02_GAN_MLP_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GAN MLP MNIST\n",
    "\n",
    "This notebook is for implementing `Generative Adversarial Network(GAN)` with [Tensorflow](https://www.tensorflow.org). <br>\n",
    "Network architecture will be `Multi-layer perceptron` and [MNIST data](http:https://yann.lecun.com/exdb/mnist/) will be used. \n",
    "\n",
    "Reference: [hwalsuklee's Github](https://github.com/hwalsuklee/tensorflow-generative-model-collections)"
   ]
  },
  {
   "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 = 'GAN'\n",
    "\n",
    "img_size   = 28\n",
    "data_size  = img_size**2\n",
    "\n",
    "batch_size = 256\n",
    "num_epoch  = 150\n",
    "\n",
    "n_hidden = 256\n",
    "n_latent = 100\n",
    "\n",
    "learning_rate_g = 2e-4\n",
    "learning_rate_d = 2e-4\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_CycleGAN_Horse2Zebra/model/model\" "
   ]
  },
  {
   "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, 784)\n",
      "y_train shape: (60000,)\n",
      "x_test shape: (10000, 784)\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",
    "x_train = np.reshape(x_train, [-1, data_size])\n",
    "x_test  = np.reshape(x_test, [-1, data_size])\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, reuse=False):\n",
    "    with tf.variable_scope('Generator', reuse=reuse):\n",
    "        xavier_init = tf.contrib.layers.xavier_initializer()\n",
    "        \n",
    "        # First hidden layer\n",
    "        w1 = tf.get_variable('w1', [x.get_shape()[1], n_hidden], initializer=xavier_init)\n",
    "        b1 = tf.get_variable('b1', [n_hidden], initializer=xavier_init)\n",
    "\n",
    "        h1 = tf.nn.leaky_relu(tf.matmul(x,w1)+b1)   \n",
    "        \n",
    "        # Second hidden layer\n",
    "        w2 = tf.get_variable('w2', [h1.get_shape()[1], n_hidden], initializer=xavier_init)\n",
    "        b2 = tf.get_variable('b2', [n_hidden], initializer=xavier_init)\n",
    "\n",
    "        h2 = tf.nn.leaky_relu(tf.matmul(h1,w2)+b2)   \n",
    "        \n",
    "        # Output layer\n",
    "        w3 = tf.get_variable('w3', [h2.get_shape()[1], data_size], initializer=xavier_init)      \n",
    "        b3 = tf.get_variable('b3', [data_size], initializer=xavier_init)\n",
    "        \n",
    "        output = tf.sigmoid(tf.matmul(h2, w3)+b3)\n",
    "        \n",
    "        return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Discriminator(x, reuse=False):\n",
    "    with tf.variable_scope('Discriminator', reuse=reuse):\n",
    "        xavier_init = tf.contrib.layers.xavier_initializer()\n",
    "\n",
    "        # First hidden layer\n",
    "        w1 = tf.get_variable('w1', [x.get_shape()[1], n_hidden], initializer=xavier_init)\n",
    "        b1 = tf.get_variable('b1', [n_hidden], initializer=xavier_init)\n",
    "\n",
    "        h1 = tf.nn.leaky_relu(tf.matmul(x,w1)+b1)\n",
    "        \n",
    "        # Second hidden layer\n",
    "        w2 = tf.get_variable('w2', [h1.get_shape()[1], n_hidden], initializer=xavier_init)\n",
    "        b2 = tf.get_variable('b2', [n_hidden], initializer=xavier_init)\n",
    "\n",
    "        h2 = tf.nn.leaky_relu(tf.matmul(h1,w2)+b2)\n",
    "              \n",
    "        # Output layer\n",
    "        wo = tf.get_variable('wo', [h2.get_shape()[1], 1], initializer=xavier_init)      \n",
    "        bo = tf.get_variable('bo', [1], initializer=xavier_init)      \n",
    "        \n",
