horizontal2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.youtube.com/watch?v=XyX5HNuv-xE\n",
    "# https://github.com/bnsreenu/python_for_microscopists/blob/master/177_semantic_segmentation_made_easy_using_segm_models.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segmentation Models: using `keras` framework.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import glob\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "import segmentation_models as sm\n",
    "from tensorflow.keras.metrics import MeanIoU\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c996a15400>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t_image = cv2.imread('rs19_val/jpgs/rs19_val/rs00062.jpg') \n",
    "t_image = t_image[:1056,:,:]\n",
    "plt.imshow(t_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels are:  [ 2  5  8  9 10 12 13 15 17]  and the counts are:  [468923  49765 922505  10135 200993 101208  27723 224439  67909]\n"
     ]
    }
   ],
   "source": [
    "temp_mask = cv2.imread('rs19_val/uint8/rs19_val/rs00062.png') #3 channels but all same. \n",
    "labels, count = np.unique(temp_mask[:,:,0], return_counts=True) #Check for each channel. All chanels are identical\n",
    "print(\"Labels are: \", labels, \" and the counts are: \", count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(temp_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#width and length of the images\n",
    "w = temp_mask.shape[0]\n",
    "l = temp_mask.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c996c53880>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "#before processing\n",
    "plt.imshow(temp_mask[:,:,0], cmap='jet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c996ed3f10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#temp_mask = cv2.imread('rs19_val/uint8/rs19_val/rs00062.png')\n",
    "plt.figure(figsize = (10,5))\n",
    "label_of_interest = 8 #veg\n",
    "#after processing\n",
    "temp_mask[:int(w/2),:,0]=0\n",
    "temp_mask[:,:,0][temp_mask[:,:,0]!=label_of_interest]=0\n",
    "plt.imshow(temp_mask[:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(temp_mask[:,:,0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprosessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Can't convert object to 'str' for 'filename'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [20], line 17\u001b[0m\n\u001b[0;32m     15\u001b[0m image \u001b[39m=\u001b[39m Image\u001b[39m.\u001b[39mopen(path_images\u001b[39m+\u001b[39mfilename) \n\u001b[0;32m     16\u001b[0m image \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(image\u001b[39m.\u001b[39mresize((\u001b[39m256\u001b[39m,\u001b[39m256\u001b[39m),\u001b[39m3\u001b[39m)) \u001b[39m#divisible by 32 because of UNET requirements\u001b[39;00m\n\u001b[1;32m---> 17\u001b[0m image \u001b[39m=\u001b[39m cv2\u001b[39m.\u001b[39;49mimread(image)\n\u001b[0;32m     18\u001b[0m mask \u001b[39m=\u001b[39m Image\u001b[39m.\u001b[39mopen(path_mask\u001b[39m+\u001b[39mfilename[:\u001b[39m7\u001b[39m]\u001b[39m+\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.png\u001b[39m\u001b[39m'\u001b[39m) \n\u001b[0;32m     19\u001b[0m mask \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(mask\u001b[39m.\u001b[39mresize((\u001b[39m256\u001b[39m,\u001b[39m256\u001b[39m),\u001b[39m3\u001b[39m))\n",
      "\u001b[1;31mTypeError\u001b[0m: Can't convert object to 'str' for 'filename'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#these codes are used to loop through the directory\n",
    "path_images = 'rs19_val/jpgs/rs19_val/'\n",
    "path_mask = 'rs19_val/uint8/rs19_val/'\n",
    "breaker = 0\n",
    "\n",
    "image_dataset = []\n",
    "mask_dataset = []\n",
    "\n",
    "label_of_interest = 8 #veg\n",
    "plt.figure(figsize = (10,5))\n",
    "\n",
    "for filename in os.listdir(path_images):\n",
    "    breaker +=1\n",
    "    if filename.endswith('.jpg'):  \n",
    "        image = Image.open(path_images+filename) \n",
    "        image = np.array(image.resize((256,256),3)) #divisible by 32 because of UNET requirements\n",
    "        image = cv2.imread(path_images+filename)\n",
    "        mask = Image.open(path_mask+filename[:7]+'.png') \n",
    "        mask = np.array(mask.resize((256,256),3))\n",
    "        mask = cv2.imread(path_mask+filename[:7]+'.png')\n",
    "        for layer in range(len(mask[0,0,:])):\n",
    "            mask[:int(w/2),:,layer]=0\n",
    "            mask[:,:,layer][mask[:,:,layer]!=label_of_interest]=0\n",
    "            mask[:,:,layer][mask[:,:,layer]==label_of_interest]=1\n",
    "                #this is because one hot encoder can only deal with 0 and 1 if there is 2 labels\n",
    "        # image = np.array(image)\n",
    "        # mask = np.array(mask)\n",
    "        image_dataset.append(image)\n",
    "        mask_dataset.append(mask)\n",
    "    if breaker==20:\n",
    "         break\n",
    "\n",
    "image_dataset = np.array(image_dataset)\n",
    "mask_dataset = np.array(mask_dataset)\n",
    "mask_dataset = mask_dataset[:,:,:,0]\n",
    "print('image: ', image_dataset.shape)\n",
    "print('mask: ', mask_dataset.shape)\n",
    "plt.imshow(mask[:,:,0])\n",
    "\n",
    "mask_dataset = np.expand_dims(mask_dataset, axis=3)\n",
    "print('image: ', image_dataset.shape)\n",
    "print('mask: ', mask_dataset.shape)\n",
    "\n",
    "print('unique labels in mask', np.unique(mask_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split\n",
    "X_train, X_test, y_train, y_test = train_test_split(image_dataset, mask_dataset, test_size = 0.20, random_state = 42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing\n",
    "# https://segmentation-models.readthedocs.io/en/latest/tutorial.html#models-and-backbones\n",
    "BACKBONE = 'inceptionv3'\n",
    "preprocess_input = sm.get_preprocessing(BACKBONE)\n",
    "\n",
    "X_train = preprocess_input(X_train)\n",
    "X_test = preprocess_input(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, None, None,  0           []                               \n",
      "                                 3)]                                                              \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, None, None,   864         ['input_1[0][0]']                \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, None, None,   96         ['conv2d[0][0]']                 \n",
      " alization)                     32)                                                               \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, None, None,   0           ['batch_normalization[0][0]']    \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, None, None,   9216        ['activation[0][0]']             \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, None, None,   96         ['conv2d_1[0][0]']               \n",
      " rmalization)                   32)                                                               \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, None, None,   0           ['batch_normalization_1[0][0]']  \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, None, None,   18432       ['activation_1[0][0]']           \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, None, None,   192        ['conv2d_2[0][0]']               \n",
      " rmalization)                   64)                                                               \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, None, None,   0           ['batch_normalization_2[0][0]']  \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, None, None,   0           ['activation_2[0][0]']           \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, None, None,   5120        ['max_pooling2d[0][0]']          \n",
      "                                80)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, None, None,   240        ['conv2d_3[0][0]']               \n",
      " rmalization)                   80)                                                               \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, None, None,   0           ['batch_normalization_3[0][0]']  \n",
      "                                80)                                                               \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, None, None,   138240      ['activation_3[0][0]']           \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, None, None,   576        ['conv2d_4[0][0]']               \n",
      " rmalization)                   192)                                                              \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, None, None,   0           ['batch_normalization_4[0][0]']  \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, None, None,   0          ['activation_4[0][0]']           \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, None, None,   12288       ['max_pooling2d_1[0][0]']        \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, None, None,   192        ['conv2d_8[0][0]']               \n",
      " rmalization)                   64)                                                               \n",
      "                                                                                                  \n",
      " activation_8 (Activation)      (None, None, None,   0           ['batch_normalization_8[0][0]']  \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, None, None,   9216        ['max_pooling2d_1[0][0]']        \n",
      "                                48)                                                               \n",
      "                                                                                                  \n",
      " conv2d_9 (Conv2D)              (None, None, None,   55296       ['activation_8[0][0]']           \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, None, None,   144        ['conv2d_6[0][0]']               \n",
