06_WGAN_GP_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WGAN_GP MNIST\n",
    "\n",
    "This notebook is for implementing `Wasserstein GAN-Gradient Panelty (WGAN_GP)` from the paper [Improved Training of Wasserstein GANs](https://arxiv.org/abs/1704.00028) with [Tensorflow](https://www.tensorflow.org). <br>\n",
    "[MNIST data](https://yann.lecun.com/exdb/mnist/) will be used.\n",
    "\n",
    "Reference: [hwalsuklee's Github](https://github.com/hwalsuklee/tensorflow-generative-model-collections), [Jonathan Hui's Blog](https://medium.com/@jonathan_hui/gan-wasserstein-gan-wgan-gp-6a1a2aa1b490)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import datetime\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "algorithm = 'WGAN_GP_MNIST'\n",
    "\n",
    "img_size   = 28\n",
    "\n",
    "batch_size = 64\n",
    "num_epoch  = 50\n",
    "\n",
    "n_latent = 100\n",
    "\n",
    "beta1 = 0\n",
    "beta2 = 0.9\n",
    "\n",
    "learning_rate_g = 0.0001\n",
    "learning_rate_d = 0.0001\n",
    "\n",
    "show_result_epoch = 5\n",
    "\n",
    "date_time = datetime.datetime.now().strftime(\"%Y%m%d-%H-%M-%S\")\n",
    "\n",
    "load_model = False\n",
    "train_model = True\n",
    "\n",
    "save_path = \"./saved_models/\" + date_time + \"_\" + algorithm\n",
    "load_path = \"./saved_models/20190809-11-04-47_WGAN_GP_MNIST/model/model\" \n",
    "\n",
    "# WGAN_GP Parateter\n",
    "n_critic = 1\n",
    "d_lambda = 10 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import MNIST Dataset\n",
    "\n",
    "Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.mnist.load_data(path='mnist.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28)\n",
      "y_train shape: (60000,)\n",
      "x_test shape: (10000, 28, 28)\n",
      "y_test shape: (10000,)\n"
     ]
    }
   ],
   "source": [
    "x_train = mnist[0][0]\n",
    "y_train = mnist[0][1]\n",
    "x_test  = mnist[1][0]\n",
    "y_test  = mnist[1][1]\n",
    "\n",
    "print('x_train shape: {}'.format(x_train.shape))\n",
    "print('y_train shape: {}'.format(y_train.shape))\n",
    "print('x_test shape: {}'.format(x_test.shape))\n",
    "print('y_test shape: {}'.format(y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Generator(x, is_training, reuse=False):\n",
    "    with tf.variable_scope('Generator', reuse=reuse):\n",
    "        xavier_init_conv = tf.contrib.layers.xavier_initializer_conv2d()\n",
    "        xavier_init = tf.contrib.layers.xavier_initializer()\n",
    "        \n",
    "        # Project and Reshape \n",
    "        w1 = tf.get_variable('w1', [x.get_shape()[1], 7*7*1024], initializer=xavier_init)\n",
    "        b1 = tf.get_variable('b1', [7*7*1024], initializer=xavier_init)\n",
    "        \n",
    "        x_project = tf.matmul(x,w1)+b1\n",
    "        x_reshape = tf.reshape(x_project, (-1, 7, 7, 1024))\n",
    "        \n",
    "        # First deconv layer\n",
    "        h1 = tf.layers.conv2d_transpose(x_reshape,filters=512, kernel_size=5, strides=1, padding='SAME', kernel_initializer=xavier_init_conv)\n",
    "        h1 = tf.nn.leaky_relu(h1)\n",
    "        \n",
    "        # Second deconv layer\n",
    "        h2 = tf.layers.conv2d_transpose(h1,filters=256, kernel_size=5, strides=2, padding='SAME', kernel_initializer=xavier_init_conv)\n",
