NestNet elu dice under construction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import random\n",
    "import warnings\n",
    "import datetime, os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm import tqdm\n",
    "from itertools import chain\n",
    "from skimage.io import imread, imshow, imread_collection, concatenate_images\n",
    "from skimage.transform import resize\n",
    "from skimage.morphology import label\n",
    "\n",
    "\n",
    "\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras.layers import concatenate\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers import Dropout, Lambda, Flatten, Activation\n",
    "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization\n",
    "from tensorflow.keras.optimizers import Adam, SGD, Adagrad, Adadelta, RMSprop\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.layers import Input, BatchNormalization, Dropout, Flatten, Reshape, Dense\n",
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from skimage.io import imread, imshow\n",
    "from skimage.transform import resize\n",
    "\n",
    "# Don't Show Warning Messages\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import six\n",
    "\n",
    "from random import randint\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-white')\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from skimage.transform import resize\n",
    "\n",
    "\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.layers import Input, Conv2D, Conv2DTranspose, MaxPooling2D, concatenate, Dropout,BatchNormalization\n",
    "from tensorflow.keras.layers import Conv2D, Concatenate, MaxPooling2D, UpSampling2D\n",
    "from tensorflow.keras.layers import UpSampling2D, Dropout, BatchNormalization\n",
    "from tqdm import tqdm_notebook\n",
    "from tensorflow.keras import initializers\n",
    "from tensorflow.keras import regularizers #Activation,SpatialDropout2D\n",
    "from tensorflow.keras import constraints\n",
    "from tensorflow.keras import backend as K\n",
    "#from tensorflow.keras.applications.imagenet_utils import _obtain_input_shape\n",
    "from tensorflow.keras.regularizers import l2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_HEIGHT = 128\n",
    "IMG_WIDTH = 128\n",
    "IMG_CHANNELS = 3\n",
    "\n",
    "NUM_TEST_IMAGES = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = os.listdir('D:/kvasir_segmentation_dataset/kvasir_segmentation_dataset/images')\n",
    "masks = os.listdir('D:/kvasir_segmentation_dataset/kvasir_segmentation_dataset/masks')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      yes\n",
       "1      yes\n",
       "2      yes\n",
       "3      yes\n",
       "4      yes\n",
       "      ... \n",
       "995    yes\n",
       "996    yes\n",
       "997    yes\n",
       "998    yes\n",
       "999    yes\n",
       "Name: has_mask, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe\n",
    "df_images = pd.DataFrame(images, columns=['image_id'])\n",
    "## example filename: cju0qkwl35piu0993l0dewei2.jpg\n",
    "\n",
    "def get_num_cells(x):\n",
    "    # split on the _\n",
    "    a = x\n",
    "    # choose the third item\n",
    "    # choose second item onwards and convert to int\n",
    "    num_cells = (a[1:])\n",
    "    \n",
    "    return num_cells\n",
    "\n",
    "# create a new column called 'num_cells'\n",
    "df_images['num_cells'] = df_images['image_id'].apply(get_num_cells)\n",
    "\n",
    "def check_for_mask(x):\n",
    "    if x in masks:\n",
    "        return 'yes'\n",
    "    else:\n",
    "        return 'no'\n",
    "    \n",
    "# create a new column called 'has_mask'\n",
    "df_images['has_mask'] = df_images['image_id'].apply(check_for_mask)\n",
    "\n",
    "df_images.head(10)\n",
    "df_images ['has_mask']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yes    1000\n",
       "Name: has_mask, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if every image has a mask.\n",
    "df_images['has_mask'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_masks = df_images[df_images['has_mask'] == 'yes']\n",
    "\n",
    "# create a new column called mask_id that is just a copy of image_id\n",
    "df_masks['mask_id'] = df_masks['image_id']\n",
    "\n",
    "df_masks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(970, 4)\n",
      "(30, 4)\n"
     ]
    }
   ],
   "source": [
    "# create a test set\n",
    "df_test = df_masks.sample(NUM_TEST_IMAGES, random_state=101)\n",
