1403 lines (1402 with data), 446.2 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, glob\n",
    "import sys\n",
    "import copy \n",
    "import pydicom\n",
    "import random\n",
    "import re\n",
    "import scipy\n",
    "import scipy.misc\n",
    "import numpy as np\n",
    "import cv2\n",
    "import imageio\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image, ImageEnhance\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from scipy.ndimage import rotate\n",
    "from skimage import exposure\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from operator import add \n",
    "plt.set_cmap('gray')\n",
    "%matplotlib inline\n",
    "## Seeding \n",
    "seed = 2019\n",
    "random.seed = seed\n",
    "np.random.seed = seed\n",
    "tf.seed = seed\n",
    "\n",
    "\n",
    "IMG_DTYPE = np.float\n",
    "SEG_DTYPE = np.uint8\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default GPU Device: /device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "if tf.test.gpu_device_name():\n",
    "    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))\n",
    "else:\n",
    "    print(\"Please install GPU version of TF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "masks_path = os.path.join('train','masks')\n",
    "training_path = os.path.join('train','patients')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "- Author IBBM\n",
    "- Date 1/3/2019 (DD/MM/YYYY)\n",
    "- Link https://github.com/IBBM/Cascaded-FCN\n",
    "\"\"\"\n",
    "def imshow(*args,**kwargs):\n",
    "    cmap = kwargs.get('cmap', 'gray')\n",
    "    title= kwargs.get('title','')\n",
    "    if len(args)==0:\n",
    "        raise ValueError(\"No images given to imshow\")\n",
    "    elif len(args)==1:\n",
    "        plt.title(title)\n",
    "        plt.imshow(args[0], interpolation='none')\n",
    "    else:\n",
    "        n=len(args)\n",
    "        if type(cmap)==str:\n",
    "            cmap = [cmap]*n\n",
    "        if type(title)==str:\n",
    "            title= [title]*n\n",
    "        plt.figure(figsize=(n*5,10))\n",
    "        for i in range(n):\n",
    "            plt.subplot(1,n,i+1)\n",
    "            plt.title(title[i])\n",
    "            plt.imshow(args[i], cmap[i])\n",
    "    plt.show()\n",
    "    \n",
    "def normalize_image(img):\n",
    "    \"\"\" Normalize image values to [0,1] \"\"\"\n",
    "    min_, max_ = float(np.min(img)), float(np.max(img))\n",
    "    return (img - min_) / (max_ - min_)\n",
    "\n",
    "def dice(im1, im2):\n",
    "\n",
    "    im1 = np.asarray(im1).astype(np.bool)\n",
    "    im2 = np.asarray(im2).astype(np.bool)\n",
    "\n",
    "    if im1.shape != im2.shape:\n",
    "        raise ValueError(\"Shape mismatch: im1 and im2 must have the same shape.\")\n",
    "\n",
    "    intersection = np.logical_and(im1, im2)\n",
    "\n",
    "    return 2. * intersection.sum() / (im1.sum() + im2.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGen(keras.utils.Sequence):\n",
    "    def __init__(self, ids, path, batch_size=8, image_size=128):\n",
    "        self.ids = ids\n",
    "        self.path = path\n",
    "        self.batch_size = batch_size\n",
    "        self.image_size = image_size\n",
    "        self.on_epoch_end()\n",
    "        \n",
    "    def __load__(self, id_name):\n",
    "\n",
    "        tumor_volume = None\n",
    "        \n",
    "        image_path = os.path.join(self.path,\"patients\", id_name)\n",
    "        mask_path = os.path.join(self.path,\"masks\")\n",
    "        all_masks = os.listdir(mask_path)\n",
    "        dicom_image = pydicom.dcmread(image_path)\n",
    "        \n",
    "        image = step1_preprocess_img_slice(dicom_image.pixel_array)\n",
    "       \n",
    "        liver_mask_id = id_name.split('_')\n",
    "        liver_mask = pydicom.dcmread(os.path.join(mask_path, liver_mask_id[0]+'_liver', id_name)).pixel_array\n",
    "        \n",
    "        image = np.multiply(image,np.clip(liver_mask,0,1))\n",
    "        \n",
    "        image = np.array(Image.fromarray(image).resize([image_size, image_size])).astype(IMG_DTYPE)\n",
    "        mask = cv2.imread(os.path.join(masks_path,'merged_livertumors', id_name+'.jpg'))\n",
    "        \n",
    "        mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)\n",
    "        retval, mask = cv2.threshold(mask, 100, 255, cv2.THRESH_BINARY)\n",
    "        mask = np.array(Image.fromarray(mask).resize([image_size, image_size])).astype(IMG_DTYPE)\n",
    "        mask = mask // 255\n",
    "        \n",
    "        mask = mask[:, :, np.newaxis]\n",
    "        \n",
    "        return image, mask\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        if(index+1)*self.batch_size > len(self.ids):\n",
    "            self.batch_size = len(self.ids) - index*self.batch_size\n",
    "        \n",
    "        files_batch = self.ids[index*self.batch_size : (index+1)*self.batch_size]\n",
    "#         print(files_batch)\n",
