Main model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imports done\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "from tensorflow.keras.models import Model \n",
    "from tensorflow.keras.layers import Conv2D, Input, Conv2DTranspose, MaxPooling2D, concatenate, BatchNormalization, Activation, Add, Dropout, DepthwiseConv2D, Flatten, Dense\n",
    "from skimage.transform import resize\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2\n",
    "\n",
    "from tensorflow import keras\n",
    "import tensorflow.keras.backend as K\n",
    "import gc\n",
    "from tensorflow.keras import optimizers\n",
    "from tqdm.notebook import tqdm_notebook\n",
    "\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "import seaborn as sns\n",
    "\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint,  CSVLogger, EarlyStopping, ReduceLROnPlateau\n",
    "\n",
    "\n",
    "print(\"Imports done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.4.1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "savePath=\"./Test_1_checkpoints/saved-{epoch:02d}-{val_my_iou_metric:.2f}.hdf5\"\n",
    "\n",
    "checkPoint = ModelCheckpoint(savePath, monitor='val_my_iou_metric', verbose=1, save_best_only= True, mode='max')\n",
    "early_stop = EarlyStopping(monitor='val_my_iou_metric', patience=15, verbose=1, mode='max')\n",
    "reduce_lr_OnPlateau = ReduceLROnPlateau(factor=0.75,monitor='val_my_iou_metric', mode='max', patience=5, min_lr=0.0001, verbose=1)\n",
    "\n",
    "#CSVLogger logs epoch, acc, loss, val_acc, val_loss\n",
    "log_csv = CSVLogger('my_logs.csv', separator=',', append=False)\n",
    "callbacks_list = [early_stop, checkPoint, reduce_lr_OnPlateau]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading the data \n",
    "For faster development, we have fixed a validation dataset. For sanity checks and debuging we have the option to load just the first \"rows\" from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading images...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05d900ce70e248139b8b65380c8e6d01",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading masks...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c5017b6c96b4fb5af768e3acc3d8175",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done loading images\n"
     ]
    }
   ],
   "source": [
    "# ------------------------------------------------------------------------------- #\n",
    "#                              Important Variables                                #\n",
    "# ------------------------------------------------------------------------------- #\n",
    "np.random.seed(42)\n",
    "data_dir = \"../../DL_data/competition_data\"\n",
    "validation_data_dir = \"../../DL_data/competition_data\"\n",
    "nrows = \"all\"                # Set to 'all' to load the whole set\n",
    "load_validation = False      # Only load the validation images and masks??\n",
    "split_train_test = True      # Split data to train and test sets??\n",
    "data_augmentation = True     # Augment the data??\n",
    "# ------------------------------------------------------------------------------- #\n",
    "\n",
    "\n",
    "# load the ids  and depths of 'nrows' from the training data set\n",
    "if isinstance(nrows, int) and nrows > 0:\n",
    "    train_df = pd.read_csv(data_dir + \"/train.csv\", index_col=\"id\", usecols=[0], nrows=nrows)\n",
    "    depths_df = pd.read_csv(data_dir + \"/depths.csv\", index_col=\"id\")\n",
    "    train_df = train_df.join(depths_df)\n",
    "    test_df = depths_df[~depths_df.index.isin(train_df.index)]\n",
    "elif isinstance(nrows, str) and nrows.upper() == \"ALL\":\n",
    "    train_df = pd.read_csv(data_dir + \"/train.csv\", index_col=\"id\", usecols=[0])\n",
    "    depths_df = pd.read_csv(data_dir + \"/depths.csv\", index_col=\"id\")\n",
    "    train_df = train_df.join(depths_df)\n",
    "    test_df = depths_df[~depths_df.index.isin(train_df.index)]\n",
    "else:\n",
    "    raise ValueError(\"Invalid nrows value\")\n",
    "\n",
    "\n",
    "# Function that loads the ids of 'nrows' from the validation data set\n",
    "def load_validation_data(data_dir, nrows):\n",
    "    if isinstance(nrows, int) and nrows > 0:\n",
    "        valid_ids = pd.read_csv(data_dir + \"/Validation_ids.csv\", usecols=[1], nrows=nrows)\n",
    "    elif isinstance(nrows, str) and nrows.upper() == \"ALL\":\n",
    "        valid_ids = pd.read_csv(data_dir + \"/Validation_ids.csv\")\n",
    "    else:\n",
    "        raise ValueError(\"Invalid nrows value\")\n",
    "    return valid_ids.ids\n",
    "\n",
    "if load_validation:\n",
    "    ids_to_load = load_validation_data(data_dir, nrows)\n",
    "    index_list = list(train_df.index)\n",
    "    ids_list = [index_list.index(i) for i in ids_to_load]\n",
    "    train_df = train_df.iloc[ids_list]\n",
    "else:\n",
    "    ids_to_load = train_df.index\n",
    "    \n",
    "    \n",
    "\n",
    "print(\"Loading images...\")\n",
    "train_df[\"images\"] = [np.array(load_img(data_dir + \"/train/images/{}.png\".format(idx),\n",
    "                                        color_mode=\"grayscale\")) / 255 for idx in tqdm_notebook(list(ids_to_load))]\n",
    "print(\"Loading masks...\")\n",
    "train_df[\"masks\"] = [np.array(load_img(data_dir + \"/train/masks/{}.png\".format(idx),\n",
    "                                       color_mode=\"grayscale\")) / 65535 for idx in tqdm_notebook(list(ids_to_load))]\n",
    "\n",
    "print(\"done loading images\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print an example of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------\n",
      "id_index =  342\n",
      "[[0.53333333 0.53333333 0.52941176 ... 0.52156863 0.55294118 0.60392157]\n",
      " [0.55294118 0.54901961 0.54509804 ... 0.50196078 0.51372549 0.54117647]\n",
      " [0.57254902 0.56862745 0.56470588 ... 0.44313725 0.43921569 0.43529412]\n",
      " ...\n",
      " [0.49019608 0.4745098  0.47058824 ... 0.56862745 0.56470588 0.56078431]\n",
      " [0.48235294 0.4627451  0.44313725 ... 0.56470588 0.56078431 0.55294118]\n",
      " [0.47058824 0.45098039 0.43137255 ... 0.56078431 0.55294118 0.54117647]]\n",
      "(101, 101)\n",
      "-------------------------------------------------------------\n",
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n",
      "(101, 101)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# printing\n",
    "print(\"-------------------------------------------------------------\")\n",
    "id = '7d5c34a95a'\n",
    "id_index = np.where(train_df.index == id)\n",
    "id_index = id_index[0][0]\n",
    "print(\"id_index = \", id_index)\n",
    "image = train_df[\"images\"][id]\n",
    "print(image)\n",
    "print(image.shape)\n",
    "print(\"-------------------------------------------------------------\")\n",
    "mask = train_df[\"masks\"][id]\n",
    "print(mask)\n",
    "print(mask.shape)\n",
    "\n",
    "def plot_image_example():\n",
    "    fig_imgshow, (axs_mask, axs_img) = plt.subplots(1, 2)\n",
    "    fig_imgshow.suptitle(\"Data example\")\n",
    "    axs_img.imshow(np.dstack((image, image, image)))  # interpolation='nearest'\n",
    "    axs_img.set(title=\"image\")\n",
    "    tmp = np.squeeze(mask).astype(np.float32)\n",
    "    axs_mask.imshow(np.dstack((tmp, tmp, tmp)))\n",
    "    axs_mask.set(title=\"mask\")\n",
    "\n",
    "\n",
    "plot_image_example()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprossessing the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Resizeing the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# either pad with zeros or resize with interpolation\n",
    "resize_to = 128\n",
    "original_size = 101\n",
    "\n",
    "\n",
    "def upsample(original_img):\n",
    "    if resize_to == original_size:\n",
    "        return original_img\n",
    "    return resize(original_img, (resize_to, resize_to), mode='constant', preserve_range=True)\n",
    "\n",
    "\n",
    "def pad_zeros(array):\n",
    "    padded_image = np.zeros(shape=(resize_to, resize_to))\n",
    "    padded_image[13:114, 13:114] = array\n",
    "    return padded_image\n",
    "\n",
    "\n",
    "resizing_function_to_use = pad_zeros\n",
    "\n",
    "images_resized = np.array(train_df.images.map(resizing_function_to_use).tolist()).reshape((-1, resize_to, resize_to, 1))\n",
    "masks_resized = np.array(train_df.masks.map(resizing_function_to_use).tolist()).reshape((-1, resize_to, resize_to, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spliting to train/ validation/ test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the salt coverage for each immage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Coverage class')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qKqCGMa82v3OqamlVLZ0/f/6wZivNSG4PmosmE073t8N1tJ8PtPZ1wKKBfgtb26baJUl6ksmE0+XAyBV3JwKXDbSf0K7aOxh4uB3++xxweJJ57UKIw1ubJElPsv14OiW5EDgE2CPJWrqr7s4CLk5yEnAv8JrW/QrgaGAN8BjwBoCqeijJnwLXt37vqqrRF1lIkjS+cKqq4zcx6tAx+hZwyibmsxxYPu7qJElzkneIkCT1juEkSeodw0mS1DuGkySpdwwnSVLvGE6SpN4xnCRJvWM4SZJ6x3CSJPWO4SRJ6h3DSZLUO4aTJKl3DCdJUu8YTpKk3jGcJEm9M+FwSvLTSW4ceD2S5K1JzkiybqD96IFpTk+yJsntSY4YzkeQJM0243rY4Fiq6nbgAIAk2wHrgEvpnnz7vqp692D/JPsDxwHPA54F/FOS/arqiYnWIEmanYZ1WO9Q4M6qunczfY4BLqqqjVV1N91j3A8c0vIlSbPIsMLpOODCgfenJlmdZHmSea1tAXDfQJ+1re0pkixLsirJqg0bNgypRGlmcnvQXDTpcEqyA/CrwMdb09nAc+gO+a0H3rO186yqc6pqaVUtnT9//mRLlGY0twfNRcPYczoK+HJV3Q9QVfdX1RNV9QPgw/zo0N06YNHAdAtbmyRJTzKMcDqegUN6SfYaGPcq4OY2fDlwXJIdk+wLLAGuG8LyJUmzzISv1gNI8gzgvwBvHGj+30kOAAq4Z2RcVd2S5GLgVuBx4BSv1JMkjWVS4VRV3wF+YlTb6zbT/0zgzMksU5I0+3mHCElS7xhOkqTeMZwkSb1jOEmSesdwkiT1juEkSeodw0mS1DuGkySpdwwnSVLvGE6SpN4xnCRJvWM4SZJ6x3CSJPWO4SRJ6p1hPKb9niQ3JbkxyarWtnuSFUnuaD/ntfYkeX+SNUlWJ3nRZJcvSZp9hrXn9IqqOqCqlrb3pwFXVtUS4Mr2HrpHui9pr2XA2UNaviRpFtlWh/WOAc5rw+cBxw60n1+dlcBuox7rLknSUMKpgM8nuSHJsta2Z1Wtb8PfAPZswwuA+wamXdvaJEn6oUk9pr15WVWtS/KTwIokXxscWVWVpLZmhi3klgHsvffeQyhRmrncHjQXTXrPqarWtZ8PAJcCBwL3jxyuaz8faN3XAYsGJl/Y2kbP85yqWlpVS+fPnz/ZEqUZze1Bc9GkwinJM5I8c2QYOBy4GbgcOLF1OxG4rA1fDpzQrto7GHh44PCfJEnA5A/r7QlcmmRkXhdU1WeTXA9cnOQk4F7gNa3/FcDRwBrgMeANk1y+JGkWmlQ4VdVdwM+O0f4gcOgY7QWcMpllSpJmP+8QIUnqHcNJktQ7hpMkqXcMJ0lS7xhOkqTeMZwkSb1jOEmSesdwkiT1juEkSeodw0mS1DuGkySpdwwnSVLvGE6SpN4xnCRJvTOMx7RLmiUWn/bpSU1/z1mvHFIlmusmvOeUZFGSLya5NcktSX6vtZ+RZF2SG9vr6IFpTk+yJsntSY4YxgeQJM0+k9