U-Net.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "import tensorflow as tf\n",
    "from keras.models import Model\n",
    "from keras import backend as K\n",
    "from keras import metrics\n",
    "from keras.datasets import mnist\n",
    "from keras.layers import Input,concatenate\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers import Conv2DTranspose,Reshape\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "batch_size = 512\n",
    "original_dim = 784\n",
    "epochs = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train the VAE on MNIST digits\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "num_train=(len(x_train))\n",
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "x_train = x_train.reshape((len(x_train), 28,28,1))\n",
    "x_test = x_test.reshape((len(x_test), 28,28,1))\n",
    "\n",
    "dm_train= copy.deepcopy(x_train)\n",
    "dm_test = copy.deepcopy(x_test)\n",
    "dm_train[:,10:19,:,:]=np.zeros([len(x_train),9,28,1])\n",
    "dm_test[:,10:19,:,:]=np.zeros([len(x_test),9,28,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 6048x12096 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_images = 6\n",
    "figure = np.zeros((28*2 , 28 * n_images))\n",
    "# Display\n",
    "showidx=np.random.randint(0,num_train,n_images)\n",
    "for i,idx in enumerate (showidx):\n",
    "    figure[0: 28,i *28: (i + 1) * 28] = np.reshape(x_train[idx], [28, 28])\n",
    "    figure[28: 56,i *28: (i + 1) * 28] = np.reshape(dm_train[idx], [28, 28])\n",
    "plt.figure(figsize=(28*3, 28*n_images))\n",
    "plt.imshow(figure, cmap='Greys_r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#encoder part\n",
    "x_noise = Input(shape=(28,28,1))\n",
    "conv_1 = Conv2D(16,(3, 3),padding='same',activation='relu')(x_noise)\n",
    "conv_2 = Conv2D(32,(3, 3),padding='same',activation='relu')(conv_1)\n",
    "pool_1 = MaxPooling2D((2, 2))(conv_2)\n",
    "conv_3 = Conv2D(64,(3, 3),padding='same',activation='relu')(pool_1)#14*14*64\n",
    "pool_2 = MaxPooling2D((2, 2))(conv_3)\n",
    "conv_4 = Conv2D(128,(3, 3),padding='valid',activation='relu')(pool_2)#5*5*128\n",
    "conv_5 = Conv2D(128,(3, 3),padding='valid',activation='relu')(conv_4)#3*3*128\n",
    "conv_6 = Conv2D(256,(3, 3),padding='valid',activation='relu')(conv_5)#1*1*256\n",
    "conv_7 = Conv2DTranspose(128,(3, 3), padding='valid',activation='relu')(conv_6)#3*3*128\n",
    "conv_8 = Conv2DTranspose(128,(3, 3), padding='valid',activation='relu')(conv_7)#5*5*128\n",
    "\n",
    "connec_1= concatenate([conv_4,conv_8],axis=-1)\n",
    "conv_9 = Conv2DTranspose(64,(3, 3), padding='valid',activation='relu')(connec_1)#7*7*64\n",
    "conv_10 = Conv2DTranspose(64,(3, 3), strides=(2, 2),padding='same',activation='relu')(conv_9)#14*14*64\n",
    "\n",
    "connec_2= concatenate([conv_3,conv_10],axis=-1)#14*14*64*2\n",
    "conv_11 = Conv2DTranspose(32,(3, 3),padding='same', activation='relu')(connec_2)#14*14*32\n",
    "conv_12 = Conv2DTranspose(32,(3, 3), strides=(2, 2),padding='same',activation='relu')(conv_11)#28*28*32\n",
    "\n",
    "connec_3= concatenate([conv_2,conv_12],axis=-1)#28*28*32*2\n",
    "conv_11 = Conv2DTranspose(16,(3, 3),padding='same', activation='relu')(connec_3)#28*28*16\n",
    "x_out = Conv2DTranspose(1,(3, 3),padding='same', activation='relu')(conv_11)#28*28*16\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 28, 28, 1)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 28, 28, 16)   160         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 28, 28, 32)   4640        conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 14, 14, 32)   0           conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 14, 14, 64)   18496       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 7, 7, 64)     0           conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 5, 5, 128)    73856       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 3, 3, 128)    147584      conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 1, 1, 256)    295168      conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 3, 3, 128)    295040      conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTrans (None, 5, 5, 128)    147584      conv2d_transpose_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 5, 5, 256)    0           conv2d_4[0][0]                   \n",
      "                                                                 conv2d_transpose_2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTrans (None, 7, 7, 64)     147520      concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_4 (Conv2DTrans (None, 14, 14, 64)   36928       conv2d_transpose_3[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 14, 14, 128)  0           conv2d_3[0][0]                   \n",