    "        image = []\n",
    "        mask  = []\n",
    "        for id_name in files_batch:\n",
    "\n",
    "            _img, _mask = self.__load__(id_name)\n",
    "            _img = np.stack((_img,)*3, axis=-1)\n",
    "            image.append(_img)\n",
    "            mask.append(_mask)\n",
    "        \n",
    "        image = np.array(image)\n",
    "        mask  = np.array(mask)\n",
    "\n",
    "        return image, mask\n",
    "    \n",
    "    def on_epoch_end(self):\n",
    "        pass\n",
    "    \n",
    "    def __len__(self):\n",
    "        return int(np.ceil(len(self.ids)/float(self.batch_size)))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16220\n"
     ]
    }
   ],
   "source": [
    "image_size =  256\n",
    "train_path = \"train\"\n",
    "batch_size = 8\n",
    "epochs = 50\n",
    "## Training Ids\n",
    "images = []\n",
    "for file in os.listdir(os.path.join(train_path, \"patients\")):\n",
    "\n",
    "    images.append(file)\n",
    "\n",
    "\n",
    "## Validation Data Size\n",
    "val_data_size = len(images)//5 # 20% validation Data\n",
    "# factor = 0\n",
    "valid_ids = images[:val_data_size]\n",
    "train_ids = images[val_data_size:]\n",
    "print(len(train_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "- Author IBBM\n",
    "- Date 1/3/2019 (DD/MM/YYYY)\n",
    "- Link https://github.com/IBBM/Cascaded-FCN\n",
    "\"\"\"\n",
    "def step1_preprocess_img_slice(img_slc):\n",
    "    \"\"\"\n",
    "    Preprocesses the image 3d volumes by performing the following :\n",
    "    1- Set pixels with hounsfield value great than 1200, to zero.\n",
    "    2- Clip all hounsfield values to the range [-100, 400]\n",
    "    3- Apply Histogram Equalization\n",
    "    \"\"\"    \n",
    "    img_slc[img_slc>1200] = 0\n",
    "    img_slc   = np.clip(img_slc, -100, 400)\n",
    "    img_slc = normalize_image(img_slc)\n",
    "\n",
    "    \n",
    "    img_slc = img_slc * 255\n",
    "    img_slc = img_slc.astype('uint8')\n",
    "    img_slc = cv2.equalizeHist(img_slc)\n",
    "    img_slc = normalize_image(img_slc)\n",
    "    return img_slc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 256, 256, 3) (8, 256, 256, 1)\n"
     ]
    }
   ],
   "source": [
    "gen = DataGen(train_ids, train_path, batch_size=batch_size, image_size=image_size)\n",
    "x, y = gen.__getitem__(0)\n",
    "print(x.shape,y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbc6c1cc668>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = random.randint(0, len(x)-1)\n",
    "r = 0\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "ax.imshow(x[r])\n",
    "print(r)\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "ax.imshow(np.reshape(y[r], (image_size, image_size)), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "- Author nikhilroxtomar\n",
    "- Date 10/4/2019 (DD/MM/YYYY)\n",
    "- Link https://github.com/IBBM/Cascaded-FCN\n",
    "\"\"\"\n",
    "def bn_act(x, act=True):\n",
    "    x = keras.layers.BatchNormalization()(x)\n",
    "    if act == True:\n",
    "        x = keras.layers.Activation(\"relu\")(x)\n",
    "    return x\n",
    "\n",
    "def conv_block(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n",
    "    conv = bn_act(x)\n",
    "    conv = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides)(conv)\n",
    "    return conv\n",
    "\n",
    "def stem(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n",
    "    conv = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides)(x)\n",
    "    conv = conv_block(conv, filters, kernel_size=kernel_size, padding=padding, strides=strides)\n",
    "    \n",
    "    shortcut = keras.layers.Conv2D(filters, kernel_size=(1, 1), padding=padding, strides=strides)(x)\n",
    "    shortcut = bn_act(shortcut, act=False)\n",
    "    \n",
    "    output = keras.layers.Add()([conv, shortcut])\n",
    "    return output\n",
    "\n",
    "def residual_block(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n",
    "    res = conv_block(x, filters, kernel_size=kernel_size, padding=padding, strides=strides)\n",
    "    res = conv_block(res, filters, kernel_size=kernel_size, padding=padding, strides=1)\n",
    "    \n",
    "    shortcut = keras.layers.Conv2D(filters, kernel_size=(1, 1), padding=padding, strides=strides)(x)\n",
    "    shortcut = bn_act(shortcut, act=False)\n",
    "    \n",
    "    output = keras.layers.Add()([shortcut, res])\n",
    "    return output\n",
    "\n",
    "def upsample_concat_block(x, xskip):\n",
    "    u = keras.layers.UpSampling2D((2, 2))(x)\n",
    "    c = keras.layers.Concatenate()([u, xskip])\n",
    "    return c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ResUNet():\n",
    "    f = [16, 32, 64, 128, 256]\n",
    "    inputs = keras.layers.Input((image_size, image_size, 3))\n",
    "    \n",
    "    ## Encoder\n",
    "    e0 = inputs\n",
    "    e1 = stem(e0, f[0])\n",
    "    e2 = residual_block(e1, f[1], strides=2)\n",