lzehx4e1V9uT2q/YYkK9q491XVuwc7J9kfOA54HvAs4J+S7FdVT0yiBknSLDThPaeqWl9VX27D3wZuAxZsZpJjgIuqamNV3U33qPYDJ7p8SdLsNZQLIpIsBl4IXNuaTk2yOsnyJPNa2wLgvoHJ1rKJMEuyLMmqJKs2bNgwjBKlGcvtQXPRpC+ISLIzcAnw1qp6JMnZwJ8C1X6+B/jtrZlnVZ0DnAOwdOnSmmyN0kzm9qCp1JeLYiYVTkmeRhdMH62qTwJU1f0D4z8MfKq9XQcsGph8YWuTpKHpyy9XTc6EwylJgI8At1XVewfa96qq9e3tq4Cb2/DlwAVJ3kt3QcQS4LqJLl9S/xgMGpbJ7Dm9FHgdcFOSG1vbHwHHJzmA7rDePcAbAarqliQXA7fSXel3ilfqSeobA7YfJhxOVXUNkDFGXbGZac4EzpzoMiVJc4N3iJDUG5Pda9HsYThJ0hB5WHA45nw4bemL5BdFfkekqTfrw8nDBJLmktnyO2/Wh5MkzSSzJVwmy+c5SZJ6xz2nLZjp5xtmev2S5ibDaRszHCRp6xlOkzTdx4e39fINV0nTwXCaZrM93CRpIgwnTcowwm2ye18GrDT7GE6adoaLpNG8lFyS1DuGkySpdwwnSVLvTHk4JTkyye1J1iQ5baqXL0nqvykNpyTbAR8EjgL2p3tq7v5TWYMkqf+mes/pQGBNVd1VVf8BXAQcM8U1SJJ6bqovJV8A3Dfwfi1w0OhOSZYBy9rbR5Pcvpl57gH8+9AqHD7rm7xe15i/2GJ9+0xq/m4PU63vNfa6vmFtD738P6eqOgc4Zzx9k6yqqqXbuKQJs77J63uN27o+t4ep1fca50p9U31Ybx2waOD9wtYmSdIPTXU4XQ8sSbJvkh2A44DLp7gGSVLPTelhvap6PMmpwOeA7YDlVXXLJGc7rsMd08j6Jq/vNfapvj7VMpa+1wf9r3FO1JeqGsZ8JEkaGu8QIUnqHcNJktQ7vQ2nLd3mKMmOST7Wxl+bZPHAuNNb++1Jjpim+t6W5NYkq5NcmWSfgXFPJLmxvbbZBSHjqPH1STYM1PLfBsadmOSO9jpxmup730BtX0/yrYFx23wdJlme5IEkN29ifJK8v9W/OsmLBsYNff25TWzz+qZ1exhnjdO2TUz59lBVvXvRXSxxJ/BsYAfgq8D+o/q8GfhQGz4O+Fgb3r/13xHYt81nu2mo7xXAj7fhN43U194/2pN1+HrgA2NMuztwV/s5rw3Pm+r6RvV/C90FNFO5Dn8BeBFw8ybGHw18BghwMHDttlp/bhOze3sYb42j+k/pNjHV20Nf95zGc5ujY4Dz2vAngEOTpLVfVFUbq+puYE2b35TWV1VfrKrH2tuVdP/TNZUmc6uoI4AVVfVQVX0TWAEcOc31HQ9cOOQaNquqrgYe2kyXY4Dzq7MS2C3JXmyb9ec2sY3r24yp2B4mUuOUbhNTvT30NZzGus3Rgk31qarHgYeBnxjntFNR36CT6P6iGLFTklVJViY5dsi1jRhvjb/edsE/kWTkH6R7tQ7b4Z99gS8MNE/FOtySTX2GbbH+3Campr7p2h62ajk93SaGuj308vZFs0mS3wKWAr840LxPVa1L8mzgC0luqqo7p6G8fwQurKqNSd5I91f3L01DHVtyHPCJqnpioK0v61BbqcfbxEzZHmAObBN93XMaz22OftgnyfbArsCD45x2KuojyWHAO4BfraqNI+1Vta79vAu4CnjhkOsbV41V9eBAXX8LvHi8005FfQOOY9Thiylah1uyqc+wLdaf28Q2rm+at4etXU4ft4nhbg/b8gTaRF90e3R30e22jpwYfN6oPqfw5JO/F7fh5/Hkk793MfyTv+Op74V0JzeXjGqfB+zYhvcA7mAzJz23cY17DQy/ClhZPzqBeXerdV4b3n2q62v9ngvcQ/uH8alch23+i9n0CeBX8uQTwNdtq/XnNjG7t4eZsk1M5fYw1MKHvBKOBr7evszvaG3vovuLC2An4ON0J3evA549MO072nS3A0dNU33/BNwP3Nhel7f2nwdual+8m4CTpnEd/jlwS6vli8BzB6b97bZu1wBvmI762vszgLNGTTcl65DuL9P1wPfpjpOfBJwMnNzGh+7hmXe2OpZuy/XnNjG7t4e+bxNTvT14+yJJUu/09ZyTJGkOM5wkSb1jOEmSesdwkiT1juEkSeodw2mGSPJTSS5KcmeSG5JckWS/6a5LmozZ/r1OclWSpdNdx0xkOM0A7eadlwJXVdVzqurFwOnAnttgWd7SSlNiKr/XbXl+t2cQw2lmeAXw/ar60EhDVX0VuCbJXya5OclNSV4L0P4SfeVI3yTnJnl1ku1a/+vbzS3f2MYfkuSf2zNgbm1t/9D+kr0lybKBeZ3UniNzXZIPJ/lAa5+f5JI27+uTvHRK1oxmsjG/11X1z+3ZQDPmu92W/+5W7+okbxmjz9ntxqy3JPmTgfaz8qPnXL27tf1Gm9dXk1w9+VU9A22r/3T2NdT/zP5d4H1jtP863e3nt6P7a/Nfgb3obr1yXuuzA90dgZ8OLAP+uLXvCKyiu1XKIcB3gH0H5r17+/l04Ga6u1s/i+62KbsDTwP+mfb8G+AC4GVteG/gtuleb776/drU97qNm1HfbbrnU30C2H7UMq6i3SlhoG271v6Ctuzb4Yc3RNit/bwJWDDYNtde7ubObC+ju4vyE8D9Sb4EvITu/lZ/nWRHuuemXF1V301yOPCCJK9u0+8KLAH+g+4+WHcPzPt3k7yqDS9q/X4K+FJVPQSQ5OPAyPmBw4D9uyM1AOySZOeqenT4H1tzwEz7bh9Gd1/DxwFG5jPKa9qe2vZ0Qbs/3d7c94CPJPkU8KnW91+Ac5NcDHxynOtsVjGcZoZbgFdvsVdTVd9LchXdQ75eS/fQMujuffWWqvrcYP8kh9D9dTn4/jDg56rqsTavnbaw2B8DDq6q7423Ts15W/W9hpn73U6yL/DfgZdU1TeTnAvsVFWPJzkQOJRuXZwK/FJVnZzkILqbqd6Q5MVV9eBElz8Tec5pZvgCsOOo4+MvAL4FvLYd755P9xjl61qXjwFvAF4OfLa1fQ54U5KntXnsl+QZYyxvV+CbbeN9Lt0dhgGuB34xybx2cvnXB6b5PN1jo0fqO2ASn1dzw5jf6yQvpzusNpO+2yuAN7ZpSbL7qPG70IXkw0n2BI5q/XYGdq2qK4DfB362tT+nqq6tqv8FbODJj5yYE9xzmgGqqtphiL9K8j/oDgPcA7wV2JnuTsQF/GFVfaNN9nng74DLqnvkM3TPqFkMfDndMYoNwLFjLPKzwMlJbqM7Hr6y1bEuyZ/R/ZJ4CPga3dNWoTt/8MEkq+m+V1fT3bFYGtMWvtfXAD/HzPlu/y3dYcDVSb4PfBj4wMBn/WqSr7T53kd32A7gmcBlSXai2/t7W2v/yyRLWtuVbT3MKd6VXFtl5Fh7+wvxUmB5VV063XVJk+V3u188rKetdUaSG+mucrob+IdprUYaHr/bPeKekySpd9xzkiT1juEkSeodw0mS1DuGkySpdwwnSVLv/H80F+hldY0o3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate salt coverage\n",
    "train_df[\"coverage\"] = train_df.masks.map(np.sum) / (101*101)\n",
    "\n",
    "\n",
    "# Generate salt coverage classes\n",
    "def cov_to_class(val):\n",
    "    for i in range(0, 11):\n",
    "        if val * 10 <= i:\n",
    "            return i\n",
    "\n",
    "#map salt coverage to salt coverage class\n",
    "train_df[\"coverage_class\"] = train_df.coverage.map(cov_to_class)\n",
    "\n",
    "\n",
    "# Plotting the salt coverage classes\n",
    "fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)\n",
    "n_bins = 20\n",
    "axs[0].hist(train_df.coverage, bins=n_bins)\n",
    "axs[1].hist(train_df.coverage, bins=10)\n",
    "\n",
    "plt.suptitle(\"Salt coverage\")\n",
    "axs[0].set_xlabel(\"Coverage\")\n",
    "axs[1].set_xlabel(\"Coverage class\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Take a test set using stratified sampling based on the salt coverage.\n",
    "Sample uniformly for each salt coverage class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train/ Test shape = 3500/ 500\n"
     ]
    }
   ],
   "source": [
    "if split_train_test:\n",
    "    (ids_trainAndValid, ids_test, x_trainAndValid, x_test, y_trainAndValid, y_test) = train_test_split(\n",
    "                                                                      train_df.index.values,\n",
    "                                                                      images_resized, masks_resized,\n",
    "                                                                      test_size=500,\n",
    "                                                                      stratify=train_df.coverage_class,\n",
    "                                                                      random_state=1337)\n",
    "else: # for the case that we only load a validation test or for debuging\n",
    "    x_trainAndValid = images_resized\n",
    "    y_trainAndValid = masks_resized\n",
    "    ids_trainAndValid = train_df.index.values\n",
    "    x_test = np.array([])  # Just to print the x_valid.shape([0]) in the end\n",
    "    y_test = np.array([])\n",
    "\n",
    "\n",
    "print(\"Train/ Test shape = %d/ %d\" % (y_trainAndValid.shape[0], y_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Recompute the coverage class of the remaining training set \n",
    "We fo that to take an additional validation set from the training set, again stratified based on the salt coverage class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Coverage class')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a temp data frame to use for spliting training and validation\n",
    "train_temp_df=pd.DataFrame(data={\"images\": list(x_trainAndValid), \"masks\":list(y_trainAndValid)}, index=list(ids_trainAndValid))\n",
    "\n",
    "train_temp_df[\"coverage\"] = train_temp_df.masks.map(np.sum) / (101*101)\n",
    "\n",
    "train_temp_df[\"coverage_class\"] = train_temp_df.coverage.map(cov_to_class)\n",
    "\n",
    "# plotting the re-computed coverage class of the remaining training set\n",
    "fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)\n",
    "n_bins = 20\n",
    "axs[0].hist(train_df.coverage, bins=n_bins)\n",
    "axs[1].hist(train_df.coverage, bins=10)\n",
    "\n",
    "plt.suptitle(\"Salt coverage\")\n",
    "axs[0].set_xlabel(\"Coverage\")\n",
    "axs[1].set_xlabel(\"Coverage class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_images = np.array(train_temp_df.images.tolist()).reshape((-1, resize_to, resize_to, 1))\n",
    "temp_masks =  np.array(train_temp_df.masks.tolist()).reshape((-1, resize_to, resize_to, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train/ Valid/ Test split = 3200/ 300/ 500\n"
     ]
    }
   ],
   "source": [
    "if split_train_test:\n",
    "    (x_train, x_valid, y_train, y_valid) = train_test_split(temp_images, temp_masks, \n",
    "                                                            test_size=300,\n",
    "                                                            stratify=train_temp_df[\"coverage_class\"],\n",
    "                                                            random_state=1337)\n",
    "else:\n",
    "    x_train = temp_images\n",
    "    y_train = temp_masks\n",
    "    x_valid = np.array([])  # Just to print the x_valid.shape([0]) in the end\n",
    "    y_valid = np.array([])\n",
    "\n",
    "\n",
    "print(\"Train/ Valid/ Test split = %d/ %d/ %d\" % (x_train.shape[0], x_valid.shape[0], x_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4987"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cleaning up\n",
    "del x_trainAndValid\n",
    "del y_trainAndValid\n",
    "del temp_images\n",
    "del temp_masks\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data augmentation\n",
    "##### Fliping the images from left to right and from up to down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fliping left/right\n",
      "fliping up/down\n",
      "x_train shape:  (9600, 128, 128, 1)\n",
      "y_train shape:  (9600, 128, 128, 1)\n"
     ]
    }
   ],
   "source": [
    "# We use the copies to avoid fliping up and down images that have been fliped left to right\n",
    "x_train_temp = np.copy(x_train)\n",
    "y_train_temp = np.copy(y_train)\n",
    "\n",
    "if data_augmentation:\n",
    "    print(\"fliping left/right\")\n",
    "    x_train = np.append(x_train, [np.fliplr(x) for x in (x_train_temp)], axis=0)\n",
    "    y_train = np.append(y_train, [np.fliplr(x) for x in (y_train_temp)], axis=0)\n",
    "    \n",
    "    print(\"fliping up/down\")\n",
    "    x_train = np.append(x_train, [np.flipud(x) for x in (x_train_temp)], axis=0)\n",
    "    y_train = np.append(y_train, [np.flipud(x) for x in (y_train_temp)], axis=0)\n",
    "    \n",
    "del x_train_temp\n",
    "del y_train_temp\n",
    "gc.collect()\n",
    "\n",
    "print(\"x_train shape: \", x_train.shape)\n",
    "print(\"y_train shape: \", y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating 3 chanels in the input images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.repeat(x_train, 3, axis=3)\n",
    "x_test = np.repeat(x_test, 3, axis=3)\n",
    "x_valid = np.repeat(x_valid, 3, axis=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape:  (9600, 128, 128, 3)\n",
      "y_train shape:  (9600, 128, 128, 1)\n",
      "x_valid shape:  (300, 128, 128, 3)\n",
      "y_valid shape:  (300, 128, 128, 1)\n",
      "x_test shape:  (500, 128, 128, 3)\n",
      "y_test shape:  (500, 128, 128, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"x_train shape: \", x_train.shape)\n",
    "print(\"y_train shape: \", y_train.shape)\n",
    "print(\"x_valid shape: \", x_valid.shape)\n",
