DCGAN_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-1-ca0ce77539e4>:9: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
     ]
    }
   ],
   "source": [
    "# Importing all the dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "# Defining the data\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the functions conv2d and max_pool_2x2 to have the same strides, padding\n",
    "def conv2d(x, w):\n",
    "    return tf.nn.conv2d(input=x, filter=w, strides=[1, 1, 1, 1], padding=\"SAME\")\n",
    "\n",
    "def max_pool_2x2(x):\n",
    "    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=\"SAME\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the generator function\n",
    "def generator(z, batch_size, z_dim, reuse=False):\n",
    "    with tf.variable_scope('generator') as scope:\n",
    "        if (reuse):\n",
    "            tf.get_variable_scope().reuse_variables()\n",
    "        g_dim = 64 #Number of filters of first layer of generator \n",
    "        c_dim = 1 #Color dimension of output (MNIST is grayscale, so c_dim = 1 for us)\n",
    "        s = 28 #Output size of the image\n",
    "        s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16) #We want to slowly upscale the image, so these values will help\n",
    "                                                                  #make that change gradual.\n",
    "\n",
    "        h0 = tf.reshape(z, [batch_size, s16+1, s16+1, 25])\n",
    "        h0 = tf.nn.relu(h0)\n",
    "        #Dimensions of h0 = batch_size x 2 x 2 x 25\n",
    "\n",
    "        #First DeConv Layer\n",
    "        output1_shape = [batch_size, s8, s8, g_dim*4]\n",
    "        W_conv1 = tf.get_variable('g_wconv1', [5, 5, output1_shape[-1], int(h0.get_shape()[-1])], \n",
    "                                  initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "        b_conv1 = tf.get_variable('g_bconv1', [output1_shape[-1]], initializer=tf.constant_initializer(.1))\n",
    "        H_conv1 = tf.nn.conv2d_transpose(h0, W_conv1, output_shape=output1_shape, strides=[1, 2, 2, 1], padding='SAME')\n",
    "        H_conv1 = tf.contrib.layers.batch_norm(inputs = H_conv1, center=True, scale=True, is_training=True, scope=\"g_bn1\")\n",
    "        H_conv1 = tf.nn.relu(H_conv1)\n",
    "        #Dimensions of H_conv1 = batch_size x 3 x 3 x 256\n",
    "\n",
    "        #Second DeConv Layer\n",
    "        output2_shape = [batch_size, s4 - 1, s4 - 1, g_dim*2]\n",
    "        W_conv2 = tf.get_variable('g_wconv2', [5, 5, output2_shape[-1], int(H_conv1.get_shape()[-1])], \n",
    "                                  initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "        b_conv2 = tf.get_variable('g_bconv2', [output2_shape[-1]], initializer=tf.constant_initializer(.1))\n",
    "        H_conv2 = tf.nn.conv2d_transpose(H_conv1, W_conv2, output_shape=output2_shape, strides=[1, 2, 2, 1], padding='SAME')\n",
    "        H_conv2 = tf.contrib.layers.batch_norm(inputs = H_conv2, center=True, scale=True, is_training=True, scope=\"g_bn2\")\n",
    "        H_conv2 = tf.nn.relu(H_conv2)\n",
    "        #Dimensions of H_conv2 = batch_size x 6 x 6 x 128\n",
    "\n",
    "        #Third DeConv Layer\n",
    "        output3_shape = [batch_size, s2 - 2, s2 - 2, g_dim*1]\n",
    "        W_conv3 = tf.get_variable('g_wconv3', [5, 5, output3_shape[-1], int(H_conv2.get_shape()[-1])], \n",
    "                                  initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "        b_conv3 = tf.get_variable('g_bconv3', [output3_shape[-1]], initializer=tf.constant_initializer(.1))\n",
