{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CycleGAN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-On8I2N6ClU",
        "colab_type": "code",
        "outputId": "d0ee9c56-e199-48a1-a582-917a2845f9f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!wget https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/monet2photo.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-08-22 12:55:44--  https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/monet2photo.zip\n",
            "Resolving people.eecs.berkeley.edu (people.eecs.berkeley.edu)... 128.32.189.73\n",
            "Connecting to people.eecs.berkeley.edu (people.eecs.berkeley.edu)|128.32.189.73|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 305231073 (291M) [application/zip]\n",
            "Saving to: ‘monet2photo.zip’\n",
            "\n",
            "monet2photo.zip     100%[===================>] 291.09M  21.2MB/s    in 15s     \n",
            "\n",
            "2019-08-22 12:55:59 (19.6 MB/s) - ‘monet2photo.zip’ saved [305231073/305231073]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g7juGuCu7uPK",
        "colab_type": "code",
        "outputId": "042304bd-c13d-4dd1-ff9c-c15732f3664a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!mkdir data\n",
        "!cd data\n",
        "\n",
        "!unzip monet2photo.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  monet2photo.zip\n",
            "   creating: monet2photo/\n",
            "   creating: monet2photo/trainA/\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWktLVd68JTs",
        "colab_type": "code",
        "outputId": "f4969979-9732-4db8-c086-0db1baeb501b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import time\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import PIL\n",
        "\n",
        "from glob import glob\n",
        "from keras import Input, Model\n",
        "from keras.callbacks import TensorBoard\n",
        "from keras.layers import Conv2D, BatchNormalization, Activation\n",
        "from keras.layers import Add, Conv2DTranspose, ZeroPadding2D, LeakyReLU\n",
        "from keras.optimizers import Adam\n",
        "from imageio import imread\n",
        "from skimage.transform import resize"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzL3qUk4fkCp",
        "colab_type": "code",
        "outputId": "aaa90111-d001-4325-e500-34a62f755671",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "!pip install git+https://www.github.com/keras-team/keras-contrib.git"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://www.github.com/keras-team/keras-contrib.git\n",
            "  Cloning https://www.github.com/keras-team/keras-contrib.git to /tmp/pip-req-build-s_z8f_t6\n",
            "  Running command git clone -q https://www.github.com/keras-team/keras-contrib.git /tmp/pip-req-build-s_z8f_t6\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.6/dist-packages (from keras-contrib==2.0.8) (2.2.4)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.12.0)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.3.1)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.0.8)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (2.8.0)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.16.4)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (1.1.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras->keras-contrib==2.0.8) (3.13)\n",
            "Building wheels for collected packages: keras-contrib\n",
            "  Building wheel for keras-contrib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-contrib: filename=keras_contrib-2.0.8-cp36-none-any.whl size=101066 sha256=cfc88de5fa2eac3e2695288ecdf4f8de4699b739e42455ffe03a614c72c4b121\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-pzwn1cn5/wheels/11/27/c8/4ed56de7b55f4f61244e2dc6ef3cdbaff2692527a2ce6502ba\n",
            "Successfully built keras-contrib\n",
            "Installing collected packages: keras-contrib\n",
            "Successfully installed keras-contrib-2.0.8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQW5UWxIf8LB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSd_diaAecm1",
        "colab_type": "text"
      },
      "source": [
        "## Residual Block"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxhtAPEK-7Cy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def residual_block(x):\n",
        "  \n",
        "  \"\"\"\n",
