Segmentation_of_Sclera.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Segmentation of Sclera.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FtkcAF6DS3Jc"
      },
      "source": [
        "# **Segmentation of Sclera**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8svvOCmRSMvw"
      },
      "source": [
        "**Special Requirements**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mjldvkk28ane",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e14d44a-bd36-4215-d80e-2cbf70d7476e"
      },
      "source": [
        "!pip install segmentation-models\n",
        "!pip install tensorflow==2.1.0\n",
        "!pip install keras==2.3.1\n",
        "#For live loss function updates\n",
        "!pip install livelossplot\n",
        "!pip install h5py==2.10.0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting segmentation-models\n",
            "  Downloading segmentation_models-1.0.1-py3-none-any.whl (33 kB)\n",
            "Collecting efficientnet==1.0.0\n",
            "  Downloading efficientnet-1.0.0-py3-none-any.whl (17 kB)\n",
            "Collecting keras-applications<=1.0.8,>=1.0.7\n",
            "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n",
            "\u001b[K     |████████████████████████████████| 50 kB 3.9 MB/s \n",
            "\u001b[?25hCollecting image-classifiers==1.0.0\n",
            "  Downloading image_classifiers-1.0.0-py3-none-any.whl (19 kB)\n",
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            "Installing collected packages: keras-applications, image-classifiers, efficientnet, segmentation-models\n",
            "Successfully installed efficientnet-1.0.0 image-classifiers-1.0.0 keras-applications-1.0.8 segmentation-models-1.0.1\n",
            "Collecting tensorflow==2.1.0\n",
            "  Downloading tensorflow-2.1.0-cp37-cp37m-manylinux2010_x86_64.whl (421.8 MB)\n",
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            "Building wheels for collected packages: gast\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-py3-none-any.whl size=7554 sha256=ac1ef8ecad6d90d1bf7e76af39ee42d1104d0d31d5d619cbeb822c6f458520aa\n",
            "  Stored in directory: /root/.cache/pip/wheels/21/7f/02/420f32a803f7d0967b48dd823da3f558c5166991bfd204eef3\n",
            "Successfully built gast\n",
            "Installing collected packages: tensorflow-estimator, tensorboard, gast, tensorflow\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.6.0\n",
            "    Uninstalling tensorflow-estimator-2.6.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.6.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.6.0\n",
            "    Uninstalling tensorboard-2.6.0:\n",
            "      Successfully uninstalled tensorboard-2.6.0\n",
            "  Attempting uninstall: gast\n",
            "    Found existing installation: gast 0.4.0\n",
            "    Uninstalling gast-0.4.0:\n",
            "      Successfully uninstalled gast-0.4.0\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.6.0\n",
            "    Uninstalling tensorflow-2.6.0:\n",
            "      Successfully uninstalled tensorflow-2.6.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow-probability 0.14.1 requires gast>=0.3.2, but you have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "Successfully installed gast-0.2.2 tensorboard-2.1.1 tensorflow-2.1.0 tensorflow-estimator-2.1.0\n",
            "Collecting keras==2.3.1\n",
            "  Downloading Keras-2.3.1-py2.py3-none-any.whl (377 kB)\n",
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            "Installing collected packages: keras\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.6.0\n",
            "    Uninstalling keras-2.6.0:\n",
            "      Successfully uninstalled keras-2.6.0\n",
            "Successfully installed keras-2.3.1\n",
            "Collecting livelossplot\n",
            "  Downloading livelossplot-0.5.4-py3-none-any.whl (22 kB)\n",
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            "Installing collected packages: livelossplot\n",
            "Successfully installed livelossplot-0.5.4\n",
            "Collecting h5py==2.10.0\n",
            "  Downloading h5py-2.10.0-cp37-cp37m-manylinux1_x86_64.whl (2.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9 MB 5.3 MB/s \n",
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            "Installing collected packages: h5py\n",
            "  Attempting uninstall: h5py\n",
            "    Found existing installation: h5py 3.1.0\n",
            "    Uninstalling h5py-3.1.0:\n",
            "      Successfully uninstalled h5py-3.1.0\n",
            "Successfully installed h5py-2.10.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUOoe7sPSj-W"
      },
      "source": [
        "**Mounting the Drive**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z69nrY_oZEWA",
        "outputId": "411ffc98-cafb-43cf-bbeb-6a1fc06fe8bd"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y6r5OJmyTLfJ"
      },
      "source": [
        "**Importing Required Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3-7YM2GBtad"
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H0DZ-15m9g-E"
      },
      "source": [
        "import glob\n",
        "import cv2\n",
        "import os\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from natsort import natsorted"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EGKj1dna7yN2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4dc44fe1-9f4e-486b-d382-02946e40ea06"
      },
      "source": [
        "import tensorflow as tf\n",
        "import segmentation_models as sm\n",
        "BACKBONE = 'resnet34'\n",
        "preprocess_input = sm.get_preprocessing(BACKBONE)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using TensorFlow backend.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Segmentation Models: using `keras` framework.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YMaY2eGiTXls"
      },
      "source": [
        "**Storing & Creating Training sets from images from mounted drive**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0y91566F-Saj"
      },
      "source": [
        "#print(os.listdir(\"membrane/train\"))\n",
        "\n",
        "#Resizing images is optional, CNNs are ok with large images\n",
        "SIZE_X = 128 #Resize images (height  = X, width = Y)\n",
        "SIZE_Y = 128\n",
        "OGX = 3000\n",
        "OGY = 1700\n",
        "\n",
        "#Capture training image info as a list\n",
        "train_images = []\n",
        "\n",
        "for directory_path in glob.glob(\"/content/drive/MyDrive/ss_train/image\"):\n",
        "    for img_path in sorted(glob.glob(os.path.join(directory_path, \"*png\"))) :\n",
        "        #print(img_path)\n",
        "        img = cv2.imread(img_path, cv2.IMREAD_COLOR )   #cv2.IMREAD_COLOR    \n",
        "        img = cv2.resize(img, (SIZE_Y, SIZE_X))\n",
        "        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
        "   \n",
        "        train_images.append(img)\n",
        "        #train_labels.append(label)\n",
        "#Convert list to array for machine learning processing  \n",
        "\n",
        "   \n",
        "train_images = np.array(train_images)\n",
        "\n",
        "#Capture mask/label info as a list\n",
        "train_masks = [] \n",
        "for directory_path in glob.glob(\"/content/drive/MyDrive/ss_train/mask\"):\n",
        "    for mask_path in sorted(glob.glob(os.path.join(directory_path, \"*.png\"))):\n",
        "        #print(mask_path)\n",
        "        mask = cv2.imread(mask_path, 0)   #,0    \n",
        "        mask = cv2.resize(mask, (SIZE_Y, SIZE_X))\n",
        "        mask = cv2.cvtColor(mask, cv2.COLOR_RGB2BGR) #\n",
        "        train_masks.append(mask)\n",
        "        #train_labels.append(label)\n",
        "#Convert list to array for machine learning processing          \n",
        "train_masks = np.array(train_masks)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K53iH_FO-XtY"
      },
      "source": [
        "#Use customary x_train and y_train variables\n",
        "X = train_images\n",
        "Y = train_masks\n",
        "#Y = np.expand_dims(Y, axis=3) #May not be necessary.. leftover from previous code \n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_val, y_train, y_val = train_test_split(X, Y, test_size=0.2, random_state=42)\n",
        "\n",
        "# preprocess input\n",
        "x_train = preprocess_input(x_train)\n",
        "x_val = preprocess_input(x_val)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pj8PEf_aTpqX"
      },
      "source": [
        "**Define Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8h3AexC--hMe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56eeb24f-3a9f-4306-bc16-12b10d650f68"
      },
      "source": [
        "# define model\n",
        "model = sm.Unet(BACKBONE, encoder_weights='imagenet')\n",
        "model.compile('Adam', loss=sm.losses.bce_jaccard_loss, metrics=[sm.metrics.iou_score],)\n",
        "\n",
