CIFAR10(DCGAN).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mathlab115/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.layers import Input, Dense, Conv2D, Add, Dot, Conv2DTranspose, Activation, Reshape, LeakyReLU, Flatten, BatchNormalization\n",
    "from SpectralNormalizationKeras import DenseSN, ConvSN2D\n",
    "from keras.models import Model, Sequential\n",
    "from keras.optimizers import Adam\n",
    "import keras.backend as K\n",
    "from keras.utils.generic_utils import Progbar\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# for resist GPU memory\n",
    "import tensorflow as tf\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth=True\n",
    "sess = tf.Session(config=config)\n",
    "K.set_session(sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load Data\n",
    "from keras.datasets import cifar100, cifar10\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 32, 32, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.concatenate((x_test,x_train))\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f67045422b0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EnxCZIucXIlPk/EJkipxfiEyR8wuRKSNN4FkyYIooQDuDSLW929OSx8lTvARSOYjMqhqP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\nnF+ITPk3aAKCHnWhsY0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67742acf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X[9487])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Hyperperemeter\n",
    "BATCHSIZE=64\n",
    "LEARNING_RATE = 0.0002\n",
    "TRAINING_RATIO = 1\n",
    "BETA_1 = 0.0\n",
    "BETA_2 = 0.9\n",
    "EPOCHS = 500\n",
    "BN_MIMENTUM = 0.1\n",
    "BN_EPSILON  = 0.00002\n",
    "SAVE_DIR = 'img/generated_img_CIFAR10_DCGAN/'\n",
    "\n",
    "GENERATE_ROW_NUM = 8\n",
    "GENERATE_BATCHSIZE = GENERATE_ROW_NUM*GENERATE_ROW_NUM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def BuildGenerator(summary=True):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(4*4*512, kernel_initializer='glorot_uniform' , input_dim=128))\n",
    "    model.add(Reshape((4,4,512)))\n",
    "    model.add(Conv2DTranspose(256, kernel_size=4, strides=2, padding='same', activation='relu',kernel_initializer='glorot_uniform'))\n",
    "    model.add(BatchNormalization(epsilon=BN_EPSILON, momentum=BN_MIMENTUM))\n",
    "    model.add(Conv2DTranspose(128, kernel_size=4, strides=2, padding='same', activation='relu',kernel_initializer='glorot_uniform'))\n",
    "    model.add(BatchNormalization(epsilon=BN_EPSILON, momentum=BN_MIMENTUM))\n",
    "    model.add(Conv2DTranspose(64,  kernel_size=4, strides=2, padding='same', activation='relu',kernel_initializer='glorot_uniform'))\n",
    "    model.add(BatchNormalization(epsilon=BN_EPSILON, momentum=BN_MIMENTUM))\n",
    "    model.add(Conv2DTranspose(3,   kernel_size=3, strides=1, padding='same', activation='tanh'))\n",
    "    if summary:\n",
    "        print(\"Generator\")\n",
    "        model.summary()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def BuildDiscriminator(summary=True, spectral_normalization=True):\n",
    "    if spectral_normalization:\n",
    "        model = Sequential()\n",
    "        model.add(ConvSN2D(64, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same', input_shape=(32,32,3) ))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(ConvSN2D(64, kernel_size=4, strides=2,kernel_initializer='glorot_uniform', padding='same'))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(ConvSN2D(128, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same'))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(ConvSN2D(128, kernel_size=4, strides=2,kernel_initializer='glorot_uniform', padding='same'))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(ConvSN2D(256, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same'))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(ConvSN2D(256, kernel_size=4, strides=2,kernel_initializer='glorot_uniform', padding='same'))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(ConvSN2D(512, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same'))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(Flatten())\n",
    "        model.add(DenseSN(1,kernel_initializer='glorot_uniform'))\n",
    "    else:\n",
    "        model = Sequential()\n",
    "        model.add(Conv2D(64, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same', input_shape=(32,32,3) ))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(Conv2D(64, kernel_size=4, strides=2,kernel_initializer='glorot_uniform', padding='same'))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(Conv2D(128, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same'))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(Conv2D(128, kernel_size=4, strides=2,kernel_initializer='glorot_uniform', padding='same'))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(Conv2D(256, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same'))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(Conv2D(256, kernel_size=4, strides=2,kernel_initializer='glorot_uniform', padding='same'))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(Conv2D(512, kernel_size=3, strides=1,kernel_initializer='glorot_uniform', padding='same'))\n",
    "        model.add(LeakyReLU(0.1))\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(1,kernel_initializer='glorot_uniform'))\n",
    "    if summary:\n",
    "        print('Discriminator')\n",
    "        print('Spectral Normalization: {}'.format(spectral_normalization))\n",
    "        model.summary()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def wasserstein_loss(y_true, y_pred):\n",
    "    return K.mean(y_true*y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generator\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 8192)              1056768   \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTr (None, 8, 8, 256)         2097408   \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 8, 8, 256)         1024      \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTr (None, 16, 16, 128)       524416    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 16, 16, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTr (None, 32, 32, 64)        131136    \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 32, 32, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_4 (Conv2DTr (None, 32, 32, 3)         1731      \n",
