Assignment_4_Babak_Ansari_ImageGeneratorDCGAN_keras.ipynb

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    "colab": {
      "name": "Assignment_4_Babak_Ansari_ImageGeneratorDCGAN_keras.ipynb",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/babakansari/DeepLearning/blob/master/Assignment_4_Babak_Ansari_ImageGeneratorDCGAN_keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJOJlagp2d20",
        "colab_type": "text"
      },
      "source": [
        "# Assignment 4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5XWlYAiT2d23",
        "colab_type": "text"
      },
      "source": [
        "Babak Ansari-Jaberi, \n",
        "Deap Learning course at UofT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "267d5504009eca2b809a2691131366baebe98436",
        "id": "XOsWSWkz2d25",
        "colab_type": "code",
        "colab": {},
        "outputId": "aea9854c-d1f8-4741-b5bb-48dd7aedb846"
      },
      "source": [
        "import os\n",
        "import time\n",
        "import keras\n",
        "import random\n",
        "import datetime\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from glob import glob\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.layers import Input, Dense, Reshape, Flatten, Dropout\n",
        "from keras.layers import BatchNormalization, Activation, ZeroPadding2D\n",
        "from keras.layers.advanced_activations import LeakyReLU\n",
        "from keras.layers.convolutional import UpSampling2D, Conv2D , Conv2DTranspose\n",
        "from keras.models import Sequential, Model\n",
        "from keras.optimizers import Adam\n",
        "import warnings\n",
        "%matplotlib inline\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iS_odZ1C2d3A",
        "colab_type": "text"
      },
      "source": [
        "Hyperparameters:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTM5Rjcy2d3B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "IMAGE_SIZE = 64\n",
        "NOISE_SIZE = 100\n",
        "LR_D = 0.00004\n",
        "LR_G = 0.0004\n",
        "BATCH_SIZE = 64\n",
        "EPOCHS = 50 # For better results increase this value\n",
        "BETA1 = 0.5\n",
        "WEIGHT_INIT_STDDEV = 0.02\n",
        "EPSILON = 0.00005\n",
        "SAMPLES_TO_SHOW = 8"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "fc376df46433261bfeb643a95793718a9d969ed1",
        "id": "8wMd5gVn2d3E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generator(z=(NOISE_SIZE,)):\n",
        "    # 4 x 4 x 512\n",
        "    input_layer = Input(z)\n",
        "    hid = Dense(4*4*512, activation='relu', name =\"Dense\")(input_layer)    \n",
        "    hid = LeakyReLU(alpha=0.2)(hid)\n",
        "    hid = Reshape((4, 4, 512))(hid)\n",
        "\n",
        "    # 4x4x512 -> 8x8x512\n",
        "    hid = Conv2DTranspose(512, kernel_size=[5,5],\n",
        "                               strides=[2,2],\n",
        "                               padding=\"same\",\n",
        "                               kernel_initializer= keras.initializers.TruncatedNormal(stddev=WEIGHT_INIT_STDDEV),name =\"trans_conv1\")(hid)\n",
        "    hid = BatchNormalization(momentum=0.9, epsilon=EPSILON, name=\"batch_trans_conv1\")(hid)\n",
        "    hid = LeakyReLU(alpha=0.2,name =\"trans_conv1_out\")(hid)\n",
        "    \n",
        "\n",
        "    # 8x8x512 -> 16x16x256\n",
        "    hid = Conv2DTranspose(256, kernel_size=[5,5],\n",
        "                               strides=[2,2],\n",
        "                               padding=\"same\",\n",
        "                               kernel_initializer= keras.initializers.TruncatedNormal(stddev=WEIGHT_INIT_STDDEV),name =\"trans_conv2\")(hid)\n",
        "    hid = BatchNormalization(momentum=0.9, epsilon=EPSILON, name=\"batch_trans_conv2\")(hid)\n",
        "    hid = LeakyReLU(alpha=0.2,name =\"trans_conv2_out\")(hid)\n",
        "    \n",
        "    \n",
        "    # 16x16x256 -> 32x32x128\n",
        "    hid = Conv2DTranspose(128, kernel_size=[5,5],\n",
        "                               strides=[2,2],\n",
        "                               padding=\"same\",\n",
        "                               kernel_initializer= keras.initializers.TruncatedNormal(stddev=WEIGHT_INIT_STDDEV),name =\"trans_conv3\")(hid)\n",
        "    hid = BatchNormalization(momentum=0.9, epsilon=EPSILON, name=\"batch_trans_conv3\")(hid)\n",
        "    hid = LeakyReLU(alpha=0.2,name =\"trans_conv3_out\")(hid)    \n",
        "    \n",
        "\n",
