gan_quickDraw.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XetlqiapRw_0"
      },
      "outputs": [],
      "source": [
        "#@title All needed imports { display-mode: \"form\" }\n",
        "import os\n",
        "import glob\n",
        "import imageio\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from tensorflow.keras.layers import Dense, Dropout, Conv2D, Flatten, MaxPooling2D, Conv2DTranspose, Reshape, Activation, BatchNormalization, GlobalAvgPool2D\n",
        "import tensorflow as tf\n",
        "import time\n",
        "import urllib\n",
        "import IPython\n",
        "import tqdm.notebook as note"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Prevent Auto-Disconnect { display-mode: \"form\" }\n",
        "#@markdown using javascript to always reconnect (at least during 6-12 hrs)\n",
        "\n",
        "js_code = '''\n",
        "function ClickConnect(){\n",
        "console.log(\"Working\");\n",
        "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
        "}\n",
        "setInterval(ClickConnect,60000)\n",
        "'''\n",
        "IPython.display.Javascript(js_code)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "h3qSv0PUnKi2",
        "outputId": "dd9aef91-db60-49f5-948f-ccda5931b103"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/javascript": [
              "\n",
              "function ClickConnect(){\n",
              "console.log(\"Working\");\n",
              "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
              "}\n",
              "setInterval(ClickConnect,60000)\n"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categories = [line.rstrip(b'\\n') for line in urllib.request.urlopen('https://raw.githubusercontent.com/googlecreativelab/quickdraw-dataset/master/categories.txt')]\n",
        "category = 'candle'\n",
        "\n",
        "#@title Download images { display-mode: \"form\" }\n",
        "#@markdown Creates a folder to download the original drawings into. <br> <br>\n",
        "#@markdown We chose to use the numpy format: <br> 1x784 pixel vectors, with values going from 0 (white) to 255 (black). We reshape them later to 28x28 grids and normalize the pixel intensity to [-1, 1]\n",
        "if not os.path.isdir('npy_files'):\n",
        "    os.mkdir('npy_files')\n",
        "    \n",
        "url = f'https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/{category}.npy'  \n",
        "urllib.request.urlretrieve(url, f'npy_files/{category}.npy')\n",
        "images = np.load(f'npy_files/{category}.npy')\n",
        "\n",
        "#@markdown <br> We have **141545** images to train on.\n",
        "dataset_size = len(images)"
      ],
      "metadata": {
        "id": "j3_OAgQunu9e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Discriminator { display-mode: \"form\" }\n",
        "class Discriminator(tf.keras.Model):\n",
        "\n",
        "    def __init__(self, DROPOUT_AMOUNT=0.2):\n",
        "        super(Discriminator, self).__init__()\n",
        "        self.lyrs = [\n",
        "                     # block 1\n",
        "                     Conv2D(\n",
        "                         filters=32, kernel_size=3, strides=2, \n",
        "                         padding=\"same\", activation=\"leaky_relu\",\n",
        "                         name='d_conv_1', kernel_regularizer=\"l2\"),\n",
        "                     MaxPooling2D((2, 2), padding='same'),\n",
        "                     Dropout(DROPOUT_AMOUNT, name=\"d_dropout\"),\n",
        "                     \n",