        "print(model.summary())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://github.com/qubvel/classification_models/releases/download/0.0.1/resnet34_imagenet_1000_no_top.h5\n",
            "85524480/85521592 [==============================] - 1s 0us/step\n",
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "data (InputLayer)               (None, None, None, 3 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "bn_data (BatchNormalization)    (None, None, None, 3 9           data[0][0]                       \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_1 (ZeroPadding2D (None, None, None, 3 0           bn_data[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv0 (Conv2D)                  (None, None, None, 6 9408        zero_padding2d_1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "bn0 (BatchNormalization)        (None, None, None, 6 256         conv0[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "relu0 (Activation)              (None, None, None, 6 0           bn0[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_2 (ZeroPadding2D (None, None, None, 6 0           relu0[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "pooling0 (MaxPooling2D)         (None, None, None, 6 0           zero_padding2d_2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit1_bn1 (BatchNormaliz (None, None, None, 6 256         pooling0[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit1_relu1 (Activation) (None, None, None, 6 0           stage1_unit1_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_3 (ZeroPadding2D (None, None, None, 6 0           stage1_unit1_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit1_conv1 (Conv2D)     (None, None, None, 6 36864       zero_padding2d_3[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit1_bn2 (BatchNormaliz (None, None, None, 6 256         stage1_unit1_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit1_relu2 (Activation) (None, None, None, 6 0           stage1_unit1_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_4 (ZeroPadding2D (None, None, None, 6 0           stage1_unit1_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit1_conv2 (Conv2D)     (None, None, None, 6 36864       zero_padding2d_4[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit1_sc (Conv2D)        (None, None, None, 6 4096        stage1_unit1_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, None, None, 6 0           stage1_unit1_conv2[0][0]         \n",
            "                                                                 stage1_unit1_sc[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit2_bn1 (BatchNormaliz (None, None, None, 6 256         add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit2_relu1 (Activation) (None, None, None, 6 0           stage1_unit2_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_5 (ZeroPadding2D (None, None, None, 6 0           stage1_unit2_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit2_conv1 (Conv2D)     (None, None, None, 6 36864       zero_padding2d_5[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit2_bn2 (BatchNormaliz (None, None, None, 6 256         stage1_unit2_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit2_relu2 (Activation) (None, None, None, 6 0           stage1_unit2_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_6 (ZeroPadding2D (None, None, None, 6 0           stage1_unit2_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit2_conv2 (Conv2D)     (None, None, None, 6 36864       zero_padding2d_6[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, None, None, 6 0           stage1_unit2_conv2[0][0]         \n",
            "                                                                 add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit3_bn1 (BatchNormaliz (None, None, None, 6 256         add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit3_relu1 (Activation) (None, None, None, 6 0           stage1_unit3_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_7 (ZeroPadding2D (None, None, None, 6 0           stage1_unit3_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit3_conv1 (Conv2D)     (None, None, None, 6 36864       zero_padding2d_7[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit3_bn2 (BatchNormaliz (None, None, None, 6 256         stage1_unit3_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit3_relu2 (Activation) (None, None, None, 6 0           stage1_unit3_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_8 (ZeroPadding2D (None, None, None, 6 0           stage1_unit3_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage1_unit3_conv2 (Conv2D)     (None, None, None, 6 36864       zero_padding2d_8[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, None, None, 6 0           stage1_unit3_conv2[0][0]         \n",
            "                                                                 add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit1_bn1 (BatchNormaliz (None, None, None, 6 256         add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit1_relu1 (Activation) (None, None, None, 6 0           stage2_unit1_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_9 (ZeroPadding2D (None, None, None, 6 0           stage2_unit1_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit1_conv1 (Conv2D)     (None, None, None, 1 73728       zero_padding2d_9[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit1_bn2 (BatchNormaliz (None, None, None, 1 512         stage2_unit1_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit1_relu2 (Activation) (None, None, None, 1 0           stage2_unit1_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_10 (ZeroPadding2 (None, None, None, 1 0           stage2_unit1_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit1_conv2 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_10[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit1_sc (Conv2D)        (None, None, None, 1 8192        stage2_unit1_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, None, None, 1 0           stage2_unit1_conv2[0][0]         \n",
            "                                                                 stage2_unit1_sc[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit2_bn1 (BatchNormaliz (None, None, None, 1 512         add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit2_relu1 (Activation) (None, None, None, 1 0           stage2_unit2_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_11 (ZeroPadding2 (None, None, None, 1 0           stage2_unit2_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit2_conv1 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_11[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit2_bn2 (BatchNormaliz (None, None, None, 1 512         stage2_unit2_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit2_relu2 (Activation) (None, None, None, 1 0           stage2_unit2_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_12 (ZeroPadding2 (None, None, None, 1 0           stage2_unit2_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit2_conv2 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_12[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, None, None, 1 0           stage2_unit2_conv2[0][0]         \n",
            "                                                                 add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit3_bn1 (BatchNormaliz (None, None, None, 1 512         add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit3_relu1 (Activation) (None, None, None, 1 0           stage2_unit3_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_13 (ZeroPadding2 (None, None, None, 1 0           stage2_unit3_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit3_conv1 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_13[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit3_bn2 (BatchNormaliz (None, None, None, 1 512         stage2_unit3_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit3_relu2 (Activation) (None, None, None, 1 0           stage2_unit3_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_14 (ZeroPadding2 (None, None, None, 1 0           stage2_unit3_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit3_conv2 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_14[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, None, None, 1 0           stage2_unit3_conv2[0][0]         \n",
            "                                                                 add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit4_bn1 (BatchNormaliz (None, None, None, 1 512         add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit4_relu1 (Activation) (None, None, None, 1 0           stage2_unit4_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_15 (ZeroPadding2 (None, None, None, 1 0           stage2_unit4_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit4_conv1 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_15[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit4_bn2 (BatchNormaliz (None, None, None, 1 512         stage2_unit4_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit4_relu2 (Activation) (None, None, None, 1 0           stage2_unit4_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_16 (ZeroPadding2 (None, None, None, 1 0           stage2_unit4_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage2_unit4_conv2 (Conv2D)     (None, None, None, 1 147456      zero_padding2d_16[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, None, None, 1 0           stage2_unit4_conv2[0][0]         \n",
