{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Generative Adversarial Networks (GANs)\n", "\n", "So far in CS231N, all the applications of neural networks that we have explored have been **discriminative models** that take an input and are trained to produce a labeled output. This has ranged from straightforward classification of image categories to sentence generation (which was still phrased as a classification problem, our labels were in vocabulary space and we’d learned a recurrence to capture multi-word labels). In this notebook, we will expand our repetoire, and build **generative models** using neural networks. Specifically, we will learn how to build models which generate novel images that resemble a set of training images.\n", "\n", "### What is a GAN?\n", "\n", "In 2014, [Goodfellow et al.](https://arxiv.org/abs/1406.2661) presented a method for training generative models called Generative Adversarial Networks (GANs for short). In a GAN, we build two different neural networks. Our first network is a traditional classification network, called the **discriminator**. We will train the discriminator to take images, and classify them as being real (belonging to the training set) or fake (not present in the training set). Our other network, called the **generator**, will take random noise as input and transform it using a neural network to produce images. The goal of the generator is to fool the discriminator into thinking the images it produced are real.\n", "\n", "We can think of this back and forth process of the generator ($G$) trying to fool the discriminator ($D$), and the discriminator trying to correctly classify real vs. fake as a minimax game:\n", "$$\\underset{G}{\\text{minimize}}\\; \\underset{D}{\\text{maximize}}\\; \\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] + \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n", "where $x \\sim p_\\text{data}$ are samples from the input data, $z \\sim p(z)$ are the random noise samples, $G(z)$ are the generated images using the neural network generator $G$, and $D$ is the output of the discriminator, specifying the probability of an input being real. In [Goodfellow et al.](https://arxiv.org/abs/1406.2661), they analyze this minimax game and show how it relates to minimizing the Jensen-Shannon divergence between the training data distribution and the generated samples from $G$.\n", "\n", "To optimize this minimax game, we will aternate between taking gradient *descent* steps on the objective for $G$, and gradient *ascent* steps on the objective for $D$:\n", "1. update the **generator** ($G$) to minimize the probability of the __discriminator making the correct choice__. \n", "2. update the **discriminator** ($D$) to maximize the probability of the __discriminator making the correct choice__.\n", "\n", "While these updates are useful for analysis, they do not perform well in practice. Instead, we will use a different objective when we update the generator: maximize the probability of the **discriminator making the incorrect choice**. This small change helps to allevaiate problems with the generator gradient vanishing when the discriminator is confident. This is the standard update used in most GAN papers, and was used in the original paper from [Goodfellow et al.](https://arxiv.org/abs/1406.2661). \n", "\n", "In this assignment, we will alternate the following updates:\n", "1. Update the generator ($G$) to maximize the probability of the discriminator making the incorrect choice on generated data:\n", "$$\\underset{G}{\\text{maximize}}\\; \\mathbb{E}_{z \\sim p(z)}\\left[\\log D(G(z))\\right]$$\n", "2. Update the discriminator ($D$), to maximize the probability of the discriminator making the correct choice on real and generated data:\n", "$$\\underset{D}{\\text{maximize}}\\; \\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] + \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n", "\n", "### What else is there?\n", "Since 2014, GANs have exploded into a huge research area, with massive [workshops](https://sites.google.com/site/nips2016adversarial/), and [hundreds of new papers](https://github.com/hindupuravinash/the-gan-zoo). Compared to other approaches for generative models, they often produce the highest quality samples but are some of the most difficult and finicky models to train (see [this github repo](https://github.com/soumith/ganhacks) that contains a set of 17 hacks that are useful for getting models working). Improving the stabiilty and robustness of GAN training is an open research question, with new papers coming out every day! For a more recent tutorial on GANs, see [here](https://arxiv.org/abs/1701.00160). There is also some even more recent exciting work that changes the objective function to Wasserstein distance and yields much more stable results across model architectures: [WGAN](https://arxiv.org/abs/1701.07875), [WGAN-GP](https://arxiv.org/abs/1704.00028).\n", "\n", "\n", "GANs are not the only way to train a generative model! For other approaches to generative modeling check out the [deep generative model chapter](http://www.deeplearningbook.org/contents/generative_models.html) of the Deep Learning [book](http://www.deeplearningbook.org). Another popular way of training neural networks as generative models is Variational Autoencoders (co-discovered [here](https://arxiv.org/abs/1312.6114) and [here](https://arxiv.org/abs/1401.4082)). Variational autoencoders combine neural networks with variational inference to train deep generative models. These models tend to be far more stable and easier to train but currently don't produce samples that are as pretty as GANs.\n", "\n", "Example pictures of what you should expect (yours might look slightly different):\n", "\n", "![caption](gan_outputs_tf.png)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function, division\n", "import tensorflow as tf\n", "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", "plt.rcParams['image.interpolation'] = 'nearest'\n", "plt.rcParams['image.cmap'] = 'gray'\n", "\n", "# A bunch of utility functions\n", "\n", "def show_images(images):\n", " images = np.reshape(images, [images.shape[0], -1]) # images reshape to (batch_size, D)\n", " sqrtn = int(np.ceil(np.sqrt(images.shape[0])))\n", " sqrtimg = int(np.ceil(np.sqrt(images.shape[1])))\n", "\n", " fig = plt.figure(figsize=(sqrtn, sqrtn))\n", " gs = gridspec.GridSpec(sqrtn, sqrtn)\n", " gs.update(wspace=0.05, hspace=0.05)\n", "\n", " for i, img in enumerate(images):\n", " ax = plt.subplot(gs[i])\n", " plt.axis('off')\n", " ax.set_xticklabels([])\n", " ax.set_yticklabels([])\n", " ax.set_aspect('equal')\n", " plt.imshow(img.reshape([sqrtimg,sqrtimg]))\n", " return\n", "\n", "def preprocess_img(x):\n", " return 2 * x - 1.0\n", "\n", "def deprocess_img(x):\n", " return (x + 1.0) / 2.0\n", "\n", "def rel_error(x,y):\n", " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n", "\n", "def count_params():\n", " \"\"\"Count the number of parameters in the current TensorFlow graph \"\"\"\n", " param_count = np.sum([np.prod(x.get_shape().as_list()) for x in tf.global_variables()])\n", " return param_count\n", "\n", "\n", "def get_session():\n", " config = tf.ConfigProto()\n", " config.gpu_options.allow_growth = True\n", " session = tf.Session(config=config)\n", " return session\n", "\n", "answers = np.load('gan-checks-tf.npz')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dataset\n", " GANs are notoriously finicky with hyperparameters, and also require many training epochs. In order to make this assignment approachable without a GPU, we will be working on the MNIST dataset, which is 60,000 training and 10,000 test images. Each picture contains a centered image of white digit on black background (0 through 9). This was one of the first datasets used to train convolutional neural networks and it is fairly easy -- a standard CNN model can easily exceed 99% accuracy. \n", "\n", "To simplify our code here, we will use the TensorFlow MNIST wrapper, which downloads and loads the MNIST dataset. See the [documentation](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/learn/python/learn/datasets/mnist.py) for more information about the interface. The default parameters will take 5,000 of the training examples and place them into a validation dataset. The data will be saved into a folder called `MNIST_data`. \n", "\n", "**Heads-up**: The TensorFlow MNIST wrapper returns images as vectors. That is, they're size (batch, 784). If you want to treat them as images, we have to resize them to (batch,28,28) or (batch,28,28,1). They are also type np.float32 and bounded [0,1]. " ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting ./cs231n/datasets/MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting ./cs231n/datasets/MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting ./cs231n/datasets/MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting ./cs231n/datasets/MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets('./cs231n/datasets/MNIST_data', one_hot=False)\n", "\n", "# show a batch\n", "show_images(mnist.train.next_batch(16)[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## LeakyReLU\n", "In the cell below, you should implement a LeakyReLU. See the [class notes](http://cs231n.github.io/neural-networks-1/) (where alpha is small number) or equation (3) in [this paper](http://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf). LeakyReLUs keep ReLU units from dying and are often used in GAN methods (as are maxout units, however those increase model size and therefore are not used in this notebook).\n", "\n", "HINT: You should be able to use `tf.maximum`" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def leaky_relu(x, alpha=0.01):\n", " \"\"\"Compute the leaky ReLU activation function.\n", " \n", " Inputs:\n", " - x: TensorFlow Tensor with arbitrary shape\n", " - alpha: leak parameter for leaky ReLU\n", " \n", " Returns:\n", " TensorFlow Tensor with the same shape as x\n", " \"\"\"\n", " # TODO: implement leaky ReLU\n", " x_leak = x*alpha\n", " y = tf.maximum(x, x_leak)\n", " return y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test your leaky ReLU implementation. You should get errors < 1e-10" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum error: 0\n" ] } ], "source": [ "def test_leaky_relu(x, y_true):\n", " tf.reset_default_graph()\n", " with get_session() as sess:\n", " y_tf = leaky_relu(tf.constant(x))\n", " y = sess.run(y_tf)\n", " print('Maximum error: %g'%rel_error(y_true, y))\n", "\n", "test_leaky_relu(answers['lrelu_x'], answers['lrelu_y'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Noise\n", "Generate a TensorFlow `Tensor` containing uniform noise from -1 to 1 with shape `[batch_size, dim]`." ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sample_noise(batch_size, dim):\n", " \"\"\"Generate random uniform noise from -1 to 1.\n", " \n", " Inputs:\n", " - batch_size: integer giving the batch size of noise to generate\n", " - dim: integer giving the dimension of the the noise to generate\n", " \n", " Returns:\n", " TensorFlow Tensor containing uniform noise in [-1, 1] with shape [batch_size, dim]\n", " \"\"\"\n", " # TODO: sample and return noise\n", " noise = tf.random_uniform([batch_size, dim], minval=-1, maxval=1)\n", " return noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure noise is the correct shape and type:" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All tests passed!\n" ] } ], "source": [ "def test_sample_noise():\n", " batch_size = 3\n", " dim = 4\n", " tf.reset_default_graph()\n", " with get_session() as sess:\n", " z = sample_noise(batch_size, dim)\n", " # Check z has the correct shape\n", " assert z.get_shape().as_list() == [batch_size, dim]\n", " # Make sure z is a Tensor and not a numpy array\n", " assert isinstance(z, tf.Tensor)\n", " # Check that we get different noise for different evaluations\n", " z1 = sess.run(z)\n", " z2 = sess.run(z)\n", " assert not np.array_equal(z1, z2)\n", " # Check that we get the correct range\n", " assert np.all(z1 >= -1.0) and np.all(z1 <= 1.0)\n", " print(\"All tests passed!\")\n", " \n", "test_sample_noise()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discriminator\n", "Our first step is to build a discriminator. You should use the layers in `tf.layers` to build the model.\n", "All fully connected layers should include bias terms.\n", "\n", "Architecture:\n", " * Fully connected layer from size 784 to 256\n", " * LeakyReLU with alpha 0.01\n", " * Fully connected layer from 256 to 256\n", " * LeakyReLU with alpha 0.01\n", " * Fully connected layer from 256 to 1\n", " \n", "The output of the discriminator should have shape `[batch_size, 1]`, and contain real numbers corresponding to the scores that each of the `batch_size` inputs is a real image." ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def discriminator(x):\n", " \"\"\"Compute discriminator score for a batch of input images.\n", " \n", " Inputs:\n", " - x: TensorFlow Tensor of flattened input images, shape [batch_size, 784]\n", " \n", " Returns:\n", " TensorFlow Tensor with shape [batch_size, 1], containing the score \n", " for an image being real for each input image.\n", " \"\"\"\n", " with tf.variable_scope(\"discriminator\"):\n", " # TODO: implement architecture\n", " init = tf.contrib.layers.xavier_initializer(uniform=True)\n", " x = tf.layers.dense(x, 256, activation=leaky_relu, kernel_initializer=init, name='first_layer')\n", " x = tf.layers.dense(x, 256, activation=leaky_relu, kernel_initializer=init, name='second_layer')\n", " logits = tf.layers.dense(x, 1, kernel_initializer=init, name='logits')\n", " return logits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test to make sure the number of parameters in the discriminator is correct:" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct number of parameters in discriminator.\n" ] } ], "source": [ "def test_discriminator(true_count=267009):\n", " tf.reset_default_graph()\n", " with get_session() as sess:\n", " y = discriminator(tf.ones((2, 784)))\n", " cur_count = count_params()\n", " if cur_count != true_count:\n", " print('Incorrect number of parameters in discriminator. {0} instead of {1}. Check your achitecture.'.format(cur_count,true_count))\n", " else:\n", " print('Correct number of parameters in discriminator.')\n", " \n", "test_discriminator()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generator\n", "Now to build a generator. You should use the layers in `tf.layers` to construct the model. All fully connected layers should include bias terms.\n", "\n", "Architecture:\n", " * Fully connected layer from tf.shape(z)[1] (the number of noise dimensions) to 1024\n", " * ReLU\n", " * Fully connected layer from 1024 to 1024 \n", " * ReLU\n", " * Fully connected layer from 1024 to 784\n", " * TanH (To restrict the output to be [-1,1])" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def generator(z):\n", " \"\"\"Generate images from a random noise vector.\n", " \n", " Inputs:\n", " - z: TensorFlow Tensor of random noise with shape [batch_size, noise_dim]\n", " \n", " Returns:\n", " TensorFlow Tensor of generated images, with shape [batch_size, 784].\n", " \"\"\"\n", " with tf.variable_scope(\"generator\"):\n", " # TODO: implement architecture\n", " init = tf.contrib.layers.xavier_initializer(uniform=True)\n", " x = tf.layers.dense(z, 1024, activation=tf.nn.relu, kernel_initializer=init, name='first_layer')\n", " x = tf.layers.dense(x, 1024, activation=tf.nn.relu, kernel_initializer=init, name='second_layer')\n", " x = tf.layers.dense(x, 784, kernel_initializer=init, name='third_layer')\n", " img = tf.tanh(x, name='image')\n", " return img" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test to make sure the number of parameters in the generator is correct:" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct number of parameters in generator.\n" ] } ], "source": [ "def test_generator(true_count=1858320):\n", " tf.reset_default_graph()\n", " with get_session() as sess:\n", " y = generator(tf.ones((1, 4)))\n", " cur_count = count_params()\n", " if cur_count != true_count:\n", " print('Incorrect number of parameters in generator. {0} instead of {1}. Check your achitecture.'.format(cur_count,true_count))\n", " else:\n", " print('Correct number of parameters in generator.')\n", " \n", "test_generator()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# GAN Loss\n", "\n", "Compute the generator and discriminator loss. The generator loss is:\n", "$$\\ell_G = -\\mathbb{E}_{z \\sim p(z)}\\left[\\log D(G(z))\\right]$$\n", "and the discriminator loss is:\n", "$$ \\ell_D = -\\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] - \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n", "Note that these are negated from the equations presented earlier as we will be *minimizing* these losses.\n", "\n", "**HINTS**: Use [tf.ones_like](https://www.tensorflow.org/api_docs/python/tf/ones_like) and [tf.zeros_like](https://www.tensorflow.org/api_docs/python/tf/zeros_like) to generate labels for your discriminator. Use [sigmoid_cross_entropy loss](https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits) to help compute your loss function. Instead of computing the expectation, we will be averaging over elements of the minibatch, so make sure to combine the loss by averaging instead of summing." ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def gan_loss(logits_real, logits_fake):\n", " \"\"\"Compute the GAN loss.\n", " \n", " Inputs:\n", " - logits_real: Tensor, shape [batch_size, 1], output of discriminator\n", " Log probability that the image is real for each real image\n", " - logits_fake: Tensor, shape[batch_size, 1], output of discriminator\n", " Log probability that the image is real for each fake image\n", " \n", " Returns:\n", " - D_loss: discriminator loss scalar\n", " - G_loss: generator loss scalar\n", " \"\"\"\n", " # TODO: compute D_loss and G_loss\n", " D_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(\n", " labels=tf.ones_like(logits_real), logits=logits_real))+tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(logits_fake), \n", " logits=logits_fake))\n", " G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(logits_fake), \n", " logits=logits_fake))\n", " return D_loss, G_loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test your GAN loss. Make sure both the generator and discriminator loss are correct. You should see errors less than 1e-5." ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum error in d_loss: 0\n", "Maximum error in g_loss: 0\n" ] } ], "source": [ "def test_gan_loss(logits_real, logits_fake, d_loss_true, g_loss_true):\n", " tf.reset_default_graph()\n", " with get_session() as sess:\n", " d_loss, g_loss = sess.run(gan_loss(tf.constant(logits_real), tf.constant(logits_fake)))\n", " print(\"Maximum error in d_loss: %g\"%rel_error(d_loss_true, d_loss))\n", " print(\"Maximum error in g_loss: %g\"%rel_error(g_loss_true, g_loss))\n", "\n", "test_gan_loss(answers['logits_real'], answers['logits_fake'],\n", " answers['d_loss_true'], answers['g_loss_true'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimizing our loss\n", "Make an `AdamOptimizer` with a 1e-3 learning rate, beta1=0.5 to mininize G_loss and D_loss separately. The trick of decreasing beta was shown to be effective in helping GANs converge in the [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498) paper. In fact, with our current hyperparameters, if you set beta1 to the Tensorflow default of 0.9, there's a good chance your discriminator loss will go to zero and the generator will fail to learn entirely. In fact, this is a common failure mode in GANs; if your D(x) learns to be too fast (e.g. loss goes near zero), your G(z) is never able to learn. Often D(x) is trained with SGD with Momentum or RMSProp instead of Adam, but here we'll use Adam for both D(x) and G(z). " ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: create an AdamOptimizer for D_solver and G_solver\n", "def get_solvers(learning_rate=1e-3, beta1=0.5):\n", " \"\"\"Create solvers for GAN training.\n", " \n", " Inputs:\n", " - learning_rate: learning rate to use for both solvers\n", " - beta1: beta1 parameter for both solvers (first moment decay)\n", " \n", " Returns:\n", " - D_solver: instance of tf.train.AdamOptimizer with correct learning_rate and beta1\n", " - G_solver: instance of tf.train.AdamOptimizer with correct learning_rate and beta1\n", " \"\"\"\n", " D_solver = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1)\n", " G_solver = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1)\n", " return D_solver, G_solver" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Putting it all together\n", "Now just a bit of Lego Construction. Read this section over carefully to understand how we'll be composing the generator and discriminator." ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tf.reset_default_graph()\n", "\n", "# number of images for each batch\n", "batch_size = 128\n", "# our noise dimension\n", "noise_dim = 96\n", "\n", "# placeholder for images from the training dataset\n", "x = tf.placeholder(tf.float32, [None, 784])\n", "# random noise fed into our generator\n", "z = sample_noise(batch_size, noise_dim)\n", "# generated images\n", "G_sample = generator(z)\n", "\n", "with tf.variable_scope(\"\") as scope:\n", " #scale images to be -1 to 1\n", " logits_real = discriminator(preprocess_img(x))\n", " # Re-use discriminator weights on new inputs\n", " scope.reuse_variables()\n", " logits_fake = discriminator(G_sample)\n", "\n", "# Get the list of variables for the discriminator and generator\n", "D_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'discriminator')\n", "G_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'generator') \n", "\n", "# get our solver\n", "D_solver, G_solver = get_solvers()\n", "\n", "# get our loss\n", "D_loss, G_loss = gan_loss(logits_real, logits_fake)\n", "\n", "# setup training steps\n", "D_train_step = D_solver.minimize(D_loss, var_list=D_vars)\n", "G_train_step = G_solver.minimize(G_loss, var_list=G_vars)\n", "D_extra_step = tf.get_collection(tf.GraphKeys.UPDATE_OPS, 'discriminator')\n", "G_extra_step = tf.get_collection(tf.GraphKeys.UPDATE_OPS, 'generator')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training a GAN!\n", "Well that wasn't so hard, was it? In the iterations in the low 100s you should see black backgrounds, fuzzy shapes as you approach iteration 1000, and decent shapes, about half of which will be sharp and clearly recognizable as we pass 3000. In our case, we'll simply train D(x) and G(z) with one batch each every iteration. However, papers often experiment with different schedules of training D(x) and G(z), sometimes doing one for more steps than the other, or even training each one until the loss gets \"good enough\" and then switching to training the other. " ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# a giant helper function\n", "def run_a_gan(sess, G_train_step, G_loss, D_train_step, D_loss, G_extra_step, D_extra_step,\\\n", " show_every=250, print_every=50, batch_size=128, num_epoch=10):\n", " \"\"\"Train a GAN for a certain number of epochs.\n", " \n", " Inputs:\n", " - sess: A tf.Session that we want to use to run our data\n", " - G_train_step: A training step for the Generator\n", " - G_loss: Generator loss\n", " - D_train_step: A training step for the Generator\n", " - D_loss: Discriminator loss\n", " - G_extra_step: A collection of tf.GraphKeys.UPDATE_OPS for generator\n", " - D_extra_step: A collection of tf.GraphKeys.UPDATE_OPS for discriminator\n", " Returns:\n", " Nothing\n", " \"\"\"\n", " # compute the number of iterations we need\n", " max_iter = int(mnist.train.num_examples*num_epoch/batch_size)\n", " for it in range(max_iter):\n", " # every show often, show a sample result\n", " if it % show_every == 0:\n", " samples = sess.run(G_sample)\n", " fig = show_images(samples[:16])\n", " plt.show()\n", " print()\n", " # run a batch of data through the network\n", " minibatch,minbatch_y = mnist.train.next_batch(batch_size)\n", " _, D_loss_curr = sess.run([D_train_step, D_loss], feed_dict={x: minibatch})\n", " _, G_loss_curr = sess.run([G_train_step, G_loss])\n", "\n", " # print loss every so often.\n", " # We want to make sure D_loss doesn't go to 0\n", " if it % print_every == 0:\n", " print('Iter: {}, D: {:.4}, G:{:.4}'.format(it,D_loss_curr,G_loss_curr))\n", " print('Final images')\n", " samples = sess.run(G_sample)\n", "\n", " fig = show_images(samples[:16])\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Train your GAN! This should take about 10 minutes on a CPU, or less than a minute on GPU." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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8eXKGDfYy93B762TZPMwHH3xwREQMHz48IorFNnHixHzAFJZwmrZn8pOz4dA4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\nLCjuxBCWLFnSzOOr03U/1GNqprHToimf+8EHH2QsKhsATSmyZRMzU2P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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 0, D: 1.733, G:0.7594\n", "Iter: 50, D: 0.408, G:1.694\n", "Iter: 100, D: 0.8904, G:1.591\n", "Iter: 150, D: 1.64, G:0.8482\n", "Iter: 200, D: 1.022, G:1.04\n" ] }, { "data": { "image/png": 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Zgb+DZCAK8eDTTz9t8SVEG/EnKQKQDMQAKUCXOHUWo4UUYhfK8V588UWzejNm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\n6Re1qU5sdpIk40kqQuaWlpaSlK+BxgpJwd3guXElj1T+yF3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CzBCTxNKsYdw0AaLVj923\n5/pbkpA5SZIxIq+poR8ISRohfgYxApQ8VoaWu6AOBRGkQfh7X19fprWtFIpEzj//fEmhwB+LBuNJ\n7BmjFc/CCm+toV9bW1tJCtaV4pPY2wClQBzi2H333VeSdMQRR0gKxRBYZp45NDRkB/NhuDmUcNpp\np0kKLDdxLeNAX1IXtbW1ud7ivpWTj7c4iEAhCqWz8VW8pNVg10FAdACZiSFBW9jYnp4ey0DgdVAk\nQqEMbXdAOPYSngwpnGKxaPr6a1pZw5j3oB00hUS+T3VVVZUd2IFxx9MExdGLQzMw0HA2W7ZssTQo\nbZEp5+WgBXuUOJiY2bdkqq2ttX072h5NDf2SJBlnUlHMjKXA2iHxRd1Yb/Jq/A0Eo3gdxpaiERi9\nRYsWGbuIdSP+okgEpIIBR+6//35JwcJ1dXVZLEn8Q1FFuXJOxsrBfhCZmKm+vt70o1Eb78HiEgfj\noXAYnxzxo48+asUJsKZYZg4yULx/9NFHSwqWmtYzcYko8wY6wpCPVs4JU0quFiEOLRQKVorL+Pz9\nVawhDD3zDnIuWbLEYm/WArad2ByWm9iZcVFjQMz8yiuvWExOmypy+ls7LBM3SpCyB0pg1JlPUBuu\nhAM9rCGvw+NbuHCh7SsKe/AwfI0Ez8BjwhtAenp6bC8ST3tPZFslIXOSJGNEKoqZiSn93btxgzdK\n07CaxC7ERJ6tpDULjPHMmTMzrXKlgOo0lOcQPKhIPAYaUcm1ZMkSs6BRw3RJmXJBi0emTZtWkoKV\nBRFBhv7+fsvr8jsYXdCCskXGgv5Y8FmzZhkS+PuWyeNSrgiacMCdz2TuFi9ebL9jHRk7c+DvmuJ+\nZmJTdCQu7OnpsTVCB48YeE78pMwRVDr66KNNR2JnxgXry2EG9hJsNvqwPosXL7Y5Zb54T3RLpulI\nayte428M3bx5s+1REJE1xFvxY+DILVVfeC5S8KrgLEB9vCt+D//gr3Zavnx57lZV9IzuvEoxc5Ik\n40le0/3MWDQsFvFBfX29oTQIBopS8UMcRnxCRRix7J/+9CfLq2K9iQdhAGF9yXsSF+EpUFP86KOP\nWrwH8mBJyzGFFOmDSBwwh6lsamoylCDHDSKjHyiKN0FsTYXTU089ZbpSOYV3QlwJi4p+oItvlvi7\n3/3OEJasgPdmvFVnDUEIrgeiqqyxsdFQHSaeccLkw10wt8SUrNdf//pX04XqNTIgoDv10Bwd9O2O\n+OyHH37YDnzAO4DyUSWi6VhVVZXZo/5YbF1dXe5ABcw0LD35cvYInABj/+Uvf2l7kUwKHhjZE+oP\nqATkWYyL78fjjz9ua4VHxJqm62mSJBmnUhEyU/eKBSGHFt9R7NvQgDLk2cgnYymJT6jV7unpsdwn\n1g3EpQk51hAmHCTm93GlGmjmBRa4s7OzKvpdRj9fPVb7f9q7d92mtiAMwKswjpQYU4RrSZWGNgXi\nARCvhpSClpIXoOA9aBECCghCFlISKdIugoJ9mvONl8cEnVAdbeZvEsfZl1mXufwza63JZOsMZaw8\n78SxnywdK8ebODk5CaYV0+le5PO9bYSwuO4p3hqGIa4Fn1XDffjwYaMB1NfzLFgS8dne3l5YXH+T\nd7ZhgLhQH4r55NaHYYiMh35lcVX6iU9ZPd6GzfjzEsbW1vGnMasPv379GjKqheBBGaPG0M7Oztaa\nAlkRY1Q+WXbBc+SnLy8v4z3VPrC8uBFW3DbKvAreSz43uocxS4Z+jP4OZZkLhZHgWnlmWiSviaWV\nhmEIyyvOYj3VHbNytJ2VKiqonjx5Ehrw+fPnrbU1iyimEYeyWJjDvBXRdDrdOoSLDOKSHnKt7ivu\nc49hGKIenRcgXhQTgniMfNY1P3v2LCyYul5MvrytPK44TNyZj2vd29sL5pPsPqsyy3AtXkDds3a5\nuLiIOPqqPgS5ZO1lXfPTp0/DYzk6OtqQ0coqXIlrHWyQZdzZ2Qmr3q10a61tH1fTy5GzGKzdMAxR\n/SbDkseTa3g3ahQw8YeHh1Hp5QDEN2/etNbWK91YZP2vLfMYnUwm8bvv8ta7/xVlmQuFkeBallk8\nRuvntaLL5TLiKLk7GpGVk3dkbbC8tlt59epVWE1VQpi/fHyH2FqMI8aVYzw9Pd06ToaGzLFma2uv\nwTuxCP2KJKtv3C9XVPFM1FtjrjGmL1++DMZZ/ISJZiWtJOJxWDWW2/Ds7CzksxZaXHvVcaC0v2om\nVq5f4ysexLLna1gS70c2LPOLFy/iGu2dDzsgoz60uV5+r8ViEW3sED7P/VUFWB6jeX33arWK67W7\nNsRQ53XjvDDj4vXr1yErj8IYlYlhkWVz8Ax5jJ6fn8fz1Rn8boz+DtciwND+0hr9KQHgO+5ePmuq\n/9/+hftiC2keBJDnIGsILS0kjSFVhoRYrVYxaBAy3ouL2J/jQz4DLxdW/Pz5M67LZwiROy+v89M9\nHzx4EItNuNmeYyBTCEg16RWTy5ZLy+UyBrhBY6Jz8xeLxS9TU+SQIuyJQn2RUyT5HGMwqQzo2WwW\nSwBtZOC9TDKLMsgopBCuKEC5vLyM/1Vm673I2J+npQ8pYEqhn8z9ZhM9slz6PW/SMJ/Pg9izS6d2\npKiNO8oc4Ygglf5aLpfhxmdFdlXhz1UoN7tQGAn+qGiElaVRuJj9Dvz5PCDXcL99T3PR/rPZLMgx\nGphWdw0Cg8vbvd/G5+l0GpY4a9luW5mts33JQbsiSm7evBkanhZHwpDPQgyuGhfN8/b397c20aPN\nvRsy0DLBfEphTw7l9s0ESy4acT5zPmObC7m7uxtehntof+2iD93D4g5jaT6fh5UWOmgn17C2wpEs\nI0yn0xgHfYqwtY1TMK48n5krLRT41eYE0l+u0Sb6jOvs82w2i8IT7Y88dU9eIje8e7+NturTnb7r\nN9f4t23KMhcKfxOuZZkfPXq0am0dxKPOadUbN25snaDoJwvZ/29ra22L1Hr//n1odcUTNJStUWlq\nWlHRiP/vy+JcK/7IG+CdnJyE1js8PFy1traUll72MtGiLIgUD+vFmrFuuazw3bt3UVqaNT6L5/na\nN3tACiwWi0VoeMU3LBGy8MuXLxta/fHjx6teNjwEXmI6nUYfZRm6fZxba2tLZuECGd++fRvtzCIC\nfoOM2o0XhAfpz87Sh2JW7YFMOz4+DhkPDg5Wra2LRdyfTJPJZON3Mre2tvR5jGav8vPnz1v7sHtH\nf8epsPrSXEg2vMT5+fnWtsDia15Nnc9cKPxl+KMTLWgf2o9Gmc/nwVbS5jke8Dza0LU9c0qL8QBY\nCNqOJVbc0DODra1jjnv37oW1pl2zBv327dvWiRZK71hvz79//34wqv5Gm5Mvs9v5fOYfP36Ehs9a\nWntirbH2ufCffA8fPox41XPyBvYfP37c0Oq21WEhtQ8P4/bt28GM+xvros94ErwTz+63inV/bSjG\n5G1I7WGxLRE0Xsh49+7deMccy+unT58+bS2WybyL/r9161Z4JXkDvdyH5DWW+wwG70q/5vJTnomU\noaIZ9/asO3fuxDjIY9S4+P79e1nmQuFvwrUsc6FQ+P+iLHOhMBLUZC4URoKazIXCSFCTuVAYCWoy\nFwojQU3mQmEkqMlcKIwENZkLhZGgJnOhMBLUZC4URoJ/AAXhh+gfOTfkAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 250, D: 1.491, G:1.44\n", "Iter: 300, D: 1.061, G:1.495\n", "Iter: 350, D: 1.006, G:0.8081\n", "Iter: 400, D: 1.718, G:1.697\n", "Iter: 450, D: 1.038, G:1.176\n" ] }, { "data": { "image/png": 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aJfBsWBffismfWZ3P5+36bEz/LFE2ZM6hMP3RQPG1fJvkqG2UzbGhoaFofuwz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\n2R89tHDhQuMSCG/5g+v93LAYqHwjPPb6669nQmLMEQsqzs2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 500, D: 1.029, G:1.433\n", "Iter: 550, D: 0.9303, G:1.766\n", "Iter: 600, D: 1.967, G:2.092\n", "Iter: 650, D: 0.9367, G:1.513\n", "Iter: 700, D: 1.115, G:2.837\n" ] }, { "data": { "image/png": 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21t14440ltRRtBOWI2rLcAs8BnwXl4RHCObLv2OgpTebcHHv27JmXPKlIIXn2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\nVLE7yduUtqVqiMKWfLIRSpRCfvvttwvuRanaOXP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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 750, D: 1.361, G:1.817\n", "Iter: 800, D: 1.108, G:1.951\n", "Iter: 850, D: 0.931, G:1.333\n", "Iter: 900, D: 2.075, G:0.9511\n", "Iter: 950, D: 1.017, G:1.282\n" ] }, { "data": { "image/png": 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UWIU/+eQTxwT4qNmlYVN2TCym7JLE/x533HHhmIuuZ1Gtzmxbm8ycOVNSaUdB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\n7bbbSvJplWHSeHiuLD2Pwl2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1000, D: 1.146, G:1.589\n", "Iter: 1050, D: 1.237, G:1.196\n", "Iter: 1100, D: 1.509, G:1.357\n", "Iter: 1150, D: 1.21, G:1.007\n", "Iter: 1200, D: 1.202, G:1.256\n" ] }, { "data": { "image/png": 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JGCGQ4HgwdRcvXlw04JCvBPKggw6S5NM7YRE+qbXtt99ekg+OUYpoyyYxlZnc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\nhRdmbavc5gRJCBv6wfjvvPOOJB+RZqQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1250, D: 1.038, G:1.281\n", "Iter: 1300, D: 1.158, G:1.049\n", "Iter: 1350, D: 1.212, G:1.049\n", "Iter: 1400, D: 1.212, G:1.239\n", "Iter: 1450, D: 1.266, G:0.9174\n" ] }, { "data": { "image/png": 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wHRSTYFMpbcWNsAuk7bq4yy67SJK23HJLSe3NxSSv58KyAwYMcHOG1WHHnXba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\nRb4zUKygXxLS9GmbQJLW7K6hocFZsSkYgaeB+Al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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1500, D: 1.259, G:0.9217\n", "Iter: 1550, D: 1.487, G:1.23\n", "Iter: 1600, D: 1.306, G:0.9402\n", "Iter: 1650, D: 1.406, G:0.8298\n", "Iter: 1700, D: 1.313, G:0.9383\n" ] }, { "data": { "image/png": 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GtWHYF9vIQn8rC6+xxhr87//+LxD0aG1ARh9q1/F14sSJQIjZ9x6fb775srEq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\ngg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1750, D: 1.316, G:0.8811\n", "Iter: 1800, D: 1.298, G:0.9701\n", "Iter: 1850, D: 1.357, G:0.8521\n", "Iter: 1900, D: 1.322, G:0.8871\n", "Iter: 1950, D: 1.249, G:0.9167\n" ] }, { "data": { "image/png": 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jnXXWyRL+47/VUpygHPQTymSmYdogLUZ8v+uvvz7TJ7vTy7dedKc9TQznoJdA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\nqaeeAroyc67WeeE8+uvZqMbI2k5ch95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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 2000, D: 1.288, G:1.157\n", "Iter: 2050, D: 1.36, G:0.8327\n", "Iter: 2100, D: 1.255, G:0.8294\n", "Iter: 2150, D: 1.228, G:1.013\n", "Iter: 2200, D: 1.316, G:0.7494\n" ] }, { "data": { "image/png": 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DkqzEhC5dTs5yVcoGbbTRRvnPTy4pPqifVDAMQ25aWbDDQGsieKPWW2+9vGQDGdIEbbCB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\n99xzkqTLLrtMknWF8ky5nU07duxo3HM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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 2250, D: 1.307, G:0.8915\n", "Iter: 2300, D: 1.323, G:0.9597\n", "Iter: 2350, D: 1.246, G:0.8454\n", "Iter: 2400, D: 1.317, G:0.869\n", "Iter: 2450, D: 1.296, G:0.9686\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 2500, D: 1.333, G:0.9693\n", "Iter: 2550, D: 1.305, G:0.8586\n", "Iter: 2600, D: 1.251, G:0.8729\n", "Iter: 2650, D: 1.29, G:1.033\n", "Iter: 2700, D: 1.31, G:0.8494\n" ] }, { "data": { "image/png": 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w4WUZqqh/UKaP3HDDDQUIOpSwSOGBBx6Y7daypuGasqc6VDkUCoVsTPq+DfBX\nejCIPy5j4zhMHx07dmzGQDLS008/DXQK0ujVjhYlikEA3bfcRufM5jhjxowChPkLrfy33HJLFmug\n9Pjggw8CwatgymklqPMqZe2zzz5AKCjh86/E5HOvf109+ZlnnulSHKHRoJHEzAkJ/QS5J1oIU9rc\nsbyOflatrXErlFIparHeJauqbxv0vvHGGwNw4oknAh3J5ZbT8RzugvqUi3e9T/o5Z9ePi+MVf6eO\nbzK6upLn1Scc/+6JJ57IEkgsAuh1LORgSqQ6siGv/i1mzJjRpUysMOV00qRJfYqZb7vtNiB0tMzp\nGg1FgCnFGH1WK5599tnMXqPurLRoSu/nP/95IEhqlpGynJZrbKmoSkjMnJDQYqiLmUeNGlWAINtn\nJynhB40jos4++2wAfvrTnwJkRcwsF7PeeusBHcwVR8Tou1WXMZpMXdJzmLigHlM8TscWx+MW73qT\nJ08uQPCJV4LnUT9XAnBn9np+b6L7ZpttlvngtaZa5iguPqgUc+aZZwLhXhYXjFcH8554jMkm06ZN\ni3f1Tr70ZiEuPiFM94yLw3cHxWt4yCGHFKC8NbsYJurIlurS1TBw4MBMOoylJ+MZ9GooiRghFsfy\nV4pjF4mZExJaDHX5mWNGFsWMrIVQXUC2sdyOaYxas2Vf/1144YUzltaaGzdHUz8R55xzDhD8hUWs\n22WslTJkttpqq05/y4BGse21117ZfNyZbUdiQcLiYnsQUj71Rw8cODCzZKo3FevTEFjV+F0bBBgj\nbpmbV199NdPJtHR7H8ul6zWbkcVXv/pVIDQqcI6WqM2TmYsRM7Lr5VrOmjUr+38t3sYn6CuOW/u4\nXvYHLxQKmc6vNBjnJAg9Iz4X2mwsPKEnphTqXavEzAkJ/QS5W7PjDBrZxfYs6rcymTpUcS6vO5I5\nosZe+7n6mEXJLSNkeaF68q3raQc6ePDgbPd2nurORoQpkWjdtqi7O/OECROyHd4otLj5mBllSigW\nANQWoI9zqaWWynzUfqfFX727J2OzxZVXXsm+++5b8rty1vfuoLtF8Itb9kKIILSQouzumNdee+1M\n4vB5iAsrxtls6sraVmxeMGTIkKr51ElnTkhoMTSUz1wOiy++eLZ7qbNtv/32QNipjFE1euvLX/5y\np3MMGDAg2yGN3xbqjrKQO6iMLIot19V2vVIoVZ4VOvSdOKNJGM/rDqwEIhMZCbTDDjt0saxr3baF\nq7nP6sGrWEFOAAAZwklEQVTqdrKu1/j73/+eWVNliKIm63XPOy+U8p06rjwZuR5odyllS/A7i/9b\nYjmWvnyWjj766E42Hghemuuuu67TuWPrtYwsGnk+y6EuMXu55ZYrQAg/FMWheXH94LIX/uQFiXv/\nDhw4MKuJpTHNY7/0pS8BXV/euGpkPfWfKqVAxi/1CiuswD/+8Y+S3wmvbTCJhhLDWddYY41MfDOw\nw1Q7VY7f/OY3QDDIGZjgtXyA5s2b16UaZDyOPLpA1oviHtQiDuOstfBBLeiumF0O5Tp4FKuCPmPF\nKY3FqEXlq3ZMErMTEloMTS/oVw7xzmxQyYcffph1rC/XE0hY5dIEhUZQygAm88VdCebPn98lnNOu\nG6bP6YJzforM9mMaMmRIZvjyPmo8M8RVQ9j48eOBwNQxG+y///6ZOy5GTzJz0bWyzxyjIbZx2qdu\nON1C1XqCVUI9Rsy8YTEI1zdGXMc8xsCBA+uqIFsJiZkTEvoJmpZo0QyYXhl3qSiHYldS3NvY3bBU\nrynZNda9Bw0alDFMuW6Qnr9S94dyPYXs4Szc7b2GhhrHN3v27Oxc5VJHa+0CWQ/iflYlrpmFRlpa\ntxkweKg4ZLXa/BZbbLGSfZ7yQjWptZ4OIiIxc0JCiyFXZi4VNF5tp6pH/6712GLWrMdSWG1+AwYM\nyKQDGUedyc9NqxNxQsbw4cMznTkek0kTMZtZQF9rfnGBBN13BiN4/935m8HM9SAP+0o1NMuaLWot\nll8JG264IRAKUpRCLf2nKyExc0JCP0FdzJyQkNB3kZg5IaGfIL3MCQn9BHXFZsfGBYMrdJX0NPIw\nrjTbeNLTMODErKneNoD1BCqtoQE5dhJdEBC7PXvEALb00ktnVlwxYMCATnG58d+1YPnll88Kw5eC\n5ywUCnW/yPWMp5Gx9zY++uij7EVekDBixIguhfIbwciRI7M4aegoS2xp4gUF8+fPbyhmPYnZCQn9\nBAtEBJiRT3GR8Dws8X1BzDZO2fKtxnPb2Kw76I6Y3RM+4jzQF9YwhnEFcYujRpD8zAkJLYY+zczl\ncl7LxTY3kiPb6K7eTNbK89ytbgDrLTRzDcshMXNCQj9BrmWD8sCgQYOy2FTbldqyVSutOqW5sBb4\n68lSOc1g5FqzwvKs0pHQGMoxrzHx5XRlC1zawD3P56jPidnDhw/P6jQVd24ohmWL9G+vttpqQMnu\neVWvV2+iRR43Py5HExv0qolojZZF+uTcfUIMrRXvvfdetmmXQ0+L2VtssUWXDp6ulURjySePUyWs\n1OW0HJKYnZDQYugzzCzbfPzxxxkjv/7660AoH2R0k4xsXW077pWqTGk1xLgbZKnommbOT7YdM2ZM\nVmro4osvBkJ3A7t8PPLII0Dobmknjxjt7e1Vd/reYma7bshckydPBsJarbnmmkAoeGhl0kbQGwYw\nK6oedNBBQCgkYbCTqlLcSTR+31Ld7ISEhC7ocWaOmXHTTTcFOrogQAc7uVNZYkXWtlytyfsm+VtX\n+rDDDgPg6quvBjp6MVWbX727er0uB4/XuDV16lRWWmklIBTw8574ufNZeeWVgVBrPC5PXIv+1Uxm\nPvzww4FgoHz55ZeBjvW67777AFh99dWBUFjBwo1+XjSuTn/bk+r++++vOo7edE3FZaOEhQztD9Yd\nJGZOSGgx9JrOLNv6r6GMjzzySFZo3u+22WYbIHSQVJc0E0a92PBHGbvRIvh5wh1bl8Rqq62W6VkP\nP/wwECQOw1Xt1KErzv7N9vhttJfWJ7/NTbqyFI4dIGRquztAWEPXqpxlujs9qHqKmc0SnDJlSiYV\n2QetaCxAcJfut99+QOgkqX3EbpDvv/9+1esmZk5IaDH0GjO7E0+cOBEIXSDb29uzXStuaRKXsf3h\nD38IhPYtDzzwABBKwTbqZ84jKEOrpjuyQQQjRozIivC5u8u0q666KhB69srYdhx0XMUdH8s1CBDl\nmNnxNOj3LPl5sS6v58G5PfPMM0Cw8m6++eZAsOC/8sordY+jaDw9wszaBrTz1AI7eg4dOhQInSXr\nQWLmhIQWQ4+Hc7pTm/anVdPdfsyYMdkO77/Tp0/v9FuZVwuxfmmLmxf3ym2kGkp3GNlyuVrk11pr\nLQBeeukloIN94wLozl0frL2s4wL62gb0r3/44Yd1RYMVoxFGjgtRiFhPHDhwYPaZlnn9zYboeo+7\nw8g9Be9tcYPEGJZDvvzyy4EgkcnEG220EdA1yi9PJGZOSOgn6DGdOW6kduSRRwKh+Zu734UXXpj5\ni0Xc0sNzyX4ygrqlhem//OUvZ0xZqmcw5KdvaWE/5ZRTgNDrV/3exmjrrbde5o/13hsF9dxzzwHB\n8q00YV/qWmK3q9WP6s4cyz0reg/UC4cMGcKZZ54JhAipm2++GYB99tmn5Hj7Yh03n51SiS9GJ2r/\nsP/2scce2+k4pSklEJ/zs846C4CTTjqpatO8pDMnJLQYetyaHevMWnBtTv7Pf/6zrD633HLLAaHB\nmvqILKTPL2a+YsRtYru7q2sVVm/ffvvtgRDRpjRh29mTTz450yMtPCc72drmsssuA+AnP/lJp+OU\nQCqtWTOY2TW7/vrrAdh9992rHm90W9yudvTo0UBY7+eff77e4XQpq9QsZjaSMJYmIMQCCCO94ug8\nJTMl0UsuuQQI9p1a7DOJmRMSWgw9bs22CZdxyPpjzTopxTpabLWIqq/IdrLh1KlTy54jbortOLoL\nz6t+u8UWWwCBsdXr77nnHqDDer/HHnsAcNtttwGw9957A8HirRX7mGOOAYI/vRY0aom3aKK55KXO\nWasUV+o4GVmYg94IM8vIlSzC3YkVsMl9zMjjxo0DOp4hPQrOVcnMMfm363/iiScC8NRTTwFw1113\n1T2uaugxMdswthVXXBHoCImDYNLXud7W1paJJrqkvDGKbJ5LsdTPK20I5VAswrS1tRXq/b0PzUkn\nnQTA8ccfD4RFNSRTsXvcuHH86Ec/AsLL68tsfWdFcw0wopFmAz1hAGsEbsyqTt1B3mL2rrvuCsCv\nf/3rkt9PnTo1C+wxpNUgGVM/fTYVu4vGV/d4kpidkNBiyFXMLlVWJ66FZMilxQIM0leULhQKmfjp\nvzri/Y2lWGR5Gc40u0bRCPO48+qamDRpEhAYOxa3zz///EzSuPXWW4EQYOBvvBeWnjF88NRTTwWC\nK6jZiANSvL+qDJVQrW60rry+AOd50UUXAcGdFmPfffcFOgxjW265JdBVAjMIKi4N1RNIzJyQ0E9Q\nl85cj04pyyyxxBIAnHDCCQAceuihnY574YUXALjjjjsAOOKII7Jdzt1N44zspo5psIVOfVmxHuSl\nbzlWg/EN9NANZfLIuHHjshRPd3WDXf785z8DHcEuEIowxEEy9aAZKZCu/1VXXQXAOeecA4R5FOuF\n5Z6VOImmm+Pp1ho6XvV3XZvx98VQstDFVDSWktfQENcIYyedOSGhxZC7NdudVuuehc9MeVNf9LoG\nE+hWuuCCC7K0SJMU3Bk91nObkGACeCPI2xLqjq3l2X8N65s6dWo2n9122w0IwQkWaDAAweO6k/jR\nDGauJ7lDN1vsCswz0aDeNXQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 