    "    e3 = residual_block(e2, f[2], strides=2)\n",
    "    e4 = residual_block(e3, f[3], strides=2)\n",
    "    e5 = residual_block(e4, f[4], strides=2)\n",
    "    \n",
    "    ## Bridge\n",
    "    b0 = conv_block(e5, f[4], strides=1)\n",
    "    b1 = conv_block(b0, f[4], strides=1)\n",
    "    \n",
    "    ## Decoder\n",
    "    u1 = upsample_concat_block(b1, e4)\n",
    "    d1 = residual_block(u1, f[4])\n",
    "    \n",
    "    u2 = upsample_concat_block(d1, e3)\n",
    "    d2 = residual_block(u2, f[3])\n",
    "    \n",
    "    u3 = upsample_concat_block(d2, e2)\n",
    "    d3 = residual_block(u3, f[2])\n",
    "    \n",
    "    u4 = upsample_concat_block(d3, e1)\n",
    "    d4 = residual_block(u4, f[1])\n",
    "    \n",
    "    outputs = keras.layers.Conv2D(1, (1, 1), padding=\"same\", activation=\"sigmoid\")(d4)\n",
    "    model = keras.models.Model(inputs, outputs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "smooth = 1.\n",
    "\n",
    "def dice_coef(y_true, y_pred):\n",
    "    y_true_f = tf.layers.flatten(y_true)\n",
    "    y_pred_f = tf.layers.flatten(y_pred)\n",
    "    intersection = tf.reduce_sum(y_true_f * y_pred_f)\n",
    "    return (2. * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)\n",
    "\n",
    "\n",
    "def dice_coef_loss(y_true, y_pred):\n",
    "    return 1.0 - dice_coef(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/amir/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From <ipython-input-12-b68f55637a1f>:4: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.flatten instead.\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 256, 256, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 256, 256, 16) 448         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1 (BatchNo (None, 256, 256, 16) 64          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 256, 256, 16) 0           batch_normalization_v1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 256, 256, 16) 64          input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 256, 256, 16) 2320        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_1 (Batch (None, 256, 256, 16) 64          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 256, 256, 16) 0           conv2d_1[0][0]                   \n",
      "                                                                 batch_normalization_v1_1[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_2 (Batch (None, 256, 256, 16) 64          add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 256, 256, 16) 0           batch_normalization_v1_2[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 128, 128, 32) 4640        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_3 (Batch (None, 128, 128, 32) 128         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 128, 128, 32) 544         add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 128, 128, 32) 0           batch_normalization_v1_3[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_4 (Batch (None, 128, 128, 32) 128         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 128, 128, 32) 9248        activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 128, 128, 32) 0           batch_normalization_v1_4[0][0]   \n",
      "                                                                 conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_5 (Batch (None, 128, 128, 32) 128         add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 128, 128, 32) 0           batch_normalization_v1_5[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 64, 64, 64)   18496       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_6 (Batch (None, 64, 64, 64)   256         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 64, 64, 64)   2112        add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 64, 64, 64)   0           batch_normalization_v1_6[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_7 (Batch (None, 64, 64, 64)   256         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 64, 64, 64)   36928       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 64, 64, 64)   0           batch_normalization_v1_7[0][0]   \n",
      "                                                                 conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_8 (Batch (None, 64, 64, 64)   256         add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 64, 64, 64)   0           batch_normalization_v1_8[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 32, 32, 128)  73856       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_9 (Batch (None, 32, 32, 128)  512         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 32, 32, 128)  8320        add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 32, 32, 128)  0           batch_normalization_v1_9[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_10 (Batc (None, 32, 32, 128)  512         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 32, 32, 128)  147584      activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 32, 32, 128)  0           batch_normalization_v1_10[0][0]  \n",
      "                                                                 conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_11 (Batc (None, 32, 32, 128)  512         add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 32, 32, 128)  0           batch_normalization_v1_11[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 16, 16, 256)  295168      activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_12 (Batc (None, 16, 16, 256)  1024        conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 16, 16, 256)  33024       add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 16, 16, 256)  0           batch_normalization_v1_12[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_13 (Batc (None, 16, 16, 256)  1024        conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 16, 16, 256)  590080      activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 16, 16, 256)  0           batch_normalization_v1_13[0][0]  \n",
      "                                                                 conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_14 (Batc (None, 16, 16, 256)  1024        add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 16, 16, 256)  0           batch_normalization_v1_14[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 16, 16, 256)  590080      activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_15 (Batc (None, 16, 16, 256)  1024        conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 16, 16, 256)  0           batch_normalization_v1_15[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 16, 16, 256)  590080      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d (UpSampling2D)    (None, 32, 32, 256)  0           conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 32, 32, 384)  0           up_sampling2d[0][0]              \n",
      "                                                                 add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_16 (Batc (None, 32, 32, 384)  1536        concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 32, 32, 384)  0           batch_normalization_v1_16[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 32, 32, 256)  884992      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_17 (Batc (None, 32, 32, 256)  1024        conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 32, 32, 256)  98560       concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 32, 32, 256)  0           batch_normalization_v1_17[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_18 (Batc (None, 32, 32, 256)  1024        conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 32, 32, 256)  590080      activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 32, 32, 256)  0           batch_normalization_v1_18[0][0]  \n",
      "                                                                 conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, 64, 64, 256)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 64, 64, 320)  0           up_sampling2d_1[0][0]            \n",
      "                                                                 add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_19 (Batc (None, 64, 64, 320)  1280        concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 64, 64, 320)  0           batch_normalization_v1_19[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 64, 64, 128)  368768      activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_20 (Batc (None, 64, 64, 128)  512         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 64, 64, 128)  41088       concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 64, 64, 128)  0           batch_normalization_v1_20[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_21 (Batc (None, 64, 64, 128)  512         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 64, 64, 128)  147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 64, 64, 128)  0           batch_normalization_v1_21[0][0]  \n",
      "                                                                 conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  (None, 128, 128, 128 0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 128, 128, 160 0           up_sampling2d_2[0][0]            \n",
      "                                                                 add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_22 (Batc (None, 128, 128, 160 640         concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 128, 128, 160 0           batch_normalization_v1_22[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 128, 128, 64) 92224       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_23 (Batc (None, 128, 128, 64) 256         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 128, 128, 64) 10304       concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 128, 128, 64) 0           batch_normalization_v1_23[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_24 (Batc (None, 128, 128, 64) 256         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 128, 128, 64) 36928       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 128, 128, 64) 0           batch_normalization_v1_24[0][0]  \n",