            "                                                                 add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit1_bn1 (BatchNormaliz (None, None, None, 1 512         add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit1_relu1 (Activation) (None, None, None, 1 0           stage3_unit1_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_17 (ZeroPadding2 (None, None, None, 1 0           stage3_unit1_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit1_conv1 (Conv2D)     (None, None, None, 2 294912      zero_padding2d_17[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit1_bn2 (BatchNormaliz (None, None, None, 2 1024        stage3_unit1_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit1_relu2 (Activation) (None, None, None, 2 0           stage3_unit1_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_18 (ZeroPadding2 (None, None, None, 2 0           stage3_unit1_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit1_conv2 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_18[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit1_sc (Conv2D)        (None, None, None, 2 32768       stage3_unit1_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "add_8 (Add)                     (None, None, None, 2 0           stage3_unit1_conv2[0][0]         \n",
            "                                                                 stage3_unit1_sc[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit2_bn1 (BatchNormaliz (None, None, None, 2 1024        add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit2_relu1 (Activation) (None, None, None, 2 0           stage3_unit2_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_19 (ZeroPadding2 (None, None, None, 2 0           stage3_unit2_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit2_conv1 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_19[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit2_bn2 (BatchNormaliz (None, None, None, 2 1024        stage3_unit2_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit2_relu2 (Activation) (None, None, None, 2 0           stage3_unit2_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_20 (ZeroPadding2 (None, None, None, 2 0           stage3_unit2_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit2_conv2 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_20[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, None, None, 2 0           stage3_unit2_conv2[0][0]         \n",
            "                                                                 add_8[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit3_bn1 (BatchNormaliz (None, None, None, 2 1024        add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit3_relu1 (Activation) (None, None, None, 2 0           stage3_unit3_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_21 (ZeroPadding2 (None, None, None, 2 0           stage3_unit3_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit3_conv1 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_21[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit3_bn2 (BatchNormaliz (None, None, None, 2 1024        stage3_unit3_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit3_relu2 (Activation) (None, None, None, 2 0           stage3_unit3_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_22 (ZeroPadding2 (None, None, None, 2 0           stage3_unit3_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit3_conv2 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_22[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, None, None, 2 0           stage3_unit3_conv2[0][0]         \n",
            "                                                                 add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit4_bn1 (BatchNormaliz (None, None, None, 2 1024        add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit4_relu1 (Activation) (None, None, None, 2 0           stage3_unit4_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_23 (ZeroPadding2 (None, None, None, 2 0           stage3_unit4_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit4_conv1 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_23[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit4_bn2 (BatchNormaliz (None, None, None, 2 1024        stage3_unit4_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit4_relu2 (Activation) (None, None, None, 2 0           stage3_unit4_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_24 (ZeroPadding2 (None, None, None, 2 0           stage3_unit4_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit4_conv2 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_24[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, None, None, 2 0           stage3_unit4_conv2[0][0]         \n",
            "                                                                 add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit5_bn1 (BatchNormaliz (None, None, None, 2 1024        add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit5_relu1 (Activation) (None, None, None, 2 0           stage3_unit5_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_25 (ZeroPadding2 (None, None, None, 2 0           stage3_unit5_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit5_conv1 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_25[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit5_bn2 (BatchNormaliz (None, None, None, 2 1024        stage3_unit5_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit5_relu2 (Activation) (None, None, None, 2 0           stage3_unit5_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_26 (ZeroPadding2 (None, None, None, 2 0           stage3_unit5_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit5_conv2 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_26[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, None, None, 2 0           stage3_unit5_conv2[0][0]         \n",
            "                                                                 add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit6_bn1 (BatchNormaliz (None, None, None, 2 1024        add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit6_relu1 (Activation) (None, None, None, 2 0           stage3_unit6_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_27 (ZeroPadding2 (None, None, None, 2 0           stage3_unit6_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit6_conv1 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_27[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit6_bn2 (BatchNormaliz (None, None, None, 2 1024        stage3_unit6_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit6_relu2 (Activation) (None, None, None, 2 0           stage3_unit6_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_28 (ZeroPadding2 (None, None, None, 2 0           stage3_unit6_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage3_unit6_conv2 (Conv2D)     (None, None, None, 2 589824      zero_padding2d_28[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "add_13 (Add)                    (None, None, None, 2 0           stage3_unit6_conv2[0][0]         \n",
            "                                                                 add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit1_bn1 (BatchNormaliz (None, None, None, 2 1024        add_13[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit1_relu1 (Activation) (None, None, None, 2 0           stage4_unit1_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_29 (ZeroPadding2 (None, None, None, 2 0           stage4_unit1_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit1_conv1 (Conv2D)     (None, None, None, 5 1179648     zero_padding2d_29[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit1_bn2 (BatchNormaliz (None, None, None, 5 2048        stage4_unit1_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit1_relu2 (Activation) (None, None, None, 5 0           stage4_unit1_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_30 (ZeroPadding2 (None, None, None, 5 0           stage4_unit1_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit1_conv2 (Conv2D)     (None, None, None, 5 2359296     zero_padding2d_30[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit1_sc (Conv2D)        (None, None, None, 5 131072      stage4_unit1_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "add_14 (Add)                    (None, None, None, 5 0           stage4_unit1_conv2[0][0]         \n",