    "        logit = tf.matmul(h2, wo)+bo\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):\n",
    "    # Generator\n",
    "    G = Generator(z)\n",
    "    \n",
    "    # Discriminator\n",
    "    D_logit_real, D_out_real = Discriminator(x)\n",
    "    D_logit_fake, D_out_fake = Discriminator(G, reuse=True)\n",
    "    \n",
    "    # get loss\n",
    "    d_loss = -(tf.reduce_mean(tf.log(D_out_real + 1e-8)) + tf.reduce_mean(tf.log( (1-D_out_fake) + 1e-8)))\n",
    "    g_loss = -tf.reduce_mean(tf.log(D_out_fake + 1e-8))\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, data_size])\n",
    "x_normalize = (tf.cast(x, tf.float32)) / (255.0)\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) \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",
    "train_step_d = tf.train.AdamOptimizer(learning_rate_d).minimize(d_loss, var_list=trainable_variables_d)\n",
    "train_step_g = tf.train.AdamOptimizer(learning_rate_g).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": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 / G Loss: 6.67235 / D Loss: 0.04604\n"
     ]
    },
    {
     "data": {
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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: 2 / G Loss: 8.00006 / D Loss: 0.02887\n",
      "Epoch: 3 / G Loss: 8.76780 / D Loss: 0.00048\n",
      "Epoch: 4 / G Loss: 8.06859 / D Loss: 0.00077\n",
      "Epoch: 5 / G Loss: 9.17308 / D Loss: 0.00073\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: 8.68714 / D Loss: 0.00098\n",
      "Epoch: 7 / G Loss: 9.28557 / D Loss: 0.00015\n",
      "Epoch: 8 / G Loss: 7.66794 / D Loss: 0.00209\n",
      "Epoch: 9 / G Loss: 9.78227 / D Loss: 0.00094\n",
      "Epoch: 10 / G Loss: 11.19008 / D Loss: 0.00920\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: 10.12480 / D Loss: 0.00069\n",
      "Epoch: 12 / G Loss: 10.29093 / D Loss: 0.00061\n",
      "Epoch: 13 / G Loss: 8.70315 / D Loss: 0.00108\n",
      "Epoch: 14 / G Loss: 9.17738 / D Loss: 0.00197\n",
      "Epoch: 15 / G Loss: 9.82064 / D Loss: 0.00207\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: 10.71913 / D Loss: 0.00036\n",
      "Epoch: 17 / G Loss: 11.19013 / D Loss: 0.00034\n",
      "Epoch: 18 / G Loss: 8.32104 / D Loss: 0.00170\n",
      "Epoch: 19 / G Loss: 11.98552 / D Loss: 0.00083\n",
      "Epoch: 20 / G Loss: 12.25039 / D Loss: 0.00038\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: 11.14770 / D Loss: 0.01826\n",
      "Epoch: 22 / G Loss: 13.21797 / D Loss: 0.00103\n",
      "Epoch: 23 / G Loss: 9.02286 / D Loss: 0.00356\n",
      "Epoch: 24 / G Loss: 9.68970 / D Loss: 0.00270\n",
      "Epoch: 25 / G Loss: 9.61295 / D Loss: 0.00727\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: 12.50169 / D Loss: 0.02954\n",
      "Epoch: 27 / G Loss: 11.21773 / D Loss: 0.00128\n",
      "Epoch: 28 / G Loss: 11.04549 / D Loss: 0.00533\n",
      "Epoch: 29 / G Loss: 11.58339 / D Loss: 0.00706\n",
      "Epoch: 30 / G Loss: 11.26721 / D Loss: 0.00632\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: 11.59486 / D Loss: 0.00445\n",
      "Epoch: 32 / G Loss: 9.30791 / D Loss: 0.02535\n",
      "Epoch: 33 / G Loss: 8.42685 / D Loss: 0.00695\n",
      "Epoch: 34 / G Loss: 11.42566 / D Loss: 0.00145\n",
      "Epoch: 35 / G Loss: 10.75725 / D Loss: 0.00722\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: 12.75472 / D Loss: 0.00050\n",
      "Epoch: 37 / G Loss: 12.27736 / D Loss: 0.00807\n",
      "Epoch: 38 / G Loss: 8.92298 / D Loss: 0.01513\n",
      "Epoch: 39 / G Loss: 10.59778 / D Loss: 0.04898\n",
