb4AlQVtPomCMohOgdq8VM3sNLHTOrzsZkUiwen8D6Q6c5cvoMP1s+nrsWZvLJ4WLWHzxtpF/2stusyaQkoBtkTCZlDCC3zap7f/8pRseHeqUNvr2OPmuIpBe6ExHkz7GyWp7ZlAtARa2xaueLw8N7S4I6MSQtm5xITkkNB05WuWzfnl/OqLhQmYnVR+xBXUgvVuqSI6xY/c0cKTKCuuE97Hx5NpNSwkmOsDIsMgh/s4nQQD/HyAiAy36/kec253b4ujMNzdz+ty+JsPrzyFWTe7SKmDUsguQIK+/sOdHhvfom1/qeX687zJRffMDTnx5l7bZjNDS3csN5aR5/TyFE79nT3uePjmXh2DhumJPGyLgQfvXuAVvLe7nNOhfFhgVyurqevJIatuWVDWiDFGf2oG4oP4iOdNMADdq6lA4lcrYRQ9KSCYmYTcqlO1JLq2ZnfrlPpF76itHxofxk2TgW9GKUhsmkyIgN5vOcUmobW/ptpU4pxa+vnMQPLxsLGCkdAKtnGE0Pqhua+fnbB7j2z59zpsGoudFa8+M39pJXWsOaa7I67ZrXne+9bFIiG4+UUNGuOcvRYmNW3yXj4nnm+unccoExrmRzdikbvyphfFIYmfGyUieEN8xKj+KHl43hoVUTUUrhbzZx0/np5JXWcuhUFdZepl+KwSkuNIATFXXc9couQgP8WD3TO63xxyeF8eOlYwflnOCBEtBJ3aoEdUIMEVHBFuaMiOZdpxTMr4qqqW5oZvoQTmPoayaT4ua56W7rxDwxMi7Esara03EG3XH+yBjmZsYAbU//rprherHecrSUfYWVALy6/Tj/3HWCuxaO6rIRS3csnZRIc6tmXbtGJ0eKjKDunktGs3BcPD9YMpZxiWEUVdWzs6CcGV3U7wkh+pfJpLh13giX0Scz041rSMmZxl6nX4rBKT4sgKPFNewqqOChVZN6NM+0L5hMilsuyBjS2UVltU1ut/f0Iasvk6BODFmXT0riWFkte2036Dtss9CmSVA36Ix0qlVI7eNZfJ2JDwsgMTyQKSkRHd4zBs1Wc/9b+5gzIprvXDSy199vYnI4w6OCXFaPATZnlxAa6EdGbFvaaVSwhUOnqqlvau3RGAQhRP8ZERtCZJBxky0jDc5N9oZVV05LYemkwdd5eijxc1PyEOBnIjhg6NWiSlAnhqxLxyfgb25LwdyRX05MSECfD/AWvWdvlmJSDFhr/h8vHceLN83EZFI8fk0Wdy3MZMM9F+JvVhw4UcV3/u9LQgL8eWz1lF4ND7ezp2BuOVpKqW3eTkurZv2h01w0Os6lJXOU07iECUlDq7uXEIOdUoosW41TgNTUnZMuHhPH8slJ/Gz5eG/vypD3wBUTmNkuY2VMYpiX9sa75GwjhqzwIH/mjoxxpGBuzy9jemrkoBvgLSAz3gjqEsOtWHo596m7hkUFOWrVLp+cxF0LR5ERG0JqdDAvfZ5PdvEZHrt6SocRA72xbFISLa2a920pmJ9+VUxZTSOXjnctwncO6pwHjQshBgd7R1p3qwjC901IDmfNNVmEDMHVoMEmMdzKvYvbxlPNHRnD09dN8+IeeY8EdWJIWzYpicKKOtbtL6KgrI7p0iRlUEqNDsbPpEiL8f4qanpMMK0abr9opKP+rq+MTQwlIzaYd3Ybq8dPf5pDQlggi8a5Nppx7vY1UEGuEKL7xtiCuhMV9V7eEyHOfRFBbTWFf71lFvFhQ/Nhp1fuBpRSi5VSh5VS2Uqp+7yxD0IALBofj8Vs4qH3DgJSTzdY+ZtNLBgbxwWZsd7eFVZOSebr01K4c0H3BhR7QinFsomJbM0tZcOhIj7LKeWmuWkdAreoYHu9jgR0QgxGY23pX7klNV7eEyHOfWFDuFGMswFfN1ZKmYEngEXAcWCbUuotrfWBgd4XIcIC/blwdCwfHigiwM/EeKlPGrSeum66t3cBMLpU9mdh/LLJSazZkM1da3cREuDH6pnDO3wmKtg21F2aMAgxKKXHGI2NvjUnzbs7IsQQMJS7fzrzxmPemUC21jpHa90IrAVWeGE/hABgme0GffKwCEllE143Kj6UUfEhVNU3c+2s4W7HQdhTTSSoE2Jw8jebyHt4Kf8xL8PbuyLEOS9ARocA3gnqkoECp9fHbdtcKKVuVUptV0ptLy4uHrCdE0PPwrHxhAX6MXdk39ZHCdFTq6amEOhv6vQpv33+zlAeOCuEEEI4mzq84wiioUTZBy8P2DdU6kpgsdb6Ftvr64BZWuvbO/ua6dOn6+3btw/ULnbbV1995e1dcGvUqFHe3gWfU17TSEign0vbeCG8paVVU1bTSGxo58NTy2oaibD6Y5LuekIIIYa46vomLH6mc37VTim1Q2vtth7FG71YC4FhTq9TbNuE8JpIpxbxQnib2aS6DOjAdayBEEIIMZSFuilVGGq8sSyxDchUSqUrpSzAauAtL+yHEEIIIYQQQvi8AV+p01o3K6VuB9YBZuAvWuv9A70fQgghhBBCCHEu8Eb6JVrrfwH/8sb3FkIIIYQQQohziXSFEEIIIYQQQggfJkGdEEIIIYQQQvgwCeqEEEIIIYQQwodJUCeEEEIIIYQQPkyCOiGEEEIIIYTwYRLUCSGEEEIIIYQPk6BOCCGEEEIIIXyY0lp7ex/OSilVDOR7ez/ciAFKvL0T4pwlx5fob3KMif4kx5foT3J8if42GI+xVK11rLs3fCKoG6yUUtu11tO9vR/i3CTHl+hvcoyJ/iTHl+hPcnyJ/uZrx5ikXwohhBBCCCGED5OgTgghhBBCCCF8mAR1vfO0t3dAnNPk+BL9TY4x0Z/k+BL9SY4v0d986hiTmjohhBBCCCGE8GGyUieEEEIIIYQQPkyCuh5QSi1WSh1WSmUrpe7z9v4I36SUGqaU+lgpdUAptV8pdadte5RS6kOl1BHbn5G27UoptcZ23O1RSk317n+B8AVKKbNSaqdS6h3b63Sl1FbbcfSKUspi2x5ge51tez/Nm/stBj+lVIRS6jWl1CGl1EGl1Hly/hJ9SSn1Xdv1cZ9S6mWlVKCcw0RPKaX+opQ6rZTa57TN43OWUuoG2+ePKKVu8MZ/izsS1HlIKWUGngCWAOOAa5RS47y7V8JHNQP3aK3HAbOB79iOpfuA9VrrTGC97TUYx1ym7Z9bgT8O/C4LH3QncNDp9f8Cj2qtRwLlwM227TcD5bbtj9o+J0RXfg+8r7UeA0zGOM7k/CX6hFIqGfhvYLrWegJgBlYj5zDRc88Di9tt8+icpZSKAn4KzAJmAj+1B4LeJkGd52YC2VrrHK11I7AWWOHlfRI+SGt9Umv9pe3fqzFuiJIxjqcXbB97AVhp+/cVwIva8DkQoZRKHODdFj5EKZUCLAWesb1WwMXAa7aPtD++7Mfda8AC2+eF6EApFQ7MA54F0Fo3aq0rkPOX6Ft+gFUp5QcEASeRc5joIa31p0BZu82enrMuBT7UWpdprcuBD+kYKHqFBHWeSwYKnF4ft20TosdsaSJZwFYgXmt90vbWKSDe9u9y7AlPPQbcC7TaXkcDFVrrZttr52PIcXzZ3q+0fV4Id9KBYuA5W3rvM0qpYOT8JfqI1roQ+C1wDCOYqwR2IOcw0bc8PWcN2nOZBHVCeJlSKgT4B3CX1rrK+T1ttKeVFrXCY0qpZcBprfUOb++LOCf5AVOBP2qts4Aa2tKWADl/id6xpbStwHiAkAQEM0hWRMS5ydfPWRLUea4QGOb0OsW2TQiPKaX8MQK6/9Nav27bXGRPS7L9edq2XY494YnzgeVKqTyMNPGLMWqgImypTOB6DDmOL9v74UDpQO6w8CnHgeNa6622169hBHly/hJ9ZSGQq7Uu1lo3Aa9jnNfkHCb6kqfnrEF7LpOgznPbgExb9yULRtHuW17eJ+GDbLn+zwIHtdaPOL31FmDvpnQD8KbT9uttHZlmA5VOKQNCuNBa/0BrnaK1TsM4T23QWn8D+Bi40vax9seX/bi70vZ5n31iKfqX1voUUKCUGm3btAA4gJy/RN85BsxWSgXZrpf2Y0zOYaIveXrOWgdcopSKtK0mX2Lb5nUyfLwHlFKXYdSqmIG/aK0f8PIuCR+klJoLbAT20lbz9EOMurq/A8OBfOAqrXWZ7aL2B4z0k1rgRq319gHfceFzlFLzge9prZcppTIwVu6igJ3AN7XWDUqpQOAljNrOMmC11jrHW/ssBj+l1BSMJjwWIAe4EeNhsZy/RJ9QSv0cuBqjW/RO4BaM+iU5hwmPKaVeBuYDMUARRhfLf+LhOUspdRPG/RrAA1rr5wbyv6MzEtQJIYQQQgghhA+T9EshhBBCCCGE8GES1AkhhBBCCCGED5OgTgghhBBCCCF8mAR1QgghhBBCCOHDJKgTQgghhBBCCB8mQZ0QQgghhBBC+DAJ6oQQQgghhBDCh0lQJ4QQQgghhBA+7P8BOwmpFQkL0hgAAAAASUVORK5CYII=\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_comparison(start_idx=200000, length=1000, train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a check, we can plot this signal directly from the resampled data-set, which looks similar." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['Odense']['Temp'][200000:200000+1000].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the same period from the original data that has not been resampled. It also looks similar.\n", + "\n", + "So either the temperature was unusually stable for a part of this period, or there is a data-error in the raw data that was obtained from the internet weather-database." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_org = weather.load_original_data()\n", + "df_org.xs('Odense')['Temp']['2002-12-23':'2003-02-04'].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example from Test-Set\n", + "\n", + "Now consider an example from the test-set. The model has not seen this data during training.\n", + "\n", + "The temperature is predicted reasonably well, although the peaks are sometimes inaccurate.\n", + "\n", + "The wind-speed has not been predicted so well. The daily oscillation-frequency seems to match, but the center-level and the peaks are quite inaccurate. A guess would be that the wind-speed is difficult to predict from the given input data, so the model has merely learnt to output sinusoidal oscillations in the daily frequency and approximately at the right center-level.\n", + "\n", + "The atmospheric pressure is predicted reasonably well, except for a lag and a more noisy signal than the true time-series." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_comparison(start_idx=200, length=1000, train=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed how to use a Recurrent Neural Network to predict several time-series from a number of input-signals. We used weather-data for 5 cities to predict tomorrow's weather for one of the cities.\n", + "\n", + "It worked reasonably well for predicting the temperature where the daily oscillations were predicted well, but the peaks were sometimes not predicted so accurately. The atmospheric pressure was also predicted reasonably well, although the predicted signal was more noisy and had a short lag. The wind-speed could not be predicted very well.\n", + "\n", + "You can use this method with different time-series but you should be careful to distinguish between *causation and correlation* in the data. The neural network may easily discover patterns in the data that are only temporary correlations which do not generalize well to unseen data.\n", + "\n", + "You should select input- and output-data where a *causal* relationship probably exists. You should have a lot of data available for training, and you should try and reduce the risk of over-fitting the model to the training-data, e.g. using early-stopping as we did in this tutorial." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Remove the wind-speed from the target-data. Does it improve prediction for the temperature and pressure?\n", + "* Train for more epochs, possibly with a lower learning-rate. Does it improve the performance on the test-set?\n", + "* Try a different architecture for the neural network, e.g. higher or lower state-size for the GRU layer, more GRU layers, dense layers before and after the GRU layers, etc.\n", + "* Use hyper-parameter optimization from Tutorial #19.\n", + "* Try using longer and shorter sequences for the batch-generator.\n", + "* Try and remove the city \"Odense\" from the input-signals.\n", + "* Try and add last year's weather-data to the input-signals.\n", + "* How good is the model at predicting the weather 3 or 7 days into the future?\n", + "* Can you train a single model with the output-signals for multiple time-shifts, so that a single model predicts the weather in e.g. 1, 3 and 7 days.\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "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:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "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." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/README.md b/README.md index 370eea1..d09884e 100644 --- a/README.md +++ b/README.md @@ -4,12 +4,6 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org) -## Donations - -All this was made by a single person who did not receive any money for doing the work. -If you find it useful then [please donate securely using PayPal](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=PY9EUURN7GRUW). -Even a few dollars are appreciated. Thanks! - ## Introduction * These tutorials are intended for beginners in Deep Learning and TensorFlow. @@ -17,54 +11,154 @@ Even a few dollars are appreciated. Thanks! * The source-code is well-documented. * There is a [YouTube video](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ) for each tutorial. -## Tutorials +## Tutorials for TensorFlow 2 + +The following tutorials have been updated and work with **TensorFlow 2** +(some of them run in "v.1 compatibility mode"). + +1. Simple Linear Model +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/01_Simple_Linear_Model.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/01_Simple_Linear_Model.ipynb)) + +2. Convolutional Neural Network +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb)) + +3-C. Keras API +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03C_Keras_API.