        "  Residual block\n",
        "  \"\"\"\n",
        "  \n",
        "  res = Conv2D(filters = 128, kernel_size = 3, strides = 1, padding = \"same\")(x)\n",
        "  res = BatchNormalization(axis = 3, momentum = 0.9, epsilon = 1e-5)(res)\n",
        "  res = Activation('relu')(res)\n",
        "  res = Conv2D(filters = 128, kernel_size = 3, strides = 1, padding = \"same\")(res)\n",
        "  res = BatchNormalization(axis = 3, momentum = 0.9, epsilon = 1e-5)(res)\n",
        "  \n",
        "  return Add()([res, x])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-RjZz0QkIoU",
        "colab_type": "text"
      },
      "source": [
        "## Generator Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4iD-p4FebVR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def build_generator():\n",
        "  \n",
        "  \"\"\"\n",
        "  Creating a generator network with the hyperparameters defined below\n",
        "  \"\"\"\n",
        "  \n",
        "  input_shape = (128, 128, 3)\n",
        "  residual_blocks = 6\n",
        "  input_layer = Input(shape = input_shape)\n",
        "  \n",
        "  \n",
        "  ## 1st Convolutional Block\n",
        "  x = Conv2D(filters = 32, kernel_size = 7, strides = 1, padding = \"same\")(input_layer)\n",
        "  x = InstanceNormalization(axis = 1)(x)\n",
        "  x = Activation(\"relu\")(x)\n",
        "  \n",
        "  ## 2nd Convolutional Block\n",
        "  x = Conv2D(filters = 64, kernel_size = 3, strides = 2, padding = \"same\")(x)\n",
        "  x = InstanceNormalization(axis = 1)(x)\n",
        "  x = Activation(\"relu\")(x)\n",
        "  \n",
        "  ## 3rd Convolutional Block\n",
        "  x = Conv2D(filters = 128, kernel_size = 3, strides = 2, padding = \"same\")(x)\n",
        "  x = InstanceNormalization(axis = 1)(x)\n",
        "  x = Activation(\"relu\")(x)\n",
        "  \n",
        "  \n",
        "  ## Residual blocks\n",
        "  for _ in range(residual_blocks):\n",
        "    x = residual_block(x)\n",
        "  \n",
        "  \n",
        "  ## 1st Upsampling Block\n",
        "  x = Conv2DTranspose(filters = 64, kernel_size = 3, strides = 2, padding = \"same\", use_bias = False)(x)\n",
        "  x = InstanceNormalization(axis = 1)(x)\n",
        "  x = Activation(\"relu\")(x)\n",
        "  \n",
        "  ## 2nd Upsampling Block\n",
        "  x = Conv2DTranspose(filters = 32, kernel_size = 3, strides = 2, padding = \"same\", use_bias = False)(x)\n",
        "  x = InstanceNormalization(axis = 1)(x)\n",
        "  x = Activation(\"relu\")(x)\n",
        "  \n",
        "  ## Last Convolutional Layer\n",
        "  x = Conv2D(filters = 3, kernel_size = 7, strides = 1, padding = \"same\")(x)\n",
        "  output = Activation(\"tanh\")(x)\n",
        "  \n",
        "  \n",
        "  model = Model(inputs = [input_layer], outputs = [output])\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gmh5NIgyzyZZ",
        "colab_type": "text"
      },
      "source": [
        "## Discriminator Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lgsx2Z3EkEQG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def build_discriminator():\n",
        "  \n",
        "  \"\"\"\n",
        "  Create a discriminator network using the hyperparameters defined below\n",
        "  \"\"\"\n",
        "  \n",
        "  input_shape = (128, 128, 3)\n",
        "  hidden_layers = 3\n",
        "  \n",
        "  input_layer = Input(shape = input_shape)\n",
        "  \n",
        "  x = ZeroPadding2D(padding = (1, 1))(input_layer)\n",
        "  \n",
        "  \n",
        "  ## 1st Convolutional Block\n",
        "  x = Conv2D(filters = 64, kernel_size = 4, strides = 2, padding = \"valid\")(x)\n",
        "  x = LeakyReLU(alpha = 0.2)(x)\n",
        "  \n",
        "  x = ZeroPadding2D(padding = (1, 1))(x)\n",
        "  \n",
        "  \n",
        "  ## 3 Hidden Convolutional Blocks\n",
        "  for i in range(1, hidden_layers + 1):\n",
        "    x = Conv2D(filters = 2 ** i * 64, kernel_size = 4, strides = 2, padding = \"valid\")(x)\n",
        "    x = InstanceNormalization(axis = 1)(x)\n",
        "    x = LeakyReLU(alpha = 0.2)(x)\n",
        "    \n",
        "    x = ZeroPadding2D(padding = (1, 1))(x)\n",
        "    \n",
        "  \n",
        "  ## Last Convolutional Layer\n",