            "                                                                 stage4_unit1_sc[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit2_bn1 (BatchNormaliz (None, None, None, 5 2048        add_14[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit2_relu1 (Activation) (None, None, None, 5 0           stage4_unit2_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_31 (ZeroPadding2 (None, None, None, 5 0           stage4_unit2_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit2_conv1 (Conv2D)     (None, None, None, 5 2359296     zero_padding2d_31[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit2_bn2 (BatchNormaliz (None, None, None, 5 2048        stage4_unit2_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit2_relu2 (Activation) (None, None, None, 5 0           stage4_unit2_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_32 (ZeroPadding2 (None, None, None, 5 0           stage4_unit2_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit2_conv2 (Conv2D)     (None, None, None, 5 2359296     zero_padding2d_32[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "add_15 (Add)                    (None, None, None, 5 0           stage4_unit2_conv2[0][0]         \n",
            "                                                                 add_14[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit3_bn1 (BatchNormaliz (None, None, None, 5 2048        add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit3_relu1 (Activation) (None, None, None, 5 0           stage4_unit3_bn1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_33 (ZeroPadding2 (None, None, None, 5 0           stage4_unit3_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit3_conv1 (Conv2D)     (None, None, None, 5 2359296     zero_padding2d_33[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit3_bn2 (BatchNormaliz (None, None, None, 5 2048        stage4_unit3_conv1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit3_relu2 (Activation) (None, None, None, 5 0           stage4_unit3_bn2[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d_34 (ZeroPadding2 (None, None, None, 5 0           stage4_unit3_relu2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "stage4_unit3_conv2 (Conv2D)     (None, None, None, 5 2359296     zero_padding2d_34[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "add_16 (Add)                    (None, None, None, 5 0           stage4_unit3_conv2[0][0]         \n",
            "                                                                 add_15[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "bn1 (BatchNormalization)        (None, None, None, 5 2048        add_16[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "relu1 (Activation)              (None, None, None, 5 0           bn1[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage0_upsampling (UpSa (None, None, None, 5 0           relu1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage0_concat (Concaten (None, None, None, 7 0           decoder_stage0_upsampling[0][0]  \n",
            "                                                                 stage4_unit1_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage0a_conv (Conv2D)   (None, None, None, 2 1769472     decoder_stage0_concat[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage0a_bn (BatchNormal (None, None, None, 2 1024        decoder_stage0a_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage0a_relu (Activatio (None, None, None, 2 0           decoder_stage0a_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage0b_conv (Conv2D)   (None, None, None, 2 589824      decoder_stage0a_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage0b_bn (BatchNormal (None, None, None, 2 1024        decoder_stage0b_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage0b_relu (Activatio (None, None, None, 2 0           decoder_stage0b_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage1_upsampling (UpSa (None, None, None, 2 0           decoder_stage0b_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage1_concat (Concaten (None, None, None, 3 0           decoder_stage1_upsampling[0][0]  \n",
            "                                                                 stage3_unit1_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage1a_conv (Conv2D)   (None, None, None, 1 442368      decoder_stage1_concat[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage1a_bn (BatchNormal (None, None, None, 1 512         decoder_stage1a_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage1a_relu (Activatio (None, None, None, 1 0           decoder_stage1a_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage1b_conv (Conv2D)   (None, None, None, 1 147456      decoder_stage1a_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage1b_bn (BatchNormal (None, None, None, 1 512         decoder_stage1b_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage1b_relu (Activatio (None, None, None, 1 0           decoder_stage1b_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage2_upsampling (UpSa (None, None, None, 1 0           decoder_stage1b_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage2_concat (Concaten (None, None, None, 1 0           decoder_stage2_upsampling[0][0]  \n",
            "                                                                 stage2_unit1_relu1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage2a_conv (Conv2D)   (None, None, None, 6 110592      decoder_stage2_concat[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage2a_bn (BatchNormal (None, None, None, 6 256         decoder_stage2a_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage2a_relu (Activatio (None, None, None, 6 0           decoder_stage2a_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage2b_conv (Conv2D)   (None, None, None, 6 36864       decoder_stage2a_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage2b_bn (BatchNormal (None, None, None, 6 256         decoder_stage2b_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage2b_relu (Activatio (None, None, None, 6 0           decoder_stage2b_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage3_upsampling (UpSa (None, None, None, 6 0           decoder_stage2b_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage3_concat (Concaten (None, None, None, 1 0           decoder_stage3_upsampling[0][0]  \n",
            "                                                                 relu0[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage3a_conv (Conv2D)   (None, None, None, 3 36864       decoder_stage3_concat[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage3a_bn (BatchNormal (None, None, None, 3 128         decoder_stage3a_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage3a_relu (Activatio (None, None, None, 3 0           decoder_stage3a_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage3b_conv (Conv2D)   (None, None, None, 3 9216        decoder_stage3a_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage3b_bn (BatchNormal (None, None, None, 3 128         decoder_stage3b_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage3b_relu (Activatio (None, None, None, 3 0           decoder_stage3b_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage4_upsampling (UpSa (None, None, None, 3 0           decoder_stage3b_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage4a_conv (Conv2D)   (None, None, None, 1 4608        decoder_stage4_upsampling[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage4a_bn (BatchNormal (None, None, None, 1 64          decoder_stage4a_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage4a_relu (Activatio (None, None, None, 1 0           decoder_stage4a_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage4b_conv (Conv2D)   (None, None, None, 1 2304        decoder_stage4a_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage4b_bn (BatchNormal (None, None, None, 1 64          decoder_stage4b_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "decoder_stage4b_relu (Activatio (None, None, None, 1 0           decoder_stage4b_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "final_conv (Conv2D)             (None, None, None, 1 145         decoder_stage4b_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "sigmoid (Activation)            (None, None, None, 1 0           final_conv[0][0]                 \n",
            "==================================================================================================\n",
            "Total params: 24,456,154\n",
            "Trainable params: 24,438,804\n",
            "Non-trainable params: 17,350\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHvKjgkfTuNR"
      },
      "source": [
        "**Model Fitting (One Time)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aSVNmw3k7qVU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8ddf656-21a7-4305-b6d6-4509705b4615"
      },
      "source": [
        "# fit model\n",
        "# if you use data generator use model.fit_generator(...) instead of model.fit(...)\n",
        "# more about `fit_generator` here: https://keras.io/models/sequential/#fit_generator\n",
        "from livelossplot import PlotLossesKeras\n",
        "#Include this as callback., but slows the training (callbacks=[PlotLossesKeras()],)\n",
        "model.fit(\n",
        "   x=x_train,\n",
        "   y=y_train,\n",
        "   batch_size=32,\n",