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/03C_Keras_API.ipynb)) + +10. Fine-Tuning +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/10_Fine-Tuning.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/10_Fine-Tuning.ipynb)) + +13-B. Visual Analysis for MNIST +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb)) + +16. Reinforcement Learning +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb)) -1. Simple Linear Model ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/01_Simple_Linear_Model.ipynb)) +19. Hyper-Parameter Optimization +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb)) -2. Convolutional Neural Network ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb)) +20. Natural Language Processing +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb)) -3. Pretty Tensor ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03_PrettyTensor.ipynb)) +21. Machine Translation +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/21_Machine_Translation.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/21_Machine_Translation.ipynb)) -4. Save & Restore ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/04_Save_Restore.ipynb)) +22. Image Captioning +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/22_Image_Captioning.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/22_Image_Captioning.ipynb)) -5. Ensemble Learning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/05_Ensemble_Learning.ipynb)) +23. Time-Series Prediction +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/23_Time-Series-Prediction.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/23_Time-Series-Prediction.ipynb)) -6. CIFAR-10 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/06_CIFAR-10.ipynb)) +## Tutorials for TensorFlow 1 -7. Inception Model ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/07_Inception_Model.ipynb)) +The following tutorials only work with the older **TensorFlow 1** API, so you +would need to install an older version of TensorFlow to run these. It would take +too much time and effort to convert these tutorials to TensorFlow 2. -8. Transfer Learning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/08_Transfer_Learning.ipynb)) +3. Pretty Tensor +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03_PrettyTensor.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/03_PrettyTensor.ipynb)) -9. Video Data ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/09_Video_Data.ipynb)) +3-B. Layers API +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03B_Layers_API.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/03B_Layers_API.ipynb)) -10. Not available yet. Please [support this issue](https://github.com/tensorflow/tensorflow/issues/5036) on GitHub so we can get it done! +4. Save & Restore +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/04_Save_Restore.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/04_Save_Restore.ipynb)) -11. Adversarial Examples ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/11_Adversarial_Examples.ipynb)) +5. Ensemble Learning +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/05_Ensemble_Learning.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/05_Ensemble_Learning.ipynb)) -12. Adversarial Noise for MNIST ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/12_Adversarial_Noise_MNIST.ipynb)) +6. CIFAR-10 +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/06_CIFAR-10.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/06_CIFAR-10.ipynb)) -13. Visual Analysis ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual_Analysis.ipynb)) +7. Inception Model +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/07_Inception_Model.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/07_Inception_Model.ipynb)) -14. DeepDream ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb)) +8. Transfer Learning +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/08_Transfer_Learning.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/08_Transfer_Learning.ipynb)) -15. Style Transfer ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb)) +9. Video Data +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/09_Video_Data.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/09_Video_Data.ipynb)) -16. Reinforcement Learning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb)) +11. Adversarial Examples +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/11_Adversarial_Examples.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/11_Adversarial_Examples.ipynb)) + +12. Adversarial Noise for MNIST +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/12_Adversarial_Noise_MNIST.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/12_Adversarial_Noise_MNIST.ipynb)) + +13. Visual Analysis +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual_Analysis.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual_Analysis.ipynb)) + +14. DeepDream +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb)) + +15. Style Transfer +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb)) + +17. Estimator API +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/17_Estimator_API.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/17_Estimator_API.ipynb)) + +18. TFRecords & Dataset API +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/18_TFRecords_Dataset_API.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/18_TFRecords_Dataset_API.ipynb)) ## Videos These tutorials are also available as [YouTube videos](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ). -## Older Versions +## Translations -Sometimes the source-code has changed from that shown in the YouTube videos. This may be due to -bug-fixes, improvements, or because code-sections are moved to separate files for easy re-use. +These tutorials have been translated to the following languages: -If you want to see the exact versions of the source-code that were used in the YouTube videos, -then you can [browse the history](https://github.com/Hvass-Labs/TensorFlow-Tutorials/commits/master) -of commits to the GitHub repository. +* [Chinese](https://github.com/Hvass-Labs/TensorFlow-Tutorials-Chinese) + +### New Translations + +You can help by translating the remaining tutorials or reviewing the ones that have already been translated. You can also help by translating to other languages. + +It is a very big job to translate all the tutorials, so you should just start with Tutorials #01, #02 and #03-C which are the most important for beginners. + +### New Videos + +You are also very welcome to record your own YouTube videos in other languages. It is strongly recommended that you get a decent microphone because good sound quality is very important. I used `vokoscreen` for recording the videos and the free [DaVinci Resolve](https://www.blackmagicdesign.com/products/davinciresolve/) for editing the videos. + +## Forks -## Downloading +See the [selected list of forks](forks.md) for community modifications to these tutorials. + +## Installation + +There are different ways of installing and running TensorFlow. This section describes how I did it +for these tutorials. You may want to do it differently and you can search the internet for instructions. + +If you are new to using Python and Linux then this may be challenging +to get working and you may need to do internet searches for error-messages, etc. +It will get easier with practice. You can also run the tutorials without installing +anything by using Google Colab, see further below. Some of the Python Notebooks use source-code located in different files to allow for easy re-use across multiple tutorials. It is therefore recommended that you download the whole repository @@ -82,33 +176,26 @@ This also makes it easy to update the tutorials, simply by executing this comman git pull -### Zip-File +### Download Zip-File You can also [download](https://github.com/Hvass-Labs/TensorFlow-Tutorials/archive/master.zip) the contents of the GitHub repository as a Zip-file and extract it manually. -## Installation - -There are different ways of installing and running TensorFlow. This section describes how I did it -for these tutorials. You may want to do it differently and you can search the internet for instructions. - -If you are new to using Python and Linux, etc. then this may be challenging -to get working and you may need to do internet searches for error-messages, etc. -It will get easier with practice. - -### Python Version 3.5 or Later - -These tutorials were developed on Linux using **Python 3.5 / 3.6** (the [Anaconda](https://www.continuum.io/downloads) distribution) and [PyCharm](https://www.jetbrains.com/pycharm/). - -There are reports that Python 2.7 gives error messages with these tutorials. Please make sure you are using **Python 3.5** or later! - ### Environment -After installing [Anaconda](https://www.continuum.io/downloads), you should create a [conda environment](http://conda.pydata.org/docs/using/envs.html) +I use [Anaconda](https://www.continuum.io/downloads) because it comes with many Python +packages already installed and it is easy to work with. After installing Anaconda, +you should create a [conda environment](http://conda.pydata.org/docs/using/envs.html) so you do not destroy your main installation in case you make a mistake somewhere: conda create --name tf python=3 +When Python gets updated to a new version, it takes a while before TensorFlow also +uses the new Python version. So if the TensorFlow installation fails, then you may +have to specify an older Python version for your new environment, such as: + + conda create --name tf python=3.6 + Now you can switch to the new environment by running the following (on Linux): source activate tf @@ -117,8 +204,6 @@ Now you can switch to the new environment by running the following (on Linux): The tutorials require several Python packages to be installed. The packages are listed in [requirements.txt](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/requirements.txt) -First you need to edit this file and select whether you want to install the CPU or GPU -version of TensorFlow. To install the required Python packages and dependencies you first have to activate the conda-environment as described above, and then you run the following command @@ -126,18 +211,59 @@ in a terminal: pip install -r requirements.txt -Note that the GPU-version of TensorFlow also requires the installation of various -NVIDIA drivers, which is not described here. +Starting with TensorFlow 2.1 it includes both the CPU and GPU versions and will +automatically switch if you have a GPU. But this requires the installation of various +NVIDIA drivers, which is a bit complicated and is not described here. -### Testing +### Python Version 3.5 or Later + +These tutorials were developed on Linux using **Python 3.5 / 3.6** (the [Anaconda](https://www.continuum.io/downloads) distribution) and [PyCharm](https://www.jetbrains.com/pycharm/). -You should now be able to run the tutorials in the Python Notebooks: +There are reports that Python 2.7 gives error messages with these tutorials. Please make sure you are using **Python 3.5** or later! + +## How To Run + +If you have followed the above installation instructions, you should +now be able to run the tutorials in the Python Notebooks: cd ~/development/TensorFlow-Tutorials/ # Your installation directory. jupyter notebook This should start a web-browser that shows the list of tutorials. Click on a tutorial to load it. +### Run in Google Colab + +If you do not want to install anything on your own computer, then the Notebooks +can be viewed, edited and run entirely on the internet by using +[Google Colab](https://colab.research.google.com). There is a +[YouTube video](https://www.youtube.com/watch?v=Hs6HI2YWchM) explaining how to do this. +You click the "Google Colab"-link next to each tutorial listed above. +You can view the Notebook on Colab but in order to run it you need to login using +your Google account. +Then you need to execute the following commands at the top of the Notebook, +which clones the contents of this repository to your work-directory on Colab. + + # Clone the repository from GitHub to Google Colab's temporary drive. + import os + work_dir = "/content/TensorFlow-Tutorials/" + if not os.path.exists(work_dir): + !git clone https://github.com/Hvass-Labs/TensorFlow-Tutorials.git + os.chdir(work_dir) + +All required packages should already be installed on Colab, otherwise you +can run the following command: + + !pip install -r requirements.txt + +## Older Versions + +Sometimes the source-code has changed from that shown in the YouTube videos. This may be due to +bug-fixes, improvements, or because code-sections are moved to separate files for easy re-use. + +If you want to see the exact versions of the source-code that were used in the YouTube videos, +then you can [browse the history](https://github.com/Hvass-Labs/TensorFlow-Tutorials/commits/master) +of commits to the GitHub repository. + ## License (MIT) These tutorials and source-code are published under the [MIT License](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/LICENSE) @@ -147,4 +273,3 @@ A few of the images used for demonstration purposes may be under copyright. Thes You are very welcome to modify these tutorials and use them in your own projects. Please keep a link to the [original repository](https://github.com/Hvass-Labs/TensorFlow-Tutorials). - diff --git a/coco.py b/coco.py new file mode 100644 index 0000000..42cecf9 --- /dev/null +++ b/coco.py @@ -0,0 +1,205 @@ +######################################################################## +# +# Functions for downloading the COCO data-set from the internet +# and loading it into memory. This data-set contains images and +# various associated data such as text-captions describing the images. +# +# http://cocodataset.org +# +# Implemented in Python 3.6 +# +# Usage: +# 1) Call set_data_dir() to set the desired storage directory. +# 2) Call maybe_download_and_extract() to download the data-set +# if it is not already located in the given data_dir. +# 3) Call load_records(train=True) and load_records(train=False) +# to load the data-records for the training- and validation sets. +# 5) Use the returned data in your own program. +# +# Format: +# The COCO data-set contains a large number of images and various +# data for each image stored in a JSON-file. +# Functionality is provided for getting a list of image-filenames +# (but not actually loading the images) along with their associated +# data such as text-captions describing the contents of the images. +# +######################################################################## +# +# This file is part of the TensorFlow Tutorials available at: +# +# https://github.com/Hvass-Labs/TensorFlow-Tutorials +# +# Published under the MIT License. See the file LICENSE for details. +# +# Copyright 2018 by Magnus Erik Hvass Pedersen +# +######################################################################## + +import json +import os +import download +from cache import cache + +######################################################################## + +# Directory where you want to download and save the data-set. +# Set this before you start calling any of the functions below. +# Use the function set_data_dir() to also update train_dir and val_dir. +data_dir = "data/coco/" + +# Sub-directories for the training- and validation-sets. +train_dir = "data/coco/train2017" +val_dir = "data/coco/val2017" + +# Base-URL for the data-sets on the internet. +data_url = "http://images.cocodataset.org/" + + +######################################################################## +# Private helper-functions. + +def _load_records(train=True): + """ + Load the image-filenames and captions + for either the training-set or the validation-set. + """ + + if train: + # Training-set. + filename = "captions_train2017.json" + else: + # Validation-set. + filename = "captions_val2017.json" + + # Full path for the data-file. + path = os.path.join(data_dir, "annotations", filename) + + # Load the file. + with open(path, "r", encoding="utf-8") as file: + data_raw = json.load(file) + + # Convenience variables. + images = data_raw['images'] + annotations = data_raw['annotations'] + + # Initialize the dict for holding our data. + # The lookup-key is the image-id. + records = dict() + + # Collect all the filenames for the images. + for image in images: + # Get the id and filename for this image. + image_id = image['id'] + filename = image['file_name'] + + # Initialize a new data-record. + record = dict() + + # Set the image-filename in the data-record. + record['filename'] = filename + + # Initialize an empty list of image-captions + # which will be filled further below. + record['captions'] = list() + + # Save the record using the the image-id as the lookup-key. + records[image_id] = record + + # Collect all the captions for the images. + for ann in annotations: + # Get the id and caption for an image. + image_id = ann['image_id'] + caption = ann['caption'] + + # Lookup the data-record for this image-id. + # This data-record should already exist from the loop above. + record = records[image_id] + + # Append the current caption to the list of captions in the + # data-record that was initialized in the loop above. + record['captions'].append(caption) + + # Convert the records-dict to a list of tuples. + records_list = [(key, record['filename'], record['captions']) + for key, record in sorted(records.items())] + + # Convert the list of tuples to separate tuples with the data. + ids, filenames, captions = zip(*records_list) + + return ids, filenames, captions + + +######################################################################## +# Public functions that you may call to download the data-set from +# the internet and load the data into memory. + + +def set_data_dir(new_data_dir): + """ + Set the base-directory for data-files and then + set the sub-dirs for training and validation data. + """ + + # Ensure we update the global variables. + global data_dir, train_dir, val_dir + + data_dir = new_data_dir + train_dir = os.path.join(new_data_dir, "train2017") + val_dir = os.path.join(new_data_dir, "val2017") + + +def maybe_download_and_extract(): + """ + Download and extract the COCO data-set if the data-files don't + already exist in data_dir. + """ + + # Filenames to download from the internet. + filenames = ["zips/train2017.zip", "zips/val2017.zip", + "annotations/annotations_trainval2017.zip"] + + # Download these files. + for filename in filenames: + # Create the full URL for the given file. + url = data_url + filename + + print("Downloading " + url) + + download.maybe_download_and_extract(url=url, download_dir=data_dir) + + +def load_records(train=True): + """ + Load the data-records for the data-set. This returns the image ids, + filenames and text-captions for either the training-set or validation-set. + + This wraps _load_records() above with a cache, so if the cache-file already + exists then it is loaded instead of processing the original data-file. + + :param train: + Bool whether to load the training-set (True) or validation-set (False). + + :return: + ids, filenames, captions for the images in the data-set. + """ + + if train: + # Cache-file for the training-set data. + cache_filename = "records_train.pkl" + else: + # Cache-file for the validation-set data. + cache_filename = "records_val.pkl" + + # Path for the cache-file. + cache_path = os.path.join(data_dir, cache_filename) + + # If the data-records already exist in a cache-file then load it, + # otherwise call the _load_records() function and save its + # return-values to the cache-file so it can be loaded the next time. + records = cache(cache_path=cache_path, + fn=_load_records, + train=train) + + return