2750, D: 1.285, G:0.766\n", "Iter: 2800, D: 1.24, G:0.9169\n", "Iter: 2850, D: 1.269, G:0.9061\n", "Iter: 2900, D: 1.335, G:0.9001\n", "Iter: 2950, D: 1.286, G:0.8886\n" ] }, { "data": { "image/png": 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Lpnyruhf6v/zkWXKTxZ767cKFC3NjZr/JnK836t60t7dz0EEHAS42QAyWV4mn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\n1RpPKnWU6G50Vw2wvJC2emW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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 3000, D: 1.263, G:0.8586\n", "Iter: 3050, D: 1.261, G:0.7921\n", "Iter: 3100, D: 1.169, G:0.904\n", "Iter: 3150, D: 1.291, G:0.931\n", "Iter: 3200, D: 1.331, G:0.7062\n" ] }, { "data": { "image/png": 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Na80mZoAunVixsYOsWbPGsHgaxSiKodAcIlVQ0nfbbbc1jOxGK5I/gK0AyYP8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\nVseHkV1JY7vttqsIIwMYOQn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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 3250, D: 1.437, G:0.7805\n", "Iter: 3300, D: 1.352, G:0.8968\n", "Iter: 3350, D: 1.231, G:0.7875\n", "Iter: 3400, D: 1.215, G:0.9672\n", "Iter: 3450, D: 1.299, G:0.8685\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 3500, D: 1.225, G:0.9669\n", "Iter: 3550, D: 1.312, G:1.26\n", "Iter: 3600, D: 1.204, G:0.8529\n", "Iter: 3650, D: 1.287, G:1.014\n", "Iter: 3700, D: 1.28, G:0.965\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 3750, D: 1.348, G:0.8638\n", "Iter: 3800, D: 1.287, G:0.8335\n", "Iter: 3850, D: 1.306, G:0.8165\n", "Iter: 3900, D: 1.287, G:0.9035\n", "Iter: 3950, D: 1.258, G:0.8157\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 4000, D: 1.283, G:0.7771\n", "Iter: 4050, D: 1.339, G:0.9076\n", "Iter: 4100, D: 1.271, G:0.8747\n", "Iter: 4150, D: 1.285, G:0.9119\n", "Iter: 4200, D: 1.244, G:0.9147\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 4250, D: 1.335, G:0.9434\n", "Final images\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with get_session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " run_a_gan(sess,G_train_step,G_loss,D_train_step,D_loss,G_extra_step,D_extra_step)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Least Squares GAN\n", "We'll now look at [Least Squares GAN](https://arxiv.org/abs/1611.04076), a newer, more stable alternative to the original GAN loss function. For this part, all we have to do is change the loss function and retrain the model. We'll implement equation (9) in the paper, with the generator loss:\n", "$$\\ell_G = \\frac{1}{2}\\mathbb{E}_{z \\sim p(z)}\\left[\\left(D(G(z))-1\\right)^2\\right]$$\n", "and the discriminator loss:\n", "$$ \\ell_D = \\frac{1}{2}\\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\left(D(x)-1\\right)^2\\right] + \\frac{1}{2}\\mathbb{E}_{z \\sim p(z)}\\left[ \\left(D(G(z))\\right)^2\\right]$$\n", "\n", "\n", "**HINTS**: Instead of computing the expectation, we will be averaging over elements of the minibatch, so make sure to combine the loss by averaging instead of summing. When plugging in for $D(x)$ and $D(G(z))$ use the direct output from the discriminator (`score_real` and `score_fake`)." ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def lsgan_loss(score_real, score_fake):\n", " \"\"\"Compute the Least Squares GAN loss.\n", " \n", " Inputs:\n", " - score_real: Tensor, shape [batch_size, 1], output of discriminator\n", " score for each real image\n", " - score_fake: Tensor, shape[batch_size, 1], output of discriminator\n", " score for each fake image \n", " \n", " Returns:\n", " - D_loss: discriminator loss scalar\n", " - G_loss: generator loss scalar\n", " \"\"\"\n", " # TODO: compute D_loss and G_loss\n", " D_loss = 0.5*tf.reduce_mean(tf.pow(score_real-1,2))+0.5*tf.reduce_mean(tf.pow(score_fake,2))\n", " G_loss = 0.5*tf.reduce_mean(tf.pow(score_fake-1,2))\n", " return D_loss, G_loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test your LSGAN loss. You should see errors less than 1e-7." ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum error in d_loss: 0\n", "Maximum error in g_loss: 0\n" ] } ], "source": [ "def test_lsgan_loss(score_real, score_fake, d_loss_true, g_loss_true):\n", " with get_session() as sess:\n", " d_loss, g_loss = sess.run(\n", " lsgan_loss(tf.constant(score_real), tf.constant(score_fake)))\n", " print(\"Maximum error in d_loss: %g\"%rel_error(d_loss_true, d_loss))\n", " print(\"Maximum error in g_loss: %g\"%rel_error(g_loss_true, g_loss))\n", "\n", "test_lsgan_loss(answers['logits_real'], answers['logits_fake'],\n", " answers['d_loss_lsgan_true'], answers['g_loss_lsgan_true'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create new training steps so we instead minimize the LSGAN loss:" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "D_loss, G_loss = lsgan_loss(logits_real, logits_fake)\n", "D_train_step = D_solver.minimize(D_loss, var_list=D_vars)\n", "G_train_step = G_solver.minimize(G_loss, var_list=G_vars)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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8keNLM9PwfJDWRlMPM3rL7DERnGinTp2yacEAuXkeDCaD/zcIKOYNN9yQFKW6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 0, D: 0.8381, G:0.4162\n", "Iter: 50, D: 0.03401, G:0.6319\n", "Iter: 100, D: 0.03015, G:0.7028\n", "Iter: 150, D: 0.6968, G:0.394\n", "Iter: 200, D: 0.1268, G:0.5025\n" ] }, { "data": { "image/png": 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Wzjdn48/MzIzcINk6r6A9m93/nZP6dT4Z62ktqj3tkbmnnm5OtFPITOrzGpOm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\n4AB/XjeWz8/MzIzsYRvG21HaqUILTCrA9ozly5eXBfeFbO8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 250, D: 0.1103, G:0.4021\n", "Iter: 300, D: 0.1535, G:0.3977\n", "Iter: 350, D: 0.06872, G:0.5046\n", "Iter: 400, D: 0.1885, G:0.4597\n", "Iter: 450, D: 0.1702, G:0.4045\n" ] }, { "data": { "image/png": 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icjRSaBMCJ6UgOQXnYs6NBABi+lzh1qzZoAPz6wzqgtxz5swpmkMsFVrjuV9r\nRQOzrMGss+fqzBELNWvLurz66qttWo/9vVlvrLEff/yxra9387CWVKB57rnnJIWyQsRfx+fSZ23F\nktL39yjYQ65HqyNy3ydPnmzrTKkqxsoZ5WxS5P+mm26SFNxlxx13nLXX8TnfnHva8FDs8JprrpEk\nHX744XlrJgVrObaoQw89VFIoqL9IygYNHz48JwUfK7W54hIwDJKFwqd30kknSQqGKB4yxG5qFW+3\n3XZ28Okkj8jy17/+VVIQQ5gsNZ8xMsX9feISQlIQ1amBvOaaa7aZPtfaGtERERHM/xaVIa50yQNE\nxwi6fyAK8gDQwQOxr5S9osdwPD9J6t27dy4el0+8j42MHNBzzjlHUugbzcPNOk+cOFFSiAfYaaed\nzOfMoee3F1xwgaSgQtFRgx7ViOpxxVJvZGJcVMtcb731bI5UkMWP62MRysrK7Iyy3tRl58z6VM/r\nrrtOUuhkOWvWLDPwcR/2ar/99pMUSi5h6CKOghRcGOEyyyxj78Gk+Ix63mWei2ZQErMTJeom1KGC\nflTUhOvApQYNGmRcD85Px0Y49T777CNJOuCAAyQFRz19f2677TZDUzgVJVjg7ojZIBoVIHG2gxxv\nv/22ITJczqdtFivo196ILCkgzahRoyTJjCu4pEBu0O27774ruO7WW28tKfSwBhGIomMNEd0YX1at\n7piyxGzvsmNMvXr1KihOgKhK2ihSFGI1BlD2kMi9eC6HHHKIpCBdgZhXXHGFpNDZ5KGHHpIU9unj\njz/OFPeLqRKc0VVWWUVSUOMwmvbu3dtqdWPEolAh10OdAzFBdaSH559/3gJOuA9rQFTZHnvsISlU\npeUeiPtc88MPPzSpyJcgKlZAozVKyJwoUTehkpB54MCBOSnoFMSSEgs9c+bMgsJ9cJkNNthAUnBr\ngNRwXRDijTfesCCEO++8U5LMqY6ejeGNWG0fkALHXWWVVUx3tAk7vWvu3LmZOnNb3SpigosikWSF\nOjY0NBjn9y421g6bASVXo9DTvGtVVla22QUjK2gECYoYaqSsOHzSv8buKykYphgfCDNt2jRzUVLC\nl/tgLwDl0Tm5BueA12HDhuWVyY0pclXZB7W1tTkpGKIwhOFma2hoKCiyANFzmYKRuJW4D+fuzjvv\n1Oqrry4p9DfDncc5x3XJeaD/OLozwVJbbbWV2YZ8H+piIcetUULmRIm6CZWUNeUD7+HQcZF69CxQ\nDe6G2wgxMwwDAAAgAElEQVQOhc6A++Xyyy+X1IJScEKslVgGcVugI3MNCrljlYyD36PsqLzP4s4G\nWZTVgzkmb7X2bhRQFJ05tqY+88wzksK6YuHFarrNNttICvo3OhxUrCxtW+TDDFl/pJq4jBDfwWpN\nYwNQld8yV0Jy46ARygQhZSA1ITFRanedddaRFKSxuACe78XVWm8ub7PhzLCXw4cPN4mI+REUwh6B\nzNwHvRt9lz2WgmvNd8xEAsUSftBBB0kKZxXpa9y4cQVuW6TWUkOAEzInStRNqEOJFj5ZgbYhca8p\nCCc5/WwJTsCKCVdEZ7v99tuNm4LmWArvuusuSUFHx6oOckNYhz/66CPjnL4fceRXbTOcMyvRQwoh\njvhaIVAM3yuWyi+++MKQFz/z7373O0mhGyHcHT0MSy+61GqrrSapJYWPeWRRVnsary9ScG/mzJkF\ne0gXSHzzSDv0JN5rr70khZzdp59+2uaPdEUABpZxWhqxt1jq8WCwrp999pnZRrLSJuM59u3bN8+P\nznfi/lEgM4g4efJkSUGf5cywzngokDLvvvtu25Pzzz9fUrDkH3zwwZKkY445RlJIQiFWgtiII488\nUlKL5fy4446TFKQHUJtzkHTmRIl6GJWEzCS2wz19on9FRYVxMax8RBiRaEEJUjgXaXN0oL/iiivM\nB7njjjtKCpE5cFJ0s6uvvrplEt8jCdUe4Mrx3NDhQRX0vFK7QGbpa3BTxoQvFsRGJ3z22WcNrX3v\naF6xCeCrJCGA11IoK9HC+26hsrIyk57Qb5nzWmutJSmE3h577LGSgn+fvb3uuuusogYeD1CNaxEZ\nduGFF0oKkhkJOq1VU/Fjj6UrzqjvixwX+ccGwfw4i1jr8Yn79FUkqX322Sev02j8XazY2ATQnfHr\nI6Fgo2hubi6IcNtqq60kBX99XOS/NUrInChRN6EOpUD6cj7EaH/77bf2HlwTjoTuAAe79dZbJQXO\nhbWvubnZ/MlYnOHWvp4Xus+2224rSRo7dmzeePv06WORXz4VL2od2iYytycijKgi9Hja0rIOIFNN\nTY1dByTwtbaQIhgziPjqq69m3j+LspDZFwFgn+bPn1+A1qApOj17SMF3EA5EqaysNIsvUhyJ996/\njT+ZZBnsIlBVVVWmV6FY2SCQ2bdFQsq79957C1qnIq1hxcbmgv8XnzCx3GVlZWYL4rt+jDRqwMqN\nfQFrNs/DPffcY3YPn0fA3iSdOVGiHkYl+ZmR8/ENEpeMnjB//nzTa7EewrVBqti/JgUkQ4989NFH\nTTdBByNtDt0FfQsLOTo2UTlYGmfMmFGgZ/kWNsVoiy22kBQsrMUQ2aM1sbeMEanCNw6bPn26fQeJ\nhmuAIsSeMw+imNDD2or6ao24FxZo0BV0aGpqsrn5+G32DGTDCowkha799ttv69lnn5UUoveQ5vBN\nY92leB5RcVFFSknFff2+1VFM7CtSG2djwoQJ9h2uiX0H9OSsYgsgDgBLP5LJ7bffbmtD7D16N+cA\n7wWSJ2u45pprSpKuvfZaSS3nqFiV26z5tUYJmRMl6iZUks6Mj9Jb39A5ysrKjCPBEeG4WCv5DfoK\n6IcFcbnlljOEPf300yXJanGTxA3qwVnJTEGnhDsvXLjQslrwa/oihKXWzfaUVagQAoVBrf79+5tv\nHR8lfnqs8KDXqquuKilwdd+M7fsxS8rW57OypjzFbVp9QYG4dLGUXyZWCjYNItZWXHFFk27OOuss\nSUG35D4gGL5d1gnpIy4Fxf3xu/qY8Tg2G53ZZ1ixT/H8kB5Hjx4tKUhA+JnRc5kfefNDhgyxc02e\nPnvFmcSOQ4w6ejf35vdPPvmk2Qm23377vO+wzu3NmupQpRHCzXio4gqcPuDAG7NwQVHdAdGNhy02\nWuH6Il2S9DJCMjk8bDwPKofw888/L6gL5pMWSnVNefIPE4wIkY0HMa4EEtUfyxsba4AYW2oXwGLU\nVqURXwky3j/PrFEDSGNlvASIwKCrq6uNqXN9HloqbiBO8z5r4WuFxUExvtIlvylWYRUDnA8eWWed\ndUys5tyiCvA+3UYJF8YVRzjnoEGD7PqcRZI0MPSxxxhGMXw99dRTee+/88479l3EeV55RlJ/5kSJ\nehh1qDgBnJF0L8Iu+/fvb6GevtI/QRMYTRCNcbvE3B+zPSGfVDyEq8Otb7jhBkkB9ceMGSMpiHA1\nNTUFaXsgRntcU22J0DF5hOY3iErx9yhk8PDDD0sKkge1piACUAhMwCAY71lrSQfff7dVMdsHRlRU\nVJikw7X5H4kMiYnfIJ7GXUoQXdkjDEQkHjBeihWwXqeccoqkcG7Ky8tNusOo1VpHC+96w5jFWVpm\nmWXMuIb6dvfdd0sK6ZqoCEiIBD7xOmvWLNsb9o7CBXyH8E5SPFFRcGlRiGKJJZbQ3nvvLUm68sor\nJQUJE+mxoaEhIXOiRD2JOpRoEbtZpHx3j+fmpM+hG4GQfE7IInrXxIkTtf/++0uSbr75ZknBQATi\nwsVBXXQdDBrc48orr7TAdwoHUhwPySFuytWZjhaem0Jwbub3yiuvGCKD0L4uNMjjr+n/32+//WyN\nKCiIqwTKSrRAMvE9kJqbmzM7PPhyUZydX/3qV5JCosWECRPMfUkSCe5FENc3iiNU1TeOO/PMM00f\nJSiIMlJITrHdg8Am5oPOHKd2so7YADhHjNW73AivZQ+fe+45Oz/o20ieuGv5Le+jK2OQ5cwuWLCg\noKgiBj8SXG688caEzIkS9SQqCZl9yZk4eF3K73AAeqJPwZEpUgci+3aZ559/vnFGuBgWcIqoUb4X\n6yncF07L+Orr682dRYCGL6jQnhTI1shbVrFiM2/WFwQ844wzLA2O1Df0SfQs0LWU4JAs/b69rimo\noqKioHMEKMQc8EBQaAKieMJ5551nAT0kFuy5556SAiLhqmEffOtf1q2pqcks71j5sTJHrYVtjr6l\nazHpBt2fcwMCMmbOHyGm6LRHH320pBb3E2OBKC7J+7gfcSeypkggzO+7774z+wLSA7o7un3qaJEo\nUQ+jkpCZAuqRJVhS4KaDBw82CyCWbnxmpMmhl6Bf8T4W2z59+pgeQhE2rKYktsNRse6CvnBDSvJW\nV1cXlO+BoiIFHUJmfg8xJsIYzz77bEkhkAUEHz58uEkn6FMQ1tq4mF1MILhPKGmN2ttrCop7UOML\nBTHQGQm3JRGBYgvYToYPH25WWzwMEPvN/rPXrCc+XSy7xbpSQq0lWvg0ybihOWmphLSCuBREYN13\n2GGHvPky1vfee8+8ANgs4lBPqaUhuxRsBdgVuBfPR+w98HuB5HbZZZclZE6UqCdRh5A51mc8wfHx\n78Fhse5RLoXiBXAqUtT22GMPK08D1+OVkDk4JT5t/MzcA10jjiaLrZlS0KGKFcFvD5GGSckiUAId\nkXI5WEIJ5xwzZoyFODI2CjdgtaeonU9T7AhlIXP0eeZvKbBARBRRT6AMdg72gwSRQw45xBCK9E4k\nFfaQ4osQEhQ6LpJOjMzeyt5a2SDOJlIG9py5c+caAjNuQjCxIhPPgOTJ95nf3LlzzbJPooovoURp\nK9Iqias46qijJAXvQ+/evQv6P/tWQcnPnChRD6OSUiBBM2Jz4b5w90022cSKr8Hd4oLtUtB/8bF6\nv/NLL71kHJDP4Nb4irkWuqXn2HD/SZMmmc6CDgUatoZ2xET7Mj3V1dW2Br6IIGPAOktUEVIGdNZZ\nZ1liPlZt1gRLb5Y+m9WdsRSK90oKSQXQ0ksvbdZidHufokc0E0kG6MzQvffea/ol/lViEm688ca8\nayFdQcw99j97+0RrJZBBOaQ19h+0O/744y3SjmQIEBEUR6fGm0IyEGfngAMOsPtgE6JzI3quLwGF\nPYH2O9h5Zs2aZRZwj8yl7m9C5kSJugl1qGyQ10PgStdcc41ZAEFoOCQcDN8dGSH4Jmntufvuu1v8\nLtE0FH3DUkz2FDoy6ZYk88cF7kEx0BDfNNy9PdZsJIUXX3zRuCjo5Psiw5kffPBBScHPCNotvvji\nJnFQypWIKvyZxO1yzfYU7s9KhczSmfke68O+Pfjgg9ZTGOu112fRmdljvA4UoNhggw0MgYgRIM0P\naY795gwRB4BuWQx9iSb0mVDFUiBBRtANK/ro0aNt/KQcEh/O++i72ApAd6La9t9/f5M48LkjaSKZ\nUTaItcR6TxRjXIifPSD2gqIIjP3rr79OOnOiRD2JSkLm1VdfPScFVIUzElu6YMEC4+JkQWHdpXwK\niffoQRtuuKGkgLo33nij5QRTQA3dEe42depUScEPCjf3ebeNjY2m/2ZF4MRc3aNWewirdlahdqKN\nQOavvvrKkBa9if99VhAcu5jXAMpqUBf9n/eFwYMH56Sg/0HYIZqbmwvuTxYRSfogI+jHGqA7P//8\n8xYBR5QYqIp9gPUA9ZCYfEmgXC6X1w5GUkHh/3iOhx9+eE4KNgvWjsy7J554wuwY6OV4EXxMBIQ0\nCQofffTRFrXFe3htmA8SJ+cPCQ3phnt99dVXJp0Qd0AEWJT3npA5UaKeRCUhc0VFRV6uKOiDz+2j\njz4q+AzuDddGhwBdKZaOJW/IkCGG/KA5jco86sHdsIiir8DdR4wYYbot6AcywA3jAuOdKbWLRMJ1\nfeWMuAoK4yRzyFtrO0Lt1Zmz5sj6xIjt95KYZfYMHR7pg/0YMGCA7SH2ARDKl5zyuq234NbW1pok\n4FsLRVZ9myNnlPvwG1r9TJgwoWCt2BukRh+LztjZr8UXX9z+JqYBKYvx8zl2Hf5nfZn3tttua5IA\npYrJqkOaaW8R/JJcU35zCVkjAKSyslIrrbSSpCD6kl5GZwMGiPuIBePwjxw50owE/AYRkN+y6Ijd\nbAbiGNecPn26LSIbwtjb03O5WI8pfxD4Dg+xXyOI+y222GK2oagFWddelMS9OISIuWVlZXl9p6Sg\nRrFnHEREyjhpQGrZc5Ji2H/Ww9cPi4NDpEIxe+HChSYqx3W84u/E5NeOqpyIsEOGDLFw2P32209S\nMIARogngYFgFEHgwR48ebUYyDKusBWPFbYo6wRn2quD48eONAXCefa+p9lISsxMl6iZUkpg9dOjQ\nnBS4KtwlFp18sTo4MZzXl7mBs5GMvthiixlX4z04MtcmWYNQOd8BIjYK+UB7/9