cycle_gan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.layers import Input\n",
    "from keras.models import Model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Reshape\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.convolutional import UpSampling2D, Convolution2D,MaxPooling2D\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.layers import Flatten, Dropout\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "from keras.datasets import mnist\n",
    "from keras.optimizers import Adam\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.datasets import fashion_mnist\n",
    "import keras.backend as K\n",
    "from keras.layers import Lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "NUM_EPOCH = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_images(images):\n",
    "    fig = plt.figure(figsize=(10,21))\n",
    "    for i, image in enumerate(images):\n",
    "        plt.subplot(int(BATCH_SIZE/5)+(BATCH_SIZE%5>0),5,i+1)\n",
    "        plt.imshow(image[:,:,0])\n",
    "        plt.gray()\n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cycle gan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-1-ebe92a8fff04>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-ebe92a8fff04>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    def generator_AB()\u001b[0m\n\u001b[0m                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "def generator_AB():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(input_dim=100, output_dim=1024))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dense(128*7*7))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Reshape((7, 7, 128), input_shape=(128*7*7,)))\n",
    "    model.add(UpSampling2D((2, 2)))\n",
    "    model.add(Convolution2D(64, (5, 5), border_mode='same'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(UpSampling2D((2, 2)))\n",
    "    model.add(Convolution2D(1, (5, 5), border_mode='same'))\n",
    "    model.add(Activation('tanh'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-2-24c97ca83870>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-2-24c97ca83870>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    def generator_BA()\u001b[0m\n\u001b[0m                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "def generator_BA():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-3-61a1fce4347b>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-61a1fce4347b>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    def discriminator()\u001b[0m\n\u001b[0m                       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "def discriminator():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-4-e4c492876b5f>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-4-e4c492876b5f>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    def train()\u001b[0m\n\u001b[0m               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "def train():\n",
    "    return generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator=train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conditional cycle gan\n",
    "\n",
    "- AtoB 柄あり　柄なし\n",
    "- BtoA 柄なしconditional 柄あり"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### high to low resolution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cgenerator_AB():\n",
    "    x_input=Input(shape=(28, 28,1), dtype='float32', name='x_input')\n",
    "    x=Convolution2D(64, (5, 5),\n",
    "                            subsample=(2, 2),\n",
    "                            border_mode='same')(x_input)\n",
    "    x=LeakyReLU(0.2)(x)\n",
    "    x=Convolution2D(128, (5, 5), subsample=(2, 2))(x)\n",
    "    x=LeakyReLU(0.2)(x)\n",
    "    x=Flatten()(x)\n",
    "    #y_input = Input(shape=(10,), dtype='float32', name='y_input')\n",
    "    #x = Dense(50)(y_input)\n",
    "    #y_output = Activation('relu')(x)\n",
    "    #x = keras.layers.concatenate([x_output, y_output])\n",
    "    x=Dense(256)(x)\n",