records + +######################################################################## diff --git a/dataset.py b/dataset.py index b4099cf..a4a04ee 100644 --- a/dataset.py +++ b/dataset.py @@ -20,6 +20,7 @@ import numpy as np import os +import shutil from cache import cache ######################################################################## @@ -37,15 +38,16 @@ def one_hot_encoded(class_numbers, num_classes=None): Assume the integers are from zero to num_classes-1 inclusive. :param num_classes: - Number of classes. If None then use max(cls)-1. + Number of classes. If None then use max(class_numbers)+1. :return: - 2-dim array of shape: [len(cls), num_classes] + 2-dim array of shape: [len(class_numbers), num_classes] """ # Find the number of classes if None is provided. + # Assumes the lowest class-number is zero. if num_classes is None: - num_classes = np.max(class_numbers) - 1 + num_classes = np.max(class_numbers) + 1 return np.eye(num_classes, dtype=float)[class_numbers] @@ -253,6 +255,75 @@ def get_test_set(self): one_hot_encoded(class_numbers=self.class_numbers_test, num_classes=self.num_classes) + def copy_files(self, train_dir, test_dir): + """ + Copy all the files in the training-set to train_dir + and copy all the files in the test-set to test_dir. + + For example, the normal directory structure for the + different classes in the training-set is: + + knifey-spoony/forky/ + knifey-spoony/knifey/ + knifey-spoony/spoony/ + + Normally the test-set is a sub-dir of the training-set: + + knifey-spoony/forky/test/ + knifey-spoony/knifey/test/ + knifey-spoony/spoony/test/ + + But some APIs use another dir-structure for the training-set: + + knifey-spoony/train/forky/ + knifey-spoony/train/knifey/ + knifey-spoony/train/spoony/ + + and for the test-set: + + knifey-spoony/test/forky/ + knifey-spoony/test/knifey/ + knifey-spoony/test/spoony/ + + :param train_dir: Directory for the training-set e.g. 'knifey-spoony/train/' + :param test_dir: Directory for the test-set e.g. 'knifey-spoony/test/' + :return: Nothing. + """ + + # Helper-function for actually copying the files. + def _copy_files(src_paths, dst_dir, class_numbers): + + # Create a list of dirs for each class, e.g.: + # ['knifey-spoony/test/forky/', + # 'knifey-spoony/test/knifey/', + # 'knifey-spoony/test/spoony/'] + class_dirs = [os.path.join(dst_dir, class_name + "/") + for class_name in self.class_names] + + # Check if each class-directory exists, otherwise create it. + for dir in class_dirs: + if not os.path.exists(dir): + os.makedirs(dir) + + # For all the file-paths and associated class-numbers, + # copy the file to the destination dir for that class. + for src, cls in zip(src_paths, class_numbers): + shutil.copy(src=src, dst=class_dirs[cls]) + + # Copy the files for the training-set. + _copy_files(src_paths=self.get_paths(test=False), + dst_dir=train_dir, + class_numbers=self.class_numbers) + + print("- Copied training-set to:", train_dir) + + # Copy the files for the test-set. + _copy_files(src_paths=self.get_paths(test=True), + dst_dir=test_dir, + class_numbers=self.class_numbers_test) + + print("- Copied test-set to:", test_dir) + ######################################################################## diff --git a/download.py b/download.py index 676abc5..57bec13 100644 --- a/download.py +++ b/download.py @@ -34,6 +34,9 @@ def _print_download_progress(count, block_size, total_size): # Percentage completion. pct_complete = float(count * block_size) / total_size + # Limit it because rounding errors may cause it to exceed 100%. + pct_complete = min(1.0, pct_complete) + # Status-message. Note the \r which means the line should overwrite itself. msg = "\r- Download progress: {0:.1%}".format(pct_complete) @@ -44,6 +47,35 @@ def _print_download_progress(count, block_size, total_size): ######################################################################## +def download(base_url, filename, download_dir): + """ + Download the given file if it does not already exist in the download_dir. + + :param base_url: The internet URL without the filename. + :param filename: The filename that will be added to the base_url. + :param download_dir: Local directory for storing the file. + :return: Nothing. + """ + + # Path for local file. + save_path = os.path.join(download_dir, filename) + + # Check if the file already exists, otherwise we need to download it now. + if not os.path.exists(save_path): + # Check if the download directory exists, otherwise create it. + if not os.path.exists(download_dir): + os.makedirs(download_dir) + + print("Downloading", filename, "...") + + # Download the file from the internet. + url = base_url + filename + file_path, _ = urllib.request.urlretrieve(url=url, + filename=save_path, + reporthook=_print_download_progress) + + print(" Done!") + def maybe_download_and_extract(url, download_dir): """ diff --git a/europarl.py b/europarl.py new file mode 100644 index 0000000..de0852b --- /dev/null +++ b/europarl.py @@ -0,0 +1,136 @@ +######################################################################## +# +# Functions for downloading the Europarl data-set from the internet +# and loading it into memory. This data-set is used for translation +# between English and most European languages. +# +# http://www.statmt.org/europarl/ +# +# Implemented in Python 3.6 +# +# Usage: +# 1) Set the variable data_dir with the desired storage directory. +# 2) Determine the language-code to use e.g. "da" for Danish. +# 3) Call maybe_download_and_extract() to download the data-set +# if it is not already located in the given data_dir. +# 4) Call load_data(english=True) and load_data(english=False) +# to load the two data-files. +# 5) Use the returned data in your own program. +# +# Format: +# The Europarl data-set contains millions of text-pairs between English +# and most European languages. The data is stored in two text-files. +# The data is returned as lists of strings by the load_data() function. +# +# The list of currently supported languages and their codes are as follows: +# +# bg - Bulgarian +# cs - Czech +# da - Danish +# de - German +# el - Greek +# es - Spanish +# et - Estonian +# fi - Finnish +# fr - French +# hu - Hungarian +# it - Italian +# lt - Lithuanian +# lv - Latvian +# nl - Dutch +# pl - Polish +# pt - Portuguese +# ro - Romanian +# sk - Slovak +# sl - Slovene +# sv - Swedish +# +######################################################################## +# +# This file is part of the TensorFlow Tutorials available at: +# +# https://github.com/Hvass-Labs/TensorFlow-Tutorials +# +# Published under the MIT License. See the file LICENSE for details. +# +# Copyright 2018 by Magnus Erik Hvass Pedersen +# +######################################################################## + +import os +import download + +######################################################################## + +# Directory where you want to download and save the data-set. +# Set this before you start calling any of the functions below. +data_dir = "data/europarl/" + +# Base-URL for the data-sets on the internet. +data_url = "http://www.statmt.org/europarl/v7/" + + +######################################################################## +# Public functions that you may call to download the data-set from +# the internet and load the data into memory. + + +def maybe_download_and_extract(language_code="da"): + """ + Download and extract the Europarl data-set if the data-file doesn't + already exist in data_dir. The data-set is for translating between + English and the given language-code (e.g. 'da' for Danish, see the + list of available language-codes above). + """ + + # Create the full URL for the file with this data-set. + url = data_url + language_code + "-en.tgz" + + download.maybe_download_and_extract(url=url, download_dir=data_dir) + + +def load_data(english=True, language_code="da", start="", end=""): + """ + Load the data-file for either the English-language texts or + for the other language (e.g. "da" for Danish). + + All lines of the data-file are returned as a list of strings. + + :param english: + Boolean whether to load the data-file for + English (True) or the other language (False). + + :param language_code: + Two-char code for the other language e.g. "da" for Danish. + See list of available codes above. + + :param start: + Prepend each line with this text e.g. "ssss " to indicate start of line. + + :param end: + Append each line with this text e.g. " eeee" to indicate end of line. + + :return: + List of strings with all the lines of the data-file. + """ + + if english: + # Load the English data. + filename = "europarl-v7.{0}-en.en".format(language_code) + else: + # Load the other language. + filename = "europarl-v7.{0}-en.