      " rmalization)                   48)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, None, None,   288        ['conv2d_9[0][0]']               \n",
      " rmalization)                   96)                                                               \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, None, None,   0           ['batch_normalization_6[0][0]']  \n",
      "                                48)                                                               \n",
      "                                                                                                  \n",
      " activation_9 (Activation)      (None, None, None,   0           ['batch_normalization_9[0][0]']  \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " average_pooling2d (AveragePool  (None, None, None,   0          ['max_pooling2d_1[0][0]']        \n",
      " ing2D)                         192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, None, None,   12288       ['max_pooling2d_1[0][0]']        \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, None, None,   76800       ['activation_6[0][0]']           \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv2d_10 (Conv2D)             (None, None, None,   82944       ['activation_9[0][0]']           \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " conv2d_11 (Conv2D)             (None, None, None,   6144        ['average_pooling2d[0][0]']      \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, None, None,   192        ['conv2d_5[0][0]']               \n",
      " rmalization)                   64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, None, None,   192        ['conv2d_7[0][0]']               \n",
      " rmalization)                   64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_10 (BatchN  (None, None, None,   288        ['conv2d_10[0][0]']              \n",
      " ormalization)                  96)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_11 (BatchN  (None, None, None,   96         ['conv2d_11[0][0]']              \n",
      " ormalization)                  32)                                                               \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, None, None,   0           ['batch_normalization_5[0][0]']  \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " activation_7 (Activation)      (None, None, None,   0           ['batch_normalization_7[0][0]']  \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " activation_10 (Activation)     (None, None, None,   0           ['batch_normalization_10[0][0]'] \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " activation_11 (Activation)     (None, None, None,   0           ['batch_normalization_11[0][0]'] \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " mixed0 (Concatenate)           (None, None, None,   0           ['activation_5[0][0]',           \n",
      "                                256)                              'activation_7[0][0]',           \n",
      "                                                                  'activation_10[0][0]',          \n",
      "                                                                  'activation_11[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_15 (Conv2D)             (None, None, None,   16384       ['mixed0[0][0]']                 \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_15 (BatchN  (None, None, None,   192        ['conv2d_15[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " activation_15 (Activation)     (None, None, None,   0           ['batch_normalization_15[0][0]'] \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv2d_13 (Conv2D)             (None, None, None,   12288       ['mixed0[0][0]']                 \n",
      "                                48)                                                               \n",
      "                                                                                                  \n",
      " conv2d_16 (Conv2D)             (None, None, None,   55296       ['activation_15[0][0]']          \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_13 (BatchN  (None, None, None,   144        ['conv2d_13[0][0]']              \n",
      " ormalization)                  48)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_16 (BatchN  (None, None, None,   288        ['conv2d_16[0][0]']              \n",
      " ormalization)                  96)                                                               \n",
      "                                                                                                  \n",
      " activation_13 (Activation)     (None, None, None,   0           ['batch_normalization_13[0][0]'] \n",
      "                                48)                                                               \n",
      "                                                                                                  \n",
      " activation_16 (Activation)     (None, None, None,   0           ['batch_normalization_16[0][0]'] \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " average_pooling2d_1 (AveragePo  (None, None, None,   0          ['mixed0[0][0]']                 \n",
      " oling2D)                       256)                                                              \n",
      "                                                                                                  \n",
      " conv2d_12 (Conv2D)             (None, None, None,   16384       ['mixed0[0][0]']                 \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv2d_14 (Conv2D)             (None, None, None,   76800       ['activation_13[0][0]']          \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv2d_17 (Conv2D)             (None, None, None,   82944       ['activation_16[0][0]']          \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " conv2d_18 (Conv2D)             (None, None, None,   16384       ['average_pooling2d_1[0][0]']    \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_12 (BatchN  (None, None, None,   192        ['conv2d_12[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_14 (BatchN  (None, None, None,   192        ['conv2d_14[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_17 (BatchN  (None, None, None,   288        ['conv2d_17[0][0]']              \n",
      " ormalization)                  96)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_18 (BatchN  (None, None, None,   192        ['conv2d_18[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " activation_12 (Activation)     (None, None, None,   0           ['batch_normalization_12[0][0]'] \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " activation_14 (Activation)     (None, None, None,   0           ['batch_normalization_14[0][0]'] \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " activation_17 (Activation)     (None, None, None,   0           ['batch_normalization_17[0][0]'] \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " activation_18 (Activation)     (None, None, None,   0           ['batch_normalization_18[0][0]'] \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " mixed1 (Concatenate)           (None, None, None,   0           ['activation_12[0][0]',          \n",
      "                                288)                              'activation_14[0][0]',          \n",
      "                                                                  'activation_17[0][0]',          \n",
      "                                                                  'activation_18[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_22 (Conv2D)             (None, None, None,   18432       ['mixed1[0][0]']                 \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_22 (BatchN  (None, None, None,   192        ['conv2d_22[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " activation_22 (Activation)     (None, None, None,   0           ['batch_normalization_22[0][0]'] \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv2d_20 (Conv2D)             (None, None, None,   13824       ['mixed1[0][0]']                 \n",
      "                                48)                                                               \n",
      "                                                                                                  \n",
      " conv2d_23 (Conv2D)             (None, None, None,   55296       ['activation_22[0][0]']          \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_20 (BatchN  (None, None, None,   144        ['conv2d_20[0][0]']              \n",
      " ormalization)                  48)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_23 (BatchN  (None, None, None,   288        ['conv2d_23[0][0]']              \n",
      " ormalization)                  96)                                                               \n",
      "                                                                                                  \n",
      " activation_20 (Activation)     (None, None, None,   0           ['batch_normalization_20[0][0]'] \n",
      "                                48)                                                               \n",
      "                                                                                                  \n",
      " activation_23 (Activation)     (None, None, None,   0           ['batch_normalization_23[0][0]'] \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " average_pooling2d_2 (AveragePo  (None, None, None,   0          ['mixed1[0][0]']                 \n",
      " oling2D)                       288)                                                              \n",
      "                                                                                                  \n",