      "Epoch: 40 / G Loss: 9.86501 / D Loss: 0.00546\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: 11.10253 / D Loss: 0.00186\n",
      "Epoch: 42 / G Loss: 13.24591 / D Loss: 0.03297\n",
      "Epoch: 43 / G Loss: 12.66667 / D Loss: 0.00066\n",
      "Epoch: 44 / G Loss: 8.62557 / D Loss: 0.01842\n",
      "Epoch: 45 / G Loss: 11.94641 / D Loss: 0.03305\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: 14.02052 / D Loss: 0.06527\n",
      "Epoch: 47 / G Loss: 10.61342 / D Loss: 0.03846\n",
      "Epoch: 48 / G Loss: 12.30398 / D Loss: 0.08951\n",
      "Epoch: 49 / G Loss: 9.69384 / D Loss: 0.00660\n",
      "Epoch: 50 / G Loss: 8.66619 / D Loss: 0.02525\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: 51 / G Loss: 10.54139 / D Loss: 0.00408\n",
      "Epoch: 52 / G Loss: 9.99116 / D Loss: 0.00464\n",
      "Epoch: 53 / G Loss: 9.18219 / D Loss: 0.01391\n",
      "Epoch: 54 / G Loss: 9.69375 / D Loss: 0.02699\n",
      "Epoch: 55 / G Loss: 9.52885 / D Loss: 0.02063\n"
     ]
    },
    {
     "data": {
      "image/png": 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FaCiGuhLUwibJdnLEEUcAcZ36Tz75BIjVuxZTPvbYY3V+zfYl1aWMTmUXS4FWInsvC3HkstkZZ5wBtJwT0P1Sap5I2bSKiKrEiKXW9096VKZKqaqhomXtFP2VbisPP/wwADfffDMAU6dOBSobyeNx5I7jOA1OLhR5JdEsu34r61FPYT09k/Vd8prZ2RqKldZqMEKRQKqKWAlqbZPevXsD8QLEUory+V500UVAvMCuRmzTpk0D4rwDRTWpnWy33XYAjBw5Emhec0TzNmpbrZEFRV4urd0DqjypEVAHj5WJ+0e5KuoLVKdFeRqff/45EFeA1Pev7HHNz7z99tst9l1qhapSuCJ3HMdpcBpGkcvXLT9Wa6hKouKMFROq9fo64tfKiqIo10eXXi1JNpQCSc/mt4d62USrB6nWjlSWkC9c0SlSUenMxvS8wnvvvQfE1RIhXp1qwoQJzfZZiiwo8nKjcU488UQgrjkzbNgwoGXNlUrU4K73/aNoLlXVFFLP6TZS6prVt6qGe3IFq3TkS2u4Inccx2lwcqfIk1meUP7qPcrGGj58OBCvJKTrlw9UUQrJ/ba1dkKtFUVra3S29rlS1HKFk9RxO2wTrXCuNV11LTNmzABg1KhRADz22GNAPEJLzo0k+fTTT4E4gmF58wflrmtZD0Wu60uPTFpDft9i/l6IIzcmTpwIwOmnn960bb/99mvTsWrdVhRlomsUqs2u7/HKK68EYMiQIUDcZ6SjXsaNGwfEmZ6ymdYGhXhNgGSOzPJwRe44jtPg5E6RJ/YFlB9RovhwVbcbMGBAs/3ceeedAJx99tlA23xY6dntevn4pkyZAsRrc5ZCftFS6lPIF9hWf14x6u33VJSARlr6zsodbZXTzhS9oIgZ5SKUOkYWfORlHBOIfei33HILEFcNFcqs1kilI/Hk9W4r6VWSpLzVZvr06QPApEmTin5ecyWq6a7RSnJNXNWM0j2obGrZMY0rcsdxnAYnF7VWiiE/ZFqZ6/fWW28NxD5R+en0BJQyU9yw6jBre7Fs0lK0pmyrjdTmcccdB8RPdVXyU8SBshul3NNIOSoCqBJKPCtIPQ0ePBiI1bK+d7WXNFoLtpx5CMWWq9573759O3raVeeLL74A4pW0FJWj6x06dCgQX7f8w4rgUGan5hzyXItGils5Ba+//joAY8aMAeJaPhqVqM5TOtv8hBNOAOIaLFrFbO7cuU3Hkj1175ZS4uXiitxxHCfneEfuOI6Tc3I/2SmXiRaS0LAnPVGnSU4NnxQSpGD9HXbYAajMMk31mqyRS6RUQScNDeWCSaOEBaUgV9JlVO8JLKGJK7kQ1E7SS/tpmPzcc88B0L9//7L3Xa7dsjDZqdT6sWPHAnESjCb0Jk+eDMTLBipMUyUtlFCX53IOaeTukEtEi86MHz8eiBPlVEgsvZi5FjhRCW21NX2ucN5tOief7HQcx2lwMj3ZmV5qCeInf48ePYB4QkEhX1JWelpqH3oaKhRIxZKkSJRoVI+FU9tDsclYTUAp+UkLTSukspQS1yhGE1tZnbBKh3m2h/T3m17qTbZUokapCUuFGm611VZNr6lo0uzZs9t9frVGJQb22GOPots14tUEoL6DXr16AZVV4pWkrUWpkugz+j41qlc/pL5E90laXffr1w+IRzEa9SVHyhq9Vaq/cUXuOI6Tc3LnI5dyluLW/+liRgoFU5EfXeeDDz4INE8hhsoq8Xr7+NJIIShcSv5NJSGkFyiuBlmzSVuRz1zp3BrJJRcg0fJo5ZIFH3kWyXtbERq1KNT5+eefb7GtXNxH7jiO0+DkTpGXQokKWqoq7RvTdcpvpQiNtqb6l0NWFIUKPMm/WU+yYpNKfd+V8Ne7Ii9OVtqKVLMKptUTV+SO4zgNTsMo8uUcE6is4m6NrCiKLOE2aYkr8uJ4W2mJK3LHcZwGJ9Nx5JWglkrccRynHrgidxzHyTk19ZE7juM4lccVueM4Ts7xjtxxHCfneEfuOI6Tc7wjdxzHyTnekTuO4+Qc78gdx3FyjnfkjuM4Occ7csdxnJzjHbnjOE7O8Y7ccRwn53hH7jiOk3O8I3ccx8k53pE7juPkHO/IHcdxco535I7jODnHO3LHcZyc4x254zhOzvGO3HEcJ+d4R+44jpNzvCN3HMfJOd6RO47j5BzvyB3HcXKOd+SO4zg55/8BxU7MD632aRAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 56 / G Loss: 8.09355 / D Loss: 0.09195\n",
      "Epoch: 57 / G Loss: 8.30151 / D Loss: 0.07570\n",
      "Epoch: 58 / G Loss: 7.69339 / D Loss: 0.03207\n",