      "=================================================================\n",
      "Total params: 3,813,251\n",
      "Trainable params: 3,812,355\n",
      "Non-trainable params: 896\n",
      "_________________________________________________________________\n",
      "Discriminator\n",
      "Spectral Normalization: True\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv_s_n2d_1 (ConvSN2D)      (None, 32, 32, 64)        1792      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv_s_n2d_2 (ConvSN2D)      (None, 16, 16, 64)        65600     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv_s_n2d_3 (ConvSN2D)      (None, 16, 16, 128)       73856     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_s_n2d_4 (ConvSN2D)      (None, 8, 8, 128)         262272    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv_s_n2d_5 (ConvSN2D)      (None, 8, 8, 256)         295168    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_s_n2d_6 (ConvSN2D)      (None, 4, 4, 256)         1048832   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)    (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv_s_n2d_7 (ConvSN2D)      (None, 4, 4, 512)         1180160   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_7 (LeakyReLU)    (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_sn_1 (DenseSN)         (None, 1)                 8193      \n",
      "=================================================================\n",
      "Total params: 2,935,873\n",
      "Trainable params: 2,935,873\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "generator = BuildGenerator()\n",
    "discriminator = BuildDiscriminator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_for_training_generator\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "sequential_1 (Sequential)    (None, 32, 32, 3)         3813251   \n",
      "_________________________________________________________________\n",
      "sequential_2 (Sequential)    (None, 1)                 2935873   \n",
      "=================================================================\n",
      "Total params: 6,749,124\n",
      "Trainable params: 6,748,228\n",
      "Non-trainable params: 896\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "Noise_input_for_training_generator = Input(shape=(128,))\n",
    "Generated_image                    = generator(Noise_input_for_training_generator)\n",
    "Discriminator_output               = discriminator(Generated_image)\n",
    "model_for_training_generator       = Model(Noise_input_for_training_generator, Discriminator_output)\n",
    "print(\"model_for_training_generator\")\n",
    "model_for_training_generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "sequential_1 (Sequential)    (None, 32, 32, 3)         3813251   \n",
      "_________________________________________________________________\n",
      "sequential_2 (Sequential)    (None, 1)                 2935873   \n",
      "=================================================================\n",
      "Total params: 6,749,124\n",
      "Trainable params: 3,812,355\n",
      "Non-trainable params: 2,936,769\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "discriminator.trainable = False\n",
    "model_for_training_generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_for_training_generator.compile(optimizer=Adam(LEARNING_RATE, beta_1=BETA_1, beta_2=BETA_2), loss=wasserstein_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_for_training_discriminator\n",
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_3 (InputLayer)             (None, 128)           0                                            \n",
      "____________________________________________________________________________________________________\n",
      "input_2 (InputLayer)             (None, 32, 32, 3)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "sequential_1 (Sequential)        (None, 32, 32, 3)     3813251     input_3[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "sequential_2 (Sequential)        (None, 1)             2935873     input_2[0][0]                    \n",
      "                                                                   sequential_1[2][0]               \n",
      "====================================================================================================\n",
      "Total params: 6,749,124\n",
      "Trainable params: 2,935,873\n",
      "Non-trainable params: 3,813,251\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "Real_image                             = Input(shape=(32,32,3))\n",
    "Noise_input_for_training_discriminator = Input(shape=(128,))\n",
    "Fake_image                             = generator(Noise_input_for_training_discriminator)\n",
    "Discriminator_output_for_real          = discriminator(Real_image)\n",
    "Discriminator_output_for_fake          = discriminator(Fake_image)\n",
    "\n",
    "model_for_training_discriminator       = Model([Real_image,\n",
    "                                                Noise_input_for_training_discriminator],\n",
    "                                               [Discriminator_output_for_real,\n",
    "                                                Discriminator_output_for_fake])\n",
    "print(\"model_for_training_discriminator\")\n",
    "generator.trainable = False\n",
    "discriminator.trainable = True\n",
    "model_for_training_discriminator.compile(optimizer=Adam(LEARNING_RATE, beta_1=BETA_1, beta_2=BETA_2), loss=[wasserstein_loss, wasserstein_loss])\n",
    "model_for_training_discriminator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "real_y = np.ones((BATCHSIZE, 1), dtype=np.float32)\n",
