Lungs_Segmentation_using_ResNet_and_DeepLabV3.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Praveen76/Lung-Segmentation-using-ResNet50-and-DeeplabV3/blob/main/Lungs_Segmentation_using_ResNet_and_DeepLabV3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DHVXkSrlCq6X"
      },
      "source": [
        "## Learning Objectives:\n",
        "\n",
        "At the end of the experiment, you will be able to:\n",
        "\n",
        "- understand, prepare, and visualize the the dataset containing image and corresponding masked image used for segmentation\n",
        "- implement DeepLabV3+ architecture\n",
        "- create a masked image (prediction)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gOGH5iqfz8ml"
      },
      "source": [
        "## Introduction\n",
        "\n",
        "Semantic segmentation is a computer vision task that involves dividing an image into different regions, each of which is labeled with a semantic category. The goal of semantic segmentation is to enable machines to understand the content of an image at a pixel level, by assigning a label to each individual pixel based on the object or region it belongs to.\n",
        "\n",
        "This technique is widely used in many applications such as self-driving cars, medical image analysis, and object recognition in robotics. It helps to extract meaningful information from images and to understand the relationships between objects and their environment.\n",
        "\n",
        "The below figure shows how semantic segmentation differs from other algorithms, such as object detection.\n",
        "\n",
        "<br>\n",
        "<img src='https://datascienceimages.s3.eu-north-1.amazonaws.com/Lungs_Segmentation_using_ResNet_and_DeepLabV3/ObjectDetection_Segmentation_Recognition.jpeg?response-content-disposition=inline&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHQaCmV1LW5vcnRoLTEiRzBFAiEAmOZQhT%2BYM40tIrgSCC0juQ3jYb2AAM%2F1Fbz6dD8McLICIHg%2Fvrzjk8lvADAhmWpZrYL9TF3PwvkzKpdWA8hZeRQoKu0CCM3%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQABoMOTc1MDUwMDY4NjU4IgyeGfj2LlL0RNsVYPMqwQJM4d9h4CmJwEyTBC7qZEzc29kl0a%2FrmErIOHAztB%2FrvmsLwfZSviafkN0S0Rgnb9ZUlQLlOLgmJAh%2Boo5wqCAD6Vph5HKDP0UmiithH%2BlXOUIiDE51ykTUEA3w1iSSHMyOs7vDBsLp%2FMFpvpCG3V2XnlQ5Iqgo422ee763aDs%2FzuanCRZpfGDaLTo%2BgTWY0vQntjWJILYrZeOw0tpLpzZArag7KROagc0J8JjBvK0M%2Fz02ApRdrd4sspTDVFraDlGzFJekxjfCDEkf0gSQy7gutoZ2hujhd%2BonPc2Sob988wEY1BUTJWathwBoXphrbMZIS6LhUp1rTnDl8w7hv54XZ4ORo9w4oXVsZdG%2FDbp2dUX6yVHFY3mjMGdU6fEQ1j2AlzySCTm4T%2FzJpXst4CisnbG6opNd6Os2b78VkYfQnKAwtdPmsQY6swKV5d%2BjoEU9poWR0HUudhbWdbIOfv1xKG64htIRW3NpNgi0OonJi5zOPTw5aWoAvi5hbnA7akONMJtwKRTgl9t30qtpFA8DwvmNL%2BUt%2FNsayqUy%2BrKPtD%2FtfVYyu82iXyxz4EyCSXPTgcCREW61a0z%2F8HtRH76JayEUAc7PAypjS861xn7SFWDzldlYWE8H2bDvjOQdf82VBKm67cP5CPWaxKpPkpTJFAR82hOQeFordG1wctY1YO5AOZnqv8Wd%2B7Tz9rQYNYk9xUihZZHgrvYzMuQWKcj%2By2eMBR7uWxP5LR48KBnL6edR4ZCv5H5LYCGjIREzJAImRO9qbDptQLHW%2B943UkCxOSqNdCIQjgRoWe84M93lB6rTQjLowcqWYU9YJDY9V73Jfw8Qz3mx6HxtnhdV&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240507T105443Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIA6GBMDKKZK2QSB44A%2F20240507%2Feu-north-1%2Fs3%2Faws4_request&X-Amz-Signature=550be8f811a5f21f0a2479b675bf3f41f99a8b3285302c6cd64456a53b254774' width=700px>\n",
        "<br><br>\n",
        "\n",
        "Moreover, in contrast to object detection, which detects and localizes objects within an image, semantic segmentation is more precise and detailed. It provides a much more granular understanding of the content of an image, allowing for more advanced and accurate applications."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "665oBbYDnVgl"
      },
      "source": [
        "## Dataset\n",
        "\n",
        "*  The Chest X-Ray dataset is made up of images and segmentated mask from two diffrent sources- Shenzhen and Montgomery dataset\n",
        "*  The CXR_png folder consists of Chest X-Rays and the masks folder has the segmented mask\n",
