Medical_Imaging_Segmentation_ICNET.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Medical_Imaging_Segmentation_ICNET",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nandinib1999/lungs-segmentation-ICNET/blob/master/Medical_Imaging_Segmentation_ICNET.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_KzqYjAUJPPq",
        "outputId": "62f6c189-ac49-41cf-e105-1faa54d65ca3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "try:\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "2.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6q3QzBKJkFg"
      },
      "source": [
        "import os\n",
        "import sys\n",
        "import random\n",
        "import warnings\n",
        "import zipfile\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tqdm import tqdm\n",
        "from itertools import chain\n",
        "from skimage.io import imread, imshow, imread_collection, concatenate_images\n",
        "from skimage.transform import resize\n",
        "from skimage.morphology import label\n",
        "\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.layers import Input, Dropout, Lambda, BatchNormalization, ZeroPadding2D, Conv2D, Conv2DTranspose\n",
        "from tensorflow.keras.layers import concatenate, MaxPooling2D, Add, Activation, AveragePooling2D, UpSampling2D\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from tensorflow.keras import backend as K\n",
        "\n",
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2a69eYRuJqcl"
      },
      "source": [
        "IMG_WIDTH = 256\n",
        "IMG_HEIGHT = 256\n",
        "IMG_CHANNELS = 3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyPrkRWbJs2o"
      },
      "source": [
        "seed = 42\n",
        "random.seed = seed\n",
        "np.random.seed = seed"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mtKyyh9XibZJ",
        "outputId": "a889ba19-b938-4d41-d837-e26c014376cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "!unzip data.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  data.zip\n",
            "   creating: data_2/\n",
            "  inflating: data_2/test.zip         \n",
            "  inflating: data_2/train.zip        \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0ctcaxCik_S",
        "outputId": "dee6c3fd-fa45-4686-98b1-b88f434a63bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!unzip data_2/test.zip -d test_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  data_2/test.zip\n",
            "   creating: test_data/test/\n",
            "   creating: test_data/test/IM-0067-0001/\n",
            "   creating: test_data/test/IM-0067-0001/images/\n",
            " extracting: test_data/test/IM-0067-0001/images/IM-0067-0001.png  \n",
            "   creating: test_data/test/IM-0069-0001/\n",
            "   creating: test_data/test/IM-0069-0001/images/\n",
            " extracting: test_data/test/IM-0069-0001/images/IM-0069-0001.png  \n",
            "   creating: test_data/test/IM-0070-0001/\n",
            "   creating: test_data/test/IM-0070-0001/images/\n",
            " extracting: test_data/test/IM-0070-0001/images/IM-0070-0001.png  \n",
            "   creating: test_data/test/IM-0071-0001/\n",
            "   creating: test_data/test/IM-0071-0001/images/\n",
            " extracting: test_data/test/IM-0071-0001/images/IM-0071-0001.png  \n",
            "   creating: test_data/test/IM-0073-0001/\n",
            "   creating: test_data/test/IM-0073-0001/images/\n",
            " extracting: test_data/test/IM-0073-0001/images/IM-0073-0001.png  \n",
            "   creating: test_data/test/IM-0075-0001/\n",
            "   creating: test_data/test/IM-0075-0001/images/\n",
            " extracting: test_data/test/IM-0075-0001/images/IM-0075-0001.png  \n",
            "   creating: test_data/test/IM-0077-0001/\n",
            "   creating: test_data/test/IM-0077-0001/images/\n",
            " extracting: test_data/test/IM-0077-0001/images/IM-0077-0001.png  \n",
            "   creating: test_data/test/IM-0079-0001/\n",
            "   creating: test_data/test/IM-0079-0001/images/\n",
            " extracting: test_data/test/IM-0079-0001/images/IM-0079-0001.png  \n",
            "   creating: test_data/test/IM-0081-0001/\n",
            "   creating: test_data/test/IM-0081-0001/images/\n",
            " extracting: test_data/test/IM-0081-0001/images/IM-0081-0001.png  \n",
            "   creating: test_data/test/IM-0083-0001/\n",
            "   creating: test_data/test/IM-0083-0001/images/\n",
            " extracting: test_data/test/IM-0083-0001/images/IM-0083-0001.png  \n",
            "   creating: test_data/test/IM-0084-0001/\n",
            "   creating: test_data/test/IM-0084-0001/images/\n",
            " extracting: test_data/test/IM-0084-0001/images/IM-0084-0001.png  \n",
            "   creating: test_data/test/IM-0085-0001/\n",
            "   creating: test_data/test/IM-0085-0001/images/\n",
            " extracting: test_data/test/IM-0085-0001/images/IM-0085-0001.png  \n",
            "   creating: test_data/test/IM-0086-0001/\n",
            "   creating: test_data/test/IM-0086-0001/images/\n",
            " extracting: test_data/test/IM-0086-0001/images/IM-0086-0001.png  \n",
            "   creating: test_data/test/IM-0087-0001/\n",
            "   creating: test_data/test/IM-0087-0001/images/\n",
            " extracting: test_data/test/IM-0087-0001/images/IM-0087-0001.png  \n",
            "   creating: test_data/test/IM-0089-0001/\n",
            "   creating: test_data/test/IM-0089-0001/images/\n",
            " extracting: test_data/test/IM-0089-0001/images/IM-0089-0001.png  \n",
            "   creating: test_data/test/IM-0101-0001/\n",
            "   creating: test_data/test/IM-0101-0001/images/\n",
            " extracting: test_data/test/IM-0101-0001/images/IM-0101-0001.png  \n",
            "   creating: test_data/test/IM-0102-0001/\n",
            "   creating: test_data/test/IM-0102-0001/images/\n",
            " extracting: test_data/test/IM-0102-0001/images/IM-0102-0001.png  \n",
            "   creating: test_data/test/IM-0103-0001/\n",
            "   creating: test_data/test/IM-0103-0001/images/\n",
            " extracting: test_data/test/IM-0103-0001/images/IM-0103-0001.png  \n",
            "   creating: test_data/test/IM-0105-0001/\n",
            "   creating: test_data/test/IM-0105-0001/images/\n",
            " extracting: test_data/test/IM-0105-0001/images/IM-0105-0001.png  \n",
            "   creating: test_data/test/IM-0107-0001/\n",
            "   creating: test_data/test/IM-0107-0001/images/\n",
            " extracting: test_data/test/IM-0107-0001/images/IM-0107-0001.png  \n",
            "   creating: test_data/test/IM-0109-0001/\n",
            "   creating: test_data/test/IM-0109-0001/images/\n",
            " extracting: test_data/test/IM-0109-0001/images/IM-0109-0001.png  \n",
            "   creating: test_data/test/IM-0110-0001/\n",
            "   creating: test_data/test/IM-0110-0001/images/\n",
            " extracting: test_data/test/IM-0110-0001/images/IM-0110-0001.png  \n",
            "   creating: test_data/test/IM-0111-0001/\n",
            "   creating: test_data/test/IM-0111-0001/images/\n",
            " extracting: test_data/test/IM-0111-0001/images/IM-0111-0001.png  \n",
            "   creating: test_data/test/NORMAL2-IM-0007-0001/\n",
            "   creating: test_data/test/NORMAL2-IM-0007-0001/images/\n",
            " extracting: test_data/test/NORMAL2-IM-0007-0001/images/NORMAL2-IM-0007-0001.png  \n",
            "   creating: test_data/test/NORMAL2-IM-0012-0001/\n",
            "   creating: test_data/test/NORMAL2-IM-0012-0001/images/\n",
            " extracting: test_data/test/NORMAL2-IM-0012-0001/images/NORMAL2-IM-0012-0001.png  \n",
            "   creating: test_data/test/NORMAL2-IM-0013-0001/\n",
            "   creating: test_data/test/NORMAL2-IM-0013-0001/images/\n",
            " extracting: test_data/test/NORMAL2-IM-0013-0001/images/NORMAL2-IM-0013-0001.png  \n",
            "   creating: test_data/test/NORMAL2-IM-0019-0001/\n",
            "   creating: test_data/test/NORMAL2-IM-0019-0001/images/\n",
            " extracting: test_data/test/NORMAL2-IM-0019-0001/images/NORMAL2-IM-0019-0001.png  \n",
            "   creating: test_data/test/NORMAL2-IM-0023-0001/\n",
            "   creating: test_data/test/NORMAL2-IM-0023-0001/images/\n",
            " extracting: test_data/test/NORMAL2-IM-0023-0001/images/NORMAL2-IM-0023-0001.png  \n",
            "   creating: test_data/test/NORMAL2-IM-0027-0001/\n",
            "   creating: test_data/test/NORMAL2-IM-0027-0001/images/\n",
            " extracting: test_data/test/NORMAL2-IM-0027-0001/images/NORMAL2-IM-0027-0001.png  \n",
            "   creating: test_data/test/NORMAL2-IM-0028-0001/\n",
            "   creating: test_data/test/NORMAL2-IM-0028-0001/images/\n",
            " extracting: test_data/test/NORMAL2-IM-0028-0001/images/NORMAL2-IM-0028-0001.png  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUFUWYpwixxP",
        "outputId": "402d1588-034a-4558-c18d-5b1bba9d580f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!unzip data_2/train.zip -d train_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  data_2/train.zip\n",
            "   creating: train_data/train/\n",
            "   creating: train_data/train/IM-0001-0001/\n",
            "   creating: train_data/train/IM-0001-0001/images/\n",
            " extracting: train_data/train/IM-0001-0001/images/IM-0001-0001.png  \n",
            "   creating: train_data/train/IM-0001-0001/mask/\n",
            " extracting: train_data/train/IM-0001-0001/mask/IM-0001-0001.png  \n",
            "   creating: train_data/train/IM-0003-0001/\n",
            "   creating: train_data/train/IM-0003-0001/images/\n",
            " extracting: train_data/train/IM-0003-0001/images/IM-0003-0001.png  \n",
            "   creating: train_data/train/IM-0003-0001/mask/\n",
            " extracting: train_data/train/IM-0003-0001/mask/IM-0003-0001.png  \n",
            "   creating: train_data/train/IM-0005-0001/\n",
            "   creating: train_data/train/IM-0005-0001/images/\n",
            " extracting: train_data/train/IM-0005-0001/images/IM-0005-0001.png  \n",
            "   creating: train_data/train/IM-0005-0001/mask/\n",
            " extracting: train_data/train/IM-0005-0001/mask/IM-0005-0001.png  \n",
            "   creating: train_data/train/IM-0006-0001/\n",
            "   creating: train_data/train/IM-0006-0001/images/\n",
            " extracting: train_data/train/IM-0006-0001/images/IM-0006-0001.png  \n",
            "   creating: train_data/train/IM-0006-0001/mask/\n",
            " extracting: train_data/train/IM-0006-0001/mask/IM-0006-0001.png  \n",
            "   creating: train_data/train/IM-0007-0001/\n",
            "   creating: train_data/train/IM-0007-0001/images/\n",
            " extracting: train_data/train/IM-0007-0001/images/IM-0007-0001.png  \n",
            "   creating: train_data/train/IM-0007-0001/mask/\n",
            " extracting: train_data/train/IM-0007-0001/mask/IM-0007-0001.png  \n",
            "   creating: train_data/train/IM-0009-0001/\n",
            "   creating: train_data/train/IM-0009-0001/images/\n",
            " extracting: train_data/train/IM-0009-0001/images/IM-0009-0001.png  \n",
            "   creating: train_data/train/IM-0009-0001/mask/\n",
            " extracting: train_data/train/IM-0009-0001/mask/IM-0009-0001.png  \n",
            "   creating: train_data/train/IM-0010-0001/\n",
            "   creating: train_data/train/IM-0010-0001/images/\n",
            " extracting: train_data/train/IM-0010-0001/images/IM-0010-0001.png  \n",
            "   creating: train_data/train/IM-0010-0001/mask/\n",
            " extracting: train_data/train/IM-0010-0001/mask/IM-0010-0001.png  \n",
            "   creating: train_data/train/IM-0011-0001/\n",
            "   creating: train_data/train/IM-0011-0001-0001/\n",
            "   creating: train_data/train/IM-0011-0001-0001/images/\n",
            " extracting: train_data/train/IM-0011-0001-0001/images/IM-0011-0001-0001.png  \n",
            "   creating: train_data/train/IM-0011-0001-0001/mask/\n",
            " extracting: train_data/train/IM-0011-0001-0001/mask/IM-0011-0001-0001.png  \n",
            "   creating: train_data/train/IM-0011-0001-0002/\n",
            "   creating: train_data/train/IM-0011-0001-0002/images/\n",
            " extracting: train_data/train/IM-0011-0001-0002/images/IM-0011-0001-0002.png  \n",
            "   creating: train_data/train/IM-0011-0001-0002/mask/\n",
            " extracting: train_data/train/IM-0011-0001-0002/mask/IM-0011-0001-0002.png  \n",
            "   creating: train_data/train/IM-0011-0001/images/\n",
            " extracting: train_data/train/IM-0011-0001/images/IM-0011-0001.png  \n",
            "   creating: train_data/train/IM-0011-0001/mask/\n",
            " extracting: train_data/train/IM-0011-0001/mask/IM-0011-0001.png  \n",
            "   creating: train_data/train/IM-0013-0001/\n",
            "   creating: train_data/train/IM-0013-0001/images/\n",
            " extracting: train_data/train/IM-0013-0001/images/IM-0013-0001.png  \n",
            "   creating: train_data/train/IM-0013-0001/mask/\n",
            " extracting: train_data/train/IM-0013-0001/mask/IM-0013-0001.png  \n",
            "   creating: train_data/train/IM-0015-0001/\n",
            "   creating: train_data/train/IM-0015-0001/images/\n",
            " extracting: train_data/train/IM-0015-0001/images/IM-0015-0001.png  \n",
            "   creating: train_data/train/IM-0015-0001/mask/\n",
            " extracting: train_data/train/IM-0015-0001/mask/IM-0015-0001.png  \n",
            "   creating: train_data/train/IM-0016-0001/\n",
            "   creating: train_data/train/IM-0016-0001/images/\n",
            " extracting: train_data/train/IM-0016-0001/images/IM-0016-0001.png  \n",
            "   creating: train_data/train/IM-0016-0001/mask/\n",
            " extracting: train_data/train/IM-0016-0001/mask/IM-0016-0001.png  \n",
            "   creating: train_data/train/IM-0017-0001/\n",
            "   creating: train_data/train/IM-0017-0001/images/\n",
            " extracting: train_data/train/IM-0017-0001/images/IM-0017-0001.png  \n",
            "   creating: train_data/train/IM-0017-0001/mask/\n",
            "  inflating: train_data/train/IM-0017-0001/mask/IM-0017-0001.png  \n",
            "   creating: train_data/train/IM-0019-0001/\n",
            "   creating: train_data/train/IM-0019-0001/images/\n",
            " extracting: train_data/train/IM-0019-0001/images/IM-0019-0001.png  \n",
            "   creating: train_data/train/IM-0019-0001/mask/\n",
            " extracting: train_data/train/IM-0019-0001/mask/IM-0019-0001.png  \n",
            "   creating: train_data/train/IM-0021-0001/\n",
            "   creating: train_data/train/IM-0021-0001/images/\n",
            " extracting: train_data/train/IM-0021-0001/images/IM-0021-0001.png  \n",
            "   creating: train_data/train/IM-0021-0001/mask/\n",
            "  inflating: train_data/train/IM-0021-0001/mask/IM-0021-0001.png  \n",
            "   creating: train_data/train/IM-0022-0001/\n",
            "   creating: train_data/train/IM-0022-0001/images/\n",
            " extracting: train_data/train/IM-0022-0001/images/IM-0022-0001.png  \n",
            "   creating: train_data/train/IM-0022-0001/mask/\n",
            " extracting: train_data/train/IM-0022-0001/mask/IM-0022-0001.png  \n",
            "   creating: train_data/train/IM-0023-0001/\n",
            "   creating: train_data/train/IM-0023-0001/images/\n",
            " extracting: train_data/train/IM-0023-0001/images/IM-0023-0001.png  \n",
            "   creating: train_data/train/IM-0023-0001/mask/\n",
            " extracting: train_data/train/IM-0023-0001/mask/IM-0023-0001.png  \n",
            "   creating: train_data/train/IM-0025-0001/\n",
            "   creating: train_data/train/IM-0025-0001/images/\n",
            " extracting: train_data/train/IM-0025-0001/images/IM-0025-0001.png  \n",
            "   creating: train_data/train/IM-0025-0001/mask/\n",
            "  inflating: train_data/train/IM-0025-0001/mask/IM-0025-0001.png  \n",
            "   creating: train_data/train/IM-0027-0001/\n",
            "   creating: train_data/train/IM-0027-0001/images/\n",
            " extracting: train_data/train/IM-0027-0001/images/IM-0027-0001.png  \n",
            "   creating: train_data/train/IM-0027-0001/mask/\n",
            " extracting: train_data/train/IM-0027-0001/mask/IM-0027-0001.png  \n",
            "   creating: train_data/train/IM-0028-0001/\n",
            "   creating: train_data/train/IM-0028-0001/images/\n",
            " extracting: train_data/train/IM-0028-0001/images/IM-0028-0001.png  \n",
            "   creating: train_data/train/IM-0028-0001/mask/\n",
            "  inflating: train_data/train/IM-0028-0001/mask/IM-0028-0001.png  \n",
            "   creating: train_data/train/IM-0029-0001/\n",
            "   creating: train_data/train/IM-0029-0001/images/\n",
            " extracting: train_data/train/IM-0029-0001/images/IM-0029-0001.png  \n",
            "   creating: train_data/train/IM-0029-0001/mask/\n",
            " extracting: train_data/train/IM-0029-0001/mask/IM-0029-0001.png  \n",
            "   creating: train_data/train/IM-0030-0001/\n",
            "   creating: train_data/train/IM-0030-0001/images/\n",
            " extracting: train_data/train/IM-0030-0001/images/IM-0030-0001.png  \n",
            "   creating: train_data/train/IM-0030-0001/mask/\n",
            " extracting: train_data/train/IM-0030-0001/mask/IM-0030-0001.png  \n",
            "   creating: train_data/train/IM-0031-0001/\n",
            "   creating: train_data/train/IM-0031-0001/images/\n",
            " extracting: train_data/train/IM-0031-0001/images/IM-0031-0001.png  \n",
            "   creating: train_data/train/IM-0031-0001/mask/\n",
            " extracting: train_data/train/IM-0031-0001/mask/IM-0031-0001.png  \n",
            "   creating: train_data/train/IM-0033-0001/\n",
            "   creating: train_data/train/IM-0033-0001-0001/\n",
            "   creating: train_data/train/IM-0033-0001-0001/images/\n",
            " extracting: train_data/train/IM-0033-0001-0001/images/IM-0033-0001-0001.png  \n",
            "   creating: train_data/train/IM-0033-0001-0001/mask/\n",
            "  inflating: train_data/train/IM-0033-0001-0001/mask/IM-0033-0001-0001.png  \n",
            "   creating: train_data/train/IM-0033-0001-0002/\n",
            "   creating: train_data/train/IM-0033-0001-0002/images/\n",
            " extracting: train_data/train/IM-0033-0001-0002/images/IM-0033-0001-0002.png  \n",
            "   creating: train_data/train/IM-0033-0001-0002/mask/\n",
            "  inflating: train_data/train/IM-0033-0001-0002/mask/IM-0033-0001-0002.png  \n",
            "   creating: train_data/train/IM-0033-0001/images/\n",
            " extracting: train_data/train/IM-0033-0001/images/IM-0033-0001.png  \n",
            "   creating: train_data/train/IM-0033-0001/mask/\n",
            " extracting: train_data/train/IM-0033-0001/mask/IM-0033-0001.png  \n",
            "   creating: train_data/train/IM-0035-0001/\n",
            "   creating: train_data/train/IM-0035-0001/images/\n",
            " extracting: train_data/train/IM-0035-0001/images/IM-0035-0001.png  \n",
            "   creating: train_data/train/IM-0035-0001/mask/\n",
            " extracting: train_data/train/IM-0035-0001/mask/IM-0035-0001.png  \n",
            "   creating: train_data/train/IM-0036-0001/\n",
            "   creating: train_data/train/IM-0036-0001/images/\n",
            " extracting: train_data/train/IM-0036-0001/images/IM-0036-0001.png  \n",
            "   creating: train_data/train/IM-0036-0001/mask/\n",
            " extracting: train_data/train/IM-0036-0001/mask/IM-0036-0001.png  \n",
            "   creating: train_data/train/IM-0037-0001/\n",
            "   creating: train_data/train/IM-0037-0001/images/\n",
            " extracting: train_data/train/IM-0037-0001/images/IM-0037-0001.png  \n",
            "   creating: train_data/train/IM-0037-0001/mask/\n",
            " extracting: train_data/train/IM-0037-0001/mask/IM-0037-0001.png  \n",
            "   creating: train_data/train/IM-0039-0001/\n",
            "   creating: train_data/train/IM-0039-0001/images/\n",
            " extracting: train_data/train/IM-0039-0001/images/IM-0039-0001.png  \n",
            "   creating: train_data/train/IM-0039-0001/mask/\n",
            " extracting: train_data/train/IM-0039-0001/mask/IM-0039-0001.png  \n",
            "   creating: train_data/train/IM-0041-0001/\n",
            "   creating: train_data/train/IM-0041-0001/images/\n",
            " extracting: train_data/train/IM-0041-0001/images/IM-0041-0001.png  \n",
            "   creating: train_data/train/IM-0041-0001/mask/\n",
            " extracting: train_data/train/IM-0041-0001/mask/IM-0041-0001.png  \n",
            "   creating: train_data/train/IM-0043-0001/\n",
            "   creating: train_data/train/IM-0043-0001/images/\n",
            " extracting: train_data/train/IM-0043-0001/images/IM-0043-0001.png  \n",
            "   creating: train_data/train/IM-0043-0001/mask/\n",
            " extracting: train_data/train/IM-0043-0001/mask/IM-0043-0001.png  \n",
            "   creating: train_data/train/IM-0045-0001/\n",
            "   creating: train_data/train/IM-0045-0001/images/\n",
            " extracting: train_data/train/IM-0045-0001/images/IM-0045-0001.png  \n",
            "   creating: train_data/train/IM-0045-0001/mask/\n",
            "  inflating: train_data/train/IM-0045-0001/mask/IM-0045-0001.png  \n",
            "   creating: train_data/train/IM-0046-0001/\n",
            "   creating: train_data/train/IM-0046-0001/images/\n",
            " extracting: train_data/train/IM-0046-0001/images/IM-0046-0001.png  \n",
            "   creating: train_data/train/IM-0046-0001/mask/\n",
            " extracting: train_data/train/IM-0046-0001/mask/IM-0046-0001.png  \n",
            "   creating: train_data/train/IM-0049-0001/\n",
            "   creating: train_data/train/IM-0049-0001/images/\n",
            " extracting: train_data/train/IM-0049-0001/images/IM-0049-0001.png  \n",
            "   creating: train_data/train/IM-0049-0001/mask/\n",
            " extracting: train_data/train/IM-0049-0001/mask/IM-0049-0001.png  \n",
            "   creating: train_data/train/IM-0050-0001/\n",
            "   creating: train_data/train/IM-0050-0001/images/\n",
            " extracting: train_data/train/IM-0050-0001/images/IM-0050-0001.png  \n",
