GANdressMNIST.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GANdressMNIST.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcyoFmSRkVgl",
        "colab_type": "code",
        "outputId": "24a33953-ac25-4417-aca9-99ff7bf985e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        }
      },
      "source": [
        "!wget http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
        "!wget http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
        "!wget http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
        "!wget http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-09-02 10:55:48--  http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Resolving fashion-mnist.s3-website.eu-central-1.amazonaws.com (fashion-mnist.s3-website.eu-central-1.amazonaws.com)... 52.219.72.198\n",
            "Connecting to fashion-mnist.s3-website.eu-central-1.amazonaws.com (fashion-mnist.s3-website.eu-central-1.amazonaws.com)|52.219.72.198|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 26421880 (25M) [binary/octet-stream]\n",
            "Saving to: ‘train-images-idx3-ubyte.gz’\n",
            "\n",
            "\r          train-ima   0%[                    ]       0  --.-KB/s               \r         train-imag  83%[===============>    ]  20.99M   105MB/s               \rtrain-images-idx3-u 100%[===================>]  25.20M   104MB/s    in 0.2s    \n",
            "\n",
            "2019-09-02 10:55:48 (104 MB/s) - ‘train-images-idx3-ubyte.gz’ saved [26421880/26421880]\n",
            "\n",
            "--2019-09-02 10:55:53--  http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Resolving fashion-mnist.s3-website.eu-central-1.amazonaws.com (fashion-mnist.s3-website.eu-central-1.amazonaws.com)... 52.219.72.162\n",
            "Connecting to fashion-mnist.s3-website.eu-central-1.amazonaws.com (fashion-mnist.s3-website.eu-central-1.amazonaws.com)|52.219.72.162|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 29515 (29K) [binary/octet-stream]\n",
            "Saving to: ‘train-labels-idx1-ubyte.gz’\n",
            "\n",
            "train-labels-idx1-u 100%[===================>]  28.82K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2019-09-02 10:55:53 (2.82 MB/s) - ‘train-labels-idx1-ubyte.gz’ saved [29515/29515]\n",
            "\n",
            "--2019-09-02 10:55:57--  http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Resolving fashion-mnist.s3-website.eu-central-1.amazonaws.com (fashion-mnist.s3-website.eu-central-1.amazonaws.com)... 52.219.73.54\n",
            "Connecting to fashion-mnist.s3-website.eu-central-1.amazonaws.com (fashion-mnist.s3-website.eu-central-1.amazonaws.com)|52.219.73.54|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4422102 (4.2M) [binary/octet-stream]\n",
            "Saving to: ‘t10k-images-idx3-ubyte.gz’\n",
            "\n",
            "t10k-images-idx3-ub 100%[===================>]   4.22M  --.-KB/s    in 0.09s   \n",
            "\n",
            "2019-09-02 10:55:57 (46.6 MB/s) - ‘t10k-images-idx3-ubyte.gz’ saved [4422102/4422102]\n",
            "\n",
            "--2019-09-02 10:56:01--  http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Resolving fashion-mnist.s3-website.eu-central-1.amazonaws.com (fashion-mnist.s3-website.eu-central-1.amazonaws.com)... 52.219.72.178\n",
            "Connecting to fashion-mnist.s3-website.eu-central-1.amazonaws.com (fashion-mnist.s3-website.eu-central-1.amazonaws.com)|52.219.72.178|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5148 (5.0K) [binary/octet-stream]\n",
            "Saving to: ‘t10k-labels-idx1-ubyte.gz’\n",
            "\n",
            "t10k-labels-idx1-ub 100%[===================>]   5.03K  --.-KB/s    in 0s      \n",
            "\n",
            "2019-09-02 10:56:01 (195 MB/s) - ‘t10k-labels-idx1-ubyte.gz’ saved [5148/5148]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wx26lBNnlSb-",
        "colab_type": "code",
        "outputId": "e4f851ec-7204-4949-c244-c943537ad5e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample_data\t\t   train-images-idx3-ubyte.gz\n",