      " conv2d_19 (Conv2D)             (None, None, None,   18432       ['mixed1[0][0]']                 \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv2d_21 (Conv2D)             (None, None, None,   76800       ['activation_20[0][0]']          \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv2d_24 (Conv2D)             (None, None, None,   82944       ['activation_23[0][0]']          \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " conv2d_25 (Conv2D)             (None, None, None,   18432       ['average_pooling2d_2[0][0]']    \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_19 (BatchN  (None, None, None,   192        ['conv2d_19[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_21 (BatchN  (None, None, None,   192        ['conv2d_21[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_24 (BatchN  (None, None, None,   288        ['conv2d_24[0][0]']              \n",
      " ormalization)                  96)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_25 (BatchN  (None, None, None,   192        ['conv2d_25[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " activation_19 (Activation)     (None, None, None,   0           ['batch_normalization_19[0][0]'] \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " activation_21 (Activation)     (None, None, None,   0           ['batch_normalization_21[0][0]'] \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " activation_24 (Activation)     (None, None, None,   0           ['batch_normalization_24[0][0]'] \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " activation_25 (Activation)     (None, None, None,   0           ['batch_normalization_25[0][0]'] \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " mixed2 (Concatenate)           (None, None, None,   0           ['activation_19[0][0]',          \n",
      "                                288)                              'activation_21[0][0]',          \n",
      "                                                                  'activation_24[0][0]',          \n",
      "                                                                  'activation_25[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_27 (Conv2D)             (None, None, None,   18432       ['mixed2[0][0]']                 \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_27 (BatchN  (None, None, None,   192        ['conv2d_27[0][0]']              \n",
      " ormalization)                  64)                                                               \n",
      "                                                                                                  \n",
      " activation_27 (Activation)     (None, None, None,   0           ['batch_normalization_27[0][0]'] \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " conv2d_28 (Conv2D)             (None, None, None,   55296       ['activation_27[0][0]']          \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_28 (BatchN  (None, None, None,   288        ['conv2d_28[0][0]']              \n",
      " ormalization)                  96)                                                               \n",
      "                                                                                                  \n",
      " activation_28 (Activation)     (None, None, None,   0           ['batch_normalization_28[0][0]'] \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " conv2d_26 (Conv2D)             (None, None, None,   995328      ['mixed2[0][0]']                 \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " conv2d_29 (Conv2D)             (None, None, None,   82944       ['activation_28[0][0]']          \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " batch_normalization_26 (BatchN  (None, None, None,   1152       ['conv2d_26[0][0]']              \n",
      " ormalization)                  384)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_29 (BatchN  (None, None, None,   288        ['conv2d_29[0][0]']              \n",
      " ormalization)                  96)                                                               \n",
      "                                                                                                  \n",
      " activation_26 (Activation)     (None, None, None,   0           ['batch_normalization_26[0][0]'] \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " activation_29 (Activation)     (None, None, None,   0           ['batch_normalization_29[0][0]'] \n",
      "                                96)                                                               \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, None, None,   0          ['mixed2[0][0]']                 \n",
      "                                288)                                                              \n",
      "                                                                                                  \n",
      " mixed3 (Concatenate)           (None, None, None,   0           ['activation_26[0][0]',          \n",
      "                                768)                              'activation_29[0][0]',          \n",
      "                                                                  'max_pooling2d_2[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_34 (Conv2D)             (None, None, None,   98304       ['mixed3[0][0]']                 \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_34 (BatchN  (None, None, None,   384        ['conv2d_34[0][0]']              \n",
      " ormalization)                  128)                                                              \n",
      "                                                                                                  \n",
      " activation_34 (Activation)     (None, None, None,   0           ['batch_normalization_34[0][0]'] \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " conv2d_35 (Conv2D)             (None, None, None,   114688      ['activation_34[0][0]']          \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_35 (BatchN  (None, None, None,   384        ['conv2d_35[0][0]']              \n",
      " ormalization)                  128)                                                              \n",
      "                                                                                                  \n",
      " activation_35 (Activation)     (None, None, None,   0           ['batch_normalization_35[0][0]'] \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " conv2d_31 (Conv2D)             (None, None, None,   98304       ['mixed3[0][0]']                 \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " conv2d_36 (Conv2D)             (None, None, None,   114688      ['activation_35[0][0]']          \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_31 (BatchN  (None, None, None,   384        ['conv2d_31[0][0]']              \n",
      " ormalization)                  128)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_36 (BatchN  (None, None, None,   384        ['conv2d_36[0][0]']              \n",
      " ormalization)                  128)                                                              \n",
      "                                                                                                  \n",
      " activation_31 (Activation)     (None, None, None,   0           ['batch_normalization_31[0][0]'] \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " activation_36 (Activation)     (None, None, None,   0           ['batch_normalization_36[0][0]'] \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " conv2d_32 (Conv2D)             (None, None, None,   114688      ['activation_31[0][0]']          \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " conv2d_37 (Conv2D)             (None, None, None,   114688      ['activation_36[0][0]']          \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_32 (BatchN  (None, None, None,   384        ['conv2d_32[0][0]']              \n",
      " ormalization)                  128)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_37 (BatchN  (None, None, None,   384        ['conv2d_37[0][0]']              \n",
      " ormalization)                  128)                                                              \n",
      "                                                                                                  \n",
      " activation_32 (Activation)     (None, None, None,   0           ['batch_normalization_32[0][0]'] \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " activation_37 (Activation)     (None, None, None,   0           ['batch_normalization_37[0][0]'] \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " average_pooling2d_3 (AveragePo  (None, None, None,   0          ['mixed3[0][0]']                 \n",
      " oling2D)                       768)                                                              \n",
      "                                                                                                  \n",