    "    x=LeakyReLU(0.2)(x)\n",
    "    x=Dropout(0.5)(x)\n",
    "    x=Dense(1200)(x)\n",
    "    x=BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "    x=Dense(128*7*7)(x)\n",
    "    x=BatchNormalization()(x)\n",
    "    x=Activation('relu')(x)\n",
    "    x=Reshape((7, 7, 128), input_shape=(128*7*7,))(x)\n",
    "    x=UpSampling2D((2, 2))(x)\n",
    "    x=Convolution2D(64, (5, 5), border_mode='same')(x)\n",
    "    x=BatchNormalization()(x)\n",
    "    x=Activation('relu')(x)\n",
    "    x=UpSampling2D((2, 2))(x)\n",
    "    x=Convolution2D(1, (5, 5), border_mode='same')(x)\n",
    "    output=Activation('tanh')(x)\n",
    "    model = Model(inputs=[x_input], outputs=[output])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### low resolution and conditional vector to high resolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cgenerator_BA():\n",
    "    x_input=Input(shape=(28, 28,1), dtype='float32', name='x_input')\n",
    "    x=Convolution2D(64, (5, 5),\n",
    "                            subsample=(2, 2),\n",
    "                            border_mode='same', name=\"con2d_1\")(x_input)\n",
    "    x=LeakyReLU(0.2, name=\"activation_1\")(x)\n",
    "    x=Convolution2D(128, (5, 5), subsample=(2, 2), name=\"conv2d_2\")(x)\n",
    "    x=LeakyReLU(0.2, name=\"activation_2\")(x)\n",
    "    x_output=Flatten(name=\"flatten_1\")(x)\n",
    "    x = Dense(200,name=\"dense_1\")(x_output)\n",
    "    x=BatchNormalization(name=\"batch_norm_1\")(x)\n",
    "    x_output=Activation('relu', name=\"activation_3\")(x)\n",
    "    y_input = Input(shape=(10,), dtype='float32', name='y_input')\n",
    "    x = Dense(200,name=\"dense_2\")(y_input)\n",
    "    x=BatchNormalization(name=\"batch_norm_2\")(x)\n",
    "    y_output = Activation('relu',name=\"activation_4\")(x)\n",
    "    x = keras.layers.concatenate([x_output, y_output],name=\"cancat_1\")\n",
    "    x = Dense(1200,name=\"dense_3\")(x)\n",
    "    x=BatchNormalization(name=\"batch_norm_3\")(x)\n",
    "    x = Activation('relu',name=\"activation_5\")(x)\n",
    "    x=Dense(128*7*7,name=\"dense_4\")(x)\n",
    "    x=BatchNormalization(name=\"batch_norm_4\")(x)\n",
    "    x=Activation('relu', name=\"activation_6\")(x)\n",
    "    x=Reshape((7, 7, 128), input_shape=(128*7*7,), name=\"reshape_1\")(x)\n",
    "    x=UpSampling2D((2, 2),name=\"upsmapling2d_1\")(x)\n",
    "    x=Convolution2D(64, (5, 5), border_mode='same',name=\"conv2d_3\")(x)\n",
    "    x=BatchNormalization(name=\"batch_norm_5\")(x)\n",
    "    x=Activation('relu',name=\"activation_7\")(x)\n",
    "    x=UpSampling2D((2, 2),name=\"upsampling2d_2\")(x)\n",
    "    x=Convolution2D(1, (5, 5), border_mode='same', name=\"conv2d_4\")(x)\n",
    "    output=Activation('tanh',name=\"output\")(x)\n",
    "    model = Model(inputs=[x_input, y_input], outputs=[output])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cdiscriminator_patch(img_dim, nb_patch, model_name=\"DCGAN_discriminator\", use_mbd=False):\n",
    "    \"\"\"\n",
    "    Discriminator model of the DCGAN\n",
    "    args : img_dim (tuple of int) num_chan, height, width\n",
    "           pretr_weights_file (str) file holding pre trained weights\n",
    "    returns : model (keras NN) the Neural Net model\n",
    "    \"\"\"\n",
    "    \n",
    "    list_input = [Input(shape=(7,7,1), name=\"disc_input_%s\" % i) for i in range(nb_patch)]\n",
    "\n",
    "    if K.image_dim_ordering() == \"th\":\n",
    "        bn_axis = 1\n",
    "    else:\n",
    "        bn_axis = -1\n",
    "\n",
    "    nb_filters = 64\n",
    "    nb_conv = 1#int(np.floor(np.log(img_dim[1]) / np.log(2)))\n",
    "    list_filters = [nb_filters * min(8, (2 ** i)) for i in range(nb_conv)]\n",
    "\n",
    "    # First conv\n",
    "    x_input = Input(shape=(7,7,1), name=\"discriminator_input\")\n",
    "    \n",