      "                                                                 conv2d_transpose_4[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_5 (Conv2DTrans (None, 14, 14, 32)   36896       concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_6 (Conv2DTrans (None, 28, 28, 32)   9248        conv2d_transpose_5[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 28, 28, 64)   0           conv2d_2[0][0]                   \n",
      "                                                                 conv2d_transpose_6[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_7 (Conv2DTrans (None, 28, 28, 16)   9232        concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_8 (Conv2DTrans (None, 28, 28, 1)    145         conv2d_transpose_7[0][0]         \n",
      "==================================================================================================\n",
      "Total params: 1,222,497\n",
      "Trainable params: 1,222,497\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "U_Net = Model(x_noise, x_out)\n",
    "U_Net.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.vis_utils import plot_model\n",
    "plot_model(U_Net, to_file='model.png',show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute VAE loss\n",
    "def AE_loss(x_origin,x_out):\n",
    "    x_origin=K.flatten(x_origin)\n",
    "    x_out=K.flatten(x_out)\n",
    "    xent_loss = original_dim * metrics.binary_crossentropy(x_origin, x_out)\n",
    "    ae_loss = K.mean(xent_loss)\n",
    "    return ae_loss\n",
    "U_Net.compile(optimizer='adam', loss=AE_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/12\n",
      "60000/60000 [==============================] - 29s 487us/step - loss: 181.2547 - val_loss: 120.9795\n",
      "Epoch 2/12\n",
      "60000/60000 [==============================] - 21s 350us/step - loss: 112.3841 - val_loss: 125.5133\n",
      "Epoch 3/12\n",
      "60000/60000 [==============================] - 21s 349us/step - loss: 108.9113 - val_loss: 99.4513\n",
      "Epoch 4/12\n",
      "60000/60000 [==============================] - 21s 350us/step - loss: 100.1429 - val_loss: 110.6896\n",
      "Epoch 5/12\n",
      "60000/60000 [==============================] - 21s 351us/step - loss: 98.4395 - val_loss: 90.6334\n",
      "Epoch 6/12\n",
      "60000/60000 [==============================] - 21s 351us/step - loss: 91.1689 - val_loss: 89.8068\n",
      "Epoch 7/12\n",
      "60000/60000 [==============================] - 21s 350us/step - loss: 89.8053 - val_loss: 88.0868\n",
      "Epoch 8/12\n",
      "60000/60000 [==============================] - 21s 350us/step - loss: 91.5694 - val_loss: 87.4944\n",
      "Epoch 9/12\n",
      "60000/60000 [==============================] - 21s 348us/step - loss: 85.6705 - val_loss: 87.2161\n",
      "Epoch 10/12\n",
      "60000/60000 [==============================] - 21s 348us/step - loss: 85.4893 - val_loss: 89.6311\n",
      "Epoch 11/12\n",
      "60000/60000 [==============================] - 21s 348us/step - loss: 93.8931 - val_loss: 100.6148\n",
      "Epoch 12/12\n",
      "60000/60000 [==============================] - 21s 347us/step - loss: 87.5281 - val_loss: 82.8633\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f78c7039048>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U_Net.fit(dm_train,x_train,\n",
    "        shuffle=True,\n",
    "        epochs=epochs,\n",
    "        batch_size=batch_size,\n",
    "        validation_data=(dm_test, x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "digit_size = 28\n",
    "figure = np.zeros((digit_size * 3, digit_size * n_images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 6048x12096 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_test=10000\n",
    "showidx=np.random.randint(0,num_test,n_images)\n",
    "x_out=U_Net.predict(x_test[showidx])\n",
    "# Display\n",
    "for i,idx in enumerate (showidx):\n",
    "    figure[0: 28,i *28: (i + 1) * 28] = np.reshape(x_test[idx], [28, 28])\n",
    "    figure[28: 28 * 2,i *28: (i + 1) * 28] = np.reshape(dm_test[idx], [28, 28])\n",
    "    figure[28 * 2: 28 * 3,i *28: (i + 1) * 28] =  np.clip(np.reshape(x_out[i], [28, 28]), 0., 1.)\n",
    "plt.figure(figsize=(28 * 3, 28*n_images))\n",
    "plt.imshow(figure, cmap='Greys_r')\n",
    "plt.savefig('result_keras_U-Net.png')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}