        "*  There are 704 images with their masks mapped with each other  \n",
        "\n",
        "<br>\n",
        "$\\quad$<img src='https://datascienceimages.s3.eu-north-1.amazonaws.com/Lungs_Segmentation_using_ResNet_and_DeepLabV3/X-Rays.png?response-content-disposition=inline&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHQaCmV1LW5vcnRoLTEiRzBFAiEAmOZQhT%2BYM40tIrgSCC0juQ3jYb2AAM%2F1Fbz6dD8McLICIHg%2Fvrzjk8lvADAhmWpZrYL9TF3PwvkzKpdWA8hZeRQoKu0CCM3%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQABoMOTc1MDUwMDY4NjU4IgyeGfj2LlL0RNsVYPMqwQJM4d9h4CmJwEyTBC7qZEzc29kl0a%2FrmErIOHAztB%2FrvmsLwfZSviafkN0S0Rgnb9ZUlQLlOLgmJAh%2Boo5wqCAD6Vph5HKDP0UmiithH%2BlXOUIiDE51ykTUEA3w1iSSHMyOs7vDBsLp%2FMFpvpCG3V2XnlQ5Iqgo422ee763aDs%2FzuanCRZpfGDaLTo%2BgTWY0vQntjWJILYrZeOw0tpLpzZArag7KROagc0J8JjBvK0M%2Fz02ApRdrd4sspTDVFraDlGzFJekxjfCDEkf0gSQy7gutoZ2hujhd%2BonPc2Sob988wEY1BUTJWathwBoXphrbMZIS6LhUp1rTnDl8w7hv54XZ4ORo9w4oXVsZdG%2FDbp2dUX6yVHFY3mjMGdU6fEQ1j2AlzySCTm4T%2FzJpXst4CisnbG6opNd6Os2b78VkYfQnKAwtdPmsQY6swKV5d%2BjoEU9poWR0HUudhbWdbIOfv1xKG64htIRW3NpNgi0OonJi5zOPTw5aWoAvi5hbnA7akONMJtwKRTgl9t30qtpFA8DwvmNL%2BUt%2FNsayqUy%2BrKPtD%2FtfVYyu82iXyxz4EyCSXPTgcCREW61a0z%2F8HtRH76JayEUAc7PAypjS861xn7SFWDzldlYWE8H2bDvjOQdf82VBKm67cP5CPWaxKpPkpTJFAR82hOQeFordG1wctY1YO5AOZnqv8Wd%2B7Tz9rQYNYk9xUihZZHgrvYzMuQWKcj%2By2eMBR7uWxP5LR48KBnL6edR4ZCv5H5LYCGjIREzJAImRO9qbDptQLHW%2B943UkCxOSqNdCIQjgRoWe84M93lB6rTQjLowcqWYU9YJDY9V73Jfw8Qz3mx6HxtnhdV&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240507T105515Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIA6GBMDKKZK2QSB44A%2F20240507%2Feu-north-1%2Fs3%2Faws4_request&X-Amz-Signature=41ce12abed0c2949531ce65beb69ac3087ec84e2f046da8ac359c219e181a33e' width=700px>\n",
        "<br><br>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y90Rgk5wG5Gc"
      },
      "source": [
        "### Problem Statement\n",
        "\n",
        "Perfrom the lungs segmentation on Chest X-Ray dataset using DeepLabV3+ model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "812a816f",
        "outputId": "380986e6-110f-4c4b-8ac4-844f07027e60"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-03-22 13:42:19--  https://cdn.iisc.talentsprint.com/AIandMLOps/MiniProjects/Datasets/LungSegmentation.zip\n",
            "Resolving cdn.iisc.talentsprint.com (cdn.iisc.talentsprint.com)... 172.105.52.210\n",
            "Connecting to cdn.iisc.talentsprint.com (cdn.iisc.talentsprint.com)|172.105.52.210|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 139904834 (133M) [application/zip]\n",
            "Saving to: ‘LungSegmentation.zip’\n",
            "\n",
            "LungSegmentation.zi 100%[===================>] 133.42M  27.1MB/s    in 5.6s    \n",
            "\n",
            "2024-03-22 13:42:25 (23.9 MB/s) - ‘LungSegmentation.zip’ saved [139904834/139904834]\n",
            "\n",
            "Data Downloaded Successfuly!!\n"
          ]
        }
      ],
      "source": [
        "\n",
        "#@title Download the data\n",
        "!wget https://cdn.iisc.talentsprint.com/AIandMLOps/MiniProjects/Datasets/LungSegmentation.zip\n",
        "!unzip -qq LungSegmentation.zip\n",
        "print(\"Data Downloaded Successfuly!!\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Download the data\n",
        "import yaml\n",
        "import json\n",
        "\n",
        "from google.colab import drive\n",
        "\n",
        "# Mount Google Drive with force remount\n",
        "drive.mount('/content/drive', force_remount=True)\n",
        "\n",
        "\n",
        "# Load relevant API Keys\n",
        "file_path = '/content/drive/MyDrive/.API_KEYS/API_KEYS.yml'\n",
        "\n",
        "with open(file_path, 'r') as file:\n",
        "    api_keys = yaml.safe_load(file)\n",
        "\n",
        "\n",
        "# Extract Kaggle username and key\n",
        "aws_access_key_id = api_keys['AWS']['AWS_ACCESS_KEY_ID']\n",
        "aws_secret_access_key = api_keys['AWS']['AWS_SECRET_ACCESS_KEY']\n"