    "\n",
    "# Reset the index.\n",
    "# This is so that we can use loc to access mask id's later.\n",
    "df_test = df_test.reset_index(drop=True)\n",
    "\n",
    "# create a list of test images\n",
    "test_images_list = list(df_test['image_id'])\n",
    "\n",
    "\n",
    "# Select only rows that are not part of the test set.\n",
    "# Note the use of ~ to execute 'not in'.\n",
    "df_masks = df_masks[~df_masks['image_id'].isin(test_images_list)]\n",
    "\n",
    "print(df_masks.shape)\n",
    "print(df_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1abd4b73128>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sample_image = 'cju0qx73cjw570799j4n5cjze.jpg'\n",
    "path_image = 'D:/kvasir_segmentation_dataset/kvasir_segmentation_dataset/images/' + sample_image\n",
    "\n",
    "# read the image using skimage\n",
    "image = imread(path_image)\n",
    "\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape:  (529, 619, 3)\n",
      "Max pixel value:  255\n",
      "Min pixel value:  0\n"
     ]
    }
   ],
   "source": [
    "print('Shape: ', image.shape)\n",
    "print('Max pixel value: ', image.max())\n",
    "print('Min pixel value: ', image.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1abd4c0c6d8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sample_mask = 'cju0qx73cjw570799j4n5cjze.jpg'\n",
    "path_mask = 'D:/kvasir_segmentation_dataset/kvasir_segmentation_dataset/masks/' + sample_mask\n",
    "\n",
    "# read the mask using skimage\n",
    "mask = imread(path_mask)\n",
    "\n",
    "plt.imshow(mask, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape:  (529, 619, 3)\n",
      "Max pixel value:  255\n",
      "Min pixel value:  0\n"
     ]
    }
   ],
   "source": [
    "print('Shape: ', mask.shape)\n",
    "print('Max pixel value: ', mask.max())\n",
    "print('Min pixel value: ', mask.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get lists of images and their masks.\n",
    "image_id_list = list(df_masks['image_id'])\n",
    "mask_id_list = list(df_masks['mask_id'])\n",
    "test_id_list = list(df_test['image_id'])\n",
    "\n",
    "# Create empty arrays\n",
    "\n",
    "X_train = np.zeros((len(image_id_list), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.float32)\n",
    "\n",
    "Y_train = np.zeros((len(image_id_list), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.float32)\n",
    "\n",
    "X_test = np.zeros((NUM_TEST_IMAGES, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(970, 128, 128, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X_train\n",
    "\n",
    "for i, image_id in enumerate(image_id_list):\n",
    "    \n",
    "    path_image = 'D:/kvasir_segmentation_dataset/kvasir_segmentation_dataset/images/' + image_id\n",
    "    \n",
    "    # read the image using skimage\n",
    "    image = imread(path_image)\n",
    "    \n",
    "    # resize the image\n",
    "    image = resize(image, (IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), mode='constant', preserve_range=True)\n",
    "    \n",
    "\n",
    "\n",
    "    \n",
    "    # insert the image into X_train\n",
    "    X_train[i] = image\n",
    "    \n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(970, 128, 128, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Y_train\n",
    "\n",
    "for i, mask_id in enumerate(mask_id_list):\n",
    "    \n",
    "    path_mask = 'D:/kvasir_segmentation_dataset/kvasir_segmentation_dataset/masks/' + mask_id\n",
    "    \n",
    "    # read the image using skimage\n",
    "    mask = imread(path_mask)\n",
    "    \n",
    "    # resize the image\n",
    "    mask = resize(mask, (IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), mode='constant', preserve_range=True)\n",
    "    \n",
    "    # use np.expand dims to add a channel axis so the shape becomes (IMG_HEIGHT, IMG_WIDTH, 1)\n",
    "    #mask = np.expand_dims(mask, axis=-1)\n",
    "    \n",
    "    # insert the image into Y_Train\n",
    "    Y_train[i] = mask\n",
    "\n",
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 128, 128, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X_test\n",
    "\n",
    "for i, image_id in enumerate(test_id_list):\n",
    "    \n",
    "    path_image = 'D:/kvasir_segmentation_dataset/kvasir_segmentation_dataset/images/' + image_id\n",
    "    \n",
    "    # read the image using skimage\n",
    "    image = imread(path_image)\n",
    "    \n",