        "  output = Conv2D(filters = 1, kernel_size = 4, strides = 1, activation = \"sigmoid\")(x)\n",
        "  \n",
        "  \n",
        "  model = Model(inputs = [input_layer], outputs = [output])\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_JvJPmVpnzG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_images(data_dir):\n",
        "  \n",
        "  imagesA = glob(data_dir + '/testA/*.*')\n",
        "  imagesB = glob(data_dir + '/testB/*.*')\n",
        "  \n",
        "  allImagesA = []\n",
        "  allImagesB = []\n",
        "  \n",
        "  for index, filename in enumerate(imagesA):\n",
        "    imgA = imread(filename, pilmode = \"RGB\")\n",
        "    imgB = imread(imagesB[index], pilmode = \"RGB\")\n",
        "    \n",
        "    imgA = resize(imgA, (128, 128))\n",
        "    imgB = resize(imgB, (128, 128))\n",
        "    \n",
        "    if np.random.random() > 0.5:\n",
        "      imgA = np.fliplr(imgA)\n",
        "      imgB = np.fliplr(imgB)\n",
        "      \n",
        "    \n",
        "    allImagesA.append(imgA)\n",
        "    allImagesB.append(imgB)\n",
        "    \n",
        "  \n",
        "  ## Normalize images\n",
        "  allImagesA = np.array(allImagesA) / 127.5 - 1.\n",
        "  allImagesB = np.array(allImagesB) / 127.5 - 1.\n",
        "  \n",
        "  return allImagesA, allImagesB"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C7tvhTzYs7Kt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_test_batch(data_dir, batch_size):\n",
        "  \n",
        "  imagesA = glob(data_dir + '/testA/*.*')\n",
        "  imagesB = glob(data_dir + '/testB/*.*')\n",
        "  \n",
        "  imagesA = np.random.choice(a = imagesA, size = batch_size)\n",
        "  imagesB = np.random.choice(a = imagesB, size = batch_size)\n",
        "  \n",
        "  allA = []\n",
        "  allB = []\n",
        "  \n",
        "  for i in range(len(imagesA)):\n",
        "    ## Load and resize images\n",
        "    imgA = resize(imread(imagesA[i], pilmode = 'RGB').astype(np.float32), (128, 128))\n",
        "    imgB = resize(imread(imagesB[i], pilmode = 'RGB').astype(np.float32), (128, 128))\n",
        "    \n",
        "    allA.append(imgA)\n",
        "    allB.append(imgB)\n",
        "    \n",
        "  return np.array(allA) / 127.5 - 1.0, np.array(allB) / 127.5 - 1.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KnGwFikvvR8y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir results\n",
        "\n",
        "def save_images(originalA, generatedB, reconstructedA,\n",
        "                originalB, generatedA, reconstructedB, path):\n",
        "  \n",
        "  \"\"\"\n",
        "  Save images\n",
        "  \"\"\"\n",
        "  \n",
        "  fig = plt.figure()\n",
        "  \n",
        "  ax = fig.add_subplot(2, 3, 1)\n",
        "  ax.imshow(originalA)\n",
        "  ax.axis(\"off\")\n",
        "  ax.set_title(\"Original\")\n",
        "  \n",
        "  ax = fig.add_subplot(2, 3, 2)\n",
        "  ax.imshow(generatedB)\n",
        "  ax.axis(\"off\")\n",
        "  ax.set_title(\"Generated\")\n",
        "  \n",
        "  ax = fig.add_subplot(2, 3, 3)\n",
        "  ax.imshow(reconstructedA)\n",
        "  ax.axis(\"off\")\n",
        "  ax.set_title(\"Reconstructed\")\n",
        "  \n",
        "  ax = fig.add_subplot(2, 3, 4)\n",
        "  ax.imshow(originalB)\n",
        "  ax.axis(\"off\")\n",
        "  ax.set_title(\"Original\")\n",
        "  \n",
        "  ax = fig.add_subplot(2, 3, 5)\n",
        "  ax.imshow(generatedA)\n",
        "  ax.axis(\"off\")\n",
        "  ax.set_title(\"Generated\")\n",
        "  \n",
        "  ax = fig.add_subplot(2, 3, 6)\n",
        "  ax.imshow(reconstructedB)\n",
        "  ax.axis(\"off\")\n",
        "  ax.set_title(\"Reconstructed\")\n",
        "  \n",
        "  plt.savefig(path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S9r7OmwCxV04",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def write_log(callback, name, loss, batch_no):\n",
        "  \n",
        "  \"\"\"\n",
        "  Write training summary to TensorBoard\n",
        "  \"\"\"\n",
        "  \n",
        "  summary = tf.Summary()\n",
        "  summary_value = summary.value.add()\n",
        "  summary_value.simple_value = loss\n",
        "  summary_value.tag = name\n",
        "  callback.writer.add_summary(summary, batch_no)\n",