      ],
      "metadata": {
        "id": "ruqB1zn7uLmg",
        "outputId": "76c515a8-edb6-44a5-d53b-9e4ed9a4a812",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ['AWS_ACCESS_KEY_ID']=aws_access_key_id\n",
        "os.environ['AWS_SECRET_ACCESS_KEY']=aws_secret_access_key\n",
        "del aws_access_key_id, aws_secret_access_key"
      ],
      "metadata": {
        "id": "NtXVznrkuY51"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install awscli\n",
        "!aws s3 cp s3://datasciencedataset/LungSegmentation.zip .\n",
        "!unzip -qq LungSegmentation.zip\n",
        "print(\"Data Downloaded Successfuly!!\")"
      ],
      "metadata": {
        "id": "5kvKzH_0uh7P",
        "outputId": "ed5ad12f-e5c0-4945-b1a9-7ff8827d1cfd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting awscli\n",
            "  Downloading awscli-1.32.99-py3-none-any.whl (4.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.5/4.5 MB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting botocore==1.34.99 (from awscli)\n",
            "  Downloading botocore-1.34.99-py3-none-any.whl (12.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.2/12.2 MB\u001b[0m \u001b[31m41.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting docutils<0.17,>=0.10 (from awscli)\n",
            "  Downloading docutils-0.16-py2.py3-none-any.whl (548 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m548.2/548.2 kB\u001b[0m \u001b[31m47.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting s3transfer<0.11.0,>=0.10.0 (from awscli)\n",
            "  Downloading s3transfer-0.10.1-py3-none-any.whl (82 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.2/82.2 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: PyYAML<6.1,>=3.10 in /usr/local/lib/python3.10/dist-packages (from awscli) (6.0.1)\n",
            "Collecting colorama<0.4.7,>=0.2.5 (from awscli)\n",
            "  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
            "Collecting rsa<4.8,>=3.1.2 (from awscli)\n",
            "  Downloading rsa-4.7.2-py3-none-any.whl (34 kB)\n",
            "Collecting jmespath<2.0.0,>=0.7.1 (from botocore==1.34.99->awscli)\n",
            "  Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)\n",
            "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.10/dist-packages (from botocore==1.34.99->awscli) (2.8.2)\n",
            "Requirement already satisfied: urllib3!=2.2.0,<3,>=1.25.4 in /usr/local/lib/python3.10/dist-packages (from botocore==1.34.99->awscli) (2.0.7)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.10/dist-packages (from rsa<4.8,>=3.1.2->awscli) (0.6.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore==1.34.99->awscli) (1.16.0)\n",
            "Installing collected packages: rsa, jmespath, docutils, colorama, botocore, s3transfer, awscli\n",
            "  Attempting uninstall: rsa\n",
            "    Found existing installation: rsa 4.9\n",
            "    Uninstalling rsa-4.9:\n",
            "      Successfully uninstalled rsa-4.9\n",
            "  Attempting uninstall: docutils\n",
            "    Found existing installation: docutils 0.18.1\n",
            "    Uninstalling docutils-0.18.1:\n",
            "      Successfully uninstalled docutils-0.18.1\n",
            "Successfully installed awscli-1.32.99 botocore-1.34.99 colorama-0.4.6 docutils-0.16 jmespath-1.0.1 rsa-4.7.2 s3transfer-0.10.1\n",
            "download: s3://datasciencedataset/LungSegmentation.zip to ./LungSegmentation.zip\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JC_nWtDfcjRt"
      },
      "source": [
        "### Import required packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "j1s-j0pdIRKV"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "from glob import glob\n",
        "from PIL import Image\n",
        "from scipy.io import loadmat\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJDtGozRcDg7"
      },
      "source": [