        "   epochs=200,\n",
        "   verbose=1,\n",
        "   validation_data=(x_val, y_val),\n",
        ")\n",
        "\n",
        "#accuracy = model.evaluate(x_val, y_val)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train on 68 samples, validate on 18 samples\n",
            "Epoch 1/200\n",
            "68/68 [==============================] - 16s 231ms/step - loss: -3.5990 - iou_score: 1.5923 - val_loss: -498.2863 - val_iou_score: 21.9596\n",
            "Epoch 2/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -53.0173 - iou_score: 5.5981 - val_loss: -275.0855 - val_iou_score: 21.1020\n",
            "Epoch 3/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -90.6574 - iou_score: 9.4268 - val_loss: -1013.8630 - val_iou_score: 21.8844\n",
            "Epoch 4/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -124.8576 - iou_score: 12.7651 - val_loss: -1216.0663 - val_iou_score: 21.7423\n",
            "Epoch 5/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -153.6363 - iou_score: 23.5687 - val_loss: -1158.2037 - val_iou_score: 21.7519\n",
            "Epoch 6/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -176.0636 - iou_score: 31.2571 - val_loss: -2198.7896 - val_iou_score: 21.6632\n",
            "Epoch 7/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -196.6432 - iou_score: 35.6058 - val_loss: -3818.6731 - val_iou_score: 21.6342\n",
            "Epoch 8/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -215.3150 - iou_score: 39.6728 - val_loss: -5463.9346 - val_iou_score: 21.6965\n",
            "Epoch 9/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -235.0772 - iou_score: 40.8732 - val_loss: -4127.9658 - val_iou_score: 22.0032\n",
            "Epoch 10/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -255.3334 - iou_score: 39.2196 - val_loss: -3928.4163 - val_iou_score: 22.7175\n",
            "Epoch 11/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -276.0503 - iou_score: 52.3537 - val_loss: -4856.8779 - val_iou_score: 23.0569\n",
            "Epoch 12/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -292.3384 - iou_score: 58.1501 - val_loss: -3591.6174 - val_iou_score: 23.1298\n",
            "Epoch 13/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -316.2293 - iou_score: 61.8376 - val_loss: -2506.9878 - val_iou_score: 23.6073\n",
            "Epoch 14/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -336.5882 - iou_score: 64.7460 - val_loss: -5603.7915 - val_iou_score: 23.6137\n",
            "Epoch 15/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -354.0930 - iou_score: 68.3929 - val_loss: -8183.4688 - val_iou_score: 23.3222\n",
            "Epoch 16/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -375.3202 - iou_score: 69.7523 - val_loss: -4681.4531 - val_iou_score: 25.2495\n",
            "Epoch 17/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -395.6763 - iou_score: 71.1661 - val_loss: -5849.6499 - val_iou_score: 24.6648\n",
            "Epoch 18/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -420.7163 - iou_score: 85.6851 - val_loss: -7213.6582 - val_iou_score: 24.5989\n",
            "Epoch 19/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -444.3611 - iou_score: 83.0773 - val_loss: -5345.8438 - val_iou_score: 23.5031\n",
            "Epoch 20/200\n",
            "68/68 [==============================] - 1s 19ms/step - loss: -466.8600 - iou_score: 95.6963 - val_loss: -3451.6270 - val_iou_score: 25.4755\n",
            "Epoch 21/200\n",
            "68/68 [==============================] - 1s 19ms/step - loss: -478.1953 - iou_score: 97.5149 - val_loss: -1703.9281 - val_iou_score: 2.4506\n",
            "Epoch 22/200\n",
            "68/68 [==============================] - 1s 19ms/step - loss: -499.2281 - iou_score: 100.9325 - val_loss: -1919.0690 - val_iou_score: 7.7083\n",
            "Epoch 23/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -526.7498 - iou_score: 109.2933 - val_loss: -2007.2904 - val_iou_score: 7.8104\n",
            "Epoch 24/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -555.8206 - iou_score: 116.9107 - val_loss: -3581.3745 - val_iou_score: 11.5537\n",
            "Epoch 25/200\n",
            "68/68 [==============================] - 1s 19ms/step - loss: -575.3915 - iou_score: 121.4727 - val_loss: -3218.4375 - val_iou_score: 28.1876\n",
            "Epoch 26/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -598.6091 - iou_score: 129.4101 - val_loss: -7197.5093 - val_iou_score: 25.5191\n",
            "Epoch 27/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -620.2858 - iou_score: 130.3238 - val_loss: -7267.8491 - val_iou_score: 26.4712\n",
            "Epoch 28/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -647.7591 - iou_score: 141.9607 - val_loss: -6363.5366 - val_iou_score: 25.3043\n",
            "Epoch 29/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -670.1249 - iou_score: 131.4227 - val_loss: -4353.6235 - val_iou_score: 23.1336\n",
            "Epoch 30/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -656.3706 - iou_score: 126.7738 - val_loss: -3620.9751 - val_iou_score: 36.5433\n",
            "Epoch 31/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -680.4161 - iou_score: 126.0995 - val_loss: -2718.7605 - val_iou_score: 37.5891\n",
            "Epoch 32/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -690.7049 - iou_score: 127.7822 - val_loss: -5816.9141 - val_iou_score: 28.9074\n",
            "Epoch 33/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -728.5372 - iou_score: 134.1727 - val_loss: -11605.0000 - val_iou_score: 23.4304\n",
            "Epoch 34/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -745.3681 - iou_score: 131.6832 - val_loss: -8466.3486 - val_iou_score: 24.1790\n",
            "Epoch 35/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -766.4519 - iou_score: 138.1921 - val_loss: -7534.7646 - val_iou_score: 25.2921\n",
            "Epoch 36/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -775.9929 - iou_score: 132.5036 - val_loss: -3717.6628 - val_iou_score: 32.6732\n",
            "Epoch 37/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -824.7812 - iou_score: 152.0389 - val_loss: -4157.7300 - val_iou_score: 35.9037\n",
            "Epoch 38/200\n",
            "68/68 [==============================] - 1s 19ms/step - loss: -837.1340 - iou_score: 146.4140 - val_loss: -3511.9326 - val_iou_score: 44.8533\n",
            "Epoch 39/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -869.3257 - iou_score: 153.1354 - val_loss: -3218.1902 - val_iou_score: 44.7131\n",
            "Epoch 40/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -888.2766 - iou_score: 152.0928 - val_loss: -5120.3911 - val_iou_score: 37.5348\n",
            "Epoch 41/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -922.0158 - iou_score: 161.3223 - val_loss: -4158.6680 - val_iou_score: 41.7112\n",
            "Epoch 42/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -942.3985 - iou_score: 152.8861 - val_loss: -3413.4062 - val_iou_score: 45.9266\n",
            "Epoch 43/200\n",
            "68/68 [==============================] - 1s 19ms/step - loss: -951.0553 - iou_score: 153.5318 - val_loss: -4403.3193 - val_iou_score: 40.4153\n",
            "Epoch 44/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -971.0893 - iou_score: 154.1513 - val_loss: -6519.3325 - val_iou_score: 23.8084\n",
            "Epoch 45/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -987.4416 - iou_score: 153.3444 - val_loss: -5509.9707 - val_iou_score: 23.0968\n",
            "Epoch 46/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1020.4929 - iou_score: 153.7527 - val_loss: -2301.8699 - val_iou_score: 14.0981\n",
            "Epoch 47/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1036.3569 - iou_score: 156.0699 - val_loss: -4597.4985 - val_iou_score: 23.3487\n",
            "Epoch 48/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1069.4349 - iou_score: 161.4214 - val_loss: -3777.0742 - val_iou_score: 26.9270\n",
            "Epoch 49/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1086.6290 - iou_score: 159.7565 - val_loss: -2621.1941 - val_iou_score: 31.8656\n",
            "Epoch 50/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1107.5800 - iou_score: 146.5883 - val_loss: -2183.6519 - val_iou_score: 36.6166\n",
            "Epoch 51/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1125.9731 - iou_score: 154.3374 - val_loss: -2270.8499 - val_iou_score: 48.4811\n",
            "Epoch 52/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1150.5067 - iou_score: 155.9540 - val_loss: -2877.1851 - val_iou_score: 34.5306\n",
            "Epoch 53/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1176.6921 - iou_score: 159.1281 - val_loss: -3889.6384 - val_iou_score: 29.8889\n",
            "Epoch 54/200\n",
            "68/68 [==============================] - 1s 19ms/step - loss: -1216.9916 - iou_score: 163.5838 - val_loss: -5260.2793 - val_iou_score: 28.7001\n",
            "Epoch 55/200\n",