    "    # resize the image\n",
    "    image = resize(image, (IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), mode='constant', preserve_range=True)\n",
    "    \n",
    "    # use np.expand dims to add a channel axis so the shape becomes (IMG_HEIGHT, IMG_WIDTH, 1)\n",
    "    #image = np.expand_dims(image, axis=-1)\n",
    "    \n",
    "    # insert the image into X_test\n",
    "    X_test[i] = image\n",
    "    \n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.append(X_train, [np.fliplr(x) for x in X_train], axis=0)\n",
    "Y_train = np.append(Y_train, [np.fliplr(x) for x in Y_train], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standard_unit(input_tensor, stage, nb_filter, kernel_size=3):\n",
    "\n",
    "    act = 'elu'\n",
    "\n",
    "    x = Conv2D(nb_filter, (kernel_size, kernel_size), activation=act, name='conv'+stage+'_1', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(input_tensor)\n",
    "    x = Dropout(dropout_rate, name='dp'+stage+'_1')(x)\n",
    "    x = Conv2D(nb_filter, (kernel_size, kernel_size), activation=act, name='conv'+stage+'_2', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(x)\n",
    "    x = Dropout(dropout_rate, name='dp'+stage+'_2')(x)\n",
    "\n",
    "    return x\n",
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Nest_Net(img_rows, img_cols, color_type=1, num_class=1, deep_supervision=False):\n",
    "\n",
    "    nb_filter = [32,64,128,256,512]\n",
    "    act = 'elu'\n",
    "\n",
    "    bn_axis = 3\n",
    "    img_input = Input(shape=(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), name='main_input')\n",
    "\n",
    "    conv1_1 = standard_unit(img_input, stage='11', nb_filter=nb_filter[0])\n",
    "    pool1 = MaxPool2D((2, 2), strides=(2, 2), name='pool1')(conv1_1)\n",
    "\n",
    "    conv2_1 = standard_unit(pool1, stage='21', nb_filter=nb_filter[1])\n",
    "    pool2 = MaxPool2D((2, 2), strides=(2, 2), name='pool2')(conv2_1)\n",
    "\n",
    "    up1_2 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up12', padding='same')(conv2_1)\n",
    "    conv1_2 = concatenate([up1_2, conv1_1], name='merge12', axis=bn_axis)\n",
    "    conv1_2 = standard_unit(conv1_2, stage='12', nb_filter=nb_filter[0])\n",
    "\n",
    "    conv3_1 = standard_unit(pool2, stage='31', nb_filter=nb_filter[2])\n",
    "    pool3 = MaxPool2D((2, 2), strides=(2, 2), name='pool3')(conv3_1)\n",
    "\n",
    "    up2_2 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up22', padding='same')(conv3_1)\n",
    "    conv2_2 = concatenate([up2_2, conv2_1], name='merge22', axis=bn_axis)\n",
    "    conv2_2 = standard_unit(conv2_2, stage='22', nb_filter=nb_filter[1])\n",
    "\n",
    "    up1_3 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up13', padding='same')(conv2_2)\n",
    "    conv1_3 = concatenate([up1_3, conv1_1, conv1_2], name='merge13', axis=bn_axis)\n",
    "    conv1_3 = standard_unit(conv1_3, stage='13', nb_filter=nb_filter[0])\n",
    "\n",
    "    conv4_1 = standard_unit(pool3, stage='41', nb_filter=nb_filter[3])\n",
    "    pool4 = MaxPool2D((2, 2), strides=(2, 2), name='pool4')(conv4_1)\n",
    "\n",
    "    up3_2 = Conv2DTranspose(nb_filter[2], (2, 2), strides=(2, 2), name='up32', padding='same')(conv4_1)\n",
    "    conv3_2 = concatenate([up3_2, conv3_1], name='merge32', axis=bn_axis)\n",
    "    conv3_2 = standard_unit(conv3_2, stage='32', nb_filter=nb_filter[2])\n",
    "\n",
    "    up2_3 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up23', padding='same')(conv3_2)\n",
    "    conv2_3 = concatenate([up2_3, conv2_1, conv2_2], name='merge23', axis=bn_axis)\n",
    "    conv2_3 = standard_unit(conv2_3, stage='23', nb_filter=nb_filter[1])\n",
    "\n",
    "    up1_4 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up14', padding='same')(conv2_3)\n",
    "    conv1_4 = concatenate([up1_4, conv1_1, conv1_2, conv1_3], name='merge14', axis=bn_axis)\n",
    "    conv1_4 = standard_unit(conv1_4, stage='14', nb_filter=nb_filter[0])\n",
    "\n",
    "    conv5_1 = standard_unit(pool4, stage='51', nb_filter=nb_filter[4])\n",
    "\n",
    "    up4_2 = Conv2DTranspose(nb_filter[3], (2, 2), strides=(2, 2), name='up42', padding='same')(conv5_1)\n",
    "    conv4_2 = concatenate([up4_2, conv4_1], name='merge42', axis=bn_axis)\n",
    "    conv4_2 = standard_unit(conv4_2, stage='42', nb_filter=nb_filter[3])\n",
    "\n",
    "    up3_3 = Conv2DTranspose(nb_filter[2], (2, 2), strides=(2, 2), name='up33', padding='same')(conv4_2)\n",