      "                                                                 conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2D)  (None, 256, 256, 64) 0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 256, 256, 80) 0           up_sampling2d_3[0][0]            \n",
      "                                                                 add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_25 (Batc (None, 256, 256, 80) 320         concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 256, 256, 80) 0           batch_normalization_v1_25[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 256, 256, 32) 23072       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_26 (Batc (None, 256, 256, 32) 128         conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 256, 256, 32) 2592        concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 256, 256, 32) 0           batch_normalization_v1_26[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_27 (Batc (None, 256, 256, 32) 128         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 256, 256, 32) 9248        activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 256, 256, 32) 0           batch_normalization_v1_27[0][0]  \n",
      "                                                                 conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 256, 256, 1)  33          add_8[0][0]                      \n",
      "==================================================================================================\n",
      "Total params: 4,723,057\n",
      "Trainable params: 4,715,761\n",
      "Non-trainable params: 7,296\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = ResUNet()\n",
    "adam = keras.optimizers.Adam()\n",
    "model.compile(optimizer=adam, loss=dice_coef_loss, metrics=[\"acc\", dice_coef])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 849s 419ms/step - loss: 0.3623 - acc: 0.9933 - dice_coef: 0.6377 - val_loss: 0.7880 - val_acc: 0.9922 - val_dice_coef: 0.2120\n",
      "\n",
      "Epoch 2/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 741s 365ms/step - loss: 0.2845 - acc: 0.9965 - dice_coef: 0.7155 - val_loss: 0.2720 - val_acc: 0.9959 - val_dice_coef: 0.7280\n",
      "\n",
      "Epoch 3/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 739s 365ms/step - loss: 0.2535 - acc: 0.9969 - dice_coef: 0.7465 - val_loss: 0.4048 - val_acc: 0.9956 - val_dice_coef: 0.5952\n",
      "\n",
      "Epoch 4/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 739s 365ms/step - loss: 0.2428 - acc: 0.9971 - dice_coef: 0.7572 - val_loss: 0.2370 - val_acc: 0.9968 - val_dice_coef: 0.7630\n",
      "\n",
      "Epoch 5/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 733s 361ms/step - loss: 0.2381 - acc: 0.9972 - dice_coef: 0.7619 - val_loss: 0.2115 - val_acc: 0.9971 - val_dice_coef: 0.7885\n",
      "\n",
      "Epoch 6/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 730s 360ms/step - loss: 0.2251 - acc: 0.9973 - dice_coef: 0.7749 - val_loss: 0.4259 - val_acc: 0.9909 - val_dice_coef: 0.5741\n",
      "\n",
      "Epoch 7/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 730s 360ms/step - loss: 0.2210 - acc: 0.9973 - dice_coef: 0.7790 - val_loss: 0.2892 - val_acc: 0.9956 - val_dice_coef: 0.7108\n",
      "\n",
      "Epoch 8/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 732s 361ms/step - loss: 0.2114 - acc: 0.9974 - dice_coef: 0.7886 - val_loss: 0.1772 - val_acc: 0.9976 - val_dice_coef: 0.8228\n",
      "\n",
      "Epoch 9/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 730s 360ms/step - loss: 0.2076 - acc: 0.9975 - dice_coef: 0.7924 - val_loss: 0.2570 - val_acc: 0.9969 - val_dice_coef: 0.7430\n",
      "\n",
      "Epoch 10/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 728s 359ms/step - loss: 0.2103 - acc: 0.9975 - dice_coef: 0.7897 - val_loss: 0.2635 - val_acc: 0.9964 - val_dice_coef: 0.7365\n",
      "\n",
      "Epoch 11/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 728s 359ms/step - loss: 0.2001 - acc: 0.9976 - dice_coef: 0.7999 - val_loss: 0.1785 - val_acc: 0.9975 - val_dice_coef: 0.8215\n",
      "\n",
      "Epoch 12/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 728s 359ms/step - loss: 0.1959 - acc: 0.9976 - dice_coef: 0.8041 - val_loss: 0.6279 - val_acc: 0.9939 - val_dice_coef: 0.3721\n",
      "\n",
      "Epoch 13/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 729s 359ms/step - loss: 0.1920 - acc: 0.9977 - dice_coef: 0.8080 - val_loss: 0.1700 - val_acc: 0.9977 - val_dice_coef: 0.8300\n",
      "\n",