    "    x = Convolution2D(list_filters[0], (5, 5), subsample=(2, 2), name=\"disc_conv2d_1\")(x_input)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = LeakyReLU(0.2)(x)\n",
    "\n",
    "    # Next convs\n",
    "    for i, f in enumerate(list_filters[1:]):\n",
    "        name = \"disc_conv2d_%s\" % (i + 2)\n",
    "        x = Convolution2D(f, (5, 5), subsample=(2, 2), name=name)(x)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = LeakyReLU(0.2)(x)\n",
    "    x_flat = Flatten()(x)\n",
    "    \n",
    "    x=Dense(256)(x_flat)\n",
    "    x=LeakyReLU(0.2)(x)\n",
    "    x=Dropout(0.5)(x)\n",
    "    \n",
    "    \n",
    "    #y_input = Input(shape=(10,), dtype='float32', name='y_input')\n",
    "    #x = Dense(50)(y_input)\n",
    "    #y_output = Activation('relu')(x)\n",
    "    #x_flat = keras.layers.concatenate([x_flat, y_output])\n",
    "    x = Dense(1, activation='softmax', name=\"disc_dense\")(x)\n",
    "    PatchGAN = Model(input=[x_input], output=[x, x_flat], name=\"PatchGAN\")\n",
    "    print(\"PatchGAN summary\")\n",
    "    PatchGAN.summary()\n",
    "\n",
    "    x = [PatchGAN(patch)[0] for patch in list_input]\n",
    "    x_mbd = [PatchGAN(patch)[1] for patch in list_input]\n",
    "\n",
    "    if len(x) > 1:\n",
    "        x = keras.layers.concatenate(x)\n",
    "    else:\n",
    "        x = x[0]\n",
    "    # use flat x\n",
    "    if use_mbd:\n",
    "        if len(x_mbd) > 1:\n",
    "            x_mbd = keras.layers.concatenate(x_mbd)\n",
    "        else:\n",
    "            x_mbd = x_mbd[0]\n",
    "\n",
    "        num_kernels = 100\n",
    "        dim_per_kernel = 5\n",
    "\n",
    "        M = Dense(num_kernels * dim_per_kernel, bias=False, activation=None)\n",
    "        #MBD = Lambda(minb_disc, output_shape=lambda_output)\n",
    "\n",
    "        x_mbd = M(x_mbd)\n",
    "        #x_mbd = Reshape((num_kernels, dim_per_kernel))(x_mbd)\n",
    "        #x_mbd = MBD(x_mbd)\n",
    "        x = keras.layers.concatenate([x,x_mbd])\n",
    "\n",
    "    x_out = Dense(1, activation=\"softmax\", name=\"disc_output\")(x)\n",
    "\n",
    "    discriminator_model = Model(input=list_input, output=[x_out], name=model_name)\n",
    "\n",
    "    return discriminator_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DCGAN_patch(generator, discriminator_model, img_dim, patch_size):#, image_dim_ordering):\n",
    "\n",
    "    #gen_input = Input(shape=(10,), name=\"DCGAN_input\")\n",
    "\n",
    "    generated_image = generator(generator.inputs)\n",
    "\n",
    "    #if image_dim_ordering == \"th\":\n",
    "    #    h, w = img_dim[1:]\n",
    "    #else:\n",
    "    h, w = img_dim[:-1]\n",
    "    ph, pw = patch_size\n",
    "\n",
    "    list_row_idx = [(i * ph, (i + 1) * ph) for i in range(int(h / ph))]\n",
    "    list_col_idx = [(i * pw, (i + 1) * pw) for i in range(int(w / pw))]\n",
    "\n",
    "    list_gen_patch = []\n",
    "    for row_idx in list_row_idx:\n",
    "        for col_idx in list_col_idx:\n",
    "            #if image_dim_ordering == \"tf\":\n",
    "            #    x_patch = Lambda(lambda z: z[:, row_idx[0]:row_idx[1], col_idx[0]:col_idx[1], :])(generated_image)\n",
    "            #else:\n",
    "            x_patch = Lambda(lambda z: z[ :, row_idx[0]:row_idx[1], col_idx[0]:col_idx[1],:])(generated_image)\n",
    "            list_gen_patch.append(x_patch)\n",
    "\n",
    "    DCGAN_output = discriminator_model(list_gen_patch)\n",
    "    DCGAN = Model(input=generator.inputs,\n",
    "                  output=[generated_image, DCGAN_output],\n",
    "                  name=\"DCGAN\")\n",
    "\n",
    "    return DCGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def l1_loss(y_true, y_pred):\n",
    "    return K.sum(K.abs(y_pred - y_true), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def produce_image28(size=28):\n",
    "    model = Sequential()\n",
    "    model.add(MaxPooling2D((2, 2),input_shape=(size, size,1)))\n",