    "        H_conv3 = tf.nn.conv2d_transpose(H_conv2, W_conv3, output_shape=output3_shape, strides=[1, 2, 2, 1], padding='SAME')\n",
    "        H_conv3 = tf.contrib.layers.batch_norm(inputs = H_conv3, center=True, scale=True, is_training=True, scope=\"g_bn3\")\n",
    "        H_conv3 = tf.nn.relu(H_conv3)\n",
    "        #Dimensions of H_conv3 = batch_size x 12 x 12 x 64\n",
    "\n",
    "        #Fourth DeConv Layer\n",
    "        output4_shape = [batch_size, s, s, c_dim]\n",
    "        W_conv4 = tf.get_variable('g_wconv4', [5, 5, output4_shape[-1], int(H_conv3.get_shape()[-1])], \n",
    "                                  initializer=tf.truncated_normal_initializer(stddev=0.1))\n",
    "        b_conv4 = tf.get_variable('g_bconv4', [output4_shape[-1]], initializer=tf.constant_initializer(.1))\n",
    "        H_conv4 = tf.nn.conv2d_transpose(H_conv3, W_conv4, output_shape=output4_shape, strides=[1, 2, 2, 1], padding='VALID')\n",
    "        H_conv4 = tf.nn.tanh(H_conv4)\n",
    "        #Dimensions of H_conv4 = batch_size x 28 x 28 x 1\n",
    "\n",
    "    return H_conv4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defiing the Discriminator function\n",
    "def discriminator(x_image, reuse=False):\n",
    "    with tf.variable_scope('discriminator') as scope:\n",
    "        if (reuse):\n",
    "            tf.get_variable_scope().reuse_variables()\n",
    "        #First Conv and Pool Layers\n",
    "        W_conv1 = tf.get_variable('d_wconv1', [5, 5, 1, 8], initializer=tf.truncated_normal_initializer(stddev=0.02))\n",
    "        b_conv1 = tf.get_variable('d_bconv1', [8], initializer=tf.constant_initializer(0))\n",
    "        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\n",
    "        h_pool1 = max_pool_2x2(h_conv1)\n",
    "\n",
    "        #Second Conv and Pool Layers\n",
    "        W_conv2 = tf.get_variable('d_wconv2', [5, 5, 8, 16], initializer=tf.truncated_normal_initializer(stddev=0.02))\n",
    "        b_conv2 = tf.get_variable('d_bconv2', [16], initializer=tf.constant_initializer(0))\n",
    "        h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n",
    "        h_pool2 = max_pool_2x2(h_conv2)\n",
    "\n",
    "        #First Fully Connected Layer\n",
    "        W_fc1 = tf.get_variable('d_wfc1', [7 * 7 * 16, 32], initializer=tf.truncated_normal_initializer(stddev=0.02))\n",
    "        b_fc1 = tf.get_variable('d_bfc1', [32], initializer=tf.constant_initializer(0))\n",
    "        h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*16])\n",
    "        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)\n",
    "\n",
    "        #Second Fully Connected Layer\n",
    "        W_fc2 = tf.get_variable('d_wfc2', [32, 1], initializer=tf.truncated_normal_initializer(stddev=0.02))\n",
    "        b_fc2 = tf.get_variable('d_bfc2', [1], initializer=tf.constant_initializer(0))\n",
    "\n",
    "        #Final Layer\n",
    "        y_conv=(tf.matmul(h_fc1, W_fc2) + b_fc2)\n",
    "    return y_conv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])  # Placeholder for the input images to the discriminator\n",
    "# shape of 'x' is [None, height, width, channels]\n",
    "z_dimensions = 100\n",
    "z = tf.placeholder(tf.float32, shape=[None, z_dimensions])  # Placeholder for the input noise vectors to the generator\n",
    "\n",
    "batch_size = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Dx = discriminator(x)  # Dx will hold the unnormalised discriminator outputs for the real MNIST images\n",
    "Gz = generator(z, batch_size, z_dimensions)  # Gz holds all the generated images\n",