        "### Data Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpD0NpBLkyMW"
      },
      "source": [
        "#### Image Visualization\n",
        "Let us have a look at one of the Chest X-Ray image and its segmented label image."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize an image\n",
        "Image.open(sorted(glob(\"/content/LungSegmentation/CXR_png/*\"))[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "sOCVdHdKQrPM",
        "outputId": "e17a80ab-59ab-4964-cea1-2ede7fff7bf3"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=P size=512x512>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize a semantic part segmentation label image\n",
        "Image.open(sorted(glob(\"/content/LungSegmentation/masks/*\"))[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "h5BQFoBvS3eo",
        "outputId": "fbd83035-ab61-4729-ae20-66bc6039abd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=L size=512x512>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "ClhuMTOPDXBf",
        "outputId": "200d5645-61a9-482c-a139-872337a5aa25"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=L size=512x512>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Visualize a semantic part segmentation label image\n",
        "Image.open(sorted(glob(\"./LungSegmentation/masks/*\"))[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJaGbpopIRKW"
      },
      "source": [
        "### Creating a TensorFlow Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8sj6gK1vIRKW"
      },
      "outputs": [],
      "source": [
        "IMAGE_SIZE = 512\n",
        "BATCH_SIZE = 4\n",
        "NUM_CLASSES = 2\n",
        "DATA_DIR = \"./LungSegmentation\"\n",
        "\n",
        "# The below line uses the glob function from the glob module to find all the image files in the CXR_png\n",
        "# subdirectory of the DATA_DIR directory. The resulting list of file paths is sorted in ascending order.\n",
        "all_images = sorted(glob(os.path.join(DATA_DIR, \"CXR_png/*\")))\n",
        "\n",
        "# The below line of code finds all the mask files in the masks\n",
        "# subdirectory of the DATA_DIR directory.\n",
        "all_masks = sorted(glob(os.path.join(DATA_DIR, \"masks/*\")))\n",
        "\n",
        "# Creating a list of validation image files by selecting every 14th image from the all_images list.\n",
        "val_images = all_images[::14]\n",
        "\n",
        "# Creating a list of validation mask files by selecting every 14th image from the all_masks list.\n",
        "val_masks = all_masks[::14]\n",
        "\n",
        "# Creating a list of test image files by selecting every 14th image starting\n",
        "# from the second image in the all_images list.\n",
        "test_images = all_images[1::14]\n",
        "\n",
        "# Creating a list of test mask files by selecting every 14th image starting\n",
        "# from the second image in the all_masks list.\n",
        "test_masks = all_masks[1::14]\n",
        "\n",
        "# Creating an empty list for the training image files.\n",
        "train_images = []\n",
        "for i in all_images:\n",
        "    if (i not in val_images) and (i not in test_images):\n",
        "        train_images.append(i)\n",
        "\n",
        "# Creating an empty list for the training image files.\n",
        "train_masks = []\n",
        "for i in all_masks:\n",
        "    if (i not in val_masks) and (i not in test_masks):\n",
        "        train_masks.append(i)\n",
        "\n",
        "# The below function reads an image file and returns a preprocessed image tensor.\n",
        "# The mask argument is set to False by default, indicating that it is an image file, not a mask file.\n",
        "def read_image(image_path, mask=False):\n",
        "    image = tf.io.read_file(image_path)\n",
        "    if mask:\n",
        "        image = tf.image.decode_png(image, channels=1) // 255\n",
        "        image.set_shape([None, None, 1])\n",