    "        h2 = tf.layers.batch_normalization(h2, training=is_training)\n",
    "        h2 = tf.nn.leaky_relu(h2)  \n",
    "        \n",
    "        # Third deconv layer \n",
    "        h3 = tf.layers.conv2d_transpose(h2, filters=128, kernel_size=5, strides=2, padding='SAME', kernel_initializer=xavier_init_conv)\n",
    "        h3 = tf.layers.batch_normalization(h3, training=is_training)\n",
    "        h3 = tf.nn.leaky_relu(h3)\n",
    "        \n",
    "        # Output layer\n",
    "        logits = tf.layers.conv2d_transpose(h3, filters=1, kernel_size=5, strides=1, padding='SAME', kernel_initializer=xavier_init_conv)     \n",
    "        output = tf.tanh(logits)\n",
    "                \n",
    "        return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Discriminator(x, is_training, reuse=False):\n",
    "    with tf.variable_scope('Discriminator', reuse=reuse):\n",
    "        xavier_init_conv = tf.contrib.layers.xavier_initializer_conv2d()\n",
    "        xavier_init = tf.contrib.layers.xavier_initializer()\n",
    "\n",
    "        # First conv layer\n",
    "        h1 = tf.layers.conv2d(x, filters=64, kernel_size=5, strides=2, padding='SAME', kernel_initializer=xavier_init_conv)\n",
    "        h1 = tf.nn.leaky_relu(h1)\n",
    "        \n",
    "        # Second conv layer\n",
    "        h2 = tf.layers.conv2d(h1, filters=128, kernel_size=5, strides=2, padding='SAME', kernel_initializer=xavier_init_conv)\n",
    "        h2 = tf.nn.leaky_relu(h2)\n",
    "        \n",
    "        # Trhid conv layer\n",
    "        h3 = tf.layers.conv2d(h2, filters=256, kernel_size=5, strides=2, padding='SAME', kernel_initializer=xavier_init_conv)\n",
    "        h3 = tf.nn.leaky_relu(h3)\n",
    "        \n",
    "        # Output layer\n",
    "        flatten = tf.reshape(h3, (-1, h3.get_shape()[1]*h3.get_shape()[2]*h3.get_shape()[3]))\n",
    "        \n",
    "        logit  = tf.layers.dense(flatten, 1, kernel_initializer=xavier_init)\n",
    "        output = tf.sigmoid(logit)  \n",
    "\n",
    "        return logit, output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GAN(x, z, is_training):\n",
    "    # Generator\n",
    "    G = Generator(z, is_training)\n",
    "    \n",
    "    # Discriminator\n",
    "    D_logit_real, D_out_real = Discriminator(x, is_training)\n",
    "    D_logit_fake, D_out_fake = Discriminator(G, is_training, reuse=True)\n",
    "    \n",
    "    # get loss \n",
    "    ########################################### WGAN GP ###########################################\n",
    "    eps = tf.random_uniform(shape=tf.shape(x),minval=0.0, maxval=1.0)\n",
    "    x_hat = (eps * x) + ((1-eps) * G)\n",
    "    D_hat, _ = Discriminator(x_hat, is_training, reuse=True)\n",
    "    grad = tf.gradients(D_hat, [x_hat])[0]\n",
    "    GP = d_lambda * tf.square(tf.norm(grad, ord=2) - 1)\n",
    "    \n",
    "    d_loss = -tf.reduce_mean(D_logit_real) + tf.reduce_mean(D_logit_fake) + GP\n",
    "    g_loss = -tf.reduce_mean(D_logit_fake) \n",
    "    ###############################################################################################\n",
    "\n",
    "    return d_loss, g_loss, G"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, shape=[None, img_size, img_size, 1])\n",