    "print(\"y_valid shape: \", y_valid.shape)\n",
    "print(\"x_test shape: \", x_test.shape)\n",
    "print(\"y_test shape: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IoU competition metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taken from: https://www.kaggle.com/iezepov/fast-iou-scoring-metric-in-pytorch-and-numpy\n",
    "\n",
    "def get_iou_vector(A, B):\n",
    "    # Numpy version\n",
    "    batch_size = A.shape[0]\n",
    "    metric = 0.0\n",
    "    for batch in range(batch_size):\n",
    "\n",
    "        t, p = A[batch], B[batch]\n",
    "        true = np.sum(t)\n",
    "        pred = np.sum(p)\n",
    "\n",
    "        # deal with empty mask first\n",
    "        if true == 0:\n",
    "            metric += (pred == 0)\n",
    "            continue\n",
    "\n",
    "        # non empty mask case.  Union is never empty\n",
    "        # hence it is safe to divide by its number of pixels\n",
    "        intersection = np.sum(t * p)\n",
    "        union = true + pred - intersection\n",
    "        iou = intersection / union\n",
    "\n",
    "        # Scale the iou function: iou -> 2*iou - 0.9\n",
    "        # This will map the threshold values [0.5, 0.55, 0.6, ...., 0.9] to the\n",
    "        # values [0.1, 0.2, 0.3, ....., 0.9]. Then multiply 2*iou - 0.9 by 10 and floor.\n",
    "        # This will map the iou values to the number of thresholds that the iou sutisfies\n",
    "        # witch is what we are intreasted in in the first playse.\n",
    "        # For example if iou=0.552 then floor(2*iou - 0.9)*10 = 2. Since\n",
    "        # eveery value <0 means an original value<0.5 we take the max with 0. Finaly we\n",
    "        # devide by 10 = |threshods|.\n",
    "        iou = np.floor(max(0, (iou - 0.45) * 20)) / 10\n",
    "\n",
    "        metric += iou\n",
    "\n",
    "    # teake the average over all images in batch\n",
    "    metric /= batch_size\n",
    "    return metric\n",
    "\n",
    "\n",
    "def my_iou_metric(label, pred):\n",
    "    # Tensorflow version\n",
    "    return tf.numpy_function(get_iou_vector, [label, pred > 0.5], tf.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Additional blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def BatchActivate(x,lambda2):\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "    if lambda2 == True:\n",
    "        x =  tf.keras.layers.Dropout(DropoutRatio)(x)\n",
    "    return x\n",
    "\n",
    "\n",
    "def convolution_block(x, filters, size, strides=(1, 1), padding='same', activation=True):\n",
    "    x = Conv2D(filters, size, strides=strides, padding=padding)(x)\n",
    "    if activation == True:\n",
    "        x = BatchActivate(x, True)\n",
    "    return x\n",
    "\n",
    "\n",
    "\n",
    "def block(block_in, expand_filters, project_filters, depthwise_stride=(1, 1), depthwise_padding=\"same\",\n",
    "          zero_padding=False):\n",
    "    x = convolution_block(block_in, expand_filters, (1, 1))\n",
    "\n",
    "    x = convolution_block(x, project_filters, (3, 3))\n",
    "\n",
    "    x = convolution_block(x, expand_filters, (1, 1), activation=False)\n",
    "    return x\n",
    "\n",
    "\n",
    "def transpose_block(block_in, to_add, expand_filters):\n",
    "    x = Conv2DTranspose(expand_filters, (3, 3), strides=(2, 2), padding=\"same\")(block_in)\n",
    "    x = concatenate([x, to_add])\n",
    "\n",
    "    x = convolution_block(x, expand_filters, (3, 3), activation=False)\n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading the backbone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cov_model_v2 = keras.models.load_model('cov_model_v2')\n",
    "\n",
    "#input_shape = (128, 128, 3)\n",
    "#backbone = MobileNetV2(input_shape=input_shape, weights='imagenet', include_top=False)\n",
    "\n",
    "backbone = cov_model_v2\n",
    "inputs = backbone.input\n",
    "\n",
    "# print(backbone.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Buiding the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_filters = 8\n",
    "DropoutRatio = 0.1\n",
    "expansion_coef = 2\n",
    "projection_coef = 1\n",
    "\n",
    "\n",
    "\n",
    "middle_1 = backbone.get_layer('block_13_expand_BN')\n",
    "middle_1 = middle_1.output\n",
    "\n",
    "# Middle\n",
    "#----------------------------------------------------------------------------------------#\n",
    "filters_m_expand = 128\n",
    "filters_m_project = 128\n",
    "convm = Conv2D(filters_m_project, (3, 3), activation=None, padding=\"same\")(middle_1)\n",
    "convm_BA = BatchActivate(convm, True )\n",
    "\n",
    "convm_1 = block(convm_BA, filters_m_expand, filters_m_project)\n",
    "convm_add_1 = Add()([convm_1, convm])\n",
    "\n",
    "convm_add_1_BA = BatchActivate(convm_add_1, True)\n",
    "convm_2 = block(convm_add_1_BA, filters_m_expand, filters_m_project)\n",
    "convm_add_2 = Add()([convm_2, convm_add_1])\n",
    "\n",
    "convm_add_2 = BatchActivate(convm_add_2, False)\n",
    "\n",
    "\n",
    "# 8 -> 16\n",
    "#----------------------------------------------------------------------------------------#\n",
    "project_filters_1 = 8 * projection_coef * base_filters   # = 64\n",
    "expand_filters_1 = expansion_coef * project_filters_1    # = 128\n",
    "\n",
    "to_add_1 = backbone.get_layer('block_6_expand_BN')\n",
    "to_add_1 = to_add_1.output\n",
    "\n",
    "uconv1_1 = transpose_block(convm_add_2, to_add_1, expand_filters_1)\n",
    "\n",
    "#res block 1\n",
    "uconv1_1_BA = BatchActivate(uconv1_1, True)\n",
    "uconv1_2 = block(uconv1_1_BA, expand_filters_1, project_filters_1)\n",
    "uconv1_add_1 = Add()([uconv1_1, uconv1_2])\n",
    "\n",
    "#res block 2\n",
    "uconv1_add_1_BA = BatchActivate(uconv1_add_1, True)\n",
    "uconv1_3 = block(uconv1_add_1_BA, expand_filters_1, project_filters_1)\n",
    "uconv1_add_2 = Add()([uconv1_add_1, uconv1_3])\n",
    "\n",
    "#res block 3\n",
    "uconv1_add_2_BA = BatchActivate(uconv1_add_2, True)\n",
    "uconv1_4 = block(uconv1_add_2_BA, expand_filters_1, project_filters_1)\n",
    "uconv1_add_3 = Add()([uconv1_add_2, uconv1_4])\n",
    "\n",
    "uconv1_add_3_BA = BatchActivate(uconv1_add_3, False)\n",
    "\n",
    "\n",
    "\n",
    "# 16 -> 32\n",
    "#----------------------------------------------------------------------------------------#\n",
    "project_filters_2 = 4 * projection_coef * base_filters  # = 32\n",
    "expand_filters_2 = expansion_coef * project_filters_2   # = 64\n",
    "\n",
    "to_add_2 = backbone.get_layer('block_3_expand_BN')\n",
    "to_add_2 = to_add_2.output\n",
    "\n",
    "uconv2_1 = transpose_block(uconv1_add_3_BA, to_add_2, expand_filters_2)\n",
    "\n",
    "#res block 1\n",
    "uconv2_1_BA = BatchActivate(uconv2_1, True)\n",
    "uconv2_2 = block(uconv2_1_BA, expand_filters_2, project_filters_2)\n",
    "uconv2_add_1 = Add()([uconv2_1, uconv2_2])\n",
    "\n",
    "#res block 2\n",
    "uconv2_add_1_BA = BatchActivate(uconv2_add_1, True)\n",
    "uconv2_3 = block(uconv2_add_1_BA, expand_filters_2, project_filters_2)\n",
    "uconv2_add_2 = Add()([uconv2_add_1, uconv2_3])\n",
    "\n",
    "#res block 3\n",
    "uconv2_add_2_BA = BatchActivate(uconv2_add_2, True)\n",
    "uconv2_4 = block(uconv2_add_2_BA, expand_filters_2, project_filters_2)\n",
    "uconv2_add_3 = Add()([uconv2_add_2, uconv2_4])\n",
    "\n",
    "uconv2_add_3_BA = BatchActivate(uconv2_add_3, False)\n",
    "\n",
    "\n",
    "\n",
    "# 32 -> 64\n",
    "#----------------------------------------------------------------------------------------#\n",
    "project_filters_3 = 2 * projection_coef * base_filters  # = 16\n",
    "expand_filters_3 = expansion_coef * project_filters_3   # = 32\n",
    "\n",
    "to_add_3 = backbone.get_layer('expanded_conv_project_BN')\n",
    "to_add_3 = to_add_3.output\n",
    "\n",
    "uconv3_1 = transpose_block(uconv2_add_3_BA, to_add_3, expand_filters_3)\n",
    "\n",
    "#res block 1\n",
    "uconv3_1_BA = BatchActivate(uconv3_1, True)\n",
    "uconv3_2 = block(uconv3_1_BA, expand_filters_3, project_filters_3)\n",
    "uconv3_add_1 = Add()([uconv3_1, uconv3_2])\n",
    "\n",
    "#res block 2\n",
    "uconv3_add_1_BA = BatchActivate(uconv3_add_1, True)\n",
    "uconv3_3 = block(uconv3_add_1_BA, expand_filters_3, project_filters_3)\n",
    "uconv3_add_2 = Add()([uconv3_add_1, uconv3_3])\n",
    "\n",
    "#res block 3\n",
    "uconv3_add_2_BA = BatchActivate(uconv3_add_2, True)\n",
    "uconv3_4 = block(uconv3_add_2_BA, expand_filters_3, project_filters_3)\n",
    "uconv3_add_3 = Add()([uconv3_add_2, uconv3_4])\n",
    "\n",
    "uconv3_add_3_BA = BatchActivate(uconv3_add_3, False)\n",
    "\n",
    "\n",
    "\n",
    "# 64 -> 128\n",
    "#----------------------------------------------------------------------------------------#\n",
    "project_filters_4 = projection_coef * base_filters      # = 8\n",
    "expand_filters_4 = expansion_coef * project_filters_4   # = 16\n",
    "\n",
    "deconv0 = Conv2DTranspose(expand_filters_4, (3, 3), strides=(2, 2), padding=\"same\")(uconv3_add_3_BA)\n",
    "uconv0 = Conv2D(expand_filters_4, (3, 3), activation=None, padding=\"same\")(deconv0)\n",
    "\n",
    "#res block 1\n",
    "uconv0_BA = BatchActivate(uconv0, True)\n",
    "uconv0_1 = block(uconv0_BA, expand_filters_4, project_filters_4)\n",
    "uconv0_add_1 = Add()([uconv0, uconv0_1])\n",
    "\n",
    "#res block 2\n",
    "uconv0_add_1_BA = BatchActivate(uconv0_add_1, True)\n",
    "uconv0_2 = block(uconv0_add_1_BA, expand_filters_4, project_filters_4)\n",
    "uconv0_add_2 = Add()([uconv0_2, uconv0_add_1])\n",
    "\n",
    "#res block 3\n",
    "uconv0_add_2_BA = BatchActivate(uconv0_add_2, True)\n",
    "uconv0_3 = block(uconv0_add_1_BA, expand_filters_4, project_filters_4)\n",
    "uconv0_add_3 = Add()([uconv0_3, uconv0_add_2])\n",
    "\n",
    "uconv0_add_3_BA = BatchActivate(uconv0_add_3, False)\n",
    "\n",
    "\n",
    "#output\n",
    "#----------------------------------------------------------------------------------------#\n",
    "output_layer_noActi = Conv2D(1, (1, 1), padding=\"same\", activation=None)(uconv0_add_3_BA)\n",