        "        image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])\n",
        "    else:\n",
        "        image = tf.image.decode_png(image, channels=3)\n",
        "        image.set_shape([None, None, 3])\n",
        "        image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])\n",
        "        image = tf.keras.applications.resnet50.preprocess_input(image)\n",
        "    return image\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b7MxNDd33VDi"
      },
      "source": [
        "The **load_data** function created below takes in two arguments, **image_list** and **mask_list**, which are lists of file paths to the images and corresponding masks, respectively. It then reads in the image and mask using the **read_image** function defined earlier. The function returns a tuple of **image** and **mask**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1c0ahnXm3SKL"
      },
      "outputs": [],
      "source": [
        "def load_data(image_list, mask_list):\n",
        "    image = read_image(image_list)\n",
        "    mask = read_image(mask_list, mask=True)\n",
        "    return image, mask"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RKh_mhVP3VdV"
      },
      "source": [
        "The **data_generator** function takes in **image_list** and **mask_list** as arguments. It creates a **tf.data.Dataset** object from the input data using **tf.data.Dataset.from_tensor_slices**. It then maps the **load_data** function to each element in the dataset using **dataset.map**. Finally, it batches the dataset into batches of size **BATCH_SIZE** using **dataset.batch** and sets **drop_remainder=True** to drop any incomplete batch at the end of the dataset. The function returns the resulting dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-4Z2Vgx3ULA",
        "outputId": "b4b8f63f-ca14-4cf9-80a6-cd59212b72ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Dataset: <_BatchDataset element_spec=(TensorSpec(shape=(4, 512, 512, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 512, 512, 1), dtype=tf.float32, name=None))>\n",
            "Val Dataset: <_BatchDataset element_spec=(TensorSpec(shape=(4, 512, 512, 3), dtype=tf.float32, name=None), TensorSpec(shape=(4, 512, 512, 1), dtype=tf.float32, name=None))>\n"
          ]
        }
      ],
      "source": [
        "def data_generator(image_list, mask_list):\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((image_list, mask_list))\n",
        "    dataset = dataset.map(load_data, num_parallel_calls=tf.data.AUTOTUNE)         # The tf.data.experimental.AUTOTUNE is simply a constant equal to -1.\n",
        "                                                                                  # This value is passed to allow certain methods to automatically set parameters based on available resources.\n",
        "    dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)\n",
        "    return dataset\n",
        "\n",
        "\n",
        "train_dataset = data_generator(train_images, train_masks)\n",
        "val_dataset = data_generator(val_images, val_masks)\n",
        "\n",
        "print(\"Train Dataset:\", train_dataset)\n",
        "print(\"Val Dataset:\", val_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dEqbkpyw2TUW"
      },
      "source": [
        "Further, let us check the total images for train and validation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vBr67cusqgfu",
        "outputId": "2182335b-a346-45de-f6d8-1a8a07c1f725"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(602, 51)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "len(train_images), len(val_images)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4g4SWTf2doR"
      },
      "source": [
        "Below are the number of batches formed in train and validation set.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVWuKj1SpsO9",
        "outputId": "d1b17ecf-7da3-4030-dc0d-4b69c52426a1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "len(train_dataset), len(val_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "od4kRMIt4bBm"