            "   creating: train_data/train/IM-0050-0001/mask/\n",
            "  inflating: train_data/train/IM-0050-0001/mask/IM-0050-0001.png  \n",
            "   creating: train_data/train/IM-0059-0001/\n",
            "   creating: train_data/train/IM-0059-0001/images/\n",
            " extracting: train_data/train/IM-0059-0001/images/IM-0059-0001.png  \n",
            "   creating: train_data/train/IM-0059-0001/mask/\n",
            "  inflating: train_data/train/IM-0059-0001/mask/IM-0059-0001.png  \n",
            "   creating: train_data/train/IM-0061-0001/\n",
            "   creating: train_data/train/IM-0061-0001/images/\n",
            " extracting: train_data/train/IM-0061-0001/images/IM-0061-0001.png  \n",
            "   creating: train_data/train/IM-0061-0001/mask/\n",
            "  inflating: train_data/train/IM-0061-0001/mask/IM-0061-0001.png  \n",
            "   creating: train_data/train/IM-0063-0001/\n",
            "   creating: train_data/train/IM-0063-0001/images/\n",
            " extracting: train_data/train/IM-0063-0001/images/IM-0063-0001.png  \n",
            "   creating: train_data/train/IM-0063-0001/mask/\n",
            "  inflating: train_data/train/IM-0063-0001/mask/IM-0063-0001.png  \n",
            "   creating: train_data/train/IM-0065-0001/\n",
            "   creating: train_data/train/IM-0065-0001/images/\n",
            " extracting: train_data/train/IM-0065-0001/images/IM-0065-0001.png  \n",
            "   creating: train_data/train/IM-0065-0001/mask/\n",
            " extracting: train_data/train/IM-0065-0001/mask/IM-0065-0001.png  \n",
            "   creating: train_data/train/IM-0067-0001/\n",
            "   creating: train_data/train/IM-0067-0001/images/\n",
            " extracting: train_data/train/IM-0067-0001/images/IM-0067-0001.png  \n",
            "   creating: train_data/train/IM-0067-0001/mask/\n",
            "  inflating: train_data/train/IM-0067-0001/mask/IM-0067-0001.png  \n",
            "   creating: train_data/train/IM-0069-0001/\n",
            "   creating: train_data/train/IM-0069-0001/images/\n",
            " extracting: train_data/train/IM-0069-0001/images/IM-0069-0001.png  \n",
            "   creating: train_data/train/IM-0069-0001/mask/\n",
            "  inflating: train_data/train/IM-0069-0001/mask/IM-0069-0001.png  \n",
            "   creating: train_data/train/IM-0070-0001/\n",
            "   creating: train_data/train/IM-0070-0001/images/\n",
            " extracting: train_data/train/IM-0070-0001/images/IM-0070-0001.png  \n",
            "   creating: train_data/train/IM-0070-0001/mask/\n",
            "  inflating: train_data/train/IM-0070-0001/mask/IM-0070-0001.png  \n",
            "   creating: train_data/train/IM-0071-0001/\n",
            "   creating: train_data/train/IM-0071-0001/images/\n",
            " extracting: train_data/train/IM-0071-0001/images/IM-0071-0001.png  \n",
            "   creating: train_data/train/IM-0071-0001/mask/\n",
            " extracting: train_data/train/IM-0071-0001/mask/IM-0071-0001.png  \n",
            "   creating: train_data/train/IM-0073-0001/\n",
            "   creating: train_data/train/IM-0073-0001/images/\n",
            " extracting: train_data/train/IM-0073-0001/images/IM-0073-0001.png  \n",
            "   creating: train_data/train/IM-0073-0001/mask/\n",
            " extracting: train_data/train/IM-0073-0001/mask/IM-0073-0001.png  \n",
            "   creating: train_data/train/IM-0075-0001/\n",
            "   creating: train_data/train/IM-0075-0001/images/\n",
            " extracting: train_data/train/IM-0075-0001/images/IM-0075-0001.png  \n",
            "   creating: train_data/train/IM-0075-0001/mask/\n",
            " extracting: train_data/train/IM-0075-0001/mask/IM-0075-0001.png  \n",
            "   creating: train_data/train/IM-0077-0001/\n",
            "   creating: train_data/train/IM-0077-0001/images/\n",
            " extracting: train_data/train/IM-0077-0001/images/IM-0077-0001.png  \n",
            "   creating: train_data/train/IM-0077-0001/mask/\n",
            " extracting: train_data/train/IM-0077-0001/mask/IM-0077-0001.png  \n",
            "   creating: train_data/train/IM-0079-0001/\n",
            "   creating: train_data/train/IM-0079-0001/images/\n",
            " extracting: train_data/train/IM-0079-0001/images/IM-0079-0001.png  \n",
            "   creating: train_data/train/IM-0079-0001/mask/\n",
            "  inflating: train_data/train/IM-0079-0001/mask/IM-0079-0001.png  \n",
            "   creating: train_data/train/IM-0081-0001/\n",
            "   creating: train_data/train/IM-0081-0001/images/\n",
            " extracting: train_data/train/IM-0081-0001/images/IM-0081-0001.png  \n",
            "   creating: train_data/train/IM-0081-0001/mask/\n",
            "  inflating: train_data/train/IM-0081-0001/mask/IM-0081-0001.png  \n",
            "   creating: train_data/train/IM-0083-0001/\n",
            "   creating: train_data/train/IM-0083-0001/images/\n",
            " extracting: train_data/train/IM-0083-0001/images/IM-0083-0001.png  \n",
            "   creating: train_data/train/IM-0083-0001/mask/\n",
            "  inflating: train_data/train/IM-0083-0001/mask/IM-0083-0001.png  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XFR-zg9lOJvU"
      },
      "source": [
        "TRAIN_PATH = 'train_data/train'\n",
        "TEST_PATH = 'test_data/test'\n",
        "\n",
        "\n",
        "# Get train and test IDs\n",
        "train_ids = os.listdir(TRAIN_PATH)\n",
        "test_ids = os.listdir(TEST_PATH)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9sk4-MOOXfo",
        "outputId": "c97eeff3-006e-4a20-b52d-6711f00454c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.uint8)\n",
        "Y_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
        "print('Getting and resizing train images and masks ... ')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Getting and resizing train images and masks ... \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qyf3fYwTOfGO",
        "outputId": "4d2a4bb3-24f6-4179-b0be-7ada3c7b5895",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)):\n",
        "    print(id_)\n",
        "    if id_ != \".ipynb_checkpoints\":\n",
        "      path = os.path.join(TRAIN_PATH, id_)\n",
        "      img = imread(os.path.join(path, 'images', id_+'.png'))\n",
        "      img = np.expand_dims(resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True), axis=-1)\n",
        "      print(img.shape)\n",
        "      X_train[n] = img\n",
        "      mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
        "      \n",
        "      mask_ = imread(path + '/mask/' + id_+'.png')\n",
        "      print(mask_.shape)\n",
        "      mask_ = np.expand_dims(resize(mask_, (IMG_HEIGHT, IMG_WIDTH), mode='constant', \n",
        "                                    preserve_range=True), axis=-1)\n",
        "      mask = np.maximum(mask, mask_)\n",
        "      \n",
        "      Y_train[n] = mask\n",
        "\n",
        "# Get and resize test images\n",
        "X_test = np.zeros((len(test_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.uint8)\n",
        "sizes_test = []\n",
        "print('Getting and resizing test images ... ')\n",
        "sys.stdout.flush()\n",
        "for n, id_ in tqdm(enumerate(test_ids), total=len(test_ids)):\n",
        "    path = os.path.join(TEST_PATH, id_)\n",
        "    img = imread(os.path.join(path, 'images', id_+'.png'))#[:,:,:IMG_CHANNELS]\n",
        "    sizes_test.append([img.shape[0], img.shape[1]])\n",
        "    img = np.expand_dims(resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True), axis=-1)\n",
        "    X_test[n] = img\n",
        "\n",
        "print('Done!')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " 20%|█▉        | 10/51 [00:00<00:00, 93.42it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "IM-0019-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0045-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0028-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0036-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0023-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0007-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0030-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0011-0001-0002\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0025-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0027-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0010-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0043-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0069-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0017-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0059-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            ".ipynb_checkpoints\n",
            "IM-0049-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0006-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0046-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0029-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0065-0001\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 59%|█████▉    | 30/51 [00:00<00:00, 93.40it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0083-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0061-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0079-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0015-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0081-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0073-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0033-0001-0002\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0016-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0075-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0033-0001-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0013-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0050-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0009-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0071-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0037-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0033-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0041-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0022-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 51/51 [00:00<00:00, 93.89it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "IM-0021-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0011-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0005-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0031-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0063-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0070-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0067-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0035-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0011-0001-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0039-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0003-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "IM-0077-0001\n",
            "(256, 256, 1)\n",
            "(256, 256)\n",
            "Getting and resizing test images ... \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "100%|██████████| 30/30 [00:00<00:00, 184.48it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Done!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZfIQNAvQAws",
        "outputId": "a30fbff3-5ad1-460a-afe5-08b1fd93415a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        }
      },
      "source": [
        "ix = random.randint(0, len(train_ids))\n",
        "imshow(np.squeeze(X_train[ix]))\n",
        "plt.show()\n",
        "imshow(np.squeeze(Y_train[ix]))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1tluz0pea97HSGSW7D+6rOQ4M7M3NTY6Ojsr/oGcUr20f91rCGF1fX+8M8ZNt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66uFsFdXFwsQkImDJTAPQLTMTxGfLxOupZujN4znWem/ujZs2fFa3mPKvoYgwbglvr9ftnoEcOG\nMeI5xuNxQRPmELg/UCtj6+UaoA6jV36YGxPHNfHOYQRrTsgcE8rBfMLjgKbstY24USx4MpTSlfiE\nrby2vr5+LaXukgGWlrhMYW5urpDToCwrNvc7GAxKu+DT09Osr6+XPuJwg8w3MoRhQe7G43FpyIbB\ns5M20e2xNkfqHXhrp2x9ZUxrOsTnu8nxXkPUNM1/k+S3k2w1TfM4yX+Y5LebpvmtJG2Sr5L8W29v\n8MdN0/zDJH+a5CLJ32jb9r35PAsqD8jr9nooAgJaf74Ou2xsbJzqNDLfRdBBAQjK5uZmp80oSobR\nAmlRSzI7O5uvvvqqbD/jVDXFjyAfDBvxuffsggNIUhYigoxmZ2fLJo9nZ2elWNEeLUkxMKxHAwGZ\n6ERxOECPrrUBbdiD8p67E7hWCSE2WnKdCmNuhXGY4DID1/Ek6ZCmNh7mYywbHMgTY2/iO+l2iGQu\nppHrbhNiJJVcIRcSFaAgGt47fKVA1siR/4+OjrKzs5Pvfe97JXzF+VF5TxLBIaKdhI2iielp+mJ9\ncpRhmXO2ugYJRrM35YeSDzBEbdv+a1Ne/vvf8fm/leRv3eQmfPN4smmfqSEfQjzNAjNwnNuemOvw\n28Qgf+Ptl5aWCqS3h3eRWdM0WV9fz6tXrzIzM5Mf//jHHR5hPL7aZcG1HX4mwgTuJUmnMBLBxxDP\nzs5mZWWlGDVCKhQB4WMcyZD1+/2yzmw8Hnf2SR+Px+U9qsG5P/NtKK3XX3G/zA+GAORIiIViJF2E\nw5jWHCBj7jQz1/JnmC97Z7iRpJvE6PV6BaVYsZwBZUzqcN+fd+aqLishbIbDQ35APxcXF+U9o2Du\nERJ8YWEhjx49yubmZlZXVwuRTaiVpIN0uT6LpnFm3BsI1xGE5RHDWhscGynro8fG8sy5bnLcispq\nhLD2jLzm//2d2qJ7cOzl6u865ZpM75PcNFd1Mtvb20VQSeNTowMiQsEWFhbyh3/4h9d6K4NIknTg\nNPdWIzs+R3jIOjEENknJmJm0RZEJJy8vJ5s6eilCkkI6ozRed+YFmYR8PAf3Wgsc1+c8fB6DnUyQ\nJOPsOeCekQGXUHAexgzPSwGn0SbK4AxaLS8Yfwwcn6s7Cbgpmkl0dyEwse51VouLi7l3715OTk7y\n5MmTMtY03ycMrCuWMTbe4fXZs2fZ2dnJnTt3ioFnzv2cHheyd7SPcdkBSQd0xrrCMxsBoRMev2nc\nkOX3/xeO6C/imGaMamNTE9c2QIah9YHHSCapymnGjfeZGOLsJJ16JCbCnMnz58/z6tWrghLqkNI7\nkNpz1OQy77N9sO+PZSEmUTGYSYrwEgqA3Fi7Vhu7OmR0EzULIpW5dVGcCWaE1USpuRjCOgs04z1N\naF3/wzxZWW1EMJY2Fg4V+C4/NobMrY30eHy1x9zh4WF5BnNeoC3OQchLGYU5v9nZ2Wxvb5ceVZDb\npODJTrpdDXPLOakn2t/fz8cff5ytra2CNqnrIiymfoj6MjoEEOqaBLd8OSFg2fXfHEZKNvx1GHyT\n41a1AakZeSMaW2MO80p8rj4svAw2xmGaNbenJyPF9esaneTKOHz55ZeFVETRSP1T5cyBUUKAUSR6\n/iwvL+fOnTu5vLzstLUgxCO8MjrysgdITNLEBwcHnfH1uPZ6V7U0RhZGGDWkx4g7fEZwbZST7tbG\nfM6hjJ0OhzsCOqFg1JVMqpvdJA1j4fM5zON8SQrqMKfE2Jnfgj9iTLkuCM2yQ3kCHB3GZn5+Pltb\nW51npFWvW/jCMTHG/X6/hMjIWq/Xy7Nnz/L1118XcpznwRjRXJ++4HR2wJDWxHOtT9RAWScMBvxj\nhPznPW4FIkI5mAgmgwPD4ZCoDtOSScsDTxzndihk4wR/4VQmnoIGVUBZBp97WVpayh/90R8VOJxc\nVa+iGEk6fYyA2jwH2ay1tbWMRqPSHN3tPfmbgkYMBciEbowmTc0hmMuixQf8Q5JSNHl0dFS8qLNN\nSYrnRmkxInhZ0BC/k4kRMJ+D4cUY1WQw9+sWKR5L5h1OC+PBOZlb0Fft0OywHMKyWBde0CG3USDz\nSWU5iojh4rkJv2mEt7i4WFARIRqyzvxwLWSVsJuQzyUTR0dH+frrr/O9732vXKPX6+XOnTulwR6y\ns7S0lN3dq4UR8Fp1lMHf5oaMhGrqw+FrPaac76Yc0a1ARAwCntVoxfBxGoSv0RCT6ewAB993it8e\nwvD0wYMHnWZb/X6/VLDCJfzpn/5pTk5OCvHZtm1Js66srHQySBhYMh7b29tFEUajUTEcLCmhnSje\nmJCLsUEgybJgJHgmhNd9iPHUoC2HEHyG65CR4WAsMR5JOuEEBYLMiXmqZFL127aTthvORl1cXK3H\no1EYn2XsTbS624C5LZ7f/BJe3krme2ZJDcaErCKH0YFRI21fqHoHoXidm7cTZ0vuXq9XMl7Myfz8\nfDY2NgrSJYRjbzM/KwmVtm1Lr3SeA/SNU8Wosh+f0b1DNGSGz9S8lf+ujZX1zMbn15YjSqa3oEyu\nN7uvj5rUnkZe17EwyxmsjExG0zSlBidJKfCzMj9+/Ljj4Zz9cOtVwjN4G5CIm4vBX9kAWyB6vV4R\nTKpqKTMAiRCa8HyEFiA5Flmy4JbxdsdEj00yySjCdcA9MD/+rOcPVGIPae/pAjuHafVCW75v8tR1\nLZ5vIzKeg/k7Ojoq9wiipc7n4uKiNIKz47CCobjMvbkoDHf9HULzhYWFTnGl7wcZwfBgSLy8xM7F\nVc8g1K+++iofffRRPvnkk/KeEynUOFGHxv0hF9PCLhZs1+9bt+oEj52Kye8PPW4FIqoPo6Hvei3p\nZp1MePNejYr8v5Wew9kQlOzg4KAgk6Zpsru7m93d3WI4uK6FxpXLTdOUlh8mhxE8cxF4KYyYDSB1\nJxC1BwcH2d/f7ygWBggvSeZkeXk5ScozIPysWmdJBnu+ExJSp4TntVIa6SGYGAsjNwupeZm6H1DN\nJyVdsjpJMXBO6TM21EqxNMVFjV7iYqQHErq8vCwr5Y+Pj8vn6nEw1+XMIyjSBnptba0TVrrZPuOH\nISIkI7Tb3NwsyLLmCJ3ZapqmFLSyENoGlbleXFzs6Ivl3/VhbdsWmeV/zwff81zWx7tWQnzXcWsQ\nkRGPB2XaALzru/6MURIetEZIjoPrOpQkxescHx93uhs+ffq09JB2BsZV0smVMbl37156vatdIs7P\nu1vo4OG4/uXlZTY2Noo3rZd7oFysQavDMjoyIggsL0hSugYmVx754OCghIiEmu5lDYcCkkJ5aoML\nErNwOumAMmFAPV/MDwaYz2McaJvLe5DMJq8Js0ajUWexJ5+nWwLXqq/Da6AxjIH5mYuLi05xZW0E\n+R5z1DRNQSLm0VZXV/Ptt9+W1/g89Wqg5PF4XNCSywz4PPPd611V5iOb3DMGnXH3any+y7MzT0aS\nLgexczC6NU9YgwOHcB963ApDhODYGLlSlc/wu86mTYOQTuMibBbGOizzuUx+IlR4oW+++abUZWAk\n8IrJJFvk/cgwOk7ZU0xotNPrTdZIoaDJ1VINe2Q8vfsIQTizceLx8XFevXpVFOTbb7/tQH/GmOth\nrJ3mN58yGo06fEySjlH1s/B83B+HU/dGsZyrRlxGDswByMH37eswnzU65prvuoeLi4vS0tVlC74G\n98lcHx8fX5OVZOLAkDPQEM6DBIZ5JRsQoz7S8zMzMzk8PCxGyQZ+fn4+P/3pT/PDH/6wkzwA6To5\ngvxxv6YlGHuPkXXJKInfteP3eW9y3ApDVB/vQkTvOzxYNVpBqZJuQSOexsaIiQEW01b18vIyOzs7\nZX9yuAFnTZJJ83rXmoBYEHQWSPJdrk/fa69VYusZe1mQCt4Tg/f69euSiTs8PCzCapRDyOnm666M\ndmEfYR8e3AqOYjlBgBC7cBJCGOVxSGwStU4rDwaDMha1YjNn5meSSSrbqKZ+DeNqxWmapmQUfY/m\nPHiP79flEO4k4BCcezU/yGJmE8wu6oSrgesBNTVNUxZeJ1fr35jfL7/8Mr/5m79Zrsl3MGqE9pQa\n2IkwH+Yma5Rro8Pna4NzUyTEcSsMkb2ZhdIC6+LCZOJdTRwiNI5dbemTXHsPAU0mxYzu3cM1V1dX\n8/u///vZ29srHhnFZYtiIyPOhzB6Zw0M4Zs3b8qWMygMTdJYEU6Wajwe5+DgoLPzBIVvGCaEFLLx\n22+/LVyRn5eDTpAXFxdlyQjozAiR8TVkd3kASopxRLH4jBEF16jRg//nM7RYIcSy4cTQm2PC0Rg1\nOANkstuo2iEKpLBRLGiR+/f5rKiWL98n9wa/Mzc3l+Pj4ywuLubs7Kxs0uBUPWNLH2zCZsoB2Kqc\nZwblfvHFF/nss88yHA7L0iEc7u7ubj777LP89Kc/LfdndMtibTudaVSHX/eYGmne9Lg1ZLU9XT0A\nDqWSbubDwlCHbXzWymdvatKV15qmydbWVme/KUKs58+fp2magmbG43HW1tayuLiY169fF6LXho7u\nh4Qa5hoIOfBKTCghChs2wn+cn59nb28vSYpxcZEdwo7iJim9tfks4+gOAnNzc8X4OovWNE3H0PAb\nQwqZzvOggJC+fs0hAPdrY064gFc2ggMV8VnOyeHzuwQDxXY4ZbTD5xkzo0OchnsdMcaURPAMoGMv\nSanrc0w2k7Hl2RwqgWCMvDz2tBlBTlZWVkroxti/ePGiyK2LNWkJQheB+h5xIHynfhYOOw7rnN+/\naWh2KwzRNAPibIOJtTrm57eNEQNlwUomTd3rUMAkatM0pSjRsfzl5WWOj4+zs7NTBHA4HObw8LBs\nLe0V5uaakkl7UzJnrjfh3DQ0s3Djgd3sykQ3XImbuMNHuHUJNUmu57Hyu5OhhZJxAe057W2uCQPB\n9tsuOrST8G8LPgbIBD5NyIxiGBcbKhsywhjG3WR3MulwyPzUxoN5YntvFo3yQzYTGbKD5F7cP5tz\nXVxc5ODgIKPRqHA9zCMG2YgCY83cYvyoA6MHEUtBrDcnJyelDS1zSBh9cXG1rdS00JOj7iuF87CO\n8D07BI/TTUO0W2GIuPGaI0i6TfQdVvm70/5OujuM+rwMFh7RSzDMJxBmUAuSTDwq2TCqaJlMSNc6\nDAD9gGhARoPBoAgqO8eOx+PSltULPRcWFkpLVwwWnhzBd29soLkRD8oHYrJCm0NjrLh3vCu8hTkp\nCyzCWSMgSgvqinnPQ43UOLyyHAeDweHazAuG0ufyD0aAa9fLb7hn7ovrYHTJEtYGDSPG/HEP/O9l\nPWdnZ2XtGNwR4Tsy4cyWx5fWsC7TsEFLrnbqffToUSGyLy8v8/r166JHpOdNLBv18IyeJ+ugEaUd\nR62bNzluhSHiQPhq0jm5Xi+UXF8nU//t2D+ZhCYuMMOzsQixTldC1FI97X3jOYeVC+9G6hUlc/aC\n73EednkgfGPf8+Pj42vCZoVzsd/l5dVSBO+xtrKyUlbf810UZGFhoRCXoESEibAUow364bP1gREj\nZCMFnUz4GpO3Nc8An/Su5IQLOz3/Vh7eYx4x1LXsOFlgbrE+n+WoXvYB8sIY4HTMGyWTanLLKeeC\nH9rd3c3Tp0+zu7tb5ihJWW4D79Y0TdbW1kqJho0fvNNwOMzp6Wkp0iRxgUMkVD0/Py/GzHpivbET\nxTAzlswtDqzW1V/luFWGyIeFwt7RQjMtNvVP0rXUDCpWPJkoCn97S18yTOxd3u/3SzdG2oYmKTDZ\nSgsKwHC5RiNJMTZJF/29fPmywyeguIQEeF2MAoQ1JLWXLgwGgywvL5ewzGnZJJ3G6oeHh9d6VIO4\nGDcKAh1iubLba8eYG9fqOAyyEYDIphulyVF7Zq5Tk80eQ9dqkQTgPDZCcDGEftwrslMjJWdBIZSZ\nU5wbhhiOL5m0PrHR5TXzYTs7O/niiy86NV084+zsbGnEDxeEXHJtDBwo+OLiIk+ePOmE1SCyXq9X\nCiaRW8aQa4L2at3ic3UUY1302H3ocSuyZuZ18PBWGENBk9gehGnW2OetrTfXwIsmkx7PyWQBLR7u\n7Owsm5ubefnyZRFUJhXCl+yGDQP37zV0GBG2iqnT4CAxFIreQxhI0q8gqrZts7a2VpYGsG3xeHy1\nlc4fHQAAIABJREFUcJe/2czx/Py89MHGa1PTwm/Oz/3j7S8uLkqVbpIO0kMZUEjGMekquZWfuTPS\nITRwiQNGxJyfv+cFxZ5nltYYwYBo4IOSSckFIRfLPjxvbduWNh4gi/n5+TIHzJ/DVNC25dgckdvG\nNE1TMlfIHYYGBOvq+radrB3kutAKh4eHpXiSe0EOBoNByUYyV3wf44KhraOTusbIOsZR6++HHLfC\nEDle9UN5EBCy5Hq3Rr5j+Izy13CZz7t+h+uSxgY14YGOj49LOt3nwGsQn7Pdby20vDc7O1vWiTkT\nZOPLMw2Hw+zv7xfBI5yEq8CrUvBmPmI0GhUFI9uWpCA0PGqSwiOQGaIGCULaHILDN4wUPXu4TxBD\nMkEpRk9e64bQG+nwutdC8TmMGujQ4WqSUiR6fn5ejIWfF7SEbBCCIFeEQHB/NBLDOME5eb7434S4\nl4KARAmLCO8hw13Xhcy/efMmjx49yt27d7O4uNjZ9JL7o/IaJGaujGc8OzvL3t5etra2sre3V557\nNBp1dvjwuPOb95AtRxWOSoxs/Zmbhmm3JjSrU/cmyJJJ+9NpaUFnYvi/5jJqWO/Bd/9jvCcDjDdG\nuRAue29CLPYcdwwPCX18fJzd3d2SOfGSgHoM7N0IsVxqAIlOyGZExw9bDGGgEFKydV6xjfHAkDkL\nlkzQDH/7PTfdRwDJanE9owLzFSgvnzXU91w6tHAYUaMu/ibkMkmNI0gmCQfCWEJnz0EyWZfnMJ/5\nGQwGZeU97YQxEmxayWugOvgr5BtDx71cXFwUR5UkL168KOvenKk078Z5nKUE/Z6enubrr7/uID/G\num3bbG5uXjNCHF7lz7NPGwsbwWkh84cetwIRveum68ExCWqDVMe6ts581l4LpSbuZ0GoYTtChceE\nI2KTRLJlkNas5wGtkOkh/HKdiY2sY+0atbFMxAWdruvAW9NkCyFzx0CE2FW/vib9cEAczkYlKWEU\nhpmwySEkYRT3iqIS0vI9F32iMPzm+s7YOTxJJgYHxIrRx/iCWBwaOpPHwl0bA+ad8/MamTEcEI6B\nDKozaLxOyAXXVleRM6bMGRXS5v4sm4PBILu7u1lbW0tyFWbCRxKeEba5NzVjjSyA2jFkzBm807Ts\nMs/sELMufbHxYbwZeydwPuS4FYbIA2BhfNfATDvquJVJqcMpK1WSa71qTFZSzIbAmFPq9/sFHrM4\nE0/f6/UKD8QaLQQUOE2YYoXr9XplcSK7QJgo5do0M6trOKhLAkVgAEwe8wwYj7m5ubInGuEkwg53\nBVQntGOMLJgcCCVel/shvKyrtu1hMQR81qFgMkFTDhdRPMbHYRzPCdJbWVnpzK+VpeZZMF6gUX5A\npIRZOB34RRrz13LnMa8ph9PT04KsqF1jHHBir1+/7iC5+fn5a3VqIHfkfDAYlJKQmiCnTAW5s4NK\n0skgW78cItf6++cJzW6FIaqNBwLgh08mkPhd1tbGCEvtc3sJgD2lLTxoh+si5EYGbsXRtm3xknt7\ne2V5BYVrhHesYwJGExb53lFiN8gi9CMUWFhYKPubuwAQZXfJP0skSMfzbCg3KKJt25KBsUA7JLTR\nqclUEBWcmFGMr+lwKulmRm2knPI2sW10wlF7ZK5rQtk8DZ9BqeHdUHA+A+pBIf1MlBtg7CxHe3t7\nmZmZyebmZg4PD0sLFScBGCvOiXFgYS+fsVHt9a4WOb98+bJzD+4tZYTH/JN9cxYMUp7v2XDwt42+\nx9bj4NeRh5q4/tDj1nBEvvma5HSmBGvP4TCMAbHQO7bFePhaDutc6ZxctX1l2UaSsr85yIHiNNDG\nYDDIy5cv8/Tp0+zv7xclspIlk8WeeFXQi40lHmwwGGRjY6OU9XsTgKZpCkeDwplUxqi58pYUOV4S\ngYObIOPnNC4GycsFnAkySvFKeO6DECGZ7IXG/3hucw0up/DBM8OHcF6nph2q2ZAmk3ov0u+EjsmV\nAoJWauTtYk/CIe7h9PQ0h4eHBQHDObJTx+LiYpaXlwt35msZeRMKItv1s7uNyuHhYR4/flz6LxGS\nwnf5HtElkig+4K/qUB1DZEeDsXfUYI7PZR/Wqw89bo0hMuFVc0M1CV0bkmncUG2gOOoaB75vrgKh\nSCYZkPF4XPq+IFCzs7NZWloqPMzBwUH29vZKjRHKakPKNZlkK6iXL1AuQKod4wDpSgEb94YBsmCZ\neJ6bmyuN1N2SBCNIGYGzdxgew3WeZ9o88borvr28hgW9h4eHOTw8zMHBQSFjMSQm8eGA+L4F3fPL\nMxPSjcfjUq2OsjAWnAdk6qpvEChdC5z25j1qdrhX5tlLIMxz8RoEttf0eetqUAshF+G/DaS3tj47\nO8vTp0/z7bfflmtQRe+F0nQRtXEhdD4+Ps7Gxka5fq1vrrfjOzid+nzTeNqbHLfCENkY2IPVrydd\n0q82NP5dGygmyul9E3UmFtu2zfLy8jXeh5jd90C18u7ubvb29jrhh3/XsNVLN+BTIMPX19ezurpa\neAHOA7FOv2TIbBCR0RnCAc9TZx3hxvDMpJMxSIwjXhjjiFJjKGyQpm1bzeG1ar43I1bQVj1WoBv+\nrjkI7sOpde6jlgFQCJ9x+h7ltQPp9XqFW/ISEGSJ9rt8j3NiRHAuznRtbm6W5vjID0Q3BggDiMwd\nHh5eoxuSZDQa5dmzZ2Ws2baodjaEcDwX98/axzpsNnfKd8zdGhFxTy5h+LVFRNNQkI1Jcn0Apr1X\nGyn/bUFKuiX4xOKEPxQLGvbaC3DP+/v72d3dLYtS+TzZifPz8w5H41DAxXQzMzO5c+dO7t692+kv\nRP2IG6lxv/RFIiO2v79fUAZEN8rIGrSaF8NAWIAQUjIxNdFd8zyUC7j2iud12pjvMi6ErSgOIRP3\n7Hlz+MLYU1/jMJz7QfFqmaizaciDZcIhspUUo0HYk6Qs8iXk5dzJZBdWxhwkRh3PvXv3SgsZHBLo\n2jVPRiPmkwjT6RZKYSWlGxRHIkvQCsg5IbGdMGMLArOj8Hs8Y/0e4/yupNK7jlthiHzjtSWtM1oc\n0+JdDiuO36sHx14AQUcJak4KRXK26vXr19nZ2SlCQjYM6I3H83lYkkGzsPPz86ytrWV7e7vE+xDP\nEI5WZowRaWOMBl4ORbm8nGxXxP2vrq52Nkj0EhOeAYId4+BSARshDCTPkUw4mF6vV0IQ5tatUTF8\noCaQmPsyYXQYN3NLhDk1OjYHd3x8XHgvZ5Mgj5umycHBQXE2h4eHJXREoY34CFMxBMwjzsKJjXq5\nB1lODBIGhXOyDIfSApSd+0XxeQ7ueTwel10+fvaznxVCezy+6hDq0J3qfBdGMscmyK1D6E1Nh6BT\nNXjwOaa9913Hrcia2aom17MihtVA7vex8+Z9nJ1wGGADZaPleN/C55R0kuzu7palAHgdJggvmaSj\nzCZak2Rra6t4SqMTPCwGDeNmJSDzAkGJcSJlvbq6WhprUVH7+vXrcn9ch/OfnZ1lfX293Csoiswb\nhhoj6SJFMk6gjF6vV/Zgd12Wx4H7MOqo0ZsRgUMNxganALozyeulEG07KYx03dA0DolsFOgMdGvZ\ngttzBtVIgdccAuKAqEnjeQjJNjc3MxqNCip35hIZ4ZoYJub95OQkjx49ysOHD6/VUlEo6aSDOxfw\nTB5LECfywn06m2Yuke9ZT25y3ApDZEPj1H1toPgsfAeCnFxfpW+4aA/sw1YbgUfZEVBSnU3TdLzT\ns2fPrsFnrg10Z4IckuGN7t692zE8CB2hFrU89SLOZLJVDeQ0FbogKmqNdnd3i4Ky/xrFds5ANU1T\n1qnhIS2MRnpGjYQx4/FkeyaMLkbYgupQi8O8gudjcXGxZPa4Jp9DuamD4pmSK+4N44sh80p7xpQ5\nA60RpqPgzuRRV8R3Pdc2OIT03COKyucwGIwFnyVEZZ+64XBY+o1fXFwUmeH65jxR/PPz84xGo7x6\n9Soff/zxNc4HY7e8vFw2XLRxT1LmHqPl61rHmHNnr3mmabTJhxy3IjRLuhyC4/h3GSMbmDpsM0Sv\neQGEBs9VD+Z4PNkMD4jLbpoM8tOnTwsawQuj0C4eNNHLPZ2dnWVlZaWzHixJMQZ19TUKwVo3QgcU\n5+joKPv7+yUbZdRG1gTFZCxAa2SBQAMOSf1MPCf3j8F0UsB1RjgLtyCZn58vWy2T0l5aWiotTSHf\nnfLHIRlxeKkNRaV8BiXlIOQDQSwtLZVmcRiqWg5wRhhSk/PTkBQy44yhUe3l5WXJ4CXd9Wz9fr9U\n6BtNER5TUc14EGbyrJyPe2jbq1X81KwlKWvscHorKysdHTIK4hnqwtHkemdLy62dv+X3JsetMURW\n4tqq1uGYPShQk/+BpB5IjI8zCc4cGMa2bVvS3IQjXlX+7NmzzMzMdJAFaIfwxILGM/T7/SwtLZV6\nIJ6FmL5t2+KJ2AEDQT45Oelk5DBEkJo8izsmAseTSdc+hyAYDGdCTk9PS6MzODPQIajEGRyUwpnO\nJKVUwHOLkDt8gpOqW384y8jnvLyHeXLyoc4UEWITpiYptT712is7riQFiXCfhGugJ1ByHeLYIIJW\nkGUjLgpPGWcQJfeDcT8+Ps5wOMxwOCzhuHmvi4uL7O/vZ39/v4NUv/zyy7K42IkD5OOTTz4pY2zD\nQ/jvMQVJOhlkWqNGue/iY9933BpDlGSqUNdGJ+k+LIOJEHAg/OYlOEww8lmuA/nHxDjF+/jx42sV\n1ggWgsIE+pxkxOowDnRxdHRU0rd0ZoSUZUwQagyXuSAUIrlSNlBT0i0gBEExrnhEvOv29nZpk2te\nwIR7Dfm9BIIDI2Uuijni81yXkLL+PoiIcKFpmlKECPJwrVLNoSALJlpdq+U1b5zToZJX29foDFQD\nwe5QzVyLnwcnxTlc88WiWaMSDB2bBgyHw2xsbJQwDo7QjpRx6vf7efHiRXFIZHNdSuDQijnh2swn\n421+z8bIvJ516aZoKLklhsgQsPZ673rN5DP/89maS7BHdkxff858QdM0nXYV+/v7OTw8LP+TsSJG\n5wA9gNQWFxdz//79a+ldalNQKBe2OXtmT4tnvry8zP7+fl69elWQU41QUAiUgKZmJvGdomY5CGOM\nwLoVLAiKcTdPwb2Donq9Xif97zDVn0WYHSI5OYByeTkM8+WaIxQNI47htkE0Oq2RNa8THsLLebEw\nn+F77CG3uLhY7pMxJ+zl/kAz3HvbtoW0tlLzWQwMu3ckV4Z0ZWWlhOmcE+LeMo5zI/NIho55oF0s\neuBIoXam76I+/Ns67PH60ONWGCKHXCYV/dsCwHdq7qhGIvVnajbfxgiPcufOnaIYCMPZ2Vm++uqr\nYiTm5+dL6IEye3kFDb22trbK/4a4ScoyC7IkRmwYHnMNoCcqkVEscxcYNqprGTO4EvNACBzXgjs5\nPT3NixcvMhqNcnBwUITLlciMNfPFok0XWDq0YXy5Z9CA3yMRUHteqq5RbIcEZPw8rsxxjbC4PoYJ\nZbN88UNyIpmQw8nESLLw189jVMR3MPagl/F4XMJfb31NQsFtX/g+skLWkxBxfX09KysrpSDRnBXH\nq1evylhxbQ6yox4vfngej6t/+2/mySi6NkofctyKrJmF2v/bSBhqm9BMrjf0xgN4HRKQmvMn3b3W\nWRmfTIi54XCYfr+fP/7jPy4NwFjRfHFx0cls4eEhYb3oFe9D+MY91/yGSc/B4Gp31cvLy9K7OElZ\n/IrRoM7o+Pg4CwsLpScSxgmi3aRm0k1t0/1xd3e33BdFlCyaxMgYVVFY1+/3S5cBwikbFK4DMfvm\nzZuicMyH27rWaC5JIdwdhnKvtbEwuiQF37ZtqaF58+ZNqa+Cc8GgUr80Pz+fvb29UjmNvNHehcQB\nRt/LIegFRL2SncB4PGkdY2fi+yc8Zo7X19dLmcjy8nJna23mlcwp5xiPr/bBe/HiRYbDYZpm0s62\nbdviOOjiaCSPTNcV566nQxdrHQbReTw+5LgViCjpEpr2VvVhInMaacZRx+l46WTCQXhQe71e6UvE\n7grLy8sFHUDmgm56vV4J3egTvLq6moWFhVJJjYGBu0gmyKL2Jr5fE+FwQSCApmlKPRCCTNsJzoc3\nAwnBATEudXYLxSLEWVxcLBW/HiOvGEe5CUngNHASzCMcj9t/+HMOnZ0R84ESODHg7CD3wLiCeCwr\ndfqd8Wf8QFcoNyHe0dFRCX8hwPnfyRXvaUdojwGo5Yz7QiZxcDwr1dVkS/k+WUJnPWuFd6KHax8c\nHJQMJuP75s2bskmjw7J3cT5G7Mx9jTz5fJ2J/pDjViCi+vCkJd0Qq+aLTP56ws0lIehWLBsurrO2\nttYhMpPkyZMnHZ6mDmnYNeOjjz4qPAye2CiNyVlaWkrbtqWmB6PgxbUUAlLFPR6Py3IOFBEjNRqN\nkqQgQHtnwri1tbWCRswnMD4+5/Lycu7du1dCNQzA+fl5QV8WNMKsi4uLa50OCQE9zigzKAgjwefM\n5VgWCKsYTxSe9yB4jabMf4AKLy4uSlgNUvE5bdScnULGyMQx/yA17gO0gzHkGtw/xDRO7fDwsHBM\nZEJ5BrguCjwZOz6L8fT2Uozt6elp1tbWcnl5mVevXuXhw4edrabq0ImSDBwgB/87iVTrk/XOMn+T\n41YYopo0nPa+vUj9kAhFzQv43GRnMAp1DU+v1yvLFU5PT7OyslI4HAYXQUZYgNG9Xq8Q2XVGDYVE\nIOld5BQv92CSG9SGcOF5LYyUF/C+l1EcHx934DVed21trRgNSFm4g5mZmayurpYwic+B7Ag9qRA2\n3+LdIzCErgDmPl3eYA6EcTMXaME3/4ZzIbzkfCAGiiExRhwOqziXixuTbv8jjz/kvTN4lB5gmDgX\nPCL/k6lknRn3ad4II0B3BUo4vGaQ6/DZubm5UlW/sbFRVtoz9hgeeL+PP/64GD/I/62trTx9+rQY\nF4MA5Mlz4iiE95gD6+FNyepbYYiS6QsqOWoPiRC78Kr+jL0sB9yFPTUTi3Cxv9js7Gxp51GncOFr\nVldXc35+noODg4KkeA+vlUwWaM7Pz+fo6Kg0ysLTkE1xv2zCO67HPZ6cnJRCN/NGhH9efwZiW1tb\nKzybi/Lwas5IebM/8yXsJOslBxR+giBANxjAGtZjcKbNO+gLhavDVSMih7XTEBQhqa+BIrlqP+ku\nu8FYJldoElkErfrZQWBGQXaGGD33pDJKoszCle2+HgkEuidQ+8TuMFS8n5+fl11bKJTlOm51++zZ\ns3z00UdFBpgv+EHrWc3XOvPIvCbddjDoocPemxzvNURN0zxM8g+S3E3SJvm9tm3/btM0G0n+uySf\nJfkqyV9p23avubrLv5vkLyc5TvLX2rb9w/ddp4Z45jIQdBe12WBNC8mmnR+B41r+LELNtZeWlvL4\n8ePiQZNJSfx4PC77YuH9nJGDxOW7Xi7Q6/XK2i2MEN+zx3YKlyZbzn6wpOLy8rJwCqAUiifdAwdO\nC0EkdUw4QtixublZEF3bXm1TVPM6XuTJXGCE7UwcWr+VpQ7ERzn5rA0P13JoUPMf5qYwrHxnZuZq\ng0JCUWcaTepyTQwSRt0hGXOOgYAA53rMtxFYjQ58/yBkKq4JzbgPHBGoZH5+vlPpzfN5PpC/7e3t\n7O/vl3tkvMnSLS0tlS2leBZKVnx9ZNV6ZtRk41/r3E2NUPJhiOgiyb/btu0fNk0zTPJ/NU3zPyf5\na0n+l7Zt/3bTNH8zyd9M8u8l+ZeTfP/tzz+b5O+9/f3OoyadQTsm+vBkybvjTzyy63o4v40VhwcZ\nYtGkLzUaGAbuYWNjo1MhjDCgDMlkPVivd9WuY2trK/v7+52CNAQV4UKhgOqMhQlW0th03INXWFxc\nzNraWhFa7+6RTDKEeH22oU5SPOzMzExZ7InieGtlFJkQjDASAeVZ8MTm55IUEthKYhgPQuM+MVR1\ndobrcj07GGTFiJhlHowB1+XeTWjDJxG+WakYe85dKythIegJx2KC3wWmzMvJyUmOj49L2AxKYv3Z\nyclJhsNhZz0iYTlrw5pm0hlic3OzsxEm33n+/Hk+//zzEsZyH/Rlrw0PzzcNdU7j8UyQ3/R4ryFq\n2/ZZkmdv/x41TfOTJB8l+Z0kv/32Y/9lkv81V4bod5L8g/bqbv6PpmnWmqa5//Y833WdzkDUzL9f\n40CwLKg1N8RrHjx7RV5nLy977d3d3TL55ngwEngm0qnspgBCcJy9t7dX3oPDgSNyWGoFx9jxGRcT\nYnD6/avdZzFCXqAJX0NtDOiBcgKjHHNB5lwwjpzHiNTLChz64QjsWVFsIyCQhrNCvFc7G5PrDhWM\nbAkLkwl5z3UtB1ZOKxrFfziVer0Z4fbJyUmnCpv35+fnSxGheSjumTownNubN28K8hyPr9YFIi+1\n8SIJgTxwz6Ah84ozMzPZ2trK4eFhJ1Pmbo6cYzQalX3PXEbAOPHMRnbv4ouYi9p5fMhxI46oaZrP\nkvzTSf4gyV0Zl+e5Ct2SKyP1jb72+O1r7zRE3LyzN3hMXfvad/ywjmtNntWpxtpAcT2Uk+vUa6Lw\n1Gtra2WVNOcDQTiLA4diz5SkrCcD+iPANqp8360uHE7wHv1lqCfykpTz8/PiGeF3MGAO4SxgZNaM\nnhhTBJewzWl6JxFQYoTR30EBHGaCjlAgwhujKowGKNNh/OLiYmfXWteY2bAeHR0VxMFcn5yclPuk\ndMOhd7/f7+zIwW/CNNc0+XrUnBn5WY5Y8+alFcge1dgkB9hJloyknQM1XVSDX1xMNkSkfsxcKuM5\nMzOTo6OjIuOE+Z57H+aL+O0fAwR+T+N6v+v4YEPUNM1ykv8+yb/Ttu3ryhK2TdPcCI81TfO7SX43\nmRRg1QTzND4Ipak9JkbB6MlQ3fyCz2/D5D2fbEAIudbW1joFcNwDws/fFLox2cBxjI+zbib/IKtR\nEO7R3MPS0lJWVlbKSnZI7l6vV7IkKAn7nZEN5HqMNdvfNG/JUsbX4+iQiwwbysu1LeC8biOHklD1\nbYfgbCDn5jrMNfwGYSXXG4/HpZe3ZaRW/l7vaq935o154odUOUS1C1Thb4xMeJbFxcUSFhF2Gm2R\nnXI5gdfVUeJBgSwGkbWCGAnzgTbufk4yoIRr9JbyFunHx8cZjUZZWVkppSMsjmXcXcuFc4Kv+i4e\n17KD/t3k+CBD1DTNTK6M0H/Vtu3/8PblF4RcTdPcT/Ly7etPkjzU1z9++1rnaNv295L8XpLMzc21\nyYQos5dNukQmnvS7DjP3PpeF36lKFAcvNTs7W7IaCD4hmT0dk2S47xACbgJh414wUgg4xm9/f788\nA99l8uFv7t+/XwxIkmJ8aKdhNMj3MEDwRs5QIViuvLZjQEDha0ABeHPQGOexIUom7XlBKRbQwWDS\nn0eylpmZmaKknIvWvV4fBxfG4awbz0/4iYLBKSFPoAieDWdB6YLlCaQEcsFgOYTBgDIHhGLMKTJp\nktzj4UQFz8eYDofD8j0T5pY5+D+M7vb2dg4ODoph2d3dzcbGRjG+fG9xcTGvX78u9+JEhCMNjLV5\nNT5nB3bT4734qbnS1r+f5Cdt2/4dvfWPkvzVt3//1ST/o17/N5qr459LcvA+foijNjImIpNulsOf\n4bd/rGTvMl6GkXgaUqr2Sk3TlDU9TChIBljMNfBI1BXRJ4hKWLw43h2h5doYPwwA97e8vJzPPvus\nVDwjzNQy0QbWXhGjiRHjvPSX5n+e0Qs4HQa4hgcBZyxsfFHIem64Tysdobj5HRTfYTfjzcEY4hRQ\nDoy555TPkX7H4FIw2rZteSbGE0OEkWEeOD/IifEFxVL7Q59qjBJFhzbCrmer+cpksm7R2b7z8/Oy\np5nXrjEvnBsuMEknzGOODg4OyrXadtIzid08eA/Di+FhjhxR8JlkEl0gGzc9PgQR/fNJ/vUk/3fT\nNH/89rX/IMnfTvIPm6b5N5M8SvJX3r73j3OVuv9FrtL3f/2mN4UQOavEZHIYItbGyH+b9LNA2GA5\nBgYKkzIfj8fZ3t7uZDPwuHh0FIeYG9gLGWg4TnjiUoH6Xon9qSUZDK7Wl3FdlHhlZaX0OIIHoVAR\ngwF5jQEirOHZnFHi2chsMW4IvMl1k9IYVZ7RBCxe00Rxko5z4UDwzdVZIXgW5oLiUObC5L7JcOQB\npEDo66QDRsoOxSiXuh0U39wjlc2MnxXUYZSdIYjHxge0Cl9GSMr5er1eXr9+3THYb968yerqahmz\net7b9qpWaGdnp4S/lFqQxXV4X+tXTfRzr/6/pjw+JGqpjw/Jmv3vSd5VJvkvTPl8m+Rv3OQmauLL\naMafMVxkYmyRHZKYMPV53t5j+Y1XGwwGpQJ2aWmpeLIkZdJcyepzE8YxqXiF3d3dDIfDkq0g5uZ5\nzI8Aycl8uSaHrobJlTdko0dqZahjYiEn98D2PijK0dFR4SxAQIwVhtM1Shg9DJGXyJjAN/pq20mF\nsb01c+qQjzmwwjOuKLSXYBhFGknxntGkK7+TlIpirre4uHittqjuG86zu4m/m805y0mRar/fL/VL\nzLPbv7TtpAEen6+VmvmE2KbMgvEkVHSBLnVkJt1JoLRtm+FwmLOzs7IMiZDPWVu+BxKGy3QY5s/Z\nQNYA4C+MrP6LPPwQ9oS1ha7T9Sabed+eqfbCHCZRk3QaQKEkKNDCwkJ2d3c7tTRJOoLLawhY01x1\n1zO3grdjok2ME0K4CC9JWRfFSnWye3Te84Z9KC/j4Gpc37s5IddGeYzsGPgfjw6BigAaARwdHRXO\nhuc0MuB/QiDm0Ne0kXKSgc8wdpQK8DrGYjwel6UURrBci3EECSbplCxQoW5i2UgJ48A4QqxjMCGc\nT05OrqX/SSTYACATjBcV9ZZV6pMwkjZSS0tL2dvbK1kyEiXmidp2spCXsM6tTpCDWt+sUxih2uAw\nbzZG03TufcetMETJ+zu71YhpWkrRD2/o7HDAIZlDh2SSSfF6IgSfFDuC4d1J7QGAy4Q3Dmenj8uI\nAAAgAElEQVQwOghTMlkcilIcHh6Wv2nrAbJZXV0t59nY2CihAtewh0IwnNUwh4Iy2zhhoC8uLkqB\nJ4aD72JMzNOY8PX4u96FscOAOGHA/aKMNkZGCnzHhCnjyLpA5gWUyXl5FuSkzupZDjg/80LYbdK/\nzoRyYEyovN/f3y/K7yJW7g8ZoKSAsXM2zDwp80MWse4m4GU0JpnH43FxaCcnJyUUsz54rGuUZCNj\nfaudmI3TTY5bY4icaTH8t4I7XEi6A81nPGA+h0luZwJAPQgWodPs7Gzu37+fJ0+elO+ykJHzA3MJ\nCZKrFiKEJ3giTyjK7yURGCyE061Bl5aWsr6+3rmv4XBYwhT38eG+jBZsZOoWGnjCJB20Mj8/X1b1\nY/j4jsOzGqZPg/eEeUlKNgyDYkU0cuE54KHqtrCQycwB5zJ3g/FEUblvluTw/KyCHw6HReFBLhhS\nihRRfmp7PJaWMaPme/fu5ac//Wm5b+YA0nx/f780luMg2UFa/fLysqDNGuVjqN68edN5BgyseTzk\n7Pj4uLNtFGPL9kGegxo18XxO3DD+3xWFvO+4Nf2IkumW2CjIqVsTgDU09Pn4zXlqqI9RIh5mwPEc\nVhBzHNwPBg2hNmmdTLbVqe/JUJhnQUiplN7a2sq9e/fKYlMQkhfW8n2+y7ndtuP09DT7+/uFA+I7\nNiAok/sxc7+Xl5M9310Tk1zfLXeaV2csGBf4N6MS3wdjiGGkWJO5wEihQPbmvIZBNgI8OTnJyclJ\np7tAMukRxfqvvb29DtIdj8flnn2/PBe9xXlu7jtJZ8tvI1Dkh7nEsIFaCJl5dopNp/GjyCqGkvlN\nUp4VI81SEZyVDTyLqa1byCuGHL0xIDBSNuK8yXGrDFHSNQ4cHvDk3az8NH6JgTFv5IxcMlmuAESH\nm7G3Y83PxcVVA3pieSbU0LtpJut+QDrAZiaXFe+Xl5OmWpDZw+Ew29vb2draSjIpVmP7HfifaWRj\nXR+EcBAOEeY5dcx42LC76tep7BrG22N6fRlGBuG1wXDW0OjKoYGNVD3HKBcK5fAKOan3F+O8zAtG\nHEXne4yBNzr0jiogr+Xl5Q6/hmFiHJJ0eDjm0PQA90kzPbgg5otnh//i+U3Ov3nzptwLjqJt206y\nxePPBo+gWy/doRTBoTFy5nnksCwYDODQb3LcmtBs2uFwzUL9IalBD9K0WJbP+FrE6ngrGzQyGSZQ\n2XkDVFDfF0Ljqt06LOn3+yU0RAju3r2bu3fvlvO17VWWx6l4DBeKiDF1qYANNkLIvUDGW+CcOqcI\nELRjpAOcB0GBHly1TdhkTskGoTY0hvl8xr1+GCsymHYkRst8ruae3GaD5wUpcK+MB+NA61xQInNF\niGRuiSUphIRkpkAS5sVAZp43jDP3gcF0fyTGZTAYFEND2IaBOT8/z/b2dkHDODfG5/DwsBRG1k6I\n8M1GDB3yvPKM1i+jpJuGZcktQ0R+eEN7BNRE9bvSgybgOJcttr1+fW3QEL9Z6+UU+cHBQfFEycRD\nIEhcB8iLwthb1yEoSGJpaSmff/55acWB919bWytFiA4PzZEgaDZWPH9dts844bm4H5cmMEZeL+XG\na/AtlBzwOmjLnrYmM7kXF+XVRX3cHwgBJcEggGhIIri+KZlsakBYZRkAbeAcSIN7Lp25855wl5eX\nBSVhhCj7qA05RtUcFcQ+2TY4OdYDmvOiJIL7AsHYUfI31768vOo9zt/IpLOZTtLUdVzWDztW65uR\nth2suaF36ee7jluHiGoIWhenWcHq79Xf9WFk4HqIJIXAhBvhN2X7JkAPDg7K5CI0ZNKYFNaXIbyG\nxAgqyApPNDc3l4cPH5b1YV7dTZMyBBTFIeOGocMj4zXhdTxm4/G4oAHH823bltXfNvYgBsIUL6kw\nmkG53W4DI2c0xfPWSyhs/Bhvyh/M+aCApKKZd5BgHaLBMeE4CJ2sKG171dfZBD9/0zGhLk2wkaGE\nwtXXHJDbZFJBv8gA6MlzaYPuKulaji3zzn6enp7m8ePH+Y3f+I3SQsTUhMNFO+U6CVTriUPo8Xjc\nMZKWh2nRx/uOW2eIknQEOZmgCOBkfRi6M0HTIKIJzDrW5Tel+iggK+ghOxF63j8/Py9wFkMER+Tu\neuZEeCZg+XA4zObmZu7fv192gkUQXfQGt8HzkRVxWGRjbMLZ2bFp3JKNPv/XAooQc09UKWNICSPq\nCnh+nBX1XGOQMXwYcDsBh9peLmF0WfNMdTGkyXDasPg7/O0FtuzUggFz7ZeNGXJwdnZW+CV3ZXCG\n02iU7CIbEzB+8DXUJbGA2tfleViy4rqqpmny9OnT3Lt3rzha5g+5s6wk3Sp/8z38GI3X+mUKxGP6\nocetDM2SifDwdzLJEtmI2EPwngd4GlQ0ikDBrfTE1Pb+yaT2hi1+4TCseJy3zo5wH5DaoIPhcJi5\nublsbW2V8A+PS9l/0t1fC69Lub6fOZn0p8azY/T8vAi1x87PWxsCp6YtrE4gMLZ439pYkML28xE2\ncI3a2TiU4P86NLBiewyMqqlOrklVeBmPD+ivNvgODclGerxAaTwzxo4yBHgbyjaYw8FgUEJQUHNd\n8MqYWC9A5CwJYuw5QJPmDJlHO2MnAFxCgdFjXBgPDI1LBerjQ3hcH7fKEH3XYcVOuiEcRz1w5pWs\nDPb8EJmGuEk6rVfJip2fn5cV8sBsOAOucXFxtR85a9UQDqC5Q6r19fUsLS11OCEE3a07UB5gejJB\nVHBHLm3gf6fPEULKADi3UdG0MMpQ3OnyumaGJQ5Gr5yTZ3AmzuEeCgWKqOuG3LOIczqrhGHwwt56\nzDAsoBTGGQXEOHgOuEeHp+b4vBHC6elpp/Xr/Px8SZW7ForlRMvLy1lYWCjbBRE6Li4uFqPJ87dt\nWz5vQ+wFymRsQW6MLY3xjUhxal7KY7LefOq7jmm8H8evPSKy0CfXtw7CW1oIbWj4jiGivSuexrxI\nksL7nJ2d5e7du0WJWbv1+vXr7O7u5vXr1+X+jo+Ps7Oz0wnJzs/PO61DnAqlLxAC/vDhw9y9ezcP\nHjzIxsZGWVWfTDJpeC1CPJ7v+Pi4s3yAdhG9Xq/wAbzH+Rg3DDWVtVZIvByhhCuMkwnpCvfCWi2U\nyFk2IyZXfiP4jKPLCZg7jPB4PNkiyJyZSWfqbkyQUzjp8NcZL9byEYbz/F7q4c6GnMsFmryPUtvw\nOVu2sLCQ9fX1YiRpC0LIxXPMzs6W/63I4/G401GTDRss08wBBggODZn+2c9+Vu6f187Pzzt7pnEt\nO4maR5vGARn917p4k+NWcUQ2ID5cmOYHT7pZFsN3Gy4bKSswB2lmewxnIdjQkEE213B5eVlCC4yS\nJxNymIPteDY3NzMcDjtCb2IawbOh43nNNxgl9PuTDfiMLEAQfM6Ihtfw9AgrsNsZJZCJ7wXFcUrX\nFdNcm2s6q1kjNsYRHo4iPpPShCleCU+444WmPr+zRMwb945h8xhg1B16YRz8DL3e1YJkzm3yd3l5\nubSCge9xVu3g4KBUeFNa4EWmcItnZ2fZ398v+9NhvBkHxsYpevfBYj6ePHmSjz76qNyrm9CZi/X4\n+bl5dp6x5pCYG655U2N0axDRNKhXpwANF2vy11kMXrfQOfQy/DQ/hCJZSWdnZ7O3t1euUaM1EA/K\nZILU90m7DjwkRsiVtAgf57NCW5m4d7w9RXnOdnl3EBAaHQIcHiXpCI89PNd2qItRAXkY3dRzyNhi\n2Bh35gZOpSaZjYot7A4RMSo+J8/OYYeGkhHeMGacHxRj5OmMrcNbn89y5jKJtm1L8Wmvd7VOkKp4\nZyS5b87DWHM/lGOQiZ2ZmSmZVTZrrKMIL6PB0O3s7BT5cUEp6xd5prpuy3PvcbWeWc45at1933Gr\nEFF9YETM6k9DQBy1UfJrPieC4HqUs7OzrK6ulkEH1tNPqLb2hqaEM2ROXMCYpPBALMpEGVzU5/of\nTzT1JPX9TxM+k48U2JlgJewhHW1jaqPEOXk2ox6yQE3TdIwm30WBQW71HBgNYfzd2qKeX+bKoTZo\nj3nCOBmZgSYZSwxenWKHf2mapiANh1pe7AoCgXgmW2oDZDoAOWDsqaI+OjpKv98vBYi0JOGzHkvO\nQ5ti7oXnm5mZbLTJ8xCmuZXMxcVFnj59mk8//bSgTDtzj0ltZHwvPJ/nuXYWtQ5+yHGrEJE9Dq/Z\n4NjD1Z7PntaDU1/DqWJ+m3Nwbc+bN29KCxC+n3QXNVKJDV+B4vjaZNqI98mQIHRGOdNK4/mcU9uE\nhe4wSFhl7ofvY0gtNIR4yaSnkIWwzlbVYXGdkTGKcdYNo5V0W3EYkdrY+D2+QyhR/ySTQkKMUD2n\ndTkB9+1sIuNJaAYRzPIJt5PlesmkhQzohXEHeZg3SlKQDE3tLHOgR9AV84TBJ03Pc2MUXcTJZp9G\nlsjNt99+W4pPGV/GoJZrz0MdpnGYU/I56uzdhxy3whDZY/ph64dB0d53Lj5jboGjJuDwkAcHBwUu\nI2js+ooio/DO6JgXSiZ8E4ToxsZGIZdZIb25uVm8n+E/xoFzQaZy2IiiNOZpuE+nxc1n1eGqCVi4\nB5AZyuieyXyn9nxc36S0080WaubA5DnKgbfne55LIyTWXlmJmE+uxzPgJCDZuab5ELKUnJNzGaVx\nbybBedbhcFiehw0NkC3ulcwcnCDrCR88eFDGChkx3wby4VlmZ6+2uiaEIkHgdZFG6KAcfh8eHpbt\n1L00h+QKSMnhYq2X1k2HlNaz9+lpfdwKQ5R0iU8fKEHSLbIz4jE34+xMTb7xuWnFfGR+gN/9fj+P\nHz8ukNrFYgikvS5KgDGA3OW8LGhcWlrqlPmjIA6/CKHwZPYy5rb4npXH2Z6aC0Jw2X4Hw0qlMcJo\nBcQzM2Ym+zHSjCtpas5rtITRbtu2s7cXY4+h90p45sbFmtyLOw8kV0jDO6baALlS3cskMEDmn5qm\n6exq4cO8XP03400rYCMcG2a4qX6/n5WVlSwvL+fOnTvF6TizyQ/ZtGTSI8l9h8yBGvEynzihs7Oz\nvHjxIouLi2WOLi4m2yvVDoNz17yoDb7/5zP1OT7kuDUcEQ/GoPpBmRhDPmdh8E7+jMm6pIuu6lDO\nJDEC7CJEyD1nXTwpXBMlxchQug/yWFlZKR61FnLD4iTFOFC9bC/ONdigj+dH6OE1zLHxGY8d58P4\nwinxGkZommc0ApumoDbIzlIxVlSnY2TgW6xcnntnAZ214bo2UoQvoBB2GiGTRBcFELbJW5O1fg9k\n8i5y3A3sOQ/nqMPkXu+qNISGe5znxYsXmZubK8tNFhYWOgZ/cXGxbBXuHlY+3KAO44OzWlhYyOvX\nrzvye3l5WRCpjUmN+hhf9MCG23TINJ72Q45bY4jsAWqDVJOpFjrHqbxvb+z3a3jJ+0mysbFRPnd+\nfp6vvvqqGCOyFaAue4Kjo6PSjS+58rjeNwpDce/evdL21ejP3ILL713ty4FXxys63ofjQcEsFHVm\nECTCanzfOylwwgiPnVPEbtJlI2fC3qUNHJ5bzxWGwCSrFQZlnZ2dLcpoQ4hSch6nr91zmpAVRU4m\nxof78DUJXYzKeA4rvHtkO6wzj8bh8LBtJzVdg8Egz58/L+l+V+5jVKi+B1UbPTqJwL3i9Fy9jk6B\nUulzbaNnvayTGvXnrFPTXvuQ49aEZsn0Cs2awa9JaBuFab/52/wI37NgoaBMJCuqOSCl6+tDpMIH\nUHBWT4hbzZoPcnhGKt9b0NQbC/6/7Z1rjF1XeYbfZXvu4xl7xpl4HEexiVMiR0IkMhE3ISSkJkEg\nWiEk+qNBFJUKgdpKjRCXP0jAj0aF0koRIqggUnpRRVslqigtVJVKUaE1KNwCBNLESiIbCGkSO3Yu\nHu/+OOfZ8+zlM84MMZwZcT5pNOeyz9rfXpf3e793rb12vUwgSWf9TLKqB5jp0dFhKPgAA3r66adb\nYAGESGsQ5QFWmOEg9lLfyMogor65JqcTGL8x+NX9wK+9SprXgICju6fMfc1co+vJwnTdzvQNMwX8\nAdysT/GaevFkgOullNJuhnfgwIFWQyRt5hYONDz6oneR9JN+ze6oc1LhlZWVHD9+vNWyaEOn4x4n\ndTpmYEq6t/x4Fm7LMqJkNa+vUwG+c2ShY1CZrqjk/LvxDSBUfo3+jpCwCFiKGRaNzZatKyu9pfs8\nBJD3NO6ll17aPg7IYET5njnBPxiSgdZiL50MZgBgkYIAqvzGDMHLBGCMdG5AL0mrU9HJATwL1mar\n+IL+UIM2bQEzI3XjM67D7JP+QHq0srL6NFR2CzCThmVYp8M3QIRz0IdchsG13gvIQjwgW9/QTCo3\naHaQcgk2tZ7CFP34+HhmZmby4IMPtsI0T/OAZZn5Um/UNfXndJ/rI3DS7vQ5bqylj3Pd9IV6UqDO\nUPjc6dpGbNMwIqcRyfk769ULyPwbm5Hdx7sjMFB4v21bb4Usa4ZoYCgwfjGDRbrGBl3j4+PZvXt3\n2wB0/rNnz2Z+fj6Li4udKMlgZ4oXkdagauAxOKBp1bpMHW05lxd0Guj48xNCak3AA8z6iNkY9czO\nku78Bnwzjlp8xi/uEk9W0xeDmgEYpoPmVjMUyqBeKJfroO4NciyrwGfE73pQmgHCkmhXs12+o+49\nKUHd12kbWuKBAwdy1VVXZX5+vvXZgdXPIYPdECzc7jAmgzM31qIDnjx5stXSBmk8HnNct60G8Y2C\nULKJGJFTsEECc00R69/Vv/X7OpXjMw9cpjr9/Hga14vaiGaUz7ahbL/pWSymaWuth3P7M8RYOnWt\nRTht8TWRZtWaCeBNh3T9MQCoS69PcdplxoVe4QkCAJu0E8bkKO+6h6nxOYPBIEO66h0Vzdyoc7d7\nrVE5LazPbcYDo+H3LBisd7mkPkihYRSAjRk5aZhBwKyCeueP5RFml7T5wsJCJ0hZsyTYNU3Tfk+/\n9M4Brifqg1QPdsie6FxHbTU4ue9wnp8HfGybBoiSdBgB72udyMJ0cv4+KDYalxTHYp1TPQadz4XV\ns2cnT55sdRZSMC8go6zx8fHs27cvs7OzbWfn+2S1MZ966qlWwPZ3RDFSLQvKdUpkOuyUi2UIRGVA\n1Z0OX5Pu0xmoK4MazObMmTOdrVL43LeQwAZoN177xl7awJvQ4YcHEXXrwU6bAs6eRfVumaRevlHU\nZeMH/aVmhfX0vGdPKZ/vYCRci3UpJgU8K8e1kt6ZBTdN7xYRNKF77723XYldSsmpU6daVoTg/OST\nT7b+r6ystLO2k5OTnUdiJWlTPu/t7aDmenIaVo+RGuiosy2rETFoMAtnNQOqI3yNzE4rBpkjUrJK\nufGDjkzD8J81F4iITInWPrIFxPz8fLtQjgHjLR4wtAizA4MH773a1umpUwXMAOTORSqTrEY4RE8G\nhJkDA991ZGAFfGrQq4MCURyxun5Io2+LGQQMBiM+Mzs0E3X7mAEbAAAFgJiUBiCDAVKOA5IfjGBm\nCKADzjt27GjXkfHdIKHX+qOvO0m7hmh6ejr33HNPfvazn7XtWwODb1HxdfomWK7l5MmT7U209TP4\n6sWy+IRZOjAge4Kgbv/nsk0DRI5szjVr9kOks5m687s6TeNzjuc3aAFeLu9lAcnqLBSdi60/eYwM\nDW3tZXFxsbPbo/WhOqdmIHF/mNkRdVAvNKS+TPs92+SOCgtxVETMhME4Jak7WLIaMZNV8dN7+Fij\n4pqsWVCPgKrbgXrlet35uWbqi8BA+V5vxWSB15IxU2TGZR/cvl5qAAsFzGgbX6frh/IAA8rld25v\nJmWYzfK6Nd/bh//sWzQ+Pp7vf//7OXbsWEevg3FRPiBKn6jHGJofzzFz2uuxUjNujy33T0/g8N+B\naz22KYCoTrkGpVumwXV0q8syWzA40RAMQr7z43m5GdTnIIo4N/bsFODEHtHLy8vt5lg0Gp2ZQc8A\npaMjpno2yXvw4GstmNYpC+VRDu+JqhjX4lso6g7H55yXjm9BnfMzIDwraGB1cLBonnT30raQ7Qjt\ntnXqSXnnzp1r95ViAsHAY13Je1JRh5wf3wbtCmAgxE9rT14ewWvapU5VmLwgNTWIM6AtlBM0rr76\n6pRS8tBDD7XplQOXgwRA5llKQAs9zwtOHfAGpVYOfB5v/PfY3OgtHpsCiGpzZK+B50IMiUjv42rQ\nojMNihReDOdB4rLGx8fbDcn9XHsGxOTkZAs4nHtqaqqdoYECe90LEdEzYMmqsOw0wqBkPQ3qbSF5\nUIdyR2GQO/XhfZL2fiQzEjMLa1KwCTNa/PbKal+LQYWB6CULlO1ZHwOPgY17rqydUZeeXOA7AkuS\nzjUb5FwvTpsN+gCJy/S10ieYXvegr7d5oU3xwze0wurGx8dz9dVXZ2pqKseOHWvP4d0XmO6nz1mU\nN0jWelUNMmak7jsOyNig7GUjtmmAyJXuHDTpiqjOQwc1enL+xk5mSR5sdAoPYla01qzIPi0uLnam\nQE2TodCsvanvHUJARDMwg6kHkAcoAOD1TcwysUK61gzc2QAIrhEmB2txvVIvfq6YxXWireuN9S8G\ngpptcb1N07QR2QzKWhOAZdDiOKcYddpcD4j6dhfqxuVYq+G3HqT1BANMtY78gDEPFKCdXX49xe3z\nur6oR/wgDSU4HTx4MOPj43nggQc6oI4ffvqJ9S0DN98zQ8miVa7ZTw+u0zGX57pxgNiIbZp1RMnq\ntpv80YBJd2fBWrfwIPKxvHYj0ejk5qaiXnNjoywq2x3nmWeeyZkzZ9r9ZGZnZ9vtHTDrNfUUqQeb\nj/d2o+T0gCZU23rWoJTVkczg5pSGDdT4Hd9Zh6nZozUMAM63hBBUKMftiD+uE/ynLTiG8wJ4tCf1\nRtlms9abOI60mPobGxvL9PR0C1AOJA5m1DPT9XXaAth4Zg126H5qgAOcCFDWy9AXKc/BD/YN2E9M\nTGRpaSkHDhxo9U18cTrr2VEH5XrseMYSf5yueRy5LWrGTbleOrEe2zRARCXVUb2m4DRQ/bvaHH0w\nUN0RwaDHOR1F0YcYtL4JkvSKCLl3797Mz8+3q6z5s3bD8XRCT+u6kZNVbQhNivqAppvt8BBIzxTV\nKaeXHTi98X1aZo6I+HRmAgXHIX6bATnlMZA6WgJU1t6oD7NR2gxfABDSGjM1ayyeqne/cjt7AakX\nsLr/MKi9gNRl8nvPFrrtqLM6UNR9mX7q4OWAxzFm/qX0tqNdXl7O8vJyK9jzPYsczdg4hr86YBvw\nB4nXtC9pMH7V4EYdbcQ2DRDVGkCdEtnqCL1WeR5UfJacv07l3Llz5w0AD14ot+8f4j8PzNu1a1c7\nbc/m7xxvDctpAhHa18AxRD8GkbUTOj6DCIbHd45mACbT9tSd77fiWICTiGxq7ro2Qzx16lQ7qMzk\nvBAUAAa4YaJe+MmA43ro1LBOP2Zn0PQyhu+Afa2BOTVzysJ9dWbFZkhuH6eIBnxYqu8f5PrNZKyL\nwYgtJtd1bmmi7i/j4+N54Qtf2Pbhbdu2tTfNcjO26x/ZAOZqBgWI1cJ5zYQoz6lpncls1DYVEFHp\nvHdKZfZic2Q3ezIdHWQWOIlsZgQMLEeNpaWltlPQ6YgkTdNkz549nY2znOI4PeIYR0jABeBgIDJI\nk7Tn9E2ZdGJ8BiCJrmZ/nglyKsB1Uvf1Uoa1WCoDCUOoBzCS1ZSDNqzb2rpDPXvk39A+DBi3cx1U\nKLNel0T9mtnU09AwQ9ePhWSn8LVOWZdr1kzZBjaDlwMcZoHcbJ6AQftPTk5m37597UQKfrE1icuA\nyfrWJQDZxzib8NgyY+IzNMdBzGi9timAyIPAOTjfGZE5ru6MHmCmtRidpdaRiERev+NKtG/MTlgY\nT3pPaVhcXOw8Gge6T4Myg4EfdZ7NAPJ1MejMGOpo5A7vp8/yO46FUrvDDarjmi0ShV2/jox0Qv67\nc3qPHgMjdefpees3LK/gPE6ZGDh+xlutSdAGvknX1+Q6T7ozVd5dALA0oDmFr/tW3bYAGeZ0hu9p\nC6d/sEbrSg5KdYpfSsnS0lL27Nlz3tNM8AEQpK1h2wbw06dPd+oUH+sg5TocxIQA143YpgCiZLWy\nTaXNgGAug8DIAwMzS/L0r6fITc8Z6DSiF6GtrKy0j55msJOCEP2XlpbabRosYFoI9f7Qg/QiBkIN\ndKRNsCmzFF8vnYqBQ3S0JmGw5v66Qb/1CmnfTmEty9GzjpAewPjntuZ/3c4AstsoSYeFbN++vTNb\nZv2MYMOG8rx3HTi1pn6tBfGZtZ2aRXpgGrwAV6dl3mbEO2i6nzlV84SGU1Zfn5nOjh07sri42O70\n6H5DOkYbERwcPAhejEFrTWb0FrwZU14YOzU1lYMHD+Y1r3lNbr755gsN9/Ns00zfu6M5XbIekXTT\nAgZKnYLVndspH53PU9Y0AmU9+eSTHY1oenq6fdICq6r53djYWA4fPpz5+fmOYGlRkfNDpc3O6KgM\nWgRctBpH1ySd97Wg6M5K+eg0PtaRyyCOWGw2tn379szMzHRAzaugndoMYiWc0wPL7Jd2pC3MFK1h\noEfRTtYuOC/aB35Rl64/fgNz8CADQDme88NGYFmkw1wjde7lEvhlkdkzfTVDrtM9fHDQHZTO8v3E\nxEQuv/zyzM3N5fjx4zlx4kSS5PTp09m5c2cH7Gh7/jPrW7cZ53V74AOgOj8/n9e//vV5+ctfnv37\n97dPst2xY0duu+22rNc2BRCZ1STdxwg5J3W0MtWkkdeqQKceTtusP3Gs14ygIc3Ozp63CTnnmJub\naxmNAY9B7MVjRF0a0qzDaUPS3fOnThm5homJifZer0EMyfVhlmkQdErmNMIAgUBcpx5EY5ZCmG2U\nsrpEwiDkMgwaZh8cX+tHgLt/Z6ChDOrcbV3P7lgXrIVn7v4HRKwr+XYMpx/MZNI/rC/VQROfHLTw\ni3M6EFDXdT+tWTPrwi655JI8+uij7ZISrslaEIDo9Mwpey1GYzDQiYmJvOQlL8lNN1rrniMAAA4b\nSURBVN2UQ4cOZc+ePe3tQx5H67VNAURJF3Hp0N5pkM5YD6Ik53Vk5/FOKyjfGoPTQWhyko7WQsrm\nbTrpJAjU0F77R/nuyHXH9AyTtQdotaPsoA7MJmGcyxv2m43YJzpt7QtpBkyBgU65DE4iOvfQeYkD\nbeDFqdSVmQ9+cB28N+gawLlutDDq2QDu1KgGTIO62Sn9Aw1tenq64yPtDvt1eV4vRV+rF2Q6AHI9\nsH6zNl8H/eX06dMdYOQ16RT9xukqTGf//v05ceJEHn/88SS9Z+s9+uij552P9k3SWS+Fj55ZhEke\nOXIkb3zjG3Po0KF2uQr+mM1txJ4TiEoplye5I8mlSZoktzdN82ellA8k+d0kP+0f+r6maT7f/817\nk7wtyUqS32+a5l+e4xxtJ7MYVmsUfF4PRg9yaz8uu06ZktVOZupcD1oYDwORGxXHx8ezsLDQLozD\nf0dbfmfAtC9Jdx8mbpK0tuHUs2ZNg6aXPVtV6xyAPNqAj6vLpkMmq6uM63Um9QCnTpkGN4P1DbWU\nOagcp968h5myuLBOuQ1klGvQ9ap9H8ugp095R0rWGdXn5HiivlNfgALf6nTKWs+gSREzwnPnVncr\n5XUtL1hHgxlPTEy0j8HevXt3y4rMuujDtfDs9ue13+/fvz/veMc7cs0117TCuK/ZAXOjth5GdDbJ\nHzVN841Sys4kXy+lfLH/3Z82TfMnPriUcjjJm5Nck2Rfki+VUn6taZo15/N27dqV3bt357HHHuvM\ndlhP4ALdQak8/nudTK0pucNv29ab2iT6MVhNVxkAgBrfESVmZmbaJ4Q6hSDK4YNBxIMYUbF+8ivm\ndIFz1DpPHXUMohznbVBdH3WZBngGibcRIQon3Qfw0T7eGgQQq1cN+/du1/r+QFN7p9P46ihNXXn9\nD8exXQfAznkMyHXaBOs1i6l9MdOsFzJSx56c4FwwXPdjmJHZuKUKf+aU033eW7ziJ9P6s7Ozrf9M\nuJiJwfYJhPQt/7GK+9Zbb83evXs7j7r2hIQDkkXz9dhzAlHTNMeTHO+/PllK+V6Syy7wkzck+dum\naZ5Ocn8p5UdJrk/yX2v9YHl5OZ/97GfzxBNP5Ctf+Uo+97nP5bHHHsuZM2faaGjtxY1k2siixFok\ndQeyxoMBRjMzM53OAQNAyKMTsC0qDQoA+lEu+MSAZVDw3nfuM1AHiZFYLWb6PDWYcO5kVYg3kHsA\nOX3h3AYYA6h94TvPNllX4BqcRg1qp6RL463h1PqStQzYIIyFz7FaMzQI+ZqtE5GewY7YaMxt4UBn\nFmHwd7sYJLG6j2C+wZXvAJ9B57VI/+yzz7b1QH3u2LEju3bt6jwolO+cFXiWrAazpaWlvPrVr87L\nXvayXHnllZ0lIQj4+EX66rZdr21IIyqlHEhybZKvJXlFkneVUm5OcjQ91vR/6YHUV/Wzh3Jh4Mq2\nbdtyySWXZNeuXdm3b19e97rX5ZlnnslDDz2UL3/5y/nCF76QRx55JEnaJ2HWVNYDcJA2AuMhovrG\nRVI0GA+dkZWp09PTrT7y1FNPtYvHmPlgQPg8lG82RKfivGYKUO+pqalODu/IVYvSvB+0Ahrg4ZpN\nmREundbCHOqyqT+v8ja7xKyBmLFaAFU/6uh6gwYqnyMWU/7Y2Fi71SnR2GtnsLUmPAgagHd9zWbZ\nDh5O7QaBYy2aOw03GzQb83KB+h4/t82gOlzL6DtOpc+ePZtTp0512p1+7t0VbJOTkzl8+HBuvPHG\nTE9PZ3Z2trMFrtsJcKzXQm3E1g1EpZTZJH+f5A+bpnmilPLxJB9MTzf6YJKPJPmdDZT39iRvT3q5\nJ3oKivzKykrm5uZy6NChvOlNb8qJEyfysY99LPfff39OnTrVgoYFwKTLmGphtO6UNLyBiOjDdH19\n39LCwkJ7CweAwzoS6OqgGxlhCTSi2Qff4y9syeXAomh01oFAtYlQBmE6Y9LVkzzw6yhI2olwmayy\nKvQT16XTDMypFSwUxuglAnW6WjMF34uHFsOaHN/DVacrSTctrtkfIFansf49dY0+ZD3JRrkwKksC\nrhNSM9igrx/ABShrYDZjo97dr/DPEgSbxI2NjbUPc0Rno2ynoKWUVqNcXFzMDTfckOXl5c5Kf+tj\ndZtTz57c2IitC4hKKWPpgdBfNU3zD/0G+LG+/2SSf+q/fTjJ5fr5/v5nHWua5vYktyfJtdde2zhn\ntsZChe/cuTMf+tCH8vDDD+fd7353u9aHCnTUqWeI+udrv6vX5JhhYXR+yieC8MwpC4WeKTHLMUs4\nd+5cC3h03JWVlc57N64pdO0nesLZs2dbTcMaSq1brCUgGiwcyepZKa6Fjd4GtOV57ALfk+42qZ7e\ntlkjMyugbhgMDiZEfOoYcMffOlXz4juDmH2g3HoW0G0BMBjE6AOuD4wA4SUB9UQD4Fqntva3rnPX\nk7VD6gObmprq7LJw5syZzrH0q7GxsbzoRS/K9ddfn/n5+c7qda4Rhjpo6t/s8aIzotJrhb9I8r2m\naT6qz5f7+lGS/GaS7/Rf35Xkr0spH01PrL4qyX9f6BwebDANzxjAkBYWFjI3N5dPfOITueuuu3Ln\nnXfm8ccfbwe6O4jLZlDR+LXQC603WC0tLeW+++5rwXHbtm2ZmZlpOzozMV41a6pqzYZGmZqa6jAh\n3ydmwHTnp9N6tgWGsn376vOsDMQuL+luGGehmM/daVmwRxn2a6102ClZzXCszXl2qdbCPMDNIOty\nzDh9C4nTVuocDY7z16kEA8ksxMK829L1Wp/Lf4MCmgOU5YK6Dll64c+s17lct4/Fbmb8YDf0XcDI\n26EAbhMTE7nsssty+PDhLCwsdNgM9cFxTtWtW7mOrNGu19bDiF6R5LeTfLuUcnf/s/cl+a1SyovT\nS80eSPJ7SdI0zXdLKX+X5J70Ztze2VxgxizpdnguyikDnxP9r7jiirz1rW/NDTfckE9/+tM5evRo\nTp8+3emYFhJ9G4fZDd8zne0Vw2ypASiePdvbOJ/IhVDN+cwGTLM5zkyAwWZwwWo2ZRpNp6B82GLN\nbOyD68+pHoDsAUGZ1pZqcDMQkgJYQK/F3RrszP4wn9fAQwrlzzkWoPLmbdYuvH7MwOPr9PV58BiY\n6T+U6/++TjM4/7dW5JlE908vjER4TrqiNddr7StJZ9N/b3ZG2YwlnjbDgxqp14WFhRw5ciQ7d+7M\nzMzMeUymDhgGc6ejtQxy0RlR0zT/mWTQLeyfv8BvPpzkwxtxxKhqAdXUG/q/ffv2zM3NZWJiIrfc\nckuOHTuWO+64I0ePHm2ftOryHNncuXlfL/DyLBQdfGJiInNzc+3AZcbAd9s7ijo1NP33mhx3UjOJ\nWmMZpDsAZr5VxccBdHzmTezxA5ZYsym+ZzCdO3eu3e7E9UldGBA9EOsUm/ox86nBmzIZnAbyQUIu\ngcG/r8HH1+m6NrD4dg6nekR/zxRak/I6JKdI7n9eBzeov1PfgIlTRwMo5zTYWB+1hoOGii61Y8eO\nVuifnJzM7t27c+TIkezdu7ddPsL5aGOuz7fu1EGSzMX18PNY2SiF+kVYKeWnSZ5M8siwfXketicj\n/4dpI/+Ha4P8v6JpmkvW8+NNAURJUko52jTNkWH78fPayP/h2sj/4drz9X/TbAMyspGN7FfXRkA0\nspGNbOi2mYDo9mE78Dxt5P9wbeT/cO15+b9pNKKRjWxkv7q2mRjRyEY2sl9RGwHRyEY2sqHb0IGo\nlHJjKeUHpZQflVLeM2x/1mOllAdKKd8updxdSjna/2yhlPLFUsoP+/93D9tPrJTyqVLKT0op39Fn\nA/0tPfvzfnt8q5Ry3fA8b30d5P8HSikP99vg7lLKa/Xde/v+/6CUcsNwvF61UsrlpZR/L6XcU0r5\nbinlD/qfb4k2uID/F68NvIL4l/2XZHuS+5K8IMl4km8mOTxMn9bp9wNJ9lSf3ZrkPf3X70nyx8P2\nU769Ksl1Sb7zXP4meW2Sf05vNf1Lk3xtk/r/gSS3DDj2cL8fTSQ52O9f24fs/3KS6/qvdya5t+/n\nlmiDC/h/0dpg2Izo+iQ/aprmf5umeSbJ36a3sdpWtDck+Uz/9WeS/MYQfelY0zT/keTR6uO1/H1D\nkjuann01ya5SyvIvx9PBtob/a1m7MV/TNPcnYWO+oVnTNMebpvlG//XJJGwuuCXa4AL+r2UbboNh\nA9FlSR7U++fcRG2TWJPkX0spXy+9fZWS5NJmdTeCE+nt8b2ZbS1/t1KbvKufunxKqfCm9r90Nxfc\ncm1Q+Z9cpDYYNhBtVXtl0zTXJbkpyTtLKa/yl02Pn26ZdRFbzd++fTzJlUlenN5Wxh8ZrjvPbaXa\nXNDfbYU2GOD/RWuDYQPRujZR22zWNM3D/f8/SfKP6dHOH0Of+/9/MjwP12Vr+bsl2qRpmh83TbPS\nNM25JJ/MKvXflP6XAZsLZgu1wSD/L2YbDBuI/ifJVaWUg6WU8fSe/nHXkH26oJVSZkrvaSYppcwk\n+fX0NoW7K8lb+oe9Jcmdw/Fw3baWv3clubk/c/PSJI8rfdg0Vmkm9cZ8by6lTJRSDmYdG/P9oq2U\nwZsLZou0wVr+X9Q2GKYarxmCe9NT1t8/bH/W4e8L0psR+GaS7+JzksUk/5bkh0m+lGRh2L7K579J\njzo/m16+/ra1/E1vpua2fnt8O8mRTer/X/b9+1a/4y/r+Pf3/f9Bkps2gf+vTC/t+laSu/t/r90q\nbXAB/y9aG4xu8RjZyEY2dBt2ajaykY1sZCMgGtnIRjZ8GwHRyEY2sqHbCIhGNrKRDd1GQDSykY1s\n6DYCopGNbGRDtxEQjWxkIxu6/T86+Ukv0/t6+wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0RET8NiJ+KemkpFu6KHYZSh7eAutK65tzzRHZvlHSJyU9Xy160PYx24/avrZatkPSW7WX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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b30SJrCeQCRh"