    "    model.add(UpSampling2D((2, 2)))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_list_gen(img_dim, patch_size, X):\n",
    "    h, w = img_dim[:-1]\n",
    "    ph, pw = patch_size\n",
    "\n",
    "    list_row_idx = [(i * ph, (i + 1) * ph) for i in range(int(h / ph))]\n",
    "    list_col_idx = [(i * pw, (i + 1) * pw) for i in range(int(w / pw))]\n",
    "    list_gen_patch = []\n",
    "    list_gen_func=lambda z: z[:,row_idx[0]:row_idx[1], col_idx[0]:col_idx[1],:]\n",
    "\n",
    "    for row_idx in list_row_idx:\n",
    "        for col_idx in list_col_idx:\n",
    "\n",
    "            x_patch = list_gen_func(X)\n",
    "\n",
    "            list_gen_patch.append(np.array(x_patch))\n",
    "    return list_gen_patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ctrain_patch(X_train, y_train):\n",
    "    \n",
    "    X_train = (X_train.astype(np.float32) - 127.5)/127.5\n",
    "    X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2],1)\n",
    "    y_train= keras.utils.to_categorical(y_train, num_classes=10)\n",
    "    #print(y_train.shape)\n",
    "    img_dim=[28,28,1]\n",
    "    nb_patch=16\n",
    "    \n",
    "    discriminatorAB = cdiscriminator_patch(img_dim, nb_patch, model_name=\"DCGAN_discriminator\", use_mbd=True)\n",
    "    discriminatorBA = cdiscriminator_patch(img_dim, nb_patch, model_name=\"DCGAN_discriminator\", use_mbd=True)\n",
    "    discriminatorAA = cdiscriminator_patch(img_dim, nb_patch, model_name=\"DCGAN_discriminator\", use_mbd=True)\n",
    "\n",
    "    \n",
    "    d_opt = Adam(lr=1e-5, beta_1=0.1)\n",
    "    discriminatorAB.compile(loss='binary_crossentropy', optimizer=d_opt)\n",
    "    discriminatorBA.compile(loss='binary_crossentropy', optimizer=d_opt)\n",
    "    discriminatorAA.compile(loss='binary_crossentropy', optimizer=d_opt)\n",
    "\n",
    "    # generator+discriminator （discriminator部分の重みは固定）\n",
    "    discriminatorAB.trainable = False\n",
    "    discriminatorBA.trainable = False\n",
    "    discriminatorAA.trainable = False\n",
    "    generatorAB = cgenerator_AB()\n",
    "    generatorAB.summary()\n",
    "    generatorBA = cgenerator_BA()\n",
    "    generatorBA.summary()\n",
    "    generatorAA_output=generatorAB(generatorBA(generatorBA.inputs))\n",
    "    generatorAA=Model(inputs=generatorBA.inputs, outputs=generatorAA_output)\n",
    "    patch_size=[7,7]\n",
    "    dcganAB=DCGAN_patch(generatorAB, discriminatorAB, img_dim, patch_size)\n",
    "    dcganBA=DCGAN_patch(generatorBA, discriminatorBA, img_dim, patch_size)\n",
    "    dcganAA=DCGAN_patch(generatorAA, discriminatorAA, img_dim, patch_size)\n",
    "    g_opt = Adam(lr=2e-4, beta_1=0.5)\n",
    "    loss = [l1_loss, 'binary_crossentropy']\n",
    "    loss_weights = [1E1, 1]\n",
    "    dcganAB.compile(loss=loss, loss_weights=loss_weights, optimizer=g_opt)\n",
    "    dcganBA.compile(loss=loss, loss_weights=loss_weights, optimizer=g_opt)\n",
    "    dcganAA.compile(loss=loss, loss_weights=loss_weights, optimizer=g_opt)\n",
    "    \n",
    "    down_image=produce_image28()\n",
    "    num_batches = int(X_train.shape[0] / BATCH_SIZE)\n",
    "    print('Number of batches:', num_batches)\n",
    "    for epoch in range(NUM_EPOCH):\n",
    "\n",
    "        for index in range(num_batches):\n",
    "            #noise = np.array([np.random.uniform(-1, 1, 100) for _ in range(BATCH_SIZE)])\n",
    "            y_noise= keras.utils.to_categorical(np.array([np.random.randint(0, 9, 1) for _ in range(BATCH_SIZE)]), num_classes=10)\n",
    "            #print(y_noise)\n",
    "            image_batch = X_train[index*BATCH_SIZE:(index+1)*BATCH_SIZE]\n",
    "            image_down = down_image.predict(image_batch)\n",
    "            y_batch = y_train[index*BATCH_SIZE:(index+1)*BATCH_SIZE]\n",
    "            #print(y_batch)\n",
    "            generated_imagesAB = generatorAB.predict(image_batch, verbose=0)\n",