        "  callback.writer.flush()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bpcRW1SpyWrK",
        "colab_type": "code",
        "outputId": "b480ba42-96f6-4202-f33a-cc25030d5b16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "if __name__ == '__main__':\n",
        "  \n",
        "  data_dir = \"monet2photo\"\n",
        "  batch_size = 1\n",
        "  epochs = 500\n",
        "  mode = 'train'\n",
        "  \n",
        "  if mode == 'train':\n",
        "    \n",
        "    \"\"\"\n",
        "    Load dataset\n",
        "    \"\"\"\n",
        "    \n",
        "    imagesA, imagesB = load_images(data_dir = data_dir)\n",
        "    \n",
        "    ## Define the common optimizer\n",
        "    common_optimizer = Adam(0.002, 0.5)\n",
        "    \n",
        "    \n",
        "    ## Build and compile discriminator networks\n",
        "    discriminatorA = build_discriminator()\n",
        "    discriminatorB = build_discriminator()\n",
        "    \n",
        "    discriminatorA.compile(loss = 'mse', \n",
        "                           optimizer = common_optimizer,\n",
        "                           metrics = ['accuracy'])\n",
        "    discriminatorB.compile(loss = 'mse',\n",
        "                           optimizer = common_optimizer,\n",
        "                           metrics = ['accuracy'])\n",
        "    \n",
        "    \n",
        "    ## Build generator networks\n",
        "    generatorA_to_B = build_generator()\n",
        "    generatorB_to_A = build_generator()\n",
        "    \n",
        "    \n",
        "    \"\"\"\n",
        "    Create an adversarial network\n",
        "    \"\"\"\n",
        "    \n",
        "    inputA = Input(shape = (128, 128, 3))\n",
        "    inputB = Input(shape = (128, 128, 3))\n",
        "    \n",
        "    \n",
        "    ## --> Generated images using both of the generator networks\n",
        "    generatedB = generatorA_to_B(inputA)\n",
        "    generatedA = generatorB_to_A(inputB)\n",
        "    \n",
        "    \n",
        "    ## --> Reconstruct the images back to the original ones\n",
        "    reconstructedA = generatorB_to_A(generatedB)\n",
        "    reconstructedB = generatorA_to_B(generatedA)\n",
        "    \n",
        "    generatedA_Id = generatorB_to_A(inputA)\n",
        "    generatedB_Id = generatorA_to_B(inputB)\n",
        "    \n",
        "    \n",
        "    ## Make both of the discriminator networks non-trainable\n",
        "    discriminatorA.trainable = False\n",
        "    discriminatorB.trainable = False\n",
        "    \n",
        "    probsA = discriminatorA(generatedA)\n",
        "    probsB = discriminatorB(generatedB)\n",
        "    \n",
        "    \n",
        "    adversarial_model = Model(inputs = [inputA, inputB],\n",
        "                              outputs = [probsA, probsB, \n",
        "                                         reconstructedA, reconstructedB,\n",
        "                                         generatedA_Id, generatedB_Id])\n",
        "    \n",
        "    adversarial_model.compile(loss = ['mse', 'mse', 'mae', 'mae', 'mae', 'mae'],\n",
        "                              loss_weights = [1, 1, 10.0, 10.0, 1.0, 1.0],\n",
        "                              optimizer = common_optimizer)\n",
        "    \n",
        "    \n",
        "    tensorboard = TensorBoard(log_dir = \"logs/{}\".format(time.time()),\n",
        "                              write_images = True, write_grads = True,\n",
        "                              write_graph = True)\n",
        "    tensorboard.set_model(generatorA_to_B)\n",
        "    tensorboard.set_model(generatorB_to_A)\n",
        "    tensorboard.set_model(discriminatorA)\n",
        "    tensorboard.set_model(discriminatorB)\n",
        "    \n",
        "    \n",
        "    real_labels = np.ones((batch_size, 7, 7, 1))\n",
        "    fake_labels = np.zeros((batch_size, 7, 7, 1))\n",
        "    \n",
        "    for epoch in range(epochs):\n",
        "      print(\"Epoch: {}\".format(epoch))\n",
        "      \n",
        "      D_losses = []\n",
        "      G_losses = []\n",
        "      \n",
        "      num_batches = int(min(imagesA.shape[0], imagesB.shape[0]) / batch_size)\n",
        "      print(\"Number of batches: {}\".format(num_batches))\n",