      },
      "source": [
        "def build_bn(width, height, n_classes, weights_path=None, train=False):\n",
        "    inp = Input(shape=(height, width, 1))\n",
        "    x = Lambda(lambda x: (x - 127.5)/255.0)(inp)\n",
        "\n",
        "    # (1/2)\n",
        "    y = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(int(x.shape[1])//2, int(x.shape[2])//2)), name='data_sub2')(x)\n",
        "    y = Conv2D(32, 3, strides=2, padding='same', activation='relu', name='conv1_1_3x3_s2')(y)\n",
        "    y = BatchNormalization(name='conv1_1_3x3_s2_bn')(y)\n",
        "    y = Conv2D(32, 3, padding='same', activation='relu', name='conv1_2_3x3')(y)\n",
        "    y = BatchNormalization(name='conv1_2_3x3_s2_bn')(y)\n",
        "    y = Conv2D(64, 3, padding='same', activation='relu', name='conv1_3_3x3')(y)\n",
        "    y = BatchNormalization(name='conv1_3_3x3_bn')(y)\n",
        "    y_ = MaxPooling2D(pool_size=3, strides=2, name='pool1_3x3_s2')(y)\n",
        "    \n",
        "    y = Conv2D(128, 1, name='conv2_1_1x1_proj')(y_)\n",
        "    y = BatchNormalization(name='conv2_1_1x1_proj_bn')(y)\n",
        "    y_ = Conv2D(32, 1, activation='relu', name='conv2_1_1x1_reduce')(y_)\n",
        "    y_ = BatchNormalization(name='conv2_1_1x1_reduce_bn')(y_)\n",
        "    y_ = ZeroPadding2D(name='padding1')(y_)\n",
        "    y_ = Conv2D(32, 3, activation='relu', name='conv2_1_3x3')(y_)\n",
        "    y_ = BatchNormalization(name='conv2_1_3x3_bn')(y_)\n",
        "    y_ = Conv2D(128, 1, name='conv2_1_1x1_increase')(y_)\n",
        "    y_ = BatchNormalization(name='conv2_1_1x1_increase_bn')(y_)\n",
        "    y = Add(name='conv2_1')([y,y_])\n",
        "    y_ = Activation('relu', name='conv2_1/relu')(y)\n",
        "\n",
        "    y = Conv2D(32, 1, activation='relu', name='conv2_2_1x1_reduce')(y_)\n",
        "    y = BatchNormalization(name='conv2_2_1x1_reduce_bn')(y)\n",
        "    y = ZeroPadding2D(name='padding2')(y)\n",
        "    y = Conv2D(32, 3, activation='relu', name='conv2_2_3x3')(y)\n",
        "    y = BatchNormalization(name='conv2_2_3x3_bn')(y)\n",
        "    y = Conv2D(128, 1, name='conv2_2_1x1_increase')(y)\n",
        "    y = BatchNormalization(name='conv2_2_1x1_increase_bn')(y)\n",
        "    y = Add(name='conv2_2')([y,y_])\n",
        "    y_ = Activation('relu', name='conv2_2/relu')(y)\n",
        "\n",
        "    y = Conv2D(32, 1, activation='relu', name='conv2_3_1x1_reduce')(y_)\n",
        "    y = BatchNormalization(name='conv2_3_1x1_reduce_bn')(y)\n",
        "    y = ZeroPadding2D(name='padding3')(y)\n",
        "    y = Conv2D(32, 3, activation='relu', name='conv2_3_3x3')(y)\n",
        "    y = BatchNormalization(name='conv2_3_3x3_bn')(y)\n",
        "    y = Conv2D(128, 1, name='conv2_3_1x1_increase')(y)\n",
        "    y = BatchNormalization(name='conv2_3_1x1_increase_bn')(y)\n",
        "    y = Add(name='conv2_3')([y,y_])\n",
        "    y_ = Activation('relu', name='conv2_3/relu')(y)\n",
        "\n",
        "    y = Conv2D(256, 1, strides=2, name='conv3_1_1x1_proj')(y_)\n",
        "    y = BatchNormalization(name='conv3_1_1x1_proj_bn')(y)\n",
        "    y_ = Conv2D(64, 1, strides=2, activation='relu', name='conv3_1_1x1_reduce')(y_)\n",
        "    y_ = BatchNormalization(name='conv3_1_1x1_reduce_bn')(y_) \n",
        "    y_ = ZeroPadding2D(name='padding4')(y_)\n",
        "    y_ = Conv2D(64, 3, activation='relu', name='conv3_1_3x3')(y_)\n",
        "    y_ = BatchNormalization(name='conv3_1_3x3_bn')(y_)\n",
        "    y_ = Conv2D(256, 1, name='conv3_1_1x1_increase')(y_)\n",
        "    y_ = BatchNormalization(name='conv3_1_1x1_increase_bn')(y_)\n",
        "    y = Add(name='conv3_1')([y,y_])\n",
        "    z = Activation('relu', name='conv3_1/relu')(y)\n",
        "\n",
        "    # (1/4)\n",
        "    y_ = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(int(x.shape[1])//2, int(x.shape[2])//2)), name='conv3_1_sub4')(z)\n",
        "    y = Conv2D(64, 1, activation='relu', name='conv3_2_1x1_reduce')(y_)\n",
        "    y = BatchNormalization(name='conv3_2_1x1_reduce_bn')(y)\n",
        "    y = ZeroPadding2D(name='padding5')(y)\n",
        "    y = Conv2D(64, 3, activation='relu', name='conv3_2_3x3')(y)\n",
        "    y = BatchNormalization(name='conv3_2_3x3_bn')(y)\n",
        "    y = Conv2D(256, 1, name='conv3_2_1x1_increase')(y)\n",
        "    y = BatchNormalization(name='conv3_2_1x1_increase_bn')(y)\n",
        "    y = Add(name='conv3_2')([y,y_])\n",
        "    y_ = Activation('relu', name='conv3_2/relu')(y)\n",
        "\n",
        "    y = Conv2D(64, 1, activation='relu', name='conv3_3_1x1_reduce')(y_)\n",
        "    y = BatchNormalization(name='conv3_3_1x1_reduce_bn')(y)\n",
        "    y = ZeroPadding2D(name='padding6')(y)\n",
        "    y = Conv2D(64, 3, activation='relu', name='conv3_3_3x3')(y)\n",
        "    y = BatchNormalization(name='conv3_3_3x3_bn')(y)\n",
        "    y = Conv2D(256, 1, name='conv3_3_1x1_increase')(y)\n",
        "    y = BatchNormalization(name='conv3_3_1x1_increase_bn')(y)\n",
        "    y = Add(name='conv3_3')([y,y_])\n",
        "    y_ = Activation('relu', name='conv3_3/relu')(y)\n",
        "\n",
        "    y = Conv2D(64, 1, activation='relu', name='conv3_4_1x1_reduce')(y_)\n",
        "    y = BatchNormalization(name='conv3_4_1x1_reduce_bn')(y)\n",
        "    y = ZeroPadding2D(name='padding7')(y)\n",
        "    y = Conv2D(64, 3, activation='relu', name='conv3_4_3x3')(y)\n",
        "    y = BatchNormalization(name='conv3_4_3x3_bn')(y)\n",
        "    y = Conv2D(256, 1, name='conv3_4_1x1_increase')(y)\n",
        "    y = BatchNormalization(name='conv3_4_1x1_increase_bn')(y)\n",
        "    y = Add(name='conv3_4')([y,y_])\n",
        "    y_ = Activation('relu', name='conv3_4/relu')(y)\n",
        "\n",
        "    y = Conv2D(512, 1, name='conv4_1_1x1_proj')(y_)\n",
        "    y = BatchNormalization(name='conv4_1_1x1_proj_bn')(y)\n",
        "    y_ = Conv2D(128, 1, activation='relu', name='conv4_1_1x1_reduce')(y_)\n",
        "    y_ = BatchNormalization(name='conv4_1_1x1_reduce_bn')(y_)\n",
        "    y_ = ZeroPadding2D(padding=2, name='padding8')(y_)\n",
        "    y_ = Conv2D(128, 3, dilation_rate=2, activation='relu', name='conv4_1_3x3')(y_)\n",
        "    y_ = BatchNormalization(name='conv4_1_3x3_bn')(y_)\n",
        "    y_ = Conv2D(512, 1, name='conv4_1_1x1_increase')(y_)\n",
        "    y_ = BatchNormalization(name='conv4_1_1x1_increase_bn')(y_)\n",
        "    y = Add(name='conv4_1')([y,y_])\n",
        "    y_ = Activation('relu', name='conv4_1/relu')(y)\n",
        "\n",
        "    y = Conv2D(128, 1, activation='relu', name='conv4_2_1x1_reduce')(y_)\n",
        "    y = BatchNormalization(name='conv4_2_1x1_reduce_bn')(y)\n",
        "    y = ZeroPadding2D(padding=2, name='padding9')(y)\n",
        "    y = Conv2D(128, 3, dilation_rate=2, activation='relu', name='conv4_2_3x3')(y)\n",
        "    y = BatchNormalization(name='conv4_2_3x3_bn')(y)\n",
        "    y = Conv2D(512, 1, name='conv4_2_1x1_increase')(y)\n",
        "    y = BatchNormalization(name='conv4_2_1x1_increase_bn')(y)\n",
        "    y = Add(name='conv4_2')([y,y_])\n",
        "    y_ = Activation('relu', name='conv4_2/relu')(y)\n",
        "\n",
        "    y = Conv2D(128, 1, activation='relu', name='conv4_3_1x1_reduce')(y_)\n",
        "    y = BatchNormalization(name='conv4_3_1x1_reduce_bn')(y)\n",
        "    y = ZeroPadding2D(padding=2, name='padding10')(y)\n",
        "    y = Conv2D(128, 3, dilation_rate=2, activation='relu', name='conv4_3_3x3')(y)\n",
        "    y = BatchNormalization(name='conv4_3_3x3_bn')(y)\n",
        "    y = Conv2D(512, 1, name='conv4_3_1x1_increase')(y)\n",
        "    y = BatchNormalization(name='conv4_3_1x1_increase_bn')(y)\n",
        "    y = Add(name='conv4_3')([y,y_])\n",
        "    y_ = Activation('relu', name='conv4_3/relu')(y)\n",
        "\n",
        "    y = Conv2D(128, 1, activation='relu', name='conv4_4_1x1_reduce')(y_)\n",
        "    y = BatchNormalization(name='conv4_4_1x1_reduce_bn')(y)\n",
        "    y = ZeroPadding2D(padding=2, name='padding11')(y)\n",
        "    y = Conv2D(128, 3, dilation_rate=2, activation='relu', name='conv4_4_3x3')(y)\n",
        "    y = BatchNormalization(name='conv4_4_3x3_bn')(y)\n",
        "    y = Conv2D(512, 1, name='conv4_4_1x1_increase')(y)\n",
        "    y = BatchNormalization(name='conv4_4_1x1_increase_bn')(y)\n",
        "    y = Add(name='conv4_4')([y,y_])\n",
        "    y_ = Activation('relu', name='conv4_4/relu')(y)\n",
        "\n",
        "    y = Conv2D(128, 1, activation='relu', name='conv4_5_1x1_reduce')(y_)\n",
        "    y = BatchNormalization(name='conv4_5_1x1_reduce_bn')(y)\n",
        "    y = ZeroPadding2D(padding=2, name='padding12')(y)\n",
        "    y = Conv2D(128, 3, dilation_rate=2, activation='relu', name='conv4_5_3x3')(y)\n",
        "    y = BatchNormalization(name='conv4_5_3x3_bn')(y)\n",
        "    y = Conv2D(512, 1, name='conv4_5_1x1_increase')(y)\n",
        "    y = BatchNormalization(name='conv4_5_1x1_increase_bn')(y)\n",
        "    y = Add(name='conv4_5')([y,y_])\n",
        "    y_ = Activation('relu', name='conv4_5/relu')(y)\n",
        "\n",
        "    y = Conv2D(128, 1, activation='relu', name='conv4_6_1x1_reduce')(y_)\n",
        "    y = BatchNormalization(name='conv4_6_1x1_reduce_bn')(y)\n",
        "    y = ZeroPadding2D(padding=2, name='padding13')(y)\n",
        "    y = Conv2D(128, 3, dilation_rate=2, activation='relu', name='conv4_6_3x3')(y)\n",
        "    y = BatchNormalization(name='conv4_6_3x3_bn')(y)\n",
        "    y = Conv2D(512, 1, name='conv4_6_1x1_increase')(y)\n",
        "    y = BatchNormalization(name='conv4_6_1x1_increase_bn')(y)\n",
        "    y = Add(name='conv4_6')([y,y_])\n",
        "    y = Activation('relu', name='conv4_6/relu')(y)\n",
        "\n",
        "    y_ = Conv2D(1024, 1, name='conv5_1_1x1_proj')(y)\n",
        "    y_ = BatchNormalization(name='conv5_1_1x1_proj_bn')(y_)\n",
        "    y = Conv2D(256, 1, activation='relu', name='conv5_1_1x1_reduce')(y)\n",
        "    y = BatchNormalization(name='conv5_1_1x1_reduce_bn')(y)\n",
        "    y = ZeroPadding2D(padding=4, name='padding14')(y)\n",
        "    y = Conv2D(256, 3, dilation_rate=4, activation='relu', name='conv5_1_3x3')(y)\n",
        "    y = BatchNormalization(name='conv5_1_3x3_bn')(y)\n",
        "    y = Conv2D(1024, 1, name='conv5_1_1x1_increase')(y)\n",
        "    y = BatchNormalization(name='conv5_1_1x1_increase_bn')(y)\n",
        "    y = Add(name='conv5_1')([y,y_])\n",
        "    y_ = Activation('relu', name='conv5_1/relu')(y)\n",
        "\n",
        "    y = Conv2D(256, 1, activation='relu', name='conv5_2_1x1_reduce')(y_)\n",
        "    y = BatchNormalization(name='conv5_2_1x1_reduce_bn')(y)\n",
        "    y = ZeroPadding2D(padding=4, name='padding15')(y)\n",
        "    y = Conv2D(256, 3, dilation_rate=4, activation='relu', name='conv5_2_3x3')(y)\n",
        "    y = BatchNormalization(name='conv5_2_3x3_bn')(y)\n",
        "    y = Conv2D(1024, 1, name='conv5_2_1x1_increase')(y)\n",
        "    y = BatchNormalization(name='conv5_2_1x1_increase_bn')(y)\n",
        "    y = Add(name='conv5_2')([y,y_])\n",
        "    y_ = Activation('relu', name='conv5_2/relu')(y)\n",
        "\n",
        "    y = Conv2D(256, 1, activation='relu', name='conv5_3_1x1_reduce')(y_)\n",
        "    y = BatchNormalization(name='conv5_3_1x1_reduce_bn')(y)\n",
        "    y = ZeroPadding2D(padding=4, name='padding16')(y)\n",
        "    y = Conv2D(256, 3, dilation_rate=4, activation='relu', name='conv5_3_3x3')(y)\n",
        "    y = BatchNormalization(name='conv5_3_3x3_bn')(y)\n",
        "    y = Conv2D(1024, 1, name='conv5_3_1x1_increase')(y)\n",
        "    y = BatchNormalization(name='conv5_3_1x1_increase_bn')(y)\n",
        "    y = Add(name='conv5_3')([y,y_])\n",
        "    y = Activation('relu', name='conv5_3/relu')(y)\n",
        "\n",
        "    h, w = y.shape[1:3].as_list()\n",
        "    pool1 = AveragePooling2D(pool_size=(h,w), strides=(h,w), name='conv5_3_pool1')(y)\n",
        "    pool1 = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(h,w)), name='conv5_3_pool1_interp')(pool1)\n",
        "    pool2 = AveragePooling2D(pool_size=(h/2,w/2), strides=(h//2,w//2), name='conv5_3_pool2')(y)\n",
        "    pool2 = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(h,w)), name='conv5_3_pool2_interp')(pool2)\n",
        "    pool3 = AveragePooling2D(pool_size=(h/3,w/3), strides=(h//3,w//3), name='conv5_3_pool3')(y)\n",
        "    pool3 = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(h,w)), name='conv5_3_pool3_interp')(pool3)\n",
        "    pool6 = AveragePooling2D(pool_size=(h/4,w/4), strides=(h//4,w//4), name='conv5_3_pool6')(y)\n",
        "    pool6 = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(h,w)), name='conv5_3_pool6_interp')(pool6)\n",
        "\n",
        "    y = Add(name='conv5_3_sum')([y, pool1, pool2, pool3, pool6])\n",
        "    y = Conv2D(256, 1, activation='relu', name='conv5_4_k1')(y)\n",
        "    y = BatchNormalization(name='conv5_4_k1_bn')(y)\n",
        "    aux_1 = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(int(x.shape[1])*2, int(x.shape[2])*2)), name='conv5_4_interp')(y)\n",
        "    y = ZeroPadding2D(padding=2, name='padding17')(aux_1)\n",
        "    y = Conv2D(128, 3, dilation_rate=2, name='conv_sub4')(y)\n",
        "    y = BatchNormalization(name='conv_sub4_bn')(y)\n",
        "    y_ = Conv2D(128, 1, name='conv3_1_sub2_proj')(z)\n",
        "    y_ = BatchNormalization(name='conv3_1_sub2_proj_bn')(y_)\n",
        "    y = Add(name='sub24_sum')([y,y_])\n",
        "    y = Activation('relu', name='sub24_sum/relu')(y)\n",
        "\n",
        "    aux_2 = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(int(x.shape[1])*2, int(x.shape[2])*2)), name='sub24_sum_interp')(y)\n",
        "    y = ZeroPadding2D(padding=2, name='padding18')(aux_2)\n",
        "    y_ = Conv2D(128, 3, dilation_rate=2, name='conv_sub2')(y)\n",
        "    y_ = BatchNormalization(name='conv_sub2_bn')(y_)\n",
        "\n",
        "    # (1)\n",
        "    y = Conv2D(32, 3, strides=2, padding='same', activation='relu', name='conv1_sub1')(x)\n",
        "    y = BatchNormalization(name='conv1_sub1_bn')(y)\n",
        "    y = Conv2D(32, 3, strides=2, padding='same', activation='relu', name='conv2_sub1')(y)\n",
        "    y = BatchNormalization(name='conv2_sub1_bn')(y)\n",
        "    y = Conv2D(64, 3, strides=2, padding='same', activation='relu', name='conv3_sub1')(y)\n",
        "    y = BatchNormalization(name='conv3_sub1_bn')(y)\n",
        "    y = Conv2D(128, 1, name='conv3_sub1_proj')(y)\n",
        "    y = BatchNormalization(name='conv3_sub1_proj_bn')(y)\n",
        "\n",
        "    y = Add(name='sub12_sum')([y,y_])\n",
        "    y = Activation('relu', name='sub12_sum/relu')(y)\n",
        "    y = Lambda(lambda x: tf.compat.v1.image.resize_bilinear(x, size=(int(x.shape[1])*2, int(x.shape[2])*2)), name='sub12_sum_interp')(y)\n",
        "    y = UpSampling2D(size=(4,4))(y)\n",
        "    out = Conv2D(n_classes, 1, activation='sigmoid', name='conv6_cls')(y)\n",
        "\n",
        "    if train:\n",
        "        aux_1 = Conv2D(n_classes, 1, activation='sigmoid', name='sub4_out')(aux_1)\n",
        "        aux_2 = Conv2D(n_classes, 1, activation='sigmoid', name='sub24_out')(aux_2)\n",
        "        model = Model(inputs=inp, outputs=[out, aux_2, aux_1])\n",
        "    else:\n",
        "        model = Model(inputs=inp, outputs=out)\n",
        "        \n",
        "    if weights_path is not None:\n",
        "        model.load_weights(weights_path, by_name=True)\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUoudHEFQRXj",
        "outputId": "e7e4ac13-614d-420a-f62b-b881ee43b9a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = build_bn(width = 256, height = 256, n_classes = 1, train=False)\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 256, 256, 1) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "lambda (Lambda)                 (None, 256, 256, 1)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "data_sub2 (Lambda)              (None, 128, 128, 1)  0           lambda[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv1_1_3x3_s2 (Conv2D)         (None, 64, 64, 32)   320         data_sub2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_1_3x3_s2_bn (BatchNormali (None, 64, 64, 32)   128         conv1_1_3x3_s2[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv1_2_3x3 (Conv2D)            (None, 64, 64, 32)   9248        conv1_1_3x3_s2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv1_2_3x3_s2_bn (BatchNormali (None, 64, 64, 32)   128         conv1_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv1_3_3x3 (Conv2D)            (None, 64, 64, 64)   18496       conv1_2_3x3_s2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv1_3_3x3_bn (BatchNormalizat (None, 64, 64, 64)   256         conv1_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "pool1_3x3_s2 (MaxPooling2D)     (None, 31, 31, 64)   0           conv1_3_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_reduce (Conv2D)     (None, 31, 31, 32)   2080        pool1_3x3_s2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_reduce_bn (BatchNor (None, 31, 31, 32)   128         conv2_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding1 (ZeroPadding2D)        (None, 33, 33, 32)   0           conv2_1_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_3x3 (Conv2D)            (None, 31, 31, 32)   9248        padding1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_3x3_bn (BatchNormalizat (None, 31, 31, 32)   128         conv2_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_proj (Conv2D)       (None, 31, 31, 128)  8320        pool1_3x3_s2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_increase (Conv2D)   (None, 31, 31, 128)  4224        conv2_1_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_proj_bn (BatchNorma (None, 31, 31, 128)  512         conv2_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1_1x1_increase_bn (BatchN (None, 31, 31, 128)  512         conv2_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1 (Add)                   (None, 31, 31, 128)  0           conv2_1_1x1_proj_bn[0][0]        \n",
            "                                                                 conv2_1_1x1_increase_bn[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2_1/relu (Activation)       (None, 31, 31, 128)  0           conv2_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_reduce (Conv2D)     (None, 31, 31, 32)   4128        conv2_1/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_reduce_bn (BatchNor (None, 31, 31, 32)   128         conv2_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding2 (ZeroPadding2D)        (None, 33, 33, 32)   0           conv2_2_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_3x3 (Conv2D)            (None, 31, 31, 32)   9248        padding2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_3x3_bn (BatchNormalizat (None, 31, 31, 32)   128         conv2_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_increase (Conv2D)   (None, 31, 31, 128)  4224        conv2_2_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2_1x1_increase_bn (BatchN (None, 31, 31, 128)  512         conv2_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2 (Add)                   (None, 31, 31, 128)  0           conv2_2_1x1_increase_bn[0][0]    \n",
            "                                                                 conv2_1/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_2/relu (Activation)       (None, 31, 31, 128)  0           conv2_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_reduce (Conv2D)     (None, 31, 31, 32)   4128        conv2_2/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_reduce_bn (BatchNor (None, 31, 31, 32)   128         conv2_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding3 (ZeroPadding2D)        (None, 33, 33, 32)   0           conv2_3_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_3x3 (Conv2D)            (None, 31, 31, 32)   9248        padding3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_3x3_bn (BatchNormalizat (None, 31, 31, 32)   128         conv2_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_increase (Conv2D)   (None, 31, 31, 128)  4224        conv2_3_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3_1x1_increase_bn (BatchN (None, 31, 31, 128)  512         conv2_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3 (Add)                   (None, 31, 31, 128)  0           conv2_3_1x1_increase_bn[0][0]    \n",
            "                                                                 conv2_2/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2_3/relu (Activation)       (None, 31, 31, 128)  0           conv2_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_reduce (Conv2D)     (None, 16, 16, 64)   8256        conv2_3/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_reduce_bn (BatchNor (None, 16, 16, 64)   256         conv3_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding4 (ZeroPadding2D)        (None, 18, 18, 64)   0           conv3_1_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_3x3 (Conv2D)            (None, 16, 16, 64)   36928       padding4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_3x3_bn (BatchNormalizat (None, 16, 16, 64)   256         conv3_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_proj (Conv2D)       (None, 16, 16, 256)  33024       conv2_3/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_increase (Conv2D)   (None, 16, 16, 256)  16640       conv3_1_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_proj_bn (BatchNorma (None, 16, 16, 256)  1024        conv3_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_1x1_increase_bn (BatchN (None, 16, 16, 256)  1024        conv3_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1 (Add)                   (None, 16, 16, 256)  0           conv3_1_1x1_proj_bn[0][0]        \n",