    "    conv3_3 = concatenate([up3_3, conv3_1, conv3_2], name='merge33', axis=bn_axis)\n",
    "    conv3_3 = standard_unit(conv3_3, stage='33', nb_filter=nb_filter[2])\n",
    "\n",
    "    up2_4 = Conv2DTranspose(nb_filter[1], (2, 2), strides=(2, 2), name='up24', padding='same')(conv3_3)\n",
    "    conv2_4 = concatenate([up2_4, conv2_1, conv2_2, conv2_3], name='merge24', axis=bn_axis)\n",
    "    conv2_4 = standard_unit(conv2_4, stage='24', nb_filter=nb_filter[1])\n",
    "\n",
    "    up1_5 = Conv2DTranspose(nb_filter[0], (2, 2), strides=(2, 2), name='up15', padding='same')(conv2_4)\n",
    "    conv1_5 = concatenate([up1_5, conv1_1, conv1_2, conv1_3, conv1_4], name='merge15', axis=bn_axis)\n",
    "    conv1_5 = standard_unit(conv1_5, stage='15', nb_filter=nb_filter[0])\n",
    "\n",
    "    nestnet_output_1 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_1', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(conv1_2)\n",
    "    nestnet_output_2 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_2', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(conv1_3)\n",
    "    nestnet_output_3 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_3', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(conv1_4)\n",
    "    nestnet_output_4 = Conv2D(num_class, (1, 1), activation='sigmoid', name='output_4', kernel_initializer = 'he_normal', padding='same', kernel_regularizer=l2(1e-4))(conv1_5)\n",
    "\n",
    "    if deep_supervision:\n",
    "        model = Model(img_input, [nestnet_output_1,nestnet_output_2,nestnet_output_3,nestnet_output_4])\n",
    "    else:\n",
    "        model = Model(img_input, [nestnet_output_4])\n",
    "    \n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dice similarity coefficient loss, brought to you by: https://github.com/nabsabraham/focal-tversky-unet\n",
    "def dsc(y_true, y_pred):\n",
    "    smooth = 1.\n",
    "    y_true_f = Flatten()(y_true)\n",
    "    y_pred_f = Flatten()(y_pred)\n",
    "    intersection = reduce_sum(y_true_f * y_pred_f)\n",
    "    score = (2. * intersection + smooth) / (reduce_sum(y_true_f) + reduce_sum(y_pred_f) + smooth)\n",
    "    return score\n",
    "\n",
    "def dice_loss(y_true, y_pred):\n",
    "    loss = 1 - dsc(y_true, y_pred)\n",
    "    return loss\n",
    "\n",
    "def bce_dice_loss(y_true, y_pred):\n",
    "    loss = binary_crossentropy(y_true, y_pred) + dice_loss(y_true, y_pred)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'tensorflow_core.compat.v1.compat' has no attribute 'v1'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-117-2ebf16b31a25>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisable_v2_behavior\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mv1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisable_eager_execution\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'tensorflow_core.compat.v1.compat' has no attribute 'v1'"
     ]
    }
   ],
   "source": [
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n",
    "\n",
    "tf.compat.v1.disable_eager_execution()\n",
    "\n",
    "\n",
    "tfd = tfp.distributions\n",
    "\n",
    "init = tf.compat.v1.global_variables_initializer()\n",
    "\n",
    "with tf.compat.v1.Session() as sess:\n",
    "    sess.run(init)\n",
    "\n",
    "    model = tf.keras.Sequential([\n",
    "      tf.keras.layers.Dense(1,kernel_initializer='glorot_uniform'),\n",
    "      tfp.layers.DistributionLambda(lambda t: tfd.Normal(loc=t, scale=1))\n",
    "    ])\n",
    "# get an instance of the model\n",
    "model = Nest_Net(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS, num_class=2, deep_supervision=False)\n",
    "# define optimizer \n",
    "adam = Adam(lr = 0.05, epsilon = 0.1)\n",
    "model.compile(optimizer=adam, loss=bce_dice_loss, metrics=[dice_loss])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'efficientnet'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-99-986364227326>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mefficientnet\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mEfficientNetB4\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mUEfficientNet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdropout_rate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     backbone = EfficientNetB4(weights='imagenet',\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'efficientnet'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W1014 