    "            generated_imagesBA = generatorBA.predict([image_down, y_batch], verbose=0)\n",
    "            generated_imagesAA = generatorAA.predict([image_down, y_batch], verbose=0)\n",
    "            #print(generated_images[0].shape)\n",
    "            # 生成画像を出力\n",
    "            if index % 500 == 0:\n",
    "                show_images(generated_imagesBA)\n",
    "                \n",
    "\n",
    "            # discriminatorを更新\n",
    "            #print(image_batch.shape)\n",
    "            #print(generated_images.shape)\n",
    "            \n",
    "            X_AB=np.concatenate((image_down,generated_imagesAB))\n",
    "            X_BA=np.concatenate((image_batch,generated_imagesBA))\n",
    "            X_AA=np.concatenate((image_batch,generated_imagesAA))\n",
    "            #print(y_batch.shape)\n",
    "            #print(y_noise.shape)\n",
    "            Y = np.concatenate((y_batch, y_noise))\n",
    "            y = [1]*BATCH_SIZE + [0]*BATCH_SIZE\n",
    "            list_gen_patchAB=get_list_gen(img_dim, patch_size, X_AB)\n",
    "            list_gen_patchBA=get_list_gen(img_dim, patch_size, X_BA)\n",
    "            list_gen_patchAA=get_list_gen(img_dim, patch_size, X_AA)\n",
    "            d_loss = discriminatorAB.train_on_batch(list_gen_patchAB, np.array(y))\n",
    "            d_loss = discriminatorBA.train_on_batch(list_gen_patchBA, np.array(y))\n",
    "            d_loss = discriminatorAA.train_on_batch(list_gen_patchAA, np.array(y))\n",
    "            # generatorを更新\n",
    "            #noise = np.array([np.random.uniform(-1, 1, 100) for _ in range(BATCH_SIZE)])\n",
    "            y_noise= keras.utils.to_categorical(np.array([np.random.randint(0, 9, 1) for _ in range(BATCH_SIZE)]), num_classes=10)\n",
    "            g_loss = dcganAB.train_on_batch(image_batch, [image_batch,np.array([1]*BATCH_SIZE)])\n",
    "            g_loss = dcganBA.train_on_batch([image_down, y_batch], [image_batch,np.array([1]*BATCH_SIZE)])\n",
    "            g_loss = dcganAA.train_on_batch([image_down, y_batch], [image_batch,np.array([1]*BATCH_SIZE)])\n",
    "            print(\"epoch: %d, batch: %d, g_loss: , d_loss: %f\" % (epoch, index, d_loss))\n",
    "            print(g_loss)\n",
    "\n",
    "        generatorAB.save_weights('ccycle_generator_patch_AB.h5')\n",
    "        discriminatorAB.save_weights('ccycle_discriminator_patch_AB.h5')\n",
    "        generatorBA.save_weights('ccycle_generator_patch_BA.h5')\n",
    "        discriminatorBA.save_weights('ccycle_discriminator_patch_BA.h5')\n",
    "        \n",
    "    return generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (_, _) = fashion_mnist.load_data()\n",
    "cgenerator_patch_fashion=ctrain_patch(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Mami/.pyenv/versions/3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:5: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (5, 5), name=\"con2d_1\", strides=(2, 2), padding=\"same\")`\n",
      "  \"\"\"\n",
      "/Users/Mami/.pyenv/versions/3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:7: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (5, 5), name=\"conv2d_2\", strides=(2, 2))`\n",
      "  import sys\n",
      "/Users/Mami/.pyenv/versions/3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:26: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (5, 5), name=\"conv2d_3\", padding=\"same\")`\n",
      "/Users/Mami/.pyenv/versions/3.6.2/lib/python3.6/site-packages/ipykernel_launcher.py:30: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(1, (5, 5), name=\"conv2d_4\", padding=\"same\")`\n"
     ]
    }
   ],
   "source": [
    "ccyclegenerator_patch_fashion=cgenerator_BA()\n",
    "ccyclegenerator_patch_fashion.load_weights('ccycle_generator_patch_BA.h5', by_name=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "down_image=produce_image28()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
    "X_test = (X_test.astype(np.float32) - 127.5)/127.5\n",
    "X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28, 1)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_images=10\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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333wAV+fkqwE8WpzmiDIhe2YH2TJbyJ7ZQbbMMIWURrgfwIsABjrndjjnZgH4BYCLnXPV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\nCCGEEAn4f8khxJz2BiBAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x205219a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_images(X_test[0:num_images])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "down_test_images=down_image.predict(X_test[0:num_images])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_noise= keras.utils.to_categorical(np.array([i for i in range(10)]), num_classes=10)\n",
    "y_noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AACjA0ggoFvNafE5FzFvzt3uJyx/MmTOnKr/yyitZnc+NqMuPiTkbPv/Gz/HHPAn/OOYX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\nOXQep2W6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207abd5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ve19ZPuOMM7K6e+65pyz7VBSjNKx42lvIP/GJT2R19vbvq6++eij7G7VPfepT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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x207f78278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SZjbimdo4FjugQghB0vclPVQUxdc7WZcM5FK1yaVEPiXV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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20cdac438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HsaynEGMc3i+GMEbSBkm3SNop6WlJn4oxrmlf89ApxLM+iGW9EM/6IJb1VXJn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\ne6/zdeCvN7Zf+c/exs1eX/2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CAAAowGAKAACgAIMpAACAAgymAAAACjCYAgAAKMBgCgAA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\nAKAAgykAAIACDKYAAAAK/H/MtV/6WLDlEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21004b198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/ztOdcZkZH5sxPVg6Ngct7l///WP6dfXq1XU77reLL764bsf95vujtORIafX1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\nLXAxBQAA0ML/D3T7coCGQMF3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21048c860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XEvk02j6f5DJq+1xK5LOhPXKZZVlL/ifpeEm3mMf/IOkfWnX8xjEnSXrUPF4q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\nAAAogcEUAABACf8/zXKM5hzfkO0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21075c908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Lt627uEzJ2rVrq3Y8lutubfZYxzTxWI01BfGrX/2q6PPbwtsYTx+bceVrT7F8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\njqUQPsZ9BfR4HNXtW//8uidGHMoTD3p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      "text/plain": [
       "<matplotlib.figure.Figure at 0x210d0e7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vkv7yL/9yZN9P//RPj+xr7ZNJ5pfX8/Zyv+yfM2afdtppfTtn1PXLyK10j9/C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\nB5kklQVUTEUDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x210f78400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tQd9sF2LZHvTN8dNLmm+/pM+mlFZLukjSp8xstaTrJG1IKa2StKHz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\nA4MpAACADAymAAAAMjCYAgAAyPD/AXBPjOMLQbFJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2115ac358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4NgexbBbi2RzEspkspdTbL5pNlbRS0gWS1ki6VdKilNK9/WseBoV4NgexbBbi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\noACDKQAAgAIMpgAAAAowmAIAACjw/wG8HXM8SIi7ewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2118c1ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "for i in range(num_images):\n",
    "    image_list=[down_test_images[i:i+1]]\n",
    "    for j in range(num_images):\n",
    "        image_list.append(ccyclegenerator_patch_fashion.predict([down_test_images[i:i+1],y_noise[j:j+1]]))\n",
    "    #print(np.array(image_list).shape)\n",
    "    show_images(np.array(image_list)[:,0,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}