            "                                                                 conv3_1_1x1_increase_bn[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1/relu (Activation)       (None, 16, 16, 256)  0           conv3_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_sub4 (Lambda)           (None, 8, 8, 256)    0           conv3_1/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_reduce (Conv2D)     (None, 8, 8, 64)     16448       conv3_1_sub4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_reduce_bn (BatchNor (None, 8, 8, 64)     256         conv3_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding5 (ZeroPadding2D)        (None, 10, 10, 64)   0           conv3_2_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_3x3 (Conv2D)            (None, 8, 8, 64)     36928       padding5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_3x3_bn (BatchNormalizat (None, 8, 8, 64)     256         conv3_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_increase (Conv2D)   (None, 8, 8, 256)    16640       conv3_2_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2_1x1_increase_bn (BatchN (None, 8, 8, 256)    1024        conv3_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2 (Add)                   (None, 8, 8, 256)    0           conv3_2_1x1_increase_bn[0][0]    \n",
            "                                                                 conv3_1_sub4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv3_2/relu (Activation)       (None, 8, 8, 256)    0           conv3_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_reduce (Conv2D)     (None, 8, 8, 64)     16448       conv3_2/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_reduce_bn (BatchNor (None, 8, 8, 64)     256         conv3_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding6 (ZeroPadding2D)        (None, 10, 10, 64)   0           conv3_3_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_3x3 (Conv2D)            (None, 8, 8, 64)     36928       padding6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_3x3_bn (BatchNormalizat (None, 8, 8, 64)     256         conv3_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_increase (Conv2D)   (None, 8, 8, 256)    16640       conv3_3_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3_1x1_increase_bn (BatchN (None, 8, 8, 256)    1024        conv3_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3 (Add)                   (None, 8, 8, 256)    0           conv3_3_1x1_increase_bn[0][0]    \n",
            "                                                                 conv3_2/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv3_3/relu (Activation)       (None, 8, 8, 256)    0           conv3_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_reduce (Conv2D)     (None, 8, 8, 64)     16448       conv3_3/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_reduce_bn (BatchNor (None, 8, 8, 64)     256         conv3_4_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding7 (ZeroPadding2D)        (None, 10, 10, 64)   0           conv3_4_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_3x3 (Conv2D)            (None, 8, 8, 64)     36928       padding7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_3x3_bn (BatchNormalizat (None, 8, 8, 64)     256         conv3_4_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_increase (Conv2D)   (None, 8, 8, 256)    16640       conv3_4_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4_1x1_increase_bn (BatchN (None, 8, 8, 256)    1024        conv3_4_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4 (Add)                   (None, 8, 8, 256)    0           conv3_4_1x1_increase_bn[0][0]    \n",
            "                                                                 conv3_3/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv3_4/relu (Activation)       (None, 8, 8, 256)    0           conv3_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_reduce (Conv2D)     (None, 8, 8, 128)    32896       conv3_4/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_reduce_bn (BatchNor (None, 8, 8, 128)    512         conv4_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding8 (ZeroPadding2D)        (None, 12, 12, 128)  0           conv4_1_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_3x3 (Conv2D)            (None, 8, 8, 128)    147584      padding8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_3x3_bn (BatchNormalizat (None, 8, 8, 128)    512         conv4_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_proj (Conv2D)       (None, 8, 8, 512)    131584      conv3_4/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_increase (Conv2D)   (None, 8, 8, 512)    66048       conv4_1_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_proj_bn (BatchNorma (None, 8, 8, 512)    2048        conv4_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1_1x1_increase_bn (BatchN (None, 8, 8, 512)    2048        conv4_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1 (Add)                   (None, 8, 8, 512)    0           conv4_1_1x1_proj_bn[0][0]        \n",
            "                                                                 conv4_1_1x1_increase_bn[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv4_1/relu (Activation)       (None, 8, 8, 512)    0           conv4_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_reduce (Conv2D)     (None, 8, 8, 128)    65664       conv4_1/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_reduce_bn (BatchNor (None, 8, 8, 128)    512         conv4_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding9 (ZeroPadding2D)        (None, 12, 12, 128)  0           conv4_2_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_3x3 (Conv2D)            (None, 8, 8, 128)    147584      padding9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_3x3_bn (BatchNormalizat (None, 8, 8, 128)    512         conv4_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_increase (Conv2D)   (None, 8, 8, 512)    66048       conv4_2_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2_1x1_increase_bn (BatchN (None, 8, 8, 512)    2048        conv4_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2 (Add)                   (None, 8, 8, 512)    0           conv4_2_1x1_increase_bn[0][0]    \n",
            "                                                                 conv4_1/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv4_2/relu (Activation)       (None, 8, 8, 512)    0           conv4_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_reduce (Conv2D)     (None, 8, 8, 128)    65664       conv4_2/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_reduce_bn (BatchNor (None, 8, 8, 128)    512         conv4_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding10 (ZeroPadding2D)       (None, 12, 12, 128)  0           conv4_3_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_3x3 (Conv2D)            (None, 8, 8, 128)    147584      padding10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_3x3_bn (BatchNormalizat (None, 8, 8, 128)    512         conv4_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_increase (Conv2D)   (None, 8, 8, 512)    66048       conv4_3_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3_1x1_increase_bn (BatchN (None, 8, 8, 512)    2048        conv4_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3 (Add)                   (None, 8, 8, 512)    0           conv4_3_1x1_increase_bn[0][0]    \n",
            "                                                                 conv4_2/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv4_3/relu (Activation)       (None, 8, 8, 512)    0           conv4_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_reduce (Conv2D)     (None, 8, 8, 128)    65664       conv4_3/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_reduce_bn (BatchNor (None, 8, 8, 128)    512         conv4_4_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding11 (ZeroPadding2D)       (None, 12, 12, 128)  0           conv4_4_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_3x3 (Conv2D)            (None, 8, 8, 128)    147584      padding11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_3x3_bn (BatchNormalizat (None, 8, 8, 128)    512         conv4_4_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_increase (Conv2D)   (None, 8, 8, 512)    66048       conv4_4_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4_1x1_increase_bn (BatchN (None, 8, 8, 512)    2048        conv4_4_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4 (Add)                   (None, 8, 8, 512)    0           conv4_4_1x1_increase_bn[0][0]    \n",
            "                                                                 conv4_3/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv4_4/relu (Activation)       (None, 8, 8, 512)    0           conv4_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_reduce (Conv2D)     (None, 8, 8, 128)    65664       conv4_4/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_reduce_bn (BatchNor (None, 8, 8, 128)    512         conv4_5_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding12 (ZeroPadding2D)       (None, 12, 12, 128)  0           conv4_5_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_3x3 (Conv2D)            (None, 8, 8, 128)    147584      padding12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_3x3_bn (BatchNormalizat (None, 8, 8, 128)    512         conv4_5_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_increase (Conv2D)   (None, 8, 8, 512)    66048       conv4_5_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5_1x1_increase_bn (BatchN (None, 8, 8, 512)    2048        conv4_5_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5 (Add)                   (None, 8, 8, 512)    0           conv4_5_1x1_increase_bn[0][0]    \n",
            "                                                                 conv4_4/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv4_5/relu (Activation)       (None, 8, 8, 512)    0           conv4_5[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_reduce (Conv2D)     (None, 8, 8, 128)    65664       conv4_5/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_reduce_bn (BatchNor (None, 8, 8, 128)    512         conv4_6_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding13 (ZeroPadding2D)       (None, 12, 12, 128)  0           conv4_6_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_3x3 (Conv2D)            (None, 8, 8, 128)    147584      padding13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_3x3_bn (BatchNormalizat (None, 8, 8, 128)    512         conv4_6_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_increase (Conv2D)   (None, 8, 8, 512)    66048       conv4_6_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6_1x1_increase_bn (BatchN (None, 8, 8, 512)    2048        conv4_6_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6 (Add)                   (None, 8, 8, 512)    0           conv4_6_1x1_increase_bn[0][0]    \n",
            "                                                                 conv4_5/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv4_6/relu (Activation)       (None, 8, 8, 512)    0           conv4_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_reduce (Conv2D)     (None, 8, 8, 256)    131328      conv4_6/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_reduce_bn (BatchNor (None, 8, 8, 256)    1024        conv5_1_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding14 (ZeroPadding2D)       (None, 16, 16, 256)  0           conv5_1_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_3x3 (Conv2D)            (None, 8, 8, 256)    590080      padding14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_3x3_bn (BatchNormalizat (None, 8, 8, 256)    1024        conv5_1_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_increase (Conv2D)   (None, 8, 8, 1024)   263168      conv5_1_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_proj (Conv2D)       (None, 8, 8, 1024)   525312      conv4_6/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_increase_bn (BatchN (None, 8, 8, 1024)   4096        conv5_1_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1_1x1_proj_bn (BatchNorma (None, 8, 8, 1024)   4096        conv5_1_1x1_proj[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1 (Add)                   (None, 8, 8, 1024)   0           conv5_1_1x1_increase_bn[0][0]    \n",
            "                                                                 conv5_1_1x1_proj_bn[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_1/relu (Activation)       (None, 8, 8, 1024)   0           conv5_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_reduce (Conv2D)     (None, 8, 8, 256)    262400      conv5_1/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_reduce_bn (BatchNor (None, 8, 8, 256)    1024        conv5_2_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding15 (ZeroPadding2D)       (None, 16, 16, 256)  0           conv5_2_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_3x3 (Conv2D)            (None, 8, 8, 256)    590080      padding15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_3x3_bn (BatchNormalizat (None, 8, 8, 256)    1024        conv5_2_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_increase (Conv2D)   (None, 8, 8, 1024)   263168      conv5_2_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2_1x1_increase_bn (BatchN (None, 8, 8, 1024)   4096        conv5_2_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2 (Add)                   (None, 8, 8, 1024)   0           conv5_2_1x1_increase_bn[0][0]    \n",
            "                                                                 conv5_1/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv5_2/relu (Activation)       (None, 8, 8, 1024)   0           conv5_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_reduce (Conv2D)     (None, 8, 8, 256)    262400      conv5_2/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_reduce_bn (BatchNor (None, 8, 8, 256)    1024        conv5_3_1x1_reduce[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "padding16 (ZeroPadding2D)       (None, 16, 16, 256)  0           conv5_3_1x1_reduce_bn[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_3x3 (Conv2D)            (None, 8, 8, 256)    590080      padding16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_3x3_bn (BatchNormalizat (None, 8, 8, 256)    1024        conv5_3_3x3[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_increase (Conv2D)   (None, 8, 8, 1024)   263168      conv5_3_3x3_bn[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_1x1_increase_bn (BatchN (None, 8, 8, 1024)   4096        conv5_3_1x1_increase[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3 (Add)                   (None, 8, 8, 1024)   0           conv5_3_1x1_increase_bn[0][0]    \n",
            "                                                                 conv5_2/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3/relu (Activation)       (None, 8, 8, 1024)   0           conv5_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_pool1 (AveragePooling2D (None, 1, 1, 1024)   0           conv5_3/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_pool2 (AveragePooling2D (None, 2, 2, 1024)   0           conv5_3/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_pool3 (AveragePooling2D (None, 4, 4, 1024)   0           conv5_3/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_pool6 (AveragePooling2D (None, 4, 4, 1024)   0           conv5_3/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_pool1_interp (Lambda)   (None, 8, 8, 1024)   0           conv5_3_pool1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_pool2_interp (Lambda)   (None, 8, 8, 1024)   0           conv5_3_pool2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_pool3_interp (Lambda)   (None, 8, 8, 1024)   0           conv5_3_pool3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_pool6_interp (Lambda)   (None, 8, 8, 1024)   0           conv5_3_pool6[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv5_3_sum (Add)               (None, 8, 8, 1024)   0           conv5_3/relu[0][0]               \n",
            "                                                                 conv5_3_pool1_interp[0][0]       \n",
            "                                                                 conv5_3_pool2_interp[0][0]       \n",
            "                                                                 conv5_3_pool3_interp[0][0]       \n",
            "                                                                 conv5_3_pool6_interp[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv5_4_k1 (Conv2D)             (None, 8, 8, 256)    262400      conv5_3_sum[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv5_4_k1_bn (BatchNormalizati (None, 8, 8, 256)    1024        conv5_4_k1[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv5_4_interp (Lambda)         (None, 16, 16, 256)  0           conv5_4_k1_bn[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "padding17 (ZeroPadding2D)       (None, 20, 20, 256)  0           conv5_4_interp[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv1_sub1 (Conv2D)             (None, 128, 128, 32) 320         lambda[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv_sub4 (Conv2D)              (None, 16, 16, 128)  295040      padding17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_sub2_proj (Conv2D)      (None, 16, 16, 128)  32896       conv3_1/relu[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv1_sub1_bn (BatchNormalizati (None, 128, 128, 32) 128         conv1_sub1[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv_sub4_bn (BatchNormalizatio (None, 16, 16, 128)  512         conv_sub4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv3_1_sub2_proj_bn (BatchNorm (None, 16, 16, 128)  512         conv3_1_sub2_proj[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_sub1 (Conv2D)             (None, 64, 64, 32)   9248        conv1_sub1_bn[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "sub24_sum (Add)                 (None, 16, 16, 128)  0           conv_sub4_bn[0][0]               \n",
            "                                                                 conv3_1_sub2_proj_bn[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2_sub1_bn (BatchNormalizati (None, 64, 64, 32)   128         conv2_sub1[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "sub24_sum/relu (Activation)     (None, 16, 16, 128)  0           sub24_sum[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv3_sub1 (Conv2D)             (None, 32, 32, 64)   18496       conv2_sub1_bn[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "sub24_sum_interp (Lambda)       (None, 32, 32, 128)  0           sub24_sum/relu[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv3_sub1_bn (BatchNormalizati (None, 32, 32, 64)   256         conv3_sub1[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "padding18 (ZeroPadding2D)       (None, 36, 36, 128)  0           sub24_sum_interp[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_sub1_proj (Conv2D)        (None, 32, 32, 128)  8320        conv3_sub1_bn[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv_sub2 (Conv2D)              (None, 32, 32, 128)  147584      padding18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv3_sub1_proj_bn (BatchNormal (None, 32, 32, 128)  512         conv3_sub1_proj[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv_sub2_bn (BatchNormalizatio (None, 32, 32, 128)  512         conv_sub2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "sub12_sum (Add)                 (None, 32, 32, 128)  0           conv3_sub1_proj_bn[0][0]         \n",
            "                                                                 conv_sub2_bn[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "sub12_sum/relu (Activation)     (None, 32, 32, 128)  0           sub12_sum[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "sub12_sum_interp (Lambda)       (None, 64, 64, 128)  0           sub12_sum/relu[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d (UpSampling2D)    (None, 256, 256, 128 0           sub12_sum_interp[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv6_cls (Conv2D)              (None, 256, 256, 1)  129         up_sampling2d[0][0]              \n",
            "==================================================================================================\n",
            "Total params: 6,739,329\n",
            "Trainable params: 6,710,785\n",
            "Non-trainable params: 28,544\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJO_L0m2Qcqy"
      },
      "source": [
        "earlystopper = EarlyStopping(patience=50, verbose=1)\n",
        "checkpointer = ModelCheckpoint('model-ICNet-initial.h5', verbose=1, save_best_only=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9LWqiDrQiQd",
        "outputId": "3db12d2b-19a4-4f03-de5e-ca11715fcd9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "results = model.fit(X_train, Y_train, validation_split=0.2, batch_size=16, epochs=400, \n",
        "                    callbacks=[earlystopper, checkpointer])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 40 samples, validate on 11 samples\n",
            "Epoch 1/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0566 - accuracy: 0.9774\n",
            "Epoch 00001: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 5s 116ms/sample - loss: 0.0569 - accuracy: 0.9770 - val_loss: 0.7197 - val_accuracy: 0.7270\n",
            "Epoch 2/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0588 - accuracy: 0.9761\n",
            "Epoch 00002: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0636 - accuracy: 0.9744 - val_loss: 0.7193 - val_accuracy: 0.7210\n",
            "Epoch 3/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0604 - accuracy: 0.9758\n",
            "Epoch 00003: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0605 - accuracy: 0.9757 - val_loss: 0.9613 - val_accuracy: 0.6962\n",
            "Epoch 4/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0594 - accuracy: 0.9760\n",
            "Epoch 00004: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0590 - accuracy: 0.9762 - val_loss: 0.8516 - val_accuracy: 0.6980\n",
            "Epoch 5/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0595 - accuracy: 0.9760\n",
            "Epoch 00005: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0605 - accuracy: 0.9755 - val_loss: 0.6631 - val_accuracy: 0.7418\n",
            "Epoch 6/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0565 - accuracy: 0.9772\n",
            "Epoch 00006: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0576 - accuracy: 0.9769 - val_loss: 0.5709 - val_accuracy: 0.7732\n",
            "Epoch 7/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0586 - accuracy: 0.9763\n",