    "x_normalize = (tf.cast(x, tf.float32) - (255.0/2)) / (255.0/2)\n",
    "\n",
    "z = tf.placeholder(tf.float32, shape=[None, n_latent])\n",
    "\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "\n",
    "d_loss, g_loss, G = GAN(x_normalize, z, is_training) \n",
    "\n",
    "# optimization\n",
    "trainable_variables = tf.trainable_variables()\n",
    "\n",
    "trainable_variables_d = [var for var in trainable_variables if var.name.startswith('Discriminator')]\n",
    "trainable_variables_g = [var for var in trainable_variables if var.name.startswith('Generator')]\n",
    "\n",
    "update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "with tf.control_dependencies(update_ops):\n",
    "    train_step_d = tf.train.AdamOptimizer(learning_rate_d, beta1, beta2).minimize(d_loss, var_list=trainable_variables_d)\n",
    "    train_step_g = tf.train.AdamOptimizer(learning_rate_g, beta1, beta2).minimize(g_loss, var_list=trainable_variables_g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize variables\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "\n",
    "sess = tf.InteractiveSession(config=config)\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Saver = tf.train.Saver()\n",
    "\n",
    "if load_model == True:\n",
    "    Saver.restore(sess, load_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 / G Loss: -1.62944 / D Loss: 0.79649\n"
     ]
    },
    {
     "data": {
      "image/png": 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p3bs3EPn05B++8cYbgcg2iqUdPXo0ECkRjbpLeWSFeB2Qv1O9EEXiKCpDPl35cl2JNw/N21BU2FFHHVW774ILLqhImeKoTmtB5XiGQ+XNUSbHddZZB4juSdFfhc+Dxo7UxmglII03NGZsqbDd0rWk9huby9wVueM4TsbJluRaBooLFoVv0bTONCslynssn7ZsoBzSUuJSn1IpJ5xwAhBlhNN+jeY//vjjZS97KejQoUPt34888ggQqSohJXbrrbcmV7AqRnVM0StSkFpRKb6tEr0e9UbVS1XUjeLmARYvXgxEClptiVYM6ty58xL71XPVPAuNDyiuXDnq7777bgA+/fRToP6Zo1Lxaqc0dqNyNtZmrsgdx3EyTtUo8jXWWAOo+wbTG7makWKYOnUqUDenimbpacaabKJc0lLyQsrj2muvLVOJy4PypkBdJS6keJT9UL0XKSLZUkpJvRKpNtUvzYaN9/R0jOKKzz77bCBSbP369VviHM1FSnfXXXcF4OKLLwbg5ZdfBiIbnH/++bXHlCoTn+wopVvoP1YZIKpvSWaH1DMgG2j25rhx44Aoogtgm222AaK5BPpOtaZtQwwePBiAm2++GYjGnkaNGgVAr169gPrzkuuZ1O9ivx9X5I7jOBkns4q8UHVqBqdGkPU7iRWsK83IkSOBSOnJJ668Eso7I0Uk35+yHQodr4iPxuacqDSqC0OGDKndVhi1JFUo5aiIHq2gvjS0yovUdqG/N16/FHevGbX6Hk4//fQlzlmqOqkyyB/dqVMnIFL+6mkdccQRtcdoVmKx61LuvffeABxzzDEAfPPNN0BkZ9WZ+EzRSkaOKSJFeYSEYr0h8kcXO46mvC2yxYMPPghE9UBjUY1ZKajY3porcsdxnIyTueyHhetR6m3fp08fIMpEphlnmq2V5MolSWT6i/dIpASUU0X3LH+bPtu3b18gslGhMpRyKFQvpaCcNpHKjivfww47DIhyssvvr/jxwhnAhei5UP2Sgi9U4vFei2YwDhw4EIBJkyYB8MUXXwB1FV+psh+qt6FxomuuuQaIYrnj37NWw9FYQEMo2+EZZ5wBRP522UW9EOUVef311wF49NFHa8/RVN94WjJlNhU9Z8899xwQ9XhVD5TTqBg8+6HjOE6V4w254zhOxsmca0XdF4V/Kbxo4sSJQNRlnD59OhANBCU54JJE11AJfQA++ugjXReIunByK2i5Mg3YCXV5tdhCOQc3y2mTQncbRK60wm696o/uWe4n2U6LAGiRAC0soYFEuRZU/5RwDKJUBypP4XTrQkq9sISuq0E2uTc0WSf+GYVEamKM3FMK4dS9xG2aLzMAkydPBqKUyArPLLRnMWTVtSLk1lP4oYgPOo8ZM6ZJ53TXiuM4TpWTOUUupHY0wDNo0CAANt98cwAuueQSAN5++20g2cURklAU8UWl33nnnUYdo3C4Hj16AFHi/CTIusoSpZxuntRSbwpHBBgxYoTOtcxjCtM5qHenJfBKNbGoPrJeV7p06QJEg9wiHvKp3tKAAQOAKFBBPZtCXJE7juNUOZlV5GkmCUURn4KuqflSWfKFyzerMLR4SFjSZF1llYNKLL6sMSQtejB06FAgqjPqwarXluS0epH1uiJvgZ5LTczSxCOIpvFLiauHHf9MHFfkjuM4VY4r8jKQdUVRDtwmdamEIs8C1VJXunXrBkSJ2eKpc6XExaJFi5Z5LlfkjuM4VY4r8jJQLYqilLhN6uKKvH6aYpeampqQP6Z8BSoSzTVQLL6WmYPSpy1wRe44jpNxMpvG1nEcp5LLyDWEyqT48XKW0RW54zhOxknUR+44juOUHlfkjuM4GccbcsdxnIzjDbnjOE7G8YbccRwn43hD7jiOk3G8IXccx8k43pA7juNkHG/IHcdxMo435I7jOBnHG3LHcZyM4w254zhOxvGG3HEcJ+N4Q+44jpNxvCF3HMfJON6QO47jZBxvyB3HcTKON+SO4zgZxxtyx3GcjOMNueM4TsbxhtxxHCfjeEPuOI6TcbwhdxzHyTjekDuO42Sc/wdZb7pQkNdbmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2 / G Loss: -1.46492 / D Loss: 0.74151\n",