      "Epoch: 59 / G Loss: 6.67812 / D Loss: 0.04719\n",
      "Epoch: 60 / G Loss: 6.43709 / D Loss: 0.04125\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: 61 / G Loss: 6.80036 / D Loss: 0.16943\n",
      "Epoch: 62 / G Loss: 7.41015 / D Loss: 0.13979\n",
      "Epoch: 63 / G Loss: 6.92441 / D Loss: 0.09361\n",
      "Epoch: 64 / G Loss: 6.77877 / D Loss: 0.15114\n",
      "Epoch: 65 / G Loss: 6.42998 / D Loss: 0.17788\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: 66 / G Loss: 6.91963 / D Loss: 0.08800\n",
      "Epoch: 67 / G Loss: 4.88726 / D Loss: 0.12640\n",
      "Epoch: 68 / G Loss: 5.26380 / D Loss: 0.17082\n",
      "Epoch: 69 / G Loss: 5.88291 / D Loss: 0.12115\n",
      "Epoch: 70 / G Loss: 5.26559 / D Loss: 0.12216\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: 71 / G Loss: 5.65261 / D Loss: 0.20106\n",
      "Epoch: 72 / G Loss: 6.01813 / D Loss: 0.06644\n",
      "Epoch: 73 / G Loss: 5.43242 / D Loss: 0.17817\n",
      "Epoch: 74 / G Loss: 5.36827 / D Loss: 0.10921\n",
      "Epoch: 75 / G Loss: 5.84803 / D Loss: 0.21597\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: 76 / G Loss: 5.08403 / D Loss: 0.11638\n",
      "Epoch: 77 / G Loss: 4.94116 / D Loss: 0.26326\n",
      "Epoch: 78 / G Loss: 4.43049 / D Loss: 0.13590\n",
      "Epoch: 79 / G Loss: 3.59281 / D Loss: 0.35211\n",
      "Epoch: 80 / G Loss: 5.05556 / D Loss: 0.21379\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: 81 / G Loss: 4.41556 / D Loss: 0.33797\n",
      "Epoch: 82 / G Loss: 4.48325 / D Loss: 0.27858\n",
      "Epoch: 83 / G Loss: 5.20034 / D Loss: 0.32856\n",
      "Epoch: 84 / G Loss: 4.81890 / D Loss: 0.25065\n",
      "Epoch: 85 / G Loss: 4.35934 / D Loss: 0.22426\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: 86 / G Loss: 4.23953 / D Loss: 0.26126\n",
      "Epoch: 87 / G Loss: 4.88443 / D Loss: 0.27263\n",
      "Epoch: 88 / G Loss: 4.58550 / D Loss: 0.22743\n",
      "Epoch: 89 / G Loss: 4.20046 / D Loss: 0.21780\n",
      "Epoch: 90 / G Loss: 4.26139 / D Loss: 0.22009\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: 91 / G Loss: 4.47031 / D Loss: 0.17267\n",
      "Epoch: 92 / G Loss: 5.07402 / D Loss: 0.26285\n",
      "Epoch: 93 / G Loss: 3.52038 / D Loss: 0.28020\n",
      "Epoch: 94 / G Loss: 3.80650 / D Loss: 0.39200\n",
      "Epoch: 95 / G Loss: 3.60964 / D Loss: 0.28431\n"
     ]
    },
    {
     "data": {
      "image/png": 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VN1w50co2UDxYtVRUD0F/py2vFOvM1pBoD5WM8WWrOUohlaoBVWqUWjzooIOAGAtXLZWPPvqoyTkUL1at9rfeeguAbbfdFog1xmXD9ijyesfIlXkj5Tl9+nQANt10UyBmQymDQcfJI1J2huYLKrEJcx5i5H/EjTfeCMTaRlpLsNtuuwHRm6vkptS17itZ7ypba13rKVTDXV6J7k0hxa4x6bbbbmty3vbgMXLHcZwGJzlF3lqUraIMDa1yVIxcO8VUkmoqCqmA0v+bFLSUg2bXNVuerUio2J8UuOolK2NH+eL3338/EFfNqg5NW6i3Ii85NxCzLJ588skm70txajWe5g00r1DJeYI8K3Kt01DcOOuhSKHLW6skeekrQvdctsphNkb+wgsvALF+fyXXHLgidxzHaXBynbXSHlQrQopLdRSU26nYXmpkc1oh7naj1XjaNUk599m9OZWFIpso9peN/w4cOBBoWvUwdeTJqNZKFuXQC9km5Yyd1iCVqd2gsn1HVUVV170joLmkrbbaCihX4sqIy+7AVcvVv67IHcdxEqdhFbn26FQ8WE9H1e5OPf+3tDqjZstVqU9t1XflAB9++OFA9FaEqruVrtyEqEIrsdo1L7QUt5TKymYsjBgxoibXlRe0QlorXrMoiyUvtYhqgeqSa7VrFq2grmfFVFfkjuM4idNwWSv77rsvEHeEUf6wFEZpjZJqUYtZ99I4nVSkdg9S9cPsTifKalGuvWLrqnhXzb6Qt0yEbOxXcw+lOy81d1wlyVPWiu4TrejU/Iq8uL59+wK1mVvKS18599xzATjjjDOA6KFqpae8Oin17F6elcSzVhzHcRqchomR6yk5ZswYoLxWcEtZCqlSGuNVlol26VYNbcXMVYNF6lLxzSFDhgDpzxe0Bs0PyBbZnF+RravRqChHWquDJ0yYAEQvbtSoUUC6WV5tQX2hf//+QLy/stkqWrlZTSW+qLgidxzHSRwfyB3HcRKnYUIrmshTSCVbFL7RFnSUhlYUJtBkp8ICL7/8MhDLFGi5tTaoVhGtjsTgwYOBuNWblpjLbVYJB7nVjYpKHKsPXH311UBML8zb5uO1IFs8a+bMmUD5RLjCcyqpnQdckTuO4yRO8opcT9EnnngCiBMVeoq2tIFAKizKBgbyQoYPHw5EtaVtybSsWgt/OiKaDNemGZMmTQJi+QF5OD169ADS3kx4UVCxOG2iIGWuzUY0CdyR0D2mzWbkySo1Uwt/9ttvvybH5wFX5I7jOImTvCIXKjOqp+SgQYOA9NPIWnrqly5UURlbbWOmgkZ9+vQBarMIKu9ojuTaa68FYrxz2LBhQJxf6NWrF1Dbgke1RP1G3ps8V22ocd111zU5vpKlWPOObCMPV6mZ2rxc5ZxlM1fkjuM4TsVIfom+sg20eYJin/369QPqoySqucRYqqG08FXnzp0BmDx5MgAjR44E4ryBNh6uZ6GjvCy7zqLYeT2ymmq1RL9bt24Lf9bilezWgSK7/Lwe1KuvyE7aXGT8+PFA3EZSZQvqgS/RdxzHaXCSV+TZzYilrOqZN14LRVG6sYTaqv+lVJUUeB5ieXlV5PUkT0Wz8oT3lXJckTuO4zQ4ySvyPFIvRbEoOef1wlVWOa7Im8f7SjmuyB3HcRqcmipyx3Ecp/K4Inccx0kcH8gdx3ESxwdyx3GcxPGB3HEcJ3F8IHccx0kcH8gdx3ESxwdyx3GcxPGB3HEcJ3F8IHccx0kcH8gdx3ESxwdyx3GcxPGB3HEcJ3F8IHccx0kcH8gdx3ESxwdyx3GcxPGB3HEcJ3F8IHccx0kcH8gdx3ESxwdyx3GcxPGB3HEcJ3F8IHccx0kcH8gdx3ESxwdyx3GcxPl/bsXjZTux6hEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 96 / G Loss: 3.72615 / D Loss: 0.24692\n",
      "Epoch: 97 / G Loss: 3.65713 / D Loss: 0.31691\n",
      "Epoch: 98 / G Loss: 3.52077 / D Loss: 0.34192\n",
      "Epoch: 99 / G Loss: 3.76807 / D Loss: 0.40834\n",
      "Epoch: 100 / G Loss: 3.67998 / D Loss: 0.36006\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: 101 / G Loss: 3.71948 / D Loss: 0.23953\n",
      "Epoch: 102 / G Loss: 3.80904 / D Loss: 0.33212\n",
      "Epoch: 103 / G Loss: 3.42375 / D Loss: 0.35181\n",
      "Epoch: 104 / G Loss: 3.56343 / D Loss: 0.33837\n",
      "Epoch: 105 / G Loss: 3.43458 / D Loss: 0.27922\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: 106 / G Loss: 3.62017 / D Loss: 0.40772\n",
      "Epoch: 107 / G Loss: 3.28994 / D Loss: 0.47482\n",
      "Epoch: 108 / G Loss: 3.16717 / D Loss: 0.33410\n",
      "Epoch: 109 / G Loss: 2.89289 / D Loss: 0.59633\n",
      "Epoch: 110 / G Loss: 2.76782 / D Loss: 0.63264\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: 111 / G Loss: 2.97070 / D Loss: 0.43250\n",
      "Epoch: 112 / G Loss: 3.49191 / D Loss: 0.47944\n",
      "Epoch: 113 / G Loss: 3.48494 / D Loss: 0.44871\n",
      "Epoch: 114 / G Loss: 3.49943 / D Loss: 0.34984\n",
      "Epoch: 115 / G Loss: 2.71638 / D Loss: 0.38446\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: 116 / G Loss: 3.45061 / D Loss: 0.47355\n",
      "Epoch: 117 / G Loss: 3.48235 / D Loss: 0.46563\n",
      "Epoch: 118 / G Loss: 3.10297 / D Loss: 0.38178\n",
      "Epoch: 119 / G Loss: 3.20176 / D Loss: 0.42922\n",
      "Epoch: 120 / G Loss: 3.47643 / D Loss: 0.33566\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: 121 / G Loss: 2.87837 / D Loss: 0.35164\n",
      "Epoch: 122 / G Loss: 2.93415 / D Loss: 0.43168\n",
      "Epoch: 123 / G Loss: 2.72580 / D Loss: 0.49909\n",
      "Epoch: 124 / G Loss: 3.22514 / D Loss: 0.37010\n",
      "Epoch: 125 / G Loss: 2.74655 / D Loss: 0.59140\n"
     ]
    },
    {
     "data": {
      "image/png": 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LojprLZZK4orccRwn41glVZaZle1kykKQn1jrIAjVUwpE0fty5AGHEBY5kXRRbaIIue5Hjhy54JgyWK666iogroOirBRF4ZMqSTbTRrt6Lh+fMjhK0UbKYZPGoBiD1JQydoSyV5QfvdlmmwGxvZRy5LY4NoHS2EXtR7+p/MFS4MnYkd6v2Y+FI0GIKwdqVUm1naa0mWq3lZtvvhmIaxA9+eSTQBzBKlNHI1it06NRm2xaShZmE1fkjuM4GadmFLn+BeUfVv64kMIo3FS5XJRDUSiPXL7/wvUzdEz+W+WeLyxvV2pLflHN+tOoRoq8FFRbZQnZRnWT7ZRzr9l5ysmXTTTDsZS+32oo8uRaMYq9CNlFPm/5j++++26g7tr1mgmreEopYghpaSsF5wBi20m2AT3X8XLM7HRF7jiOU+NkPmtFu3po55aGVKTWKc8qytMtnJGWPKaVHQvV+l+h3Hp9Tj51+dwnT57c+AKnFClSZS3Jz6nVLrUv64EHHgjAbbfdBjSsxrJA4b61yXqorSRjRRqpyy8sJS60IqZmuEqh1yKyRUO/vWyqUZ3sXUmbuCJ3HMfJOJn1kSd3uG7IP6WZaNopvhKkzceXRH5h+X/1XH7Q5CzYUpAWm0iRK8tCa7Mrv1qr9l1++