    "output_layer = Activation('sigmoid')(output_layer_noActi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### BCE dice loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taken from: https://www.kaggle.com/meaninglesslives/getting-0-87-on-private-lb-using-kaggle-kernel\n",
    "\n",
    "from tensorflow.keras.losses import binary_crossentropy\n",
    "\n",
    "def dice_coef(y_true, y_pred):\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred = K.cast(y_pred, 'float32')\n",
    "    y_pred_f = K.cast(K.greater(K.flatten(y_pred), 0.5), 'float32')\n",
    "    intersection = y_true_f * y_pred_f\n",
    "    score = 2. * K.sum(intersection) / (K.sum(y_true_f) + K.sum(y_pred_f))\n",
    "    return score\n",
    "\n",
    "def dice_loss(y_true, y_pred):\n",
    "    smooth = 1.\n",
    "    y_true_f = K.flatten(y_true)\n",
    "    y_pred_f = K.flatten(y_pred)\n",
    "    intersection = y_true_f * y_pred_f\n",
    "    score = (2. * K.sum(intersection) + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)\n",
    "    return 1. - score\n",
    "\n",
    "def bce_dice_loss(y_true, y_pred):\n",
    "    return binary_crossentropy(y_true, y_pred) + dice_loss(y_true, y_pred)\n",
    "\n",
    "def bce_logdice_loss(y_true, y_pred):\n",
    "    return binary_crossentropy(y_true, y_pred) - K.log(1. - dice_loss(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compiling and training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            [(None, 128, 128, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Conv1 (Conv2D)                  (None, 64, 64, 32)   864         input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bn_Conv1 (BatchNormalization)   (None, 64, 64, 32)   128         Conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_relu (ReLU)               (None, 64, 64, 32)   0           bn_Conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise (Depthw (None, 64, 64, 32)   288         Conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_BN (Bat (None, 64, 64, 32)   128         expanded_conv_depthwise[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_relu (R (None, 64, 64, 32)   0           expanded_conv_depthwise_BN[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project (Conv2D)  (None, 64, 64, 16)   512         expanded_conv_depthwise_relu[0][0\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project_BN (Batch (None, 64, 64, 16)   64          expanded_conv_project[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand (Conv2D)         (None, 64, 64, 96)   1536        expanded_conv_project_BN[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_BN (BatchNormali (None, 64, 64, 96)   384         block_1_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_relu (ReLU)      (None, 64, 64, 96)   0           block_1_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_pad (ZeroPadding2D)     (None, 65, 65, 96)   0           block_1_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise (DepthwiseCon (None, 32, 32, 96)   864         block_1_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_BN (BatchNorm (None, 32, 32, 96)   384         block_1_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_relu (ReLU)   (None, 32, 32, 96)   0           block_1_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project (Conv2D)        (None, 32, 32, 24)   2304        block_1_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project_BN (BatchNormal (None, 32, 32, 24)   96          block_1_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand (Conv2D)         (None, 32, 32, 144)  3456        block_1_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_BN (BatchNormali (None, 32, 32, 144)  576         block_2_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_relu (ReLU)      (None, 32, 32, 144)  0           block_2_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise (DepthwiseCon (None, 32, 32, 144)  1296        block_2_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_BN (BatchNorm (None, 32, 32, 144)  576         block_2_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_relu (ReLU)   (None, 32, 32, 144)  0           block_2_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project (Conv2D)        (None, 32, 32, 24)   3456        block_2_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project_BN (BatchNormal (None, 32, 32, 24)   96          block_2_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_add (Add)               (None, 32, 32, 24)   0           block_1_project_BN[0][0]         \n",
      "                                                                 block_2_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand (Conv2D)         (None, 32, 32, 144)  3456        block_2_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_BN (BatchNormali (None, 32, 32, 144)  576         block_3_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_relu (ReLU)      (None, 32, 32, 144)  0           block_3_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_pad (ZeroPadding2D)     (None, 33, 33, 144)  0           block_3_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise (DepthwiseCon (None, 16, 16, 144)  1296        block_3_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_BN (BatchNorm (None, 16, 16, 144)  576         block_3_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_relu (ReLU)   (None, 16, 16, 144)  0           block_3_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project (Conv2D)        (None, 16, 16, 32)   4608        block_3_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project_BN (BatchNormal (None, 16, 16, 32)   128         block_3_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand (Conv2D)         (None, 16, 16, 192)  6144        block_3_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_4_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_4_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise (DepthwiseCon (None, 16, 16, 192)  1728        block_4_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_BN (BatchNorm (None, 16, 16, 192)  768         block_4_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_relu (ReLU)   (None, 16, 16, 192)  0           block_4_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project (Conv2D)        (None, 16, 16, 32)   6144        block_4_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project_BN (BatchNormal (None, 16, 16, 32)   128         block_4_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_add (Add)               (None, 16, 16, 32)   0           block_3_project_BN[0][0]         \n",
      "                                                                 block_4_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand (Conv2D)         (None, 16, 16, 192)  6144        block_4_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_5_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_5_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise (DepthwiseCon (None, 16, 16, 192)  1728        block_5_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_BN (BatchNorm (None, 16, 16, 192)  768         block_5_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_relu (ReLU)   (None, 16, 16, 192)  0           block_5_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project (Conv2D)        (None, 16, 16, 32)   6144        block_5_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project_BN (BatchNormal (None, 16, 16, 32)   128         block_5_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_5_add (Add)               (None, 16, 16, 32)   0           block_4_add[0][0]                \n",
      "                                                                 block_5_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand (Conv2D)         (None, 16, 16, 192)  6144        block_5_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_BN (BatchNormali (None, 16, 16, 192)  768         block_6_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_relu (ReLU)      (None, 16, 16, 192)  0           block_6_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_pad (ZeroPadding2D)     (None, 17, 17, 192)  0           block_6_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise (DepthwiseCon (None, 8, 8, 192)    1728        block_6_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_BN (BatchNorm (None, 8, 8, 192)    768         block_6_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_relu (ReLU)   (None, 8, 8, 192)    0           block_6_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project (Conv2D)        (None, 8, 8, 64)     12288       block_6_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project_BN (BatchNormal (None, 8, 8, 64)     256         block_6_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand (Conv2D)         (None, 8, 8, 384)    24576       block_6_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_7_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_7_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_7_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_7_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_7_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project (Conv2D)        (None, 8, 8, 64)     24576       block_7_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project_BN (BatchNormal (None, 8, 8, 64)     256         block_7_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_add (Add)               (None, 8, 8, 64)     0           block_6_project_BN[0][0]         \n",
      "                                                                 block_7_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand (Conv2D)         (None, 8, 8, 384)    24576       block_7_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_8_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_8_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_8_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_8_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_8_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project (Conv2D)        (None, 8, 8, 64)     24576       block_8_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project_BN (BatchNormal (None, 8, 8, 64)     256         block_8_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_8_add (Add)               (None, 8, 8, 64)     0           block_7_add[0][0]                \n",
