segmentation.ipynb~

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.layers import Activation\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.models import Model\n",
    "from keras.layers import Input\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.layers import UpSampling2D\n",
    "from keras.layers import Concatenate\n",
    "from keras.layers import Lambda \n",
    "from keras.utils import to_categorical\n",
    "import tensorflow as tf\n",
    "from keras.layers import Add \n",
    "\n",
    "from keras.layers import Reshape\n",
    "\n",
    "from keras import backend as K\n",
    "from keras import regularizers, optimizers\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as scio\n",
    "import numpy as np    \n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import re\n",
    "from scipy.misc import imsave\n",
    "from scipy import ndimage, misc\n",
    "from numpy import unravel_index\n",
    "from operator import sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import ReduceLROnPlateau, CSVLogger,EarlyStopping,ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.load('/home/sachin/Desktop/AI_Assignment2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=np.load('/home/sachin/Desktop/AI_Assignment2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 256, 256, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 256, 256)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "train_indices = np.random.choice(2000,1500,replace = False)\n",
    "print(sorted(train_indices))\n",
    "x_train_images = []\n",
    "y_train_labels = [] \n",
    "for i in train_indices:\n",
    "    x_train_images.append(x[i])\n",
    "    y_train_labels.append(y[i])\n",
    "\n",
    "test_indices = [xy for xy in range(2000) if xy not in train_indices]\n",
    "print(test_indices)\n",
    "x_test_images = []\n",
    "y_test_labels = []\n",
    "\n",
    "for i in test_indices:\n",
    "    x_test_images.append(x[i])\n",
    "    y_test_labels.append(y[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.array(x_train_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1500, 256, 256, 3)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = np.array(x_test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 256, 256, 3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.array(y_train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1500, 256, 256)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = np.array(y_test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 256, 256)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#z-score\n",
    "mean = np.mean(x_train,axis=(0,1,2,3))\n",
    "std = np.std(x_train,axis=(0,1,2,3))\n",
    "x_train = (x_train-mean)/(std+1e-7)\n",
    "x_test = (x_test-mean)/(std+1e-7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"x_train2.npy\",x_train)\n",
    "np.save(\"x_test2.npy\", x_test)\n",
    "np.save(\"y_train2.npy\",y_train)\n",
    "np.save(\"y_test2,npy\", y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.load('x_train2.npy')\n",
    "x_test = np.load('x_test2.npy')\n",
    "y_train = np.load('y_train2.npy')\n",
    "y_test = np.load('y_test2,npy.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = 256\n",
    "cols = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_shape = 216*64\n",
    "weight_decay = 0.0001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, None, None, 3 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, None, None, 6 1792        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, None, None, 6 256         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, None, None, 6 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, None, None, 6 0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, None, None, 1 73856       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, None, None, 1 512         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, None, None, 1 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, None, None, 1 0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, None, None, 1 147584      max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, None, None, 1 512         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, None, None, 1 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv_dil_1 (Conv2D)             (None, None, None, 1 147584      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, None, None, 1 512         conv_dil_1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, None, None, 1 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv_dil_2 (Conv2D)             (None, None, None, 1 147584      activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, None, None, 1 512         conv_dil_2[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, None, None, 1 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv_dil_3 (Conv2D)             (None, None, None, 1 147584      activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, None, None, 1 512         conv_dil_3[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, None, None, 1 0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "skip_conv_1 (Conv2D)            (None, None, None, 1 147584      max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, None, None, 1 0           activation_6[0][0]               \n",
      "                                                                 skip_conv_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, None, None, 1 0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, None, None, 1 147584      up_sampling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, None, None, 1 512         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, None, None, 1 0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "skip_conv_2 (Conv2D)            (None, None, None, 1 73856       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, None, None, 1 0           activation_7[0][0]               \n",