      "Epoch 14/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 728s 359ms/step - loss: 0.1826 - acc: 0.9978 - dice_coef: 0.8174 - val_loss: 0.1889 - val_acc: 0.9975 - val_dice_coef: 0.8111\n",
      "\n",
      "Epoch 15/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 728s 359ms/step - loss: 0.1869 - acc: 0.9978 - dice_coef: 0.8131 - val_loss: 0.1809 - val_acc: 0.9977 - val_dice_coef: 0.8191\n",
      "\n",
      "Epoch 16/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1827 - acc: 0.9978 - dice_coef: 0.8173 - val_loss: 0.1948 - val_acc: 0.9971 - val_dice_coef: 0.8052\n",
      "\n",
      "Epoch 17/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 722s 356ms/step - loss: 0.1795 - acc: 0.9978 - dice_coef: 0.8205 - val_loss: 0.1634 - val_acc: 0.9978 - val_dice_coef: 0.8366\n",
      "\n",
      "Epoch 18/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1852 - acc: 0.9978 - dice_coef: 0.8148 - val_loss: 0.2330 - val_acc: 0.9966 - val_dice_coef: 0.7670\n",
      "\n",
      "Epoch 19/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 726s 358ms/step - loss: 0.1724 - acc: 0.9979 - dice_coef: 0.8276 - val_loss: 0.1640 - val_acc: 0.9978 - val_dice_coef: 0.8360\n",
      "\n",
      "Epoch 20/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 726s 358ms/step - loss: 0.1732 - acc: 0.9979 - dice_coef: 0.8268 - val_loss: 0.1685 - val_acc: 0.9978 - val_dice_coef: 0.8315\n",
      "\n",
      "Epoch 21/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 726s 358ms/step - loss: 0.1682 - acc: 0.9980 - dice_coef: 0.8318 - val_loss: 0.1567 - val_acc: 0.9978 - val_dice_coef: 0.8433\n",
      "\n",
      "Epoch 22/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 726s 358ms/step - loss: 0.1653 - acc: 0.9980 - dice_coef: 0.8347 - val_loss: 0.1579 - val_acc: 0.9979 - val_dice_coef: 0.8421\n",
      "\n",
      "Epoch 23/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1722 - acc: 0.9979 - dice_coef: 0.8278 - val_loss: 0.3536 - val_acc: 0.9960 - val_dice_coef: 0.6464\n",
      "\n",
      "Epoch 24/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1630 - acc: 0.9980 - dice_coef: 0.8370 - val_loss: 0.1598 - val_acc: 0.9979 - val_dice_coef: 0.8402\n",
      "\n",
      "Epoch 25/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1563 - acc: 0.9981 - dice_coef: 0.8437 - val_loss: 0.1500 - val_acc: 0.9979 - val_dice_coef: 0.8500\n",
      "\n",
      "Epoch 26/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 358ms/step - loss: 0.1580 - acc: 0.9981 - dice_coef: 0.8420 - val_loss: 0.1561 - val_acc: 0.9979 - val_dice_coef: 0.8439\n",
      "\n",
      "Epoch 27/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1571 - acc: 0.9981 - dice_coef: 0.8429 - val_loss: 0.1717 - val_acc: 0.9978 - val_dice_coef: 0.8283\n",
      "\n",
      "Epoch 28/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1506 - acc: 0.9982 - dice_coef: 0.8494 - val_loss: 0.1507 - val_acc: 0.9980 - val_dice_coef: 0.8493\n",
      "\n",
      "Epoch 29/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1497 - acc: 0.9982 - dice_coef: 0.8503 - val_loss: 0.1570 - val_acc: 0.9978 - val_dice_coef: 0.8430\n",
      "\n",
      "Epoch 30/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 358ms/step - loss: 0.1477 - acc: 0.9982 - dice_coef: 0.8523 - val_loss: 0.1477 - val_acc: 0.9980 - val_dice_coef: 0.8523\n",
      "\n",
      "Epoch 31/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1478 - acc: 0.9982 - dice_coef: 0.8522 - val_loss: 0.2773 - val_acc: 0.9965 - val_dice_coef: 0.7227\n",
      "\n",
      "Epoch 32/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1456 - acc: 0.9982 - dice_coef: 0.8544 - val_loss: 0.1512 - val_acc: 0.9980 - val_dice_coef: 0.8488\n",
      "\n",
      "Epoch 33/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 358ms/step - loss: 0.1455 - acc: 0.9982 - dice_coef: 0.8545 - val_loss: 0.2388 - val_acc: 0.9966 - val_dice_coef: 0.7612\n",
      "\n",
      "Epoch 34/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1428 - acc: 0.9983 - dice_coef: 0.8572 - val_loss: 0.1377 - val_acc: 0.9981 - val_dice_coef: 0.8623\n",
      "\n",
      "Epoch 35/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 726s 358ms/step - loss: 0.1393 - acc: 0.9983 - dice_coef: 0.8607 - val_loss: 0.2255 - val_acc: 0.9970 - val_dice_coef: 0.7745\n",
      "\n",
      "Epoch 36/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 358ms/step - loss: 0.1371 - acc: 0.9983 - dice_coef: 0.8629 - val_loss: 0.1780 - val_acc: 0.9977 - val_dice_coef: 0.8220\n",
      "\n",
      "Epoch 37/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 728s 359ms/step - loss: 0.1400 - acc: 0.9983 - dice_coef: 0.8600 - val_loss: 0.1500 - val_acc: 0.9979 - val_dice_coef: 0.8500\n",
      "\n",
      "Epoch 38/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1364 - acc: 0.9984 - dice_coef: 0.8636 - val_loss: 0.1927 - val_acc: 0.9974 - val_dice_coef: 0.8073\n",
      "\n",
      "Epoch 39/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1305 - acc: 0.9984 - dice_coef: 0.8695 - val_loss: 0.1332 - val_acc: 0.9982 - val_dice_coef: 0.8668\n",