        "      \n",
        "      \n",
        "      for index in range(num_batches):\n",
        "        print(\"Batch: {}\".format(index))\n",
        "        \n",
        "        ## Sample images\n",
        "        batchA = imagesA[index * batch_size: (index + 1) * batch_size]\n",
        "        batchB = imagesB[index * batch_size: (index + 1) * batch_size]\n",
        "        \n",
        "        ## Translate images to opposite domain\n",
        "        generatedB = generatorA_to_B.predict(batchA)\n",
        "        generatedA = generatorB_to_A.predict(batchB)\n",
        "        \n",
        "        ## Train the discriminator A on real and fake images\n",
        "        D_A_Loss1 = discriminatorA.train_on_batch(batchA, real_labels)\n",
        "        D_A_Loss2 = discriminatorA.train_on_batch(generatedA, fake_labels)\n",
        "        \n",
        "        ## Train the discriminator B on real and fake images\n",
        "        D_B_Loss1 = discriminatorB.train_on_batch(batchB, real_labels)\n",
        "        D_B_Loss2 = discriminatorB.train_on_batch(generatedB, fake_labels)\n",
        "        \n",
        "        ## Calculate the total discriminator loss\n",
        "        D_loss = 0.5 * np.add(0.5 * np.add(D_A_Loss1, D_A_Loss2), \n",
        "                              0.5 * np.add(D_B_Loss1, D_B_Loss2))\n",
        "        \n",
        "        print(\"D_Loss: {}\".format(D_loss))\n",
        "        \n",
        "        \n",
        "        \"\"\"\n",
        "        Train the generator networks\n",
        "        \"\"\"\n",
        "        \n",
        "        G_loss = adversarial_model.train_on_batch([batchA, batchB],\n",
        "                                                  [real_labels, real_labels,\n",
        "                                                   batchA, batchB,\n",
        "                                                   batchA, batchB])\n",
        "        \n",
        "        print(\"G_Loss: {}\".format(G_loss))\n",
        "        \n",
        "        D_losses.append(D_loss)\n",
        "        G_losses.append(G_loss)\n",
        "        \n",
        "        \n",
        "      \"\"\"\n",
        "      Save losses to TensorBoard after every epoch\n",
        "      \"\"\"\n",
        "      \n",
        "      write_log(tensorboard, 'discriminator_loss', np.mean(D_losses), epoch)\n",
        "      write_log(tensorboard, 'generator_loss', np.mean(G_losses), epoch)\n",
        "      \n",
        "      \n",
        "      ## Sample and save images after every 10 epochs\n",
        "      if epoch % 10 == 0:\n",
        "        \n",
        "        ## Get a batch of test data\n",
        "        batchA, batchB = load_test_batch(data_dir = data_dir, batch_size = 2)\n",
        "        \n",
        "        ## Generate images\n",
        "        generatedB = generatorA_to_B.predict(batchA)\n",
        "        generatedA = generatorB_to_A.predict(batchB)\n",
        "        \n",
        "        ## Get reconstructed images\n",
        "        recons_A = generatorB_to_A.predict(generatedB)\n",
        "        recons_B = generatorA_to_B.predict(generatedA)\n",
        "        \n",
        "        ## Save original, generated and reconstructed images\n",
        "        for i in range(len(generatedA)):\n",
        "          save_images(originalA = batchA[i], generatedB = generatedB[i], reconstructedA = recons_A[i],\n",
        "                      originalB = batchB[i], generatedA = generatedA[i], reconstructedB = recons_B[i],\n",
        "                      path = \"results/gen_{}_{}\".format(epoch, i))\n",
        "          \n",
        "    \n",
        "    ## Save models\n",
        "    generatorA_to_B.save_weights(\"generatorA_to_B.h5\")\n",
        "    generatorB_to_A.save_weights(\"generatorB_to_A.h5\")\n",
        "    discriminatorA.save_weights(\"discriminatorA.h5\")\n",
        "    discriminatorB.save_weights(\"discriminatorB.h5\")\n",
        "    \n",
        "    \n",
        "  elif mode == 'predict':\n",
        "    \n",
        "    ## Build generator networks\n",
        "    generatorA_to_B = build_generator()\n",
        "    generatorB_to_A = build_generator()\n",
        "    \n",
        "    generatorA_to_B.load_weights(\"generatorA_to_B.h5\")\n",
        "    generatorB_to_A.load_weights(\"generatorB_to_A.h5\")\n",