      },
      "source": [
        "Now, we have created our TensorFlow Dataset. Further, let us try to understand the DeepLabV3+ model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rb9boz_8rA2e"
      },
      "source": [
        "### Building the DeepLabV3+ model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEPE701wIRKX"
      },
      "source": [
        "Downsampling is widely adopted in deep convolutional neural networks (CNN) for reducing memory consumption while preserving the transformation invariance to some degree.\n",
        "\n",
        "Multiple downsampling of a CNN will lead the feature map resolution to become smaller, resulting in lower prediction accuracy and loss of boundary information in semantic segmentation.\n",
        "\n",
        "DeepLabv3+ helps in solving these issues by including **atrous convolutions**. They aggregate context around a feature which helps in segmenting it better.\n",
        "\n",
        "<br>\n",
        "\n",
        "#### **Atrous Convolution/Dilated Convolution**\n",
        "\n",
        "It is a tool for refining the effective field of view of the convolution. It modifies the field of view using a parameter termed ***atrous rate*** or ***dilation rate***.\n",
        "\n",
        "With dilated convolution, as we go deeper in the network, we can keep the stride constant but with larger field-of-view without increasing the number of parameters or the amount of computation. It also enables larger output feature maps, which is useful for semantic segmentation.\n",
        "\n",
        "In the below figure, Atrous/Dilated Convolution has wider field of view with same number of parameters as Normal convolution.\n",
        "\n",
        "<br>\n",
        "<img src=\"https://datascienceimages.s3.eu-north-1.amazonaws.com/Lungs_Segmentation_using_ResNet_and_DeepLabV3/Normal_Vs_Dialted_Convolution.jpeg?response-content-disposition=inline&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHQaCmV1LW5vcnRoLTEiRzBFAiEAmOZQhT%2BYM40tIrgSCC0juQ3jYb2AAM%2F1Fbz6dD8McLICIHg%2Fvrzjk8lvADAhmWpZrYL9TF3PwvkzKpdWA8hZeRQoKu0CCM3%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQABoMOTc1MDUwMDY4NjU4IgyeGfj2LlL0RNsVYPMqwQJM4d9h4CmJwEyTBC7qZEzc29kl0a%2FrmErIOHAztB%2FrvmsLwfZSviafkN0S0Rgnb9ZUlQLlOLgmJAh%2Boo5wqCAD6Vph5HKDP0UmiithH%2BlXOUIiDE51ykTUEA3w1iSSHMyOs7vDBsLp%2FMFpvpCG3V2XnlQ5Iqgo422ee763aDs%2FzuanCRZpfGDaLTo%2BgTWY0vQntjWJILYrZeOw0tpLpzZArag7KROagc0J8JjBvK0M%2Fz02ApRdrd4sspTDVFraDlGzFJekxjfCDEkf0gSQy7gutoZ2hujhd%2BonPc2Sob988wEY1BUTJWathwBoXphrbMZIS6LhUp1rTnDl8w7hv54XZ4ORo9w4oXVsZdG%2FDbp2dUX6yVHFY3mjMGdU6fEQ1j2AlzySCTm4T%2FzJpXst4CisnbG6opNd6Os2b78VkYfQnKAwtdPmsQY6swKV5d%2BjoEU9poWR0HUudhbWdbIOfv1xKG64htIRW3NpNgi0OonJi5zOPTw5aWoAvi5hbnA7akONMJtwKRTgl9t30qtpFA8DwvmNL%2BUt%2FNsayqUy%2BrKPtD%2FtfVYyu82iXyxz4EyCSXPTgcCREW61a0z%2F8HtRH76JayEUAc7PAypjS861xn7SFWDzldlYWE8H2bDvjOQdf82VBKm67cP5CPWaxKpPkpTJFAR82hOQeFordG1wctY1YO5AOZnqv8Wd%2B7Tz9rQYNYk9xUihZZHgrvYzMuQWKcj%2By2eMBR7uWxP5LR48KBnL6edR4ZCv5H5LYCGjIREzJAImRO9qbDptQLHW%2B943UkCxOSqNdCIQjgRoWe84M93lB6rTQjLowcqWYU9YJDY9V73Jfw8Qz3mx6HxtnhdV&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240507T105618Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIA6GBMDKKZK2QSB44A%2F20240507%2Feu-north-1%2Fs3%2Faws4_request&X-Amz-Signature=2ba402d0c7a43345e2308913c66cfffe9bc3aa927a238f57ee63bf88bbc2823d\" width=500px>\n",
        "<br><br>\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lxZoQ8hE7pLF"
      },
      "source": [
        "#### **DeepLabv3+**\n",
        "\n",
        "Earlier version, DeepLabv3 has a problem of consuming too much time to process high-resolution images. DeepLabv3+ is a semantic segmentation architecture that improves upon DeepLabv3 with several improvements, such as adding an effective decoder module to refine the segmentation results.\n",
        "\n",