rZFMi2qCNoSxgl\nQTKdIS9mE5KLe7E14xqUVazAd8NA3ampqbF1B3n9NSgwgJvRfx6vW1trF89x5MiROSmocT7RJk5s\n4D0kOoJICFPlrGIg4xzusMMOVveaskAQUiJhnpTAApGRVHDn9evXz9Qu358Zg2gygCVK1MOoQ8UJ\nPMVlb+D4vsQtHBKuDueFC4Hy8+bNK9Cj4Gq+GB/XRqcj+CIO1udvdHk4Ivr37NmzFykydwV1Rpdu\nywBWDBG9LprVn5rfYmSKkxv8WLOuAdpxjbiQH9/n72jPJAXkjBMRFltssTzJwxsmY7cWY8EWgIHP\nlyHGVco1Pv/8czOkQiRYkCTj9XvsENh7YsMfKZcka/h+0N98801C5kSJehKVZM3OcivA4QYOHGjo\nCYf1JWd9QAIWPD4fNmyYuTV8zyM6R1IiFR2Ne8E5sWaXlZUZWuBC4L5w2x+aYruCJ1I9sbJCHUHk\nuEOFv398Tf9/ZWVlZgmfLHRlz7lW3759bU8gzgq6JnPkfPDKPeI5cz9/H6y+MWGB5/ecHc7EyJEj\nDdFxl2J59/YcpDr0YoJMllpqKbNS+1RLkJ+zyDWx//A+c5HCM0ChfPbOF69oixIyJ0rUTagknTlR\nokQ/XkrInChRN6H0MCdK1E2oJAMYrqk4o0XKN7aQz0sdYQwCXpz3wSTUiDr11FOtb89VV10lKdQJ\nIzOFa2GQwLmOQ55w0KamJvuur9lF+N306dMzay5DcZAEtcrIV/ZBMhhnMJ4wP+KZX3/9dYsTJv+a\n2HaMdBhVGCNGHdxrxDvH4yR+nTDaqGtDSXWzpeAC8lVA2qLYmNZRd1pHfhe738hnLlZ1VGpZs4MO\nOkiSdNttt0kKxigfUIRLlN/SneTKK6+0v+mlRaDP/fffLykY0aj9RrbeyJEjJYUsu9iNR60xDHPE\niM+YMaPrW7ouu+yyOSn4yrx1cauttrKBEKfLA8fhIhXM+4rjh54H3fuXGSsLhaUQSyXX5Hdz5861\naxFpxoPOb+PWJv369ctJ+SmU8fzWWmsti8nlOxx8xuB95MVKAHFvPsuaT+RHLbpmc+fOtWvgG8Wv\nGl27pIe5NWv7j5Xih5kzSlyzT8rYZJNNCopYcEbZ57iJnlR4Dpubm+1cZbXGZc849/wWxsAezpo1\ny65P/gDRa9xj7ty5yc+cKFFPopKQmegaskh8mmPsI86KIvKiDEhOBtbAgQONq3pfKFRMvJdCJBgR\nQtXV1SbCIhF4aSKOeyVuGdTl+syvqqqqIDEexM0qJICKgMpQU1NTEC+cJVpyTT/fmOsTV8w1fSy0\nj+tdFFFuHRXLu4piZCZ/wBc7QMoZPny4nVHvR/frzRmmjBOlgAYOHFhQNirrtyAz/8dSo9SC0CAy\nEgGfRVJdQuZEiXoSdSg2G24DZyMi684777Tid7GCLwWEovg33JycZUq5DB8+vKAhHNyMPFOyWkAl\nr1NjOJo9e7ZxZNCVyKRiBdQxgPFdEJoxjh8/3kroUNqF61PInvK0xNX6CKC+ffsW6NfoRqwrnLlY\nCZ34+w0NDQV500WyxNqFzHF0V1Z7Wu7bnlzwH5LiOVJ0El2WOZD4/+ijjxa0P2LNaI1DvjMGKEru\nUixj6NChBQUxkAoxbvJdzih77SPT5syZY+vKK6gfSQ4JmRMl6klUEjLD1ZHxMc+DlC+88IIhEVwN\nlIXbgza4UigSTjH2P/zhD1Z03OsZXJMcWIrxU/jvb3/7m6R8KzTxu1gukRxA1jjjhvlRfQMLOFkt\njz/+uCGYb6Pi49ThsuiytDT917/+pTfeeENS4LxwaayqNKbHdUKMrv+dFKpf4GFgvvzfXmt2llW2\nFIrRMKvJgLezgPJZcd/toRi5mB+le2nohhdj3Lhxdm8ymXBt+jOKWwl3H7kB119/vZUV9kUteSVf\n+9RTT5UU2sZSvheJtayszM4bUuOaa64pSXrttdf4bte7ppZbbrm8xG8WjNRDKRw0RFOqJJLY4FPS\nqKd85plnSmrxx7LI1A+mrAsdFAh8Z7KIvhgQ2JTq6uqCEj1sJMHtNTU1tlBDhgzJc2t4RtDU1GSb\nB2OhM+WJJ54oKbhCSO2kvjJj3GWXXUwNoIY0vafoO82YEffoT8Q4mMvChQsL3FqIdagt6667bqcN\nYNTzZrye/Bi23XZbY4D4YenMSUEJzh1dPehBVgq9+OKLkqQNN9zQ5sgZxeDp/bxxmi4iMTW86SrK\n+rNPPMTMYe2117YHDzDiHFBbm2fi8ccflxR6qWF8g/FVVlYWnFHOL89Z//79k5idKFFPopIiwOB2\nRFqBbqBVbW2tiRkYF+A6uKBA6Kuvvjp/IN9z9T//+c/GRelkQecBOl0gOvEb+hdRGxnjxKRJk4zb\n+WAKnxonBURGdEVFAHn69etXkI6HJELkD3XCt9lmG0mhGyTzPv/88+0aJ598sqQgkvFd1hdRnU4L\niHbMe9KkSQXFH5g7klFnOkhCSCHUiCZVjzVElARtHnnkEXONofqgOrBeXtxmfQjKgVobPypTLF1+\n8sknkqQVVlhBUuEZ7du3r0lNiN7eBfXLX/5SUkBbVBbGfO655xryoortu+++koILknPA/EB1VCdS\nKKdMmWLSFGiP6uELd7RFCZkTJeomVJLOTLgjv6FXEjrsggULCsqlQPSjImYb1403KI0bN864OIY1\nDECUdwEB4JiUSMWQ8Oijj0pqQQPQDnT1hQUXLlxo+kifPn3ygkYo30Is9OzZsws6MXI9jBaECqJv\n8X247scff2ySxWOPPZb3W8aPnofOxNqAvnD1pZde2vpB+cAa5tdWbHYphqf2frexsTGvy2E8Pq6B\nBOeLGBQrTVRKQb+BAwfmxdeD+JzROXPm2Bn1a+ZL/yCheXvEww8/XFDjeoMNNpAUzqgvC815wB6E\nlLXWWmvZGUWXj4OUpBTOmShRj6OSdGbQDY6BZQ79ceDAgQW6Kb2k0CXIWMEdgNMdq+biiy9uHBP3\nEa4BdGlcNFwTFxmvcL8pU6aY3gmnjoqkZc4TJET/Qg+tra0tCN/keoyJwBbWhvW45JJLbF2YH5ZO\nvotNgoADXHCsISV34vJI3qVUrHBda1SKTs13fXkd6De/+Y2kljnyGS5H0JX1wAqMfgpyEVwDlZr0\n4ZNSyERjDxdbbLGC4pKMgfK4dK7Ee4B+zHlbYokl7IygZ2Mj4sy89NJLkqT77rtPUvAIEDyFvWHq\n1KkFOrN3mbWXEjInStRNqEM6s08UIM/2008/LUhHvOeeeyRJhxxyiCTp9NNPlyQdccQRkmShdXDy\nBx54wLgcfs0zzjgj77f4MNFX0IvIVSYsb/bs2eajhmPDEdHl4iB2dGafjIEO+9lnnxXoWUgacGh0\nQbg8hetAnAceeMA6BILmhIJeccUVkgJqkfuKP5Vr47OePHmyoTio6YMx2hvO2RrR0REdHqIQHgFA\nrPF3331nLYvw4WIZJmcXpPS2Bt82Z/bs2XbdLIrn2L9//7xGDVyPQvZfffVVQToiZ+SPf/yjpGCZ\nJnYA/Rhp87rrrrMzCvISA8E8kTSIlcCug/SFtfubb76xs8I8fcKQD/zJooTMiRJ1EyoJmanigEwf\nNxuTWjgXQekkWmDdo/UHPlXQFF8f+u7YsWPNEgjHJxIL9KFrHqiP3ghyw+FifRCuCsrif4yLE5Bo\n4UM1mV9ZWZnNh/mhoyJhEK1F+CaIvccee0hq0XsZA4gMCjGP66+/XpJ06aWXSgr+b6K6iiGVt4Ai\nIdXX15eMzFkpmYxvzJgxkoKfHGssuueTTz5pfm4kIW/9x0PAufD2iVKoWKIFyOh9xOXl5SZJ4tPm\nfyLACA/G/uElvssuu8zsBqTuIr2wZsREUJGGPUP6QnKIzyh7R8M6pLl58+YlZE6UqCdRSdZsyLeJ\nQQ969dVXDWnhUHBcLHZwcXy4IBtoeMYZZ1hkD5ZiOBSWZNAd3yFWXzgb/sHZs2cbp0cnx7rZGvko\nJRJLvvnmG7unT0InSgorJlZ0JBAklosvvtjQCt8k/nTGiJ7NOpMU7xPh40IHILEvJt8RypLWmBM6\nJkREGHu4yy672BoSM/Dqq69KCmtLlBv/g2ydLXCAVMHagaBIVB988IHp+swTKQ1rMmPFZhHH+kst\n+jHXA0WxJ3BN4u2xjxx77LF54+NMzZ07tyDyixwDb59pixIyJ0rUTagkZIbrY0GFI8OVYvmf6DA4\nE9lDRGeNHz9eUkBsON24cePMwkk1TnQX/MweyeCGcFLGF38GhwbVvI9UCiiB7o11E72rrq7Ofk+6\nIr7IZ599VlLwN/JKfC3ze+GFF3TLLbdIknbbbTdJAfHwN5NpRLockgY2BKSDuXPn5unzUn5DstYo\ny0LdGuEBAD353+vDCxcutO9gmfXFGJdffnlJQVJijh41SyUkFKzMrDFoV19fb/H6ZE0Rx8Ce4WVg\nT7Fp4O+/5557tPHGG0sKZwWLN/PhDHJWsQNRJRYJLp4ryO8LObaXEjInStRNqCRrtq8rDRqALLlc\nzjgvuc6gLJZhOBeIDec65phjJLX4kPktTdXRyUFeUHGTTTaRFPzRvozuF198Yf5syhX5bJ358+eb\nYlJeXp6L5+XL9ORyOeOW6POMBVQFpdCDyDTCmr3ccsvZmpx//vmSpK233lpSsBaD9nwP9CR/mzVu\nbGw03Yu5c39eO1I321NbtaxBPfatV69eFhvAHqIjUxKHV1APqYi5l0Kt1c32bWfjM0rGE9ZsfoOX\nhrh3IvVOO+00SS32EeaDL5osOeqjI5FxZu+9915JIaoLW8c333xj+8932DvO3Zw5c7q+OAEHAWMP\nolRsBGKQvEcqGiIyBxjnOSGK1M7q06dPQbXE/fffX1JITPBpc7ghWCgOyCeffGLXQpxngRARW6s0\nEgWW2Hi8WIvbDPcRwTEwK0JSER/79OljwQAYPDbddFNJIV2Q9YVZeTGM12+//dbWmcPKuKLuil1e\nnRMRnT1FtC12lniPPSMABjGbQ98ZKlZpBPcpZzRuLOCDnjBOAjSXX365pCB+E8ZJAsaQIUMMnHjF\nKIu7lHNGkhDnD2bFmZ06dWpeF1UpAE5kGEuuqUSJehKVhMxeDN15550lBWPDsGHDzI2E6AJ3o20N\nXA5RBsNHnBKHUYESLAQn4DIg3JGQQQxUiNQYMPr27WvBCz7gn9cYuZgfhIjE/WpqagxNQUL+R+xC\n5ETsxeAR11UmoAQEQHxDSuGaGMJQG2jbgwRSWVlpkg+SACjZ3o4WHW0j8/21Jalo2itzRAXCQIQh\nFKJcFFIWaYiljDFGZvaQ+Y8ePVpSCP1dfPHF7eyxdgSwnHDCCZKChIRbldrrnNFZs2aZeM34+R/j\nJEFF3JczCoJjbBs0aJCdEYxmXkWI03Rbo4TMiRJ1EyoJmUlEgOsRkhZXpvS1ldEpqU4I5wLZ0Fdw\nVY0fP94QEZQhwAQJAN2C4HkMQ1wLrvfRRx/ZdwgMIJSS706cONG4Xk1NTV7dbOYXp/zxN9wTNwfI\nAkKiBxECSEjga6+9ZpIGRjmkExCXgAf0SgwxoD/3OPfcc+1alKPBvoAr5LvvvusynTmrOAHGH9bk\nueeeM8MfbkVva+Aa/pr+/0033dRcRATT3HzzzXn3j5G5trY2T3pEP+ZcNjY22v5iV8BwRzgnqIo0\nhY0AI+fTTz9tAT+cc8I6b7rpJknhfHOWcUX5cOdXXnnF9GrCS3GZsf9vvvlmQuZEiXoSdco15VGq\npqbGkApUJZwPHZLCfgTlg5xYAXfYYQe7HwhMoT7uQyA8SAaHxfrM+ObPn29oRgraxIkT834bc/WK\nityOf4cAAB2oSURBVIq8+fmQSLh7fE+4OhZqdHPQivXlvhdccIG54whCoKQSnBk9DAs5uqlPIGls\nbDTLNxIHKB51LOxyazaSATonxP5ceOGFVlKXpAwSaShCcc4550gqTVfP0p2LuaZ8+V/WrE+fPgXS\nI2cUjwP7wx7iworTWr1bFgkJRMbeg1sRqzr35HXevHl2RpFisdHgvksdLRIl6mHUoRRI342Qayy9\n9NJWYoXUNnyoWPsIAUXHJLQR7vjll19qrbXWyvstJXexjIKGIAP6ClZhrOyVlZWZPlAkgg8//LCA\nq8N14e5xmVZ0MPR4OLEv9YNORMABQSb9+vWzxHTS47B0ogtyX6QXfJSsIWVbq6urC/ps+bDOzgSN\n+C6bIC8SBJZi9px1WnHFFa0rCUEtUXE6SUGKAo0gJLOHHnqovcPMQy7sHv6MMvaVV17ZEJhgDc4e\n0gTvM3aCObA6T58+3YJDSAriHOOh4LfozOjB2DSwnMdn1HcQjWwmCZkTJepJVFKihe9BC8UF/vDF\n0QcKCyQWWzgTepdP2hgwYID5jymujs8SHYJyvegykyZNkhQSM+JwOFAOBIVTE4EWk/dBQ+jOoLIU\nQkwpJADC+FBACvSPGjVKknTwwQebpIHP9aKLLpIUfJL4OYlEI2oIrh+XC46T7qXCCLDOkE/whyhO\nR8QUvnWiuY488khLDSRCCumJggzYFjyVgsjFyNtxfEjuP//5TzujSHC0xsGugq6MnYeEF5I2hg8f\nbrEMxE9Q/hjJAzsPCM55AOXZr5qaGkNmPBD8j5+7vZSQOVGibkIlITMWOErhwMHgJHvttZehDeVz\nfI9huB0pkli30f1WXXVV8+vB1YlRplwt4yA2GG4M94PDfvrpp4YuSA8gp9dP4ve8TguNHDnSEJ1m\nYj7FEhQFvZBAuPYjjzxifkXitlkb4tORIrBmg7bEZMdlb+DmUbRQ3riyCK8D6w6Vl5fbeDwiex8x\n9gC/TkcddZRZ15Gi2HdfLDFrnB2NTEN6AgGJb+CM7rrrrua3RvKLE2mkIOlxRr2HYPHFFzd7jW+U\nSIQj9yMCkvNBvANn9JNPPino5c26pxTIRIl6KJVkza6qqsprTwPaoasec8wxFlsLupAlg/UXpCQi\nhswQ9MHdd9/dfM5Ydymujm5GIzW4I6WH8M+BvuXl5aabYCEHKdGlZ8+eXVDQD24LEhITfdVVV1mM\nMRZd344TGwFRaXBmMm5GjRplqI4vEm5OVhgSCBwb/ZMosrjAPQjGdxgP0kt7C/qBFJ9//nkmakP4\njGmxQmQUUsmAAQPMwo3nASkD6y9RehBrHTfyay8VK+iH9MB1ScU877zzrFQxdhwkJM4oaa3oxejB\nlAI67LDDbA/ZVyRRUBxdmmvxrHAesP9UVFTYGeX+WNuRsmbNmpWs2YkS9SQqCZm32WabnBTiqL2+\n8OGHHxp6YjGkSRrcG30ETgXSoX9dfPHF2myzzSQFfyo+W8ZKlgvITLw3iBLH5RIhBYeEM0YF+Yzr\nLbvssjkpRIfB3dGpGhoaDPEYPxZRChWCjPiG4bZ8/vjjj5v/cNddd81bK+aFLQBfONye+TGG5uZm\ns6IzZ9/SpCsiwEDXrGKIzBWJYebMmfYeY+a1WInZzlKMzBtttFFOCmcGCSyORMTyzLpTJhnrMfsA\nqlNQEW/DhRdeaNejyD/nGkkTGwH2DcbDOnD+FixYYF4LzqovnRQ3amiNEjInStRNqCRkJnY5+l9S\niOZ68803C8qDwt1AGxASqyO6XlwoD4sfSADH8kXhQCWuBVfkWquuuqpxSNAQXS1CisyyQV4fRer4\n/ruSAkKjz1Lqx2dpwX379+9vCMBvQGDflC5uEPf9WPM+j6tmgDLkk0eF/LssNhsd2qML64Q1tqam\nxvRMxs54FgXFyIzdI/pfUsg4e/fddwt88khenA0ym7ABIAHGzQKJxUevJueZPUR6Qi9nPbgm41p9\n9dXNjsM1GAclmWfOnNkuZC7JNeU3jyB6EgNqa2vNFUAqGC4aBspCEe7GopBU8dOf/tQOAEEhuF9Y\ndIwpHFwW2Yuh06ZNK+g674PsY/IPsS+wUF5eboYiGA2BEoTrMQbm5+tKrbTSSiZGEerH4YJpYRzx\n1S99Esu8efMKDk1XBo144v6QZ9zFihUsyoe4NWJsFKxAHK6trTWXKmIzBjECgXCNAgCABA/XGmus\nYYUy2HcMvuwVewiIsSaoHxi3vvzyy4KQXK+2tJeSmJ0oUTehksTspZZaKicFUcFzlDjgwIfRIRLD\nbfgcpENcHDlypBkTCNDwIYt/+tOfJIVet9wD7si1KisrC4IofGJ6bCAaMGBATgpc06NbWVlZQcgn\n4pIXNUEpEBNpon///oassXsiJlxxFC/waXwxInpDkv/OokiB7ArCaIoxtTMUi9kYMVlbX7yvoqKi\noN46kh/I6881SI0Uueyyy5r6hoTmzyiusLPPPltSOJucVcTuysrKuGOnpCDNRIbOZABLlKgnUUnI\n3Lt377ygEa8jNTQ0FPRgIvABZOK76JK4PUCSGTNmmK7Ie+jbGI7iRAMp6Nve+FNfX29GJOpy06Ui\nKs7WpgEsfmXcPijBF9KDvERSV1dn6+bTFZEEeB8dlTmgdzPv+vp6+y09q0GKSEr5USJzV1KMzJxR\nL7HEXSL4jFcQGd3YdwUBmdn76dOn23fZK+w7nFHux/ngHrjF4j1HR8a+RAgs53r69OkJmRMl6klU\nkjXbh9qBHHCsESNGGLr4znpe70NPBElAnxEjRhQEFvAZJVhAaqQAuJ23ZudyOdN/xo4dm3dfrJAx\neeusTxLp06ePXRu9OquEEr+Ny+JKLdZv1ob7ISVcffXVkkJyAPpesaJ0UIwW8TXxGvzYCDsAZXY6\nQ8W6JHrpBgklPqPsHZIc5xq09LorthvO4fLLL29uRa938z6FDHynyWJnlPNOUUbGQW+x9lJC5kSJ\nugmVpDMnSpTox0sJmRMl6iaUHuZEiboJlWQAa49bg/BGn73TFmEwampqKgi8aG/VCf+7rOvHVKyD\nYEfv8WMkX3MZ91tWnbNcLmddOOh15etpQawFv6W+27XXXltQrYVMOHKgIQxHBFEQBovBKHYx+Qbx\nGATj+HPf0tXPr7y83PKSCTn21UAwonF9fksI58iRIy1Pn/xsam2TgcVaEdePsW3VVVeVFDLhYncu\nmVjch3jy9lbn7FBL1yyqqqoqGjUldawx2Q9BpT7M/9fIP8z9+/fPSSHKyTPM9ddf35IHsLhibefB\n4sHzUW/x3sM8s5hfkUg1SYUxBPX19QWx60QgFmuOt+666+akkEborfzrrbeeRXKROguTIEb7mmuu\nyfsNDyRek1wuZ5ZufMFYpLGU+0Qc3of4/fz58y0JZ5dddpEUinIQo/H5558nP3OiRD2JuhSZO0Jw\nNrjtD02LGpmzxPsfijwyEyEFUoA+IEnfvn0NufzZyJKyEKV33HFH7pFZnL818V4qjKirqKiwUlOc\nES/9xXNcffXVc1Io6+Tvs9lmm+mZZ56RVDzWXSqMyENCwc88ZMgQE5vjvIT4WlmSBpFixHbX1NSY\naE75YV+uKbWnSZSoh1GXIDNRNgsXLsxrmh4T3JVslh+LLl0qMreFMF1ZDqcrKMsA5iPWQIfnn3/e\n8npJkmeuBx54oKQQOxxH7UnBqNO3b98CnTyOgJNCSxs/DihuZu9Rz2c9FWsch/EKyY9Ck5deeqlF\nVnFGsQHQ/ogIPIjyVaeffrqklugukB/0xpBHWx5aC1GcwmcAUpBi6tSpFhVG0UlKTEWGx4TMiRL1\nJOoUMsNlouyODg8Ei15cPcPfB10Grotu11qZVo+YHlnbg8xdgbql6M5difKeqzNHsnhom0Ppmtde\ne60grhmU9QiJdZl9x5V11VVXWZ6yz2snQ+iMM86QJJ100kmSQhO6Rx99VFL+3BkrCIreCdrF+b7M\nD0SkwggVR7744gs7R7h+kEB4H0nj8MMPl1TYYne//fYzO4HX9XmlfC8NDyjNi/sutu6zjpxfcqCR\nBGJrfWvUqYe5PaIy1Svp1wNxuBF12ORdd93VrkeHB7o70qOJz+nax8EoZS6IVHvttVenDGB0DKTL\nhydvANpiiy3scLImJLLTnwiinjZ1u0shqkGuttpqeQdhyJAhOSmkU3rjUjxmfL5UTl1vvfUkBYMQ\nDztiIdVGN954Y1O36B3Feh988MGSgh+Zhx6xlAcmLvrAIec9Xoll6Nu3r81xpZVWykmhwiapoRis\nysvL7UGi7BUMBRWA+cG86CVN/boJEybY9QAU+k4jzqNyMEaqtMYlqKSW6rV///vfJQWmxXyj3mZJ\nzE6UqCdRSRFgUCnGK0Rwev/AvSmnQ4kckvhvv/32AjETURDHPKIMBhhK0NC3it97R31MJILDSTtK\ncH5KHVGkD5GN0kZIIvTiiomoII/ioJuPfGoP0X3Q7xGIDOqCHLE4DPKBPhh5MPpQ6PD888+XFGqi\ng/JXXXWVIRYlnujXjYGI8bFXSCHnnXeepCCxffzxxwWuIlQVkCwWyXH5cH0kjrhsFOmJjA3Jx/cq\no9AAVVnpbDp+/HhLdUSMpsslxS/oVgLRpYXzzrq///77poL4ohxZxuQsSsicKFE3oU7pzKX0B4rd\nV1JheR246/z58y2xnpA8OBeIAYrTLQ9OxjjiggFtxVD/0OGcuVyuoNxvVq3xjvRdKnK/vIvTiwl0\nQyoA0RYuXFhQLgny9g/cMYwX5Jw6dap1UKTnFrWnKSwBqrKHoDp7PHHiRBsfUo+XXIqFc/bp0ycn\nBf0WWwyoG68/68t16ZN9yimnSCrUoTFeHXLIIebKo2caCE2HFnR2JDLcXcRyE/8+atQoC9/0ddGZ\nn+8XlkUJmRMl6ibUIZ0Zak+hda+/wil9CVTKyFRUVBhqYP4HXdEp4IZwO5Da65T/GwEcWfYE3BtS\nQEWyY6KeQpKCREJwA0EFkXWzw+TDDNEL2Z+4NBLfwYVz3333SQp9udGpCQS66667JAXEloKODAIT\nLEL2FHorbiD0VyS5zz77rEBnxlZSrEg8a4hOyrw4QyNGjLAxML9LL71UknTiiSdKkq677rq8ebAe\nO+20k6QWSZF9Zh5YpHGf4e664447JAWbAciM6+yRRx4xJEZH5r5Y/NtLCZkTJeom1CXhnK0FOWBV\nnDx5sqSAQvgXyXOFy06dOtX0JRztxx9/vKRgTQRN6DAJ9/dO/6+//rpNNOuszozDH6kBAhmw2jPv\nuro6s7qfcMIJkgLiEQwBimR1XwSpFy5c2KZHwevMhDt6qQod89tvvy0IsMGHir8f3Zm+3FhysRy/\n9NJL5udmblhx2UvQDl0WiQ0/M/7oDz74wCQ035UzanBgc+zXr18uvj5rydouscQSJhFxHdYXTwTn\nj73jffqIjR071s4vMQ6cQbwjV1xxhaRQOJKYCXpbI4msvfba1m+c+XhPw4IFC5LOnChRT6IuT4HM\nSkYnXBNLIVwevyuRVPfdd5/5jUEm9CeQlyoPcG/C+tCFSqGusmYjneB7ZX4kwGM1Hj9+vEUDYQX2\nVmP0SxABDt2RZnBZiRZe74wt2Pi1sXBjaSbFEf0fqYv1R1J67LHH7PokFBBvwDqR7oe+yh7zv29z\nFP8WFCfMtKGhweaItR5UjSO/uB6/R5pCAmQ/dthhh7yxY8Mg0o1IOClINCA/e/XKK69ICmeSa7NG\nSA5NTU0FkhDojQ1i7ty5CZkTJepJ1CXI3J6IMEqvwMkoTYNOSyRVVVWVcVH0DSyGXB8uDkfDh4me\nVgp1FTLDeeH6WHjjhmW88h4+V5IconHk/ZbyMURvlUIememx7SUorKuxfo5lmLnhG2VfiJUnMgzk\nzuVypl8S+YRlljnR0xvkJLkAazb3bG5uLmis5v3gTU1NNseamprc96+SgjSz2267SZJuueUWa9Xq\ni9tTG4zyPdg2iEAEMRdbbDGTCokph3y66Lhx4ySFBBK+Tzz+ySefbLoykgZWbfzgvsd2FiVkTpSo\nm1CH/MzEtvoWHTF5Cze6AzG7WKThinDS6dOn23ex6nF99Ltf/OIXkoIvj2t4f2RHyUeYtYfgov7V\n68NNTU0FCfsQ88RCTkwukUgdQWZP7AeZUEQsoevF1VFZfwrZgS5UosQSzd4Rs/3GG2/YHhEthvRE\nnDONztFX0cv5Ht6PmFjL1tJJQXH2DkkQhCwrK7MziM8XyzR2nSwvAxVHL7roIkNPpBFispkfa8j6\nsnes0aGHHiqpRQLhrHQ2niAhc6JE3YQWWUE/z0WzamDD3bH2Dhw4UIcddpikYBnecMMNJQV9Dj0L\nazdITPZOsXFkzXNRx2ZjMQVty8vLLX8WnRPLK4jh84WZgy/01h5qb91skCSXyxl6Y4EmVh60AzlA\nbOKv0UuXXnpp20/infkf5EWKAu3uvPNOSSHflzHMmzfPYhGQJnyJ3zh2GWu2byCHDh5LHoyFwghE\neD355JOSpEsuuURSi3VeCr7jWbNmWSz2HnvsISmcUTwx6N2bbrqppKBvs5ZIW7fccov5oCn1y7OC\nH9y35c2iRV6d0z9M1JdCjCL8DZGpvLy8oH8uIg2iGYYjRJa27tkadfXD7JP9o4ommb9hvKwJYh1i\nV2coq9JIXGMrHl9VVVWB+MrBw6hHUj+GL0IWCbbo27evXYODieEHtyJzw/3DA8Z4CGmMVQsYgP9u\nbABjfr5jJmfp17/+tSU28BkPHtVBKDSB0Y60XVxWQ4cONRUE4yQF9VFFmD8P75577ikpdPpkjz/4\n4ANbe5gnqgHJGrHrrTVKYnaiRN2EOoXMpRicfH1kRMY4KP/7e2j77beXFAwQGBkI64OOOeYYScFQ\nQ0J4PKe20gkXlZjt3SgxESBDcP4GG2wgKRhiIAwxtC3Zd999OzKOosjMuBAPQcza2loTo31Nc1w0\nBPqgFmA4Yq3r6urMxXTZZZdJCqV5ECkxcpI0gzrCHFGZqqqqLDiFVEgfZBEjM2oEhLuJElMrr7yy\nBYFQOIJAFYyyuM1QI0jXROr67rvvzLDHXiJGI7JzP6QVpJpTTz1VUiijtPLKK2u//faTFMoToZKx\nvikFMlGiHkY/WEeLLITE6IDjfNy4cca9t9xyS0mFydroSuhdoLy/x0YbbWR60CGHHCKppalZTIva\nAAbqgnaTJk0yBCBQo6O04oorml6VVTjRI3NVVVVOykt8V/x/LpcrcP3g+gOFCM5Bd8UN9dZbb0lq\nMYiB+IyPYgW4iLwRCjcjAR0E0rz11lvmCmXd0LNxIX7xxRc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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 