        "    \n",
        "    \n",
        "    ## Get a batch of test data\n",
        "    batchA, batchB = load_test_batch(data_dir = data_dir, batch_size = 2)\n",
        "    \n",
        "    \n",
        "    ## Save images\n",
        "    generatedB = generatorA_to_B.predict(batchA)\n",
        "    generatedA = generatorB_to_A.predict(batchB)\n",
        "    \n",
        "    reconsA = generatorB_to_A.predict(generatedB)\n",
        "    reconsB = generatorA_to_B.predict(generatedA)\n",
        "    \n",
        "    for i in range(len(generatedA)):\n",
        "      save_images(originalA = batchA[i], generatedB = generatedB[i], reconstructedA = recons_A[i],\n",
        "                  originalB = batchB[i], generatedA = generatedA[i], reconstructedB = recons_B[i],\n",
        "                  path = \"results/test_{}\".format(i))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0822 12:57:49.734445 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0822 12:57:49.736517 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0822 12:57:49.752670 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0822 12:57:50.060953 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "W0822 12:57:50.235987 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "W0822 12:57:50.237730 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "W0822 12:57:53.725808 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
            "\n",
            "W0822 12:58:08.037763 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:850: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n",
            "\n",
            "W0822 12:58:08.039646 139716881598336 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:853: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0\n",
            "Number of batches: 121\n",
            "Batch: 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
            "  'Discrepancy between trainable weights and collected trainable'\n",
            "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
            "  'Discrepancy between trainable weights and collected trainable'\n",
            "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
            "  'Discrepancy between trainable weights and collected trainable'\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "D_Loss: [0.5305587  0.49489796]\n",
            "G_Loss: [25.205275, 5.0539395e-09, 6.623143e-08, 1.2187737, 1.0739226, 1.2182144, 1.0600963]\n",
            "Batch: 1\n",
            "D_Loss: [0.4999975 0.5      ]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
            "  'Discrepancy between trainable weights and collected trainable'\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "G_Loss: [3.9817982, 8.908386e-11, 2.0940565e-11, 0.20176497, 0.16031894, 0.19941089, 0.16154826]\n",
            "Batch: 2\n",
            "D_Loss: [0.4999979 0.5      ]\n",
            "G_Loss: [0.14613247, 1.06901446e-10, 3.266916e-11, 0.008199459, 0.0050833183, 0.008200543, 0.0051041497]\n",
            "Batch: 3\n",
            "D_Loss: [0.49999782 0.5       ]\n",
            "G_Loss: [0.07484748, 1.3021768e-10, 4.5673253e-11, 0.0035987599, 0.0032056177, 0.0035989643, 0.0032047378]\n",
            "Batch: 4\n",
            "D_Loss: [0.49999744 0.5       ]\n",
            "G_Loss: [0.055565186, 1.7880945e-10, 7.669714e-11, 0.0039672605, 0.001084133, 0.003967266, 0.0010839804]\n",
            "Batch: 5\n",
            "D_Loss: [0.4999969 0.5      ]\n",
            "G_Loss: [0.08253879, 2.7708774e-10, 1.625803e-10, 0.0032744212, 0.004229109, 0.0032744072, 0.0042290743]\n",
            "Batch: 6\n",
            "D_Loss: [0.4999958 0.5      ]\n",
            "G_Loss: [0.10562783, 5.279189e-10, 5.5828697e-10, 0.0035202028, 0.00608233, 0.0035201842, 0.0060823206]\n",
            "Batch: 7\n",
            "D_Loss: [0.49999303 0.5       ]\n",
            "G_Loss: [0.086792976, 1.5879688e-09, 1.0446463e-08, 0.00399099, 0.0038992828, 0.0039909696, 0.0038992679]\n",
            "Batch: 8\n",
            "D_Loss: [0.49997103 0.5       ]\n",
            "G_Loss: [1.0719333, 3.3966444e-08, 0.9998367, 0.0031124214, 0.003441812, 0.00311242, 0.0034418332]\n",
            "Batch: 9\n",
            "D_Loss: [0.74946296 0.25      ]\n",
            "G_Loss: [2.074207, 0.9999405, 0.9958127, 0.0041293586, 0.0030027935, 0.004129373, 0.003002779]\n",