        "    # 32x32x128 -> 64x64x64\n",
        "    hid = Conv2DTranspose(64, kernel_size=[5,5],\n",
        "                               strides=[2,2],\n",
        "                               padding=\"same\",\n",
        "                               kernel_initializer= keras.initializers.TruncatedNormal(stddev=WEIGHT_INIT_STDDEV),name =\"trans_conv4\")(hid)\n",
        "    hid = BatchNormalization(momentum=0.9, epsilon=EPSILON, name=\"batch_trans_conv4\")(hid)\n",
        "    hid = LeakyReLU(alpha=0.2,name =\"trans_conv4_out\")(hid)        \n",
        "    \n",
        "\n",
        "    # 64x64x64 -> 64x64x3\n",
        "    hid = Conv2DTranspose(3, kernel_size=[5,5],\n",
        "                               strides=[1,1],\n",
        "                               padding=\"same\",\n",
        "                               kernel_initializer= keras.initializers.TruncatedNormal(stddev=WEIGHT_INIT_STDDEV),name =\"logits\")(hid)\n",
        "  \n",
        "    out = Activation(\"tanh\", name =\"out\")(hid)\n",
        "    \n",
        "    model = Model(inputs=input_layer, outputs=out)\n",
        "    model.summary()\n",
        "  \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "ba53a4bb09dbcd57d3e3392f74ccd054ecf23ecb",
        "id": "edzPY-ts2d3I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def discriminator(input_shape=(IMAGE_SIZE, IMAGE_SIZE,3)):\n",
        "  # 64x64x3 -> 32x32x32\n",
        "    input_layer = Input(input_shape)\n",
        "    hid = Conv2D(filters=32,\n",
        "                    kernel_size=[5,5],\n",
        "                    strides=[2,2],\n",
        "                    padding=\"same\",\n",
        "                    kernel_initializer= keras.initializers.TruncatedNormal(stddev=WEIGHT_INIT_STDDEV),\n",
        "                    name = \"conv1\")(input_layer)\n",
        "    hid = BatchNormalization(momentum=0.9, epsilon=EPSILON, name=\"batch_norm1\")(hid)\n",
        "    hid = LeakyReLU(alpha=0.2, name=\"conv1_out\")(hid)    \n",
        "    \n",
        "    # 32x32x32-> 16x16x64 \n",
        "    hid = Conv2D(filters=64,\n",
        "                        kernel_size=[5,5],\n",
        "                        strides=[2,2],\n",
        "                        padding=\"same\",\n",
        "                        kernel_initializer= keras.initializers.TruncatedNormal(stddev=WEIGHT_INIT_STDDEV),\n",
        "                        name = \"conv2\")(hid)\n",
        "    hid = BatchNormalization(momentum=0.9, epsilon=EPSILON, name=\"batch_norm2\")(hid)\n",
        "    hid = LeakyReLU(alpha=0.2, name=\"conv2_out\")(hid) \n",
        "    \n",
        "    # 16x16x64  -> 8x8x128  \n",
        "    hid = Conv2D(filters=128,\n",
        "                    kernel_size=[5,5],\n",
        "                    strides=[2,2],\n",
        "                    padding=\"same\",\n",
        "                    kernel_initializer= keras.initializers.TruncatedNormal(stddev=WEIGHT_INIT_STDDEV),\n",
        "                    name = \"conv3\")(hid)\n",
        "    hid = BatchNormalization(momentum=0.9, epsilon=EPSILON, name=\"batch_norm3\")(hid)\n",
        "    hid = LeakyReLU(alpha=0.2, name=\"conv3_out\")(hid)\n",
        "    \n",
        "    # 8x8x128 -> 8x8x256\n",
        "    hid = Conv2D(filters=256,\n",
        "                    kernel_size=[5,5],\n",
        "                    strides=[1,1],\n",
        "                    padding=\"same\",\n",
        "                    kernel_initializer= keras.initializers.TruncatedNormal(stddev=WEIGHT_INIT_STDDEV),\n",
        "                    name = \"conv4\")(hid)\n",
        "    hid = BatchNormalization(momentum=0.9, epsilon=EPSILON, name=\"batch_norm4\")(hid)\n",
        "    hid = LeakyReLU(alpha=0.2, name=\"conv4_out\")(hid)\n",
        "    \n",
        "\n",
        "    # 8x8x256 -> 4x4x512\n",
        "    hid = Conv2D(filters=512,\n",
        "                    kernel_size=[5,5],\n",
        "                    strides=[2,2],\n",
        "                    padding=\"same\",\n",
        "                    kernel_initializer= keras.initializers.TruncatedNormal(stddev=WEIGHT_INIT_STDDEV),\n",
        "                    name = \"conv5\")(hid)  \n",
        "    hid = BatchNormalization(momentum=0.9, epsilon=EPSILON, name=\"batch_norm5\")(hid)\n",
        "    hid = LeakyReLU(alpha=0.2, name=\"conv5_out\")(hid)\n",
        "    \n",
        "    hid = Flatten(name = \"flatten\")(hid)\n",