    "Dg = discriminator(Gz, reuse=True)  # Dg will hold the unnormalised discriminator outputs for generated images\n",
    "\n",
    "# The loss functions\n",
    "g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dg, labels=tf.ones_like(Dg)))\n",
    "\n",
    "d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dx, labels=tf.ones_like(Dx)))\n",
    "d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dg, labels=tf.zeros_like(Dg)))\n",
    "d_loss = d_loss_fake + d_loss_real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Once we have our 2 loss functions (d_loss and g_loss), we need to define our optimizers. Keep in mind that the optimizer for the generator network needs to only update the generator’s weights, not those of the discriminator. In order to make this distinction, we need to create 2 lists, one with the discriminator’s weights and one with the generator’s weights. This is where naming all of your Tensorflow variables can come in handy.\n",
    "tvars = tf.trainable_variables()\n",
    "d_vars = [var for var in tvars if 'd_' in var.name]\n",
    "g_vars = [var for var in tvars if 'g_' in var.name]\n",
    "\n",
    "# Optimizers for the discriminator and the generator respectively\n",
    "trainerD = tf.train.AdamOptimizer().minimize(d_loss, var_list=d_vars)\n",
    "trainerG = tf.train.AdamOptimizer().minimize(g_loss, var_list=g_vars)\n",
    "\n",
    "n_epochs = 600 #The higher the number of epochs, the better are the results, 600 is a low value actually\n",
    "\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  1  is now complete, discriminator & generator losses are:  1.3856956 ,  0.69429755\n",
      "Epoch  2  is now complete, discriminator & generator losses are:  1.3844444 ,  0.6954523\n",
      "Epoch  3  is now complete, discriminator & generator losses are:  1.3828034 ,  0.69721925\n",
      "Epoch  4  is now complete, discriminator & generator losses are:  1.3805934 ,  0.69954467\n",
      "Epoch  5  is now complete, discriminator & generator losses are:  1.3774719 ,  0.7029327\n",
      "Epoch  6  is now complete, discriminator & generator losses are:  1.374014 ,  0.70705885\n",
      "Epoch  7  is now complete, discriminator & generator losses are:  1.3688502 ,  0.7129564\n",
      "Epoch  8  is now complete, discriminator & generator losses are:  1.3627725 ,  0.71991855\n",
      "Epoch  9  is now complete, discriminator & generator losses are:  1.3555746 ,  0.7288376\n",
      "Epoch  10  is now complete, discriminator & generator losses are:  1.3480064 ,  0.74065983\n",
      "Epoch  11  is now complete, discriminator & generator losses are:  1.3377292 ,  0.75430906\n",
      "Epoch  12  is now complete, discriminator & generator losses are:  1.3301547 ,  0.7685522\n",
      "Epoch  13  is now complete, discriminator & generator losses are:  1.3236481 ,  0.78359836\n",
      "Epoch  14  is now complete, discriminator & generator losses are:  1.3121042 ,  0.8039913\n",
      "Epoch  15  is now complete, discriminator & generator losses are:  1.2992566 ,  0.83464354\n",
      "Epoch  16  is now complete, discriminator & generator losses are:  1.280363 ,  0.87517023\n",
      "Epoch  17  is now complete, discriminator & generator losses are:  1.2763985 ,  0.92923534\n",
      "Epoch  18  is now complete, discriminator & generator losses are:  1.2471566 ,  1.0054939\n",
      "Epoch  19  is now complete, discriminator & generator losses are:  1.2361832 ,  1.1060224\n",
      "Epoch  20  is now complete, discriminator & generator losses are:  1.2332647 ,  1.2068392\n",