        "                     # block 2\n",
        "                     Conv2D(\n",
        "                         filters=64, kernel_size=3, strides=2, \n",
        "                         padding=\"same\", activation=\"leaky_relu\",\n",
        "                         name='d_conv_1', kernel_regularizer=\"l2\"),\n",
        "                     MaxPooling2D((2, 2), padding='same'),\n",
        "                     Dropout(DROPOUT_AMOUNT, name=\"d_dropout\"),\n",
        "\n",
        "                     Conv2D(\n",
        "                         filters=128, kernel_size=3, strides=2, \n",
        "                         padding=\"same\", activation=\"leaky_relu\",\n",
        "                         name='d_conv_1', kernel_regularizer=\"l2\"),\n",
        "                     Dropout(DROPOUT_AMOUNT, name=\"d_dropout\"),\n",
        "\n",
        "                     # flatten\n",
        "                     GlobalAvgPool2D(),\n",
        "                     # applying bottleneck (binary classification)\n",
        "                     tf.keras.layers.Dense(units=1, activation=\"sigmoid\", name='d_output_layer')\n",
        "                     ]\n",
        "    @tf.function\n",
        "    def call(self, data, training=True):\n",
        "        d = data\n",
        "        for l in self.layers:\n",
        "            if training or l.name != \"d_dropout\":\n",
        "                d = l(d)\n",
        "        return d"
      ],
      "metadata": {
        "id": "7wpTZe3n5nkF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Generator { display-mode: \"form\" }\n",
        "class Generator(tf.keras.Model):\n",
        "\n",
        "    def __init__(self, DROPOUT_AMOUNT=0.2, N_FAKE_DATA=100):\n",
        "        super(Generator, self).__init__()\n",
        "        self.lyrs = [\n",
        "                     # block 1\n",
        "                     Dense(7*7*128, use_bias=False, input_shape=(N_FAKE_DATA,),\n",
        "                           name='g_input_layer', kernel_regularizer=\"l2\"),\n",
        "                     BatchNormalization(name='g_input_bn'),\n",
        "                     Activation(\"leaky_relu\"),\n",
        "                     Reshape((7,7,128), name='reshape_Layer'),\n",
        "                     Dropout(DROPOUT_AMOUNT, name=\"g_dropout\"),\n",
        "                    \n",
        "                     # block 2\n",
        "                     Conv2DTranspose(\n",
        "                         filters=64, kernel_size=4, strides=1,\n",
        "                         padding=\"same\", name='g_conv_1',\n",
        "                         kernel_regularizer=\"l2\"),\n",
        "                     BatchNormalization(name='g_bn_1'),\n",
        "                     Activation(\"leaky_relu\"),\n",
        "                     Dropout(DROPOUT_AMOUNT, name=\"g_dropout\"),\n",
        "\n",
        "                     # block 3\n",
        "                     Conv2DTranspose(\n",
        "                         filters=32, kernel_size=4, strides=2,\n",
        "                         padding=\"same\", name='g_conv_1',\n",
        "                         kernel_regularizer=\"l2\"),\n",
        "                     BatchNormalization(name='g_bn_1'),\n",
        "                     Activation(\"leaky_relu\"),\n",
        "                     Dropout(DROPOUT_AMOUNT, name=\"g_dropout\"),\n",
        "                    \n",
        "                     # block 5\n",
        "                     Conv2DTranspose(\n",