500, D: 0.1925, G:0.3532\n", "Iter: 550, D: 0.1784, G:0.2953\n", "Iter: 600, D: 0.1352, G:0.4192\n", "Iter: 650, D: 0.1339, G:0.4904\n", "Iter: 700, D: 0.1677, G:0.3159\n" ] }, { "data": { "image/png": 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KxoUGkYTo+pMpR4YX2Xn4AdBYyMenbWqpxgMgrtCiR48eeam4BBG0tLS4QhU0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\nNigNoF6hy18tsCKloFYSqyS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/jo+GqEE0WSYUWgQEBBQgk2QOCAhYcREkc0BAF0F4mAMC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\niXI0xaVgT/X99tsvL0m33nqrJO+gITyTBrVOuZT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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 750, D: 0.1063, G:0.4903\n", "Iter: 800, D: 0.119, G:0.6916\n", "Iter: 850, D: 0.1834, G:0.3702\n", "Iter: 900, D: 0.1444, G:0.3067\n", "Iter: 950, D: 0.1219, G:0.3401\n" ] }, { "data": { "image/png": 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5Up3r7rHHHpJ8sgHjamhoKEpasPNNsre4PlIi8Nk6icJ48a0++eSTBWOAAU2K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\nvDTCrfTd737X7aJINSQlv8OaWvdGKYlM+1Nb1id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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1000, D: 0.166, G:0.5049\n", "Iter: 1050, D: 0.1402, G:0.3886\n", "Iter: 1100, D: 0.1495, G:0.2111\n", "Iter: 1150, D: 0.2057, G:0.139\n", "Iter: 1200, D: 0.1519, G:0.2411\n" ] }, { "data": { "image/png": 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7vnBjA86bzdGWrkWS3oABA7LP1Hfj6+p9JkMqVZkBlV8fdV9tJ/rR9dosvfTS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\n/OUvAXj66acBsvK+6tRa4S2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1250, D: 0.1719, G:0.2454\n", "Iter: 1300, D: 0.1373, G:0.4168\n", "Iter: 1350, D: 0.1437, G:0.2796\n", "Iter: 1400, D: 0.1152, G:0.3075\n", "Iter: 1450, D: 0.1682, G:0.2893\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1500, D: 0.1264, G:0.3125\n", "Iter: 1550, D: 0.1938, G:0.2529\n", "Iter: 1600, D: 0.1703, G:0.2891\n", "Iter: 1650, D: 0.1654, G:0.1759\n", "Iter: 1700, D: 0.1995, G:0.2025\n" ] }, { "data": { "image/png": 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hbNezC2RRIEteF4xZ0lrSWqb9jrYAxzp58uSMNa1HLVOrk+66664l847TLUWl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\neooheA796f7tObT4Wi7I1i3Gv1dDM8x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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1750, D: 0.2101, G:0.212\n", "Iter: 1800, D: 0.2151, G:0.1983\n", "Iter: 1850, D: 0.2095, G:0.1635\n", "Iter: 1900, D: 0.2044, G:0.2116\n", "Iter: 1950, D: 0.1994, G:0.1896\n" ] }, { "data": { "image/png": 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NiyU2GNKAMbPBUCdIhJkLMXI5pX2KfdePkTVfee2jo/O604ZKK6lQg18AoRowZjYY6gRV\nt2Zrh1Zsq0qzTJgwIfiO2ruqufc999wDZBchh1xGVpysjt3c3FxUj1+TLaHhMk1RMGt2fEiaVHFG\nZVGNHj0agCeffJJnn30WcMX7Bw4cCLiS0mqUOGfOHCC3WGAcpFo2qBwo5VAlWF599VXA9epRTaXV\nq1eXXMlR4rZ+qwsbpxJktR/mHj16lF1upxxRtSMe5jgFDnSzP/PMMwDsu+++AKy33nolny+pNdT1\n3XnnnQG48sorAddHW/fq0qVL2WqrrQBXoVTjVd+w119/XePRGLOO/X//938lj8tcUwZDg6HqYnaU\nQSyOe0sizRtvvAFEM1YtiNm+se7qq68OulwMHToUIBDZNttsMyBZV1+titnqbCGxVLW31fdYVVA/\n++yzWKpEOfPz7x9JeCpkoc4Wkio/+OCDoHOJGFhdTdWPu9S1K6f0U+SxSjqjwWCoeaTOzFL8Fy9e\nDBDUU1YXSO16+X7j12kWVIP7qquuAgjqbGu3v+yyy0oeX7V0Zs1p4cKFwfj9zg/azVWGWMbBcqDr\nvGTJkppiZtVC//vf/571viSX+fPnA3D++ecDrs9YIVS6hiodvGLFCsAV9FOBP0Fju+iiizj11FOB\n9u4W4NZSUleoLFXc4eTAmNlgaDCkzsw6vlh0xx13BFxnvVKgPszST8Rg0nXUgSBfJ4Y4+kgSrPXk\nk08CsPvuuwNOH95+++2BdjeHCsepz7R0M9kTxACSNNRhshwreDV15jgBQroGfqcLWYyFpUuXBlJN\n1HErXUONRawqiWjkyJE6JgCnnHIKAMOHD2eTTTYBXGcW9XRetGhR3NMXhTGzwdBgSCScMx+ksyXR\n/W/WrFmA20HFyNqpL7jgAgB+97vfAS7YJOnQwfBcNAY/CECMrPA+X1poaWkJghDEQmIn/UatfYYP\nHw44vdEPiqk1SA8eNmxYEPhz9dVX5/2uroE/9x/+8IcAXH/99YC7zpUin4dAfuTTTjsNgPfffx+A\nUaNGZf1W913Pnj2DY2l8999/PxBdfrdY+d4kYcxsMNQJUtOZ0wis186/zjrrAM5CLPivS0Fa1uxf\n/OIXgNvtx40bB7Sz7iOPPAIQRBHJwiuEm+hVimrozJJYxE6rVq0K1igK7733HuAipvxe3JJC5AUp\nhGpH8X366aeB5CnLt9bqzTffBJz/PAmYzmwwNBhS05m1006bNg2An/zkJ4DbkcuBdmvtgorUkTVb\neO211wAXUVVNSIeWZOLrhIsXLw6s2JqPmFh6Xd++fQueo5y00jShxBetbSlx1ZJGNIe7774bcL5e\nrW1SOnMcSGe+/PLLAScJPvzww0B7jMS6664LuPH+7W9/A5z+LcmsmjBmNhjqBKmVDTr++OOB3Kyo\ncvDBBx8AzkK+cOFCwEXfKKtFcdCbb7552ecqhihWlAVa10KSye233w7AscceC7TrhIceemjWd32L\n+Isvvgjk6tJCRzGyH2cu/7/iknfYYQfA+c/zQWNXnEG+CEDoGEYWfvvb3wIE8dey1WhevXv3ZurU\nqYC715T99cADDwC5NqNKUiBLhTGzwVAniMXM7777LpCr0+XLEJkyZQrg8j0V1VQOZPEUAz/44IMA\n3HLLLYDTmXfZZRfAJZWnAZ8VfX+zn6d68MEHZ71uaWkJ2F27thhA0UPyVfvJ8Uns6hMnTgTgmGOO\nif1bMfJ+++0HwH333Qc4C7RimWfOnBnYCmS513XQbxWb//jjjwMUtX5XE/JAyK/8y1/+EnDFMXr1\n6sVGG20EuKg8SaB+DICkF0k1cfKY40onqbmmtDhyLUgM9QPsC8Efm9LjlGY2efJkwG0USmC44oor\ngPawvGqFcyo9Toabv/zlL4AzxmkjgtxgBUHBLttssw3gHmKJseUgSdeU1Jp58+YBsM8++wBu4w5v\n6vrs0Ucf1XkBp1rIyDRjxgwAnnrqqXKHlbhrSg+e5qlKN0rIOPLIIyODoWTo1NrpgbztttsAt4nG\nSW8115TB0GBInJklMn7yySeAY9M99tgDgEmTJgFOlNlwww0B527KZDIcd9xxgAvpe/nllwHHemJB\nP0jkhhtuAOCEE04IjlUM5e7qH3/8MeDYSjuxkuo1L4miCuJvbW0NDHgSzfzgkHBifqWohJl99tE4\nJe2ozpXcMnLTtLa2Fr32SYT5Ckkxs8bkJ4HIuKVe05dffnlOeurFF18MuNBjqRd+mGc54Z3GzAZD\ngyE1ndkvMCBdQkaeJUuWAC7AQN+fOXMm2267LQA//vGPAWfoUjG0p59+GshlCiFOKGnS+pZvtNJu\n/vbbbwffkR1Buv15552XNV5JHrIvfPTRR2WPJ0mdWddb45HrzDfMtbW1RRpvkmRkIek1lP6+5557\nAo5dZYhdtGhRzjzkNpXxUoYxud5kGNN1kRRTCoyZDYYGQ2rMLJ15zJgxgLPgqqSqihScfvrpAPz0\npz8FYMCAAQGLybqrXU7uDVkV5ToQy/vlXvbff/9Ad4lCoV1dzCP9uBzIbRMuEaQAGs1D8JPwoxL4\n46ASZpYVXTq8P86ddtoJgBdeeEHHBtrnqlLKCqrQsbRmSQa+JM3MYmAVJzj88MMB55LNZ4mWlX7I\nkCEAnHvuuUD7/QzuXpK9RK6tuXPnFh2PMbPB0GBInJn9IArtQLJ4+tD3pC+2tLTw7W9/G4DZs2dn\nfVd6qCzFa6+9NuBK7k6fPh1wJVxKQVK7uizTpVigR4wYAbhiCvqtmE1svmDBAqCyNNJKmFnBOX7f\nJK2x5qqgChUieOyxx4I1EIunoSsLSa2hpMkDDjgAcCWBTjrpJMD1kZozZ06kpV/PkyROJfsoJPnX\nv/511u/uvffegPmjYMxsMDQYEk+BlJ/ND1k86KCDABctJCjaRqV/fv7znwf6s5/UoHA6ReZo11ex\nPOljSi1cd911g7S8tLsQRjGyP4fbbruNo48+Ous78kErSk4WcFnxq4lw/yoxsqzvihnwrdeSoCRR\n7bnnnsF1ll2gmuVzyoViJIYNGwY4yUT3le7hpqamYH66r2ThP+OMMwBXKli/VSqwjzArV5qMYcxs\nMNQJUrNma0eS9U7WPp1Pu590iSeeeAJoZ1X5ouWz9X3W2g3FuooDVuqgdNBSdri4+la5DK9Y9fnz\n53PrrbcCcNhhhwFOd1YyvCSPJJCEn1mJLGJZMZYSW1S0XyVzJkyYEMQISJ8Wa6eBtMoGKa5e96rK\nJutezgdFhmlNdZ8ceeSRQGlF/X2YzmwwNBgqYmY/lrWtrS3HB6fYZd8yLfisWwqkEytLSj7Mc845\nB8jNRiqEtIvBSboQoy9atCiwG6h9qSQJNcQTwyWBJCPAfClLTKW0Rq3lqlWrcmIA0kQpa1hJqSWV\nDZbEdOeddwZzlh1B8/zzn/8MwG677Zb3fLKLxEkJNmY2GBoMVWvpqvhW6bfPP/88EG+nTKOQXdLM\nLNaSjixfpSSTgQMHBkysuHT5k9NAksys66+4YuUiK2Zen2+xxRbsvffegCvBkybSkq6UT6AIPBXx\n++STT4KYhpNPPhlwcRTK7EsSxswGQ4Mhlp/5wAMPBOChhx7K+/ns2bMjrZby0UnfLYdda6W0LOS2\nARVUYURx4qpSceKJJwLt7CVfpNhLVShqAYMGDQpiBQTph8riUjyxGFn2ANkH/vrXv/LKK69UZbzl\nwI/eirqv/Jh4zXPq1KnB+gpicf8c1bxnUxOzfTdSNRHnQlYqovluMIlZErflflKtq8WLFzN+/HjA\nBRikGVBRiZgdtWEJaQfilIpqd7SoNkzMNhgaDIkyc7jDoQ8lRaTRvzYKzc3NRQunpb2rS0rI1zWy\nGsyWpAGsGrWfy4ExczuMmQ2GOkGizNy5c+ea27V9+Lp8tXb1cE/pahpFqtEFsqOxpjOzb+PxO8QY\nMxsMDYaqBY3EQTXT5db0Xb0YjJnXfBgzGwwNhljMbDAYahfGzAZDncAeZoOhThArNrvejQudO3fO\nQHQQR5cuXQKjnG+kk7riuxnihJaWY/grdvxiBjDFV6vVaNeuXcvO/OmIeOQvzleyAWzzzTcPanqv\nKSjVAFaT1uxqInyhOnXqlPniPSB/QH5U1FbUjVxOlFex34TP5fskfZg1e82HWbMNhgaDMXMJu3oh\n9pNo7H/ms7tavCxdujRIclcZHjGwvxZ+cryPcFncKMlgTWHmSuLUjZnbYcxsMNQJjJnz7OpRyeud\nOnUK/h+lVws+m4eZU+1nxEYqhK4GeWrQrmR/3yAVPqfPyGkwcxKGLeV9q4FeviyyclELzPy1r30N\nqKz9bhSMmQ2GBkPi7WnSgMq2qui9Co2roLjybCstphbFyGLZ1atXRzKh3hebDh48OGvMc+bMAeC5\n554LPlP7FzVdV5uaSZMmAY6xNX+xmtisubk5x42VUtHDvO/HOZffvke6ca3mSMeFpKqORNUeZnVF\n0AMXtYg9evTIuTCqSbzFFlsArluAKiSqnrEqJerzuPBvSt2s4TH6KZR6rQdOJXb0EOvvdtttB0D/\n/v2Dut7qOaXyQeru0b9/fwA23XRTwNUFv/jiiwH42c9+BrQbxjTGHj16AOneVP6cox7iTp06FTVk\n6beqqz5u3DgA9tprL8AZFmsR+eanNfKRr7Y8RLsSKxpX4kc0GAwdgjXCACYmuv766wFXg1tspIqf\n6q8rUXf58uVFj53PeOJLEUK4+EK42ED47/rrr5/1W4nS+rvHHnsEtbT/9Kc/Aa7qpSQQVev0z6W/\n2tUHDx4cdFDQd/OoAVVzTcURu1XpVfWmVdVU7rjjjjsOgIkTJxYVwdM2gKlzyoQJEwA44ogjciL/\n/DlLrZLkUSioqAQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 2000, D: 0.2059, G:0.1875\n", "Iter: 2050, D: 0.2143, G:0.1829\n", "Iter: 2100, D: 0.2208, G:0.1794\n", "Iter: 2150, D: 0.2192, G:0.1917\n", "Iter: 2200, D: 0.2181, G:0.2936\n" ] }, { "data": { "image/png": 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wrW99C3CliGKev64Pc2dnZ1A66HOf+5zOC8DVV18NuHI1aaAeD7PW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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 2250, D: 0.2094, G:0.2222\n", "Iter: 2300, D: 0.2284, G:0.1589\n", "Iter: 2350, D: 0.2265, G:0.2022\n", "Iter: 2400, D: 0.2356, G:0.1755\n", "Iter: 2450, D: 0.2259, G:0.1732\n" ] }, { "data": { "image/png": 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2FSTZ1ffZZ5+ssi9qcfLss88CjnkWXnhhwOlUakdS6QZq5Si1G+eZ8a280inL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\nXmaDoUZgL7PBUCP4f7/QSMnqbItjAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 2500, D: 0.2172, G:0.1828\n", "Iter: 2550, D: 0.2091, G:0.2036\n", "Iter: 2600, D: 0.2191, G:0.1898\n", "Iter: 2650, D: 0.2278, G:0.1887\n", "Iter: 2700, D: 0.2159, G:0.182\n" ] }, { "data": { "image/png": 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AAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 2750, D: 0.2315, G:0.1855\n", "Iter: 2800, D: 0.2453, G:0.1728\n", "Iter: 2850, D: 0.2441, G:0.1436\n", "Iter: 2900, D: 0.2165, G:0.1545\n", "Iter: 2950, D: 0.2405, G:0.167\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 3000, D: 0.22, G:0.1692\n", "Iter: 3050, D: 0.2127, G:0.1688\n", "Iter: 3100, D: 0.2174, G:0.1758\n", "Iter: 3150, D: 0.2287, G:0.1706\n", "Iter: 3200, D: 0.2301, G:0.1707\n" ] }, { "data": { "image/png": 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XqktmX5fwOwvecMMNQQcB6b7+GJ977jnAWYN91LNp6623jjKeqkjm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\nRiKzAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 3250, D: 0.2362, G:0.1485\n", "Iter: 3300, D: 0.2411, G:0.1582\n", "Iter: 3350, D: 0.2347, G:0.1684\n", "Iter: 3400, D: 0.2189, G:0.1695\n", "Iter: 3450, D: 0.2242, G:0.1873\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 3500, D: 0.2302, G:0.1635\n", "Iter: 3550, D: 0.2417, G:0.1723\n", "Iter: 3600, D: 0.2166, G:0.1762\n", "Iter: 3650, D: 0.2272, G:0.1778\n", "Iter: 3700, D: 0.2134, G:0.1862\n" ] }, { "data": { "image/png": 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jTihq1VS9Oxfq/fyg/s+xWueXZGJJVAdY7EsgDaOWKFQkMS5qwfdk02zDqBNM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\n2TAajMSVWR5BKa6fxSW7ZN9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 3750, D: 0.2286, G:0.1608\n", "Iter: 3800, D: 0.2114, G:0.1563\n", "Iter: 3850, D: 0.2348, G:0.1661\n", "Iter: 3900, D: 0.2157, G:0.1883\n", "Iter: 3950, D: 0.234, G:0.1992\n" ] }, { "data": { "image/png": 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y1hjM70466SQg5MRqkbaYnYwso+fjYL2euouMLKO5+1kmKC609u677xbm2NZT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\nmVk7lpjpb+gLNRpJ9PT0ZEw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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 4000, D: 0.2442, G:0.1577\n", "Iter: 4050, D: 0.2126, G:0.1727\n", "Iter: 4100, D: 0.2251, G:0.186\n", "Iter: 4150, D: 0.2343, G:0.1642\n", "Iter: 4200, D: 0.2198, G:0.1627\n" ] }, { "data": { "image/png": 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No5qIbWvKP+tNmjQp49/9M9dLL73EoEGDABg2bBjgVE2qftVVVwFug/6KK66I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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 4250, D: 0.243, G:0.1724\n", "Final images\n" ] }, { "data": { "image/png": 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R0k6jf//+AIwbNw5w/ahOP/10ABZeeOGCY0oaSApECTBJUmeWFNH8xHl2dB+kQ/qeCPUi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\nzDfddBPgdGVZCCU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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with get_session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " run_a_gan(sess, G_train_step, G_loss, D_train_step, D_loss, G_extra_step, D_extra_step)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# INLINE QUESTION 1:\n", "Describe how the visual quality of the samples changes over the course of training. Do you notice anything about the distribution of the samples? How do the results change across different training runs?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the beginning, the network outputs similar images for different noise inputs. After further training, the number of classes it starts to generate diversifies." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Deep Convolutional GANs\n", "In the first part of the notebook, we implemented an almost direct copy of the original GAN network from Ian Goodfellow. However, this network architecture allows no real spatial reasoning. It is unable to reason about things like \"sharp edges\" in general because it lacks any convolutional layers. Thus, in this section, we will implement some of the ideas from [DCGAN](https://arxiv.org/abs/1511.06434), where we use convolutional networks as our discriminators and generators.\n", "\n", "#### Discriminator\n", "We will use a discriminator inspired by the TensorFlow MNIST classification [tutorial](https://www.tensorflow.org/get_started/mnist/pros), which is able to get above 99% accuracy on the MNIST dataset fairly quickly. *Be sure to check the dimensions of x and reshape when needed*, fully connected blocks expect [N,D] Tensors while conv2d blocks expect [N,H,W,C] Tensors. \n", "\n", "Architecture:\n", "* 32 Filters, 5x5, Stride 1, Leaky ReLU(alpha=0.01)\n", "* Max Pool 2x2, Stride 2\n", "* 64 Filters, 5x5, Stride 1, Leaky ReLU(alpha=0.01)\n", "* Max Pool 2x2, Stride 2\n", "* Flatten\n", "* Fully Connected size 4 x 4 x 64, Leaky ReLU(alpha=0.01)\n", "* Fully Connected size 1" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct number of parameters in discriminator.\n" ] } ], "source": [ "def discriminator(x):\n", " \"\"\"Compute discriminator score for a batch of input images.\n", " \n", " Inputs:\n", " - x: TensorFlow Tensor of flattened input images, shape [batch_size, 784]\n", " \n", " Returns:\n", " TensorFlow Tensor with shape [batch_size, 1], containing the score \n", " for an image being real for each input image.\n", " \"\"\"\n", " with tf.variable_scope(\"discriminator\"):\n", " # TODO: implement architecture\n", " init = tf.contrib.layers.xavier_initializer(uniform=True)\n", " x = tf.reshape(x, [-1, 28, 28, 1])\n", " x = tf.layers.conv2d(x, 32, 5, activation=leaky_relu, padding='valid',\n", " kernel_initializer=init, name='first_convolution')\n", " x = tf.layers.max_pooling2d(x, 2, 2, padding='same', name='first_maxpool')\n", " x = tf.layers.conv2d(x, 64, 5, activation=leaky_relu, padding='valid', \n", " kernel_initializer=init, name='second_convolution')\n", " x = tf.layers.max_pooling2d(x, 2, 2, padding='same', name='second_maxpool')\n", " x = tf.reshape(x, [-1, 1024])\n", " x = tf.layers.dense(x, 1024, activation=leaky_relu, \n", " kernel_initializer=init, name='dense_layer')\n", " logits = tf.layers.dense(x, 1, kernel_initializer=init, name='logits')\n", " return logits\n", "test_discriminator(1102721)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Generator\n", "For the generator, we will copy the architecture exactly from the [InfoGAN paper](https://arxiv.org/pdf/1606.03657.pdf). See Appendix C.1 MNIST. See the documentation for [tf.nn.conv2d_transpose](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_transpose). We are always \"training\" in GAN mode.\n", "\n", "Architecture:\n", "* Fully connected of size 1024, ReLU\n", "* BatchNorm\n", "* Fully connected of size 7 x 7 x 128, ReLU\n", "* BatchNorm\n", "* Resize into Image Tensor\n", "* 64 conv2d^T (transpose) filters of 4x4, stride 2, ReLU\n", "* BatchNorm\n", "* 1 conv2d^T (transpose) filter of 4x4, stride 2, TanH" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct number of parameters in generator.\n" ] } ], "source": [ "def generator(z):\n", " \"\"\"Generate images from a random noise vector.\n", " \n", " Inputs:\n", " - z: TensorFlow Tensor of random noise with shape [batch_size, noise_dim]\n", " \n", " Returns:\n", " TensorFlow Tensor of generated images, with shape [batch_size, 784].\n", " \"\"\"\n", " with tf.variable_scope(\"generator\"):\n", " # TODO: implement architecture\n", " init = tf.contrib.layers.xavier_initializer(uniform=True)\n", " z = tf.layers.dense(z, 1024, activation=tf.nn.relu, kernel_initializer=init, name='dense_0')\n", " z = tf.layers.batch_normalization(z, name='batchnorm_0')\n", " z = tf.layers.dense(z, 6272, activation=tf.nn.relu, kernel_initializer=init, name='dense_1')\n", " z = tf.layers.batch_normalization(z, name='batchnorm_1')\n", " z = tf.reshape(z, [-1, 7, 7, 128])\n", " z = tf.layers.conv2d_transpose(z, 64, 4, strides=2, padding='same', activation=tf.nn.relu,\n", " kernel_initializer=init, name='conv_0')\n", " z = tf.layers.batch_normalization(z, name='batchnorm_2')\n", " z = tf.layers.conv2d_transpose(z, 1, 4, strides=2, padding='same', kernel_initializer=init, \n", " name='conv_1')\n", " z = tf.tanh(z)\n", " img = tf.reshape(z, [-1, 784])\n", " return img\n", "test_generator(6595521)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have to recreate our network since we've changed our functions." ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tf.reset_default_graph()\n", "\n", "batch_size = 128\n", "# our noise dimension\n", "noise_dim = 96\n", "\n", "# placeholders for images from the training dataset\n", "x = tf.placeholder(tf.float32, [None, 784])\n", "z = sample_noise(batch_size, noise_dim)\n", "# generated images\n", "G_sample = generator(z)\n", "\n", "with tf.variable_scope(\"\") as scope:\n", " #scale images to be -1 to 1\n", " logits_real = discriminator(preprocess_img(x))\n", " # Re-use discriminator weights on new inputs\n", " scope.reuse_variables()\n", " logits_fake = discriminator(G_sample)\n", "\n", "# Get the list of variables for the discriminator and generator\n", "D_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,'discriminator')\n", "G_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,'generator') \n", "\n", "D_solver,G_solver = get_solvers()\n", "D_loss, G_loss = lsgan_loss(logits_real, logits_fake)\n", "D_train_step = D_solver.minimize(D_loss, var_list=D_vars)\n", "G_train_step = G_solver.minimize(G_loss, var_list=G_vars)\n", "D_extra_step = tf.get_collection(tf.GraphKeys.UPDATE_OPS,'discriminator')\n", "G_extra_step = tf.get_collection(tf.GraphKeys.UPDATE_OPS,'generator')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train and evaluate a DCGAN\n", "This is the one part of A3 that significantly benefits from using a GPU. It takes 3 minutes on a GPU for the requested five epochs. Or about 50 minutes on a dual core laptop on CPU (feel free to use 3 epochs if you do it on CPU)." ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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asiiiueW1FoXZZ599diOiCKGVpAnRVl555UwpCDsJK2R9D5fFVA2/p06dmjK/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\n9thct6IY866u4Je//GXNmWurbUWyFnFmnBRyyIfiAX//+9/Tw1Kz8a/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 0, D: 0.8544, G:0.415\n", "Iter: 50, D: 0.01302, G:0.538\n", "Iter: 100, D: 0.01642, G:0.5688\n", "Iter: 150, D: 0.1102, G:0.3321\n", "Iter: 200, D: 0.006611, G:0.4826\n" ] }, { "data": { "image/png": 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FxnH2WJv/au1Z63mEL3YXCfV+hlMszmkT+jlswOfg7z9jxvov//IvkqSzzjor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\n7KG14MUr3RkXLVrkjv3RgWE4YZt9M3a8n+wcpOL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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 250, D: 0.01454, G:0.5366\n", "Iter: 300, D: 0.0338, G:0.596\n", "Iter: 350, D: 0.1805, G:0.3736\n", "Iter: 400, D: 0.05014, G:0.405\n", "Iter: 450, D: 0.08969, G:0.2656\n" ] }, { "data": { "image/png": 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vPiCt5xUaDlT6dSXkuVRqGbhqGbyy5w4NEmH43fTp05MLrr322hUg6dJoVpDn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\nrheBGDJzUURmjohoEzTUOK6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SrAfii1/8IpD6aet5O7yu45szZ05TCvo55jDG32g2JZEy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\nlzkiok0QX+aIiDbB/wdnPP9ogjswUgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 500, D: 0.08032, G:0.4675\n", "Iter: 550, D: 0.1063, G:0.2358\n", "Iter: 600, D: 0.1038, G:0.3205\n", "Iter: 650, D: 0.1203, G:0.2682\n", "Iter: 700, D: 0.1231, G:0.3264\n" ] }, { "data": { "image/png": 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JOywVvvLKKyMRs6uNiooKz9b/zW9+A7gsNWWyqY2u6rMPOuggwG3pmkgkaiiy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\ngg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 750, D: 0.1555, G:0.1912\n", "Iter: 800, D: 0.1177, G:0.2805\n", "Iter: 850, D: 0.1614, G:0.3657\n", "Iter: 900, D: 0.1661, G:0.3876\n", "Iter: 950, D: 0.1464, G:0.2839\n" ] }, { "data": { "image/png": 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UTbFoldjV76dMmQK4UkrKQygrLh0kym9IPqawyhyXhymzYcSEvCmz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\nNoyY8P+E45qLpJFWqQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1000, D: 0.1466, G:0.2401\n", "Iter: 1050, D: 0.1562, G:0.2521\n", "Iter: 1100, D: 0.1431, G:0.249\n", "Iter: 1150, D: 0.1772, G:0.2415\n", "Iter: 1200, D: 0.1899, G:0.2535\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1250, D: 0.1946, G:0.1856\n", "Iter: 1300, D: 0.1642, G:0.2688\n", "Iter: 1350, D: 0.1658, G:0.2186\n", "Iter: 1400, D: 0.1561, G:0.246\n", "Iter: 1450, D: 0.169, G:0.2318\n" ] }, { "data": { "image/png": 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Jlvpo1Zequ6Ro8f7vtGnTgEIrCVx9vTjxxBMBGDNmDJAda0XnpfPUecycOTPh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\npVzbKnKXryy/SiqnNjXlAcU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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1500, D: 0.1854, G:0.2193\n", "Iter: 1550, D: 0.1564, G:0.2316\n", "Iter: 1600, D: 0.1644, G:0.259\n", "Iter: 1650, D: 0.1616, G:0.2567\n", "Iter: 1700, D: 0.194, G:0.2143\n" ] }, { "data": { "image/png": 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MiqixqZKTk8PYsWMBl06qh1jrkUOyIg0EdP1lnshc0TXUBuFPnAH33ZZmsqSD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\nmdWmdfHixd7Ui6qCihLSnS1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1750, D: 0.1709, G:0.1472\n", "Iter: 1800, D: 0.1641, G:0.21\n", "Iter: 1850, D: 0.1804, G:0.2542\n", "Iter: 1900, D: 0.1596, G:0.2273\n", "Iter: 1950, D: 0.1963, G:0.1929\n" ] }, { "data": { "image/png": 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PBJxnWbRv3x5Iz/5r3LgxADNnzgScdNQxy25N5phl06eLbGDZxuVB/oMXXnih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\n7ZRqUasdMx3K2wxO9rsqntS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 2000, D: 0.1917, G:0.2353\n", "Iter: 2050, D: 0.1839, G:0.2061\n", "Iter: 2100, D: 0.1691, G:0.1832\n", "Final images\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with get_session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " run_a_gan(sess,G_train_step,G_loss,D_train_step,D_loss,G_extra_step,D_extra_step,num_epoch=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# INLINE QUESTION 2:\n", "What differences do you see between the DCGAN results and the original GAN results?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The images generated by DCGAN are less noisy. The shapes of the digits are much clearer and smoother. I have changed the original loss to the least squares loss as it obtained much better results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "------\n", "\n", "# Extra Credit \n", "** Be sure you don't destroy your results above, but feel free to copy+paste code to get results below **\n", "* For a small amount of extra credit, you can implement additional new GAN loss functions below, provided they converge. See AFI, BiGAN, Softmax GAN, Conditional GAN, InfoGAN, etc. They should converge to get credit.\n", "* Likewise for an improved architecture or using a convolutional GAN (or even implement a VAE)\n", "* For a bigger chunk of extra credit, load the CIFAR10 data (see last assignment) and train a compelling generative model on CIFAR-10\n", "* Demonstrate the value of GANs in building semi-supervised models. In a semi-supervised example, only some fraction of the input data has labels; we can supervise this in MNIST by only training on a few dozen or hundred labeled examples. This was first described in [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498).\n", "* Something new/cool.\n", "\n", "#### Describe what you did here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# WGAN-GP (Small Extra Credit)\n", "\n", "Please only attempt after you have completed everything above.\n", "\n", "We'll now look at [Improved Wasserstein GAN](https://arxiv.org/abs/1704.00028) as a newer, more stable alernative to the original GAN loss function. For this part, all we have to do is change the loss function and retrain the model. We'll implement Algorithm 1 in the paper. \n", "\n", "You'll also need to use a discriminator and corresponding generator without max-pooling. So we cannot use the one we currently have from DCGAN. Pair the DCGAN Generator (from InfoGAN) with the discriminator from [InfoGAN](https://arxiv.org/pdf/1606.03657.pdf) Appendix C.1 MNIST (We don't use Q, simply implement the network up to D). You're also welcome to define a new generator and discriminator in this notebook, in case you want to use the fully-connected pair of D(x) and G(z) you used at the top of this notebook.