      "Epoch  21  is now complete, discriminator & generator losses are:  1.2291471 ,  1.2912093\n",
      "Epoch  22  is now complete, discriminator & generator losses are:  1.3034366 ,  1.3057578\n",
      "Epoch  23  is now complete, discriminator & generator losses are:  1.3105433 ,  1.2983465\n",
      "Epoch  24  is now complete, discriminator & generator losses are:  1.2997884 ,  1.2093432\n",
      "Epoch  25  is now complete, discriminator & generator losses are:  1.2912021 ,  1.1024724\n",
      "Epoch  26  is now complete, discriminator & generator losses are:  1.2602847 ,  1.0296667\n",
      "Epoch  27  is now complete, discriminator & generator losses are:  1.237772 ,  0.9725355\n",
      "Epoch  28  is now complete, discriminator & generator losses are:  1.1911604 ,  0.9498068\n",
      "Epoch  29  is now complete, discriminator & generator losses are:  1.1674429 ,  0.9262502\n",
      "Epoch  30  is now complete, discriminator & generator losses are:  1.1149813 ,  0.9573686\n",
      "Epoch  31  is now complete, discriminator & generator losses are:  1.0896561 ,  0.9535235\n",
      "Epoch  32  is now complete, discriminator & generator losses are:  1.0893934 ,  0.91492444\n",
      "Epoch  33  is now complete, discriminator & generator losses are:  1.091067 ,  0.8834684\n",
      "Epoch  34  is now complete, discriminator & generator losses are:  1.1262765 ,  0.82010967\n",
      "Epoch  35  is now complete, discriminator & generator losses are:  1.18636 ,  0.7443776\n",
      "Epoch  36  is now complete, discriminator & generator losses are:  1.2022974 ,  0.6987983\n",
      "Epoch  37  is now complete, discriminator & generator losses are:  1.2702596 ,  0.67262244\n",
      "Epoch  38  is now complete, discriminator & generator losses are:  1.2850975 ,  0.66813993\n",
      "Epoch  39  is now complete, discriminator & generator losses are:  1.2768463 ,  0.7096263\n",
      "Epoch  40  is now complete, discriminator & generator losses are:  1.2759658 ,  0.7700091\n",
      "Epoch  41  is now complete, discriminator & generator losses are:  1.249548 ,  0.8892001\n",
      "Epoch  42  is now complete, discriminator & generator losses are:  1.16974 ,  1.0561931\n",
      "Epoch  43  is now complete, discriminator & generator losses are:  1.1664814 ,  1.3564233\n",
      "Epoch  44  is now complete, discriminator & generator losses are:  1.2571442 ,  1.5087278\n",
      "Epoch  45  is now complete, discriminator & generator losses are:  1.1261894 ,  1.838541\n",
      "Epoch  46  is now complete, discriminator & generator losses are:  1.1456084 ,  1.9845849\n",
      "Epoch  47  is now complete, discriminator & generator losses are:  1.1420375 ,  1.8971194\n",
      "Epoch  48  is now complete, discriminator & generator losses are:  1.1604514 ,  1.6552042\n",
      "Epoch  49  is now complete, discriminator & generator losses are:  0.9859489 ,  1.5160311\n",
      "Epoch  50  is now complete, discriminator & generator losses are:  1.094466 ,  1.2693889\n",
      "Epoch  51  is now complete, discriminator & generator losses are:  1.123872 ,  1.0143883\n",
      "Epoch  52  is now complete, discriminator & generator losses are:  1.1558065 ,  0.8706512\n",
      "Epoch  53  is now complete, discriminator & generator losses are:  1.2890153 ,  0.7686538\n",
      "Epoch  54  is now complete, discriminator & generator losses are:  1.2838165 ,  0.7383737\n",
      "Epoch  55  is now complete, discriminator & generator losses are:  1.2231576 ,  0.7543136\n",
      "Epoch  56  is now complete, discriminator & generator losses are:  1.1948483 ,  0.8231623\n",