      "                                                                 skip_conv_2[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  (None, None, None, 1 0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, None, None, 6 73792       up_sampling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, None, None, 6 256         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, None, None, 6 0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, None, None, 1 65          activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, None, None, 1 0           conv2d_6[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 1,112,449\n",
      "Trainable params: 1,110,657\n",
      "Non-trainable params: 1,792\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Defines the input tensor\n",
    "inputs = Input(shape=(None,None,3))\n",
    "\n",
    "L1 = Conv2D(64,kernel_size=(3,3),padding = \"same\",kernel_regularizer=regularizers.l2(weight_decay))(inputs)\n",
    "L2 = BatchNormalization()(L1)\n",
    "L2 = Activation('relu')(L2)\n",
    "#L3 = Lambda(maxpool_1,output_shape = shape)(L2)\n",
    "L3 = MaxPooling2D(pool_size=(2,2))(L2)\n",
    "L4 = Conv2D(128,kernel_size=(3,3),padding = \"same\",kernel_regularizer=regularizers.l2(weight_decay))(L3)\n",
    "L5 = BatchNormalization()(L4)\n",
    "L5 = Activation('relu')(L5)\n",
    "#L6 = Lambda(maxpool_2,output_shape = shape)(L5)\n",
    "L6 = MaxPooling2D(pool_size=(2,2))(L5)\n",
    "L7 = Conv2D(128,kernel_size=(3,3),padding = \"same\",kernel_regularizer=regularizers.l2(weight_decay))(L6)\n",
    "L8 = BatchNormalization()(L7)\n",
    "L9 = Activation('relu')(L8)\n",
    "L10 = Conv2D(128,(3,3),dilation_rate= (2,2), padding = \"same\", activation='relu', name = \"conv_dil_1\")(L9)\n",
    "L11 = BatchNormalization()(L10)\n",
    "L12 = Activation('relu')(L11)\n",
    "L13 = Conv2D(128,(3,3),dilation_rate= (4,4), padding = \"same\", activation='relu', name = \"conv_dil_2\")(L12)\n",
    "L14 = BatchNormalization()(L13)\n",
    "L15 = Activation('relu')(L14)\n",
    "L16 = Conv2D(128,(3,3),dilation_rate= (8,8), padding = \"same\", activation='relu', name = \"conv_dil_3\")(L15)\n",
    "L17 = BatchNormalization()(L16)\n",
    "L18 = Activation('relu')(L17)\n",
    "L19 = Conv2D(128,kernel_size=(3,3),padding = \"same\",kernel_regularizer=regularizers.l2(weight_decay),\n",
    "             name=\"skip_conv_1\")(L6)\n",
    "L20 = Add()([L18,L19])\n",
    "L21 = UpSampling2D( size = (2,2)) (L20)\n",
    "#L21 = Deconvolution2D(128, kernel_size = (3,3), strides = (2,2), activation = \"relu\", \n",
    " #                     name = \"ct_deconv_1\", padding = \"same\")(L20)\n",
    "L21 = Conv2D(128,(3,3), padding = \"same\", kernel_regularizer=regularizers.l2(weight_decay))(L21)\n",
    "L22 = BatchNormalization()(L21)\n",
    "L23 = Activation('relu')(L22)\n",
    "L24 = Conv2D(128,kernel_size=(3,3),padding = \"same\",kernel_regularizer=regularizers.l2(weight_decay),\n",
    "             name=\"skip_conv_2\")(L3)\n",
    "L24 = Add()([L23,L24])\n",
    "L25 = UpSampling2D(size = (2,2))(L24)\n",
    "L25 = Conv2D(64, (3,3), padding = \"same\", kernel_regularizer=regularizers.l2(weight_decay))(L25)\n",
    "#L25 = Deconvolution2D(64, kernel_size = (3,3), strides = (2,2), activation = \"relu\", \n",
    " #                     name = \"ct_deconv_2\", padding = \"same\")(L24)\n",
    "#L25 = \n",
    "L26 = BatchNormalization()(L25)\n",
    "L27 = Activation('relu')(L26)\n",
    "L28 = Conv2D(1,kernel_size=(1,1),padding = \"same\",kernel_regularizer=regularizers.l2(weight_decay))(L27)#\n",
    "\n",
    "L30 = Activation('sigmoid')(L28)\n",
    "model = Model(inputs = inputs, outputs = L30)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1500, 256, 256, 3)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 256, 256, 3)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1500, 256, 256)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.reshape(y_train.shape[0],256,256,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test.reshape(y_test.shape[0],256,256,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "smooth = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dice_coef(y_true, y_pred):\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    intersection = K.sum(y_true_f * y_pred_f)\n",
    "    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dice_coef_loss(y_true, y_pred):\n",
    "    return -dice_coef(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def customized_loss(y_true,y_pred):\n",
    "    return (1*K.binary_crossentropy(y_true, y_pred))+(0.5*dice_coef_loss(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimiser = optimizers.Adam(lr = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=optimiser,loss=dice_coef_loss,metrics=['accuracy',dice_coef])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining Callback functions which will be called by model during runtime when specified condition satisfies\n",
    "lr_reducer = ReduceLROnPlateau(factor=0.5, cooldown=0, patience=6, min_lr=0.5e-6)\n",
    "csv_logger = CSVLogger('segmentation_lr_e2_bs4.csv')\n",
    "model_chekpoint = ModelCheckpoint(\"segmentation_lr_e2_bs4.hdf5\",monitor = 'val_loss',verbose = 1,save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1500 samples, validate on 500 samples\n",
      "Epoch 1/20\n",
      "1500/1500 [==============================] - 10841s 7s/step - loss: -0.3307 - acc: 0.6218 - dice_coef: 0.3821 - val_loss: -0.3519 - val_acc: 0.6959 - val_dice_coef: 0.3823\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to -0.35187, saving model to segmentation_lr_e2_bs4.hdf5\n"
     ]
    },
    {
     "ename": "ImportError",