      "Epoch: 3 / G Loss: -1.18724 / D Loss: 0.84228\n",
      "Epoch: 4 / G Loss: -0.96359 / D Loss: 0.90265\n",
      "Epoch: 5 / G Loss: -0.85765 / D Loss: 0.91554\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 6 / G Loss: -0.88647 / D Loss: 0.88533\n",
      "Epoch: 7 / G Loss: -0.70785 / D Loss: 0.90547\n",
      "Epoch: 8 / G Loss: -0.68087 / D Loss: 0.89776\n",
      "Epoch: 9 / G Loss: -0.92192 / D Loss: 0.90113\n",
      "Epoch: 10 / G Loss: -0.78550 / D Loss: 0.77135\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 11 / G Loss: -0.66251 / D Loss: 0.79097\n",
      "Epoch: 12 / G Loss: -0.64042 / D Loss: 0.85479\n",
      "Epoch: 13 / G Loss: -0.61477 / D Loss: 0.73297\n",
      "Epoch: 14 / G Loss: -0.64609 / D Loss: 0.85537\n",
      "Epoch: 15 / G Loss: -0.64114 / D Loss: 0.92180\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 16 / G Loss: -0.53677 / D Loss: 0.67377\n",
      "Epoch: 17 / G Loss: -0.56631 / D Loss: 0.69013\n",
      "Epoch: 18 / G Loss: -0.55932 / D Loss: 0.93784\n",
      "Epoch: 19 / G Loss: -0.70444 / D Loss: 0.82597\n",
      "Epoch: 20 / G Loss: -0.44856 / D Loss: 0.87988\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 21 / G Loss: -0.63634 / D Loss: 0.86060\n",
      "Epoch: 22 / G Loss: -0.57229 / D Loss: 0.76459\n",
      "Epoch: 23 / G Loss: -0.24838 / D Loss: 0.88559\n",
      "Epoch: 24 / G Loss: -0.54387 / D Loss: 1.01582\n",
      "Epoch: 25 / G Loss: -0.42747 / D Loss: 0.85571\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 26 / G Loss: -0.11469 / D Loss: 0.84010\n",
      "Epoch: 27 / G Loss: -0.58286 / D Loss: 0.66911\n",
      "Epoch: 28 / G Loss: -0.18663 / D Loss: 1.06307\n",
      "Epoch: 29 / G Loss: -0.63823 / D Loss: 0.98817\n",
      "Epoch: 30 / G Loss: -0.27712 / D Loss: 0.79499\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 31 / G Loss: -0.43570 / D Loss: 0.80832\n",
      "Epoch: 32 / G Loss: -0.38540 / D Loss: 0.86206\n",
      "Epoch: 33 / G Loss: -0.37699 / D Loss: 0.87056\n",
      "Epoch: 34 / G Loss: -0.25752 / D Loss: 1.03787\n",
      "Epoch: 35 / G Loss: 0.13506 / D Loss: 0.82616\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 36 / G Loss: -0.23106 / D Loss: 0.75532\n",
      "Epoch: 37 / G Loss: -0.24361 / D Loss: 0.76778\n",
      "Epoch: 38 / G Loss: -0.08059 / D Loss: 0.93509\n",
      "Epoch: 39 / G Loss: -0.39276 / D Loss: 0.93896\n",
      "Epoch: 40 / G Loss: -0.33909 / D Loss: 0.90729\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 41 / G Loss: -0.33547 / D Loss: 0.65333\n",
      "Epoch: 42 / G Loss: -0.38235 / D Loss: 0.81878\n",
      "Epoch: 43 / G Loss: -0.24610 / D Loss: 0.75616\n",
      "Epoch: 44 / G Loss: -0.20341 / D Loss: 1.02102\n",
      "Epoch: 45 / G Loss: -0.01807 / D Loss: 0.97392\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 46 / G Loss: -0.47904 / D Loss: 0.67215\n",
      "Epoch: 47 / G Loss: -0.38656 / D Loss: 0.90133\n",
      "Epoch: 48 / G Loss: -0.60181 / D Loss: 0.71048\n",
