C_GAN_dressGen.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "C_GAN_dressGen.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcyoFmSRkVgl",
        "colab_type": "code",
        "outputId": "8e88b15b-8720-4fc5-ec60-43ca76db7b03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        }
      },
      "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": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-09-22 12:59:00--  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)... 52.219.73.106\n",
            "Connecting to fashion-mnist.s3-website.eu-central-1.amazonaws.com (fashion-mnist.s3-website.eu-central-1.amazonaws.com)|52.219.73.106|: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.2’\n",
            "\n",
            "\r          train-ima   0%[                    ]       0  --.-KB/s               \r         train-imag  84%[===============>    ]  21.38M   107MB/s               \rtrain-images-idx3-u 100%[===================>]  25.20M   105MB/s    in 0.2s    \n",
            "\n",
            "2019-09-22 12:59:00 (105 MB/s) - ‘train-images-idx3-ubyte.gz.2’ saved [26421880/26421880]\n",
            "\n",
            "--2019-09-22 12:59:05--  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)... 52.219.74.170\n",
            "Connecting to fashion-mnist.s3-website.eu-central-1.amazonaws.com (fashion-mnist.s3-website.eu-central-1.amazonaws.com)|52.219.74.170|: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.2’\n",
            "\n",
            "train-labels-idx1-u 100%[===================>]  28.82K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2019-09-22 12:59:05 (2.70 MB/s) - ‘train-labels-idx1-ubyte.gz.2’ saved [29515/29515]\n",
            "\n",
            "--2019-09-22 12:59:10--  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)... 52.219.72.194\n",
            "Connecting to fashion-mnist.s3-website.eu-central-1.amazonaws.com (fashion-mnist.s3-website.eu-central-1.amazonaws.com)|52.219.72.194|: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.2’\n",
            "\n",
            "t10k-images-idx3-ub 100%[===================>]   4.22M  --.-KB/s    in 0.09s   \n",
            "\n",
            "2019-09-22 12:59:10 (45.3 MB/s) - ‘t10k-images-idx3-ubyte.gz.2’ saved [4422102/4422102]\n",
            "\n",
            "--2019-09-22 12:59:15--  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)... 52.219.72.186\n",
            "Connecting to fashion-mnist.s3-website.eu-central-1.amazonaws.com (fashion-mnist.s3-website.eu-central-1.amazonaws.com)|52.219.72.186|: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.2’\n",
            "\n",
            "t10k-labels-idx1-ub 100%[===================>]   5.03K  --.-KB/s    in 0s      \n",
            "\n",
            "2019-09-22 12:59:15 (332 MB/s) - ‘t10k-labels-idx1-ubyte.gz.2’ saved [5148/5148]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wx26lBNnlSb-",
        "colab_type": "code",
        "outputId": "8744bf0d-5676-4d2f-da13-e0beacab8962",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "log\t\t\t     t10k-labels-idx1-ubyte.gz.2\n",
            "MNIST_Fashion\t\t     train-images-idx3-ubyte.gz\n",
            "sample_data\t\t     train-images-idx3-ubyte.gz.1\n",
            "t10k-images-idx3-ubyte.gz    train-images-idx3-ubyte.gz.2\n",
            "t10k-images-idx3-ubyte.gz.1  train-labels-idx1-ubyte.gz\n",
            "t10k-images-idx3-ubyte.gz.2  train-labels-idx1-ubyte.gz.1\n",
            "t10k-labels-idx1-ubyte.gz    train-labels-idx1-ubyte.gz.2\n",
            "t10k-labels-idx1-ubyte.gz.1\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\n",
        "import matplotlib.gridspec as gridspec"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ac0FYa8Xlede",
        "colab_type": "code",
        "outputId": "963c48de-db6d-4bf3-d472-20e1bc1fbfe8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "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/\", one_hot = True)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘MNIST_Fashion’: File exists\n",
            "Extracting MNIST_Fashion/train-images-idx3-ubyte.gz\n",
            "Extracting MNIST_Fashion/train-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (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.one_hot on tensors.\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",
        "Y_dimension = 10 # The number of classes\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 + Y_dimension, disc_hidd_dim])),\n",
        "    \"disc_final\": tf.Variable(xavier_init([disc_hidd_dim,1])),\n",
        "    \"gen_H\": tf.Variable(xavier_init([z_noise_dim + Y_dimension, 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": "-hn90CdeTXRg",
        "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_input\")\n",
        "Y_input = tf.placeholder(tf.float32, shape = [None, Y_dimension], name = \"Labels\")\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Od--kA7nvAch",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Discriminator(x,y):\n",
        "  inputs = tf.concat(axis = 1, values = [x,y])\n",