        "                         filters=1, kernel_size=4, strides=2, padding=\"same\",\n",
        "                         activation='tanh', name='g_output_layer')\n",
        "                     ]\n",
        "    @tf.function\n",
        "    def call(self, data, training=True):\n",
        "        d = data\n",
        "        for l in self.layers:\n",
        "            if training or l.name != \"g_dropout\":\n",
        "                d = l(d)\n",
        "        return d"
      ],
      "metadata": {
        "id": "Wc1dXNTFGOf6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title GAN { display-mode: \"form\" }\n",
        "class GAN():\n",
        "\n",
        "    def __init__(self, BATCH_SIZE=32, LEARNING_RATE=0.0001, DROPOUT_AMOUNT=0.2):\n",
        "        self.G = Generator(DROPOUT_AMOUNT, N_FAKE_DATA)\n",
        "        self.D = Discriminator(DROPOUT_AMOUNT)\n",
        "        self.G_optimizer = tf.optimizers.Adam(LEARNING_RATE)\n",
        "        self.D_optimizer = tf.optimizers.Adam(LEARNING_RATE)\n",
        "        self.loss = tf.keras.losses.BinaryCrossentropy()\n",
        "        self.batch_size = BATCH_SIZE\n",
        "        self.summary_writer_train = tf.summary.create_file_writer(f\"drive/MyDrive/Tensorflow/Week09/eval_logs/train\")\n",
        "        self.summary_writer_test = tf.summary.create_file_writer(f\"drive/MyDrive/Tensorflow/Week09/eval_logs/test\")\n",
        "\n",
        "\n",
        "    def training_step(self, train_data, N_FAKE_DATA=100):\n",
        "        z = tf.random.normal((self.batch_size, N_FAKE_DATA))\n",
        "\n",
        "        with tf.GradientTape() as G_Tape, tf.GradientTape() as D_Tape:\n",
        "            \n",
        "            fake_data = self.G(z)\n",
        "            fake_discriminated = self.D(fake_data)\n",
        "            real_discriminated = self.D(train_data)\n",
        "            \n",
        "            G_loss_train = self.loss(fake_discriminated, tf.ones_like(fake_discriminated))\n",
        "            D_fake_loss = self.loss(fake_discriminated, tf.zeros_like(fake_discriminated))\n",
        "            D_real_loss = self.loss(real_discriminated, tf.ones_like(real_discriminated))\n",
        "            D_loss_train = D_fake_loss + D_real_loss\n",
        "            \n",
        "            D_gradients = D_Tape.gradient(D_loss_train, self.D.trainable_variables)\n",
        "            self.D_optimizer.apply_gradients(zip(D_gradients, self.D.trainable_variables))\n",
        "            G_gradients = G_Tape.gradient(G_loss_train, self.G.trainable_variables)\n",
        "            self.G_optimizer.apply_gradients(zip(G_gradients, self.G.trainable_variables))\n",
        "\n",
        "        return np.array([G_loss_train, D_loss_train])\n",
        "\n",
        "\n",
        "    def test(self, test_data, N_FAKE_DATA=100):\n",
        "        z = tf.random.normal((self.batch_size,N_FAKE_DATA))\n",
        "        G_loss_agg = np.empty(len(test_data))\n",
        "        D_loss_agg = np.empty(len(test_data))\n",
        "\n",
        "        fake_data = 0\n",
        "\n",
        "        for i,d in enumerate(note.tqdm(test_data,position=0,leave=True)):\n",
        "\n",
        "            fake_data = self.G(z, training=False)\n",
        "\n",
        "            fake_discriminated = self.D(fake_data, training=False)\n",
        "            real_discriminated = self.D(d, training=False)\n",
        "\n",
        "            G_loss_test = self.loss(fake_discriminated, tf.ones_like(fake_discriminated))\n",