      "Epoch  57  is now complete, discriminator & generator losses are:  1.3252757 ,  0.8941065\n",
      "Epoch  58  is now complete, discriminator & generator losses are:  1.211052 ,  1.0101273\n",
      "Epoch  59  is now complete, discriminator & generator losses are:  1.1353908 ,  1.1505554\n",
      "Epoch  60  is now complete, discriminator & generator losses are:  1.2816601 ,  1.2356255\n",
      "Epoch  61  is now complete, discriminator & generator losses are:  1.2727263 ,  1.3596025\n",
      "Epoch  62  is now complete, discriminator & generator losses are:  1.3895824 ,  1.3138694\n",
      "Epoch  63  is now complete, discriminator & generator losses are:  1.4182208 ,  1.1923459\n",
      "Epoch  64  is now complete, discriminator & generator losses are:  1.301808 ,  1.124264\n",
      "Epoch  65  is now complete, discriminator & generator losses are:  1.3799361 ,  1.008363\n",
      "Epoch  66  is now complete, discriminator & generator losses are:  1.3646286 ,  0.91336554\n",
      "Epoch  67  is now complete, discriminator & generator losses are:  1.3824896 ,  0.8001704\n",
      "Epoch  68  is now complete, discriminator & generator losses are:  1.3698981 ,  0.72598886\n",
      "Epoch  69  is now complete, discriminator & generator losses are:  1.3319647 ,  0.7230774\n",
      "Epoch  70  is now complete, discriminator & generator losses are:  1.3731914 ,  0.66847897\n",
      "Epoch  71  is now complete, discriminator & generator losses are:  1.3932102 ,  0.653314\n",
      "Epoch  72  is now complete, discriminator & generator losses are:  1.3930538 ,  0.6315807\n",
      "Epoch  73  is now complete, discriminator & generator losses are:  1.405501 ,  0.63947827\n",
      "Epoch  74  is now complete, discriminator & generator losses are:  1.3605431 ,  0.63996804\n",
      "Epoch  75  is now complete, discriminator & generator losses are:  1.3591887 ,  0.66302305\n",
      "Epoch  76  is now complete, discriminator & generator losses are:  1.3647169 ,  0.6997409\n",
      "Epoch  77  is now complete, discriminator & generator losses are:  1.4073513 ,  0.7175545\n",
      "Epoch  78  is now complete, discriminator & generator losses are:  1.3288579 ,  0.7668611\n",
      "Epoch  79  is now complete, discriminator & generator losses are:  1.3585565 ,  0.7728764\n",
      "Epoch  80  is now complete, discriminator & generator losses are:  1.4266059 ,  0.7678331\n",
      "Epoch  81  is now complete, discriminator & generator losses are:  1.3439682 ,  0.76442015\n",
      "Epoch  82  is now complete, discriminator & generator losses are:  1.4232911 ,  0.7447736\n",
      "Epoch  83  is now complete, discriminator & generator losses are:  1.3858804 ,  0.731027\n",
      "Epoch  84  is now complete, discriminator & generator losses are:  1.390533 ,  0.679515\n",
      "Epoch  85  is now complete, discriminator & generator losses are:  1.415038 ,  0.64670205\n",
      "Epoch  86  is now complete, discriminator & generator losses are:  1.4241936 ,  0.61547405\n",
      "Epoch  87  is now complete, discriminator & generator losses are:  1.3555485 ,  0.6164003\n",
      "Epoch  88  is now complete, discriminator & generator losses are:  1.3628154 ,  0.6142694\n",
      "Epoch  89  is now complete, discriminator & generator losses are:  1.3842118 ,  0.6342683\n",
      "Epoch  90  is now complete, discriminator & generator losses are:  1.3628333 ,  0.65844136\n",
      "Epoch  91  is now complete, discriminator & generator losses are:  1.3491701 ,  0.6867626\n",