        "\n",
        "    out = Dense(1, activation='sigmoid', name = \"ligit\")(hid)\n",
        "    model = Model(inputs= input_layer, outputs=out)\n",
        "\n",
        "    model.summary()\n",
        "\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hp9cxjH2d3L",
        "colab_type": "text"
      },
      "source": [
        "Discriminator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RFoE58L12d3N",
        "colab_type": "code",
        "colab": {},
        "outputId": "c1b7476e-1e55-4ece-d6d7-eb4f4c6d02ca"
      },
      "source": [
        "d = discriminator((IMAGE_SIZE, IMAGE_SIZE,3))\n",
        "d.compile(loss='binary_crossentropy',optimizer=Adam(lr=LR_D, beta_1=BETA1),metrics=['accuracy'])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         (None, 64, 64, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv1 (Conv2D)               (None, 32, 32, 32)        2432      \n",
            "_________________________________________________________________\n",
            "batch_norm1 (BatchNormalizat (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv1_out (LeakyReLU)        (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2 (Conv2D)               (None, 16, 16, 64)        51264     \n",
            "_________________________________________________________________\n",
            "batch_norm2 (BatchNormalizat (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2_out (LeakyReLU)        (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv3 (Conv2D)               (None, 8, 8, 128)         204928    \n",
            "_________________________________________________________________\n",
            "batch_norm3 (BatchNormalizat (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv3_out (LeakyReLU)        (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv4 (Conv2D)               (None, 8, 8, 256)         819456    \n",
            "_________________________________________________________________\n",
            "batch_norm4 (BatchNormalizat (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "conv4_out (LeakyReLU)        (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv5 (Conv2D)               (None, 4, 4, 512)         3277312   \n",
            "_________________________________________________________________\n",
            "batch_norm5 (BatchNormalizat (None, 4, 4, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv5_out (LeakyReLU)        (None, 4, 4, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 8192)              0         \n",
            "_________________________________________________________________\n",
            "ligit (Dense)                (None, 1)                 8193      \n",
            "=================================================================\n",
            "Total params: 4,367,553\n",
            "Trainable params: 4,365,569\n",
            "Non-trainable params: 1,984\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3k-kmL772d3R",
        "colab_type": "text"
      },
      "source": [
        "Only train the generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kaDOCCL72d3S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "d.trainable = False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zyyCL-CJ2d3V",
        "colab_type": "text"
      },
      "source": [
        "Generator:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMFqEcF72d3W",
        "colab_type": "code",
        "colab": {},
        "outputId": "3b457598-f24d-4579-ee24-3983a71da269"
      },
      "source": [
        "g = generator((NOISE_SIZE,))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "Dense (Dense)                (None, 8192)              827392    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)    (None, 8192)              0         \n",
            "_________________________________________________________________\n",
            "reshape_1 (Reshape)          (None, 4, 4, 512)         0         \n",
            "_________________________________________________________________\n",
            "trans_conv1 (Conv2DTranspose (None, 8, 8, 512)         6554112   \n",
            "_________________________________________________________________\n",
            "batch_trans_conv1 (BatchNorm (None, 8, 8, 512)         2048      \n",
            "_________________________________________________________________\n",
            "trans_conv1_out (LeakyReLU)  (None, 8, 8, 512)         0         \n",