            "Epoch 00007: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0598 - accuracy: 0.9757 - val_loss: 0.5996 - val_accuracy: 0.7490\n",
            "Epoch 8/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0585 - accuracy: 0.9762\n",
            "Epoch 00008: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0586 - accuracy: 0.9763 - val_loss: 0.6221 - val_accuracy: 0.7495\n",
            "Epoch 9/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0604 - accuracy: 0.9756\n",
            "Epoch 00009: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0605 - accuracy: 0.9756 - val_loss: 0.7045 - val_accuracy: 0.7396\n",
            "Epoch 10/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0579 - accuracy: 0.9764\n",
            "Epoch 00010: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0597 - accuracy: 0.9758 - val_loss: 0.7250 - val_accuracy: 0.7280\n",
            "Epoch 11/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0588 - accuracy: 0.9763\n",
            "Epoch 00011: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0597 - accuracy: 0.9759 - val_loss: 0.8451 - val_accuracy: 0.7118\n",
            "Epoch 12/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0585 - accuracy: 0.9764\n",
            "Epoch 00012: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0576 - accuracy: 0.9767 - val_loss: 0.9177 - val_accuracy: 0.7069\n",
            "Epoch 13/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0550 - accuracy: 0.9778\n",
            "Epoch 00013: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0553 - accuracy: 0.9777 - val_loss: 0.8442 - val_accuracy: 0.7107\n",
            "Epoch 14/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0558 - accuracy: 0.9777\n",
            "Epoch 00014: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0569 - accuracy: 0.9770 - val_loss: 0.8936 - val_accuracy: 0.7106\n",
            "Epoch 15/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0533 - accuracy: 0.9785\n",
            "Epoch 00015: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0534 - accuracy: 0.9785 - val_loss: 0.9242 - val_accuracy: 0.7168\n",
            "Epoch 16/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0536 - accuracy: 0.9782\n",
            "Epoch 00016: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0534 - accuracy: 0.9782 - val_loss: 0.7795 - val_accuracy: 0.7271\n",
            "Epoch 17/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0522 - accuracy: 0.9787\n",
            "Epoch 00017: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0536 - accuracy: 0.9781 - val_loss: 0.7394 - val_accuracy: 0.7460\n",
            "Epoch 18/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0538 - accuracy: 0.9781\n",
            "Epoch 00018: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0600 - accuracy: 0.9756 - val_loss: 0.8562 - val_accuracy: 0.7223\n",
            "Epoch 19/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0544 - accuracy: 0.9782\n",
            "Epoch 00019: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0544 - accuracy: 0.9780 - val_loss: 0.7483 - val_accuracy: 0.7272\n",
            "Epoch 20/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0528 - accuracy: 0.9789\n",
            "Epoch 00020: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0541 - accuracy: 0.9782 - val_loss: 0.6499 - val_accuracy: 0.7727\n",
            "Epoch 21/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0538 - accuracy: 0.9783\n",
            "Epoch 00021: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0545 - accuracy: 0.9778 - val_loss: 0.6381 - val_accuracy: 0.7724\n",
            "Epoch 22/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0510 - accuracy: 0.9793\n",
            "Epoch 00022: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0523 - accuracy: 0.9789 - val_loss: 0.6799 - val_accuracy: 0.7613\n",
            "Epoch 23/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0518 - accuracy: 0.9793\n",
            "Epoch 00023: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0521 - accuracy: 0.9790 - val_loss: 0.6829 - val_accuracy: 0.7746\n",
            "Epoch 24/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0515 - accuracy: 0.9790\n",
            "Epoch 00024: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0528 - accuracy: 0.9787 - val_loss: 0.6524 - val_accuracy: 0.7838\n",
            "Epoch 25/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0521 - accuracy: 0.9787\n",
            "Epoch 00025: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0521 - accuracy: 0.9787 - val_loss: 0.5659 - val_accuracy: 0.7977\n",
            "Epoch 26/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0525 - accuracy: 0.9789\n",
            "Epoch 00026: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0536 - accuracy: 0.9784 - val_loss: 0.6473 - val_accuracy: 0.7877\n",
            "Epoch 27/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0504 - accuracy: 0.9796\n",
            "Epoch 00027: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0516 - accuracy: 0.9790 - val_loss: 0.6682 - val_accuracy: 0.7790\n",
            "Epoch 28/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0519 - accuracy: 0.9790\n",
            "Epoch 00028: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0514 - accuracy: 0.9791 - val_loss: 0.5627 - val_accuracy: 0.8132\n",
            "Epoch 29/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0492 - accuracy: 0.9799\n",
            "Epoch 00029: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0509 - accuracy: 0.9792 - val_loss: 0.5819 - val_accuracy: 0.8146\n",
            "Epoch 30/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0495 - accuracy: 0.9796\n",
            "Epoch 00030: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0510 - accuracy: 0.9792 - val_loss: 0.5984 - val_accuracy: 0.8011\n",
            "Epoch 31/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0503 - accuracy: 0.9793\n",
            "Epoch 00031: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0503 - accuracy: 0.9794 - val_loss: 0.5606 - val_accuracy: 0.8150\n",
            "Epoch 32/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0491 - accuracy: 0.9799\n",
            "Epoch 00032: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0525 - accuracy: 0.9785 - val_loss: 0.5639 - val_accuracy: 0.8170\n",
            "Epoch 33/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0513 - accuracy: 0.9790\n",
            "Epoch 00033: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0530 - accuracy: 0.9784 - val_loss: 0.5813 - val_accuracy: 0.8162\n",
            "Epoch 34/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0513 - accuracy: 0.9791\n",
            "Epoch 00034: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0528 - accuracy: 0.9783 - val_loss: 0.5706 - val_accuracy: 0.8175\n",
            "Epoch 35/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0525 - accuracy: 0.9785\n",
            "Epoch 00035: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0553 - accuracy: 0.9774 - val_loss: 0.6454 - val_accuracy: 0.7943\n",
            "Epoch 36/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0539 - accuracy: 0.9779\n",
            "Epoch 00036: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0538 - accuracy: 0.9779 - val_loss: 0.5775 - val_accuracy: 0.8136\n",
            "Epoch 37/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0500 - accuracy: 0.9794\n",
            "Epoch 00037: val_loss did not improve from 0.53102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0504 - accuracy: 0.9793 - val_loss: 0.5324 - val_accuracy: 0.8317\n",
            "Epoch 38/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0495 - accuracy: 0.9799\n",
            "Epoch 00038: val_loss improved from 0.53102 to 0.49569, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 23ms/sample - loss: 0.0515 - accuracy: 0.9791 - val_loss: 0.4957 - val_accuracy: 0.8466\n",
            "Epoch 39/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0481 - accuracy: 0.9803\n",
            "Epoch 00039: val_loss improved from 0.49569 to 0.46027, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 3s 74ms/sample - loss: 0.0497 - accuracy: 0.9797 - val_loss: 0.4603 - val_accuracy: 0.8500\n",
            "Epoch 40/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0502 - accuracy: 0.9794\n",
            "Epoch 00040: val_loss did not improve from 0.46027\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0511 - accuracy: 0.9791 - val_loss: 0.5311 - val_accuracy: 0.8312\n",
            "Epoch 41/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0509 - accuracy: 0.9792\n",
            "Epoch 00041: val_loss did not improve from 0.46027\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0510 - accuracy: 0.9792 - val_loss: 0.6165 - val_accuracy: 0.8060\n",
            "Epoch 42/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0504 - accuracy: 0.9794\n",
            "Epoch 00042: val_loss did not improve from 0.46027\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0511 - accuracy: 0.9790 - val_loss: 0.5625 - val_accuracy: 0.8244\n",
            "Epoch 43/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0492 - accuracy: 0.9801\n",
            "Epoch 00043: val_loss did not improve from 0.46027\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0493 - accuracy: 0.9800 - val_loss: 0.5565 - val_accuracy: 0.8271\n",
            "Epoch 44/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0499 - accuracy: 0.9799\n",
            "Epoch 00044: val_loss did not improve from 0.46027\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0502 - accuracy: 0.9796 - val_loss: 0.5690 - val_accuracy: 0.8276\n",
            "Epoch 45/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0496 - accuracy: 0.9795\n",
            "Epoch 00045: val_loss did not improve from 0.46027\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0498 - accuracy: 0.9795 - val_loss: 0.5714 - val_accuracy: 0.8275\n",
            "Epoch 46/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0492 - accuracy: 0.9797\n",
            "Epoch 00046: val_loss did not improve from 0.46027\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0491 - accuracy: 0.9797 - val_loss: 0.5712 - val_accuracy: 0.8236\n",
            "Epoch 47/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0510 - accuracy: 0.9790\n",
            "Epoch 00047: val_loss did not improve from 0.46027\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0521 - accuracy: 0.9788 - val_loss: 0.5246 - val_accuracy: 0.8508\n",
            "Epoch 48/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0499 - accuracy: 0.9794\n",
            "Epoch 00048: val_loss did not improve from 0.46027\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0499 - accuracy: 0.9793 - val_loss: 0.5190 - val_accuracy: 0.8493\n",
            "Epoch 49/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0476 - accuracy: 0.9805\n",
            "Epoch 00049: val_loss improved from 0.46027 to 0.44252, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 24ms/sample - loss: 0.0489 - accuracy: 0.9799 - val_loss: 0.4425 - val_accuracy: 0.8697\n",
            "Epoch 50/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0474 - accuracy: 0.9806\n",
            "Epoch 00050: val_loss improved from 0.44252 to 0.40648, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 3s 74ms/sample - loss: 0.0487 - accuracy: 0.9800 - val_loss: 0.4065 - val_accuracy: 0.8775\n",
            "Epoch 51/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0492 - accuracy: 0.9797\n",
            "Epoch 00051: val_loss did not improve from 0.40648\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0506 - accuracy: 0.9791 - val_loss: 0.4126 - val_accuracy: 0.8800\n",
            "Epoch 52/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0530 - accuracy: 0.9783\n",
            "Epoch 00052: val_loss did not improve from 0.40648\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0526 - accuracy: 0.9784 - val_loss: 0.4911 - val_accuracy: 0.8506\n",
            "Epoch 53/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0488 - accuracy: 0.9799\n",
            "Epoch 00053: val_loss did not improve from 0.40648\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0496 - accuracy: 0.9796 - val_loss: 0.4252 - val_accuracy: 0.8704\n",
            "Epoch 54/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0482 - accuracy: 0.9802\n",
            "Epoch 00054: val_loss improved from 0.40648 to 0.34061, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 2s 56ms/sample - loss: 0.0487 - accuracy: 0.9799 - val_loss: 0.3406 - val_accuracy: 0.8976\n",
            "Epoch 55/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0491 - accuracy: 0.9797\n",
            "Epoch 00055: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0492 - accuracy: 0.9797 - val_loss: 0.4037 - val_accuracy: 0.8849\n",
            "Epoch 56/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0472 - accuracy: 0.9805\n",
            "Epoch 00056: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0478 - accuracy: 0.9802 - val_loss: 0.4629 - val_accuracy: 0.8705\n",
            "Epoch 57/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0471 - accuracy: 0.9805\n",
            "Epoch 00057: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0480 - accuracy: 0.9802 - val_loss: 0.4315 - val_accuracy: 0.8765\n",
            "Epoch 58/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0481 - accuracy: 0.9801\n",
            "Epoch 00058: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0484 - accuracy: 0.9800 - val_loss: 0.3758 - val_accuracy: 0.8870\n",
            "Epoch 59/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0467 - accuracy: 0.9806\n",
            "Epoch 00059: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0491 - accuracy: 0.9797 - val_loss: 0.3947 - val_accuracy: 0.8846\n",
            "Epoch 60/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0460 - accuracy: 0.9809\n",
            "Epoch 00060: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0476 - accuracy: 0.9803 - val_loss: 0.4129 - val_accuracy: 0.8756\n",
            "Epoch 61/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0474 - accuracy: 0.9803\n",
            "Epoch 00061: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0488 - accuracy: 0.9798 - val_loss: 0.4191 - val_accuracy: 0.8767\n",
            "Epoch 62/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0485 - accuracy: 0.9800\n",
            "Epoch 00062: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0484 - accuracy: 0.9800 - val_loss: 0.3846 - val_accuracy: 0.8889\n",
            "Epoch 63/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0489 - accuracy: 0.9797\n",
            "Epoch 00063: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0492 - accuracy: 0.9796 - val_loss: 0.3980 - val_accuracy: 0.8862\n",
            "Epoch 64/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0478 - accuracy: 0.9800\n",
            "Epoch 00064: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0506 - accuracy: 0.9788 - val_loss: 0.4032 - val_accuracy: 0.8869\n",
            "Epoch 65/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0479 - accuracy: 0.9799\n",
            "Epoch 00065: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0482 - accuracy: 0.9798 - val_loss: 0.3425 - val_accuracy: 0.8952\n",
            "Epoch 66/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0490 - accuracy: 0.9797\n",
            "Epoch 00066: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0487 - accuracy: 0.9799 - val_loss: 0.3811 - val_accuracy: 0.8905\n",
            "Epoch 67/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0471 - accuracy: 0.9803\n",
            "Epoch 00067: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0473 - accuracy: 0.9804 - val_loss: 0.3559 - val_accuracy: 0.8981\n",
            "Epoch 68/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0491 - accuracy: 0.9796\n",
            "Epoch 00068: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0486 - accuracy: 0.9798 - val_loss: 0.3647 - val_accuracy: 0.8916\n",
            "Epoch 69/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0473 - accuracy: 0.9803\n",
            "Epoch 00069: val_loss did not improve from 0.34061\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0481 - accuracy: 0.9799 - val_loss: 0.3687 - val_accuracy: 0.8933\n",
            "Epoch 70/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0471 - accuracy: 0.9804\n",
            "Epoch 00070: val_loss improved from 0.34061 to 0.33726, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 23ms/sample - loss: 0.0488 - accuracy: 0.9796 - val_loss: 0.3373 - val_accuracy: 0.8998\n",
            "Epoch 71/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0468 - accuracy: 0.9804\n",
            "Epoch 00071: val_loss improved from 0.33726 to 0.28102, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 3s 74ms/sample - loss: 0.0472 - accuracy: 0.9802 - val_loss: 0.2810 - val_accuracy: 0.9121\n",
            "Epoch 72/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0467 - accuracy: 0.9806\n",
            "Epoch 00072: val_loss did not improve from 0.28102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0478 - accuracy: 0.9802 - val_loss: 0.3118 - val_accuracy: 0.9081\n",
            "Epoch 73/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0457 - accuracy: 0.9809\n",
            "Epoch 00073: val_loss did not improve from 0.28102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0462 - accuracy: 0.9807 - val_loss: 0.3208 - val_accuracy: 0.9061\n",
            "Epoch 74/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0471 - accuracy: 0.9802\n",
            "Epoch 00074: val_loss did not improve from 0.28102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0470 - accuracy: 0.9804 - val_loss: 0.2997 - val_accuracy: 0.9113\n",
            "Epoch 75/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0468 - accuracy: 0.9808\n",
            "Epoch 00075: val_loss did not improve from 0.28102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0469 - accuracy: 0.9805 - val_loss: 0.3189 - val_accuracy: 0.9072\n",
            "Epoch 76/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0470 - accuracy: 0.9807\n",
            "Epoch 00076: val_loss did not improve from 0.28102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0471 - accuracy: 0.9804 - val_loss: 0.3188 - val_accuracy: 0.9056\n",
            "Epoch 77/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0462 - accuracy: 0.9808\n",
            "Epoch 00077: val_loss did not improve from 0.28102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0464 - accuracy: 0.9807 - val_loss: 0.3137 - val_accuracy: 0.9093\n",
            "Epoch 78/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0474 - accuracy: 0.9803\n",
            "Epoch 00078: val_loss did not improve from 0.28102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0471 - accuracy: 0.9803 - val_loss: 0.2901 - val_accuracy: 0.9148\n",
            "Epoch 79/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0470 - accuracy: 0.9804\n",
            "Epoch 00079: val_loss did not improve from 0.28102\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0464 - accuracy: 0.9807 - val_loss: 0.3216 - val_accuracy: 0.9096\n",
            "Epoch 80/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0469 - accuracy: 0.9803\n",
            "Epoch 00080: val_loss improved from 0.28102 to 0.27929, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 23ms/sample - loss: 0.0477 - accuracy: 0.9799 - val_loss: 0.2793 - val_accuracy: 0.9162\n",
            "Epoch 81/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0468 - accuracy: 0.9806\n",
            "Epoch 00081: val_loss improved from 0.27929 to 0.26949, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 3s 75ms/sample - loss: 0.0467 - accuracy: 0.9806 - val_loss: 0.2695 - val_accuracy: 0.9203\n",
            "Epoch 82/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0475 - accuracy: 0.9801\n",
            "Epoch 00082: val_loss did not improve from 0.26949\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0484 - accuracy: 0.9798 - val_loss: 0.2809 - val_accuracy: 0.9172\n",
            "Epoch 83/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0469 - accuracy: 0.9803\n",
            "Epoch 00083: val_loss did not improve from 0.26949\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0483 - accuracy: 0.9798 - val_loss: 0.2814 - val_accuracy: 0.9168\n",
            "Epoch 84/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0452 - accuracy: 0.9808\n",
            "Epoch 00084: val_loss improved from 0.26949 to 0.23943, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 24ms/sample - loss: 0.0461 - accuracy: 0.9805 - val_loss: 0.2394 - val_accuracy: 0.9268\n",
            "Epoch 85/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0459 - accuracy: 0.9808\n",
            "Epoch 00085: val_loss improved from 0.23943 to 0.23440, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 3s 73ms/sample - loss: 0.0463 - accuracy: 0.9806 - val_loss: 0.2344 - val_accuracy: 0.9285\n",
            "Epoch 86/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0438 - accuracy: 0.9815\n",
            "Epoch 00086: val_loss did not improve from 0.23440\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0452 - accuracy: 0.9809 - val_loss: 0.2633 - val_accuracy: 0.9245\n",
            "Epoch 87/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0449 - accuracy: 0.9811\n",
            "Epoch 00087: val_loss did not improve from 0.23440\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0460 - accuracy: 0.9808 - val_loss: 0.2484 - val_accuracy: 0.9258\n",
            "Epoch 88/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0444 - accuracy: 0.9815\n",
            "Epoch 00088: val_loss did not improve from 0.23440\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0455 - accuracy: 0.9810 - val_loss: 0.2416 - val_accuracy: 0.9282\n",
            "Epoch 89/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0442 - accuracy: 0.9813\n",
            "Epoch 00089: val_loss improved from 0.23440 to 0.21349, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 24ms/sample - loss: 0.0445 - accuracy: 0.9813 - val_loss: 0.2135 - val_accuracy: 0.9352\n",
            "Epoch 90/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0451 - accuracy: 0.9811\n",
            "Epoch 00090: val_loss did not improve from 0.21349\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0452 - accuracy: 0.9810 - val_loss: 0.2312 - val_accuracy: 0.9320\n",
