Wasserstin_GAN.ipynb

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  "metadata": {
    "colab": {
      "name": "Wasserstin_GAN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "pK63Bpxv7ZUg",
        "colab_type": "code",
        "outputId": "916ed95d-130b-4fd6-ada7-8290af584581",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 63
        }
      },
      "source": [
        "from tensorflow.keras.layers import Input, Conv2D, Flatten, Dense, Conv2DTranspose, Reshape, Lambda, Activation, BatchNormalization, LeakyReLU, Dropout, ZeroPadding2D, UpSampling2D, Dropout, Softmax\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from tensorflow.keras import backend as K\n",
        "from tensorflow.keras.optimizers import Adam, RMSprop\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras.initializers import RandomNormal\n",
        "\n",
        "import numpy as np\n",
        "import json\n",
        "import os\n",
        "import pickle as pkl\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "_-gKwTUgAT3l",
        "colab_type": "code",
        "outputId": "5226553e-819f-4090-abfe-d22fad089da7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        }
      },
      "source": [
        "from keras.layers import Input, Conv2D, Flatten, Dense, Conv2DTranspose, Reshape, Lambda, Activation, BatchNormalization, LeakyReLU, Dropout, ZeroPadding2D, UpSampling2D, Dropout\n",
        "from keras.layers.merge import _Merge\n",
        "\n",
        "from keras.models import Model, Sequential\n",
        "from keras import backend as K\n",
        "from keras.optimizers import Adam, RMSprop\n",
        "from keras.utils import plot_model\n",
        "from keras.initializers import RandomNormal\n",
        "\n",
        "import numpy as np\n",
        "import json\n",
        "import os\n",
        "import pickle as pkl\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJ7oIx2CRjoM",
        "colab_type": "code",
        "outputId": "ce85db2c-866e-49b6-c6bf-201226fb8bff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "!wget \"https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/camel.npy\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-12-29 09:03:49--  https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/camel.npy\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 108.177.126.128, 2a00:1450:4013:c06::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|108.177.126.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 95176896 (91M) [application/octet-stream]\n",
            "Saving to: ‘camel.npy’\n",
            "\n",
            "camel.npy           100%[===================>]  90.77M  69.9MB/s    in 1.3s    \n",
            "\n",
            "2019-12-29 09:03:51 (69.9 MB/s) - ‘camel.npy’ saved [95176896/95176896]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gR8Tipyj9N91",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = np.load(\"camel.npy\")\n",
        "data = data.reshape(data.shape[0], 28, 28, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9T2NuJ_7ZYZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class WGAN():\n",
        "\n",
        "    def  __init__(self, \n",
        "                  input_dim,\n",
        "                  disc_filter,\n",
        "                  disc_kernel,\n",
        "                  disc_strides,\n",
        "                  disc_dropout,\n",
        "                  disc_lr,\n",
        "                  gen_filter,\n",
        "                  gen_kernel,\n",
        "                  gen_strides,\n",
        "                  gen_upsample,\n",
        "                  gen_lr,\n",
        "                  z_dim,\n",
        "                  batch_size):\n",
        "      \n",
        "\n",
        "          self.input_dim = input_dim\n",
        "          self.disc_filter = disc_filter\n",
        "          self.disc_kernel = disc_kernel\n",
        "          self.disc_strides = disc_strides\n",
        "          self.disc_dropout = disc_dropout\n",
        "          self.disc_lr = disc_lr\n",
        "          self.gen_filter = gen_filter\n",
        "          self.gen_kernel = gen_kernel\n",
        "          self.gen_strides = gen_strides\n",
        "          self.gen_upsample = gen_upsample\n",
        "          self.gen_lr = gen_lr\n",
        "          self.z_dim = z_dim\n",
        "          self.batch_size = batch_size\n",
        "\n",
        "          self.d_losses = []\n",
        "          self.g_losses = []\n",
        "          self.epoch = 0\n",
        "\n",
        "          self.Discriminator()\n",