            "t10k-images-idx3-ubyte.gz  train-labels-idx1-ubyte.gz\n",
            "t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BazpF0bmJM0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ac0FYa8Xlede",
        "colab_type": "code",
        "outputId": "8978b4e8-8ec7-42e8-afb1-dc8c45be55b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "!mkdir MNIST_Fashion\n",
        "!cp *.gz MNIST_Fashion/\n",
        "from tensorflow.examples.tutorials.mnist import input_data\n",
        "mnist = input_data.read_data_sets(\"MNIST_Fashion/\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0902 10:56:21.823908 139623493531520 deprecation.py:323] From <ipython-input-4-8b24037dcc55>:4: 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",
            "W0902 10:56:21.825902 139623493531520 deprecation.py:323] From /usr/local/lib/python3.6/dist-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",
            "W0902 10:56:21.835679 139623493531520 deprecation.py:323] From /usr/local/lib/python3.6/dist-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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting MNIST_Fashion/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "W0902 10:56:22.173902 139623493531520 deprecation.py:323] From /usr/local/lib/python3.6/dist-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",
            "W0902 10:56:22.236248 139623493531520 deprecation.py:323] From /usr/local/lib/python3.6/dist-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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting MNIST_Fashion/train-labels-idx1-ubyte.gz\n",
            "Extracting MNIST_Fashion/t10k-images-idx3-ubyte.gz\n",
            "Extracting MNIST_Fashion/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMupEFAhmh6S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Training PArams\n",
        "learning_rate = 0.0002\n",
        "batch_size = 128\n",
        "epochs = 100000\n",
        "\n",
        "#Network params\n",
        "image_dim = 784 #img sz is 28x28\n",
        "gen_hidd_dim = 256\n",
        "disc_hidd_dim  = 256\n",
        "z_noise_dim = 100\n",
        "\n",
        "def xavier_init(shape):\n",
        "  return tf.random_normal(shape = shape, stddev= 1./tf.sqrt(shape[0]/2.0))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZT-GzeQtaQN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "weights = {\n",
        "    \"disc_H\" : tf.Variable(xavier_init([image_dim, disc_hidd_dim])),\n",
        "    \"disc_final\": tf.Variable(xavier_init([disc_hidd_dim,1])),\n",
        "    \"gen_H\": tf.Variable(xavier_init([z_noise_dim, gen_hidd_dim])),\n",
        "    \"gen_final\": tf.Variable(xavier_init([gen_hidd_dim, image_dim]))\n",
        "}\n",
        "\n",
        "bias = {\n",
        "    \"disc_H\" : tf.Variable(xavier_init([disc_hidd_dim])),\n",
        "    \"disc_final\": tf.Variable(xavier_init([1])),\n",
        "    \"gen_H\": tf.Variable(xavier_init([gen_hidd_dim])),\n",
        "    \"gen_final\": tf.Variable(xavier_init([image_dim]))\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Od--kA7nvAch",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Discriminator(x):\n",
        "  hidden_layer = tf.nn.relu(tf.add(tf.matmul(x, weights[\"disc_H\"]), bias[\"disc_H\"]))\n",
        "  final_layer = (tf.add(tf.matmul(hidden_layer, weights[\"disc_final\"]), bias[\"disc_final\"]))\n",
        "  disc_output = tf.nn.sigmoid(final_layer)\n",
        "  return final_layer, disc_output\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96s5eXy8wELi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Generator NW\n",
        "def Generator(x):\n",
        "  hidden_layer = tf.nn.relu(tf.add(tf.matmul(x, weights[\"gen_H\"]), bias[\"gen_H\"]))\n",
        "  final_layer = (tf.add(tf.matmul(hidden_layer, weights[\"gen_final\"]), bias[\"gen_final\"]))\n",
        "  gen_output = tf.nn.sigmoid(final_layer)\n",
        "  return gen_output\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWg-TWTDwsK1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#define placeholders for external input\n",