    "fake_y = -real_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = X/255*2-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f66d8660710>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eAMMAvlqrE5tZC4DvAfiiu3+gTFAt1yTgR83XxJdRNLdS1iL4zwDYetXPW8pj\nNcfdz5T/HwHwAyx8JFkrKiqIutq4+/nyhVcC8E3UaE3MLIuFgPu2u3+/PFzzNQn5sVZrUj73NRfN\nrZS1CP7XAewws21mVgfgd7FQDLSmmFmzmbVeeQzg1wAcSJ61qlwXBVGvXFxlPosarIkt9Jh6EsAh\nd//aVaaargnzo9ZrUrOiubXawVy0m/kpLOykHgfwb9fIh+1YUBreBnCwln4A+A4W3j7OY2HP4zEA\n67DQ9uwogJ8C6FojP/4KwDsA9pcvtt4a+PExLLyF3Q/grfK/T9V6TRL8qOmaALgbwM/L5zsA4N+V\nx1d0PfQNPyEiJfYNPyGiRcEvRKQo+IWIFAW/EJGi4BciUhT8QkSKgl+ISFHwCxEp/xefEQhVTghf\nRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66d86e1d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow((X[8787]+1)/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1 of 500\n",
      "number of batches: 937\n",
      "936/937 [============================>.] - ETA: 0s\n",
      "epoch time: 65.86544990539551\n",
      "32/64 [==============>...............] - ETA: 0s570.5799560546875\n",
      "32/64 [==============>...............] - ETA: 0s-570.5798645019531\n",
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      "plot generated_image\n",
      "epoch 2 of 500\n",
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      "epoch 3 of 500\n",
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      "plot generated_image\n",
      "epoch 4 of 500\n",
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      "epoch 5 of 500\n",
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      "epoch 6 of 500\n",
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      "epoch 7 of 500\n",
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      "epoch 8 of 500\n",
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      "epoch 9 of 500\n",
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      "plot generated_image\n",
      "epoch 10 of 500\n",
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      "plot generated_image\n",
      "epoch 11 of 500\n",
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      "plot generated_image\n",
      "epoch 12 of 500\n",
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      "epoch 13 of 500\n",
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      "epoch 14 of 500\n",
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      "epoch 15 of 500\n",
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      "epoch 16 of 500\n",
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      "plot generated_image\n",
      "epoch 17 of 500\n",
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      "epoch 18 of 500\n",
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      "epoch 19 of 500\n",
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      "plot generated_image\n",
      "epoch 20 of 500\n",
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      "epoch 21 of 500\n",
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      "epoch 22 of 500\n",
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      "epoch 23 of 500\n",
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      "epoch 24 of 500\n",
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      "epoch 25 of 500\n",
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      "epoch 26 of 500\n",
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      "epoch 27 of 500\n",
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      "epoch 28 of 500\n",
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      "epoch 29 of 500\n",
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      "epoch 30 of 500\n",
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      "plot generated_image\n",
      "epoch 31 of 500\n",
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      "epoch 32 of 500\n",
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      "epoch 33 of 500\n",
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      "epoch 34 of 500\n",
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      "epoch 35 of 500\n",
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      "epoch 36 of 500\n",
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      "epoch 37 of 500\n",
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      "epoch 38 of 500\n",
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      "epoch 39 of 500\n",
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      "plot generated_image\n",
      "epoch 40 of 500\n",
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      "epoch 41 of 500\n",
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      "epoch 43 of 500\n",
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      "epoch 44 of 500\n",
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      "epoch 46 of 500\n",
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      "epoch 50 of 500\n",
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      "epoch 51 of 500\n",
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      "epoch 54 of 500\n",
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      "epoch 55 of 500\n",
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      "epoch 56 of 500\n",
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      "epoch 57 of 500\n",
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      "epoch 58 of 500\n",
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      "epoch 59 of 500\n",
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      "epoch 60 of 500\n",
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      "epoch 61 of 500\n",
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      "epoch 62 of 500\n",
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      "epoch 63 of 500\n",
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      "epoch 64 of 500\n",
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      "epoch 65 of 500\n",
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      "epoch 66 of 500\n",
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      "epoch 67 of 500\n",
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      "epoch 68 of 500\n",
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      "epoch 69 of 500\n",
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      "epoch 70 of 500\n",
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      "epoch 71 of 500\n",
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      "epoch 72 of 500\n",
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      "epoch 73 of 500\n",