      "                                                                 block_8_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand (Conv2D)         (None, 8, 8, 384)    24576       block_8_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_BN (BatchNormali (None, 8, 8, 384)    1536        block_9_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_relu (ReLU)      (None, 8, 8, 384)    0           block_9_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise (DepthwiseCon (None, 8, 8, 384)    3456        block_9_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_BN (BatchNorm (None, 8, 8, 384)    1536        block_9_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_relu (ReLU)   (None, 8, 8, 384)    0           block_9_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project (Conv2D)        (None, 8, 8, 64)     24576       block_9_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project_BN (BatchNormal (None, 8, 8, 64)     256         block_9_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_9_add (Add)               (None, 8, 8, 64)     0           block_8_add[0][0]                \n",
      "                                                                 block_9_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand (Conv2D)        (None, 8, 8, 384)    24576       block_9_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_BN (BatchNormal (None, 8, 8, 384)    1536        block_10_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_relu (ReLU)     (None, 8, 8, 384)    0           block_10_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise (DepthwiseCo (None, 8, 8, 384)    3456        block_10_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_BN (BatchNor (None, 8, 8, 384)    1536        block_10_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_relu (ReLU)  (None, 8, 8, 384)    0           block_10_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project (Conv2D)       (None, 8, 8, 96)     36864       block_10_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project_BN (BatchNorma (None, 8, 8, 96)     384         block_10_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand (Conv2D)        (None, 8, 8, 576)    55296       block_10_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_11_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_11_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise (DepthwiseCo (None, 8, 8, 576)    5184        block_11_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_BN (BatchNor (None, 8, 8, 576)    2304        block_11_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_relu (ReLU)  (None, 8, 8, 576)    0           block_11_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project (Conv2D)       (None, 8, 8, 96)     55296       block_11_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project_BN (BatchNorma (None, 8, 8, 96)     384         block_11_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_add (Add)              (None, 8, 8, 96)     0           block_10_project_BN[0][0]        \n",
      "                                                                 block_11_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand (Conv2D)        (None, 8, 8, 576)    55296       block_11_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_12_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_relu (ReLU)     (None, 8, 8, 576)    0           block_12_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise (DepthwiseCo (None, 8, 8, 576)    5184        block_12_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_BN (BatchNor (None, 8, 8, 576)    2304        block_12_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_relu (ReLU)  (None, 8, 8, 576)    0           block_12_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project (Conv2D)       (None, 8, 8, 96)     55296       block_12_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project_BN (BatchNorma (None, 8, 8, 96)     384         block_12_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_12_add (Add)              (None, 8, 8, 96)     0           block_11_add[0][0]               \n",
      "                                                                 block_12_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand (Conv2D)        (None, 8, 8, 576)    55296       block_12_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_BN (BatchNormal (None, 8, 8, 576)    2304        block_13_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 8, 8, 128)    663680      block_13_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 8, 8, 128)    512         conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 8, 8, 128)    0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 8, 8, 128)    0           activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 8, 8, 128)    16512       dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 8, 8, 128)    512         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 8, 8, 128)    0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 8, 8, 128)    0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 8, 8, 128)    147584      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 8, 8, 128)    512         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 8, 8, 128)    0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 8, 8, 128)    0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 8, 8, 128)    16512       dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 8, 8, 128)    0           conv2d_3[0][0]                   \n",
      "                                                                 conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 8, 8, 128)    512         add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 8, 8, 128)    0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 8, 8, 128)    0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 8, 8, 128)    16512       dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 8, 8, 128)    512         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 8, 8, 128)    0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 8, 8, 128)    0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 8, 8, 128)    147584      dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 8, 8, 128)    512         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 8, 8, 128)    0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 8, 8, 128)    0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 8, 8, 128)    16512       dropout_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 8, 8, 128)    0           conv2d_6[0][0]                   \n",
      "                                                                 add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 8, 8, 128)    512         add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 8, 8, 128)    0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose (Conv2DTranspo (None, 16, 16, 128)  147584      activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 16, 16, 320)  0           conv2d_transpose[0][0]           \n",
      "                                                                 block_6_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 16, 16, 128)  368768      concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 16, 16, 128)  512         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 16, 16, 128)  0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_6 (Dropout)             (None, 16, 16, 128)  0           activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 16, 16, 128)  16512       dropout_6[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 16, 16, 128)  512         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 16, 16, 128)  0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_7 (Dropout)             (None, 16, 16, 128)  0           activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 16, 16, 64)   73792       dropout_7[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 16, 16, 64)   256         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 16, 16, 64)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (None, 16, 16, 64)   0           activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 16, 16, 128)  8320        dropout_8[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 16, 16, 128)  0           conv2d_7[0][0]                   \n",
      "                                                                 conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 16, 16, 128)  512         add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 16, 16, 128)  0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (None, 16, 16, 128)  0           activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 16, 16, 128)  16512       dropout_9[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 16, 16, 128)  512         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 16, 16, 128)  0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_10 (Dropout)            (None, 16, 16, 128)  0           activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 16, 16, 64)   73792       dropout_10[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 16, 16, 64)   256         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 16, 16, 64)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_11 (Dropout)            (None, 16, 16, 64)   0           activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 16, 16, 128)  8320        dropout_11[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 16, 16, 128)  0           add_2[0][0]                      \n",
      "                                                                 conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 16, 16, 128)  512         add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 16, 16, 128)  0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_12 (Dropout)            (None, 16, 16, 128)  0           activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 16, 16, 128)  16512       dropout_12[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 16, 16, 128)  512         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 16, 16, 128)  0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_13 (Dropout)            (None, 16, 16, 128)  0           activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 16, 16, 64)   73792       dropout_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 16, 16, 64)   256         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 16, 16, 64)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_14 (Dropout)            (None, 16, 16, 64)   0           activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 16, 16, 128)  8320        dropout_14[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 16, 16, 128)  0           add_3[0][0]                      \n",