        "\n",
        "z_input = tf.placeholder(tf.float32, shape = [None, z_noise_dim], name = \"input_noise\")\n",
        "x_input = tf.placeholder(tf.float32, shape = [None, image_dim], name = \"real_noise\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfq_esohxQOo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# building the GEN NW\n",
        "with tf.name_scope(\"Generator\") as scope:\n",
        "  output_Gen = Generator(z_input) #G(z)\n",
        " \n",
        " # Building the Disc NW\n",
        "with tf.name_scope(\"Generator\") as scope:\n",
        "  real_output1_Disc, real_output_disc = Discriminator(x_input) #implements D(x)\n",
        "  fake_output1_Disc, fake_output_disc = Discriminator(output_Gen) # implements D(G(x))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09_G-6jyyxIl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#first kind of loss\n",
        "with tf.name_scope(\"Discriminator_Loss\") as scope:\n",
        "  Discriminator_Loss = -tf.reduce_mean(tf.log(real_output_disc+ 0.0001)+tf.log(1.- fake_output_disc+0.0001))\n",
        "  \n",
        "with tf.name_scope(\"Genetator_Loss\") as scope:\n",
        "  Generator_Loss = -tf.reduce_mean(tf.log(fake_output_disc+ 0.0001)) # due to max log(D(G(x)))\n",
        "\n",
        "  # T-board summary\n",
        "  \n",
        "  Disc_loss_total = tf.summary.scalar(\"Disc_Total_loss\", Discriminator_Loss)\n",
        "  Gen_loss_total = tf.summary.scalar(\"Gen_loss\", Generator_Loss)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "beVetM5N1xsX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#second kind of loss\n",
        "with tf.name_scope(\"Discriminator_Loss\") as scope:\n",
        "  Disc_real_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = real_output1_Disc, labels = tf.ones_like(real_output1_Disc)))\n",
        "  Disc_fake_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = fake_output1_Disc, labels = tf.zeros_like(fake_output1_Disc)))\n",
        "  Discriminator_Loss = Disc_real_loss + Disc_fake_loss\n",
        "\n",
        "  \n",
        "with tf.name_scope(\"Genetator_Loss\") as scope:\n",
        "  Generator_Loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = fake_output1_Disc, labels = tf.ones_like(fake_output1_Disc)))\n",
        "  \n",
        "  # Tensorboarf summary\n",
        "  \n",
        "Disc_loss_real_summary = tf.summary.scalar('Disc_loss_real', Disc_real_loss)\n",
        "Disc_loss_fake_summary = tf.summary.scalar('Disc_loss_fake', Disc_fake_loss)\n",
        "Disc_loss_summary = tf.summary.scalar('Disc_total_loss', Discriminator_Loss)\n",
        "\n",
        "Disc_loss_total = tf.summary.merge([Disc_loss_real_summary, Disc_loss_fake_summary, Disc_loss_summary])\n",
        "Gen_loss_total = tf.summary.scalar('Gen_loss', Generator_Loss)\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DmvjYQaKuXpi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define the variables\n",
        "\n",
        "Generator_var = [weights[\"gen_H\"], weights[\"gen_final\"], bias[\"gen_H\"], bias[\"gen_final\"]]\n",
        "Discriminator_var = [weights[\"disc_H\"], weights[\"disc_final\"], bias[\"disc_H\"], bias[\"disc_final\"]]\n",
        "\n",
        "#Define the optimizer\n",
        "with tf.name_scope(\"Optimizer_Discriminator\") as scope:\n",
        "  Discriminator_optimize = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(Discriminator_Loss, var_list = Discriminator_var)\n",
        "\n",
        "with tf.name_scope(\"Optimizer_Generator\") as scope:\n",
        "  Generator_optimize = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(Generator_Loss, var_list = Generator_var)\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B6nBmWh6wLLq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 905
        },
        "outputId": "7ea7c1b3-a324-45ac-bef2-0861bca96bb6"
      },
      "source": [
        "# Initialize the variables\n",
        "\n",
        "init = tf.global_variables_initializer()\n",
        "sess = tf.Session()\n",
        "\n",
        "sess.run(init)\n",
        "writer = tf.summary.FileWriter(\"./log\", sess.graph)\n",
        "\n",
        "for epoch in range(epochs):\n",
        "  x_batch, _ = mnist.train.next_batch(batch_size)\n",
        "  \n",
        "  #Generate noise to feed Discriminator\n",
        "  z_noise = np.random.uniform(-1.,1.,size = [batch_size, z_noise_dim])\n",
        "  _, Disc_loss_epoch = sess.run([Discriminator_optimize, Discriminator_Loss], feed_dict = {x_input:x_batch, z_input:z_noise})\n",
        "  _, Gen_loss_epoch = sess.run([Generator_optimize, Generator_Loss], feed_dict = {z_input:z_noise})  \n",
        "  \n",
        "  #Running the Discriminator summary\n",
        "  summary_Disc_loss = sess.run(Disc_loss_total, feed_dict = {x_input:x_batch, z_input:z_noise})\n",
        "  # Adding the Discriminator summary\n",
        "  writer.add_summary(summary_Disc_loss, epoch)\n",
        "  \n",
        "  #Running the Generator summary\n",
        "  summary_Gen_loss = sess.run(Gen_loss_total, feed_dict = {z_input:z_noise})\n",
        "  # Adding the Generator summary\n",
        "  writer.add_summary(summary_Gen_loss, epoch)\n",
        "  \n",
        "  if epoch % 2000 == 0:\n",
        "    print(\"Steps: {0}: Generator Loss: {1}, Discriminator Loss:{2}\".format(epoch, Gen_loss_epoch, Disc_loss_epoch))\n",
        "  \n",
        "  \n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Steps: 0: Generator Loss: 0.2859601676464081, Discriminator Loss:2.233637571334839\n",
            "Steps: 2000: Generator Loss: 3.0023133754730225, Discriminator Loss:0.2748841345310211\n",
            "Steps: 4000: Generator Loss: 2.8812737464904785, Discriminator Loss:0.28830602765083313\n",
            "Steps: 6000: Generator Loss: 3.231123924255371, Discriminator Loss:0.2344113290309906\n",
            "Steps: 8000: Generator Loss: 2.633310079574585, Discriminator Loss:0.31073784828186035\n",
            "Steps: 10000: Generator Loss: 2.414632797241211, Discriminator Loss:0.4111747741699219\n",
            "Steps: 12000: Generator Loss: 2.3037047386169434, Discriminator Loss:0.4018137753009796\n",
            "Steps: 14000: Generator Loss: 2.192345142364502, Discriminator Loss:0.41327720880508423\n",
            "Steps: 16000: Generator Loss: 3.166553497314453, Discriminator Loss:0.35595840215682983\n",
            "Steps: 18000: Generator Loss: 2.51501727104187, Discriminator Loss:0.5612295866012573\n",
            "Steps: 20000: Generator Loss: 2.9820990562438965, Discriminator Loss:0.49208712577819824\n",
            "Steps: 22000: Generator Loss: 2.6812961101531982, Discriminator Loss:0.5584867000579834\n",
            "Steps: 24000: Generator Loss: 2.812584400177002, Discriminator Loss:0.41504138708114624\n",
            "Steps: 26000: Generator Loss: 2.933314323425293, Discriminator Loss:0.4651210606098175\n",
            "Steps: 28000: Generator Loss: 2.1647796630859375, Discriminator Loss:0.7624726295471191\n",
            "Steps: 30000: Generator Loss: 2.664511203765869, Discriminator Loss:0.6443354487419128\n",
            "Steps: 32000: Generator Loss: 2.3114120960235596, Discriminator Loss:0.5428123474121094\n",
            "Steps: 34000: Generator Loss: 2.532151699066162, Discriminator Loss:0.8082764148712158\n",
            "Steps: 36000: Generator Loss: 2.4026713371276855, Discriminator Loss:0.7856625318527222\n",
            "Steps: 38000: Generator Loss: 2.313305139541626, Discriminator Loss:0.635770857334137\n",
            "Steps: 40000: Generator Loss: 1.9234436750411987, Discriminator Loss:0.7314289808273315\n",
            "Steps: 42000: Generator Loss: 1.8774704933166504, Discriminator Loss:0.8333190679550171\n",
            "Steps: 44000: Generator Loss: 2.3576712608337402, Discriminator Loss:0.5700790882110596\n",
            "Steps: 46000: Generator Loss: 2.2355618476867676, Discriminator Loss:0.8112173080444336\n",
            "Steps: 48000: Generator Loss: 2.9110002517700195, Discriminator Loss:0.5595995783805847\n",
            "Steps: 50000: Generator Loss: 2.230238676071167, Discriminator Loss:0.621875524520874\n",