        "          self.Generator()\n",
        "\n",
        "          self.full_model()\n",
        "\n",
        "    def wasserstein(self, y_true, y_pred):\n",
        "        return -K.mean(y_true * y_pred)\n",
        "\n",
        "    def Discriminator(self):\n",
        "\n",
        "        disc_input = Input(shape=self.input_dim, name='discriminator_input')\n",
        "        x = disc_input\n",
        "\n",
        "        for i in range(len(self.disc_filter)):\n",
        "            x = Conv2D(filters=self.disc_filter[i], kernel_size=self.disc_kernel[i], strides=self.disc_strides[i], padding='same', name='disc_'+str(i))(x)\n",
        "            x = LeakyReLU()(x)\n",
        "            x = Dropout(self.disc_dropout)(x)\n",
        "            x = BatchNormalization()(x)\n",
        "\n",
        "        x = Flatten()(x)\n",
        "        disc_output = Dense(1, activation=\"linear\")(x)\n",
        "        self.discriminator = Model(disc_input, disc_output)\n",
        "\n",
        "    def Generator(self):\n",
        "\n",
        "        gen_input = Input(shape=(self.z_dim,), name='generator_input')\n",
        "        x = gen_input\n",
        "\n",
        "        x = Dense(7*7*self.batch_size)(x)\n",
        "        x = LeakyReLU()(x)\n",
        "        x = BatchNormalization()(x)\n",
        "        x = Reshape(target_shape=(7,7,self.batch_size))(x)\n",
        "        \n",
        "        for i in range(len(self.gen_filter)):\n",
        "            if self.gen_upsample[i]==2:\n",
        "                x = UpSampling2D(size=self.gen_upsample[i], name='upsample_'+str(i/2))(x)\n",
        "                x = Conv2D(filters=self.gen_filter[i], kernel_size=self.gen_kernel[i], strides=self.gen_strides[i], padding='same', name='gen_'+str(i))(x)\n",
        "\n",
        "            else:\n",
        "                x = Conv2DTranspose(filters=self.gen_filter[i], kernel_size=self.gen_kernel[i], strides=self.gen_strides[i], padding='same', name='gen_'+str(i))(x)\n",
        "            \n",
        "            if i<len(self.gen_filter)-1:\n",
        "                #x = BatchNormalization()(x)\n",
        "                x = LeakyReLU()(x)\n",
        "\n",
        "            else:\n",
        "                x = Activation(\"tanh\")(x)\n",
        "\n",
        "        gen_output = x\n",
        "        self.generator = Model(gen_input, gen_output)\n",
        "\n",
        "      \n",
        "    def set_trainable(self, model, val):\n",
        "        model.trainable=val\n",
        "                  \n",
        "    def full_model(self):\n",
        "        \n",
        "        ### COMPILE DISCRIMINATOR\n",
        "        self.discriminator.compile(optimizer= Adam(self.disc_lr), loss = self.wasserstein, metrics=['accuracy'])\n",
        "    \n",
        "        ### COMPILE THE FULL GAN\n",
        "\n",
        "        self.set_trainable(self.discriminator, False)\n",
        "        self.discriminator.compile(optimizer= Adam(self.disc_lr), loss = self.wasserstein, metrics=['accuracy'])\n",
        "\n",
        "        model_input = Input(shape=(self.z_dim,), name='model_input')\n",
        "        model_output = self.discriminator(self.generator(model_input))\n",
        "        self.model = Model(model_input, model_output)\n",
        "\n",
        "        self.model.compile(optimizer= Adam(self.disc_lr), loss = self.wasserstein, metrics=['accuracy'])\n",
        "\n",
        "        self.set_trainable(self.discriminator, True)\n",
        "\n",
        "    def train_generator(self, batch_size):\n",
        "        valid = np.ones((batch_size,1))\n",
        "        noise = np.random.normal(0, 1, (batch_size, self.z_dim))\n",
        "        return self.model.train_on_batch(noise, valid)\n",
        "\n",
        "    def train_discriminator(self, x_train, batch_size, using_generator):\n",
        "        valid = np.ones((batch_size,1))\n",
        "        fake = np.zeros((batch_size,1))\n",
        "        \n",
        "        if using_generator:\n",
        "            true_imgs = next(x_train)[0]\n",
        "            if true_imgs.shape[0] != batch_size:\n",
        "                true_imgs = next(x_train)[0]\n",
        "        else:\n",
        "            idx = np.random.randint(0, x_train.shape[0], batch_size)\n",
        "            true_imgs = x_train[idx]\n",
        "        \n",
        "        noise = np.random.normal(0, 1, (batch_size, self.z_dim))\n",
        "        gen_imgs = self.generator.predict(noise)\n",