      "Epoch  92  is now complete, discriminator & generator losses are:  1.3339148 ,  0.7253477\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  93  is now complete, discriminator & generator losses are:  1.3121045 ,  0.7850694\n",
      "Epoch  94  is now complete, discriminator & generator losses are:  1.3017693 ,  0.83891517\n",
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      "Epoch  182  is now complete, discriminator & generator losses are:  1.3039645 ,  0.90379393\n",
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      "Epoch  184  is now complete, discriminator & generator losses are:  1.2195058 ,  0.79935205\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  185  is now complete, discriminator & generator losses are:  1.138946 ,  0.7978023\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch  275  is now complete, discriminator & generator losses are:  0.6152114 ,  2.2816062\n",
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      "Epoch  364  is now complete, discriminator & generator losses are:  0.50676095 ,  1.4157712\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  365  is now complete, discriminator & generator losses are:  0.2866213 ,  2.0631788\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  457  is now complete, discriminator & generator losses are:  0.099141315 ,  2.6924548\n",
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      "Epoch  536  is now complete, discriminator & generator losses are:  0.45680922 ,  3.401813\n",
      "Epoch  537  is now complete, discriminator & generator losses are:  0.5721114 ,  1.1905295\n",
      "Epoch  538  is now complete, discriminator & generator losses are:  0.31792617 ,  1.6351259\n",
      "Epoch  539  is now complete, discriminator & generator losses are:  0.04279663 ,  4.333063\n",
      "Epoch  540  is now complete, discriminator & generator losses are:  0.31948274 ,  6.148204\n",
      "Epoch  541  is now complete, discriminator & generator losses are:  0.46339044 ,  5.612242\n",
      "Epoch  542  is now complete, discriminator & generator losses are:  0.28921145 ,  3.78508\n",
      "Epoch  543  is now complete, discriminator & generator losses are:  0.2787061 ,  2.0147738\n",
      "Epoch  544  is now complete, discriminator & generator losses are:  0.49643233 ,  1.1009135\n",
      "Epoch  545  is now complete, discriminator & generator losses are:  0.22225523 ,  2.251361\n",
      "Epoch  546  is now complete, discriminator & generator losses are:  0.23379032 ,  3.3241029\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  547  is now complete, discriminator & generator losses are:  0.21067142 ,  4.8690863\n",
      "Epoch  548  is now complete, discriminator & generator losses are:  0.23339093 ,  5.3765545\n",
      "Epoch  549  is now complete, discriminator & generator losses are:  0.31434947 ,  4.168039\n",
      "Epoch  550  is now complete, discriminator & generator losses are:  0.21075767 ,  2.6562111\n",
      "Epoch  551  is now complete, discriminator & generator losses are:  0.43935946 ,  1.3476646\n",
      "Epoch  552  is now complete, discriminator & generator losses are:  0.40805992 ,  1.33166\n",
      "Epoch  553  is now complete, discriminator & generator losses are:  0.07209359 ,  2.968408\n",
      "Epoch  554  is now complete, discriminator & generator losses are:  0.3845345 ,  4.1972847\n",
      "Epoch  555  is now complete, discriminator & generator losses are:  0.25646982 ,  4.703309\n",
      "Epoch  556  is now complete, discriminator & generator losses are:  0.17133778 ,  4.3173103\n",
      "Epoch  557  is now complete, discriminator & generator losses are:  0.22199795 ,  3.1488407\n",