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      "epoch 74 of 500\n",
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      "epoch 76 of 500\n",
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      "plot generated_image\n",
      "epoch 77 of 500\n",
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      "epoch 78 of 500\n",
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      "epoch 79 of 500\n",
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      "epoch 81 of 500\n",
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      "epoch 88 of 500\n",
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      "epoch 90 of 500\n",
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      "epoch 91 of 500\n",
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      "epoch 93 of 500\n",
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      "epoch 95 of 500\n",
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      "936/937 [============================>.] - ETA: 0s\n",
      "epoch time: 140.1712543964386\n",
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      "wasserstein_loss: -0.01171875\n",
      "plot generated_image\n",
      "epoch 267 of 500\n",
      "number of batches: 937\n",
      "936/937 [============================>.] - ETA: 0s\n",
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      "wasserstein_loss: -0.001953125\n",
      "plot generated_image\n",
      "epoch 268 of 500\n",
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      "936/937 [============================>.] - ETA: 0s\n",
      "epoch time: 140.50038814544678\n",
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      "wasserstein_loss: -0.015625\n",
      "plot generated_image\n",
      "epoch 269 of 500\n",
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      "936/937 [============================>.] - ETA: 0s\n",
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      "wasserstein_loss: 0.046875\n",
      "plot generated_image\n",
      "epoch 270 of 500\n",
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      "936/937 [============================>.] - ETA: 0s\n",
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      "wasserstein_loss: -0.75\n",
      "plot generated_image\n",
      "epoch 271 of 500\n",
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      "wasserstein_loss: 1.25\n",
      "plot generated_image\n",
      "epoch 272 of 500\n",
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      "plot generated_image\n",
      "epoch 273 of 500\n",
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      "plot generated_image\n",
      "epoch 274 of 500\n",
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      "wasserstein_loss: -0.00390625\n",
      "plot generated_image\n",
      "epoch 275 of 500\n",
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      "wasserstein_loss: 0.375\n",
      "plot generated_image\n",
      "epoch 276 of 500\n",
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      "wasserstein_loss: -0.03125\n",
      "plot generated_image\n",
      "epoch 277 of 500\n",
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      "wasserstein_loss: -0.009765625\n",
      "plot generated_image\n",
      "epoch 278 of 500\n",
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      "wasserstein_loss: -0.006591796875\n",
      "plot generated_image\n",
      "epoch 279 of 500\n",
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      "wasserstein_loss: -0.1875\n",
      "plot generated_image\n",
      "epoch 280 of 500\n",
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      "plot generated_image\n",
      "epoch 281 of 500\n",
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      "plot generated_image\n",
      "epoch 282 of 500\n",
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      "epoch 283 of 500\n",
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      "epoch 284 of 500\n",
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      "epoch 285 of 500\n",
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      "epoch 286 of 500\n",
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      "plot generated_image\n",
      "epoch 287 of 500\n",
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      "wasserstein_loss: -0.00390625\n",
      "plot generated_image\n",
      "epoch 288 of 500\n",
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      "epoch 289 of 500\n",
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      "epoch 290 of 500\n",
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      "plot generated_image\n",
      "epoch 291 of 500\n",
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      "plot generated_image\n",
      "epoch 292 of 500\n",
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      "epoch 293 of 500\n",
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      "epoch 294 of 500\n",
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      "plot generated_image\n",
      "epoch 295 of 500\n",
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      "plot generated_image\n",
      "epoch 296 of 500\n",
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      "epoch 297 of 500\n",
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      "plot generated_image\n",
      "epoch 298 of 500\n",
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      "plot generated_image\n",
      "epoch 299 of 500\n",
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      "epoch time: 140.14342141151428\n",
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      "plot generated_image\n",
      "epoch 300 of 500\n",
      "number of batches: 937\n",
      "936/937 [============================>.] - ETA: 0s\n",
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      "plot generated_image\n",
      "epoch 301 of 500\n",
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      "wasserstein_loss: -0.03125\n",
      "plot generated_image\n",
      "epoch 302 of 500\n",
      "number of batches: 937\n",
      "936/937 [============================>.] - ETA: 0s\n",
      "epoch time: 140.12926054000854\n",