            "_________________________________________________________________\n",
            "trans_conv2 (Conv2DTranspose (None, 16, 16, 256)       3277056   \n",
            "_________________________________________________________________\n",
            "batch_trans_conv2 (BatchNorm (None, 16, 16, 256)       1024      \n",
            "_________________________________________________________________\n",
            "trans_conv2_out (LeakyReLU)  (None, 16, 16, 256)       0         \n",
            "_________________________________________________________________\n",
            "trans_conv3 (Conv2DTranspose (None, 32, 32, 128)       819328    \n",
            "_________________________________________________________________\n",
            "batch_trans_conv3 (BatchNorm (None, 32, 32, 128)       512       \n",
            "_________________________________________________________________\n",
            "trans_conv3_out (LeakyReLU)  (None, 32, 32, 128)       0         \n",
            "_________________________________________________________________\n",
            "trans_conv4 (Conv2DTranspose (None, 64, 64, 64)        204864    \n",
            "_________________________________________________________________\n",
            "batch_trans_conv4 (BatchNorm (None, 64, 64, 64)        256       \n",
            "_________________________________________________________________\n",
            "trans_conv4_out (LeakyReLU)  (None, 64, 64, 64)        0         \n",
            "_________________________________________________________________\n",
            "logits (Conv2DTranspose)     (None, 64, 64, 3)         4803      \n",
            "_________________________________________________________________\n",
            "out (Activation)             (None, 64, 64, 3)         0         \n",
            "=================================================================\n",
            "Total params: 11,691,395\n",
            "Trainable params: 11,689,475\n",
            "Non-trainable params: 1,920\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uRTLJacb2d3a",
        "colab_type": "text"
      },
      "source": [
        "The discriminator takes generated images as input and determines validity\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FUHt6jQP2d3b",
        "colab_type": "code",
        "colab": {},
        "outputId": "08920712-b630-41f9-88d4-1d6767d9f3c7"
      },
      "source": [
        "\n",
        "gan_input = Input(shape=(NOISE_SIZE,))\n",
        "x = g(gan_input)\n",
        "gan_out = d(x)\n",
        "gan = Model(gan_input, gan_out)\n",
        "gan.summary()\n",
        "\n",
        "gan.compile(loss='binary_crossentropy',optimizer=Adam(lr=LR_G, beta_1=BETA1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "model_3 (Model)              (None, 64, 64, 3)         11691395  \n",
            "_________________________________________________________________\n",
            "model_2 (Model)              (None, 1)                 4367553   \n",
            "=================================================================\n",
            "Total params: 16,058,948\n",
            "Trainable params: 11,689,475\n",
            "Non-trainable params: 4,369,473\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eF5iB-GJ2d3f",
        "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",
        "\n",
        "    plt.show()\n",
        "    plt.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HOxDYICX2d3i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "def test(input_z, epoch):\n",
        "    samples = g.predict(input_z[:SAMPLES_TO_SHOW])\n",
        "    sample_images = [((sample + 1.0) * 127.5).astype(np.uint8) for sample in samples]\n",
        "    show_samples(sample_images, OUTPUT_DIR + \"samples\", epoch)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "0ed8f1c378f936ac81ae89b35d5b6914cd6efbbb",
        "id": "_OWyhpQf2d3l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def summarize_epoch(d_losses, g_losses , data_shape, epoch, duration, input_z):\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",
        "    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(input_z, epoch)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ozpI3ZT2d3p",
        "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 np.array(batches)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "d857345ed6c3ba7e4f614fa90b5ca42adb9917cf",
        "id": "KWGFaIim2d3s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Paths\n",