        "  hidden_layer = tf.nn.relu(tf.add(tf.matmul(inputs, 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,y):\n",
        "  inputs = tf.concat(axis = 1, values = [x,y])\n",
        "  hidden_layer = tf.nn.relu(tf.add(tf.matmul(inputs, 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": "vfq_esohxQOo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# building the GEN NW\n",
        "output_Gen = Generator(z_input, Y_input) #G(z/y)\n",
        " \n",
        " # Building the Disc NW\n",
        "real_output1_Disc, real_output_disc = Discriminator(x_input, Y_input) #implements D(x/y)\n",
        "fake_output1_Disc, fake_output_disc = Discriminator(output_Gen, Y_input) # implements D(G(x/y))\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",
        "outputId": "a016d9ad-60e7-444e-fd48-3720955217d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        }
      },
      "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, Y_label = 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, Y_input:Y_label, z_input:z_noise})\n",
        "  _, Gen_loss_epoch = sess.run([Generator_optimize, Generator_Loss], feed_dict = {z_input:z_noise, Y_input:Y_label})  \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, Y_input:Y_label})\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, Y_input:Y_label})\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": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Steps: 0: Generator Loss: 1.2810556888580322, Discriminator Loss:1.4036788940429688\n",
            "Steps: 2000: Generator Loss: 6.665197849273682, Discriminator Loss:0.02185380831360817\n",
            "Steps: 4000: Generator Loss: 4.120672702789307, Discriminator Loss:0.13705849647521973\n",
            "Steps: 6000: Generator Loss: 4.001176834106445, Discriminator Loss:0.15926459431648254\n",
            "Steps: 8000: Generator Loss: 3.998939037322998, Discriminator Loss:0.1976121962070465\n",
            "Steps: 10000: Generator Loss: 3.755772829055786, Discriminator Loss:0.37067463994026184\n",
            "Steps: 12000: Generator Loss: 4.238238334655762, Discriminator Loss:0.17916974425315857\n",
            "Steps: 14000: Generator Loss: 3.8317372798919678, Discriminator Loss:0.3688141107559204\n",
            "Steps: 16000: Generator Loss: 3.7119901180267334, Discriminator Loss:0.4597961902618408\n",
            "Steps: 18000: Generator Loss: 3.534891366958618, Discriminator Loss:0.38630104064941406\n",
            "Steps: 20000: Generator Loss: 2.8810877799987793, Discriminator Loss:0.46687525510787964\n",
            "Steps: 22000: Generator Loss: 2.328599452972412, Discriminator Loss:0.5129964351654053\n",
            "Steps: 24000: Generator Loss: 2.4169230461120605, Discriminator Loss:0.5382898449897766\n",
            "Steps: 26000: Generator Loss: 2.520730495452881, Discriminator Loss:0.5078528523445129\n",
            "Steps: 28000: Generator Loss: 2.0571329593658447, Discriminator Loss:0.7263427972793579\n",
            "Steps: 30000: Generator Loss: 2.2126357555389404, Discriminator Loss:0.5324649214744568\n",
            "Steps: 32000: Generator Loss: 2.391488552093506, Discriminator Loss:0.7062442898750305\n",
            "Steps: 34000: Generator Loss: 2.031740188598633, Discriminator Loss:0.6795985698699951\n",
            "Steps: 36000: Generator Loss: 2.0562562942504883, Discriminator Loss:0.8967874050140381\n",
            "Steps: 38000: Generator Loss: 1.6431206464767456, Discriminator Loss:0.8702189922332764\n",
            "Steps: 40000: Generator Loss: 1.8524537086486816, Discriminator Loss:0.8787059187889099\n",
            "Steps: 42000: Generator Loss: 2.0923521518707275, Discriminator Loss:0.7313941121101379\n",
            "Steps: 44000: Generator Loss: 2.082287073135376, Discriminator Loss:1.0506993532180786\n",
            "Steps: 46000: Generator Loss: 1.8415436744689941, Discriminator Loss:0.9871857762336731\n",
            "Steps: 48000: Generator Loss: 2.445986747741699, Discriminator Loss:0.8463912010192871\n",
            "Steps: 50000: Generator Loss: 1.6479865312576294, Discriminator Loss:1.177063226699829\n",
            "Steps: 52000: Generator Loss: 1.8386491537094116, Discriminator Loss:0.8278873562812805\n",
            "Steps: 54000: Generator Loss: 1.5918254852294922, Discriminator Loss:1.0101335048675537\n",
            "Steps: 56000: Generator Loss: 1.9227936267852783, Discriminator Loss:1.015535593032837\n",
            "Steps: 58000: Generator Loss: 1.6866161823272705, Discriminator Loss:0.8705646395683289\n",
            "Steps: 60000: Generator Loss: 1.6198217868804932, Discriminator Loss:1.0817625522613525\n",
            "Steps: 62000: Generator Loss: 1.691160798072815, Discriminator Loss:1.0951712131500244\n",
            "Steps: 64000: Generator Loss: 2.045804738998413, Discriminator Loss:1.0415313243865967\n",
            "Steps: 66000: Generator Loss: 1.9435594081878662, Discriminator Loss:0.867792010307312\n",
            "Steps: 68000: Generator Loss: 1.2355430126190186, Discriminator Loss:1.3247641324996948\n",