21:21:51.602864  1904 deprecation.py:323] From C:\\Users\\zakel\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\compat\\v2_compat.py:65: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "module 'tensorflow' has no attribute 'placeholder'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-94-8169e55f39c1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mv1\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisable_v2_behavior\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mseg_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mUResNet34\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m768\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m768\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mencoder_weights\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[0m",
      "\u001b[1;32m<ipython-input-90-781b561472c5>\u001b[0m in \u001b[0;36mUResNet34\u001b[1;34m(input_shape, classes, decoder_filters, decoder_block_type, encoder_weights, input_tensor, activation, **kwargs)\u001b[0m\n\u001b[0;32m      7\u001b[0m                          \u001b[0mclasses\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclasses\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m                          \u001b[0minclude_top\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m                          block_type='basic')\n\u001b[0m\u001b[0;32m     10\u001b[0m     \u001b[0mbackbone\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'resnet34'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-86-e6ba5d5212c0>\u001b[0m in \u001b[0;36mbuild_resnet\u001b[1;34m(repetitions, include_top, input_tensor, input_shape, classes, block_type)\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0minput_tensor\u001b[0m \u001b[1;32mis\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;32m---> 37\u001b[1;33m         \u001b[0mimg_input\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mInput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'data'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     38\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_keras_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\engine\\input_layer.py\u001b[0m in \u001b[0;36mInput\u001b[1;34m(shape, batch_shape, name, dtype, sparse, tensor)\u001b[0m\n\u001b[0;32m    176\u001b[0m                              \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    177\u001b[0m                              \u001b[0msparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 178\u001b[1;33m                              input_tensor=tensor)\n\u001b[0m\u001b[0;32m    179\u001b[0m     \u001b[1;31m# Return tensor including _keras_shape and _keras_history.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m     \u001b[1;31m# Note that in this case train_output and test_output are the same pointer.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\legacy\\interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[0;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[1;32m---> 91\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\engine\\input_layer.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, input_shape, batch_size, batch_input_shape, dtype, input_tensor, sparse, name)\u001b[0m\n\u001b[0;32m     85\u001b[0m                                          \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m                                          \u001b[0msparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 87\u001b[1;33m                                          name=self.name)\n\u001b[0m\u001b[0;32m     88\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_placeholder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36mplaceholder\u001b[1;34m(shape, ndim, dtype, sparse, name)\u001b[0m\n\u001b[0;32m    515\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msparse_placeholder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    516\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 517\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    518\u001b[0m     \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_keras_shape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    519\u001b[0m     \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_uses_learning_phase\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'placeholder'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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