            "68/68 [==============================] - 1s 19ms/step - loss: -1233.3896 - iou_score: 158.7756 - val_loss: -5220.4360 - val_iou_score: 28.1977\n",
            "Epoch 56/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1258.0704 - iou_score: 159.3482 - val_loss: -4573.7998 - val_iou_score: 26.8617\n",
            "Epoch 57/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1281.2504 - iou_score: 165.9609 - val_loss: -5989.2031 - val_iou_score: 25.7256\n",
            "Epoch 58/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1304.8530 - iou_score: 131.6116 - val_loss: -8136.1553 - val_iou_score: 25.5987\n",
            "Epoch 59/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1333.8054 - iou_score: 158.7862 - val_loss: -6545.2695 - val_iou_score: 24.4190\n",
            "Epoch 60/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1355.5627 - iou_score: 151.9869 - val_loss: -2664.3564 - val_iou_score: 17.1693\n",
            "Epoch 61/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1389.2098 - iou_score: 170.9246 - val_loss: -1806.2157 - val_iou_score: 16.7836\n",
            "Epoch 62/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1421.9685 - iou_score: 178.4740 - val_loss: -2411.3064 - val_iou_score: 24.5498\n",
            "Epoch 63/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1449.7731 - iou_score: 177.4064 - val_loss: -2374.0371 - val_iou_score: 29.3094\n",
            "Epoch 64/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1476.0905 - iou_score: 174.4716 - val_loss: -2267.0156 - val_iou_score: 50.4189\n",
            "Epoch 65/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1478.1806 - iou_score: 163.5423 - val_loss: -2311.4312 - val_iou_score: 68.7635\n",
            "Epoch 66/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1497.6008 - iou_score: 168.6833 - val_loss: -2387.0842 - val_iou_score: 64.4324\n",
            "Epoch 67/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1540.5733 - iou_score: 169.7489 - val_loss: -2439.2588 - val_iou_score: 66.7234\n",
            "Epoch 68/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1572.6896 - iou_score: 174.3132 - val_loss: -2307.6189 - val_iou_score: 71.5269\n",
            "Epoch 69/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1600.7081 - iou_score: 173.5046 - val_loss: -2362.8647 - val_iou_score: 70.0970\n",
            "Epoch 70/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1632.3284 - iou_score: 179.1853 - val_loss: -2072.0305 - val_iou_score: 60.0470\n",
            "Epoch 71/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1656.3744 - iou_score: 163.7534 - val_loss: -2008.4553 - val_iou_score: 42.4523\n",
            "Epoch 72/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1675.1265 - iou_score: 169.0556 - val_loss: -2049.0991 - val_iou_score: 38.3695\n",
            "Epoch 73/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1676.0222 - iou_score: 164.5758 - val_loss: -1764.4854 - val_iou_score: 36.1994\n",
            "Epoch 74/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1714.4583 - iou_score: 157.2806 - val_loss: -1857.6018 - val_iou_score: 47.9667\n",
            "Epoch 75/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1719.4751 - iou_score: 153.9163 - val_loss: -2386.0825 - val_iou_score: 73.8525\n",
            "Epoch 76/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1734.2728 - iou_score: 112.2734 - val_loss: -2433.7368 - val_iou_score: 60.6771\n",
            "Epoch 77/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1784.5294 - iou_score: 154.8443 - val_loss: -2220.6140 - val_iou_score: 44.3028\n",
            "Epoch 78/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1821.3017 - iou_score: 159.4058 - val_loss: -2682.1318 - val_iou_score: 48.8854\n",
            "Epoch 79/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1867.3755 - iou_score: 168.4528 - val_loss: -2788.0620 - val_iou_score: 46.6851\n",
            "Epoch 80/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1899.6068 - iou_score: 171.6576 - val_loss: -2669.8181 - val_iou_score: 42.5007\n",
            "Epoch 81/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1931.5154 - iou_score: 174.3857 - val_loss: -3067.3057 - val_iou_score: 42.0520\n",
            "Epoch 82/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1967.8630 - iou_score: 177.1617 - val_loss: -3212.0652 - val_iou_score: 45.5012\n",
            "Epoch 83/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -1998.1178 - iou_score: 177.2214 - val_loss: -2926.9761 - val_iou_score: 41.0028\n",
            "Epoch 84/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2032.7836 - iou_score: 167.5964 - val_loss: -2620.6531 - val_iou_score: 40.4276\n",
            "Epoch 85/200\n",
            "68/68 [==============================] - 1s 19ms/step - loss: -2070.5303 - iou_score: 178.1793 - val_loss: -2447.9690 - val_iou_score: 44.6080\n",
            "Epoch 86/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2099.6244 - iou_score: 177.7506 - val_loss: -2285.9282 - val_iou_score: 47.5848\n",
            "Epoch 87/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2117.7885 - iou_score: 179.0208 - val_loss: -2347.0957 - val_iou_score: 53.3777\n",
            "Epoch 88/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2133.2352 - iou_score: 172.6759 - val_loss: -2222.5081 - val_iou_score: 28.4358\n",
            "Epoch 89/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2169.8786 - iou_score: 173.2392 - val_loss: -2253.9316 - val_iou_score: 27.1543\n",
            "Epoch 90/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2198.6801 - iou_score: 175.1406 - val_loss: -2393.5276 - val_iou_score: 36.0380\n",
            "Epoch 91/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2239.6331 - iou_score: 177.0976 - val_loss: -2286.2603 - val_iou_score: 33.5933\n",
            "Epoch 92/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2277.6380 - iou_score: 167.6067 - val_loss: -2171.5461 - val_iou_score: 36.7366\n",
            "Epoch 93/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2319.3520 - iou_score: 177.0891 - val_loss: -2043.7114 - val_iou_score: 32.2151\n",
            "Epoch 94/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2315.3819 - iou_score: 156.5745 - val_loss: -2082.3860 - val_iou_score: 27.9201\n",
            "Epoch 95/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2324.0060 - iou_score: 154.4334 - val_loss: -2232.7026 - val_iou_score: 27.7060\n",
            "Epoch 96/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2367.9501 - iou_score: 161.4384 - val_loss: -2163.2312 - val_iou_score: 17.1298\n",
            "Epoch 97/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2410.4878 - iou_score: 158.9471 - val_loss: -2364.8721 - val_iou_score: 19.2427\n",
            "Epoch 98/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2456.7935 - iou_score: 168.7121 - val_loss: -2790.3799 - val_iou_score: 38.1305\n",
            "Epoch 99/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2478.1420 - iou_score: 165.5539 - val_loss: -3193.1431 - val_iou_score: 47.6803\n",
            "Epoch 100/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2510.8325 - iou_score: 132.6721 - val_loss: -3302.7297 - val_iou_score: 50.6568\n",
            "Epoch 101/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2557.5429 - iou_score: 169.2038 - val_loss: -3128.3564 - val_iou_score: 46.9793\n",
            "Epoch 102/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2595.4835 - iou_score: 165.2666 - val_loss: -2805.6216 - val_iou_score: 39.9657\n",
            "Epoch 103/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2619.6662 - iou_score: 162.9555 - val_loss: -2893.9480 - val_iou_score: 50.0484\n",
            "Epoch 104/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2680.7147 - iou_score: 178.9068 - val_loss: -3111.0693 - val_iou_score: 70.2614\n",
            "Epoch 105/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2719.5147 - iou_score: 173.8357 - val_loss: -3169.7458 - val_iou_score: 71.1658\n",
            "Epoch 106/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2756.4473 - iou_score: 182.1765 - val_loss: -2895.2925 - val_iou_score: 67.7139\n",
            "Epoch 107/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2800.0534 - iou_score: 184.1213 - val_loss: -2624.0300 - val_iou_score: 63.6629\n",
            "Epoch 108/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2833.4608 - iou_score: 187.2288 - val_loss: -2734.5728 - val_iou_score: 62.2228\n",
            "Epoch 109/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2861.5838 - iou_score: 177.9537 - val_loss: -3012.1267 - val_iou_score: 61.3895\n",
            "Epoch 110/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2908.4846 - iou_score: 186.4987 - val_loss: -3087.5273 - val_iou_score: 46.3068\n",
            "Epoch 111/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2943.0028 - iou_score: 180.8394 - val_loss: -2996.2288 - val_iou_score: 38.4152\n",
            "Epoch 112/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -2986.0008 - iou_score: 186.5328 - val_loss: -2688.7642 - val_iou_score: 31.6456\n",