            "Epoch 91/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0445 - accuracy: 0.9813\n",
            "Epoch 00091: val_loss did not improve from 0.21349\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0448 - accuracy: 0.9811 - val_loss: 0.2278 - val_accuracy: 0.9322\n",
            "Epoch 92/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0444 - accuracy: 0.9813\n",
            "Epoch 00092: val_loss did not improve from 0.21349\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0451 - accuracy: 0.9810 - val_loss: 0.2301 - val_accuracy: 0.9327\n",
            "Epoch 93/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0435 - accuracy: 0.9813\n",
            "Epoch 00093: val_loss did not improve from 0.21349\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0448 - accuracy: 0.9810 - val_loss: 0.2162 - val_accuracy: 0.9362\n",
            "Epoch 94/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0442 - accuracy: 0.9813\n",
            "Epoch 00094: val_loss did not improve from 0.21349\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0452 - accuracy: 0.9808 - val_loss: 0.2288 - val_accuracy: 0.9340\n",
            "Epoch 95/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0454 - accuracy: 0.9808\n",
            "Epoch 00095: val_loss did not improve from 0.21349\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0461 - accuracy: 0.9805 - val_loss: 0.2276 - val_accuracy: 0.9335\n",
            "Epoch 96/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0465 - accuracy: 0.9804\n",
            "Epoch 00096: val_loss did not improve from 0.21349\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0459 - accuracy: 0.9806 - val_loss: 0.2396 - val_accuracy: 0.9316\n",
            "Epoch 97/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0457 - accuracy: 0.9807\n",
            "Epoch 00097: val_loss did not improve from 0.21349\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0463 - accuracy: 0.9805 - val_loss: 0.2168 - val_accuracy: 0.9353\n",
            "Epoch 98/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0445 - accuracy: 0.9812\n",
            "Epoch 00098: val_loss improved from 0.21349 to 0.19406, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 23ms/sample - loss: 0.0456 - accuracy: 0.9808 - val_loss: 0.1941 - val_accuracy: 0.9396\n",
            "Epoch 99/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0449 - accuracy: 0.9810\n",
            "Epoch 00099: val_loss did not improve from 0.19406\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0469 - accuracy: 0.9802 - val_loss: 0.2013 - val_accuracy: 0.9385\n",
            "Epoch 100/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0465 - accuracy: 0.9805\n",
            "Epoch 00100: val_loss did not improve from 0.19406\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0470 - accuracy: 0.9802 - val_loss: 0.2050 - val_accuracy: 0.9377\n",
            "Epoch 101/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0454 - accuracy: 0.9809\n",
            "Epoch 00101: val_loss did not improve from 0.19406\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0466 - accuracy: 0.9803 - val_loss: 0.2205 - val_accuracy: 0.9352\n",
            "Epoch 102/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0452 - accuracy: 0.9806\n",
            "Epoch 00102: val_loss improved from 0.19406 to 0.18751, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 2s 60ms/sample - loss: 0.0455 - accuracy: 0.9805 - val_loss: 0.1875 - val_accuracy: 0.9416\n",
            "Epoch 103/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0457 - accuracy: 0.9806\n",
            "Epoch 00103: val_loss did not improve from 0.18751\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0461 - accuracy: 0.9805 - val_loss: 0.1951 - val_accuracy: 0.9408\n",
            "Epoch 104/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0467 - accuracy: 0.9804\n",
            "Epoch 00104: val_loss did not improve from 0.18751\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0466 - accuracy: 0.9805 - val_loss: 0.2009 - val_accuracy: 0.9398\n",
            "Epoch 105/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0462 - accuracy: 0.9804\n",
            "Epoch 00105: val_loss did not improve from 0.18751\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0464 - accuracy: 0.9803 - val_loss: 0.1974 - val_accuracy: 0.9407\n",
            "Epoch 106/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0433 - accuracy: 0.9815\n",
            "Epoch 00106: val_loss improved from 0.18751 to 0.17694, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 24ms/sample - loss: 0.0447 - accuracy: 0.9810 - val_loss: 0.1769 - val_accuracy: 0.9437\n",
            "Epoch 107/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0438 - accuracy: 0.9815\n",
            "Epoch 00107: val_loss did not improve from 0.17694\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0449 - accuracy: 0.9810 - val_loss: 0.1828 - val_accuracy: 0.9435\n",
            "Epoch 108/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0442 - accuracy: 0.9812\n",
            "Epoch 00108: val_loss did not improve from 0.17694\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0452 - accuracy: 0.9808 - val_loss: 0.1847 - val_accuracy: 0.9442\n",
            "Epoch 109/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0442 - accuracy: 0.9813\n",
            "Epoch 00109: val_loss did not improve from 0.17694\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0456 - accuracy: 0.9806 - val_loss: 0.1820 - val_accuracy: 0.9425\n",
            "Epoch 110/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0442 - accuracy: 0.9812\n",
            "Epoch 00110: val_loss improved from 0.17694 to 0.17508, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 2s 58ms/sample - loss: 0.0455 - accuracy: 0.9807 - val_loss: 0.1751 - val_accuracy: 0.9439\n",
            "Epoch 111/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0451 - accuracy: 0.9810\n",
            "Epoch 00111: val_loss did not improve from 0.17508\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0453 - accuracy: 0.9809 - val_loss: 0.1848 - val_accuracy: 0.9447\n",
            "Epoch 112/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0456 - accuracy: 0.9805\n",
            "Epoch 00112: val_loss did not improve from 0.17508\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0467 - accuracy: 0.9801 - val_loss: 0.1753 - val_accuracy: 0.9445\n",
            "Epoch 113/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0438 - accuracy: 0.9814\n",
            "Epoch 00113: val_loss improved from 0.17508 to 0.16784, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 25ms/sample - loss: 0.0449 - accuracy: 0.9809 - val_loss: 0.1678 - val_accuracy: 0.9452\n",
            "Epoch 114/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0470 - accuracy: 0.9800\n",
            "Epoch 00114: val_loss did not improve from 0.16784\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0476 - accuracy: 0.9799 - val_loss: 0.1689 - val_accuracy: 0.9454\n",
            "Epoch 115/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0456 - accuracy: 0.9805\n",
            "Epoch 00115: val_loss did not improve from 0.16784\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0464 - accuracy: 0.9802 - val_loss: 0.1822 - val_accuracy: 0.9440\n",
            "Epoch 116/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0459 - accuracy: 0.9802\n",
            "Epoch 00116: val_loss improved from 0.16784 to 0.15914, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 24ms/sample - loss: 0.0458 - accuracy: 0.9803 - val_loss: 0.1591 - val_accuracy: 0.9471\n",
            "Epoch 117/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0440 - accuracy: 0.9814\n",
            "Epoch 00117: val_loss did not improve from 0.15914\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0453 - accuracy: 0.9810 - val_loss: 0.1671 - val_accuracy: 0.9460\n",
            "Epoch 118/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0440 - accuracy: 0.9813\n",
            "Epoch 00118: val_loss did not improve from 0.15914\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0454 - accuracy: 0.9808 - val_loss: 0.1684 - val_accuracy: 0.9463\n",
            "Epoch 119/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0442 - accuracy: 0.9813\n",
            "Epoch 00119: val_loss did not improve from 0.15914\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0454 - accuracy: 0.9809 - val_loss: 0.1647 - val_accuracy: 0.9465\n",
            "Epoch 120/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0443 - accuracy: 0.9812\n",
            "Epoch 00120: val_loss did not improve from 0.15914\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0443 - accuracy: 0.9812 - val_loss: 0.1663 - val_accuracy: 0.9458\n",
            "Epoch 121/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0448 - accuracy: 0.9809\n",
            "Epoch 00121: val_loss did not improve from 0.15914\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0452 - accuracy: 0.9808 - val_loss: 0.1666 - val_accuracy: 0.9463\n",
            "Epoch 122/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0447 - accuracy: 0.9809\n",
            "Epoch 00122: val_loss did not improve from 0.15914\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0450 - accuracy: 0.9809 - val_loss: 0.1649 - val_accuracy: 0.9471\n",
            "Epoch 123/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0448 - accuracy: 0.9811\n",
            "Epoch 00123: val_loss did not improve from 0.15914\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0451 - accuracy: 0.9808 - val_loss: 0.1599 - val_accuracy: 0.9475\n",
            "Epoch 124/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0450 - accuracy: 0.9809\n",
            "Epoch 00124: val_loss did not improve from 0.15914\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0450 - accuracy: 0.9809 - val_loss: 0.1640 - val_accuracy: 0.9461\n",
            "Epoch 125/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0445 - accuracy: 0.9809\n",
            "Epoch 00125: val_loss did not improve from 0.15914\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0443 - accuracy: 0.9811 - val_loss: 0.1598 - val_accuracy: 0.9480\n",
            "Epoch 126/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0443 - accuracy: 0.9811\n",
            "Epoch 00126: val_loss did not improve from 0.15914\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0439 - accuracy: 0.9814 - val_loss: 0.1650 - val_accuracy: 0.9468\n",
            "Epoch 127/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0425 - accuracy: 0.9818\n",
            "Epoch 00127: val_loss improved from 0.15914 to 0.15465, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 25ms/sample - loss: 0.0436 - accuracy: 0.9814 - val_loss: 0.1546 - val_accuracy: 0.9486\n",
            "Epoch 128/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0430 - accuracy: 0.9816\n",
            "Epoch 00128: val_loss did not improve from 0.15465\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0437 - accuracy: 0.9813 - val_loss: 0.1672 - val_accuracy: 0.9468\n",
            "Epoch 129/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0436 - accuracy: 0.9813\n",
            "Epoch 00129: val_loss did not improve from 0.15465\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0441 - accuracy: 0.9811 - val_loss: 0.1626 - val_accuracy: 0.9477\n",
            "Epoch 130/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0436 - accuracy: 0.9813\n",
            "Epoch 00130: val_loss did not improve from 0.15465\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0435 - accuracy: 0.9814 - val_loss: 0.1600 - val_accuracy: 0.9484\n",
            "Epoch 131/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0435 - accuracy: 0.9814\n",
            "Epoch 00131: val_loss did not improve from 0.15465\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0433 - accuracy: 0.9815 - val_loss: 0.1657 - val_accuracy: 0.9477\n",
            "Epoch 132/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0436 - accuracy: 0.9813\n",
            "Epoch 00132: val_loss did not improve from 0.15465\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0436 - accuracy: 0.9813 - val_loss: 0.1594 - val_accuracy: 0.9489\n",
            "Epoch 133/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0420 - accuracy: 0.9818\n",
            "Epoch 00133: val_loss did not improve from 0.15465\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0449 - accuracy: 0.9807 - val_loss: 0.1604 - val_accuracy: 0.9481\n",
            "Epoch 134/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0436 - accuracy: 0.9811\n",
            "Epoch 00134: val_loss did not improve from 0.15465\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0444 - accuracy: 0.9810 - val_loss: 0.1645 - val_accuracy: 0.9476\n",
            "Epoch 135/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0427 - accuracy: 0.9816\n",
            "Epoch 00135: val_loss did not improve from 0.15465\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0437 - accuracy: 0.9813 - val_loss: 0.1568 - val_accuracy: 0.9492\n",
            "Epoch 136/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0460 - accuracy: 0.9804\n",
            "Epoch 00136: val_loss did not improve from 0.15465\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0462 - accuracy: 0.9804 - val_loss: 0.1582 - val_accuracy: 0.9485\n",
            "Epoch 137/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0451 - accuracy: 0.9807\n",
            "Epoch 00137: val_loss did not improve from 0.15465\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0467 - accuracy: 0.9801 - val_loss: 0.1607 - val_accuracy: 0.9478\n",
            "Epoch 138/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0429 - accuracy: 0.9816\n",
            "Epoch 00138: val_loss improved from 0.15465 to 0.15403, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 23ms/sample - loss: 0.0435 - accuracy: 0.9814 - val_loss: 0.1540 - val_accuracy: 0.9492\n",
            "Epoch 139/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0433 - accuracy: 0.9814\n",
            "Epoch 00139: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0436 - accuracy: 0.9812 - val_loss: 0.1678 - val_accuracy: 0.9477\n",
            "Epoch 140/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0426 - accuracy: 0.9818\n",
            "Epoch 00140: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0435 - accuracy: 0.9813 - val_loss: 0.1639 - val_accuracy: 0.9485\n",
            "Epoch 141/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0444 - accuracy: 0.9809\n",
            "Epoch 00141: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0447 - accuracy: 0.9810 - val_loss: 0.1636 - val_accuracy: 0.9483\n",
            "Epoch 142/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0433 - accuracy: 0.9814\n",
            "Epoch 00142: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0432 - accuracy: 0.9815 - val_loss: 0.1625 - val_accuracy: 0.9487\n",
            "Epoch 143/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0417 - accuracy: 0.9820\n",
            "Epoch 00143: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0428 - accuracy: 0.9816 - val_loss: 0.1628 - val_accuracy: 0.9488\n",
            "Epoch 144/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0433 - accuracy: 0.9814\n",
            "Epoch 00144: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0446 - accuracy: 0.9810 - val_loss: 0.1618 - val_accuracy: 0.9491\n",
            "Epoch 145/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0423 - accuracy: 0.9818\n",
            "Epoch 00145: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0436 - accuracy: 0.9812 - val_loss: 0.1599 - val_accuracy: 0.9496\n",
            "Epoch 146/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0430 - accuracy: 0.9816\n",
            "Epoch 00146: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0437 - accuracy: 0.9812 - val_loss: 0.1655 - val_accuracy: 0.9487\n",
            "Epoch 147/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0434 - accuracy: 0.9813\n",
            "Epoch 00147: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0437 - accuracy: 0.9813 - val_loss: 0.1584 - val_accuracy: 0.9500\n",
            "Epoch 148/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0427 - accuracy: 0.9817\n",
            "Epoch 00148: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0437 - accuracy: 0.9813 - val_loss: 0.1684 - val_accuracy: 0.9488\n",
            "Epoch 149/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0428 - accuracy: 0.9815\n",
            "Epoch 00149: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0434 - accuracy: 0.9813 - val_loss: 0.1592 - val_accuracy: 0.9500\n",
            "Epoch 150/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0452 - accuracy: 0.9808\n",
            "Epoch 00150: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0457 - accuracy: 0.9805 - val_loss: 0.1590 - val_accuracy: 0.9500\n",
            "Epoch 151/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0443 - accuracy: 0.9808\n",
            "Epoch 00151: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0447 - accuracy: 0.9809 - val_loss: 0.1606 - val_accuracy: 0.9502\n",
            "Epoch 152/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0442 - accuracy: 0.9810\n",
            "Epoch 00152: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0442 - accuracy: 0.9810 - val_loss: 0.1549 - val_accuracy: 0.9505\n",
            "Epoch 153/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0441 - accuracy: 0.9809\n",
            "Epoch 00153: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0452 - accuracy: 0.9807 - val_loss: 0.1613 - val_accuracy: 0.9492\n",
            "Epoch 154/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0434 - accuracy: 0.9812\n",
            "Epoch 00154: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0438 - accuracy: 0.9812 - val_loss: 0.1615 - val_accuracy: 0.9499\n",
            "Epoch 155/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0433 - accuracy: 0.9814\n",
            "Epoch 00155: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0448 - accuracy: 0.9809 - val_loss: 0.1572 - val_accuracy: 0.9509\n",
            "Epoch 156/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0424 - accuracy: 0.9818\n",
            "Epoch 00156: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0433 - accuracy: 0.9815 - val_loss: 0.1544 - val_accuracy: 0.9507\n",
            "Epoch 157/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0437 - accuracy: 0.9812\n",
            "Epoch 00157: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0435 - accuracy: 0.9812 - val_loss: 0.1555 - val_accuracy: 0.9505\n",
            "Epoch 158/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0441 - accuracy: 0.9810\n",
            "Epoch 00158: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0453 - accuracy: 0.9806 - val_loss: 0.1600 - val_accuracy: 0.9502\n",
            "Epoch 159/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0422 - accuracy: 0.9818\n",
            "Epoch 00159: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0429 - accuracy: 0.9814 - val_loss: 0.1553 - val_accuracy: 0.9500\n",
            "Epoch 160/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0436 - accuracy: 0.9813\n",
            "Epoch 00160: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0442 - accuracy: 0.9810 - val_loss: 0.1575 - val_accuracy: 0.9504\n",
            "Epoch 161/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0423 - accuracy: 0.9818\n",
            "Epoch 00161: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0428 - accuracy: 0.9816 - val_loss: 0.1577 - val_accuracy: 0.9503\n",
            "Epoch 162/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0425 - accuracy: 0.9816\n",
            "Epoch 00162: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0432 - accuracy: 0.9814 - val_loss: 0.1544 - val_accuracy: 0.9508\n",
            "Epoch 163/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0418 - accuracy: 0.9819\n",
            "Epoch 00163: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0426 - accuracy: 0.9816 - val_loss: 0.1641 - val_accuracy: 0.9485\n",
            "Epoch 164/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0422 - accuracy: 0.9818\n",
            "Epoch 00164: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0427 - accuracy: 0.9815 - val_loss: 0.1584 - val_accuracy: 0.9505\n",
            "Epoch 165/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0438 - accuracy: 0.9813\n",
            "Epoch 00165: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0435 - accuracy: 0.9813 - val_loss: 0.1611 - val_accuracy: 0.9498\n",
            "Epoch 166/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0417 - accuracy: 0.9819\n",
            "Epoch 00166: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0432 - accuracy: 0.9813 - val_loss: 0.1552 - val_accuracy: 0.9505\n",
            "Epoch 167/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0426 - accuracy: 0.9816\n",
            "Epoch 00167: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0426 - accuracy: 0.9816 - val_loss: 0.1588 - val_accuracy: 0.9495\n",
            "Epoch 168/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0428 - accuracy: 0.9814\n",
            "Epoch 00168: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0424 - accuracy: 0.9816 - val_loss: 0.1588 - val_accuracy: 0.9506\n",
            "Epoch 169/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0421 - accuracy: 0.9818\n",
            "Epoch 00169: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0421 - accuracy: 0.9817 - val_loss: 0.1641 - val_accuracy: 0.9491\n",
            "Epoch 170/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0431 - accuracy: 0.9814\n",
            "Epoch 00170: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0428 - accuracy: 0.9815 - val_loss: 0.1606 - val_accuracy: 0.9496\n",
            "Epoch 171/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0418 - accuracy: 0.9818\n",
            "Epoch 00171: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0438 - accuracy: 0.9811 - val_loss: 0.1551 - val_accuracy: 0.9502\n",
            "Epoch 172/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0421 - accuracy: 0.9817\n",
            "Epoch 00172: val_loss did not improve from 0.15403\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0425 - accuracy: 0.9816 - val_loss: 0.1590 - val_accuracy: 0.9489\n",