        "\n",
        "        d_loss_real, d_acc_real =  self.discriminator.train_on_batch(true_imgs, valid)\n",
        "        d_loss_fake, d_acc_fake =  self.discriminator.train_on_batch(gen_imgs, fake)\n",
        "        d_loss = 0.5 * (d_loss_real + d_loss_fake)\n",
        "        d_acc = 0.5 * (d_acc_real + d_acc_fake)\n",
        "\n",
        "        for l in self.discriminator.layers:\n",
        "            weights = l.get_weights()\n",
        "            weights = [np.clip(w, -0.01, 0.01) for w in weights]\n",
        "            l.set_weights(weights)\n",
        "\n",
        "        return [d_loss, d_loss_real, d_loss_fake, d_acc, d_acc_real, d_acc_fake]\n",
        "\n",
        "    def train(self, x_train, batch_size, epochs, print_every_n_batches = 50, using_generator = False):\n",
        "\n",
        "        for epoch in range(self.epoch, self.epoch + epochs):\n",
        "\n",
        "            d = self.train_discriminator(x_train, batch_size, using_generator)\n",
        "            g = self.train_generator(batch_size)\n",
        "            if self.epoch % print_every_n_batches == 0:\n",
        "                print (\"%d [D loss: (%.3f)(R %.3f, F %.3f)] [D acc: (%.3f)(%.3f, %.3f)] [G loss: %.3f] [G acc: %.3f]\" % (epoch, d[0], d[1], d[2], d[3], d[4], d[5], g[0], g[1]))\n",
        "\n",
        "            self.d_losses.append(d)\n",
        "            self.g_losses.append(g)\n",
        "\n",
        "            self.epoch+=1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6gBlh_R7ZdA",
        "colab_type": "code",
        "outputId": "88bdb3e8-b016-45a4-9922-9bcb98fa87d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        }
      },
      "source": [
        "wgan = WGAN(input_dim = (28,28,1)\n",
        "        , disc_filter = [64,64,128,128]\n",
        "        , disc_kernel = [5,5,5,5]\n",
        "        , disc_strides = [2,2,2,1]\n",
        "        , disc_dropout = 0.4\n",
        "        , disc_lr = 0.0008\n",
        "        , gen_upsample = [2,2, 1, 1]\n",
        "        , gen_filter = [128,64, 64,1]\n",
        "        , gen_kernel = [5,5,5,5]\n",
        "        , gen_strides = [1,1, 1, 1]\n",
        "        , gen_lr = 0.0004\n",
        "        , z_dim = 100\n",
        "        , batch_size=64\n",
        "        )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2041: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2239: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1Qpq5a87Zjw",
        "colab_type": "code",
        "outputId": "f62a1783-fbdc-4df5-dc06-975ff73a06de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        }
      },
      "source": [
        "wgan.discriminator.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "discriminator_input (InputLa (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "disc_0 (Conv2D)              (None, 14, 14, 64)        1664      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)    (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 14, 14, 64)        256       \n",
            "_________________________________________________________________\n",
            "disc_1 (Conv2D)              (None, 7, 7, 64)          102464    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_2 (LeakyReLU)    (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 7, 7, 64)          256       \n",
            "_________________________________________________________________\n",
            "disc_2 (Conv2D)              (None, 4, 4, 128)         204928    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_3 (LeakyReLU)    (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 4, 4, 128)         512       \n",
            "_________________________________________________________________\n",
            "disc_3 (Conv2D)              (None, 4, 4, 128)         409728    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_4 (LeakyReLU)    (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 4, 4, 128)         512       \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 2049      \n",
            "=================================================================\n",
            "Total params: 768\n",
            "Trainable params: 0\n",
            "Non-trainable params: 768\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:493: 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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dz9n6-EV8-Hl",
        "colab_type": "code",
        "outputId": "18646e48-a572-4508-99b2-7466c6169e01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        }