{0}".format(language_code) + + # Full path for the data-file. + path = os.path.join(data_dir, filename) + + # Open and read all the contents of the data-file. + with open(path, encoding="utf-8") as file: + # Read the line from file, strip leading and trailing whitespace, + # prepend the start-text and append the end-text. + texts = [start + line.strip() + end for line in file] + + return texts + + +######################################################################## diff --git a/forks.md b/forks.md new file mode 100644 index 0000000..aa88cf4 --- /dev/null +++ b/forks.md @@ -0,0 +1,10 @@ +# TensorFlow Tutorials - Forks + +These are forks of the [original TensorFlow Tutorials by Hvass-Labs](https://github.com/Hvass-Labs/TensorFlow-Tutorials). +They are not developed or even reviewed by the original author, who takes no reponsibility for these forks. + +If you have made a fork of the TensorFlow Tutorials with substantial modifications that you feel may be useful to others, +then please [open a new issue on GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials/issues) with a link and short description. + +* [Keras port of some tutorials.](https://github.com/chidochipotle/TensorFlow-Tutorials) +* [The Inception model as an OpenFaaS function.](https://github.com/faas-and-furious/inception-function) diff --git a/images/10_transfer_learning_flowchart.png b/images/10_transfer_learning_flowchart.png new file mode 100644 index 0000000..177e32b Binary files /dev/null and b/images/10_transfer_learning_flowchart.png differ diff --git a/images/10_transfer_learning_flowchart.svg b/images/10_transfer_learning_flowchart.svg new file mode 100644 index 0000000..eeba118 --- /dev/null +++ b/images/10_transfer_learning_flowchart.svg @@ -0,0 +1,907 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + Original VGG16 Model + + Dense 2 + + + + Dense 1 + + + + ConvolutionBlock 5 + + + + ConvolutionBlock 4 + + + + ConvolutionBlock 3 + + + + ConvolutionBlock 2 + + + + ConvolutionBlock 1 + + + + Input Layer + + + + Output LayerSoftmax + + + + + + + + + + + + + Output LayerSoftmax + + + + Dense 1 + + + + Dropout + + + + + + + Old Classifier + New Classifier + + New Model + + New Model + + diff --git a/images/13b_visual_analysis_flowchart.png b/images/13b_visual_analysis_flowchart.png new file mode 100644 index 0000000..df89de9 Binary files /dev/null and b/images/13b_visual_analysis_flowchart.png differ diff --git a/images/13b_visual_analysis_flowchart.svg b/images/13b_visual_analysis_flowchart.svg new file mode 100644 index 0000000..1053f6e --- /dev/null +++ b/images/13b_visual_analysis_flowchart.svg @@ -0,0 +1,779 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + Conv.Layer 1 + + + + Conv.Layer 2 + + + + DenseLayer 1 + + + + DenseLayer 2 + + + + Softmax + + + + + + + + Use gradient to find image thatmaximizes a feature. + + + + + + + + + + + Use gradient to optimize network weights. + + + + Cross-Entropy + + + + + + + + + + + + True class: 7 + + + + + + diff --git a/images/19_flowchart_bayesian_optimization.png b/images/19_flowchart_bayesian_optimization.png new file mode 100644 index 0000000..3fc83c3 Binary files /dev/null and b/images/19_flowchart_bayesian_optimization.png differ diff --git a/images/19_flowchart_bayesian_optimization.svg b/images/19_flowchart_bayesian_optimization.svg new file mode 100644 index 0000000..a0fac20 --- /dev/null +++ b/images/19_flowchart_bayesian_optimization.svg @@ -0,0 +1,971 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + ClassificationAccuracyon Validation-Set + + Hyper-Parameters:- Learning-rate- Number of Dense Layers- Number of Dense Nodes- Activation Function + + + Evaluate Performanceof Neural Network + + + + Create Neural Networkusing Hyper-Parameters + + + + Train theNeural Network + + + Neural Network + + + UpdateGaussianModel + + + + Sample the Model to MaximizeExpected Improvement + + + Hyper-Parameter Optimization using Gaussian Process + + + + + + + + + + + diff --git a/images/20_natural_language_flowchart.png b/images/20_natural_language_flowchart.png new file mode 100644 index 0000000..70ada9b Binary files /dev/null and b/images/20_natural_language_flowchart.png differ diff --git a/images/20_natural_language_flowchart.svg b/images/20_natural_language_flowchart.svg new file mode 100644 index 0000000..0e9162e --- /dev/null +++ b/images/20_natural_language_flowchart.svg @@ -0,0 +1,440 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + [11, 6, 21, 3, 49, 17] + [[0.67, 0.36, ..., 0.39], [0.76, 0.61, ..., 0.70], ...] + Raw Text + "This is not a good movie!" + + + + Tokenizer + Converts text to integer-tokens + + + + + + Embedding + Converts integer-tokensto real-valued vectors. + + + + + + Recurrent Neural Network + Process sequences of arbitrary length + + + + + + + Sigmoid + Dense output layer to predict class + + + + + + 0.0 (Negative) + + + + 1.0 (Positive) + + + + + + + + + + diff --git a/images/20_recurrent_unit.png b/images/20_recurrent_unit.png new file mode 100644 index 0000000..b8affb0 Binary files /dev/null and b/images/20_recurrent_unit.png differ diff --git a/images/20_recurrent_unit.svg b/images/20_recurrent_unit.svg new file mode 100644 index 0000000..279c4ca --- /dev/null +++ b/images/20_recurrent_unit.svg @@ -0,0 +1,336 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + Old State + + + + New State + + + + Output + + + + Input + + + + Gate + + + + Gate + + + + + + + + + + + diff --git a/images/20_unrolled_3layers_flowchart.png b/images/20_unrolled_3layers_flowchart.png new file mode 100644 index 0000000..2cbd421 Binary files /dev/null and b/images/20_unrolled_3layers_flowchart.png differ diff --git a/images/20_unrolled_3layers_flowchart.svg b/images/20_unrolled_3layers_flowchart.svg new file mode 100644 index 0000000..8233cfb --- /dev/null +++ b/images/20_unrolled_3layers_flowchart.svg @@ -0,0 +1,1847 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + "this" + + + + "is" + + + + "not" + + + + "a" + + + + "very" + + + + "good" + + + + "movie" + + + + + PositiveorNegative + + + + + + + RU1 + + + FirstStateZero + + + + + + + + + + + + + + + + + + FirstStateZero + + + + + + + + + + + + + + + + + + FirstStateZero + + + + + + + + + + + + + + + + + Layer 1 + Layer 2 + Layer 3 + + + + RU1 + + + + RU1 + + + + RU1 + + + + RU1 + + + + RU1 + + + + RU1 + + + + RU2 + + + + RU2 + + + + RU2 + + + + RU2 + + + + RU2 + + + + RU2 + + + + RU2 + + + + RU3 + + + + RU3 + + + + RU3 + + + + RU3 + + + + RU3 + + + + RU3 + + + + RU3 + + + diff --git a/images/20_unrolled_flowchart.png b/images/20_unrolled_flowchart.png new file mode 100644 index 0000000..d6a1bc5 Binary files /dev/null and b/images/20_unrolled_flowchart.png differ diff --git a/images/20_unrolled_flowchart.svg b/images/20_unrolled_flowchart.svg new file mode 100644 index 0000000..6d0a7fc --- /dev/null +++ b/images/20_unrolled_flowchart.svg @@ -0,0 +1,849 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + RU + + + + "this" + + + + "is" + + + + "not" + + + + "a" + + + + "very" + + + + "good" + + + + "movie" + + + + FirstStateZero + + + + RU + + + + RU + + + + RU + + + + RU + + + + RU + + + + RU + + + + PositiveorNegative + + + + + + + + + + + + + + + + + + (Output) + (States) + + + + + + diff --git a/images/21_machine_translation_flowchart.png b/images/21_machine_translation_flowchart.png new file mode 100644 index 0000000..6437f12 Binary files /dev/null and b/images/21_machine_translation_flowchart.png differ diff --git a/images/21_machine_translation_flowchart.svg b/images/21_machine_translation_flowchart.svg new file mode 100644 index 0000000..b8f8a61 --- /dev/null +++ b/images/21_machine_translation_flowchart.svg @@ -0,0 +1,4475 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + GRU3 + + + + GRU3 + + + ThoughtVector + + + Source (Danish) + + + + + diff --git a/images/22_image_captioning_flowchart.png b/images/22_image_captioning_flowchart.png new file mode 100644 index 0000000..af3446f Binary files /dev/null and b/images/22_image_captioning_flowchart.png differ diff --git a/images/22_image_captioning_flowchart.svg b/images/22_image_captioning_flowchart.svg new file mode 100644 index 0000000..2df11f7 --- /dev/null +++ b/images/22_image_captioning_flowchart.svg @@ -0,0 +1,4209 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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0000000..079319f --- /dev/null +++ b/images/23_time_series_flowchart.svg @@ -0,0 +1,769 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + Temp + Pressure + WindSpeed + WindDir + + + + + + + + + + + + + + + + + + Aalborg + + + + + + + + + + + + + + + + + + + + + Temp + Pressure + WindSpeed + WindDir + + Roskilde + + + + + + + + + + + + + + + + + Temp + Pressure + WindSpeed + + Odense + + + + + + Gated Recurrent Unit + + + + Dense + + + + + + + + + + + + + + + + + + + + + + + + Input Signals + Output Signals + 512 + + NeuralNetwork + + diff --git a/images/Denmark.jpg b/images/Denmark.jpg new file mode 100644 index 0000000..239d139 Binary files /dev/null and b/images/Denmark.jpg differ diff --git a/images/Europe.jpg b/images/Europe.jpg new file mode 100644 index 0000000..b5a6219 Binary files /dev/null and b/images/Europe.jpg differ diff --git a/imdb.py b/imdb.py new file mode 100644 index 0000000..db3094f --- /dev/null +++ b/imdb.py @@ -0,0 +1,121 @@ +######################################################################## +# +# Functions for downloading the IMDB Review data-set from the internet +# and loading it into memory. +# +# Implemented in Python 3.6 +# +# Usage: +# 1) Set the variable data_dir with the desired storage directory. +# 2) Call maybe_download_and_extract() to download the data-set +# if it is not already located in the given data_dir. +# 3) Call load_data(train=True) to load the training-set. +# 4) Call load_data(train=False) to load the test-set. +# 5) Use the returned data in your own program. +# +# Format: +# The IMDB Review data-set consists of 50000 reviews of movies +# that are split into 25000 reviews for the training- and test-set, +# and each of those is split into 12500 positive and 12500 negative reviews. +# These are returned as lists of strings by the load_data() function. +# +######################################################################## +# +# This file is part of the TensorFlow Tutorials available at: +# +# https://github.com/Hvass-Labs/TensorFlow-Tutorials +# +# Published under the MIT License. See the file LICENSE for details. +# +# Copyright 2018 by Magnus Erik Hvass Pedersen +# +######################################################################## + +import os +import download +import glob + +######################################################################## + +# Directory where you want to download and save the data-set. +# Set this before you start calling any of the functions below. +data_dir = "data/IMDB/" + +# URL for the data-set on the internet. +data_url = "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz" + + +######################################################################## +# Private helper-functions. + +def _read_text_file(path): + """ + Read and return all the contents of the text-file with the given path. + It is returned as a single string where all lines are concatenated. + """ + + with open(path, 'rt', encoding='utf-8') as file: + # Read a list of strings. + lines = file.readlines() + + # Concatenate to a single string. + text = " ".join(lines) + + return text + + +######################################################################## +# Public functions that you may call to download the data-set from +# the internet and load the data into memory. + + +def maybe_download_and_extract(): + """ + Download and extract the IMDB Review data-set if it doesn't already exist + in data_dir (set this variable first to the desired directory). + """ + + download.maybe_download_and_extract(url=data_url, download_dir=data_dir) + + +def load_data(train=True): + """ + Load all the data from the IMDB Review data-set for sentiment analysis. + + :param train: Boolean whether to load the training-set (True) + or the test-set (False). + + :return: A list of all the reviews as text-strings, + and a list of the corresponding sentiments + where 1.0 is positive and 0.0 is negative. + """ + + # Part of the path-name for either training or test-set. + train_test_path = "train" if train else "test" + + # Base-directory where the extracted data is located. + dir_base = os.path.join(data_dir, "aclImdb", train_test_path) + + # Filename-patterns for the data-files. + path_pattern_pos = os.path.join(dir_base, "pos", "*.txt") + path_pattern_neg = os.path.join(dir_base, "neg", "*.txt") + + # Get lists of all the file-paths for the data. + paths_pos = glob.glob(path_pattern_pos) + paths_neg = glob.glob(path_pattern_neg) + + # Read all the text-files. + data_pos = [_read_text_file(path) for path in paths_pos] + data_neg = [_read_text_file(path) for path in paths_neg] + + # Concatenate the positive and negative data. + x = data_pos + data_neg + + # Create a list of the sentiments for the text-data. + # 1.0 is a positive sentiment, 0.0 is a negative sentiment. + y = [1.0] * len(data_pos) + [0.0] * len(data_neg) + + return x, y + + +######################################################################## diff --git a/knifey.py b/knifey.py index 7b741e6..6079529 100644 --- a/knifey.py +++ b/knifey.py @@ -28,6 +28,12 @@ # Set this before you start calling any of the functions below. data_dir = "data/knifey-spoony/" +# Directory for the training-set after copying the files using copy_files(). +train_dir = os.path.join(data_dir, "train/") + +# Directory for the test-set after copying the files using copy_files(). +test_dir = os.path.join(data_dir, "test/") + # URL for the data-set on the internet. data_url = "https://github.com/Hvass-Labs/knifey-spoony/raw/master/knifey-spoony.tar.gz" @@ -41,6 +47,9 @@ # Number of channels in each image, 3 channels: Red, Green, Blue. num_channels = 3 +# Shape of the numpy-array for an image. +img_shape = [img_size, img_size, num_channels] + # Length of an image when flattened to a 1-dim array. img_size_flat = img_size * img_size * num_channels @@ -88,6 +97,27 @@ def load(): return dataset +def copy_files(): + """ + Copy all the files in the training-set to train_dir + and copy all the files in the test-set to test_dir. + + This creates the directories if they don't already exist, + and it overwrites the images if they already exist. + + The images are originally stored in a directory-structure + that is incompatible with e.g. the Keras API. This function + copies the files to a dir-structure that works with e.g. Keras. + """ + + # Load the Knifey-Spoony dataset. + # This is very fast as it only gathers lists of the files + # and does not actually load the images into memory. + dataset = load() + + # Copy the files to separate training- and test-dirs. + dataset.copy_files(train_dir=train_dir, test_dir=test_dir) + ######################################################################## if __name__ == '__main__': diff --git a/mnist.py b/mnist.py new file mode 100644 index 0000000..6b7bbb4 --- /dev/null +++ b/mnist.py @@ -0,0 +1,186 @@ +######################################################################## +# +# Downloads the MNIST data-set for recognizing hand-written digits. +# +# Implemented in Python 3.6 +# +# Usage: +# 1) Create a new object instance: data = MNIST(data_dir="data/MNIST/") +# This automatically downloads the files to the given dir. +# 2) Use the training-set as data.x_train, data.y_train and data.y_train_cls +# 3) Get random batches of training data using data.random_batch() +# 4) Use the test-set as data.x_test, data.y_test and data.y_test_cls +# +######################################################################## +# +# This file is part of the TensorFlow Tutorials available at: +# +# https://github.com/Hvass-Labs/TensorFlow-Tutorials +# +# Published under the MIT License. See the file LICENSE for details. +# +# Copyright 2016-18 by Magnus Erik Hvass Pedersen +# +######################################################################## + +import numpy as np +import gzip +import os +from dataset import one_hot_encoded +from download import download + +######################################################################## + +# Base URL for downloading the data-files from the internet. +base_url = "https://storage.googleapis.com/cvdf-datasets/mnist/" + +# Filenames for the data-set. +filename_x_train = "train-images-idx3-ubyte.gz" +filename_y_train = "train-labels-idx1-ubyte.gz" +filename_x_test = "t10k-images-idx3-ubyte.gz" +filename_y_test = "t10k-labels-idx1-ubyte.gz" + +######################################################################## + + +class MNIST: + """ + The MNIST data-set for recognizing hand-written digits. + This automatically downloads the data-files if they do + not already exist in the local data_dir. + + Note: Pixel-values are floats between 0.0 and 1.0. + """ + + # The images are 28 pixels in each dimension. + img_size = 28 + + # The images are stored in one-dimensional arrays of this length. + img_size_flat = img_size * img_size + + # Tuple with height and width of images used to reshape arrays. + img_shape = (img_size, img_size) + + # Number of colour channels for the images: 1 channel for gray-scale. + num_channels = 1 + + # Tuple with height, width and depth used to reshape arrays. + # This is used for reshaping in Keras. + img_shape_full = (img_size, img_size, num_channels) + + # Number of classes, one class for each of 10 digits. + num_classes = 10 + + def __init__(self, data_dir="data/MNIST/"): + """ + Load the MNIST data-set. Automatically downloads the files + if they do not already exist locally. + + :param data_dir: Base-directory for downloading files. + """ + + # Copy args to self. + self.data_dir = data_dir + + # Number of images in each sub-set. + self.num_train = 55000 + self.num_val = 5000 + self.num_test = 10000 + + # Download / load the training-set. + x_train = self._load_images(filename=filename_x_train) + y_train_cls = self._load_cls(filename=filename_y_train) + + # Split the training-set into train / validation. + # Pixel-values are converted from ints between 0 and 255 + # to floats between 0.0 and 1.0. + self.x_train = x_train[0:self.num_train] / 255.0 + self.x_val = x_train[self.num_train:] / 255.0 + self.y_train_cls = y_train_cls[0:self.num_train] + self.y_val_cls = y_train_cls[self.num_train:] + + # Download / load the test-set. + self.x_test = self._load_images(filename=filename_x_test) / 255.0 + self.y_test_cls = self._load_cls(filename=filename_y_test) + + # Convert the class-numbers from bytes to ints as that is needed + # some places in TensorFlow. + self.y_train_cls = self.y_train_cls.astype(np.int) + self.y_val_cls = self.y_val_cls.astype(np.int) + self.y_test_cls = self.y_test_cls.astype(np.int) + + # Convert the integer class-numbers into one-hot encoded arrays. + self.y_train = one_hot_encoded(class_numbers=self.y_train_cls, + num_classes=self.num_classes) + self.y_val = one_hot_encoded(class_numbers=self.y_val_cls, + num_classes=self.num_classes) + self.y_test = one_hot_encoded(class_numbers=self.y_test_cls, + num_classes=self.num_classes) + + def _load_data(self, filename, offset): + """ + Load the data in the given file. Automatically downloads the file + if it does not already exist in the data_dir. + + :param filename: Name of the data-file. + :param offset: Start offset in bytes when reading the data-file. + :return: The data as a numpy array. + """ + + # Download the file from the internet if it does not exist locally. + download(base_url=base_url, filename=filename, download_dir=self.data_dir) + + # Read the data-file. + path = os.path.join(self.data_dir, filename) + with gzip.open(path, 'rb') as f: + data = np.frombuffer(f.read(), np.uint8, offset=offset) + + return data + + def _load_images(self, filename): + """ + Load image-data from the given file. + Automatically downloads the file if it does not exist locally. + + :param filename: Name of the data-file. + :return: Numpy array. + """ + + # Read the data as one long array of bytes. + data = self._load_data(filename=filename, offset=16) + + # Reshape to 2-dim array with shape (num_images, img_size_flat). + images_flat = data.reshape(-1, self.img_size_flat) + + return images_flat + + def _load_cls(self, filename): + """ + Load class-numbers from the given file. + Automatically downloads the file if it does not exist locally. + + :param filename: Name of the data-file. + :return: Numpy array. + """ + return self._load_data(filename=filename, offset=8) + + def random_batch(self, batch_size=32): + """ + Create a random batch of training-data. + + :param batch_size: Number of images in the batch. + :return: 3 numpy arrays (x, y, y_cls) + """ + + # Create a random index into the training-set. + idx = np.random.randint(low=0, high=self.num_train, size=batch_size) + + # Use the index to lookup random training-data. + x_batch = self.x_train[idx] + y_batch = self.y_train[idx] + y_batch_cls = self.y_train_cls[idx] + + return x_batch, y_batch, y_batch_cls + + +######################################################################## diff --git a/reinforcement_learning.py b/reinforcement_learning.py index 9c4c2f8..d5f0e09 100644 --- a/reinforcement_learning.py +++ b/reinforcement_learning.py @@ -7,7 +7,7 @@ # To train a Neural Network for playing the Atari game Breakout, # run the following command in a terminal window. # -# python reinforcement-learning.py --env 'Breakout-v0' --training +# python reinforcement_learning.py --env 'Breakout-v0' --training # # The agent should start to improve after a few hours, but a full # training run required 150 hours on a 2.6 GHz CPU and GTX 1070 GPU. @@ -18,13 +18,12 @@ # Once the Neural Network has been trained, you can test it and # watch it play the game by running this command in the terminal: # -# python reinforcement-learning.py --env 'Breakout-v0' --render --episodes 2 +# python reinforcement_learning.py --env 'Breakout-v0' --render --episodes 2 # # Requirements: # # - Python 3.6 (Python 2.7 may not work) -# - TensorFlow 1.0.1 -# - PrettyTensor 0.7.4 +# - TensorFlow 1.1.0 # - OpenAI Gym 0.8.1 # # Summary: @@ -156,17 +155,19 @@ # ######################################################################## +# Use TensorFlow v.2 with this old v.1 code. +# E.g. placeholder variables and sessions have changed in TF2. +import tensorflow.compat.v1 as tf +tf.disable_v2_behavior() + import numpy as np -import tensorflow as tf -import prettytensor as pt import gym -import scipy.ndimage +import PIL.Image import sys import os import time import csv import argparse -import download ######################################################################## # File-paths are global variables for convenience so they don't @@ -217,29 +218,6 @@ def update_paths(env_name): # File-path for the log-file for Q-values. log_q_values_path = os.path.join(checkpoint_dir, "log_q_values.txt") -######################################################################## -# Download TensorFlow checkpoints. - -# URL's for the checkpoint-files. -_checkpoint_url = { - "Breakout-v0": "http://hvass-labs.org/projects/tensorflow/tutorial16/Breakout-v0.tar.gz", - "SpaceInvaders-v0": "http://hvass-labs.org/projects/tensorflow/tutorial16/SpaceInvaders-v0.tar.gz" -} - - -def maybe_download_checkpoint(env_name): - """ - Download and extract the TensorFlow checkpoint for the given - environment-name, if it does not already exist in checkpoint_base_dir. - You should first set this dir and call update_paths(). - """ - - # Get the url for the game-environment. - url = _checkpoint_url[env_name] - - # Download and extract the file if it does not already exist. - download.maybe_download_and_extract(url=url, - download_dir=checkpoint_base_dir) ######################################################################## # Classes used for logging data during training. @@ -431,6 +409,9 @@ def print_progress(msg): # Size of each image in the state. state_img_size = np.array([state_height, state_width]) +# Size of each image in the state. Reversed order used by PIL.Image. +state_img_size_reverse = tuple(reversed(state_img_size)) + # Number of images in the state. state_channels = 2 @@ -461,12 +442,19 @@ def _pre_process_image(image): """Pre-process a raw image from the game-environment.""" # Convert image to gray-scale. - img = _rgb_to_grayscale(image) + img_gray = _rgb_to_grayscale(image=image) + + # Create PIL-object from numpy array. + img = PIL.Image.fromarray(img_gray) - # Resize to the desired size using SciPy for convenience. - img = scipy.misc.imresize(img, size=state_img_size, interp='bicubic') + # Resize the image. + img_resized = img.resize(size=state_img_size_reverse, + resample=PIL.Image.LINEAR) - return img + # Convert 8-bit pixel values back to floating-point. + img_resized = np.float32(img_resized) + + return img_resized class MotionTracer: @@ -1151,36 +1139,10 @@ def __init__(self, num_actions, replay_memory): self.count_episodes_increase = tf.assign(self.count_episodes, self.count_episodes + 1) - # Initializer for the layers in the Neural Network. - # If you change the architecture of the network, particularly - # if you add or remove layers, then you may have to change - # the stddev-parameter here. The initial weights must result - # in the Neural Network outputting Q-values that are very close - # to zero - but the network weights must not be too low either - # because it will make it hard to train the network. - # You can experiment with values between 1e-2 and 1e-3. - init = tf.truncated_normal_initializer(mean=0.0, stddev=2e-2) - - # Wrap the input to the Neural Network in a PrettyTensor object. - x_pretty = pt.wrap(self.x) - - # Create the convolutional Neural Network using Pretty Tensor. - with pt.defaults_scope(activation_fn=tf.nn.relu): - self.q_values = x_pretty. \ - conv2d(kernel=3, depth=16, stride=2, name='layer_conv1', weights=init). \ - conv2d(kernel=3, depth=32, stride=2, name='layer_conv2', weights=init). \ - conv2d(kernel=3, depth=64, stride=1, name='layer_conv3', weights=init). \ - flatten(). \ - fully_connected(size=1024, name='layer_fc1', weights=init). \ - fully_connected(size=1024, name='layer_fc2', weights=init). \ - fully_connected(size=1024, name='layer_fc3', weights=init). \ - fully_connected(size=1024, name='layer_fc4', weights=init). \ - fully_connected(size=num_actions, name='layer_fc_out', weights=init, - activation_fn=None) - + # The Neural Network will be constructed in the following. # Note that the architecture of this Neural Network is very # different from that used in the original DeepMind papers, - # which were something like this: + # which was something like this: # Input image: 84 x 84 x 4 (4 gray-scale images of 84 x 84 pixels). # Conv layer 1: 16 filters 8 x 8, stride 4, relu. # Conv layer 2: 32 filters 4 x 4, stride 2, relu. @@ -1196,10 +1158,88 @@ def __init__(self, num_actions, replay_memory): # optimization iteration was performed after each step of the game, # and some more tricks. - # Loss-function which must be optimized. This the mean-squared error - # between the Q-values that are output by the Neural Network - # and the target Q-values. - self.loss = self.q_values.l2_regression(target=self.q_values_new) + # Initializer for the layers in the Neural Network. + # If you change the architecture of the network, particularly + # if you add or remove layers, then you may have to change + # the stddev-parameter here. The initial weights must result + # in the Neural Network outputting Q-values that are very close + # to zero - but the network weights must not be too low either + # because it will make it hard to train the network. + # You can experiment with values between 1e-2 and 1e-3. + init = tf.truncated_normal_initializer(mean=0.0, stddev=2e-2) + + # This builds the Neural Network using the tf.layers API, + # which is very verbose and inelegant, but should work for everyone. + + # Padding used for the convolutional layers. + padding = 'SAME' + + # Activation function for all convolutional and fully-connected + # layers, except the last. + activation = tf.nn.relu + + # Reference to the lastly added layer of the Neural Network. + # This makes it easy to add or remove layers. + net = self.x + + # First convolutional layer. + net = tf.layers.conv2d(inputs=net, name='layer_conv1', + filters=16, kernel_size=3, strides=2, + padding=padding, + kernel_initializer=init, activation=activation) + + # Second convolutional layer. + net = tf.layers.conv2d(inputs=net, name='layer_conv2', + filters=32, kernel_size=3, strides=2, + padding=padding, + kernel_initializer=init, activation=activation) + + # Third convolutional layer. + net = tf.layers.conv2d(inputs=net, name='layer_conv3', + filters=64, kernel_size=3, strides=1, + padding=padding, + kernel_initializer=init, activation=activation) + + # Flatten output of the last convolutional layer so it can + # be input to a fully-connected (aka. dense) layer. + net = tf.layers.flatten(net) + + # First fully-connected (aka. dense) layer. + net = tf.layers.dense(inputs=net, name='layer_fc1', units=1024, + kernel_initializer=init, activation=activation) + + # Second fully-connected layer. + net = tf.layers.dense(inputs=net, name='layer_fc2', units=1024, + kernel_initializer=init, activation=activation) + + # Third fully-connected layer. + net = tf.layers.dense(inputs=net, name='layer_fc3', units=1024, + kernel_initializer=init, activation=activation) + + # Fourth fully-connected layer. + net = tf.layers.dense(inputs=net, name='layer_fc4', units=1024, + kernel_initializer=init, activation=activation) + + # Final fully-connected layer. + net = tf.layers.dense(inputs=net, name='layer_fc_out', units=num_actions, + kernel_initializer=init, activation=None) + + # The output of the Neural Network is the estimated Q-values + # for each possible action in the game-environment. + self.q_values = net + + # TensorFlow has a built-in loss-function for doing regression: + # self.loss = tf.nn.l2_loss(self.q_values - self.q_values_new) + # But it uses tf.reduce_sum() rather than tf.reduce_mean() + # which is used by PrettyTensor. This means the scale of the + # gradient is different and hence the hyper-parameters + # would have to be re-tuned, because they were tuned for + # the original version of this tutorial using PrettyTensor. + # So instead we calculate the L2-loss similarly to how it is + # done in PrettyTensor. + squared_error = tf.square(self.q_values - self.q_values_new) + sum_squared_error = tf.reduce_sum(squared_error, axis=1) + self.loss = tf.reduce_mean(sum_squared_error) # Optimizer used for minimizing the loss-function. # Note the learning-rate is a placeholder variable so we can @@ -1384,8 +1424,11 @@ def get_weights_variable(self, layer_name): you must use the function get_variable_value() for