      "                                                                 conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 16, 16, 128)  512         add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 16, 16, 128)  0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 32, 32, 64)   73792       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 32, 32, 208)  0           conv2d_transpose_1[0][0]         \n",
      "                                                                 block_3_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 32, 32, 64)   119872      concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 32, 32, 64)   256         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 32, 32, 64)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_15 (Dropout)            (None, 32, 32, 64)   0           activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 32, 32, 64)   4160        dropout_15[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 32, 32, 64)   256         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 32, 32, 64)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_16 (Dropout)            (None, 32, 32, 64)   0           activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 32, 32, 32)   18464       dropout_16[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 32, 32, 32)   128         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 32, 32, 32)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_17 (Dropout)            (None, 32, 32, 32)   0           activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 32, 32, 64)   2112        dropout_17[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 32, 32, 64)   0           conv2d_17[0][0]                  \n",
      "                                                                 conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 32, 32, 64)   256         add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 32, 32, 64)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_18 (Dropout)            (None, 32, 32, 64)   0           activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 32, 32, 64)   4160        dropout_18[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 32, 32, 64)   256         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 32, 32, 64)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_19 (Dropout)            (None, 32, 32, 64)   0           activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 32, 32, 32)   18464       dropout_19[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 32, 32, 32)   128         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 32, 32, 32)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_20 (Dropout)            (None, 32, 32, 32)   0           activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 32, 32, 64)   2112        dropout_20[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 32, 32, 64)   0           add_5[0][0]                      \n",
      "                                                                 conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 32, 32, 64)   256         add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 32, 32, 64)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_21 (Dropout)            (None, 32, 32, 64)   0           activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 32, 32, 64)   4160        dropout_21[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 32, 32, 64)   256         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 32, 32, 64)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_22 (Dropout)            (None, 32, 32, 64)   0           activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 32, 32, 32)   18464       dropout_22[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 32, 32, 32)   128         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 32, 32, 32)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_23 (Dropout)            (None, 32, 32, 32)   0           activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 32, 32, 64)   2112        dropout_23[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 32, 32, 64)   0           add_6[0][0]                      \n",
      "                                                                 conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 32, 32, 64)   256         add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 32, 32, 64)   0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTrans (None, 64, 64, 32)   18464       activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 64, 64, 48)   0           conv2d_transpose_2[0][0]         \n",
      "                                                                 expanded_conv_project_BN[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 64, 64, 32)   13856       concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 64, 64, 32)   128         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 64, 64, 32)   0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_24 (Dropout)            (None, 64, 64, 32)   0           activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 64, 64, 32)   1056        dropout_24[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 64, 64, 32)   128         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 64, 64, 32)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_25 (Dropout)            (None, 64, 64, 32)   0           activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 64, 64, 16)   4624        dropout_25[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 64, 64, 16)   64          conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 64, 64, 16)   0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_26 (Dropout)            (None, 64, 64, 16)   0           activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 64, 64, 32)   544         dropout_26[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 64, 64, 32)   0           conv2d_27[0][0]                  \n",
      "                                                                 conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 64, 64, 32)   128         add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 64, 64, 32)   0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_27 (Dropout)            (None, 64, 64, 32)   0           activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 64, 64, 32)   1056        dropout_27[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 64, 64, 32)   128         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 64, 64, 32)   0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_28 (Dropout)            (None, 64, 64, 32)   0           activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 64, 64, 16)   4624        dropout_28[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 64, 64, 16)   64          conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 64, 64, 16)   0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_29 (Dropout)            (None, 64, 64, 16)   0           activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 64, 64, 32)   544         dropout_29[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 64, 64, 32)   0           add_8[0][0]                      \n",
      "                                                                 conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 64, 64, 32)   128         add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 64, 64, 32)   0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_30 (Dropout)            (None, 64, 64, 32)   0           activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 64, 64, 32)   1056        dropout_30[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 64, 64, 32)   128         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 64, 64, 32)   0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_31 (Dropout)            (None, 64, 64, 32)   0           activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 64, 64, 16)   4624        dropout_31[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 64, 64, 16)   64          conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 64, 64, 16)   0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_32 (Dropout)            (None, 64, 64, 16)   0           activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 64, 64, 32)   544         dropout_32[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 64, 64, 32)   0           add_9[0][0]                      \n",
      "                                                                 conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 64, 64, 32)   128         add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 64, 64, 32)   0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTrans (None, 128, 128, 16) 4624        activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 128, 128, 16) 2320        conv2d_transpose_3[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 128, 128, 16) 64          conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 128, 128, 16) 0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_33 (Dropout)            (None, 128, 128, 16) 0           activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 128, 128, 16) 272         dropout_33[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 128, 128, 16) 64          conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 128, 128, 16) 0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_34 (Dropout)            (None, 128, 128, 16) 0           activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 128, 128, 8)  1160        dropout_34[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 128, 128, 8)  32          conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 128, 128, 8)  0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_35 (Dropout)            (None, 128, 128, 8)  0           activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 128, 128, 16) 144         dropout_35[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 128, 128, 16) 0           conv2d_37[0][0]                  \n",