            "Epoch 113/200\n",
            "68/68 [==============================] - 1s 19ms/step - loss: -3017.9709 - iou_score: 185.6528 - val_loss: -2785.2454 - val_iou_score: 42.9139\n",
            "Epoch 114/200\n",
            "68/68 [==============================] - 1s 19ms/step - loss: -3055.7255 - iou_score: 186.2537 - val_loss: -2902.7527 - val_iou_score: 57.0156\n",
            "Epoch 115/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3095.6213 - iou_score: 182.2838 - val_loss: -2977.7371 - val_iou_score: 54.1559\n",
            "Epoch 116/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3130.5094 - iou_score: 188.1121 - val_loss: -3030.8342 - val_iou_score: 55.9152\n",
            "Epoch 117/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3175.4666 - iou_score: 190.7728 - val_loss: -2967.6575 - val_iou_score: 56.7816\n",
            "Epoch 118/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3208.1839 - iou_score: 186.7794 - val_loss: -2956.8965 - val_iou_score: 59.3853\n",
            "Epoch 119/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3249.1196 - iou_score: 183.4851 - val_loss: -3039.2610 - val_iou_score: 59.3634\n",
            "Epoch 120/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3288.4074 - iou_score: 183.8907 - val_loss: -3089.4622 - val_iou_score: 52.5354\n",
            "Epoch 121/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3332.3987 - iou_score: 193.4467 - val_loss: -2992.5640 - val_iou_score: 48.5660\n",
            "Epoch 122/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3373.0275 - iou_score: 193.8112 - val_loss: -2776.6960 - val_iou_score: 52.6943\n",
            "Epoch 123/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3411.2408 - iou_score: 182.8868 - val_loss: -2691.3069 - val_iou_score: 51.7266\n",
            "Epoch 124/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3454.6093 - iou_score: 194.5480 - val_loss: -2808.0811 - val_iou_score: 47.3798\n",
            "Epoch 125/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3496.9957 - iou_score: 192.7407 - val_loss: -3019.2341 - val_iou_score: 55.6309\n",
            "Epoch 126/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3535.6116 - iou_score: 191.7090 - val_loss: -3113.1816 - val_iou_score: 55.0149\n",
            "Epoch 127/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3575.0201 - iou_score: 178.1060 - val_loss: -3127.0439 - val_iou_score: 48.2855\n",
            "Epoch 128/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3620.9726 - iou_score: 189.7015 - val_loss: -3098.5654 - val_iou_score: 44.8099\n",
            "Epoch 129/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3661.8693 - iou_score: 191.3726 - val_loss: -2964.8213 - val_iou_score: 46.4684\n",
            "Epoch 130/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3699.3767 - iou_score: 190.0162 - val_loss: -2908.9631 - val_iou_score: 48.6553\n",
            "Epoch 131/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3736.3152 - iou_score: 187.5422 - val_loss: -3027.1162 - val_iou_score: 45.7111\n",
            "Epoch 132/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3776.6987 - iou_score: 183.1869 - val_loss: -3165.3711 - val_iou_score: 41.2104\n",
            "Epoch 133/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3812.6735 - iou_score: 190.5715 - val_loss: -3227.6370 - val_iou_score: 46.7654\n",
            "Epoch 134/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3861.2762 - iou_score: 191.1740 - val_loss: -3190.5403 - val_iou_score: 50.0717\n",
            "Epoch 135/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3902.2614 - iou_score: 189.1490 - val_loss: -3141.5571 - val_iou_score: 46.3570\n",
            "Epoch 136/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3950.2679 - iou_score: 186.9798 - val_loss: -3218.9956 - val_iou_score: 46.3312\n",
            "Epoch 137/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -3989.9303 - iou_score: 190.4332 - val_loss: -3392.3464 - val_iou_score: 45.3351\n",
            "Epoch 138/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4034.6725 - iou_score: 192.4680 - val_loss: -3448.3706 - val_iou_score: 44.3725\n",
            "Epoch 139/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4072.0314 - iou_score: 176.3464 - val_loss: -3428.0500 - val_iou_score: 40.4741\n",
            "Epoch 140/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4112.9802 - iou_score: 165.7075 - val_loss: -3722.8286 - val_iou_score: 50.1456\n",
            "Epoch 141/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4156.3278 - iou_score: 186.8621 - val_loss: -4266.9971 - val_iou_score: 63.5689\n",
            "Epoch 142/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4197.5163 - iou_score: 180.7966 - val_loss: -4472.5732 - val_iou_score: 54.2670\n",
            "Epoch 143/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4239.4029 - iou_score: 189.3184 - val_loss: -4296.3345 - val_iou_score: 45.8731\n",
            "Epoch 144/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4288.1669 - iou_score: 183.9258 - val_loss: -4028.1936 - val_iou_score: 45.3180\n",
            "Epoch 145/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4326.3419 - iou_score: 168.7228 - val_loss: -4551.6421 - val_iou_score: 60.6984\n",
            "Epoch 146/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4375.5417 - iou_score: 189.2898 - val_loss: -5594.0913 - val_iou_score: 64.8140\n",
            "Epoch 147/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4416.7459 - iou_score: 189.0291 - val_loss: -6142.7363 - val_iou_score: 65.5089\n",
            "Epoch 148/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4450.6894 - iou_score: 178.4689 - val_loss: -6068.1226 - val_iou_score: 58.4330\n",
            "Epoch 149/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4480.3233 - iou_score: 169.4387 - val_loss: -5980.5303 - val_iou_score: 71.1040\n",
            "Epoch 150/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4517.4968 - iou_score: 177.0579 - val_loss: -6196.6255 - val_iou_score: 84.0408\n",
            "Epoch 151/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4541.1934 - iou_score: 178.2393 - val_loss: -6711.6577 - val_iou_score: 73.4230\n",
            "Epoch 152/200\n",
            "68/68 [==============================] - 1s 19ms/step - loss: -4545.6271 - iou_score: 160.1204 - val_loss: -7037.0488 - val_iou_score: 66.8751\n",
            "Epoch 153/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4582.9813 - iou_score: 132.6983 - val_loss: -4740.2856 - val_iou_score: 28.4669\n",
            "Epoch 154/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4643.3715 - iou_score: 161.2214 - val_loss: -7662.1875 - val_iou_score: 23.7897\n",
            "Epoch 155/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4688.7950 - iou_score: 166.2678 - val_loss: -32364.0137 - val_iou_score: 23.3696\n",
            "Epoch 156/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4758.7118 - iou_score: 160.9731 - val_loss: -61831.8477 - val_iou_score: 23.7232\n",
            "Epoch 157/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4818.7872 - iou_score: 169.3557 - val_loss: -97956.5234 - val_iou_score: 23.2049\n",
            "Epoch 158/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4871.5363 - iou_score: 170.9417 - val_loss: -45598.1484 - val_iou_score: 23.4235\n",
            "Epoch 159/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4914.7829 - iou_score: 155.5637 - val_loss: -17487.9062 - val_iou_score: 21.9363\n",
            "Epoch 160/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -4979.4392 - iou_score: 169.8379 - val_loss: -8065.2993 - val_iou_score: 24.3091\n",
            "Epoch 161/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5027.4278 - iou_score: 180.8581 - val_loss: -8301.2324 - val_iou_score: 27.3714\n",
            "Epoch 162/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5064.5353 - iou_score: 178.3322 - val_loss: -12375.0947 - val_iou_score: 28.8820\n",
            "Epoch 163/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5125.3231 - iou_score: 176.0841 - val_loss: -7957.5044 - val_iou_score: 33.3053\n",
            "Epoch 164/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5181.6392 - iou_score: 179.2161 - val_loss: -3142.6694 - val_iou_score: 20.3204\n",
            "Epoch 165/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5230.7659 - iou_score: 170.6303 - val_loss: -1693.8315 - val_iou_score: 5.9829\n",
            "Epoch 166/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5281.1023 - iou_score: 184.9528 - val_loss: -2830.2688 - val_iou_score: 7.6554\n",
            "Epoch 167/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5331.7474 - iou_score: 180.0289 - val_loss: -3403.7083 - val_iou_score: 8.7209\n",
            "Epoch 168/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5378.2513 - iou_score: 165.9424 - val_loss: -4492.2417 - val_iou_score: 12.5943\n",