        "            D_fake_loss = self.loss(fake_discriminated, tf.zeros_like(fake_discriminated))\n",
        "            D_real_loss = self.loss(real_discriminated, tf.ones_like(real_discriminated))\n",
        "            D_loss_test = D_fake_loss + D_real_loss\n",
        "            \n",
        "            G_loss_agg[i] = G_loss_test\n",
        "            D_loss_agg[i] = D_loss_test\n",
        "        \n",
        "        G_loss_test = np.mean(G_loss_agg)\n",
        "        D_loss_test = np.mean(D_loss_agg)\n",
        "\n",
        "        return np.array([G_loss_test, D_loss_test]), fake_data\n",
        "\n",
        "\n",
        "    def train(self, train_data, test_data, N_EPOCHS=10, N_FAKE_DATA=100):\n",
        "\n",
        "        start_time = time.time()\n",
        "\n",
        "        for e in range(N_EPOCHS):\n",
        "\n",
        "            epoch_loss_agg = np.empty(len(train_data), dtype=object)\n",
        "            for i,d in enumerate(note.tqdm(train_data, position=0, leave=True)):\n",
        "                epoch_loss_agg[i] = self.training_step(d, N_FAKE_DATA)\n",
        "\n",
        "            with self.summary_writer_train.as_default():\n",
        "                for val,name in zip(np.mean(epoch_loss_agg, axis=0), [\"Generator Loss\",\"Discriminator Loss\"]):\n",
        "                    tf.summary.scalar(name, val, step=e)\n",
        "            print(\" Generator loss: \",np.mean(epoch_loss_agg,axis=0)[0],\"\\n\",\"Discriminator loss: \",np.mean(epoch_loss_agg,axis=0)[1])\n",
        "            \n",
        "            with self.summary_writer_test.as_default():\n",
        "                test_losses, generated_images = self.test(test_data, N_FAKE_DATA)\n",
        "                for val,name in zip(test_losses, [\"Generator Loss\",\"Discriminator Loss\"]):\n",
        "                    tf.summary.scalar(name, val, step=e)\n",
        "            \n",
        "                tf.summary.image(\"Generated images\", data=generated_images, max_outputs=self.batch_size, step=e)\n",
        "                \n",
        "            plt.imshow(generated_images.numpy()[np.random.randint(0, self.batch_size)].reshape(28,28))\n",
        "            plt.show()\n",
        "            \n",
        "            used_time = np.round(time.time() - start_time)\n",
        "            print(f\"Finished epoch {e + 1} in {int(used_time // 60)} minutes and {int(used_time % 60)} seconds.\")\n",
        "            start_time = time.time()"
      ],
      "metadata": {
        "id": "77HpF9qM9S_i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Define Hyperparameters { display-mode: \"form\" }\n",
        "\n",
        "# Hyperparams\n",
        "#@markdown Dataset split\n",
        "train_size = 10000 #@param {type: \"number\"}\n",
        "test_size = 1000 #@param {type: \"number\"}\n",
        "#@markdown Dataset preprocessing\n",
        "BATCH_SIZE =  32#@param {type: \"number\"}\n",
        "SHUFFLE_SIZE = 100 #@param {type: \"number\"}\n",
        "PREFETCH_SIZE=100 #@param {type: \"number\"}\n",
        "#@markdown Training\n",
        "LEARNING_RATE = 0.0001 #@param {type: \"number\"}\n",
        "N_EPOCHS =  10#@param {type: \"number\"}\n",
        "DROPOUT_AMOUNT = 0.2 #@param {type: \"number\"}\n",
        "N_FAKE_DATA = 100 #@param {type: \"number\"}\n",
        "\n",
        "train_images = images[:train_size]\n",
        "test_images = images[train_size:train_size+test_size]\n",