            "Batch: 10\n",
            "D_Loss: [0.6524923  0.33163264]\n",
            "G_Loss: [2.0854123, 0.9999276, 0.99994147, 0.0026702269, 0.0051064147, 0.0026702278, 0.0051064114]\n",
            "Batch: 11\n",
            "D_Loss: [0.49996725 0.5       ]\n",
            "G_Loss: [2.0732508, 0.9998624, 0.99994475, 0.0046732277, 0.0020034672, 0.0046732393, 0.0020034793]\n",
            "Batch: 12\n",
            "D_Loss: [0.4999519 0.5      ]\n",
            "G_Loss: [2.076531, 0.99949414, 0.9999042, 0.0031265437, 0.0038855155, 0.0031265533, 0.0038854443]\n",
            "Batch: 13\n",
            "D_Loss: [0.4999232 0.5      ]\n",
            "G_Loss: [2.0755465, 0.9983372, 0.999865, 0.005020872, 0.0020104526, 0.005020882, 0.0020104344]\n",
            "Batch: 14\n",
            "D_Loss: [0.52557886 0.46428573]\n",
            "G_Loss: [2.0670457, 1.0, 0.9998054, 0.003332845, 0.0027798975, 0.003332844, 0.0027799131]\n",
            "Batch: 15\n",
            "D_Loss: [0.49995142 0.5       ]\n",
            "G_Loss: [2.0627306, 1.0, 0.99967253, 0.0035078381, 0.0022247022, 0.0035078109, 0.0022247187]\n",
            "Batch: 16\n",
            "D_Loss: [0.49991855 0.5       ]\n",
            "G_Loss: [2.0548084, 1.0, 0.9991885, 0.0022493354, 0.002807031, 0.0022493303, 0.0028070305]\n",
            "Batch: 17\n",
            "D_Loss: [0.4998095 0.5      ]\n",
            "G_Loss: [2.070156, 1.0, 0.99236864, 0.0031272955, 0.0039442857, 0.003127303, 0.0039442796]\n",
            "Batch: 18\n",
            "D_Loss: [0.74809253 0.25      ]\n",
            "G_Loss: [1.0848153, 1.0, 0.0, 0.0033807512, 0.00432973, 0.0033807652, 0.0043297005]\n",
            "Batch: 19\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0574374, 1.0, 0.0, 0.0034567295, 0.0017648425, 0.0034567132, 0.0017648609]\n",
            "Batch: 20\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0851662, 1.0, 0.0, 0.0030887434, 0.0046536615, 0.0030885967, 0.004653617]\n",
            "Batch: 21\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0874918, 1.0, 0.0, 0.0042378055, 0.003715997, 0.004237742, 0.0037159426]\n",
            "Batch: 22\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0767947, 1.0, 0.0, 0.003374986, 0.0036064032, 0.003374503, 0.0036063932]\n",
            "Batch: 23\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0570321, 1.0, 0.0, 0.0031483816, 0.0020362842, 0.0031492207, 0.0020362176]\n",
            "Batch: 24\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.051461, 1.0, 0.0, 0.0022763265, 0.0024026777, 0.0022682818, 0.0024027082]\n",
            "Batch: 25\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0806954, 1.0, 0.0, 0.0036099807, 0.0037259345, 0.0036102007, 0.003725971]\n",
            "Batch: 26\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0783005, 1.0, 0.0, 0.004731375, 0.0023868494, 0.00473141, 0.0023868706]\n",
            "Batch: 27\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0667522, 1.0, 0.0, 0.0033675889, 0.002700807, 0.0033674338, 0.0027008553]\n",
            "Batch: 28\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.052085, 1.0, 0.0, 0.0022735798, 0.002461434, 0.0022734143, 0.0024614318]\n",
            "Batch: 29\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.1021444, 1.0, 0.0, 0.0048889676, 0.0043968046, 0.004889828, 0.004396752]\n",
            "Batch: 30\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0780519, 1.0, 0.0, 0.003395278, 0.0037004033, 0.0033947781, 0.0037003746]\n",
            "Batch: 31\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0768307, 1.0, 0.0, 0.0032819586, 0.0037026913, 0.0032816841, 0.0037025642]\n",
            "Batch: 32\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0663079, 1.0, 0.0, 0.0033780397, 0.0026499787, 0.0033778278, 0.0026499734]\n",
            "Batch: 33\n",
            "D_Loss: [0.5 0.5]\n",
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "W0822 12:59:28.696646 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 12:59:28.715202 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 12:59:28.736211 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 12:59:28.753946 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 12:59:28.772412 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 12:59:28.792045 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 12:59:28.878107 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 12:59:28.897553 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 12:59:28.917304 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 12:59:28.937082 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 12:59:28.957372 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 12:59:28.978284 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