      },
      "source": [
        "wgan.generator.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "generator_input (InputLayer) (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 3136)              316736    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_5 (LeakyReLU)    (None, 3136)              0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 3136)              12544     \n",
            "_________________________________________________________________\n",
            "reshape_1 (Reshape)          (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "upsample_0.0 (UpSampling2D)  (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "gen_0 (Conv2D)               (None, 14, 14, 128)       204928    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_6 (LeakyReLU)    (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "upsample_0.5 (UpSampling2D)  (None, 28, 28, 128)       0         \n",
            "_________________________________________________________________\n",
            "gen_1 (Conv2D)               (None, 28, 28, 64)        204864    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_7 (LeakyReLU)    (None, 28, 28, 64)        0         \n",
            "_________________________________________________________________\n",
            "gen_2 (Conv2DTranspose)      (None, 28, 28, 64)        102464    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_8 (LeakyReLU)    (None, 28, 28, 64)        0         \n",
            "_________________________________________________________________\n",
            "gen_3 (Conv2DTranspose)      (None, 28, 28, 1)         1601      \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 28, 28, 1)         0         \n",
            "=================================================================\n",
            "Total params: 843,137\n",
            "Trainable params: 836,865\n",
            "Non-trainable params: 6,272\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-irphQ44V9H0",
        "colab_type": "code",
        "outputId": "42799552-7cde-4f51-9bfb-dd8010dfe54c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "wgan.model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "model_input (InputLayer)     (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "model_2 (Model)              (None, 28, 28, 1)         843137    \n",
            "_________________________________________________________________\n",
            "model_1 (Model)              (None, 1)                 722369    \n",
            "=================================================================\n",
            "Total params: 843,905\n",
            "Trainable params: 836,865\n",
            "Non-trainable params: 7,040\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:493: 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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_f1l--Gj9CsG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BATCH_SIZE = 64\n",
        "EPOCHS = 100\n",
        "PRINT_EVERY_N_BATCHES = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sbt4W3Pf9C59",
        "colab_type": "code",
        "outputId": "cd7e7437-bd27-4ca8-f260-aed87fc47d4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        }
      },
      "source": [
        "wgan.train(     \n",
        "    data\n",
        "    , batch_size = BATCH_SIZE\n",
        "    , epochs = EPOCHS\n",
        "    , print_every_n_batches = PRINT_EVERY_N_BATCHES\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:493: 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": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "0 [D loss: (0.088)(R 0.175, F -0.000)] [D acc: (0.258)(0.234, 0.281)] [G loss: 0.000] [G acc: 0.000]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:493: 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": [
            "10 [D loss: (0.000)(R 0.000, F -0.000)] [D acc: (0.500)(0.000, 1.000)] [G loss: 0.000] [G acc: 0.000]\n",
            "20 [D loss: (0.000)(R 0.000, F -0.000)] [D acc: (0.500)(0.000, 1.000)] [G loss: -0.000] [G acc: 0.000]\n",
            "30 [D loss: (0.000)(R 0.000, F -0.000)] [D acc: (0.500)(0.000, 1.000)] [G loss: -0.000] [G acc: 0.000]\n",
            "40 [D loss: (-0.000)(R -0.000, F -0.000)] [D acc: (0.500)(0.000, 1.000)] [G loss: -0.000] [G acc: 0.000]\n",
            "50 [D loss: (-0.000)(R -0.000, F -0.000)] [D acc: (0.500)(0.000, 1.000)] [G loss: -0.000] [G acc: 0.000]\n",
            "60 [D loss: (-0.000)(R -0.000, F -0.000)] [D acc: (0.500)(0.000, 1.000)] [G loss: -0.000] [G acc: 0.000]\n",