      "Epoch  558  is now complete, discriminator & generator losses are:  0.12881643 ,  2.603918\n",
      "Epoch  559  is now complete, discriminator & generator losses are:  0.15383019 ,  2.291185\n",
      "Epoch  560  is now complete, discriminator & generator losses are:  0.1673421 ,  2.2666647\n",
      "Epoch  561  is now complete, discriminator & generator losses are:  0.124654844 ,  2.7337775\n",
      "Epoch  562  is now complete, discriminator & generator losses are:  0.08064873 ,  3.786026\n",
      "Epoch  563  is now complete, discriminator & generator losses are:  0.2986564 ,  4.183749\n",
      "Epoch  564  is now complete, discriminator & generator losses are:  0.13213691 ,  3.915572\n",
      "Epoch  565  is now complete, discriminator & generator losses are:  0.19488983 ,  2.8751283\n",
      "Epoch  566  is now complete, discriminator & generator losses are:  0.10941182 ,  2.5850027\n",
      "Epoch  567  is now complete, discriminator & generator losses are:  0.32381976 ,  2.0462422\n",
      "Epoch  568  is now complete, discriminator & generator losses are:  0.15868549 ,  2.489425\n",
      "Epoch  569  is now complete, discriminator & generator losses are:  0.18314877 ,  3.072628\n",
      "Epoch  570  is now complete, discriminator & generator losses are:  0.22250882 ,  3.3067079\n",
      "Epoch  571  is now complete, discriminator & generator losses are:  0.072539344 ,  3.6969812\n",
      "Epoch  572  is now complete, discriminator & generator losses are:  0.4914229 ,  3.4572868\n",
      "Epoch  573  is now complete, discriminator & generator losses are:  0.20402932 ,  2.7306912\n",
      "Epoch  574  is now complete, discriminator & generator losses are:  0.30981445 ,  1.9057477\n",
      "Epoch  575  is now complete, discriminator & generator losses are:  0.30282784 ,  1.7953696\n",
      "Epoch  576  is now complete, discriminator & generator losses are:  0.21905787 ,  2.2763643\n",
      "Epoch  577  is now complete, discriminator & generator losses are:  0.50493205 ,  2.9891691\n",
      "Epoch  578  is now complete, discriminator & generator losses are:  0.062335707 ,  4.167762\n",
      "Epoch  579  is now complete, discriminator & generator losses are:  0.09707812 ,  3.8620684\n",
      "Epoch  580  is now complete, discriminator & generator losses are:  0.12735395 ,  4.388692\n",
      "Epoch  581  is now complete, discriminator & generator losses are:  0.059735358 ,  4.5567274\n",
      "Epoch  582  is now complete, discriminator & generator losses are:  0.18829848 ,  3.1392567\n",
      "Epoch  583  is now complete, discriminator & generator losses are:  0.16573825 ,  2.882259\n",
      "Epoch  584  is now complete, discriminator & generator losses are:  0.4331978 ,  1.5129244\n",
      "Epoch  585  is now complete, discriminator & generator losses are:  0.2952566 ,  1.5724436\n",
      "Epoch  586  is now complete, discriminator & generator losses are:  0.2933914 ,  2.7747922\n",
      "Epoch  587  is now complete, discriminator & generator losses are:  0.19518664 ,  3.8359158\n",
      "Epoch  588  is now complete, discriminator & generator losses are:  0.43028453 ,  4.0332613\n",
      "Epoch  589  is now complete, discriminator & generator losses are:  0.2950797 ,  2.7131276\n",
      "Epoch  590  is now complete, discriminator & generator losses are:  0.2593143 ,  2.0813942\n",
      "Epoch  591  is now complete, discriminator & generator losses are:  0.19390233 ,  2.0265803\n",
      "Epoch  592  is now complete, discriminator & generator losses are:  0.21599734 ,  2.3326738\n",
      "Epoch  593  is now complete, discriminator & generator losses are:  0.18308568 ,  3.4107673\n",