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      "wasserstein_loss: 0.01953125\n",
      "plot generated_image\n",
      "epoch 303 of 500\n",
      "number of batches: 937\n",
      "936/937 [============================>.] - ETA: 0s\n",
      "epoch time: 139.9989321231842\n",
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      "plot generated_image\n",
      "epoch 304 of 500\n",
      "number of batches: 937\n",
      "936/937 [============================>.] - ETA: 0s\n",
      "epoch time: 139.90365266799927\n",
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      "wasserstein_loss: 0.015625\n",
      "plot generated_image\n",
      "epoch 305 of 500\n",
      "number of batches: 937\n",
      " 78/937 [=>............................] - ETA: 127s"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-9c2c40cad8e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0mdiscriminator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0mgenerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m         \u001b[0mgenerator_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_for_training_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBATCHSIZE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\nepoch time: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mstart_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/mathlab115/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1760\u001b[0m             \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1762\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1763\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1764\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/mathlab115/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2271\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[1;32m   2272\u001b[0m                               \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2273\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2274\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/mathlab115/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/mathlab115/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    980\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/mathlab115/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1030\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/home/mathlab115/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1037\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1040\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/mathlab115/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "test_noise = np.random.randn(GENERATE_BATCHSIZE, 128)\n",
    "W_loss = []\n",
    "discriminator_loss = []\n",
    "generator_loss = []\n",
    "for epoch in range(EPOCHS):\n",
    "    np.random.shuffle(X)\n",
    "    \n",
    "    print(\"epoch {} of {}\".format(epoch+1, EPOCHS))\n",
    "    num_batches = int(X.shape[0] // BATCHSIZE)\n",
    "    \n",
    "    print(\"number of batches: {}\".format(int(X.shape[0] // (BATCHSIZE))))\n",
    "    \n",
    "    progress_bar = Progbar(target=int(X.shape[0] // (BATCHSIZE * TRAINING_RATIO)))\n",
    "    minibatches_size = BATCHSIZE * TRAINING_RATIO\n",
    "    \n",
    "    start_time = time()\n",
    "    for index in range(int(X.shape[0] // (BATCHSIZE * TRAINING_RATIO))):\n",
    "        progress_bar.update(index)\n",
    "        discriminator_minibatches = X[index * minibatches_size:(index + 1) * minibatches_size]\n",
    "        \n",
    "        for j in range(TRAINING_RATIO):\n",
    "            image_batch = discriminator_minibatches[j * BATCHSIZE : (j + 1) * BATCHSIZE]\n",
    "            noise = np.random.randn(BATCHSIZE, 128).astype(np.float32)\n",
    "            discriminator.trainable = True\n",
    "            generator.trainable = False\n",
    "            discriminator_loss.append(model_for_training_discriminator.train_on_batch([image_batch, noise],\n",
    "                                                                                      [real_y, fake_y]))\n",
    "        discriminator.trainable = False\n",
    "        generator.trainable = True\n",
    "        generator_loss.append(model_for_training_generator.train_on_batch(np.random.randn(BATCHSIZE, 128), real_y))\n",
    "    \n",
    "    print('\\nepoch time: {}'.format(time()-start_time))\n",
    "    \n",
    "    W_real = model_for_training_generator.evaluate(test_noise, real_y)\n",
    "    print(W_real)\n",
    "    W_fake = model_for_training_generator.evaluate(test_noise, fake_y)\n",
    "    print(W_fake)\n",
    "    W_l = W_real+W_fake\n",
    "    print('wasserstein_loss: {}'.format(W_l))\n",
    "    W_loss.append(W_l)\n",
    "    #Generate image\n",
    "    generated_image = generator.predict(test_noise)\n",
    "    generated_image = (generated_image+1)/2\n",
    "    for i in range(GENERATE_ROW_NUM):\n",
    "        new = generated_image[i*GENERATE_ROW_NUM:i*GENERATE_ROW_NUM+GENERATE_ROW_NUM].reshape(32*GENERATE_ROW_NUM,32,3)\n",
    "        if i!=0:\n",
    "            old = np.concatenate((old,new),axis=1)\n",
    "        else:\n",
    "            old = new\n",
    "    print('plot generated_image')\n",
    "    plt.imsave('{}/SN_epoch_{}.png'.format(SAVE_DIR, epoch), old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f668a461ef0>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0WjUPrt7L+QtOvGobiwsWVLDju1cAidNQI5Hj97LyaxfE3kevYrc3t8c6zRdM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\nycuQPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f668a4cf0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(W_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f668a19a550>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2d1ldW+XPvvYyQgq2exM++9Vv8ksfewobBgS1iKRwpFnBZHyHt69eI9CSWhKi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\nnw9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diqkFzfdUDTS3ySubKzHLKUdOg5Y73fUsl75tTjrR7Lud4OH7taevFw7h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\nXmL8nLIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f668a235240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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