that. """ + # The tf.layers API uses this name for the weights in a conv-layer. + variable_name = 'kernel' + with tf.variable_scope(layer_name, reuse=True): - variable = tf.get_variable('weights') + variable = tf.get_variable(variable_name) return variable diff --git a/requirements.txt b/requirements.txt index 43c0e28..d303a8b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,6 +7,7 @@ # Python packages by running the following commands in a shell: # # conda create --name tf python=3 +# source activate tf # pip install -r requirements.txt # # Note that you have to edit this file to select whether you @@ -23,20 +24,22 @@ Pillow scikit-learn ################################################################ -# TensorFlow can be installed either as CPU or GPU versions. -# You select which one to install by (un)commenting these lines. +# TensorFlow v.2.1 and above include both CPU and GPU versions. -tensorflow # CPU Version of TensorFlow. -# tensorflow-gpu # GPU version of TensorFlow. - -# Builder API for TensorFlow used in many of the tutorials. -prettytensor +tensorflow ################################################################ -# The tutorial on Reinforcement Learning uses OpenAI Gym. -# Uncomment this line if you want to run that tutorial. +# Some tutorials use other individual Python packages. +# Uncomment the relevant lines for the tutorials you want to run. -# gym[atari] +# gym[atari] # Tutorial #16 on Reinforcement Learning. +# pandas # Tutorial #23 on Time-Series Prediction. ################################################################ +# PrettyTensor was used as the builder API for several of the +# earlier tutorials. PrettyTensor is apparently no longer being +# maintained and may not work with newer versions of TensorFlow. + +# prettytensor +################################################################ diff --git a/weather.py b/weather.py new file mode 100644 index 0000000..e706034 --- /dev/null +++ b/weather.py @@ -0,0 +1,255 @@ +######################################################################## +# +# Functions for downloading and re-sampling weather-data +# for 5 cities in Denmark between 1980-2018. +# +# The raw data was obtained from: +# +# National Climatic Data Center (NCDC) in USA +# https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd +# +# Note that the NCDC's database functionality may change soon, and +# that the CSV-file needed some manual editing before it could be read. +# See the function _convert_raw_data() below for inspiration if you +# want to convert a new data-file from NCDC's database. +# +# Implemented in Python 3.6 +# +# Usage: +# 1) Set the desired storage directory in the data_dir variable. +# 2) Call maybe_download_and_extract() to download the data-set +# if it is not already located in the given data_dir. +# 3) Either call load_original_data() or load_resampled_data() +# to load the original or resampled data for use in your program. +# +# Format: +# The raw data-file from NCDC is not included in the downloaded archive, +# which instead contains a cleaned-up version of the raw data-file +# referred to as the "original data". This data has not yet been resampled. +# The original data-file is available as a pickled file for fast reloading +# with Pandas, and as a CSV-file for broad compatibility. +# +######################################################################## +# +# This file is part of the TensorFlow Tutorials available at: +# +# https://github.com/Hvass-Labs/TensorFlow-Tutorials +# +# Published under the MIT License. See the file LICENSE for details. +# +# Copyright 2018 by Magnus Erik Hvass Pedersen +# +######################################################################## + +import pandas as pd +import os +import download + +######################################################################## + +# Directory where you want to download and save the data-set. +# Set this before you start calling any of the functions below. +data_dir = "data/weather-denmark/" + + +# Full path for the pickled data-file. (Original data). +def path_original_data_pickle(): + return os.path.join(data_dir, "weather-denmark.pkl") + + +# Full path for the comma-separated text-file. (Original data). +def path_original_data_csv(): + return os.path.join(data_dir, "weather-denmark.csv") + + +# Full path for the resampled data as a pickled file. +def path_resampled_data_pickle(): + return os.path.join(data_dir, "weather-denmark-resampled.pkl") + + +# URL for the data-set on the internet. +data_url = "https://github.com/Hvass-Labs/weather-denmark/raw/master/weather-denmark.tar.gz" + + +# List of the cities in this data-set. These are cities in Denmark. +cities = ['Aalborg', 'Aarhus', 'Esbjerg', 'Odense', 'Roskilde'] + + +######################################################################## +# Private helper-functions. + + +def _date_string(x): + """Convert two integers to a string for the date and time.""" + + date = x[0] # Date. Example: 19801231 + time = x[1] # Time. Example: 1230 + + return "{0}{1:04d}".format(date, time) + + +def _usaf_to_city(usaf): + """ + The raw data-file uses USAF-codes to identify weather-stations. + If you download another data-set from NCDC then you will have to + change this function to use the USAF-codes in your new data-file. + """ + + table = \ + { + 60300: 'Aalborg', + 60700: 'Aarhus', + 60800: 'Esbjerg', + 61200: 'Odense', + 61700: 'Roskilde' + } + + return table[usaf] + + +def _convert_raw_data(path): + """ + This converts a raw data-file obtained from the NCDC database. + This function may be useful as an inspiration if you want to + download another raw data-file from NCDC, but you will have + to modify this function to match the data you have downloaded. + + Note that you may also have to manually edit the raw data-file, + e.g. because the header is not in a proper comma-separated format. + """ + + # The raw CSV-file uses various markers for "not-available" (NA). + # (This is one of several oddities with NCDC's file-format.) + na_values = ['999', '999.0', '999.9', '9999.9'] + + # Use Pandas to load the comma-separated file. + # Note that you may have to manually edit the file's header + # to get this to load correctly. + df_raw = pd.read_csv(path, sep=',', header=1, + index_col=False, na_values=na_values) + + # Create a new data-frame containing only the data + # we are interested in. + df = pd.DataFrame() + + # Get the city-name / weather-station name from the USAF code. + df['City'] = df_raw['USAF '].apply(_usaf_to_city) + + # Convert the integer date-time to a proper date-time object. + datestr = df_raw[['Date ', 'HrMn']].apply(_date_string, axis=1) + df['DateTime'] = pd.to_datetime(datestr, format='%Y%m%d%H%M') + + # Get the data we are interested in. + df['Temp'] = df_raw['Temp '] + df['Pressure'] = df_raw['Slp '] + df['WindSpeed'] = df_raw['Spd '] + df['WindDir'] = df_raw['Dir'] + + # Set the city-name and date-time as the index. + df.set_index(['City', 'DateTime'], inplace=True) + + # Save the new data-frame as a pickle for fast reloading. + df.to_pickle(path_original_data_pickle()) + + # Save the new data-frame as a CSV-file for general readability. + df.to_csv(path_original_data_csv()) + + return df + + +def _resample(df): + """ + Resample the contents of a Pandas data-frame by first + removing empty rows and columns, then up-sampling and + interpolating the data for 1-minute intervals, and + finally down-sampling to 60-minute intervals. + """ + + # Remove all empty rows. + df_res = df.dropna(how='all') + + # Upsample so the time-series has data for every minute. + df_res = df_res.resample('1T') + + # Fill in missing values. + df_res = df_res.interpolate(method='time') + + # Downsample so the time-series has data for every hour. + df_res = df_res.resample('60T') + + # Finalize the resampling. (Is this really necessary?) + df_res = df_res.interpolate() + + # Remove all empty rows. + df_res = df_res.dropna(how='all') + + return df_res + + +######################################################################## +# Public functions that you may call to download the data-set from +# the internet and load the data into memory. + + +def maybe_download_and_extract(): + """ + Download and extract the weather-data if the data-files don't + already exist in the data_dir. + """ + + download.maybe_download_and_extract(url=data_url, download_dir=data_dir) + + +def load_original_data(): + """ + Load and return the original data that has not been resampled. + + Note that this is not the raw data obtained from NCDC. + It is a cleaned-up version of that data, as written by the + function _convert_raw_data() above. + """ + + return pd.read_pickle(path_original_data_pickle()) + + +def load_resampled_data(): + """ + Load and return the resampled weather-data. + + This has data-points at regular 60-minute intervals where + missing data has been linearly interpolated. + + This uses a cache-file for saving and quickly reloading the data, + so the original data is only resampled once. + """ + + # Path for the cache-file with the resampled data. + path = path_resampled_data_pickle() + + # If the cache-file exists ... + if os.path.exists(path): + # Reload the cache-file. + df = pd.read_pickle(path) + else: + # Otherwise resample the original data and save it in a cache-file. + + # Load the original data. + df_org = load_original_data() + + # Split the original data into separate data-frames for each city. + df_cities = [df_org.xs(city) for city in cities] + + # Resample the data for each city. + df_resampled = [_resample(df_city) for df_city in df_cities] + + # Join the resampled data into a single data-frame. + df = pd.concat(df_resampled, keys=cities, + axis=1, join='inner') + + # Save the resampled data in a cache-file for quick reloading. + df.to_pickle(path) + + return df + + +########################################################################