      "\n",
      "Epoch 40/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1317 - acc: 0.9984 - dice_coef: 0.8683 - val_loss: 0.2249 - val_acc: 0.9970 - val_dice_coef: 0.7751\n",
      "\n",
      "Epoch 41/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1316 - acc: 0.9984 - dice_coef: 0.8684 - val_loss: 0.2441 - val_acc: 0.9966 - val_dice_coef: 0.7559\n",
      "\n",
      "Epoch 42/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1238 - acc: 0.9985 - dice_coef: 0.8762 - val_loss: 0.1293 - val_acc: 0.9983 - val_dice_coef: 0.8707\n",
      "\n",
      "Epoch 43/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1267 - acc: 0.9985 - dice_coef: 0.8733 - val_loss: 0.1293 - val_acc: 0.9983 - val_dice_coef: 0.8707\n",
      "\n",
      "Epoch 44/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1263 - acc: 0.9985 - dice_coef: 0.8737 - val_loss: 0.1250 - val_acc: 0.9983 - val_dice_coef: 0.8750\n",
      "\n",
      "Epoch 45/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1216 - acc: 0.9985 - dice_coef: 0.8784 - val_loss: 0.1171 - val_acc: 0.9984 - val_dice_coef: 0.8829\n",
      "\n",
      "Epoch 46/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1248 - acc: 0.9985 - dice_coef: 0.8752 - val_loss: 0.1144 - val_acc: 0.9984 - val_dice_coef: 0.8856\n",
      "\n",
      "Epoch 47/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1252 - acc: 0.9985 - dice_coef: 0.8748 - val_loss: 0.1699 - val_acc: 0.9979 - val_dice_coef: 0.8301\n",
      "\n",
      "Epoch 48/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1227 - acc: 0.9985 - dice_coef: 0.8773 - val_loss: 0.1450 - val_acc: 0.9980 - val_dice_coef: 0.8550\n",
      "\n",
      "Epoch 49/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1180 - acc: 0.9986 - dice_coef: 0.8820 - val_loss: 0.1664 - val_acc: 0.9979 - val_dice_coef: 0.8336\n",
      "\n",
      "Epoch 50/50\n",
      "2027/2027 [==============================]2027/2027 [==============================] - 727s 359ms/step - loss: 0.1162 - acc: 0.9986 - dice_coef: 0.8838 - val_loss: 0.1185 - val_acc: 0.9983 - val_dice_coef: 0.8815\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x6b39ab3cc860>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_gen = DataGen(train_ids, train_path, image_size=image_size, batch_size=batch_size)\n",
    "valid_gen = DataGen(valid_ids, train_path, image_size=image_size, batch_size=batch_size)\n",
    "# test_gen  = DataGenTest(train_ids, train_path, image_size=image_size, batch_size=batch_size)\n",
    "\n",
    "train_steps = len(train_ids)//batch_size\n",
    "valid_steps = len(valid_ids)//batch_size\n",
    "\n",
    "model.fit_generator(train_gen, validation_data=valid_gen, steps_per_epoch=train_steps, validation_steps=valid_steps, \n",
    "                    epochs=epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save('tumor_model_50epochs.h5')  # creates a HDF5 file 'my_model.h5'\n",
    "# model.save_weights(\"tumor_weights_final_50epochs.h5\")\n",
    "model = keras.models.load_model(os.path.join('models','tumor_model_50epochs.h5'),compile=False)\n",
    "valid_gen = DataGen(valid_ids, train_path, image_size=image_size, batch_size=batch_size)\n",
    "# model.summary()\n",
    "\n",
    "# del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = valid_gen.__getitem__(2)\n",
    "result = model.predict(x)\n",
    "\n",
    "# result = result > 0.5\n",
    "\n",
    "imshow(x[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbbadeb6be0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "ax.imshow(np.reshape(y[1]*255, (image_size, image_size)), cmap=\"gray\")\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "ax.imshow(np.reshape(result[1]*255, (image_size, image_size)), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.6/site-packages/matplotlib/pyplot.py:528: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b39f0193c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b39d047a860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2qnpFVb0xexe/++66xzdXVVVJPp/kiTHGZ/b9yLG9RIfNs+MaDqRWXBG14kb4\nPF0Tn6mroVZcj03UitedbchnN8Z4vqo+kuQfkrwsyb1jjMc3PKxdcX2Sr+4dV7kuyd+NMf6+qr6X\n5MGq+lCSnyV53wbHOFtV9aUktyZ5XVVdSfLJJH+dg+f260nenb0Lg/0myQfXPuAZO2Sub62qW7LX\nLvlkkg8nyRjj8ap6MMmPsneV/zvHGC9sYtwz9Y4kf5nkh1X16LTtE3FsL9th8/x+xzW8mFpxpdSK\nK6RWXB+14lqpFddj7bVijeF0PwAAAICutuG0MgAAAAA2RDgEAAAA0JhwCAAAAKAx4RAAAABAY8Ih\nAAAAgMaEQwAAAACNCYcAAAAAGvv/Mxm0m49lbfsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b39d03c7160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b39d035ed30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b39d02d7dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b39d024ea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b39d01cacc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c67f4be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c6776390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c66e5320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c665aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vXJKHxhi/H2P8PMmlJLedYnwAAGwwtSIAzN9p9hz6eFX9eNFK/KbFsRuTPLvnNZcXx16l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\ntPCzU/B2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c5d98630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rd1XVrdlpl3w6yUeTZIzxRFU9lOQn2bnK/11jjJfWMe6FemeSv0zy46p6bFr2\nydi2T9tB8/xB2zW8nFpxpdSKK6RWnI9acVZqxXnMXivWGE73AwAAAOhqE04rAwAAAGBNhEMAAAAA\njQmHAAAAABoTDgEAAAA0JhwCAAAAaEw4BAAAANCYcAgAAACgsf8H6Loqty5EC5IAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c5d12a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c5c82e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c5c7ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c5bf4320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c5b6f6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c5ade710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c5a5a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c59c7b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PUe9nNQgAYDOmrBk31aWk+xxg+4RDO27KMOikn+MLHwBgvdYdEm3zhiQWGAGm57QyAAAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c5943fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ye5J3VdX5JJ9N8tfZe22/neTO7FwY7LdJPjb7hDfYPmt9e1Xdkp12yReTfCJJ\nxhjPVdVjSX6Snav83zvGeH0V895Q70/yl0l+XFVPT2OfiX170fZb54/Yr+GN1IpLpVZcIrXifNSK\ns1IrzmP2WrHGcLofAAAAQFfrcFoZAAAAACsiHAIAAABoTDgEAAAA0JhwCAAAAKAx4RAAAABAY8Ih\nAAAAgMaEQwAAAACN/T9KCVSxyNzdIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c593c240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c58b1e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38c5826cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b389c1dabe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b389c08a6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b3897793a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b38977600b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# imshow(np.reshape(y[2]*255, (image_size, image_size)), cmap=\"gray\")\n",
    "for i in range(1, 5, 1):\n",
    "    ## Dataset for prediction\n",
    "    x, y = valid_gen.__getitem__(i)\n",
    "    result = model.predict(x)\n",
    "    result = result > 0.4\n",
    "#     print(id_name)\n",
    "    for j in range(len(result)):\n",
    "        fig = plt.figure(figsize=(20,20))\n",
    "        fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "\n",
    "        ax = fig.add_subplot(1, 2, 1)\n",
    "        ax.imshow(np.reshape(y[j]*255, (image_size, image_size)), cmap=\"gray\")\n",
    "#         ax.imshow(y[i])\n",
    "        ax = fig.add_subplot(1, 2, 2)\n",
    "        ax.imshow(np.reshape(result[j]*255, (image_size, image_size)), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "2\n",
      "[[62930   216]\n",
      " [   38  2352]]\n",
      "[62930   216    38  2352]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_batch = random.randint(0, len(valid_ids)//batch_size - 1)\n",
    "random_sample = random.randint(0, batch_size-1)\n",
    "random_batch = 4\n",
    "random_sample = 2\n",
    "print(random_batch)\n",
    "print(random_sample)\n",
    "x, y = valid_gen.__getitem__(random_batch)\n",
    "result =  model.predict(x)\n",
    "result = result > 0\n",
    "\n",
    "fig2 = plt.figure(figsize=(6,6))\n",
    "ax2 = fig2.add_subplot(1,1,1)\n",
    "ax2.imshow(x[random_sample])\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "ax.imshow(np.reshape(y[random_sample]*255, (image_size, image_size)), cmap=\"gray\")\n",
    "#         ax.imshow(y[i])\n",
    "ax = fig.add_subplot(1, 2, 2)\n",
    "ax.imshow(np.reshape(result[random_sample]*255, (image_size, image_size)), cmap=\"gray\")\n",
    "\n",
    "# imshow(np.reshape(y[random_sample]*255, (image_size, image_size)), cmap=\"gray\")\n",
    "cm_2d = confusion_matrix(y[random_sample].flatten(), result[random_sample].flatten())\n",
    "cm = cm_2d.ravel()\n",
    "\n",
    "# (tn, fp, fn, tp)\n",
    "print(cm_2d)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixel Accuracy 99.6124267578125%\n",
      "True Positive Accuracy 98.41004184100419%\n",
      "Dice Coefficient 0.9487696651875757\n"
     ]
    }
   ],
   "source": [
    "print(\"Pixel Accuracy \" + str(((cm[3]+cm[0])/(cm[3]+cm[0]+cm[1]+cm[2])*100))+'%' )\n",
    "print(\"True Positive Accuracy \" + str(((cm[3])/(cm[3]+cm[2])*100))+'%' )\n",
    "print(\"Dice Coefficient \" + str(dice(y[random_sample], result[random_sample])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax= plt.subplot()\n",
    "cm_2d = cm_2d.astype('float') / cm_2d.sum(axis=1)[:, np.newaxis]\n",
    "sns.heatmap(cm_2d, annot=True, ax = ax); #annot=True to annotate cells\n",
    "\n",
    "print(count_cms)\n",
    "# labels, title and ticks\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
    "ax.set_title('Confusion Matrix'); \n",
    "ax.xaxis.set_ticklabels(['No Tumor', 'Tumor']); ax.yaxis.set_ticklabels(['No Tumor', 'Tumor']);\n",
    "\n"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}