        "INPUT_DATA_DIR = \"simpsons-faces/cropped/\" # Path to the folder with input images. For more info check simspons_dataset.txt\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": "markdown",
      "metadata": {
        "id": "ypN_S42d2d3w",
        "colab_type": "text"
      },
      "source": [
        "Training:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "1ee6349afccb4d2f29f36d6605dd2f156350821a",
        "scrolled": false,
        "id": "REYoJrwc2d3x",
        "colab_type": "code",
        "colab": {},
        "outputId": "77711374-15a3-495e-a89d-dd32fe6399e5"
      },
      "source": [
        "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",
        "np.random.shuffle(input_images)\n",
        "\n",
        "sample_images = random.sample(list(input_images), SAMPLES_TO_SHOW)\n",
        "show_samples(sample_images, OUTPUT_DIR + \"inputs\", 0)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input: (9877, 64, 64, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 4608x4608 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xioOZb3m2d34",
        "colab_type": "text"
      },
      "source": [
        "Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTLkEL4i2d36",
        "colab_type": "code",
        "colab": {},
        "outputId": "4e0c961d-e81a-471b-d790-f9cfa6cf2a24"
      },
      "source": [
        "print(\"Training Starts!\")\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "d_losses = []\n",
        "g_losses = []\n",
        "cum_d_loss = 0\n",
        "cum_g_loss = 0\n",
        "\n",
        "for epoch in range(EPOCHS):\n",
        "    epoch += 1\n",
        "    start_time = time.time()\n",
        "    \n",
        "    for batch_images in get_batches(input_images):\n",
        "        \n",
        "        noise_data = np.random.normal(0, 1, size=(BATCH_SIZE, NOISE_SIZE))\n",
        "        # We use same labels for generated images as in the real training batch\n",
        "        generated_images = g.predict(noise_data)\n",
        "        \n",
        "        noise_prop = 0.05 # Randomly flip 5% of targets\n",
        "        real_labels = np.zeros((BATCH_SIZE, 1)) + np.random.uniform(low=0.0, high=0.1, size=(BATCH_SIZE, 1))\n",
        "        flipped_idx = np.random.choice(np.arange(len(real_labels)), size=int(noise_prop*len(real_labels)))\n",
        "        real_labels[flipped_idx] = 1 - real_labels[flipped_idx]\n",
        "        \n",
        "        # Train discriminator on real data\n",
        "        d_loss_real = d.train_on_batch(batch_images, real_labels)\n",
        "\n",
        "\n",
        "        # Prepare labels for generated data\n",
        "        fake_labels = np.ones((BATCH_SIZE, 1)) - np.random.uniform(low=0.0, high=0.1, size=(BATCH_SIZE, 1))\n",
        "        flipped_idx = np.random.choice(np.arange(len(fake_labels)), size=int(noise_prop*len(fake_labels)))\n",
        "        fake_labels[flipped_idx] = 1 - fake_labels[flipped_idx]\n",
        "        \n",
        "        # Train discriminator on generated data\n",
        "        d_loss_fake = d.train_on_batch(generated_images, fake_labels)\n",
        "        \n",
        "        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
        "        cum_d_loss += d_loss\n",
        "        d_losses.append(d_loss[0])\n",
        "        \n",
        "        \n",
        "        # Train generator\n",
        "        noise_data = np.random.normal(0, 1, size=(BATCH_SIZE, NOISE_SIZE))\n",
        "        g_loss = gan.train_on_batch(noise_data, np.zeros((BATCH_SIZE, 1)))\n",
        "        cum_g_loss += g_loss\n",
        "        g_losses.append(g_loss)\n",
        "        \n",
        "    if epoch > 0 and epoch % 20 == 0 :\n",
        "        print(\"saving model\")\n",
        "        d.save_weights(\"desc-simposon-model.h5-\" + str(epoch))\n",
        "        gan.save_weights(\"gan-simposon-model.h5-\" + str(epoch))\n",
        "        \n",
        "    # Plot the progress\n",
        "    summarize_epoch(d_losses, g_losses, input_images.shape, epoch, time.time()-start_time, noise_data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Starts!\n",
            "Epoch 1/50 \n",
            "Duration: 978.87105 \n",
            "D Loss: 1.36481 \n",
            "G Loss: 2.79073\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 4608x4608 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RcaWTdpo2d3-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}