      "                                                                 conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 128, 128, 16) 64          add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 128, 128, 16) 0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_36 (Dropout)            (None, 128, 128, 16) 0           activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 128, 128, 16) 272         dropout_36[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 128, 128, 16) 272         dropout_36[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 128, 128, 16) 64          conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 128, 128, 16) 64          conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 128, 128, 16) 0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 128, 128, 16) 0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_37 (Dropout)            (None, 128, 128, 16) 0           activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_40 (Dropout)            (None, 128, 128, 16) 0           activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 128, 128, 8)  1160        dropout_37[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 128, 128, 8)  1160        dropout_40[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 128, 128, 8)  32          conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 128, 128, 8)  32          conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 128, 128, 8)  0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 128, 128, 8)  0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_38 (Dropout)            (None, 128, 128, 8)  0           activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_41 (Dropout)            (None, 128, 128, 8)  0           activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 128, 128, 16) 144         dropout_38[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 128, 128, 16) 144         dropout_41[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 128, 128, 16) 0           conv2d_43[0][0]                  \n",
      "                                                                 add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 128, 128, 16) 0           conv2d_46[0][0]                  \n",
      "                                                                 add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 128, 128, 16) 64          add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 128, 128, 16) 0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 128, 128, 1)  17          activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 128, 128, 1)  0           conv2d_47[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 2,795,609\n",
      "Trainable params: 2,772,489\n",
      "Non-trainable params: 23,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Model(inputs=[inputs], outputs=[output_layer])\n",
    "adam_optimizer = optimizers.Adam(learning_rate=0.005)\n",
    "model.compile(optimizer=adam_optimizer, loss=bce_dice_loss, metrics=[my_iou_metric])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "960/960 [==============================] - 402s 403ms/step - loss: 0.5234 - my_iou_metric: 0.4769 - val_loss: 0.5742 - val_my_iou_metric: 0.3380\n",
      "\n",
      "Epoch 00001: val_my_iou_metric improved from -inf to 0.33800, saving model to C:/Users/gauss/Documents/TGS/Notebooks/Test_1_checkpoints\\saved-01-0.34.hdf5\n",
      "Epoch 2/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.3379 - my_iou_metric: 0.6343 - val_loss: 0.4380 - val_my_iou_metric: 0.5590\n",
      "\n",
      "Epoch 00002: val_my_iou_metric improved from 0.33800 to 0.55900, saving model to C:/Users/gauss/Documents/TGS/Notebooks/Test_1_checkpoints\\saved-02-0.56.hdf5\n",
      "Epoch 3/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.2992 - my_iou_metric: 0.6714 - val_loss: 0.3329 - val_my_iou_metric: 0.6866\n",
      "\n",
      "Epoch 00003: val_my_iou_metric improved from 0.55900 to 0.68660, saving model to C:/Users/gauss/Documents/TGS/Notebooks/Test_1_checkpoints\\saved-03-0.69.hdf5\n",
      "Epoch 4/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.2660 - my_iou_metric: 0.6938 - val_loss: 0.3110 - val_my_iou_metric: 0.6806\n",
      "\n",
      "Epoch 00004: val_my_iou_metric did not improve from 0.68660\n",
      "Epoch 5/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.2540 - my_iou_metric: 0.7157 - val_loss: 0.3357 - val_my_iou_metric: 0.6740\n",
      "\n",
      "Epoch 00005: val_my_iou_metric did not improve from 0.68660\n",
      "Epoch 6/50\n",
      "960/960 [==============================] - 387s 403ms/step - loss: 0.2535 - my_iou_metric: 0.7043 - val_loss: 0.2856 - val_my_iou_metric: 0.6888\n",
      "\n",
      "Epoch 00006: val_my_iou_metric improved from 0.68660 to 0.68880, saving model to C:/Users/gauss/Documents/TGS/Notebooks/Test_1_checkpoints\\saved-06-0.69.hdf5\n",
      "Epoch 7/50\n",
      "960/960 [==============================] - 387s 403ms/step - loss: 0.2404 - my_iou_metric: 0.7265 - val_loss: 0.3179 - val_my_iou_metric: 0.6986\n",
      "\n",
      "Epoch 00007: val_my_iou_metric improved from 0.68880 to 0.69860, saving model to C:/Users/gauss/Documents/TGS/Notebooks/Test_1_checkpoints\\saved-07-0.70.hdf5\n",
      "Epoch 8/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.2303 - my_iou_metric: 0.7199 - val_loss: 0.2716 - val_my_iou_metric: 0.6696\n",
      "\n",
      "Epoch 00008: val_my_iou_metric did not improve from 0.69860\n",
      "Epoch 9/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.2333 - my_iou_metric: 0.7311 - val_loss: 1.0563 - val_my_iou_metric: 0.1266\n",
      "\n",
      "Epoch 00009: val_my_iou_metric did not improve from 0.69860\n",
      "Epoch 10/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.2242 - my_iou_metric: 0.7369 - val_loss: 0.3201 - val_my_iou_metric: 0.7210\n",
      "\n",
      "Epoch 00010: val_my_iou_metric improved from 0.69860 to 0.72100, saving model to C:/Users/gauss/Documents/TGS/Notebooks/Test_1_checkpoints\\saved-10-0.72.hdf5\n",
      "Epoch 11/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.2114 - my_iou_metric: 0.7366 - val_loss: 0.2775 - val_my_iou_metric: 0.6990\n",
      "\n",
      "Epoch 00011: val_my_iou_metric did not improve from 0.72100\n",
      "Epoch 12/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1984 - my_iou_metric: 0.7463 - val_loss: 0.2988 - val_my_iou_metric: 0.7342\n",
      "\n",
      "Epoch 00012: val_my_iou_metric improved from 0.72100 to 0.73420, saving model to C:/Users/gauss/Documents/TGS/Notebooks/Test_1_checkpoints\\saved-12-0.73.hdf5\n",
      "Epoch 13/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.2150 - my_iou_metric: 0.7392 - val_loss: 0.2880 - val_my_iou_metric: 0.7046\n",
      "\n",
      "Epoch 00013: val_my_iou_metric did not improve from 0.73420\n",
      "Epoch 14/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1852 - my_iou_metric: 0.7563 - val_loss: 0.2940 - val_my_iou_metric: 0.7314\n",
      "\n",
      "Epoch 00014: val_my_iou_metric did not improve from 0.73420\n",
      "Epoch 15/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1878 - my_iou_metric: 0.7488 - val_loss: 0.3090 - val_my_iou_metric: 0.7126\n",
      "\n",
      "Epoch 00015: val_my_iou_metric did not improve from 0.73420\n",
      "Epoch 16/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1961 - my_iou_metric: 0.7511 - val_loss: 0.3710 - val_my_iou_metric: 0.6606\n",
      "\n",
      "Epoch 00016: val_my_iou_metric did not improve from 0.73420\n",
      "Epoch 17/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1936 - my_iou_metric: 0.7521 - val_loss: 0.2800 - val_my_iou_metric: 0.7272\n",
      "\n",
      "Epoch 00017: val_my_iou_metric did not improve from 0.73420\n",
      "\n",
      "Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.0037499999161809683.\n",
      "Epoch 18/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1711 - my_iou_metric: 0.7777 - val_loss: 0.2623 - val_my_iou_metric: 0.7226\n",
      "\n",
      "Epoch 00018: val_my_iou_metric did not improve from 0.73420\n",
      "Epoch 19/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1609 - my_iou_metric: 0.7739 - val_loss: 0.2933 - val_my_iou_metric: 0.7212\n",
      "\n",
      "Epoch 00019: val_my_iou_metric did not improve from 0.73420\n",
      "Epoch 20/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1634 - my_iou_metric: 0.7750 - val_loss: 0.3082 - val_my_iou_metric: 0.6382\n",
      "\n",
      "Epoch 00020: val_my_iou_metric did not improve from 0.73420\n",
      "Epoch 21/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1544 - my_iou_metric: 0.7814 - val_loss: 0.6844 - val_my_iou_metric: 0.1940\n",
      "\n",
      "Epoch 00021: val_my_iou_metric did not improve from 0.73420\n",
      "Epoch 22/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1578 - my_iou_metric: 0.7768 - val_loss: 0.3981 - val_my_iou_metric: 0.6720\n",
      "\n",
      "Epoch 00022: val_my_iou_metric did not improve from 0.73420\n",
      "\n",
      "Epoch 00022: ReduceLROnPlateau reducing learning rate to 0.002812499937135726.\n",
      "Epoch 23/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.1405 - my_iou_metric: 0.7839 - val_loss: 0.3163 - val_my_iou_metric: 0.7112\n",
      "\n",
      "Epoch 00023: val_my_iou_metric did not improve from 0.73420\n",
      "Epoch 24/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.1366 - my_iou_metric: 0.7953 - val_loss: 1.0311 - val_my_iou_metric: 0.1360\n",
      "\n",
      "Epoch 00024: val_my_iou_metric did not improve from 0.73420\n",
      "Epoch 25/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.1473 - my_iou_metric: 0.7857 - val_loss: 0.2441 - val_my_iou_metric: 0.7406\n",
      "\n",
      "Epoch 00025: val_my_iou_metric improved from 0.73420 to 0.74060, saving model to C:/Users/gauss/Documents/TGS/Notebooks/Test_1_checkpoints\\saved-25-0.74.hdf5\n",