\n", "\n", "Architecture:\n", "* 64 Filters of 4x4, stride 2, LeakyReLU\n", "* 128 Filters of 4x4, stride 2, LeakyReLU\n", "* BatchNorm\n", "* Flatten\n", "* Fully connected 1024, LeakyReLU\n", "* Fully connected size 1" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct number of parameters in discriminator.\n" ] } ], "source": [ "def discriminator(x):\n", " with tf.variable_scope('discriminator'):\n", " # TODO: implement architecture\n", " init = tf.contrib.layers.xavier_initializer()\n", " x = tf.reshape(x, [-1, 28, 28, 1])\n", " x = tf.layers.conv2d(x, 64, 4, activation=leaky_relu, strides=2, padding='valid',\n", " kernel_initializer=init, name='conv_0')\n", " x = tf.layers.conv2d(x, 128, 4, activation=leaky_relu, strides=2, padding='valid',\n", " kernel_initializer=init, name='conv_1')\n", " x = tf.layers.batch_normalization(x, name='batchnorm_0')\n", " x = tf.reshape(x, [-1, 3200])\n", " x = tf.layers.dense(x, 1024, activation=leaky_relu, kernel_initializer=init,\n", " name='dense_0')\n", " logits = tf.layers.dense(x, 1, kernel_initializer=init, name='logits')\n", " return logits\n", "test_discriminator(3411649)" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tf.reset_default_graph()\n", "\n", "batch_size = 128\n", "# our noise dimension\n", "noise_dim = 96\n", "\n", "# placeholders for images from the training dataset\n", "x = tf.placeholder(tf.float32, [None, 784])\n", "z = sample_noise(batch_size, noise_dim)\n", "# generated images\n", "G_sample = generator(z)\n", "\n", "with tf.variable_scope(\"\") as scope:\n", " #scale images to be -1 to 1\n", " logits_real = discriminator(preprocess_img(x))\n", " # Re-use discriminator weights on new inputs\n", " scope.reuse_variables()\n", " logits_fake = discriminator(G_sample)\n", "\n", "# Get the list of variables for the discriminator and generator\n", "D_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,'discriminator')\n", "G_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,'generator')\n", "\n", "D_solver, G_solver = get_solvers()" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def wgangp_loss(logits_real, logits_fake, batch_size, x, G_sample):\n", " \"\"\"Compute the WGAN-GP loss.\n", " \n", " Inputs:\n", " - logits_real: Tensor, shape [batch_size, 1], output of discriminator\n", " Log probability that the image is real for each real image\n", " - logits_fake: Tensor, shape[batch_size, 1], output of discriminator\n", " Log probability that the image is real for each fake image\n", " - batch_size: The number of examples in this batch\n", " - x: the input (real) images for this batch\n", " - G_sample: the generated (fake) images for this batch\n", " \n", " Returns:\n", " - D_loss: discriminator loss scalar\n", " - G_loss: generator loss scalar\n", " \"\"\"\n", " # TODO: compute D_loss and G_loss\n", " D_loss = tf.reduce_mean(logits_fake-logits_real)\n", " G_loss = -tf.reduce_mean(logits_fake)\n", "\n", " # lambda from the paper\n", " lam = 10\n", " \n", " # random sample of batch_size (tf.random_uniform)\n", " eps = tf.random_uniform([batch_size,1], minval=0.0, maxval=1.0)\n", " x_hat = eps*x+(1-eps)*G_sample\n", " \n", " # Gradients of Gradients is kind of tricky!\n", " with tf.variable_scope('',reuse=True) as scope:\n", " grad_D_x_hat = tf.gradients(discriminator(x_hat), x_hat)\n", "\n", " grad_norm = tf.norm(grad_D_x_hat[0], axis=1, ord='euclidean')\n", " grad_pen = tf.reduce_mean(lam*tf.square(grad_norm-1))\n", " \n", " D_loss += grad_pen\n", "\n", " return D_loss, G_loss\n", "\n", "D_loss, G_loss = wgangp_loss(logits_real, logits_fake, 128, x, G_sample)\n", "D_train_step = D_solver.minimize(D_loss, var_list=D_vars)\n", "G_train_step = G_solver.minimize(G_loss, var_list=G_vars)\n", "D_extra_step = tf.get_collection(tf.GraphKeys.UPDATE_OPS,'discriminator')\n", "G_extra_step = tf.get_collection(tf.GraphKeys.UPDATE_OPS,'generator')" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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yCSNHjmz2YnbXMkfuiyPj/cYBupk3b17uOjN+9INlHW31SdYimD1p0qRGRAED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\ndY011kjkgJv6rrHjoSEFir/NG6Jwly5dsnLLRoLqts2pU6c24Vu2eeq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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 0, D: 8.406, G:-0.03445\n", "Iter: 50, D: -20.98, G:1.386\n", "Iter: 100, D: -16.3, G:-13.59\n", "Iter: 150, D: -14.67, G:0.3505\n", "Iter: 200, D: -10.58, G:4.452\n" ] }, { "data": { "image/png": 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W5jRu3DjAL2NULsQnn3ziyh4pXVWFM0KkeZZzbZP6dUwyG0ZKqFputogrZKDX\n8nqqBGuhTCDNUKFXO9xmMa+wxq44o2ycaiNfgDzBmUzGHbOWBa4sKKd5/PjxQPFGAroWp556KuCz\n2iSpihXOL5WOLl7Q0k3dVypCoOYDxxxzDOCvob737rvvOqmu7LhQimqRkHwG5aDzXSommQ0jJdQt\nzqw43f777w8Ut3NlMz///PM5n4sknsJwuVu1kAf0oosuynn/tddec7bXytTGNIo89AcffHDO+zr/\nixcvdjayIhDSRlS8sDOhe0Ied2Unyjej45KP4OKLL3aFMcLySLq29cgdMMlsGCmh6pI5LDqvFU5a\ndfLOO+8AubOgZnFJZEkAxS7DHOawTWgxW0rjkEe2WkgCRTPboO04q12Av1poxU+YKaXsNrWYWbp0\nqSusoOsrO7SUa1RtdE9OnjwZ8Nfo0EMPLfj9r3zlK0Bu4QTZ28p+0zVV7vl1111X4VG3j0lmw0gJ\nVSsbFOZAy6bQzKzG6cqC2mOPPYA2j6JikypKLs9zaIeEudvh3+hYtV+tAKqEN7UYKimrxnjKaqtV\nG5ZKZFYpRq5MKJ3DsMlAoQiEpJiO+9ZbbwUqK5HLya/PZDIusqDji0MaYFjKKJPJuBLRQvd3JSVy\n0lK7mYSlgCqW1aALoSWQUktHjBjhAvEKY5TTfTIO3eRaHL9gwYJM5LPOnbVRBtlsNueprsQxakLU\nAox6Ez3GShyfHF1h6mkhamE2hNcwdixVG4FhGDUlkWRubGzMQsdmobA4nxInpLr16tWLGTNm5PxG\nwfNQvSsneV3ppEojffXVV00yr+RUWjJ3NkwyG0YXo6zQVCkSMfyOJHK0cwX4ND8lF3zyySeujI6C\n+NqGEj3iJHRIdHySyHJIrazhoZWB5ubmoqWcVnZ69uxZ1vLbamOS2TBSQiLJrGQBFRZQTx4lYMya\nNcuVGtXif7n/lYqp5WRaiqjvv/XWWwAMHTrUBfFVSE2hBIUDFPaR3a1i+JK6on///u632q+OYe21\n1449ziS2uLQFhTHC7pfy/KpvVK9evZw2Uo0F67oWcQkQQqE5dc6MdiXUNnQsSs1Uf2aFopTUo2sd\nvW5KqlDyiHpMldoJQn6ZpqamvEKCOudjx47N+722f/755wPws5/9LOcYPvroI9dkQaEfjeW9994D\n/L354Ycf5hyDvPfDhg1zIVWlb+p1e8cXpn82Nzc7n5A6YyqxavTo0QW3EYdJZsNICYm82YZhdF5M\nMhtGSrCdufMdAAAUSElEQVSH2TBSQtLc7NTp5F0t4aCpqSkLlVlLHefskTMN4hOMSunBFYdqvckx\nNX/+/C51DeOoW252Z6GrPcxpOsbIhNClrmEcpmYbRkqoedkgw6gUUXXeMMlsGKnBJLNRNtGssRBl\n5ylLLERF8pV1laSdbVzD9q6OSWbDSAnmzTZvdsVpampyrVwlcVXA8bLLLgN8rr7a2naErnYN47CH\nucY3wpgxY3jssccAr55KXdWCAsVeK1GDuRIPs+K6Guemm24K+PrSOg4tUCjWA0oP99y5cwG/4KWc\ntGItlPjf//5nDzOmZhtGauh0DrCGhgZXUO30008H/PIyVe1Uob/ob6BzLRRX8YU333wT8NItShha\nCb8j55GWFkryVRudT/Vf3mmnnQDylkaWUwFUyxd/8pOfFHxfjjMtEy3kXNNyxrCPd1fHJLNhpIS6\nS2bNrlqQHZVWqld8/fXXA/Dcc88V3EYtutKXisofvfzyy0B5PYRlV8pWjgvvFCOuLJLGIy0gLMEU\nPYfSLhQK0oL6uGSN6G/jpLa6Ij700EM5v9HCfHWIUK+uiRMn8uSTT+Zsc6+99gJwpXtqTd++fTn3\n3HMBOPHEE3M+0/lVlw8Vw1AJrGpiktkwUkLdJXOhDg9nnXUWAFdeeSWQW0KmELK3kvazrQbf+c53\nAG/rFZLMKj8jqfT3v/8dgO233x6Ajz/+GMB1GlS/piRIioboXMZ9Hv3OjjvuCMB+++2XM56nn346\n5zjUL1ve5dGjR7vtS6qrSOO2226b81uhkkTS0KSVffjhh3law5133gn4/mTVJow2ZLPZWO0ktPFr\nIZGFSWbDSAk1jzOrwJ/S+EJaW1td1z1JAiUcqAtkyH333QfAvvvum3g8lYozSwJrzEo1HDduHOBn\n9ffee6+mfZnDGGVDQ0P28/fb/a1aCMmGl8SOS+OM+i6KpXqWwjrrrAO0aWPhdZefRV7tefPmVSXO\nHHeOVqxYwR133AHAYYcdBvhzFdrxuid1j5Y5DoszG0ZXomaSOZKtU/BzSS7Zv4WIG6tKvcreSkK5\nkjnsKqlOlVtssQXgZ2hpIirbumjRonbjo9q2OgrKZr7kkkuA4vZuSNIMsEwm4zzhGqdey7+h+PNu\nu+0GeOkrn8ayZctcr+bZs2cXHZ+k+QYbbJCzbd0vAwYMcL4Ele/VPbLhhhsC8OKLL9ZUMhfy1Oua\nxWkrHRyHSWbD6ErUzJutwvUhkqaSruDtTc3uKrYfh76nQvq14Gtf+xrgFwpIQkvDUBF25SDL7ped\nVwxJvGOOOQbwtqr+Dh48GPAe4kLExZkl1SQxNN5oFp32o2NSBp7Ye++9C25bzQjGjx/frkSWFJck\nkw9Fmo20jxUrVrjYtMaqZghJ+xd3lEJSVtezHK2w0phkNoyUUHWbOWpHxWwz7/uKzSlGWcK4kg7L\n0VFvtvKVlS+uzKaTTjqp7DFVko54s+tJNP87ziMeaYtTUZtZUlZSt9D9pffkA5I3W4QNEzuC2cyG\n0cWous0cN6sq71fITp4xY0ZJdiV4GyrMM64FGr+0CK3L1RrkkFLs3FoQ53UthaRN3zqCtlXsmpaT\ns14KajZ3xRVXFPw8k8m0e4xhhlstqLqaHVfsvJSeykIX9Nvf/jbg0zwHDRqU81edFpOQRM3u3bu3\nMxcOOeQQwIegbr31Vm0D8BNMOeqsnFfahpxB5UxWcWq2CB/Qfv36ueWHoeNN39XClx//+MeAD7sl\nITw/+qt9RR1y7YUkK6Vm6/jkjFNYMaTYNQ0TayqBqdmG0cWoWdLIf//7X8DPuFryJsLZP9hvzmtJ\nYG3j3nvvBfyCgCQkkcz9+/d3qt1dd90FwLXXXgvAH//4x5zvTp06FYCtt94ayO3bLE0iDNcpbBQm\nhSgEU85CknBW79WrV1bjAC/tpXH079/fjVWSTyq5ygRttNFGgF8QotBgoUUzIVdffTUA3//+93Pe\n13h0H8iReNRRRzFr1qyc72pccj7Onj27JmWDij0rlUgOKbJfk8yG0ZWoWdKIHEBKwRNKGSw0s0ni\nhuy6666AT14vRyKXQ9SpoX0qKSRERe8kRZQ0suWWW3LbbbcV/I1CcVrY/uijjwKVXdopGz9Oyixc\nuDAvpVYVNSWRda2UpFOKRNZvpkyZAvgCA9/61reAfJ/KLrvsErstaQSdIVFD2kFnwCSzYaSEupfa\nlaSIBt0XLFgAeGkeztqSYBq7JFk5lJs0ohlZy/NWX331nM9lgx5//PEA/PrXvwZg+vTp7L///gC8\n9tprgPeays5W4b5wAUM5hPZWY2NjFuLDR42NjS7sds455wD5mo/GpfGOHj0agBEjRgBtCyJkA0ur\n0EIUpcHKLld5JfkWwvTSYiGgSEJSTWxmFVZcY401StJGKoXZzIbRxah72SAVPJOE69evHzNnzgTy\nZ+VbbrkFgOOOOw6ob5kgSQUtT5RkVhKJPNZarKAYeZ8+fVz5XUkfeWvDUrqSbpUsWBie06iXHeDQ\nQw9l5513BmD33XfP+a7G8atf/QqAH/3oRznvT5s2DWhbtqgkGi1f1AKKML1R5ZO1eOOpp54CvHbW\n0NBQVIuoJWruns1mOfPMMwE4++yzazqGYphkNoyUUDfJrBlaS+WiduFmm20G+EwoxV2PPvpowM/i\nKuxWDyS1xo4dC/i488033wx4H4BsK0mm559/njXWWAPwSwvjMqjkrS1HIsdJLZ13xa51bnX+Bw0a\n5EriHHDAAYC/RtKMlO4YagyynYcPH84zzzwDtElpiF80IxtZRQFll44ZMwZoW2Ia2qfS5hTfrgcT\nJkwA4IILLgCSFYyoFiaZDSMl1M2bLelUqGiB7BDZJdWko0sgJZ1U/H7UqFEAzu6U5/erX/0q0BZX\nVTbcdtttB8CNN96Ysy3FqOXt7gjlNI7TOFRUT1Ly7rvvBry2Ed470ga6d+/uPOATJ04EvA9BKJNu\nyJAhQHkx40gRvZp4s6Plk2vZEsm82YbRxaibZI7LxT7ooINcfFNF3qpJpSTzeuutB/jjmjx5MuBz\n0g888ECgzdstG1TZYfqNln5WYgmh6EhLV2WChTnj4eqtsKxuU1OTk+Lybyju/vDDDwOw5557anwl\nH0tIJL+8KpJZ0lctc1QWKZPJ1LQlkklmw+hi1NybreyicJ2zbKvf/e53zjPcGdG4p0+fDnibX21X\nQsLCdeBtztCOlDTfY489KjjiZHTr1s1dC0UPJk2aBPg13PJQ77DDDgDOcy2y2ayTyDpfikx84xvf\ncN8pRjT+HVdyqtotXWWTjx8/Puf9OXPm5GX8dQZMMhtGWlAlh1L+AdmO/stkMtlMJpNtaWnJtrS0\nFPxOz549sz179nTfrcR+4/4lOb5MJpNdvHhxdvHixdn2WLZsWXbZsmVx+8yhtbU129raWtHjamxs\nzDY2NuYcXzabRec0PLd6fcopp2SXLl2aXbp0ad6xaJxiwoQJ2QkTJuTt+84778z7rujdu3e2d+/e\n7Y6/R48e2R49ehS9hwYOHJgdOHBgomuY5N/w4cOzw4cPr+r9l/QeLfav7gst6k1HHWAh6jqhkjr1\npiMOsMhvcl6rKEQpZZpqUZ+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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 250, D: -6.649, G:6.121\n", "Iter: 300, D: -6.268, G:-10.84\n", "Iter: 350, D: -5.309, G:-7.715\n", "Iter: 400, D: -6.116, G:-1.98\n", "Iter: 450, D: -5.485, G:-0.9872\n" ] }, { "data": { "image/png": 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bsGBBQXB+Vy4fJOIki0iKq0Nikv2by0la0XcVVCG/t1IDtXKQH3qFFVYIrPj+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\n7GE2jIxgD7NhZAR7mA0jI9jDbBgZ4f8AqTohzzgOrtoAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 500, D: -5.008, G:-3.572\n", "Iter: 550, D: -4.651, G:-2.434\n", "Iter: 600, D: -3.985, G:-0.8994\n", "Iter: 650, D: -3.326, G:4.645\n", "Iter: 700, D: -2.839, G:1.115\n" ] }, { "data": { "image/png": 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S/rLk+7HNYdRLZ/71r39d9FkjlBKq+ctc7oXs7u4OkvUVFii3SSn3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\nAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 750, D: -2.754, G:-1.415\n", "Iter: 800, D: -3.173, G:0.4315\n", "Iter: 850, D: -2.476, G:0.7514\n", "Iter: 900, D: -2.235, G:2.259\n", "Iter: 950, D: -1.18, G:-0.4736\n" ] }, { "data": { "image/png": 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DDz5IPqOjjjqq3U/t+W233RYIuy0/O/tt53EvR2WOROqEmvRmO2ng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\njEBLAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1000, D: -1.439, G:-2.755\n", "Iter: 1050, D: -0.7006, G:5.957\n", "Iter: 1100, D: -1.078, G:1.944\n", "Iter: 1150, D: -0.9947, G:-0.7764\n", "Iter: 1200, D: -0.2449, G:-0.6648\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1250, D: -0.9923, G:-1.337\n", "Iter: 1300, D: -0.8734, G:0.3115\n", "Iter: 1350, D: -0.1571, G:6.24\n", "Iter: 1400, D: -0.05282, G:8.609\n", "Iter: 1450, D: -1.785, G:1.382\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1500, D: -0.01148, G:1.856\n", "Iter: 1550, D: 0.07895, G:2.482\n", "Iter: 1600, D: -0.7232, G:2.943\n", "Iter: 1650, D: -0.4414, G:-0.02294\n", "Iter: 1700, D: -0.822, G:-2.287\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 1750, D: 0.04871, G:-8.025\n", "Iter: 1800, D: -0.8545, G:-1.413\n", "Iter: 1850, D: -0.05131, G:0.9242\n", "Iter: 1900, D: -1.639, G:-4.617\n", "Iter: 1950, D: -0.4222, G:-5.719\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Iter: 2000, D: -0.5203, G:2.802\n", "Iter: 2050, D: 0.08011, G:-1.55\n", "Iter: 2100, D: -0.2934, G:0.2779\n", "Final images\n" ] }, { "data": { "image/png": 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2WKbMhhETfFfmSZMmAU6RNCMffPDBvn2GvL6PPvooAIcddhjg8n6VQdWmTRs+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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with get_session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " run_a_gan(sess,G_train_step,G_loss,D_train_step,D_loss,G_extra_step,D_extra_step,batch_size=128,num_epoch=5)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }