Simpsons_Faces_DCGAN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Simpsons_Faces_DCGAN.ipynb",
      "provenance": [],
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyPSeS6RWpOOOYtYjCQZOz3O",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bosecodes/slytherin-slingshot/blob/master/Simpsons_Faces_DCGAN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuCdpf6WsS6c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q kaggle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPQV_BuUvc3h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0g2Lz79evc5l",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 113
        },
        "outputId": "b5fe7a7b-d342-46de-d9ce-22a4c79aaabc"
      },
      "source": [
        "files.upload()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-606920db-2dad-4c3a-a54a-0c7b67cf5941\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-606920db-2dad-4c3a-a54a-0c7b67cf5941\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"bosecodes\",\"key\":\"6778ca7506fc805f6168d85338f1de9b\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYeWAGKavc9-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! mkdir ~/.kaggle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cweT5bc6vc_9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp kaggle.json ~/.kaggle/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRPmGUUqvdEF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHsaf8JSvdGI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "beb9d7b5-ac54-4d7d-91d2-1afb3c338e7b"
      },
      "source": [
        "!kaggle datasets download -d kostastokis/simpsons-faces"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading simpsons-faces.zip to /content\n",
            " 97% 427M/442M [00:03<00:00, 146MB/s]\n",
            "100% 442M/442M [00:04<00:00, 109MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "op88EUO4vdKO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "585c6b9f-1bcf-4547-c971-f72dddab9841"
      },
      "source": [
        "!unzip simpsons-faces.zip"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  inflating: simplified/539.png      \n",
            "  inflating: simplified/5390.png     \n",
            "  inflating: simplified/5391.png     \n",
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            "  inflating: simplified/54.png       \n",
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            "  inflating: simplified/5400.png     \n",
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            "  inflating: simplified/5403.png     \n",
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            "  inflating: simplified/5416.png     \n",
            "  inflating: simplified/5417.png     \n",
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            "  inflating: simplified/5420.png     \n",
            "  inflating: simplified/5421.png     \n",
            "  inflating: simplified/5422.png     \n",
            "  inflating: simplified/5423.png     \n",
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            "  inflating: simplified/5430.png     \n",
            "  inflating: simplified/5431.png     \n",
            "  inflating: simplified/5432.png     \n",
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            "  inflating: simplified/5434.png     \n",
            "  inflating: simplified/5435.png     \n",
            "  inflating: simplified/5436.png     \n",
            "  inflating: simplified/5437.png     \n",
            "  inflating: simplified/5438.png     \n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHiO_vYuvdMD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import time\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from glob import glob \n",
        "import datetime\n",
        "import random\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGWcXkH5vdQW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generator(z, output_channel_dim, training):\n",
        "  with tfvariable_scope(\"generator\", reuse = not training):\n",
        "\n",
        "    # 8x8x1024\n",
        "    fully_connected = tf.layers.dense(z, 8*8*1024)\n",
        "    fully_connected = tf.reshape(fully_connected, (-1, 8, 8, 1024))\n",
        "\n",
        "    # 8x8x1024 -> 16x16x512\n",
        "    trans_conv1 = tf.layers.conv2d_transpose(inputs = fully_connected, \n",
        "                                             filters = 512, \n",
        "                                             kernel_size = [5,5],\n",
        "                                             strides = [2,2],\n",
        "                                             padding = 'SAME',\n",
        "                                             kernel_initializer = tf.truncated_normal_initializer(stddev = WEIGHT_INIT_STDDEV),\n",
        "                                             name = \"trans_conv1\")\n",
        "    \n",
        "    batch_trans_conv1 = tf.layers.batch_normalization(inputs = trans_conv1, \n",
        "                                                      training = training,\n",
        "                                                      epsilon = EPSILON, \n",
        "                                                      name = 'batch_trans_conv1')\n",
        "    \n",
        "    trans_conv1_out = tf.nn.leaky_relu(batch_trans_conv1, name = \"trans_conv1_out\")\n",
        "\n",
        "    # 16x16x512 -> 32x32x256\n",
        "\n",
        "    trans_conv2 = tf.layers.conv2d_transpose(inputs = trans_conv1_out,\n",
        "                                             filters = 256,\n",
        "                                             kernel_size = [5,5],\n",
        "                                             strides = [2,2],\n",
        "                                             padding = 'SAME',\n",
        "                                             kernel_initializer = tf.truncated_normal_initializer(stddev = WEIGHT_INIT_STDDEV))\n",
        "    \n",
        "    batch_trans_conv2 = tf.layers.batch_normalization(inputs = trans_conv2,\n",
        "                                                      training = training,\n",
        "                                                      epsilon = EPSILON,\n",
        "                                                      name = \"batch_trans_conv2\")\n",
        "    \n",
        "    trans_conv2_out = tf.nn.leaky_relu(batch_trans_conv2, name = \"trans_conv2_out\")\n",
        "\n",
        "    # 32x32x256 -> 64x64x128\n",
        "    trans_conv3 = tf.layers.conv2d_transpose(inputs = trans_conv2_out,\n",
        "                                             filters = 128,\n",
        "                                             kernel_size = [5,5],\n",
        "                                             strides = [2,2],\n",
        "                                             padding = 'SAME',\n",
        "                                             kernel_initializer = tf.truncated_normal_initializer(stddev = WEIGHT_INIT_STDDEV))\n",
        "    batch_trans_conv3 = tf.layers.batch_normalization(inputs = trans_conv3,\n",
        "                                                      training = training,\n",
        "                                                      epsilon = EPSILON,\n",
        "                                                      name = 'batch_trans_conv3')\n",
        "    trans_conv3_out = tf.nn.leaky_relu(batch_trans_conv3, name = 'trans_conv3_out')\n",
        "\n",
        "    # 64x64x128 -> 128x128x64\n",
        "    trans_conv4 = tf.layers.conv2d_transpose(inputs = trans_conv3_out,\n",
        "                                             filters = 64,\n",
        "                                             kernel_size = [5,5],\n",
        "                                             strides = [2,2],\n",
        "                                             padding = 'SAME',\n",
        "                                             kernel_initializer = tf.truncated_normal_initializer(stddev = WEIGHTS_INIT_STDDEV))\n",
        "    batch_trans_conv3 = tf.layers.batch_normalization(inputs = trans_conv4,\n",
        "                                                      training = training,\n",
        "                                                      epsilon = EPSILON,\n",
        "                                                      name = 'batch_trans_conv3')\n",
        "    trans_conv4_out = tf.nn.leaky_relu(batch_trans_conv3, name = 'trans_conv4_out')\n",
        "\n",
        "    # 128x128x64 -> 128x128x3\n",
        "    logits = tf.layers.conv2d_transpose(inputs = trans_conv4_out,\n",
        "                                        filters = 3,\n",
        "                                        kernel_size = [5,5],\n",
        "                                        strides = [1,1],\n",
        "                                        padding = 'SAME',\n",
        "                                        kernel_initializer = tf.truncated_normal_initilizer(stddev = WEIGHTS_INIT_STDDEV),\n",
        "                                        name = 'logits')\n",
        "    out =  tf.tanh(logits, name = 'out')\n",
        "    return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STLraaP-vdR-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def discriminator(x, reuse):\n",
        "  with tf.variable_scope(\"discriminator\", reuse = reuse):\n",
        "\n",
        "    # 128x128x3 -> 64x64x64\n",
        "    conv1 = tf.layers.conv2d(inputs = x,\n",
        "                             filters = 64,\n",
        "                             kernel_size = [5,5],\n",
        "                             strides = [2,2],\n",
        "                             padding = 'SAME',\n",
        "                             kernel_initializer = tf.truncated_normal_initializer(stddev = WEIGHT_INIT_STDDEV),\n",
        "                             name = 'conv1')\n",
        "    batch_norm1 = tf.layers.batch_normalization(conv1, \n",
        "                                                training = True,\n",
        "                                                epsilon = EPSILON,\n",
        "                                                name = 'batch_norm1')\n",
        "    conv1_out = tf.nn.leaky_relu(batch_norm1, name = 'conv1_out')\n",
        "\n",
        "    #64x64x64 -> 32x32x128\n",
        "    conv2 = tf.layers.conv2d(inputs = conv1_out,\n",
        "                             filters = 128,\n",
        "                             kernel_size = [5,5],\n",
        "                             strides = [2,2],\n",
        "                             padding = 'SAME',\n",
        "                             kernel_initializer = tf.truncated_normal_initializer(stddev = WEIGHTS_INIT_STDDEV),\n",
        "                             name = 'conv2')\n",
        "    batch_norm2 = tf.layers.batch_normalization(conv2, training = True,\n",
        "                                                epsilon = EPSILON,\n",
        "                                                name = 'batch_norm2')\n",
        "    conv2_out = tf.nn.leaky_relu(batch_norm2, name = 'conv2_out')\n",
        "\n",
        "    #32x32x128 -> 16x16x256\n",
        "    conv3 = tf.layers.conv2d(inputs = conv2_out,\n",
        "                             filters = 128,\n",
        "                             kernel_size = [5,5],\n",
        "                             strides = [2,2],\n",
        "                             padding = 'SAME',\n",
        "                             kernel_initializer = tf.truncated_normal_initializer(stddev = WEIGHTS_INIT_STDDEV),\n",
        "                             name = 'conv3')\n",
        "    batch_norm3 = tf.layers.batch_normalization(conv3, training = True,\n",
        "                                                epsilon = EPSILON,\n",
        "                                                name = 'batch_norm3')\n",
        "    conv3_out = tf.nn.leaky_relu(batch_norm3, name = 'conv2_out')\n",
        "\n",
        "    #16x16x256 -> 16x16x512\n",
        "    conv4 = tf.layers.conv2d(inputs = conv3_out,\n",
        "                             filters = 128,\n",
        "                             kernel_size = [5,5],\n",
        "                             strides = [2,2],\n",
        "                             padding = 'SAME',\n",
        "                             kernel_initializer = tf.truncated_normal_initializer(stddev = WEIGHTS_INIT_STDDEV),\n",
        "                             name = 'conv4')\n",
        "    batch_norm4 = tf.layers.batch_normalization(conv4, training = True,\n",
        "                                                epsilon = EPSILON, \n",
        "                                                name = 'batch_norm4')\n",
        "    conv4_out = tf.nn.leaky_relu(batch_norm4, name = 'conv4_out')\n",
        "\n",
        "    # 16x16x512 -> 8x8x1024\n",
        "    conv5 = tf.layers.conv2d(inputs = conv4_out, \n",
        "                             filters = 128,\n",
        "                             kernel_size = [5,5],\n",
        "                             strides = [2,2],\n",
        "                             padding = 'SAME',\n",
        "                             kernel_initializer = tf.truncated_normal_initializer(stddev = WEIGHTS_INIT_STDDEV),\n",
        "                             name = 'conv5')\n",
        "    batch_norm5 = tf.layers.batch_normalization(conv5, training = True,\n",
        "                                                epsilon = EPSILON,\n",
        "                                                name = 'batch_norm4')\n",
        "    conv4_out = tf.nn.leaky_relu(batch_norm4, name = 'conv5_out')\n",
        "\n",
        "    flatten = tf.reshape(conv5_out, (-1, 8*8*1024))\n",
        "    logits = tf.layers.dense(inputs = flatten,\n",
        "                             units = 1,\n",
        "                             activation = None)\n",
        "    out = tf.sigmoid(logits)\n",
        "    return out, logits"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sz46upfzvdV-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def model_loss(input_real, input_z, output_channel_dim):\n",
        "  g_model = generator(input_z, output_channel_dim, True)\n",
        "  \n",
        "  noisy_input_real = input_real + tf.random_normal(shape = tf.shape(input_real),\n",
        "                                                   mean = 0.0,\n",
        "                                                   stddev = random.uniform(0.0,0.1),\n",
        "                                                   dtype = tf.float32)\n",
        "  \n",
        "  d_model_real, d_logits_real = discriminator(noisy_input_real, reuse = False)\n",
        "  d_model_fake, d_logits_fake = discriminator(g_model, reuse = True)\n",
        "\n",
        "  d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_real,\n",
        "                                                                       bels = tf.ones_like(d_model_fake)))\n",
        "  d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake,\n",
        "                               bels = tf.zeros_like(d_model_fake)))\n",
        "  \n",
        "  d_loss = tf.reduce_mean(0.5 * (d_loss_real + d_loss_fake))\n",
        "  g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake, labels = tf.ones_like(d_model_fake)))\n",
        "\n",
        "  return d_loss, g_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJv4X4WEvdX9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def model_optimizers(d_loss, g_loss):\n",
        "  t_vars = tf.trainable_variables()\n",
        "  g_vars = [var for var in t_vars if var.name.startswith('generator')]\n",
        "  d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n",
        "\n",
        "  update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
        "  gen_updates = [op for op in update_ops if op.name.startswith('generator')]\n",
        "\n",
        "  with tf.control_dependencies(gen_updates):\n",
        "    d_train_opt = tf.train.AdamOptimizer(learning_rate = LR_D, beta1 = BETA1).minimize(d_loss, var_list = d_vars)\n",
        "    g_trian_opt = tf.train.AdamOptimizer(learning_rate = LR_G, beta1 = BETA1).minimize(g_loss, var_list = g_vars)\n",
        "    return d_train_opt, g_train_opt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Z5V3Dzsvdbq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def model_inputs(real_dim, z_dim):\n",
        "  inputs_real = tf.placeholder(tf.float32, (None, *real_dim), name = 'inputs_real')\n",
        "  inputs_z = tf.placeholder(tf.float32, (None, z_dim), name = 'input_z')\n",
        "  learning_rate_G = tf.placeholder(tf.float32, (None, z_dim), name = 'lr_g')\n",
        "  learning_rate_D = tf.placeholder(tf.float32, (None, z_dim), name = 'lr_d')\n",
        "  return inputs_real, inputs_z, learning_rate_G, learning_rate_D"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VACrjsN1vdgK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def show_samples(sample_images, name, epoch):\n",
        "  figure, axes = plt.subplots(1, len(sample_images), figsize = (IMAGE_SIZE, IMAGE_SIZE))\n",
        "  for index, axis in enumerate(axes):\n",
        "    axis.axis('off')\n",
        "    image_array = sample_images[index]\n",
        "    axis.imshow(image_array)\n",
        "    image = Image.fromarray(image_array)\n",
        "    image.save(name + \"_\" + str(epoch) + \"_\" + str(index) + '.png')\n",
        "    plt.savefig(name + \"_\" + str(epoch) + '.png', bbox_inches = 'tight', pad_inches = 0)\n",
        "    plt.show()\n",
        "    plt.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4CtvdSF_Wxj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def test(sess, input_z, out_channel_dim, epoch):\n",
        "  example_z = np.random.uniform(-1, 1, size = [SAMPLES_TO_SHOW, input_z.get_shape().as_list()[-1]])\n",
        "  samples = sess.run(generator(input_z, out_channel_dim, False), feed_dict = {input_z: example_z})\n",
        "  sample_images = [((sample+ 1.0) * 127.5).astype(np.uint8) for sample in samples]\n",
        "  show_samples(sample_images, OUTPUT_DIR + 'samples', epoch)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4aXdRhHz_Wz_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def summarize_epoch(epoch, duration, sess, d_losses, g_losses, input_z, data_shape):\n",
        "  minibatch_size = int(data_shape[0] / BATCH_SIZE)\n",
        "  print('Epoch {}/{}'.format(epoch, EPOCHS),\n",
        "        '/nDuration: {:.5f}'.format(duration),\n",
        "        '/nD Loss: {:.5f}'.format(np.mean(d_losses[-minibatch_size:])),\n",
        "        '/nG Loss: {:.5f}'.format(np.mean(g_losses[-minibatch_size:])))\n",
        "  \n",
        "  fig, ax = plt.subplots()\n",