            "Epoch 173/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0430 - accuracy: 0.9813\n",
            "Epoch 00173: val_loss improved from 0.15403 to 0.15364, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 23ms/sample - loss: 0.0432 - accuracy: 0.9812 - val_loss: 0.1536 - val_accuracy: 0.9513\n",
            "Epoch 174/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0431 - accuracy: 0.9816\n",
            "Epoch 00174: val_loss did not improve from 0.15364\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0428 - accuracy: 0.9816 - val_loss: 0.1595 - val_accuracy: 0.9494\n",
            "Epoch 175/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0420 - accuracy: 0.9817\n",
            "Epoch 00175: val_loss did not improve from 0.15364\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0434 - accuracy: 0.9812 - val_loss: 0.1546 - val_accuracy: 0.9507\n",
            "Epoch 176/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0426 - accuracy: 0.9815\n",
            "Epoch 00176: val_loss did not improve from 0.15364\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0429 - accuracy: 0.9815 - val_loss: 0.1574 - val_accuracy: 0.9502\n",
            "Epoch 177/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0418 - accuracy: 0.9818\n",
            "Epoch 00177: val_loss did not improve from 0.15364\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0424 - accuracy: 0.9816 - val_loss: 0.1539 - val_accuracy: 0.9511\n",
            "Epoch 178/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0406 - accuracy: 0.9825\n",
            "Epoch 00178: val_loss did not improve from 0.15364\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0419 - accuracy: 0.9819 - val_loss: 0.1601 - val_accuracy: 0.9486\n",
            "Epoch 179/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0417 - accuracy: 0.9819\n",
            "Epoch 00179: val_loss did not improve from 0.15364\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0425 - accuracy: 0.9816 - val_loss: 0.1612 - val_accuracy: 0.9491\n",
            "Epoch 180/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0416 - accuracy: 0.9820\n",
            "Epoch 00180: val_loss did not improve from 0.15364\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0429 - accuracy: 0.9814 - val_loss: 0.1545 - val_accuracy: 0.9510\n",
            "Epoch 181/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0424 - accuracy: 0.9817\n",
            "Epoch 00181: val_loss did not improve from 0.15364\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0429 - accuracy: 0.9815 - val_loss: 0.1572 - val_accuracy: 0.9509\n",
            "Epoch 182/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0428 - accuracy: 0.9814\n",
            "Epoch 00182: val_loss did not improve from 0.15364\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0432 - accuracy: 0.9813 - val_loss: 0.1568 - val_accuracy: 0.9498\n",
            "Epoch 183/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0418 - accuracy: 0.9819\n",
            "Epoch 00183: val_loss did not improve from 0.15364\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0420 - accuracy: 0.9819 - val_loss: 0.1589 - val_accuracy: 0.9496\n",
            "Epoch 184/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0428 - accuracy: 0.9815\n",
            "Epoch 00184: val_loss improved from 0.15364 to 0.15212, saving model to model-ICNet-initial.h5\n",
            "40/40 [==============================] - 1s 25ms/sample - loss: 0.0429 - accuracy: 0.9814 - val_loss: 0.1521 - val_accuracy: 0.9512\n",
            "Epoch 185/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0422 - accuracy: 0.9815\n",
            "Epoch 00185: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0421 - accuracy: 0.9817 - val_loss: 0.1554 - val_accuracy: 0.9501\n",
            "Epoch 186/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0437 - accuracy: 0.9811\n",
            "Epoch 00186: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0429 - accuracy: 0.9815 - val_loss: 0.1565 - val_accuracy: 0.9495\n",
            "Epoch 187/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0421 - accuracy: 0.9818\n",
            "Epoch 00187: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0417 - accuracy: 0.9819 - val_loss: 0.1555 - val_accuracy: 0.9501\n",
            "Epoch 188/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0405 - accuracy: 0.9822\n",
            "Epoch 00188: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0423 - accuracy: 0.9815 - val_loss: 0.1568 - val_accuracy: 0.9496\n",
            "Epoch 189/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0424 - accuracy: 0.9816\n",
            "Epoch 00189: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0428 - accuracy: 0.9815 - val_loss: 0.1583 - val_accuracy: 0.9489\n",
            "Epoch 190/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0418 - accuracy: 0.9818\n",
            "Epoch 00190: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0414 - accuracy: 0.9819 - val_loss: 0.1576 - val_accuracy: 0.9513\n",
            "Epoch 191/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0428 - accuracy: 0.9815\n",
            "Epoch 00191: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0438 - accuracy: 0.9811 - val_loss: 0.1597 - val_accuracy: 0.9498\n",
            "Epoch 192/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0416 - accuracy: 0.9819\n",
            "Epoch 00192: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0426 - accuracy: 0.9816 - val_loss: 0.1605 - val_accuracy: 0.9490\n",
            "Epoch 193/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0429 - accuracy: 0.9814\n",
            "Epoch 00193: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0437 - accuracy: 0.9811 - val_loss: 0.1544 - val_accuracy: 0.9505\n",
            "Epoch 194/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0432 - accuracy: 0.9812\n",
            "Epoch 00194: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0427 - accuracy: 0.9814 - val_loss: 0.1542 - val_accuracy: 0.9504\n",
            "Epoch 195/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0421 - accuracy: 0.9815\n",
            "Epoch 00195: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0423 - accuracy: 0.9815 - val_loss: 0.1692 - val_accuracy: 0.9469\n",
            "Epoch 196/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0417 - accuracy: 0.9819\n",
            "Epoch 00196: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0432 - accuracy: 0.9813 - val_loss: 0.1624 - val_accuracy: 0.9488\n",
            "Epoch 197/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0411 - accuracy: 0.9822\n",
            "Epoch 00197: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0418 - accuracy: 0.9818 - val_loss: 0.1561 - val_accuracy: 0.9503\n",
            "Epoch 198/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0417 - accuracy: 0.9818\n",
            "Epoch 00198: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0417 - accuracy: 0.9818 - val_loss: 0.1686 - val_accuracy: 0.9473\n",
            "Epoch 199/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0429 - accuracy: 0.9813\n",
            "Epoch 00199: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0426 - accuracy: 0.9815 - val_loss: 0.1662 - val_accuracy: 0.9483\n",
            "Epoch 200/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0420 - accuracy: 0.9818\n",
            "Epoch 00200: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0416 - accuracy: 0.9818 - val_loss: 0.1591 - val_accuracy: 0.9496\n",
            "Epoch 201/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0420 - accuracy: 0.9818\n",
            "Epoch 00201: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0420 - accuracy: 0.9817 - val_loss: 0.1618 - val_accuracy: 0.9495\n",
            "Epoch 202/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0413 - accuracy: 0.9819\n",
            "Epoch 00202: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0411 - accuracy: 0.9819 - val_loss: 0.1645 - val_accuracy: 0.9488\n",
            "Epoch 203/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0420 - accuracy: 0.9816\n",
            "Epoch 00203: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0417 - accuracy: 0.9819 - val_loss: 0.1609 - val_accuracy: 0.9494\n",
            "Epoch 204/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0405 - accuracy: 0.9823\n",
            "Epoch 00204: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0426 - accuracy: 0.9815 - val_loss: 0.1622 - val_accuracy: 0.9498\n",
            "Epoch 205/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0423 - accuracy: 0.9814\n",
            "Epoch 00205: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0424 - accuracy: 0.9814 - val_loss: 0.1630 - val_accuracy: 0.9495\n",
            "Epoch 206/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0420 - accuracy: 0.9818\n",
            "Epoch 00206: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0425 - accuracy: 0.9816 - val_loss: 0.1562 - val_accuracy: 0.9505\n",
            "Epoch 207/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0410 - accuracy: 0.9821\n",
            "Epoch 00207: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0429 - accuracy: 0.9814 - val_loss: 0.1551 - val_accuracy: 0.9510\n",
            "Epoch 208/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0416 - accuracy: 0.9817\n",
            "Epoch 00208: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0422 - accuracy: 0.9815 - val_loss: 0.1628 - val_accuracy: 0.9489\n",
            "Epoch 209/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0436 - accuracy: 0.9810\n",
            "Epoch 00209: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0436 - accuracy: 0.9810 - val_loss: 0.1571 - val_accuracy: 0.9504\n",
            "Epoch 210/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0418 - accuracy: 0.9819\n",
            "Epoch 00210: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0422 - accuracy: 0.9817 - val_loss: 0.1557 - val_accuracy: 0.9511\n",
            "Epoch 211/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0418 - accuracy: 0.9818\n",
            "Epoch 00211: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0416 - accuracy: 0.9819 - val_loss: 0.1635 - val_accuracy: 0.9490\n",
            "Epoch 212/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0417 - accuracy: 0.9817\n",
            "Epoch 00212: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0419 - accuracy: 0.9816 - val_loss: 0.1604 - val_accuracy: 0.9502\n",
            "Epoch 213/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0408 - accuracy: 0.9821\n",
            "Epoch 00213: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0411 - accuracy: 0.9820 - val_loss: 0.1650 - val_accuracy: 0.9486\n",
            "Epoch 214/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0414 - accuracy: 0.9819\n",
            "Epoch 00214: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0424 - accuracy: 0.9815 - val_loss: 0.1598 - val_accuracy: 0.9491\n",
            "Epoch 215/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0417 - accuracy: 0.9818\n",
            "Epoch 00215: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0419 - accuracy: 0.9817 - val_loss: 0.1587 - val_accuracy: 0.9502\n",
            "Epoch 216/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0413 - accuracy: 0.9818\n",
            "Epoch 00216: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0413 - accuracy: 0.9818 - val_loss: 0.1598 - val_accuracy: 0.9499\n",
            "Epoch 217/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0411 - accuracy: 0.9819\n",
            "Epoch 00217: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0414 - accuracy: 0.9818 - val_loss: 0.1622 - val_accuracy: 0.9491\n",
            "Epoch 218/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0409 - accuracy: 0.9820\n",
            "Epoch 00218: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0412 - accuracy: 0.9819 - val_loss: 0.1553 - val_accuracy: 0.9510\n",
            "Epoch 219/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0419 - accuracy: 0.9816\n",
            "Epoch 00219: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0427 - accuracy: 0.9814 - val_loss: 0.1561 - val_accuracy: 0.9508\n",
            "Epoch 220/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0417 - accuracy: 0.9816\n",
            "Epoch 00220: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0412 - accuracy: 0.9819 - val_loss: 0.1659 - val_accuracy: 0.9480\n",
            "Epoch 221/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0420 - accuracy: 0.9817\n",
            "Epoch 00221: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0422 - accuracy: 0.9816 - val_loss: 0.1591 - val_accuracy: 0.9495\n",
            "Epoch 222/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0404 - accuracy: 0.9823\n",
            "Epoch 00222: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0411 - accuracy: 0.9820 - val_loss: 0.1549 - val_accuracy: 0.9507\n",
            "Epoch 223/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0406 - accuracy: 0.9821\n",
            "Epoch 00223: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0413 - accuracy: 0.9819 - val_loss: 0.1623 - val_accuracy: 0.9488\n",
            "Epoch 224/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0411 - accuracy: 0.9819\n",
            "Epoch 00224: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0428 - accuracy: 0.9814 - val_loss: 0.1638 - val_accuracy: 0.9483\n",
            "Epoch 225/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0424 - accuracy: 0.9814\n",
            "Epoch 00225: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0427 - accuracy: 0.9814 - val_loss: 0.1570 - val_accuracy: 0.9504\n",
            "Epoch 226/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0408 - accuracy: 0.9821\n",
            "Epoch 00226: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0409 - accuracy: 0.9820 - val_loss: 0.1575 - val_accuracy: 0.9502\n",
            "Epoch 227/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0415 - accuracy: 0.9816\n",
            "Epoch 00227: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0421 - accuracy: 0.9815 - val_loss: 0.1599 - val_accuracy: 0.9495\n",
            "Epoch 228/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0423 - accuracy: 0.9815\n",
            "Epoch 00228: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0417 - accuracy: 0.9818 - val_loss: 0.1638 - val_accuracy: 0.9493\n",
            "Epoch 229/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0400 - accuracy: 0.9824\n",
            "Epoch 00229: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0414 - accuracy: 0.9819 - val_loss: 0.1629 - val_accuracy: 0.9505\n",
            "Epoch 230/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0406 - accuracy: 0.9820\n",
            "Epoch 00230: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0409 - accuracy: 0.9820 - val_loss: 0.1652 - val_accuracy: 0.9496\n",
            "Epoch 231/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0407 - accuracy: 0.9822\n",
            "Epoch 00231: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0422 - accuracy: 0.9815 - val_loss: 0.1690 - val_accuracy: 0.9480\n",
            "Epoch 232/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0412 - accuracy: 0.9819\n",
            "Epoch 00232: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0416 - accuracy: 0.9817 - val_loss: 0.1574 - val_accuracy: 0.9507\n",
            "Epoch 233/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0422 - accuracy: 0.9814\n",
            "Epoch 00233: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0422 - accuracy: 0.9815 - val_loss: 0.1610 - val_accuracy: 0.9504\n",
            "Epoch 234/400\n",
            "32/40 [=======================>......] - ETA: 0s - loss: 0.0405 - accuracy: 0.9821\n",
            "Epoch 00234: val_loss did not improve from 0.15212\n",
            "40/40 [==============================] - 0s 6ms/sample - loss: 0.0416 - accuracy: 0.9818 - val_loss: 0.1733 - val_accuracy: 0.9480\n",
            "Epoch 00234: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltL3uLbfS1ja"
      },
      "source": [
        "model.save_weights(\"model-ICNet-trained.h5\", overwrite=True)\n",
        "model.save(\"model-ICNet-trained-model.h5\", overwrite=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3rX-RwMTJhN",
        "outputId": "fbfc9f8f-6320-413f-ce7b-385e95125c79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "model = load_model('model-ICNet-trained-model.h5')\n",
        "preds_train = model.predict(X_train[:int(X_train.shape[0]*0.9)], verbose=1)\n",
        "preds_val = model.predict(X_train[int(X_train.shape[0]*0.9):], verbose=1)\n",
        "preds_test = model.predict(X_test, verbose=1)\n",
        "\n",
        "# Threshold predictions\n",
        "preds_train_t = (preds_train > 0.5).astype(np.uint8)\n",
        "preds_val_t = (preds_val > 0.5).astype(np.uint8)\n",
        "preds_test_t = (preds_test > 0.5).astype(np.uint8)\n",
        "\n",
        "# Create list of upsampled test masks\n",
        "preds_test_upsampled = []\n",
        "for i in range(len(preds_test)):\n",
        "    preds_test_upsampled.append(resize(np.squeeze(preds_test[i]), \n",
        "                                       (sizes_test[i][0], sizes_test[i][1]), \n",
        "                                       mode='constant', preserve_range=True))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "45/45 [==============================] - 1s 21ms/sample\n",
            "6/6 [==============================] - 0s 3ms/sample\n",
            "30/30 [==============================] - 0s 2ms/sample\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OnLzHRFTTlZf",
        "outputId": "06b3abfc-08d5-4784-8c15-3f077318f73a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 911
        }
      },
      "source": [
        "ix = random.randint(0, len(preds_train_t))\n",
        "imshow(np.squeeze(X_train[ix]))\n",
        "plt.show()\n",
        "imshow(np.squeeze(Y_train[ix]))\n",
        "plt.show()\n",
        "imshow(np.squeeze(preds_train_t[ix]))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jYUJHRIXk6pK2KiI4UchHoDULUdp0Uj9+/NjQFa9Bv3R7e6vpdKqLiwszAE/e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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.\n",
            "  lo, hi, cmap = _get_display_range(image)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AnwC/UVV/m+TksaqqZHkF3SQ7aKrUnMXrl/NRSR20JjpVAJKcTpMM/7Cq/rTd/cxcVbj9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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XH50z60HUQTL",
        "outputId": "102ba929-bb1a-4462-c8fe-7cc9bad8d044",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        }
      },
      "source": [
        "ix = random.randint(0, len(preds_val_t)-1)\n",
        "print(len(preds_val_t))\n",
        "print(ix)\n",
        "imshow(np.squeeze(X_train[int(X_train.shape[0]*0.9):][ix]))\n",
        "plt.show()\n",
        "imshow(np.squeeze(Y_train[int(Y_train.shape[0]*0.9):][ix]))\n",
        "plt.show()\n",
        "imshow(np.squeeze(preds_val_t[ix]))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6\n",
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2tVxuKvXJUOIMIpFNH2yQJGucMBOnBzryqBljA0GNoaTv9Nu3bw3FYQwIu3Bo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\nPKl+N6smeUrwGbabN282kpwhY8DsPVShGFH7YFXg2nNYc6n4v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qQjsnZiYqLdN+ZYhrlq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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.\n",
            "  lo, hi, cmap = _get_display_range(image)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qmR60IY6cEJO8DvhD4Fer6q+TnHivqipZXkE3yU66KjVn8NrlfFRSD62LThWAJKfSJcPf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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "seTSz4cgU7F9",
        "outputId": "a136d9af-5190-4567-9646-f4cf90b73008",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        }
      },
      "source": [
        "ix = random.randint(0, len(preds_val_t))\n",
        "imshow(np.squeeze(X_test[ix]))\n",
        "plt.show()\n",
        "imshow(np.squeeze(preds_test_t[ix]))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CQxBXTA/DMZIYFUhs5qbVamXkiXcCODAa7tGi/MiXh0qMj/NK7sU4V+lZR1qeK/Tmz/ax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\nnCcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.\n",
            "  lo, hi, cmap = _get_display_range(image)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cWFVH21vfBy5s+xcBT/d87HA7dhSdZLud1p0paEMcOCEmeRPwB8CHq+qvkpx8r6oqWV5B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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3AIE3woP8ZN4"
      },
      "source": [
        "Denormalizing the output segmentation map"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ahSf6X927QCX"
      },
      "source": [
        "preds_test_t[ix] = (preds_test_t[ix] + 127.5) * 255"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8MfE2uv08c03",
        "outputId": "92be8b40-40e3-4e6e-c6da-1041c327aeca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "preds_test_t[ix].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(256, 256, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A--n7Nt48qSb"
      },
      "source": [
        "import cv2\n",
        "backtorgb = cv2.cvtColor(preds_test_t[ix],cv2.COLOR_GRAY2RGB)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "buXM65rS8vFb",
        "outputId": "8040fa0e-64cd-4310-cf09-f458f4257a23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.imshow(backtorgb)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb54bddc898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sg4jEKDGISIwSg4jEKDGISIwSg4jEKDGISExFD9TSk82bN49PPvkEgLVr1/L2228HjkjK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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wdWQDUXk2E86",
        "outputId": "b0ec632a-87d6-45c7-b6f0-ace25d52f7fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "%matplotlib inline\n",
        "img = cv2.addWeighted(X_test[ix], 1, preds_test_t[ix], 0.8, 0)\n",
        "plt.imshow(img, cmap='gray')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb549c2b748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D9LDZ3vjMMtGVcIm8BSQX5joNzXWn02lafBRdCj47T5goytramp599tlC+BOz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\nBU3QQjEsaEELmqD/B5R8wbT4VVTwAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}