      " conv2d_30 (Conv2D)             (None, None, None,   147456      ['mixed3[0][0]']                 \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_33 (Conv2D)             (None, None, None,   172032      ['activation_32[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_38 (Conv2D)             (None, None, None,   172032      ['activation_37[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_39 (Conv2D)             (None, None, None,   147456      ['average_pooling2d_3[0][0]']    \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_30 (BatchN  (None, None, None,   576        ['conv2d_30[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_33 (BatchN  (None, None, None,   576        ['conv2d_33[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_38 (BatchN  (None, None, None,   576        ['conv2d_38[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_39 (BatchN  (None, None, None,   576        ['conv2d_39[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_30 (Activation)     (None, None, None,   0           ['batch_normalization_30[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_33 (Activation)     (None, None, None,   0           ['batch_normalization_33[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_38 (Activation)     (None, None, None,   0           ['batch_normalization_38[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_39 (Activation)     (None, None, None,   0           ['batch_normalization_39[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " mixed4 (Concatenate)           (None, None, None,   0           ['activation_30[0][0]',          \n",
      "                                768)                              'activation_33[0][0]',          \n",
      "                                                                  'activation_38[0][0]',          \n",
      "                                                                  'activation_39[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_44 (Conv2D)             (None, None, None,   122880      ['mixed4[0][0]']                 \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_44 (BatchN  (None, None, None,   480        ['conv2d_44[0][0]']              \n",
      " ormalization)                  160)                                                              \n",
      "                                                                                                  \n",
      " activation_44 (Activation)     (None, None, None,   0           ['batch_normalization_44[0][0]'] \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " conv2d_45 (Conv2D)             (None, None, None,   179200      ['activation_44[0][0]']          \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_45 (BatchN  (None, None, None,   480        ['conv2d_45[0][0]']              \n",
      " ormalization)                  160)                                                              \n",
      "                                                                                                  \n",
      " activation_45 (Activation)     (None, None, None,   0           ['batch_normalization_45[0][0]'] \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " conv2d_41 (Conv2D)             (None, None, None,   122880      ['mixed4[0][0]']                 \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " conv2d_46 (Conv2D)             (None, None, None,   179200      ['activation_45[0][0]']          \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_41 (BatchN  (None, None, None,   480        ['conv2d_41[0][0]']              \n",
      " ormalization)                  160)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_46 (BatchN  (None, None, None,   480        ['conv2d_46[0][0]']              \n",
      " ormalization)                  160)                                                              \n",
      "                                                                                                  \n",
      " activation_41 (Activation)     (None, None, None,   0           ['batch_normalization_41[0][0]'] \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " activation_46 (Activation)     (None, None, None,   0           ['batch_normalization_46[0][0]'] \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " conv2d_42 (Conv2D)             (None, None, None,   179200      ['activation_41[0][0]']          \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " conv2d_47 (Conv2D)             (None, None, None,   179200      ['activation_46[0][0]']          \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_42 (BatchN  (None, None, None,   480        ['conv2d_42[0][0]']              \n",
      " ormalization)                  160)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_47 (BatchN  (None, None, None,   480        ['conv2d_47[0][0]']              \n",
      " ormalization)                  160)                                                              \n",
      "                                                                                                  \n",
      " activation_42 (Activation)     (None, None, None,   0           ['batch_normalization_42[0][0]'] \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " activation_47 (Activation)     (None, None, None,   0           ['batch_normalization_47[0][0]'] \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " average_pooling2d_4 (AveragePo  (None, None, None,   0          ['mixed4[0][0]']                 \n",
      " oling2D)                       768)                                                              \n",
      "                                                                                                  \n",
      " conv2d_40 (Conv2D)             (None, None, None,   147456      ['mixed4[0][0]']                 \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_43 (Conv2D)             (None, None, None,   215040      ['activation_42[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_48 (Conv2D)             (None, None, None,   215040      ['activation_47[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_49 (Conv2D)             (None, None, None,   147456      ['average_pooling2d_4[0][0]']    \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_40 (BatchN  (None, None, None,   576        ['conv2d_40[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_43 (BatchN  (None, None, None,   576        ['conv2d_43[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_48 (BatchN  (None, None, None,   576        ['conv2d_48[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_49 (BatchN  (None, None, None,   576        ['conv2d_49[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_40 (Activation)     (None, None, None,   0           ['batch_normalization_40[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_43 (Activation)     (None, None, None,   0           ['batch_normalization_43[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_48 (Activation)     (None, None, None,   0           ['batch_normalization_48[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_49 (Activation)     (None, None, None,   0           ['batch_normalization_49[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " mixed5 (Concatenate)           (None, None, None,   0           ['activation_40[0][0]',          \n",
      "                                768)                              'activation_43[0][0]',          \n",
      "                                                                  'activation_48[0][0]',          \n",
      "                                                                  'activation_49[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_54 (Conv2D)             (None, None, None,   122880      ['mixed5[0][0]']                 \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_54 (BatchN  (None, None, None,   480        ['conv2d_54[0][0]']              \n",
      " ormalization)                  160)                                                              \n",
      "                                                                                                  \n",
      " activation_54 (Activation)     (None, None, None,   0           ['batch_normalization_54[0][0]'] \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " conv2d_55 (Conv2D)             (None, None, None,   179200      ['activation_54[0][0]']          \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_55 (BatchN  (None, None, None,   480        ['conv2d_55[0][0]']              \n",
      " ormalization)                  160)                                                              \n",
      "                                                                                                  \n",
      " activation_55 (Activation)     (None, None, None,   0           ['batch_normalization_55[0][0]'] \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " conv2d_51 (Conv2D)             (None, None, None,   122880      ['mixed5[0][0]']                 \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " conv2d_56 (Conv2D)             (None, None, None,   179200      ['activation_55[0][0]']          \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_51 (BatchN  (None, None, None,   480        ['conv2d_51[0][0]']              \n",
      " ormalization)                  160)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_56 (BatchN  (None, None, None,   480        ['conv2d_56[0][0]']              \n",
      " ormalization)                  160)                                                              \n",
      "                                                                                                  \n",
      " activation_51 (Activation)     (None, None, None,   0           ['batch_normalization_51[0][0]'] \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " activation_56 (Activation)     (None, None, None,   0           ['batch_normalization_56[0][0]'] \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " conv2d_52 (Conv2D)             (None, None, None,   179200      ['activation_51[0][0]']          \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " conv2d_57 (Conv2D)             (None, None, None,   179200      ['activation_56[0][0]']          \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_52 (BatchN  (None, None, None,   480        ['conv2d_52[0][0]']              \n",
      " ormalization)                  160)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_57 (BatchN  (None, None, None,   480        ['conv2d_57[0][0]']              \n",