        "  plt.plot(d_losses, label = 'Discriminator', alpha = 0.6)\n",
        "  plt.plot(g_losses, label = 'Generator', alpha = 0.6)\n",
        "  plt.title('Losses')\n",
        "  plt.legend()\n",
        "  plt.savefig(OUTPUT_DIR + 'losses_' + str(epoch) + '.png')\n",
        "  plt.show()\n",
        "  plt.close()\n",
        "  test(sess, input_z, data_shape[3], epoch)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HLQl2DcY_XTz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_batches(data):\n",
        "  batches = []\n",
        "  for i in range(int(data.shape[0]//BATCH_SIZE)):\n",
        "    batch = data[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]\n",
        "    augmented_images = []\n",
        "    for img in batch:\n",
        "      image = Image.fromarray(img)\n",
        "      if random.choice([True, False]):\n",
        "        image = image.transpose(Image.FLIP_LEFT_RIGHT)\n",
        "      augmented_images.append(np.asarray(image))\n",
        "      batch = np.asarray(augmented_images)\n",
        "      normalized_batch = (batch / 127.5) - 1.0\n",
        "      batches.append(normalized_batch)\n",
        "  return batches"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yEalSiJRvdiA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(get_batches, data_shape, checkpoint_to_load = None):\n",
        "  input_images, input_z, lr_G, lr_D = model_inputs(data_shape[1:], NOISE_SIZE)\n",
        "  d_loss, g_loss = model_loss(input_images, input_z, data_shape[3])\n",
        "  d_opt, g_opt = model_optimizers(d_loss, g_loss)\n",
        "\n",
        "  with tf.Session() as sess:\n",
        "    sess.run(tf.global_variables_initializer())\n",
        "    epoch = 0\n",
        "    iteration = 0\n",
        "    d_losses = []\n",
        "    g_losses = []\n",
        "\n",
        "    for epoch in range(EPOCHS):\n",
        "      epoch += 1\n",
        "      start_time = time.time()\n",
        "\n",
        "      for batch_images in get_batches:\n",
        "        iteration += 1\n",
        "        batch_z = np.random.uniform(-1, 1, size = (BATCH_SIZE, NOISE_SIZE))\n",
        "\n",
        "        _ = sess.run(d_opt, feed_dict = {input_images: batch_images, \n",
        "                                         input_z: batch_z,\n",
        "                                         lr_D: LR_D})\n",
        "        _ = sess.run(g_opt, feed_dict = {input_images: batch_images,\n",
        "                                         input_z: batch_z,\n",
        "                                         lr_G: LR_G})\n",
        "        d_losses.append(d_loss.eval({input_z: batch_z, input_images: batch_images}))\n",
        "        g_losses.append(g_loss.eval({input_z: batch_z}))\n",
        "\n",
        "        summarize_epoch(epoch, time.time()-start_time, sess, d_losses, g_losses, input_z, data_shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1m1LDsUPvdmL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# PATHS\n",
        "INPUT_DATA_DIR = \"./simplified/\" # PATH to the folder with the input images\n",
        "OUTPUT_DIR = './{date:%Y-%m-%d_%H:%M:%S}/'.format(date=datetime.datetime.now())\n",
        "if not os.path.exists(OUTPUT_DIR):\n",
        "  os.makedirs(OUTPUT_DIR)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "do_Omp3xvdoP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Hyperparameters\n",
        "\n",
        "IMAGE_SIZE = 128\n",
        "NOISE_SIZE = 100\n",
        "LR_D = 0.00004\n",
        "LR_G = 0.0004\n",
        "BATCH_SIZE = 64\n",
        "EPOCHS = 300\n",
        "BETA1 = 0.5\n",
        "WEIGHT_INIT_STDDEV = 0.02\n",
        "EPSILON = 0.00005\n",
        "SAMPLES_TO_SHOW = 5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1inkms0vdsG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "outputId": "aec213ae-42aa-4ca1-fb5b-e5d9c841a55a"
      },
      "source": [
        "# Training\n",
        "input_images = np.asarray([np.asarray(Image.open(file).resize((IMAGE_SIZE, IMAGE_SIZE))) for file in glob(INPUT_DATA_DIR + '*')])\n",
        "print('Input: ' + str(input_images.shape))\n",
        "\n",
        "np.random.shuffle(input_images)\n",
        "sample_images = random.sample(list(input_images), SAMPLES_TO_SHOW)\n",
        "show_samples(sample_images, OUTPUT_DIR + \"inputs\", 0)\n",
        "\n",
        "with tf.Graph().as_default():\n",
        "    train(get_batches(input_images), input_images.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input: (9877, 128, 128, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 9216x9216 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
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}