      "Epoch 26/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.1301 - my_iou_metric: 0.7954 - val_loss: 0.2428 - val_my_iou_metric: 0.7358\n",
      "\n",
      "Epoch 00026: val_my_iou_metric did not improve from 0.74060\n",
      "Epoch 27/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.1178 - my_iou_metric: 0.7986 - val_loss: 0.2867 - val_my_iou_metric: 0.7134\n",
      "\n",
      "Epoch 00027: val_my_iou_metric did not improve from 0.74060\n",
      "Epoch 28/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.1227 - my_iou_metric: 0.7977 - val_loss: 0.3414 - val_my_iou_metric: 0.6870\n",
      "\n",
      "Epoch 00028: val_my_iou_metric did not improve from 0.74060\n",
      "Epoch 29/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1101 - my_iou_metric: 0.8052 - val_loss: 0.3079 - val_my_iou_metric: 0.6356\n",
      "\n",
      "Epoch 00029: val_my_iou_metric did not improve from 0.74060\n",
      "Epoch 30/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1233 - my_iou_metric: 0.8091 - val_loss: 0.3013 - val_my_iou_metric: 0.7140\n",
      "\n",
      "Epoch 00030: val_my_iou_metric did not improve from 0.74060\n",
      "\n",
      "Epoch 00030: ReduceLROnPlateau reducing learning rate to 0.002109374909196049.\n",
      "Epoch 31/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1131 - my_iou_metric: 0.8005 - val_loss: 0.2720 - val_my_iou_metric: 0.7272\n",
      "\n",
      "Epoch 00031: val_my_iou_metric did not improve from 0.74060\n",
      "Epoch 32/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1048 - my_iou_metric: 0.8085 - val_loss: 0.2640 - val_my_iou_metric: 0.7256\n",
      "\n",
      "Epoch 00032: val_my_iou_metric did not improve from 0.74060\n",
      "Epoch 33/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1119 - my_iou_metric: 0.8040 - val_loss: 0.2748 - val_my_iou_metric: 0.7052\n",
      "\n",
      "Epoch 00033: val_my_iou_metric did not improve from 0.74060\n",
      "Epoch 34/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1133 - my_iou_metric: 0.8015 - val_loss: 0.2813 - val_my_iou_metric: 0.7156\n",
      "\n",
      "Epoch 00034: val_my_iou_metric did not improve from 0.74060\n",
      "Epoch 35/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1131 - my_iou_metric: 0.8107 - val_loss: 0.2841 - val_my_iou_metric: 0.7234\n",
      "\n",
      "Epoch 00035: val_my_iou_metric did not improve from 0.74060\n",
      "\n",
      "Epoch 00035: ReduceLROnPlateau reducing learning rate to 0.001582031138241291.\n",
      "Epoch 36/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1019 - my_iou_metric: 0.8100 - val_loss: 0.2752 - val_my_iou_metric: 0.7376\n",
      "\n",
      "Epoch 00036: val_my_iou_metric did not improve from 0.74060\n",
      "Epoch 37/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1000 - my_iou_metric: 0.8129 - val_loss: 0.2742 - val_my_iou_metric: 0.7410\n",
      "\n",
      "Epoch 00037: val_my_iou_metric improved from 0.74060 to 0.74100, saving model to C:/Users/gauss/Documents/TGS/Notebooks/Test_1_checkpoints\\saved-37-0.74.hdf5\n",
      "Epoch 38/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.0961 - my_iou_metric: 0.8109 - val_loss: 0.3146 - val_my_iou_metric: 0.7322\n",
      "\n",
      "Epoch 00038: val_my_iou_metric did not improve from 0.74100\n",
      "Epoch 39/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.0987 - my_iou_metric: 0.8128 - val_loss: 0.2589 - val_my_iou_metric: 0.7364\n",
      "\n",
      "Epoch 00039: val_my_iou_metric did not improve from 0.74100\n",
      "Epoch 40/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.0903 - my_iou_metric: 0.8179 - val_loss: 0.2871 - val_my_iou_metric: 0.7310\n",
      "\n",
      "Epoch 00040: val_my_iou_metric did not improve from 0.74100\n",
      "Epoch 41/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.1040 - my_iou_metric: 0.8105 - val_loss: 0.2787 - val_my_iou_metric: 0.7314\n",
      "\n",
      "Epoch 00041: val_my_iou_metric did not improve from 0.74100\n",
      "Epoch 42/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.0871 - my_iou_metric: 0.8220 - val_loss: 0.2732 - val_my_iou_metric: 0.7292\n",
      "\n",
      "Epoch 00042: val_my_iou_metric did not improve from 0.74100\n",
      "\n",
      "Epoch 00042: ReduceLROnPlateau reducing learning rate to 0.0011865233536809683.\n",
      "Epoch 43/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.0884 - my_iou_metric: 0.8265 - val_loss: 0.3101 - val_my_iou_metric: 0.7324\n",
      "\n",
      "Epoch 00043: val_my_iou_metric did not improve from 0.74100\n",
      "Epoch 44/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.0852 - my_iou_metric: 0.8246 - val_loss: 0.2829 - val_my_iou_metric: 0.7490\n",
      "\n",
      "Epoch 00044: val_my_iou_metric improved from 0.74100 to 0.74900, saving model to C:/Users/gauss/Documents/TGS/Notebooks/Test_1_checkpoints\\saved-44-0.75.hdf5\n",
      "Epoch 45/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.0866 - my_iou_metric: 0.8205 - val_loss: 0.2693 - val_my_iou_metric: 0.7490\n",
      "\n",
      "Epoch 00045: val_my_iou_metric improved from 0.74900 to 0.74900, saving model to C:/Users/gauss/Documents/TGS/Notebooks/Test_1_checkpoints\\saved-45-0.75.hdf5\n",
      "Epoch 46/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.0836 - my_iou_metric: 0.8248 - val_loss: 0.2470 - val_my_iou_metric: 0.7368\n",
      "\n",
      "Epoch 00046: val_my_iou_metric did not improve from 0.74900\n",
      "Epoch 47/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.0864 - my_iou_metric: 0.8263 - val_loss: 0.2488 - val_my_iou_metric: 0.7382\n",
      "\n",
      "Epoch 00047: val_my_iou_metric did not improve from 0.74900\n",
      "Epoch 48/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.0837 - my_iou_metric: 0.8274 - val_loss: 0.2686 - val_my_iou_metric: 0.7340\n",
      "\n",
      "Epoch 00048: val_my_iou_metric did not improve from 0.74900\n",
      "Epoch 49/50\n",
      "960/960 [==============================] - 386s 402ms/step - loss: 0.0788 - my_iou_metric: 0.8302 - val_loss: 0.2686 - val_my_iou_metric: 0.7386\n",
      "\n",
      "Epoch 00049: val_my_iou_metric did not improve from 0.74900\n",
      "\n",
      "Epoch 00049: ReduceLROnPlateau reducing learning rate to 0.0008898925152607262.\n",
      "Epoch 50/50\n",
      "960/960 [==============================] - 385s 401ms/step - loss: 0.0773 - my_iou_metric: 0.8285 - val_loss: 0.2679 - val_my_iou_metric: 0.7266\n",
      "\n",
      "Epoch 00050: val_my_iou_metric did not improve from 0.74900\n"
     ]
    }
   ],
   "source": [
    "epochs = 50\n",
    "batch_len = 10\n",
    "history = model.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=epochs, shuffle=True, batch_size=batch_len, callbacks = callbacks_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting and saving the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_save_dir = \"C:/Users/gauss/Documents/TGS/Notebooks/Saved_models/test_1_latest\"\n",
    "\n",
    "def plot_history(hs, epochs, metric):\n",
    "    print()\n",
    "    plt.style.use('dark_background')\n",
    "    plt.rcParams['figure.figsize'] = [15, 8]\n",
    "    plt.rcParams['font.size'] = 16\n",
    "    plt.clf()\n",
    "    for label in hs:\n",
    "        print(len(hs[label].history['loss']))\n",
    "        plt.plot(hs[label].history[metric], label='{0:s} train {1:s}'.format(label, metric), linewidth=2)\n",
    "        plt.plot(hs[label].history['val_{0:s}'.format(metric)], label='{0:s} validation {1:s}'.format(label, metric), linewidth=2)\n",
    "    \n",
    "    \n",
    "    \n",
    "    x_ticks = np.arange(0, epochs + 1, epochs / 10)\n",
    "    x_ticks [0] += 1\n",
    "    plt.xticks(x_ticks)\n",
    "    plt.ylim((0, 1))\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss' if metric=='loss' else 'Accuracy')\n",
    "    plt.legend()\n",
    "    plt.savefig('{0:s}/fig_{1:s}.png'.format(model_save_dir, metric))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(hs={'': history}, epochs=epochs, metric='loss')\n",
    "plot_history(hs={'': history}, epochs=epochs, metric='my_iou_metric')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the results to txt files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "loss_history = np.array(history.history[\"loss\"]).reshape((50, 1))\n",
    "IoU_history = (np.array(history.history[\"my_iou_metric\"])).reshape((50 , 1))\n",
    "val_loss_history = np.array(history.history[\"val_loss\"]).reshape((50 , 1))\n",
    "val_IoU_history = (np.array(history.history[\"val_my_iou_metric\"])).reshape((50 , 1))\n",
    "\n",
    "np.savetxt(model_save_dir+\"/logs/IoU.txt\", \n",
    "           IoU_history, delimiter=\",\")\n",
    "np.savetxt(model_save_dir+\"/logs/Loss.txt\", \n",
    "           loss_history, delimiter=\",\")\n",
    "np.savetxt(model_save_dir+\"/logs/val_IoU.txt\", \n",
    "           val_IoU_history, delimiter=\",\")\n",
    "np.savetxt(model_save_dir+\"/logs/val_Loss.txt\", \n",
    "           val_loss_history, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = keras.models.load_model(\"./Test_1_checkpoints\\saved-45-0.75.hdf5\", \n",
    "                                    custom_objects={\"my_iou_metric\": my_iou_metric, \"bce_dice_loss\": bce_dice_loss}, compile=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model.compile(optimizer=adam_optimizer, loss=bce_dice_loss, metrics=[my_iou_metric])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500/500 [==============================] - 22s 34ms/step - loss: 0.5885 - my_iou_metric: 0.7546\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5752320885658264, 0.7489995360374451]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.evaluate(x=x_test, y=y_test, batch_size=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31290"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K.clear_session()\n",
    "gc.collect()\n",
    "del backbone\n",
    "del model\n",
    "del best_model\n",
    "del train_df\n",
    "del x_train\n",
    "del x_test\n",
    "del x_valid\n",
    "del y_train\n",
    "del y_test\n",
    "del y_valid\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "KERNEL_DISPLAY_NAME",
   "language": "python",
   "name": "environment_name"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}