            "Epoch 169/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5419.3543 - iou_score: 182.8870 - val_loss: -5648.2026 - val_iou_score: 49.0938\n",
            "Epoch 170/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5473.6998 - iou_score: 178.7007 - val_loss: -5662.9243 - val_iou_score: 41.9880\n",
            "Epoch 171/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5526.3951 - iou_score: 179.0488 - val_loss: -5374.5791 - val_iou_score: 40.0760\n",
            "Epoch 172/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5572.4519 - iou_score: 185.8105 - val_loss: -5253.2817 - val_iou_score: 43.0480\n",
            "Epoch 173/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5621.2175 - iou_score: 183.0400 - val_loss: -5033.2212 - val_iou_score: 45.0840\n",
            "Epoch 174/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5673.9151 - iou_score: 182.7579 - val_loss: -5175.3340 - val_iou_score: 40.3739\n",
            "Epoch 175/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5740.4487 - iou_score: 187.5072 - val_loss: -5439.8247 - val_iou_score: 36.9970\n",
            "Epoch 176/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5785.0352 - iou_score: 185.9122 - val_loss: -5618.1138 - val_iou_score: 46.6541\n",
            "Epoch 177/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5818.4274 - iou_score: 168.3204 - val_loss: -5655.0562 - val_iou_score: 43.2719\n",
            "Epoch 178/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5869.2528 - iou_score: 182.9681 - val_loss: -5679.6548 - val_iou_score: 36.5052\n",
            "Epoch 179/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5943.7190 - iou_score: 182.9641 - val_loss: -6224.0034 - val_iou_score: 58.3502\n",
            "Epoch 180/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -5971.9719 - iou_score: 176.8942 - val_loss: -6162.9863 - val_iou_score: 55.0755\n",
            "Epoch 181/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6035.4694 - iou_score: 156.9491 - val_loss: -6489.8457 - val_iou_score: 54.2024\n",
            "Epoch 182/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6097.5131 - iou_score: 179.2289 - val_loss: -6850.8276 - val_iou_score: 71.9645\n",
            "Epoch 183/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6139.8221 - iou_score: 153.9353 - val_loss: -6716.4648 - val_iou_score: 68.5181\n",
            "Epoch 184/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6200.9679 - iou_score: 175.9954 - val_loss: -6585.9077 - val_iou_score: 63.4599\n",
            "Epoch 185/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6254.3187 - iou_score: 160.5387 - val_loss: -6583.5000 - val_iou_score: 71.0992\n",
            "Epoch 186/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6309.9984 - iou_score: 177.6806 - val_loss: -6866.9209 - val_iou_score: 75.4211\n",
            "Epoch 187/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6358.2833 - iou_score: 167.2807 - val_loss: -6923.5142 - val_iou_score: 57.4680\n",
            "Epoch 188/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6424.9217 - iou_score: 182.2719 - val_loss: -6648.9917 - val_iou_score: 44.8273\n",
            "Epoch 189/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6448.1767 - iou_score: 173.8999 - val_loss: -6601.6895 - val_iou_score: 48.0258\n",
            "Epoch 190/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6525.6745 - iou_score: 185.2247 - val_loss: -6709.4404 - val_iou_score: 47.4279\n",
            "Epoch 191/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6573.4885 - iou_score: 184.9678 - val_loss: -6818.5806 - val_iou_score: 47.8535\n",
            "Epoch 192/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6640.8870 - iou_score: 174.4718 - val_loss: -6692.0347 - val_iou_score: 38.7512\n",
            "Epoch 193/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6703.8299 - iou_score: 182.5319 - val_loss: -6382.2588 - val_iou_score: 30.2651\n",
            "Epoch 194/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6748.2061 - iou_score: 181.9841 - val_loss: -6565.1909 - val_iou_score: 37.7166\n",
            "Epoch 195/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6819.9802 - iou_score: 182.1859 - val_loss: -6701.7036 - val_iou_score: 42.2431\n",
            "Epoch 196/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6877.6138 - iou_score: 172.9783 - val_loss: -6836.9297 - val_iou_score: 40.9237\n",
            "Epoch 197/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6940.9231 - iou_score: 185.6243 - val_loss: -6873.6992 - val_iou_score: 42.6902\n",
            "Epoch 198/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -6986.9752 - iou_score: 170.1379 - val_loss: -7041.0166 - val_iou_score: 46.4065\n",
            "Epoch 199/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -7047.9399 - iou_score: 188.0965 - val_loss: -7349.7793 - val_iou_score: 47.3676\n",
            "Epoch 200/200\n",
            "68/68 [==============================] - 1s 20ms/step - loss: -7099.2161 - iou_score: 177.3561 - val_loss: -7273.8599 - val_iou_score: 47.2011\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7f2613d5bb50>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TnHRBr0FT3yk"
      },
      "source": [
        "**Saving the Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQJjSyBi6vgn"
      },
      "source": [
        "model.save('/content/drive/MyDrive/ss_train/Sclera_140.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zoX7ah85T8UL"
      },
      "source": [
        "**Testing Area**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOpjUls97AF3"
      },
      "source": [
        "from tensorflow import keras\n",
        "model = keras.models.load_model('/content/drive/MyDrive/ss_train/Sclera_140.h5', compile=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G6JfoeArUM7E"
      },
      "source": [
        "**Upload & Read Image**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNITJruv-nux",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "df11427d-b607-49cf-dff4-4559d405014c"
      },
      "source": [
        "\n",
        "#Test on a different image\n",
        "#READ EXTERNAL IMAGE...\n",
        "test_img = cv2.imread('/content/13R_s_5.jpg', cv2.IMREAD_COLOR) \n",
        "test_img = cv2.cvtColor(test_img, cv2.COLOR_RGB2BGR)\n",
        "test_image = test_img \n",
        "test_img = cv2.resize(test_img, (SIZE_Y, SIZE_X))\n",
        "     \n",
        "\n",
        "test_img = np.expand_dims(test_img, axis=0)\n",
        "\n",
        "prediction = model.predict(test_img)\n",
        "\n",
        "test_image = cv2.resize(test_image, (OGX, OGY))\n",
        "plt.imshow(test_image)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f33eb39c690>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8ATKTtQUAmE"
      },
      "source": [
        "**Segmentate Mask**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OFeTYBA64E0H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "8f1e4193-b9a8-4e74-8222-0280fb6bc126"
      },
      "source": [
        "#View and Save segmented image\n",
        "prediction_image = prediction.reshape((SIZE_X,SIZE_Y))\n",
        "\n",
        "prediction_image = cv2.resize(prediction_image, (OGX, OGY))\n",
        "plt.imshow(prediction_image, cmap='gray')\n",
        "plt.imsave('/content/drive/MyDrive/testing/segmented.jpg', prediction_image, cmap='gray')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B4T0UVjVUFAn"
      },
      "source": [
        "**Test Image + Segmentate Mask**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3POqtlQaDTL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "29c01c0b-f79d-42b4-924b-af9da6686c75"
      },
      "source": [
        "imgg = cv2.imread('/content/drive/MyDrive/testing/segmented.jpg')  \n",
        "  \n",
        "#imgg2 = cv2.resize(imgg2, (SIZE_Y, SIZE_X))   \n",
        "imgg = cv2.resize(imgg, (OGX, OGY))\n",
        "imgg =  cv2.cvtColor(imgg, cv2.COLOR_RGB2BGR) \n",
        "annd = cv2.bitwise_and(imgg, test_image)\n",
        "plt.imshow(annd, cmap='gray')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f33eb2e1d50>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vn3yHQ2mUVhU"
      },
      "source": [
        "**Filter for veins**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cl3WaAAXesTp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "2b83a0e1-0221-4332-eb52-605dd21ccb03"
      },
      "source": [
        "\n",
        "from skimage.filters import frangi, hessian\n",
        "\n",
        "\n",
        "\n",
        "image = test_image\n",
        "#image = cv2.imread('/content/53L_s_1.jpg', cv2.IMREAD_COLOR)\n",
        "seg=(hessian(image) )\n",
        "\n",
        "plt.imsave('/content/drive/MyDrive/eye/final2.jpg', seg, cmap='gray')\n",
        "\n",
        "seg2= cv2.imread('/content/drive/MyDrive/eye/final2.jpg')\n",
        "\n",
        "annd2 = cv2.bitwise_and(seg2, annd)\n",
        "plt.imshow(annd2, cmap='gray')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f33de11a710>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}