      " ormalization)                  160)                                                              \n",
      "                                                                                                  \n",
      " activation_52 (Activation)     (None, None, None,   0           ['batch_normalization_52[0][0]'] \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " activation_57 (Activation)     (None, None, None,   0           ['batch_normalization_57[0][0]'] \n",
      "                                160)                                                              \n",
      "                                                                                                  \n",
      " average_pooling2d_5 (AveragePo  (None, None, None,   0          ['mixed5[0][0]']                 \n",
      " oling2D)                       768)                                                              \n",
      "                                                                                                  \n",
      " conv2d_50 (Conv2D)             (None, None, None,   147456      ['mixed5[0][0]']                 \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_53 (Conv2D)             (None, None, None,   215040      ['activation_52[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_58 (Conv2D)             (None, None, None,   215040      ['activation_57[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_59 (Conv2D)             (None, None, None,   147456      ['average_pooling2d_5[0][0]']    \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_50 (BatchN  (None, None, None,   576        ['conv2d_50[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_53 (BatchN  (None, None, None,   576        ['conv2d_53[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_58 (BatchN  (None, None, None,   576        ['conv2d_58[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_59 (BatchN  (None, None, None,   576        ['conv2d_59[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_50 (Activation)     (None, None, None,   0           ['batch_normalization_50[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_53 (Activation)     (None, None, None,   0           ['batch_normalization_53[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_58 (Activation)     (None, None, None,   0           ['batch_normalization_58[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_59 (Activation)     (None, None, None,   0           ['batch_normalization_59[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " mixed6 (Concatenate)           (None, None, None,   0           ['activation_50[0][0]',          \n",
      "                                768)                              'activation_53[0][0]',          \n",
      "                                                                  'activation_58[0][0]',          \n",
      "                                                                  'activation_59[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_64 (Conv2D)             (None, None, None,   147456      ['mixed6[0][0]']                 \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_64 (BatchN  (None, None, None,   576        ['conv2d_64[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_64 (Activation)     (None, None, None,   0           ['batch_normalization_64[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_65 (Conv2D)             (None, None, None,   258048      ['activation_64[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_65 (BatchN  (None, None, None,   576        ['conv2d_65[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_65 (Activation)     (None, None, None,   0           ['batch_normalization_65[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_61 (Conv2D)             (None, None, None,   147456      ['mixed6[0][0]']                 \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_66 (Conv2D)             (None, None, None,   258048      ['activation_65[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_61 (BatchN  (None, None, None,   576        ['conv2d_61[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_66 (BatchN  (None, None, None,   576        ['conv2d_66[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_61 (Activation)     (None, None, None,   0           ['batch_normalization_61[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_66 (Activation)     (None, None, None,   0           ['batch_normalization_66[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_62 (Conv2D)             (None, None, None,   258048      ['activation_61[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_67 (Conv2D)             (None, None, None,   258048      ['activation_66[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_62 (BatchN  (None, None, None,   576        ['conv2d_62[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_67 (BatchN  (None, None, None,   576        ['conv2d_67[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_62 (Activation)     (None, None, None,   0           ['batch_normalization_62[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_67 (Activation)     (None, None, None,   0           ['batch_normalization_67[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " average_pooling2d_6 (AveragePo  (None, None, None,   0          ['mixed6[0][0]']                 \n",
      " oling2D)                       768)                                                              \n",
      "                                                                                                  \n",
      " conv2d_60 (Conv2D)             (None, None, None,   147456      ['mixed6[0][0]']                 \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_63 (Conv2D)             (None, None, None,   258048      ['activation_62[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_68 (Conv2D)             (None, None, None,   258048      ['activation_67[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_69 (Conv2D)             (None, None, None,   147456      ['average_pooling2d_6[0][0]']    \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_60 (BatchN  (None, None, None,   576        ['conv2d_60[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_63 (BatchN  (None, None, None,   576        ['conv2d_63[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_68 (BatchN  (None, None, None,   576        ['conv2d_68[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_69 (BatchN  (None, None, None,   576        ['conv2d_69[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_60 (Activation)     (None, None, None,   0           ['batch_normalization_60[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_63 (Activation)     (None, None, None,   0           ['batch_normalization_63[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_68 (Activation)     (None, None, None,   0           ['batch_normalization_68[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_69 (Activation)     (None, None, None,   0           ['batch_normalization_69[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " mixed7 (Concatenate)           (None, None, None,   0           ['activation_60[0][0]',          \n",
      "                                768)                              'activation_63[0][0]',          \n",
      "                                                                  'activation_68[0][0]',          \n",
      "                                                                  'activation_69[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_72 (Conv2D)             (None, None, None,   147456      ['mixed7[0][0]']                 \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_72 (BatchN  (None, None, None,   576        ['conv2d_72[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_72 (Activation)     (None, None, None,   0           ['batch_normalization_72[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_73 (Conv2D)             (None, None, None,   258048      ['activation_72[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_73 (BatchN  (None, None, None,   576        ['conv2d_73[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_73 (Activation)     (None, None, None,   0           ['batch_normalization_73[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_70 (Conv2D)             (None, None, None,   147456      ['mixed7[0][0]']                 \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_74 (Conv2D)             (None, None, None,   258048      ['activation_73[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_70 (BatchN  (None, None, None,   576        ['conv2d_70[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_74 (BatchN  (None, None, None,   576        ['conv2d_74[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_70 (Activation)     (None, None, None,   0           ['batch_normalization_70[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " activation_74 (Activation)     (None, None, None,   0           ['batch_normalization_74[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " conv2d_71 (Conv2D)             (None, None, None,   552960      ['activation_70[0][0]']          \n",
      "                                320)                                                              \n",
      "                                                                                                  \n",
      " conv2d_75 (Conv2D)             (None, None, None,   331776      ['activation_74[0][0]']          \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_71 (BatchN  (None, None, None,   960        ['conv2d_71[0][0]']              \n",
      " ormalization)                  320)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_75 (BatchN  (None, None, None,   576        ['conv2d_75[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_71 (Activation)     (None, None, None,   0           ['batch_normalization_71[0][0]'] \n",