eVAzEjQdVLKmb+V8pEXXhN6vLBsLY3SNLNT2T36nHaf0gzZUpKWtrIwZBvNKldmk2xTStxH7jiOU+Nk1kcuZTV+/Hgg5ge3a9cOiLOwDj/88CqULt1IgSuDR2pTa7nXMsn16RVTUYxFK0TKRmpXUl9ZpHDUnczAaEiZJ+ctCM3PKIcSzxpJ22llyGrgitxxHCfjpEqRJ9eK1rrZWn0Pok9c++ppxTEpDPmnlA+cxSyDcvPWW28BcVSj6LryyWsRtS2tGSKlLeWt17X/YnJ3+FohOZ9C97pOlHGhHXSSM0FPP/30yhU25ShjTsg7UA1ckTuO42Qc78gdx3EyTqpcKxq+afpw586dgbg0JsR0MQ0RNWSeMGEC4EGYv0LBGA2fZUO5D9K4SW6pkItAG0kng3jaNEATZNK0LVk5kCtFwV+52bSBhFLq5MrUBhS6d2L6rq6r7t27V60srsgdx3EyTqoUeTLYefLJJwNRhRei4KbQpglOwyiApYCwbKhp1rW0gUQSKUu1rWT7UfCzviUQahmNPDRK0zWnpVqVnqnRWi22jcaiNqRU1mq2HVfkjuM4GSdVilz/9koJ69ChA1CcQphchrZWtuAqJ1LamnYtf6gmufTr1686Basg2p5LaNnVsWPHAnGLwCWN5HK2ipcoDbN///4ADB8+HHBFDjG+MmDAAABOPPFEIG6h2KVLl4qXyRW54zhOxkn1ollSjpoYBNCrVy8Axo0bB0RFlSbStuiPRjGDBw8G4ibNAwcOBOCJJ54Ayjt5qto2kQ06deoExCyncmz1t6hUY2OJhpB9WrduDcQJUdpYYsyYMUBlsnmq3VYWhuIJ2mxEbUrLg2g7wFL2rb5oluM4To2TakWeVdKqKLScQbdu3QAYNWoUUBlVmlabVJM0KfI0kfa2omyVrl27AnGTkt69ewPw7LPPAq7IHcdxnMXAFXkZSLuiqAZuk7q4Iq8fbyt1cUXuOI5T41RUkTuO4zilxxW54zhOxvGO3HEcJ+N4R+44jpNxvCN3HMfJON6RO47jZBzvyB3HcTKOd+SO4zgZxztyx3GcjOMdueM4TsbxjtxxHCfjeEfuOI6TcbwjdxzHyTjekTuO42Qc78gdx3EyjnfkjuM4Gcc7csdxnIzjHbnjOE7G8Y7ccRwn43hH7jiOk3G8I3ccx8k43pE7juNkHO/IHcdxMo535I7jOBnn/wECD0BR0PT0xAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 126 / G Loss: 2.23377 / D Loss: 0.54434\n",
      "Epoch: 127 / G Loss: 2.56489 / D Loss: 0.49231\n",
      "Epoch: 128 / G Loss: 2.51639 / D Loss: 0.61378\n",
      "Epoch: 129 / G Loss: 2.93069 / D Loss: 0.40005\n",
      "Epoch: 130 / G Loss: 3.02428 / D Loss: 0.45549\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: 131 / G Loss: 2.58594 / D Loss: 0.49273\n",
      "Epoch: 132 / G Loss: 2.83252 / D Loss: 0.47270\n",
      "Epoch: 133 / G Loss: 2.82994 / D Loss: 0.33703\n",
      "Epoch: 134 / G Loss: 2.82337 / D Loss: 0.50046\n",
      "Epoch: 135 / G Loss: 2.76491 / D Loss: 0.42704\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: 136 / G Loss: 2.44113 / D Loss: 0.58643\n",
      "Epoch: 137 / G Loss: 2.78043 / D Loss: 0.50638\n",
      "Epoch: 138 / G Loss: 2.93066 / D Loss: 0.56899\n",
      "Epoch: 139 / G Loss: 2.72635 / D Loss: 0.45649\n",
      "Epoch: 140 / G Loss: 2.32083 / D Loss: 0.45475\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: 141 / G Loss: 2.54252 / D Loss: 0.42625\n",
      "Epoch: 142 / G Loss: 2.51941 / D Loss: 0.40433\n",
      "Epoch: 143 / G Loss: 2.82983 / D Loss: 0.51171\n",
      "Epoch: 144 / G Loss: 2.20074 / D Loss: 0.45445\n",
      "Epoch: 145 / G Loss: 2.45398 / D Loss: 0.51286\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: 146 / G Loss: 2.22986 / D Loss: 0.49986\n",
      "Epoch: 147 / G Loss: 2.46172 / D Loss: 0.46607\n",
      "Epoch: 148 / G Loss: 2.88607 / D Loss: 0.48938\n",
      "Epoch: 149 / G Loss: 2.57252 / D Loss: 0.41396\n",
      "Epoch: 150 / G Loss: 2.36961 / D Loss: 0.42403\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAABpCAYAAAAnQqjlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvDW2N/gAAE2ZJREFUeJztnXeQVVW2h79lQEyIWcAxYc5pxIyKT1HEgI5aRqqUUhRTaemU5Sg+s4gYUEHLsix0FJhyTJh9KCa0TJjTKIoiiiQFxLjfH/f+et8+3T1A9w1n315fVRfNuemc1fvs+9srbQsh4DiO46TLErU+AcdxHKdt+ETuOI6TOD6RO47jJI5P5I7jOInjE7njOE7i+ETuOI6TOD6RO47jJE7NJ3Izm2xm+9b6PBaGmfUys4/MbL6ZjTezdSv4Wbm3iZl1MLN/Fc81mNleVfjMFOyys5k9bWYzzWy6mY01sy4V/LwUbLK5mb1uZrOKP8+Y2eYV/Lzc26QUM7u4eA+1+pxrPpGngJmtBjwA/ANYBXgdGF3Tk8oHLwLHAdNqfSI5YmXgdmA9YF3gJ+CuWp5QDpgKHEHh3lkNeBi4v6ZnlBPMrDvwN+DbtrxPriZyM+tvZi+Z2TAzm21mn5vZrsXjU8zsezM7seT5fczsLTP7sfj44Mz7nWBmX5rZDDP7R+k3tZktYWZ/N7P/FB8fY2artHBq/YD3QwhjQwgLgMHANma2aWUs0egacmmTEMKvIYQbQggvAn9U0gbNkWO7PF4cJz+GEOYDw4HdKmiK0mvIq01mhxAmh0IZuVEYLxtWzhKNriGXNinhFuAC4Ne2XGeuJvIiPYB3gFWBf1L45v4rhT/8ccBwM1uh+Nx5wAlAZ6APMNDMDoXCcg64FTgW6AKsBHQr+ZwzgEOBnkBXYBYFozbHFsAk/SeEMA/4T/F4NcijTfJACnbZE3i/dZfXKnJrEzObDSwAbgaubON1Lg65tImZ/Q34JYTwWJuvMIRQ0x9gMrBv8ff+wKclj20FBGDNkmMzgG1beK8bgGHF3y8G7it5bDkK33r6rA+BXiWPdwF+A5Zq5n3vBK7OHHsJ6N9ebZL5jK+BvXysNPmMrYGZwB5uk4bnLQ+cBvRpzzYBVgQ+BdbLnnNrfpYif3xX8vvPACGE7LEVAMysB3A1sCXQAVgGGFt8Xldgil4UQphvZjNK3mdd4N9m9mfJsT+ANYFvMuc0F+iUOdaJgv+zGuTRJnkgt3Yxsw2Bx4GzQggvLPaVtZ7c2qT4PvPMbAQw3cw2CyF8v3iX1yryaJPBwKgQwuRWXVGGPLpWFod/Ugic/CWEsBIwgoIPDgrBg7X1RDNblsLSSkwBDgghdC756RhCaG4Qvg9sU/JeywPdqe6SeVGplk1So2p2sUJG0zPAZSGEURW4lnJRq7GyBAU1221hT6wB1bJJL+BMM5tmZtOAvwBjzOyC1px06hP5isDMEMICM9sJOKbksX8BfYuBjQ4UvgGt5PERwBXFmw4zW93MDmnhc/4NbGlmh5tZRwpLrHdCCB+V+XrKQbVsgpktU7QHQAcz62hm1tLza0xV7GJm3YD/A4aHEEZU4DrKSbVs8j9mtp2ZLWlmnYDrKfiPPyz/JbWZat0/vSio/m2LP1OBU2hlTCr1ifw04H/N7CcKk+sYPRBCeJ9C8OF+Ct+kc4HvgV+KT7mRwjfvU8XXT6QQFGlCCGE6cDhwBYUB2AM4ugLXUw6qYpMiH1NYlnYDniz+XrH8+jZSLbucDGwADDazufqpwPWUg2rZpDNwHzCHQpJAd6B3KGSA5Y1qzSkzQgjT9EPBBTMrhNCqsWJFR3vdU4xKzwY2CiF8UevzyQNuk+ZxuzTFbdKUPNkkdUX+XzGzvma2XNGnfR3wLoXocLvFbdI8bpemuE2akleb1PVEDhxCwfc0FdgIODq0lyVIy7hNmsft0hS3SVNyaZN241pxHMepV+pdkTuO49Q9PpE7juMkTlUrO82sXfhxQgiLnEvtNmmK26R53C5NcZsUcEXuOI6TOD6RO47jJI5P5I7jOInjE7njOE7i+ETuOI6TOD6RO47jJI5P5I7jOImTxx2CyoraY2+22WYAHHDAAQDceuutAPz888+1OTGnpmhc6N9VVy3sD/Djjz8C8PvvvwPwxx9V31faaQdo3JWrRYorcsdxnMSpatOsalZhdejQAYjKavLkyQCstdZaAMybNw+Ayy+/HIChQ4eW7bPzUpm29NJLA7DmmmsCsNdeewHwwAMPAPDLL4V++NVQnXmxyRJLFLTL/vvvD8B5550HwIorrgjA008/DcRxUckVm1d2Nk9exsrCWHLJJQHYdNNNAejWrbBzXf/+/QF44YXCVq0vvfRSk9deccUVAJx88skA/PDDD0DL96JXdjqO49Q5ySryrI8pu1Wkjq+zzjoATJgwAYB11228E9n33xc28ZZqLQe1VhRadVx77bUAHH/88UBcnSy1VOPQiFYr66+/frlPpYFa20R06tQJgClTpuizAFhuueWAOE4US9GqpRKkpMg1ZrLzxWqrrQbAjBmFzeS33HJLoPFYevDBB5t9bUvkZawIreJkA80pQ4YMAWCfffYBYIUVVtA5AfDrr78CMH36dAD+/PPPhve8/fbbgRir0735008/NXsOrsgdx3HqnOSyVrp27QrAyy+/DES/rxRB9hvtu+++A2I2QpannnqqEqdZE/StLv+/1KdoaYP79dZbr9Hj9bzZyKmnngpEn7h8k7LZww8/DMDyyy8PRFtIXbUX5P+VndZee20AdtllFwA6d+4MwOqrrw7AbrvtBkSF3qVLl4b3euSRRwA49thjK33aZUWrtI033hiAc889F4AjjzwSiHG4hXHzzTcD8O233zYck7qfM2cO0FittwZX5I7jOInjE7njOE7iJOFaWWmllRp+P+iggwD44osvAJg9ezYAc+fObfa1cje0FMgbNWpU2c6zVmgJqOWwXCq//fYbAHvuuScAb731FgDXX389AAMHDgRiylPHjh2B+iySWmONNYCYKqZrlq0eeughAMaPHw/AjjvuCMDUqVMB+OCDD4C2L4HzzpVXXgnAHnvsAcDWW28NRNfSmDFjgOhm6N27NwBnnnkmEN0Npe65s88+u9KnXVbkYlSQ8/DDDwdiAFxpvWLBggWN/l122WUb/f+bb74BYNy4cQ2vmT9/PlC+8eSK3HEcJ3GSUOQKCADcfffdANx3331Ay+k6YplllgFialAWKbDUkFqAWOCjb3cF7rSSyQYvBw0aBEQ1uvvuuwNwwgknADBy5MhKnXbN2XDDDYFoK6UXKiD1+eefA1GVPfroowAMGzYMgBtvvLF6J1sFdJ0qiDvttNOAuLp7/PHHAZg0aRIAI0aMAKJCf+655wB45513ANh+++2bfIZWhnlHttB9o9W6ArkK/Op+mjlzJgADBgwA4mpEK16NsYkTJwKNEy7KvbJzRe44jpM4SSjyUqSgFrVQ47HHHmv2uBLyU1EL/w35/6XSZ82aBbScRqjn3XLLLQAcc8wxABx11FFAfSryCy64AICVV14ZiKsY2eCNN94Aon/z0ksvBaIK69OnDwA33XQTkH6KpsaAWhL07NkTiGPnpJNOAmJ6rq43e98pBqVCIK0GX3nllYbntJT6mzcUI+rbty8QVfOHH34IxGuU8lbBnVJWtepX/EA2njZtWqP3qwSuyB3HcRInOUW+qOhbU0ojyymnnFLN0yk7pd/uWb+vlEMWKQRluRx22GGNjstWKvJQaXHKyO+pbAtlC7z55ptA9F9KWQoVvqyyyioAbLXVVkBsh6wsltTQ316Ke6eddgJiptKzzz4LRJWZRfZUQZBiClKzikkdfPDBDa9JJdNHY2CDDTYAYraJ5hLdJ4rLyYeeLQySLbQ6qUYWmCtyx3GcxKk7Ra5vzffee6/Zx5988slqnk7FKC23l8qUj+61114Dmkbbb7vtNgB23XVXIKozIdvJR96vX7+KnHs1USn0ddddB8TVizINskp8m222AWKJvtDGE1qtpERp3vPo0aOB2DxO9lHbXrVXzaLYguIpyt7RmNF4/Oijj4A0Y0/K1Hn++ecBOP3004HYWE/xA9WsKIf+3XffBeIqRGNKq/5qxFNckTuO4yRO3SryjTbaqNnHlX1QT1x00UUA3HDDDUCsXlSTHqkwKQipJSkQNRZTK996spGU57bbbgvErBQpbNlA42b48OFAzBHOrlrUwlTPT8H/e9VVVzX8rviJfP8777wzEFVlFtUYZJuJyW5CdlATuxSzepSRoywwXaMqo9UQTKsTZa3ce++9QMyh16pf2S6uyB3HcZyFUneKfN999232uCLN9bKZbum3vHKhr7nmGiD6RLV5wqGHHgpEJa4MDil2RdcHDx7c6PXqN/L6669X5iKqgDbZkC9Yqks9e7QNl5S3VnKygTIOlMGg3iOKQ3z88ceVvYBWIH+1/P2KiUBsWaz7IVutqDiLVm/a+EAqNIuU+GWXXQbElUyKbLHFFgCcf/75AHTv3h2Iqy/NHcpS0epO9ReqglXufTXnGlfkjuM4iVM3ilyKSxWbWdQcXj4+RZalKFKMsgtdi/JXpRiyW7opP1ZReP2r7m5Cik4bASjnOgV/sJDSlCqV4pTSVp+ZCy+8EIBNNtkEiNlO8nfKlspW0Ya62h4vj+haL774YiB2vYToE9d9oMrO0t49EMeOMqI0VtRFUmhMqAdLNgsoBeT7vueee4AYY8qi+0JzheosNKb0urfffhuobpzAFbnjOE7i1I0il1LQllRCPnP57lSRJuUgZSJ/cj2gznTZ7ckURc+qL/UdkfqS70+qLCUlLnQtql5VfrO2BFQWi3y/ykb5+uuvgRg3kKq68847gej/rOSmzG1Fq4gzzjgDgOOOO67hMfX9UIZSNp6i+0cVm0888QTQdNUmVXrWWWcB0V6pUJp1o2vbfPPNgaZKWr5uxVU0VtQlUVle2hshuxKuBq7IHcdxEseq6ccxs4p9mFSj/FhS3FIn2X4Iyk7o0aMHAHfddVej92kLIYTmdzluhkraZFGRQleGhhSFVi8L6/m+KFTDJqXVrvp7b7fddgAcccQRQFyhqX+2lOk555wDxMwd9VbRik3+U/nUy8Hi2AQW3S7ZTbbV/wRiHEX54cqRltpU5aZWsDvssAMAY8eOBeL9oapF2aWcMaZqjBVtoAyx6lcbRut+0MbcqgbWKky5+FLwe++9d6PjJ554IgCffPIJUJ05xRW54zhO4iSvyBWFL+1/DLEfgnJB5cOTUldln3ym8ieXwx6pKXLlFn/66adAzFKRbVO0iVSp/JnKA89mrUhxq7pVmQfKE5eqUpfAcqxORKUUeVuQGlX3R3VD1Arn1VdfBaKSr0S2VyXHiq5jwoQJDce6du0KxNiQ9i3VrlGaM2Qb9arX6k7H1aNlyJAhQHm7h7oidxzHqXOSz1rJ7o+nb0ftJaiqK+UHZ3M8pdCyGR7tCfXZkG9cOdTK6EixWk9/3y+//BKIWQpSV1LWUk0aN7169Wr0f8VSunXrBsTsl3pHPnD512W3gQMHAunWXSgDRf2JIPrLFRdRX5lsZabqMJTlokw5zR1a/ZVz1baouCJ3HMdJnOQV+bhx44D4bSo/l/4vJSUf6dVXXw1EJaZ/U+zW1laUyaH+GkL+ZeXF1gNSV7q2BQsWAHElp9XIIYccAjQdD3PmzKnKedYarVyOPvroRseVl69sn1TR3189eCBmo6inkOJrGhPK9Bk6dCgQ42uqHv7ss8+AmNGksVVNXJE7juMkTvKKXFF1+bmU86lvXPmrlO8qX7mqslKsWmwtUiPaOeiOO+5o9LhU6Isvvtjo//VENgNBMRL15540aRIQszbUIVKKtN7Zb7/9gKbVicqRTv1+0d9fFasAffv2BWK21rBhw4C4ilf2llb5Qv1n1O9d8Zha4IrccRwncXwidxzHSZxkXSta+qmsVgEHNf4Xak96ySWXAPDMM88A8NVXX1XlPPOENk9QQYOKYrKpehMnTmx0vB6Ri0CuFQW0VKKuAjJtFpDnJlnlQK4mleLLDafrrpemctkWHhC3A1TxoBIk1LpYttFr1Hxu1KhRANx///1AbTetcUXuOI6TOMkqcikqldqqib5Sf6SwlE6l5vrtufBHKlOlx2r2I5Wi4o/SzXrrHSlOBb+1gbXas44fP742J1ZlVPyl+0NjYtCgQTU7p0qgVWZp+qG2qVM7Zynw7AbTsok20Rg5ciSQjwCwK3LHcZzESV6RZxvZHHjggQD069cPiKXEKi2uRflsXpDCUFsDqRMpDTWQUlFVeymCgVjooo0X1JZA/tBabBZQDfS3V9m54iYqBhs9enRtTqzCKKYGsQ2DtvHr2bMnEOcYNdbTRtRS5HmKIbkidxzHSZzk29jmkby3sVUL0gEDBgAxU0NKXKuYco6NvNukmXMAKqu6atnGVteXbU0gtamYUu/evYHqZmTUaqxoda/mV8rUkTKvpQL3NraO4zh1jivyCpCa+qwGbpOm5GljCeXTS33WMifax0pTXJE7juPUOfUZinccZ7FIdaMIp4ArcsdxnMSpqo/ccRzHKT+uyB3HcRLHJ3LHcZzE8YnccRwncXwidxzHSRyfyB3HcRLHJ3LHcZzE8YnccRwncXwidxzHSRyfyB3HcRLHJ3LHcZzE8YnccRwncXwidxzHSRyfyB3HcRLHJ3LHcZzE8YnccRwncXwidxzHSRyfyB3HcRLHJ3LHcZzE8YnccRwncXwidxzHSRyfyB3HcRLHJ3LHcZzE8YnccRwncf4fNHgiw8aIXcQAAAAASUVORK5CYII=\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",
    "            sampled_z = np.random.uniform(-1, 1, size=(data_x_in.shape[0] , n_latent))\n",
    "\n",
    "            # Run Optimizer!\n",
    "            _, loss_d = sess.run([train_step_d, d_loss], feed_dict = {x: data_x_in, z: sampled_z})\n",
    "            _, loss_g = sess.run([train_step_g, g_loss], feed_dict = {x: data_x_in, z: sampled_z})\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})\n",
    "        output_reshape = np.reshape(G_out, [5, img_size, img_size])\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(output_reshape[j,:,:], 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": 12,
   "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})\n",
    "output_reshape = np.reshape(G_result, [num_test**2, img_size, img_size])\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] = output_reshape[i,:,:]\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
}