        "\n",
        "def np_to_preprocessed_ds(np_array, SHUFFLE_SIZE=1000, BATCH_SIZE=32, PREFETCH_SIZE=1000):\n",
        "    min_val = np.amin(np_array)\n",
        "    max_val = np.amax(np_array)\n",
        "    ds = tf.data.Dataset.from_tensor_slices(np_array)\n",
        "    ds = ds.map(lambda x: tf.reshape(x, (28,28,1)))\n",
        "    ds = ds.map(lambda x: tf.cast(x,tf.float32)/128 -1)\n",
        "    return ds.shuffle(SHUFFLE_SIZE).batch(BATCH_SIZE).prefetch(PREFETCH_SIZE)\n",
        "\n",
        "train_ds = np_to_preprocessed_ds(train_images, BATCH_SIZE)\n",
        "test_ds = np_to_preprocessed_ds(test_images, BATCH_SIZE)\n",
        "print(f'Train set: {train_ds}')\n",
        "print(f'Test set: {test_ds}')\n",
        "\n",
        "try: \n",
        "    %reload_ext tensorboard \n",
        "except: \n",
        "    %load_ext tensorboard\n",
        "if os.path.isdir('drive/MyDrive/Tensorflow/Week09/eval_logs/'):\n",
        "    !rm -r drive/MyDrive/Tensorflow/Week09/eval_logs/*"
      ],
      "metadata": {
        "id": "ALtSW_zFqmKi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf516bf1-3390-4817-a65e-fd695f1e30e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set: <PrefetchDataset shapes: (None, 28, 28, 1), types: tf.float32>\n",
            "Test set: <PrefetchDataset shapes: (None, 28, 28, 1), types: tf.float32>\n",
            "rm: cannot remove 'drive/MyDrive/Tensorflow/Week09/eval_logs/*': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Training\n",
        "tf.keras.backend.clear_session()\n",
        "Model = GAN(BATCH_SIZE, LEARNING_RATE, DROPOUT_AMOUNT);\n",
        "Model.train(train_ds, test_ds, N_EPOCHS, N_FAKE_DATA)"
      ],
      "metadata": {
        "id": "aiQps_pA22sV",
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          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "outputId": "42cbe52f-206f-4b69-a310-d0523c7b1db1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "980d82178b0649eb96bad2f5b92a135c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Generator loss:  11.714255 \n",
            " Discriminator loss:  6.3767815\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b6545053c56f4af09955d6aeb7130a71",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 1 in 1 minutes and 24 seconds.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a187ffbe7a57491a9568358c8960d396",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Generator loss:  12.168313 \n",
            " Discriminator loss:  6.1017632\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d385bd23a1f94aabb5a1e8f7c3862626",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PuH559R8/Tcce7B6m8ZEree+0z4dr1bVWjqwbT5YpBwAskC2br8yfomO/updvdT2Z5XX45+e3BmPbDo7RsS3f5MeO8QI/b7RWHnku8u3Hq+hnN7NBM/u+mT1lZk+a2Ucrt/eZ2VEzO1H5yHc6EJGGWsuv8UsAPu7uVwC4CsCtZnYFgE8AeMDdDwB4oPJ/EWlS0WR391F3f6Ty+TSApwHsBHADgDsrX3YngPfXapIiUr1X9Te7me0FcCWAnwIYcPfRSugsgIHAmMMADgNAO/j15yJSO2t+N97MugB8F8DH3P23dvPz5dX1Vn1nwN2PuPuQuw/l0FbVZEVk/daU7GaWw3Kif8Pd76rcPGZm2yvx7QDGazNFEdkI0V/jzcwAfAXA0+7++RWhewHcDOD2ysd7ovdmBsuF75Iukbs8l/DY6Vk6tryLt1N2jPHSXGF3uFQyMruZjt2Xn6DxfI6332IrX+YaF6Z4vAqZyNbDVuJlokWytPHXzr6Vjs2N8aWiu4b5fZ+4GC69nX9o1b86f3PfO/lzsbWL/5aaneVlQzxzMhzb2keHGnuuz4Vfv9fyN/vbAHwIwBNm9ljltk9iOcm/Y2a3AHgBwAfWcCwRaZBosrv7DxG+dOJdGzsdEakVXS4rkgglu0gilOwiiVCyiyRCyS6SiPq3uJL2vdiyxdZN2lh7+dbBk2/k8fw4r6vmzoVPVdt+PvaDPQ/R+Nu7nqHxz+z7GxrPnSA12xhy7QIAWGcnjbdO81p3Ozn+1GIHHdvzLA2j/1F+fcHwdeHnS3FPpHX3x/y5mDt5lsbLA7xWnuki5zW2LDpreSb5pVd2kUQo2UUSoWQXSYSSXSQRSnaRRCjZRRKhZBdJRF3r7GYGayU9ypnIzx6yDS7t8QXQOsO3980u8LiTcvSzj/Atmdv382OfXeL98G1jkV59Visn/eRrifscX8Z68y/43LJkremTZ/rp2P7Irsk2zJeD/svX/yoYu+uzf0rH5k+dp3GPbMk8v6uLxttJP3umly8tjjbSS0+eC3plF0mEkl0kEUp2kUQo2UUSoWQXSYSSXSQRSnaRRNS3n914z7rTrWgBIz3AxUG+tvr8Fv5zbWo/Xwe8/43hmu5btz1Px26OXD9wY+cojX/pAO/F73ychilriTwFIvEXL+P15BLZQvizb/4uHfuZnvfSeOHsPhqfWjoXjOX/6gwdO3ORryvf0c3X05/dxs9bfu9gMLa0ha8hkJsl1z6QSy70yi6SCCW7SCKU7CKJULKLJELJLpIIJbtIIpTsIolYy/7sgwC+BmAAgAM44u5fNLPbAPw9gJeKmZ909/v4wTIA2e/bJybXNutVtLw4R+Pdp8P7qwNA/gcXaPz8eHh/97vfyfvRjw/uoPFnn7+ExgfaImu7R9bbZ5ysM758bP56kF3k46959G+DsblH+bURW47z6y4WyTYCAPDtB8P7v++7p0jHts4VaDw7OUPj+XN8TXyMha8ByBV5r7yzPRImw8+FtVxUswTg4+7+iJl1A3jYzI5WYl9w939awzFEpMHWsj/7KIDRyufTZvY0gJ21npiIbKxX9Te7me0FcCWAn1Zu+oiZPW5md5hZb2DMYTM7ZmbHFst8iSMRqZ01J7uZdQH4LoCPuftFAF8C8DoAB7H8yv+51ca5+xF3H3L3odZM5O8YEamZNSW7meWwnOjfcPe7AMDdx9y95O5lAF8GcKh20xSRakWT3cwMwFcAPO3un19x+/YVX3YjgOMbPz0R2ShreTf+bQA+BOAJM3usctsnAdxkZgexXI47BeDD0SO5A4vhkoexJXIBeCG8za738FbLtgleSkGk3NH3VLjUUmqL3Pdf8GNn8zy+5UfjNL7EWoNjS0lneNmufIFvi9x1mr8PMzoaLhNtilRaex6f4F8QKQvmz4T/bPQsL2dmn+dbMmOBb/ncdYyXgmnJM/JcNPZ4l8PLlq/l3fgfYvUuWV5TF5GmoivoRBKhZBdJhJJdJBFKdpFEKNlFEqFkF0lEXZeSXtrcjonrLwvGt/ws3PYHAHOvX/XyewCARbb3zU3zlsbyZr58b6ktXI/uHuZ10Wf/Zz+N95/gWzpPXM1bZPs6yPUJJX5sY8sSI379wvwAX1K599Hw68mm0/wxKezhrcO2xK8hWOgNP727fzlNx5Z386WkY0rtPLUyC/w5w0xdGn6uLv1HuJVbr+wiiVCyiyRCyS6SCCW7SCKU7CKJULKLJELJLpII81i/80bemdk5AC+suKkfwPm6TeDVada5Neu8AM1tvTZybnvcfetqgbom+yvu3OyYuw81bAJEs86tWecFaG7rVa+56dd4kUQo2UUS0ehkP9Lg+2eadW7NOi9Ac1uvusytoX+zi0j9NPqVXUTqRMkukoiGJLuZXWtmvzCz58zsE42YQ4iZnTKzJ8zsMTM71uC53GFm42Z2fMVtfWZ21MxOVD6Gm/zrP7fbzGykcu4eM7PrGjS3QTP7vpk9ZWZPmtlHK7c39NyRedXlvNX9b3YzywJ4FsC7AQwDeAjATe7+VF0nEmBmpwAMuXvDL8Aws7cDmAHwNXd/Q+W2zwKYdPfbKz8oe939H5pkbrcBmGn0Nt6V3Yq2r9xmHMD7Afw1GnjuyLw+gDqct0a8sh8C8Jy7n3T3RQDfBnBDA+bR9Nz9QQAv3zflBgB3Vj6/E8tPlroLzK0puPuouz9S+XwawEvbjDf03JF51UUjkn0ngNMr/j+M5trv3QHcb2YPm9nhRk9mFQPuPlr5/CyA6tZP2njRbbzr6WXbjDfNuVvP9ufV0ht0r3S1u/8RgPcCuLXy62pT8uW/wZqpdrqmbbzrZZVtxn+tkeduvdufV6sRyT4CYHDF/3dVbmsK7j5S+TgO4G4031bUYy/toFv5yHd9rKNm2sZ7tW3G0QTnrpHbnzci2R8CcMDM9plZK4APAri3AfN4BTPrrLxxAjPrBHANmm8r6nsB3Fz5/GYA9zRwLr+lWbbxDm0zjgafu4Zvf+7udf8H4DosvyP/SwCfasQcAvPaD+DnlX9PNnpuAL6F5V/rilh+b+MWAFsAPADgBIDvAehrorl9HcATAB7HcmJtb9Dcrsbyr+iPA3is8u+6Rp87Mq+6nDddLiuSCL1BJ5IIJbtIIpTsIolQsoskQskukgglu0gilOwiifh/Z61Q+iLLSJsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 2 in 1 minutes and 14 seconds.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0095cce896fe46e3984da3c4aeb4ffd1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Generator loss:  9.501773 \n",
            " Discriminator loss:  9.567358\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7974f7e54210490d94636eb8bcdeffc2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 3 in 1 minutes and 25 seconds.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8b48eee920bc492caf68d6a7493d33b6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Generator loss:  9.944883 \n",
            " Discriminator loss:  11.159606\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "99e18f6e5b0a47b9bae6dc499a6956f9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 4 in 1 minutes and 24 seconds.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5edc60fd214d450aa3c4dddb958ab653",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Generator loss:  12.254231 \n",
            " Discriminator loss:  12.222675\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9d9b669f7f404822a17588a7b4edd63b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N5Hnj47JAEhygA5Ig7EAShB1IgrADSRB2IAnCDiRB2IEk/h8Tglyjikun2AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 5 in 1 minutes and 14 seconds.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b5187869121541429e79c0a61eedef80",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Generator loss:  2.6222472 \n",