      "                                320)                                                              \n",
      "                                                                                                  \n",
      " activation_75 (Activation)     (None, None, None,   0           ['batch_normalization_75[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " max_pooling2d_3 (MaxPooling2D)  (None, None, None,   0          ['mixed7[0][0]']                 \n",
      "                                768)                                                              \n",
      "                                                                                                  \n",
      " mixed8 (Concatenate)           (None, None, None,   0           ['activation_71[0][0]',          \n",
      "                                1280)                             'activation_75[0][0]',          \n",
      "                                                                  'max_pooling2d_3[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_80 (Conv2D)             (None, None, None,   573440      ['mixed8[0][0]']                 \n",
      "                                448)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_80 (BatchN  (None, None, None,   1344       ['conv2d_80[0][0]']              \n",
      " ormalization)                  448)                                                              \n",
      "                                                                                                  \n",
      " activation_80 (Activation)     (None, None, None,   0           ['batch_normalization_80[0][0]'] \n",
      "                                448)                                                              \n",
      "                                                                                                  \n",
      " conv2d_77 (Conv2D)             (None, None, None,   491520      ['mixed8[0][0]']                 \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " conv2d_81 (Conv2D)             (None, None, None,   1548288     ['activation_80[0][0]']          \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_77 (BatchN  (None, None, None,   1152       ['conv2d_77[0][0]']              \n",
      " ormalization)                  384)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_81 (BatchN  (None, None, None,   1152       ['conv2d_81[0][0]']              \n",
      " ormalization)                  384)                                                              \n",
      "                                                                                                  \n",
      " activation_77 (Activation)     (None, None, None,   0           ['batch_normalization_77[0][0]'] \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " activation_81 (Activation)     (None, None, None,   0           ['batch_normalization_81[0][0]'] \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " conv2d_78 (Conv2D)             (None, None, None,   442368      ['activation_77[0][0]']          \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " conv2d_79 (Conv2D)             (None, None, None,   442368      ['activation_77[0][0]']          \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " conv2d_82 (Conv2D)             (None, None, None,   442368      ['activation_81[0][0]']          \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " conv2d_83 (Conv2D)             (None, None, None,   442368      ['activation_81[0][0]']          \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " average_pooling2d_7 (AveragePo  (None, None, None,   0          ['mixed8[0][0]']                 \n",
      " oling2D)                       1280)                                                             \n",
      "                                                                                                  \n",
      " conv2d_76 (Conv2D)             (None, None, None,   409600      ['mixed8[0][0]']                 \n",
      "                                320)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_78 (BatchN  (None, None, None,   1152       ['conv2d_78[0][0]']              \n",
      " ormalization)                  384)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_79 (BatchN  (None, None, None,   1152       ['conv2d_79[0][0]']              \n",
      " ormalization)                  384)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_82 (BatchN  (None, None, None,   1152       ['conv2d_82[0][0]']              \n",
      " ormalization)                  384)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_83 (BatchN  (None, None, None,   1152       ['conv2d_83[0][0]']              \n",
      " ormalization)                  384)                                                              \n",
      "                                                                                                  \n",
      " conv2d_84 (Conv2D)             (None, None, None,   245760      ['average_pooling2d_7[0][0]']    \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_76 (BatchN  (None, None, None,   960        ['conv2d_76[0][0]']              \n",
      " ormalization)                  320)                                                              \n",
      "                                                                                                  \n",
      " activation_78 (Activation)     (None, None, None,   0           ['batch_normalization_78[0][0]'] \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " activation_79 (Activation)     (None, None, None,   0           ['batch_normalization_79[0][0]'] \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " activation_82 (Activation)     (None, None, None,   0           ['batch_normalization_82[0][0]'] \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " activation_83 (Activation)     (None, None, None,   0           ['batch_normalization_83[0][0]'] \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_84 (BatchN  (None, None, None,   576        ['conv2d_84[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_76 (Activation)     (None, None, None,   0           ['batch_normalization_76[0][0]'] \n",
      "                                320)                                                              \n",
      "                                                                                                  \n",
      " mixed9_0 (Concatenate)         (None, None, None,   0           ['activation_78[0][0]',          \n",
      "                                768)                              'activation_79[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, None, None,   0           ['activation_82[0][0]',          \n",
      "                                768)                              'activation_83[0][0]']          \n",
      "                                                                                                  \n",
      " activation_84 (Activation)     (None, None, None,   0           ['batch_normalization_84[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " mixed9 (Concatenate)           (None, None, None,   0           ['activation_76[0][0]',          \n",
      "                                2048)                             'mixed9_0[0][0]',               \n",
      "                                                                  'concatenate[0][0]',            \n",
      "                                                                  'activation_84[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_89 (Conv2D)             (None, None, None,   917504      ['mixed9[0][0]']                 \n",
      "                                448)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_89 (BatchN  (None, None, None,   1344       ['conv2d_89[0][0]']              \n",
      " ormalization)                  448)                                                              \n",
      "                                                                                                  \n",
      " activation_89 (Activation)     (None, None, None,   0           ['batch_normalization_89[0][0]'] \n",
      "                                448)                                                              \n",
      "                                                                                                  \n",
      " conv2d_86 (Conv2D)             (None, None, None,   786432      ['mixed9[0][0]']                 \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " conv2d_90 (Conv2D)             (None, None, None,   1548288     ['activation_89[0][0]']          \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_86 (BatchN  (None, None, None,   1152       ['conv2d_86[0][0]']              \n",
      " ormalization)                  384)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_90 (BatchN  (None, None, None,   1152       ['conv2d_90[0][0]']              \n",
      " ormalization)                  384)                                                              \n",
      "                                                                                                  \n",
      " activation_86 (Activation)     (None, None, None,   0           ['batch_normalization_86[0][0]'] \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " activation_90 (Activation)     (None, None, None,   0           ['batch_normalization_90[0][0]'] \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " conv2d_87 (Conv2D)             (None, None, None,   442368      ['activation_86[0][0]']          \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " conv2d_88 (Conv2D)             (None, None, None,   442368      ['activation_86[0][0]']          \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " conv2d_91 (Conv2D)             (None, None, None,   442368      ['activation_90[0][0]']          \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " conv2d_92 (Conv2D)             (None, None, None,   442368      ['activation_90[0][0]']          \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " average_pooling2d_8 (AveragePo  (None, None, None,   0          ['mixed9[0][0]']                 \n",