            "70 [D loss: (-0.000)(R -0.000, F -0.000)] [D acc: (0.500)(0.000, 1.000)] [G loss: -0.000] [G acc: 0.000]\n",
            "80 [D loss: (-0.000)(R -0.000, F -0.000)] [D acc: (0.500)(0.000, 1.000)] [G loss: -0.000] [G acc: 0.000]\n",
            "90 [D loss: (-0.000)(R -0.000, F -0.000)] [D acc: (0.500)(0.000, 1.000)] [G loss: -0.000] [G acc: 0.000]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oMAt5MhYCLTx",
        "colab_type": "code",
        "outputId": "b9265919-35dc-41f1-cd99-b17d1267da06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        }
      },
      "source": [
        "plt.imshow(data[0].reshape(28,28), interpolation='nearest', cmap='gray')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lBxJRl7Kb2QVm9qmZrTOzO+sxhzxmtt7MlmfLUNd1fbpsDb1WM1vRYdthZvamma3Nvne6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\n2YFEUHYgEZQdSARlBxJB2YFE/D+iUgdhagg2owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgOSAlsiCzrC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "noise = np.random.normal(0, 1, (64, 100))\n",
        "arr = wgan.generator.predict(noise)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kBdTPpVC4PC",
        "colab_type": "code",
        "outputId": "6ee54ecc-1995-4864-fc70-d125102abdb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for img in arr[:10]:\n",
        "    plt.imshow(data[0].reshape(28,28), interpolation='nearest', cmap='gray')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lBxJRl7Kb2QVm9qmZrTOzO+sxhzxmtt7MlmfLUNd1fbpsDb1WM1vRYdthZvamma3Nvne6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\n2YFEUHYgEZQdSARlBxJB2YFE/D+iUgdhagg2owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lBxJRl7Kb2QVm9qmZrTOzO+sxhzxmtt7MlmfLUNd1fbpsDb1WM1vRYdthZvamma3Nvne6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\n2YFEUHYgEZQdSARlBxJB2YFE/D+iUgdhagg2owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lBxJRl7Kb2QVm9qmZrTOzO+sxhzxmtt7MlmfLUNd1fbpsDb1WM1vRYdthZvamma3Nvne6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\n2YFEUHYgEZQdSARlBxJB2YFE/D+iUgdhagg2owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lBxJRl7Kb2QVm9qmZrTOzO+sxhzxmtt7MlmfLUNd1fbpsDb1WM1vRYdthZvamma3Nvne6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\n2YFEUHYgEZQdSARlBxJB2YFE/D+iUgdhagg2owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lBxJRl7Kb2QVm9qmZrTOzO+sxhzxmtt7MlmfLUNd1fbpsDb1WM1vRYdthZvamma3Nvne6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\n2YFEUHYgEZQdSARlBxJB2YFE/D+iUgdhagg2owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lBxJRl7Kb2QVm9qmZrTOzO+sxhzxmtt7MlmfLUNd1fbpsDb1WM1vRYdthZvamma3Nvne6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\n2YFEUHYgEZQdSARlBxJB2YFE/D+iUgdhagg2owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lBxJRl7Kb2QVm9qmZrTOzO+sxhzxmtt7MlmfLUNd1fbpsDb1WM1vRYdthZvamma3Nvne6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\n2YFEUHYgEZQdSARlBxJB2YFE/D+iUgdhagg2owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lBxJRl7Kb2QVm9qmZrTOzO+sxhzxmtt7MlmfLUNd1fbpsDb1WM1vRYdthZvamma3Nvne6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\n2YFEUHYgEZQdSARlBxJB2YFE/D+iUgdhagg2owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lBxJRl7Kb2QVm9qmZrTOzO+sxhzxmtt7MlmfLUNd1fbpsDb1WM1vRYdthZvamma3Nvne6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\n2YFEUHYgEZQdSARlBxJB2YFE/D+iUgdhagg2owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lBxJRl7Kb2QVm9qmZrTOzO+sxhzxmtt7MlmfLUNd1fbpsDb1WM1vRYdthZvamma3Nvne6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\n2YFEUHYgEZQdSARlBxJB2YFE/D+iUgdhagg2owAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1tZJ9oPFFBU",
        "colab_type": "code",
        "outputId": "571cf524-ea03-4558-baf5-9724a2b81f25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        }
      },
      "source": [
        "for l in wgan.discriminator.layers:\n",
        "  print(l.get_config()['name'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "discriminator_input\n",
            "disc_0\n",
            "leaky_re_lu_45\n",
            "dropout_25\n",
            "batch_normalization_30\n",
            "disc_1\n",
            "leaky_re_lu_46\n",
            "dropout_26\n",
            "batch_normalization_31\n",
            "disc_2\n",
            "leaky_re_lu_47\n",
            "dropout_27\n",
            "batch_normalization_32\n",
            "disc_3\n",
            "leaky_re_lu_48\n",
            "dropout_28\n",
            "batch_normalization_33\n",
            "flatten_7\n",
            "dense_11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sjo0MdadQJFe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}