     "evalue": "`save_model` requires h5py.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-45-bd354e96f25d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlr_reducer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcsv_logger\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel_chekpoint\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\python35\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m   1710\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1711\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1712\u001b[1;33m                               validation_steps=validation_steps)\n\u001b[0m\u001b[0;32m   1713\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1714\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[1;32mc:\\python35\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[1;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[0;32m   1253\u001b[0m                             \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_outs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1254\u001b[0m                                 \u001b[0mepoch_logs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'val_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1255\u001b[1;33m             \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1256\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mcallback_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1257\u001b[0m                 \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python35\\lib\\site-packages\\keras\\callbacks.py\u001b[0m in \u001b[0;36mon_epoch_end\u001b[1;34m(self, epoch, logs)\u001b[0m\n\u001b[0;32m     75\u001b[0m         \u001b[0mlogs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogs\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 77\u001b[1;33m             \u001b[0mcallback\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     78\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     79\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mon_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python35\\lib\\site-packages\\keras\\callbacks.py\u001b[0m in \u001b[0;36mon_epoch_end\u001b[1;34m(self, epoch, logs)\u001b[0m\n\u001b[0;32m    445\u001b[0m                             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    446\u001b[0m                         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 447\u001b[1;33m                             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    448\u001b[0m                     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    449\u001b[0m                         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python35\\lib\\site-packages\\keras\\engine\\topology.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, filepath, overwrite, include_optimizer)\u001b[0m\n\u001b[0;32m   2574\u001b[0m         \"\"\"\n\u001b[0;32m   2575\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msave_model\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2576\u001b[1;33m         \u001b[0msave_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minclude_optimizer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2577\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2578\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msave_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python35\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36msave_model\u001b[1;34m(model, filepath, overwrite, include_optimizer)\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mh5py\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 60\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'`save_model` requires h5py.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_json_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: `save_model` requires h5py."
     ]
    }
   ],
   "source": [
    "model.fit(x_train,y_train,batch_size=4,epochs=20,validation_data=(x_test, y_test),callbacks=[lr_reducer, csv_logger,model_chekpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "`load_weights` requires h5py.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-d27d3f2471a0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'segmentation_lr_e2_bs4.hdf5'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\python35\\lib\\site-packages\\keras\\engine\\topology.py\u001b[0m in \u001b[0;36mload_weights\u001b[1;34m(self, filepath, by_name, skip_mismatch, reshape)\u001b[0m\n\u001b[0;32m   2640\u001b[0m         \"\"\"\n\u001b[0;32m   2641\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mh5py\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2642\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'`load_weights` requires h5py.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2643\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mh5py\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2644\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;34m'layer_names'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m'model_weights'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: `load_weights` requires h5py."
     ]
    }
   ],
   "source": [
    "model.load_weights('segmentation_lr_e2_bs4.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x25d26695630>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25d2613b470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x25d24aaa9e8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25d2613b2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(y[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256, 256, 1)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt = y_test[0].reshape((256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x25d24b61c50>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VCNNWHQy9P0W7qi4111eArzBpwZ7daBmb6yurq/BFtqqrd+u5enra/qyvGKCH67Xrr0JYdTA8DBxOcmOSlzH5rsjTK67p/yV5RfM9lyR5BfA2JqeXnwaONYsdAx5cTYUtW9V1Gnh3M4t+G/CzjdZ4Vfp42v5WXzFAz9brVnUudZ3uxyzqDjOsdzKZVf0B8NFV17Opttcxmc39LvD4Rn3A7wJngKea62tWUNsXmbSL/8vkHeG9W9XFpJX8u2YdPwoc6UGt/9DUcr7ZcA9MLf/RptYngTv2sc43M2mxzwOPNJc7+7Zet6lzaevUIx8ltax6V0JSDxkMkloMBkktBoOkFoNBUovBIKnFYJDUYjBIavk/aNbJHgC3aRUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25d24b125f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(gt, cmap = \"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_image = x_test[0].reshape((1,256,256,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(testing_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 256, 256, 1)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = prediction.reshape((256,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = prediction > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x25d24ba4898>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25d24b75cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(sample, cmap = \"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x25d24be10b8>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25d24b93978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.imshow(sample, cmap = \"gray\")\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(gt, cmap = \"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}