      " oling2D)                       2048)                                                             \n",
      "                                                                                                  \n",
      " conv2d_85 (Conv2D)             (None, None, None,   655360      ['mixed9[0][0]']                 \n",
      "                                320)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_87 (BatchN  (None, None, None,   1152       ['conv2d_87[0][0]']              \n",
      " ormalization)                  384)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_88 (BatchN  (None, None, None,   1152       ['conv2d_88[0][0]']              \n",
      " ormalization)                  384)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_91 (BatchN  (None, None, None,   1152       ['conv2d_91[0][0]']              \n",
      " ormalization)                  384)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_92 (BatchN  (None, None, None,   1152       ['conv2d_92[0][0]']              \n",
      " ormalization)                  384)                                                              \n",
      "                                                                                                  \n",
      " conv2d_93 (Conv2D)             (None, None, None,   393216      ['average_pooling2d_8[0][0]']    \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_85 (BatchN  (None, None, None,   960        ['conv2d_85[0][0]']              \n",
      " ormalization)                  320)                                                              \n",
      "                                                                                                  \n",
      " activation_87 (Activation)     (None, None, None,   0           ['batch_normalization_87[0][0]'] \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " activation_88 (Activation)     (None, None, None,   0           ['batch_normalization_88[0][0]'] \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " activation_91 (Activation)     (None, None, None,   0           ['batch_normalization_91[0][0]'] \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " activation_92 (Activation)     (None, None, None,   0           ['batch_normalization_92[0][0]'] \n",
      "                                384)                                                              \n",
      "                                                                                                  \n",
      " batch_normalization_93 (BatchN  (None, None, None,   576        ['conv2d_93[0][0]']              \n",
      " ormalization)                  192)                                                              \n",
      "                                                                                                  \n",
      " activation_85 (Activation)     (None, None, None,   0           ['batch_normalization_85[0][0]'] \n",
      "                                320)                                                              \n",
      "                                                                                                  \n",
      " mixed9_1 (Concatenate)         (None, None, None,   0           ['activation_87[0][0]',          \n",
      "                                768)                              'activation_88[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate)    (None, None, None,   0           ['activation_91[0][0]',          \n",
      "                                768)                              'activation_92[0][0]']          \n",
      "                                                                                                  \n",
      " activation_93 (Activation)     (None, None, None,   0           ['batch_normalization_93[0][0]'] \n",
      "                                192)                                                              \n",
      "                                                                                                  \n",
      " mixed10 (Concatenate)          (None, None, None,   0           ['activation_85[0][0]',          \n",
      "                                2048)                             'mixed9_1[0][0]',               \n",
      "                                                                  'concatenate_1[0][0]',          \n",
      "                                                                  'activation_93[0][0]']          \n",
      "                                                                                                  \n",
      " decoder_stage0_upsampling (UpS  (None, None, None,   0          ['mixed10[0][0]']                \n",
      " ampling2D)                     2048)                                                             \n",
      "                                                                                                  \n",
      " decoder_stage0_concat (Concate  (None, None, None,   0          ['decoder_stage0_upsampling[0][0]\n",
      " nate)                          2816)                            ',                               \n",
      "                                                                  'mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " decoder_stage0a_conv (Conv2D)  (None, None, None,   6488064     ['decoder_stage0_concat[0][0]']  \n",
      "                                256)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage0a_bn (BatchNorma  (None, None, None,   1024       ['decoder_stage0a_conv[0][0]']   \n",
      " lization)                      256)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage0a_relu (Activati  (None, None, None,   0          ['decoder_stage0a_bn[0][0]']     \n",
      " on)                            256)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage0b_conv (Conv2D)  (None, None, None,   589824      ['decoder_stage0a_relu[0][0]']   \n",
      "                                256)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage0b_bn (BatchNorma  (None, None, None,   1024       ['decoder_stage0b_conv[0][0]']   \n",
      " lization)                      256)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage0b_relu (Activati  (None, None, None,   0          ['decoder_stage0b_bn[0][0]']     \n",
      " on)                            256)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage1_upsampling (UpS  (None, None, None,   0          ['decoder_stage0b_relu[0][0]']   \n",
      " ampling2D)                     256)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage1_concat (Concate  (None, None, None,   0          ['decoder_stage1_upsampling[0][0]\n",
      " nate)                          544)                             ',                               \n",
      "                                                                  'mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " decoder_stage1a_conv (Conv2D)  (None, None, None,   626688      ['decoder_stage1_concat[0][0]']  \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage1a_bn (BatchNorma  (None, None, None,   512        ['decoder_stage1a_conv[0][0]']   \n",
      " lization)                      128)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage1a_relu (Activati  (None, None, None,   0          ['decoder_stage1a_bn[0][0]']     \n",
      " on)                            128)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage1b_conv (Conv2D)  (None, None, None,   147456      ['decoder_stage1a_relu[0][0]']   \n",
      "                                128)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage1b_bn (BatchNorma  (None, None, None,   512        ['decoder_stage1b_conv[0][0]']   \n",
      " lization)                      128)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage1b_relu (Activati  (None, None, None,   0          ['decoder_stage1b_bn[0][0]']     \n",
      " on)                            128)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage2_upsampling (UpS  (None, None, None,   0          ['decoder_stage1b_relu[0][0]']   \n",
      " ampling2D)                     128)                                                              \n",
      "                                                                                                  \n",
      " decoder_stage2_concat (Concate  (None, None, None,   0          ['decoder_stage2_upsampling[0][0]\n",
      " nate)                          320)                             ',                               \n",
      "                                                                  'activation_4[0][0]']           \n",
      "                                                                                                  \n",
      " decoder_stage2a_conv (Conv2D)  (None, None, None,   184320      ['decoder_stage2_concat[0][0]']  \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage2a_bn (BatchNorma  (None, None, None,   256        ['decoder_stage2a_conv[0][0]']   \n",
      " lization)                      64)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage2a_relu (Activati  (None, None, None,   0          ['decoder_stage2a_bn[0][0]']     \n",
      " on)                            64)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage2b_conv (Conv2D)  (None, None, None,   36864       ['decoder_stage2a_relu[0][0]']   \n",
      "                                64)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage2b_bn (BatchNorma  (None, None, None,   256        ['decoder_stage2b_conv[0][0]']   \n",
      " lization)                      64)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage2b_relu (Activati  (None, None, None,   0          ['decoder_stage2b_bn[0][0]']     \n",
      " on)                            64)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage3_upsampling (UpS  (None, None, None,   0          ['decoder_stage2b_relu[0][0]']   \n",
      " ampling2D)                     64)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage3_concat (Concate  (None, None, None,   0          ['decoder_stage3_upsampling[0][0]\n",
      " nate)                          128)                             ',                               \n",
      "                                                                  'activation_2[0][0]']           \n",
      "                                                                                                  \n",
      " decoder_stage3a_conv (Conv2D)  (None, None, None,   36864       ['decoder_stage3_concat[0][0]']  \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage3a_bn (BatchNorma  (None, None, None,   128        ['decoder_stage3a_conv[0][0]']   \n",
      " lization)                      32)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage3a_relu (Activati  (None, None, None,   0          ['decoder_stage3a_bn[0][0]']     \n",
      " on)                            32)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage3b_conv (Conv2D)  (None, None, None,   9216        ['decoder_stage3a_relu[0][0]']   \n",
      "                                32)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage3b_bn (BatchNorma  (None, None, None,   128        ['decoder_stage3b_conv[0][0]']   \n",
      " lization)                      32)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage3b_relu (Activati  (None, None, None,   0          ['decoder_stage3b_bn[0][0]']     \n",
      " on)                            32)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage4_upsampling (UpS  (None, None, None,   0          ['decoder_stage3b_relu[0][0]']   \n",
      " ampling2D)                     32)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage4a_conv (Conv2D)  (None, None, None,   4608        ['decoder_stage4_upsampling[0][0]\n",
      "                                16)                              ']                               \n",
      "                                                                                                  \n",
      " decoder_stage4a_bn (BatchNorma  (None, None, None,   64         ['decoder_stage4a_conv[0][0]']   \n",
      " lization)                      16)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage4a_relu (Activati  (None, None, None,   0          ['decoder_stage4a_bn[0][0]']     \n",
      " on)                            16)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage4b_conv (Conv2D)  (None, None, None,   2304        ['decoder_stage4a_relu[0][0]']   \n",
      "                                16)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage4b_bn (BatchNorma  (None, None, None,   64         ['decoder_stage4b_conv[0][0]']   \n",
      " lization)                      16)                                                               \n",
      "                                                                                                  \n",
      " decoder_stage4b_relu (Activati  (None, None, None,   0          ['decoder_stage4b_bn[0][0]']     \n",
      " on)                            16)                                                               \n",
      "                                                                                                  \n",
      " final_conv (Conv2D)            (None, None, None,   145         ['decoder_stage4b_relu[0][0]']   \n",
      "                                1)                                                                \n",
      "                                                                                                  \n",
      " sigmoid (Activation)           (None, None, None,   0           ['final_conv[0][0]']             \n",
      "                                1)                                                                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 29,933,105\n",
      "Trainable params: 8,145,553\n",
      "Non-trainable params: 21,787,552\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# define the model\n",
    "metrics = [sm.metrics.IOUScore(threshold=0.5), 'mse']\n",
    "model = sm.Unet(BACKBONE, encoder_weights='imagenet', encoder_freeze = True, classes=1)\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=metrics)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1051, in train_function  *\n        return step_function(self, iterator)\n    File \"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1040, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1030, in run_step  **\n        outputs = model.train_step(data)\n    File \"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 889, in train_step\n        y_pred = self(x, training=True)\n    File \"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\backend.py\", line 3313, in concatenate\n        return tf.concat([to_dense(x) for x in tensors], axis)\n\n    ValueError: Exception encountered when calling layer \"decoder_stage1_concat\" (type Concatenate).\n    \n    Dimension 1 in both shapes must be equal, but are 136 and 135. Shapes are [?,136,240] and [?,135,240]. for '{{node model/decoder_stage1_concat/concat}} = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32](model/decoder_stage1_upsampling/resize/ResizeNearestNeighbor, model/mixed2/concat, model/decoder_stage1_concat/concat/axis)' with input shapes: [?,136,240,256], [?,135,240,288], [] and with computed input tensors: input[2] = <3>.\n    \n    Call arguments received by layer \"decoder_stage1_concat\" (type Concatenate):\n      • inputs=['tf.Tensor(shape=(None, 136, 240, 256), dtype=float32)', 'tf.Tensor(shape=(None, 135, 240, 288), dtype=float32)']\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m history \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mfit(    X_train,\n\u001b[0;32m      2\u001b[0m                         y_train,\n\u001b[0;32m      3\u001b[0m                         batch_size\u001b[39m=\u001b[39;49m\u001b[39m15\u001b[39;49m,\n\u001b[0;32m      4\u001b[0m                         epochs\u001b[39m=\u001b[39;49m\u001b[39m2\u001b[39;49m,\n\u001b[0;32m      5\u001b[0m                         verbose\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m,\n\u001b[0;32m      6\u001b[0m                         validation_data\u001b[39m=\u001b[39;49m(X_test,y_test))\n",
      "File \u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py:67\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:  \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[0;32m     66\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n\u001b[1;32m---> 67\u001b[0m   \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mwith_traceback(filtered_tb) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n\u001b[0;32m     68\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     69\u001b[0m   \u001b[39mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32m~\\AppData\\Local\\Temp\\__autograph_generated_filesmi6ffhg.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m     14\u001b[0m     do_return \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m---> 15\u001b[0m     retval_ \u001b[39m=\u001b[39m ag__\u001b[39m.\u001b[39mconverted_call(ag__\u001b[39m.\u001b[39mld(step_function), (ag__\u001b[39m.\u001b[39mld(\u001b[39mself\u001b[39m), ag__\u001b[39m.\u001b[39mld(iterator)), \u001b[39mNone\u001b[39;00m, fscope)\n\u001b[0;32m     16\u001b[0m \u001b[39mexcept\u001b[39;00m:\n\u001b[0;32m     17\u001b[0m     do_return \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1051, in train_function  *\n        return step_function(self, iterator)\n    File \"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1040, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1030, in run_step  **\n        outputs = model.train_step(data)\n    File \"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 889, in train_step\n        y_pred = self(x, training=True)\n    File \"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\backend.py\", line 3313, in concatenate\n        return tf.concat([to_dense(x) for x in tensors], axis)\n\n    ValueError: Exception encountered when calling layer \"decoder_stage1_concat\" (type Concatenate).\n    \n    Dimension 1 in both shapes must be equal, but are 136 and 135. Shapes are [?,136,240] and [?,135,240]. for '{{node model/decoder_stage1_concat/concat}} = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32](model/decoder_stage1_upsampling/resize/ResizeNearestNeighbor, model/mixed2/concat, model/decoder_stage1_concat/concat/axis)' with input shapes: [?,136,240,256], [?,135,240,288], [] and with computed input tensors: input[2] = <3>.\n    \n    Call arguments received by layer \"decoder_stage1_concat\" (type Concatenate):\n      • inputs=['tf.Tensor(shape=(None, 136, 240, 256), dtype=float32)', 'tf.Tensor(shape=(None, 135, 240, 288), dtype=float32)']\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(    X_train,\n",
    "                        y_train,\n",
    "                        batch_size=15,\n",
    "                        epochs=2,\n",
    "                        verbose=1,\n",
    "                        validation_data=(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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