      "Epoch  594  is now complete, discriminator & generator losses are:  0.43578833 ,  4.1140747\n",
      "Epoch  595  is now complete, discriminator & generator losses are:  0.15870929 ,  3.9998555\n",
      "Epoch  596  is now complete, discriminator & generator losses are:  0.35912102 ,  2.156212\n",
      "Epoch  597  is now complete, discriminator & generator losses are:  0.25167403 ,  1.807009\n",
      "Epoch  598  is now complete, discriminator & generator losses are:  0.28467017 ,  1.6229026\n",
      "Epoch  599  is now complete, discriminator & generator losses are:  0.21142544 ,  2.97927\n",
      "Epoch  600  is now complete, discriminator & generator losses are:  0.22093314 ,  3.150179\n"
     ]
    }
   ],
   "source": [
    "#Starting the session\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "for i in range(n_epochs):\n",
    "    z_batch = np.random.normal(-1, 1, size=[batch_size, z_dimensions])\n",
    "    real_image_batch = mnist.train.next_batch(batch_size)\n",
    "    real_image_batch = np.reshape(real_image_batch[0],[batch_size,28,28,1])\n",
    "    sess.run(trainerD,feed_dict={z:z_batch,x:real_image_batch}) #Update the discriminator\n",
    "    sess.run(trainerG,feed_dict={z:z_batch}) #Update the generator\n",
    "    train_loss_d = sess.run(d_loss, feed_dict={z: z_batch, x: real_image_batch})\n",
    "    train_loss_g = sess.run(g_loss, feed_dict={z: z_batch})\n",
    "    print(\"Epoch \", i + 1, \" is now complete, discriminator & generator losses are: \", train_loss_d, \", \", train_loss_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wr7fm4wP+9Q8W7xqBkbDuflalPHr9AH6qqm+LyGQAb4nI7qT2C1V9Kr/hEVFe3PCrajeA7uT9PhE5AOCWvAdGRPn6Wq9tRGQugIUA/pzc9IiI/EVEtojIsHsricgaEekSkS5vySgiqp6Swy8iDQB+D+AnqvoZgF8CmA9gAQZfGfx8uONUdaOqdqhqR1NTUwWGTESVUFL4RWQcBoP/a1X9AwCo6klVvaaqAwA2AVic3zCJqNLc8Mvgn6I3AzigqhuG3D5029rvAXiv8sMjoryU8tf+JQA6AewXkXeT29YBWCUiCwAogMMAfpTLCEeBrK28LMuGey0r776ztsSsdpw3Xdh73LxWoHW8d98RWoGl/LX/TwCGe6RGbU+fKIKR/98XEZWF4ScKiuEnCorhJwqK4ScKiuEnCopLd48AeW4n7R2b53Rhb2luyhef+YmCYviJgmL4iYJi+ImCYviJgmL4iYJi+ImCkixzxb/2yUROAfh4yE1NAGp1Yb9aHVutjgvg2MpVybF9Q1XtfdcTVQ3/V04u0qWqHYUNwFCrY6vVcQEcW7mKGhtf9hMFxfATBVV0+DcWfH5LrY6tVscFcGzlKmRshf7OT0TFKfqZn4gKUkj4ReQ+EfmbiBwSkbVFjCGNiBwWkf0i8q6IdBU8li0i0iMi7w25bbqI7BaRg8nbYbdJK2hs60Xkk+Sxe1dE7i9obK0iskdEDojIX0XkX5PbC33sjHEV8rhV/WW/iIwB8HcAywEcA7APwCpVfb+qA0khIocBdKhq4T1hEflnAOcBPKuqdyS3/QeAM6r6RPIfZ6Oq/luNjG09gPNF79ycbCjTPHRnaQAPAvgXFPjYGeN6CAU8bkU88y8GcEhVP1TVKwB+A2BFAeOoeaq6F8CZG25eAWBr8v5WDP7wVF3K2GqCqnar6tvJ+30Aru8sXehjZ4yrEEWE/xYAR4d8fAy1teW3AvijiLwlImuKHswwZifbpl/fPn1WweO5kbtzczXdsLN0zTx25ex4XWlFhH+4daNqqeWwRFUXAfgOgB8nL2+pNCXt3Fwtw+wsXRPK3fG60ooI/zEArUM+bgFwvIBxDEtVjydvewBsR+3tPnzy+iapyduegsfzhVrauXm4naVRA49dLe14XUT49wFoF5FvikgdgJUAXihgHF8hIvXJH2IgIvUAvo3a2334BQCrk/dXA9hR4Fi+pFZ2bk7bWRoFP3a1tuN1IRf5JK2M/wQwBsAWVf33qg9iGCIyD4PP9sDgysbbihybiDwHYCkGZ32dBPAzAP8N4HcA2gAcAfB9Va36H95SxrYUgy9dv9i5+frv2FUe2z8B+F8A+wFc3253HQZ/vy7ssTPGtQoFPG68wo8oKF7hRxQUw08UFMNPFBTDTxQUw08UFMNPFBTDTxQUw08U1P8BvqDmZRnDOu4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x192c6190438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating the images\n",
    "sample_image = generator(z, 1, z_dimensions, reuse=True)\n",
    "z_batch = np.random.normal(-1, 1, size=[1, z_dimensions])\n",
    "temp = sess.run(sample_image, feed_dict={z: z_batch})\n",
    "plt.imshow(temp.reshape([28,28]), cmap=\"Greys\", interpolation=\"nearest\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Closing the session\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}