            "Steps: 70000: Generator Loss: 1.5091404914855957, Discriminator Loss:1.3332843780517578\n",
            "Steps: 72000: Generator Loss: 1.8564181327819824, Discriminator Loss:0.9653632640838623\n",
            "Steps: 74000: Generator Loss: 1.8958728313446045, Discriminator Loss:0.9530302286148071\n",
            "Steps: 76000: Generator Loss: 1.4985740184783936, Discriminator Loss:1.2420713901519775\n",
            "Steps: 78000: Generator Loss: 1.9713279008865356, Discriminator Loss:1.105647325515747\n",
            "Steps: 80000: Generator Loss: 1.5458425283432007, Discriminator Loss:1.2899222373962402\n",
            "Steps: 82000: Generator Loss: 1.553359866142273, Discriminator Loss:1.1438515186309814\n",
            "Steps: 84000: Generator Loss: 1.8715550899505615, Discriminator Loss:1.231022834777832\n",
            "Steps: 86000: Generator Loss: 1.8320751190185547, Discriminator Loss:1.0993586778640747\n",
            "Steps: 88000: Generator Loss: 1.6383163928985596, Discriminator Loss:1.1759893894195557\n",
            "Steps: 90000: Generator Loss: 1.896270751953125, Discriminator Loss:1.1564751863479614\n",
            "Steps: 92000: Generator Loss: 1.6164828538894653, Discriminator Loss:1.3400644063949585\n",
            "Steps: 94000: Generator Loss: 1.32316255569458, Discriminator Loss:1.249334454536438\n",
            "Steps: 96000: Generator Loss: 1.4251748323440552, Discriminator Loss:1.1683566570281982\n",
            "Steps: 98000: Generator Loss: 1.2588375806808472, Discriminator Loss:1.395728588104248\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gSHkcV7-EP7z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "60c3faf1-3791-4341-abc7-3001a638694b"
      },
      "source": [
        "print(x_batch.shape)\n",
        "print(x_input.shape)\n",
        "print(Y_input.shape)\n",
        "print(Y_label.shape)\n",
        "print(z_input.shape)\n",
        "print(z_noise.shape)\n",
        "\n"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10, 784)\n",
            "(?, 784)\n",
            "(?, 10)\n",
            "(10, 10)\n",
            "(?, 100)\n",
            "(128, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4NBuJPt2iwm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_plot(samples):\n",
        "  fig = plt.figure(figsize = (4,4))\n",
        "  gs = gridspec.GridSpec(4,4)\n",
        "  gs.update(wspace = 0.05, hspace = 0.05)\n",
        "  \n",
        "  for i, sample in enumerate(samples):\n",
        "    ax = plt.subplot(gs[i])\n",
        "    plt.axis('off')\n",
        "    ax.set_xticks([])\n",
        "    ax.set_yticks([])\n",
        "    ax.set_aspect('equal')\n",
        "    plt.imshow(sample.reshape(28,28), cmap = 'gray')\n",
        "  return fig  \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ad1rGeTg-ypT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create(inp):\n",
        "  feature_map = { \"t-shirt\":0,\n",
        "                 \"trouser\":1,\n",
        "                 \"pullover\":2,\n",
        "                 \"dress\":3,\n",
        "                 \"coat\":4,\n",
        "                 \"sandal\":5,\n",
        "                 \"sirt\":6,\n",
        "                 \"sneaker\":7,\n",
        "                 \"bag\":8,\n",
        "                 \"ankle boot\": 9\n",
        "                }\n",
        "  samples = 16\n",
        "  z_noise = np.random.uniform(-1.,1.,size = [samples, z_noise_dim])\n",
        "  #one hot encoding\n",
        "  Y_label = np.zeros(shape = [samples, Y_dimension])\n",
        "  Y_label[:, feature_map[inp]] = 1\n",
        "  \n",
        "  # run the traineg generator excluding Discriminator\n",
        "  generated_samples = sess.run(output_Gen, feed_dict = {z_input:z_noise, Y_input:Y_label})\n",
        "  #plot images\n",
        "  \n",
        "  generate_plot(generated_samples)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AnLixT6yIYnA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "81929d86-f936-4358-ebf6-eb5ece606dbe"
      },
      "source": [
        "create(\"coat\")"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOwAAADnCAYAAAAdFLrXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "izEbokHnJwC_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "aa23a421-c10b-4171-d524-a681aea3dcfb"
      },
      "source": [
        "create('trouser')"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOwAAADnCAYAAAAdFLrXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIX1gExJqCOkDm5BQQ/h/kRe1iQTYuGYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7W4Sj7zbKVQ7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "9c2c196a-878b-4bd3-8229-84a971973658"
      },
      "source": [
        "create('dress')"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOwAAADnCAYAAAAdFLrXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGxjsw4s0AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGvnSGzIKXP1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}