      "Epoch: 49 / G Loss: -0.25816 / D Loss: 0.83003\n",
      "Epoch: 50 / G Loss: -0.34854 / D Loss: 0.77147\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if train_model:\n",
    "    # Training\n",
    "    data_x = x_train\n",
    "    len_data = x_train.shape[0]\n",
    "\n",
    "    for i in range(num_epoch):\n",
    "        # Shuffle the data \n",
    "        np.random.shuffle(data_x)\n",
    "\n",
    "        # Making mini-batch\n",
    "        for j in range(0, len_data, batch_size):\n",
    "            if j + batch_size < len_data:\n",
    "                data_x_in = data_x[j : j + batch_size, :]\n",
    "            else:\n",
    "                data_x_in = data_x[j : len_data, :]\n",
    "\n",
    "            data_x_in = data_x_in.reshape((-1, img_size, img_size, 1))\n",
    "\n",
    "            sampled_z = np.random.uniform(-1, 1, size=(data_x_in.shape[0] , n_latent))\n",
    "\n",
    "            # Run Optimizer!\n",
    "            G_out = sess.run(G, feed_dict = {x: data_x_in, z: sampled_z, is_training: True})\n",
    "\n",
    "            ########################################### WGAN ###########################################\n",
    "            for iter_d in range(n_critic):\n",
    "                _, loss_d = sess.run([train_step_d, d_loss], feed_dict = {x: data_x_in, z: sampled_z, is_training: True})\n",
    "            ############################################################################################\n",
    "\n",
    "            _, loss_g = sess.run([train_step_g, g_loss], feed_dict = {x: data_x_in, z: sampled_z, is_training: True})\n",
    "\n",
    "            print(\"Batch: {} / {}\".format(j, len_data), end=\"\\r\")\n",
    "\n",
    "        # Print Progess\n",
    "        print(\"Epoch: {} / G Loss: {:.5f} / D Loss: {:.5f}\".format((i+1), loss_g, loss_d))\n",
    "\n",
    "        # Show test images \n",
    "        z_test = np.random.uniform(-1, 1, size=(5, n_latent))\n",
    "        G_out = sess.run(G, feed_dict = {z: z_test, is_training: False})\n",
    "\n",
    "        if i == 0 or (i+1) % show_result_epoch == 0:\n",
    "            f, ax = plt.subplots(1,5)\n",
    "            for j in range(5):\n",
    "                ax[j].imshow(G_out[j,:,:,0], cmap = 'gray')\n",
    "                ax[j].axis('off')\n",
    "                ax[j].set_title('Image '+str(j))\n",
    "\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_test = 10\n",
    "\n",
    "img = np.zeros([img_size * num_test, img_size * num_test])\n",
    "\n",
    "z_result = np.random.uniform(-1, 1, size=(num_test**2, n_latent))\n",
    "G_result = sess.run(G, feed_dict = {z: z_result, is_training: False})\n",
    "\n",
    "for i in range(num_test**2):\n",
    "    row_num = int(i/num_test)\n",
    "    col_num = int(i%num_test)\n",
    "    \n",
    "    img[row_num * img_size : (row_num + 1) * img_size, (col_num) * img_size : (col_num + 1) * img_size] = G_result[i,:,:,0]\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(img, cmap='gray')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.mkdir(save_path)\n",
    "\n",
    "Saver.save(sess, save_path + \"/model/model\")\n",
    "print(\"Model is saved in {}\".format(save_path + \"/model/model\"))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}