            "Steps: 52000: Generator Loss: 1.6985931396484375, Discriminator Loss:0.9084687232971191\n",
            "Steps: 54000: Generator Loss: 2.407982587814331, Discriminator Loss:0.830478310585022\n",
            "Steps: 56000: Generator Loss: 2.0505809783935547, Discriminator Loss:0.7255258560180664\n",
            "Steps: 58000: Generator Loss: 2.6849453449249268, Discriminator Loss:0.7400216460227966\n",
            "Steps: 60000: Generator Loss: 2.7776036262512207, Discriminator Loss:0.8257851600646973\n",
            "Steps: 62000: Generator Loss: 1.8865808248519897, Discriminator Loss:0.749656617641449\n",
            "Steps: 64000: Generator Loss: 2.1922802925109863, Discriminator Loss:0.8696221113204956\n",
            "Steps: 66000: Generator Loss: 1.8173532485961914, Discriminator Loss:0.8903434872627258\n",
            "Steps: 68000: Generator Loss: 2.6272659301757812, Discriminator Loss:0.6751720905303955\n",
            "Steps: 70000: Generator Loss: 1.8914060592651367, Discriminator Loss:0.79111248254776\n",
            "Steps: 72000: Generator Loss: 2.5205678939819336, Discriminator Loss:0.6208642721176147\n",
            "Steps: 74000: Generator Loss: 2.4295969009399414, Discriminator Loss:0.8749495148658752\n",
            "Steps: 76000: Generator Loss: 2.1805500984191895, Discriminator Loss:0.8991968631744385\n",
            "Steps: 78000: Generator Loss: 2.5251080989837646, Discriminator Loss:0.9849644899368286\n",
            "Steps: 80000: Generator Loss: 1.9961838722229004, Discriminator Loss:0.8828095197677612\n",
            "Steps: 82000: Generator Loss: 2.280056953430176, Discriminator Loss:0.742890477180481\n",
            "Steps: 84000: Generator Loss: 2.3696675300598145, Discriminator Loss:0.9417824745178223\n",
            "Steps: 86000: Generator Loss: 1.7252144813537598, Discriminator Loss:0.9523500204086304\n",
            "Steps: 88000: Generator Loss: 2.180276870727539, Discriminator Loss:0.8354692459106445\n",
            "Steps: 90000: Generator Loss: 2.030958652496338, Discriminator Loss:1.0317438840866089\n",
            "Steps: 92000: Generator Loss: 2.4610633850097656, Discriminator Loss:0.8401101231575012\n",
            "Steps: 94000: Generator Loss: 2.000363826751709, Discriminator Loss:1.1329814195632935\n",
            "Steps: 96000: Generator Loss: 1.5011600255966187, Discriminator Loss:0.9772607088088989\n",
            "Steps: 98000: Generator Loss: 2.447108268737793, Discriminator Loss:0.8664214015007019\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4NBuJPt2iwm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "2fb4ba2f-fe32-4241-c7d7-3aeea60ecc44"
      },
      "source": [
        "#Testing\n",
        "# Generate images from noise, using the generator network\n",
        "\n",
        "n = 6\n",
        "canvas = np.empty((28*n, 28*n))\n",
        "for i in range(n):\n",
        "  #Noise input\n",
        "  z_noise = np.random.uniform(-1.,1., size = [batch_size, z_noise_dim])\n",
        "  # Generate image from noise\n",
        "  g = sess.run(output_Gen, feed_dict = {z_input:z_noise})\n",
        "  # Reverse colors for better display\n",
        "  g=-1*(g-1)\n",
        "  for j in range(n):\n",
        "    #Draw the generated digits\n",
        "    canvas[i*28:(i+1)*28, j*28:(j+1)*28] = g[j].reshape([28, 28])\n",
        "    \n",
        "plt.figure(figsize = (n,n))\n",
        "plt.imshow(canvas, origin = \"upper\", cmap = \"gray\")\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2EDfNPl6VEkSNFR6JrEAcsbbLZNTGG2+8sSBy82ls8c2UHWqZ4+dX1PC5YjCU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\n2k9V5nwObUDYMkRlt9VRek/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t6hTCcl2RbZ588slJkiOOOCLJhIfDezJ341KrlLaP9ejN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bOO+Ma9Zk3pd0wcE8KsFgJ8mDT9/NF82nKuGF/KyNeNYcszjtlTp7AEqRww1O22265vK+2BYfLb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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ad1rGeTg-ypT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}