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            "Batch: 0\n",
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            "Batch: 1\n",
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            "Batch: 2\n",
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            "Batch: 3\n",
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "W0822 13:05:44.663225 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:05:44.688777 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:05:44.707403 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
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            "G_Loss: [1.0395207, 1.0, 0.0, 0.0017244318, 0.0018683599, 0.0017247358, 0.001868157]\n"
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          "text": [
            "W0822 13:05:44.726286 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:05:44.747255 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:05:44.765892 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:05:44.837347 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:05:44.854864 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:05:44.872599 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:05:44.891363 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:05:44.910731 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:05:44.929961 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
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          "text": [
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            "Batch: 0\n",
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          "name": "stdout"
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        {
          "output_type": "stream",
          "text": [
            "W0822 13:12:00.810707 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:12:00.835649 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:12:00.855693 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
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          "name": "stderr"
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            "G_Loss: [1.039357, 1.0, 0.0, 0.0017186594, 0.0018592626, 0.0017186969, 0.0018590989]\n"
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          "text": [
            "W0822 13:12:00.874701 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:12:00.895823 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:12:00.913496 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:12:00.991259 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:12:01.010306 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:12:01.029375 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:12:01.048117 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:12:01.066426 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "W0822 13:12:01.084241 139716881598336 image.py:648] Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
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          "text": [
            "Epoch: 21\n",
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            "Batch: 0\n",
            "D_Loss: [0.5 0.5]\n",
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            "G_Loss: [1.0320256, 1.0, 0.0, 0.0017160284, 0.0011955148, 0.0017146135, 0.0011955099]\n",
            "Batch: 3\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0510559, 1.0, 0.0, 0.0028536946, 0.0017877315, 0.0028541593, 0.0017875144]\n",
            "Batch: 4\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0260245, 1.0, 0.0, 0.0012029654, 0.0011628708, 0.0012032909, 0.0011627255]\n",
            "Batch: 5\n",
            "D_Loss: [0.5 0.5]\n",
            "G_Loss: [1.0420709, 1.0, 0.0, 0.0010179211, 0.002806708, 0.0010178435, 0.0028068065]\n",
            "Batch: 6\n",
            "D_Loss: [0.5 0.5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDD37FBMlLA2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}