            " Discriminator loss:  14.394448\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ffe1fe55f61841fab7a0677bfb6f84a1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 6 in 1 minutes and 14 seconds.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "af858c39cc804f70bc4fe9c505341fd7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Generator loss:  0.0013266632 \n",
            " Discriminator loss:  15.431449\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a382ad42c77e4b189bb281623d0937d9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 7 in 1 minutes and 13 seconds.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "50b71ad868c441b987410cf453d0166f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Generator loss:  0.0005520628 \n",
            " Discriminator loss:  15.426934\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ba3b11889a1a47c88ab732aa33c9bc76",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 8 in 1 minutes and 24 seconds.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a0db76e7dcfe4c18ac5d6c266209edc0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Generator loss:  0.00029789336 \n",
            " Discriminator loss:  15.427006\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9dc37f08dab7442ba3f0202d318642a2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+wHDejX8ewFCNoxxTF7lU6RN0IpFQ2EUiobCLREJhF4mEwi4SCYVdJBKlXUqaAKvDh0wdNy3nFrzOsVmTchpTtlxGn193t+gFAOf4E58/6rc9fdavp8gdOubW+8+Gl9GuOvaO2zb1/2jaNNW08+62TTl6ylg46F9HU/+fOqqmTAo/7pHw74Ku7CKRUNhFIqGwi0RCYReJhMIuEgmFXSQSCrtIJFLnsxf0YOQxAG8NumkCgLdL1oGLU6l9q9R+AepbvgrZtyvMbOJQhZKG/T0HJ9vMrKVsHXBUat8qtV+A+pavUvVNT+NFIqGwi0Si3GFfU+bjeyq1b5XaL0B9y1dJ+lbW1+wiUjrlvrKLSIko7CKRKEvYSd5K8g2Se0muLEcfQkjuJ7mD5DaSbWXuyzqSHSR3DrqtieRmknuSr/7i66Xt2yqS7cm520ZyUZn6Np3kz0nuJrmL5F3J7WU9d06/SnLeSv6anWQVgF8D+HMABwG8BGCZme0uaUcCSO4H0GJmZf8ABskPATgL4CEzuza57T8AHDez1ckfynFm9q8V0rdVAM6WexvvZLei5sHbjANYAuAfUMZz5/RrKUpw3spxZZ8PYK+Z7TOzbgCPAVhchn5UPDN7DsDxd928GMD65Pv1GPhlKblA3yqCmR02s1eS788AuLDNeFnPndOvkihH2KcBODDo54OorP3eDcAmki+TbC13Z4YwedA2W0cAhPd+Ko/UbbxL6V3bjFfMuctn+/Os9Abde91kZtcDuA3Ap5OnqxXJBl6DVdLY6bC28S6VIbYZ/61ynrt8tz/PqhxhbwcwfdDPlye3VQQza0++dgB4EpW3FfXRCzvoJl/9nRlLqJK28R5qm3FUwLkr5/bn5Qj7SwBmk7yKZC2AOwBsLEM/3oNkY/LGCUg2AliIytuKeiOA5cn3ywE8Vca+/I5K2cY7tM04ynzuyr79uZmV/B+ARRh4R/5NAP9ejj4E+jUTwGvJv13l7huARzHwtK4HA+9t3AlgPIAtAPYA+CmApgrq239jYGvv7RgIVnOZ+nYTBp6ibwewLfm3qNznzulXSc6bPi4rEgm9QScSCYVdJBIKu0gkFHaRSCjsIpFQ2EUiobCLROL/AZNpFz+WypxFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 9 in 1 minutes and 14 seconds.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "45948cafdc5441a0a6e9941b13105cc2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/313 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Generator loss:  0.00016867832 \n",
            " Discriminator loss:  15.425995\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ea71172aa03c4579b7e878706148d293",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/32 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tJr+Rf07S3/Sih5q+3iXpyervmV73Jul+Tb6tG9Xkdxs3SXqbpK2SnpX0HUln9FFv90p6WtJTmgzW0h71dpkm36I/JWl79be219uu0FdXths/lwWS4As6IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUji/wEh6Vh2kYgkaAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finished epoch 10 in 1 minutes and 25 seconds.\n"
          ]
        }
      ]
    }
  ]
}