        "The below figure shows the typical architecture of DeepLabv3+. The encoder module processes multiscale contextual information by applying dilated/atrous convolution at multiple scales, while the decoder module refines the segmentation results along object boundaries.\n",
        "\n",
        "<br>\n",
        "<img src=\"https://datascienceimages.s3.eu-north-1.amazonaws.com/Lungs_Segmentation_using_ResNet_and_DeepLabV3/Encoder_Decoder.png?response-content-disposition=inline&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHQaCmV1LW5vcnRoLTEiRzBFAiEAmOZQhT%2BYM40tIrgSCC0juQ3jYb2AAM%2F1Fbz6dD8McLICIHg%2Fvrzjk8lvADAhmWpZrYL9TF3PwvkzKpdWA8hZeRQoKu0CCM3%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQABoMOTc1MDUwMDY4NjU4IgyeGfj2LlL0RNsVYPMqwQJM4d9h4CmJwEyTBC7qZEzc29kl0a%2FrmErIOHAztB%2FrvmsLwfZSviafkN0S0Rgnb9ZUlQLlOLgmJAh%2Boo5wqCAD6Vph5HKDP0UmiithH%2BlXOUIiDE51ykTUEA3w1iSSHMyOs7vDBsLp%2FMFpvpCG3V2XnlQ5Iqgo422ee763aDs%2FzuanCRZpfGDaLTo%2BgTWY0vQntjWJILYrZeOw0tpLpzZArag7KROagc0J8JjBvK0M%2Fz02ApRdrd4sspTDVFraDlGzFJekxjfCDEkf0gSQy7gutoZ2hujhd%2BonPc2Sob988wEY1BUTJWathwBoXphrbMZIS6LhUp1rTnDl8w7hv54XZ4ORo9w4oXVsZdG%2FDbp2dUX6yVHFY3mjMGdU6fEQ1j2AlzySCTm4T%2FzJpXst4CisnbG6opNd6Os2b78VkYfQnKAwtdPmsQY6swKV5d%2BjoEU9poWR0HUudhbWdbIOfv1xKG64htIRW3NpNgi0OonJi5zOPTw5aWoAvi5hbnA7akONMJtwKRTgl9t30qtpFA8DwvmNL%2BUt%2FNsayqUy%2BrKPtD%2FtfVYyu82iXyxz4EyCSXPTgcCREW61a0z%2F8HtRH76JayEUAc7PAypjS861xn7SFWDzldlYWE8H2bDvjOQdf82VBKm67cP5CPWaxKpPkpTJFAR82hOQeFordG1wctY1YO5AOZnqv8Wd%2B7Tz9rQYNYk9xUihZZHgrvYzMuQWKcj%2By2eMBR7uWxP5LR48KBnL6edR4ZCv5H5LYCGjIREzJAImRO9qbDptQLHW%2B943UkCxOSqNdCIQjgRoWe84M93lB6rTQjLowcqWYU9YJDY9V73Jfw8Qz3mx6HxtnhdV&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240507T105643Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIA6GBMDKKZK2QSB44A%2F20240507%2Feu-north-1%2Fs3%2Faws4_request&X-Amz-Signature=960611acde801d429a14e566d68771a592ec1054731fa1a7477dab0700a96cea\" width=1000px>\n",
        "<br><br>\n",
        "\n",
        "Deeplabv3+ employs Aligned Xception network as its main feature extractor (encoder), although with substantial modifications. Depth-wise separable convolution replaces all max pooling procedures.\n",
        "\n",
        "In Model Playground, we can select feature extraction (encoding) network to use as either **Resnet** or EfficientNet.\n",
        "\n",
        "The reason for using **Dilated Spatial Pyramid Pooling** is that it was shown that as the sampling rate becomes larger, the number of valid filter weights (i.e., weights that are applied to the valid feature region, instead of padded zeros) becomes smaller.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VZqBPgQZOirC"
      },
      "source": [
        "### Create the model\n",
        "First, create different functions  to implement DeepLabV3+ architecture."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "51JFQYafBWKV"
      },
      "source": [
        "Create a function, **convolution_block()**, to add a convolution layer, a BatchNormalization layer, and apply ReLu activation in one go."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "swj5dBq3Nuhd"
      },
      "outputs": [],
      "source": [
        "def convolution_block(block_input, num_filters=256, kernel_size=3, dilation_rate=1, padding=\"same\", use_bias=False):\n",
        "    x = layers.Conv2D(num_filters, kernel_size=kernel_size, dilation_rate=dilation_rate,\n",
        "                      padding=\"same\", use_bias=use_bias,\n",
        "                      kernel_initializer=keras.initializers.HeNormal())(block_input)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    return tf.nn.relu(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZ97G7zKDEwM"
      },
      "source": [
        "Create another function to perform **Dilated Spatial Pyramid Pooling**. Use above function to add different convolution blocks."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4ak0-MaLIRKX"
      },
      "outputs": [],
      "source": [
        "def DilatedSpatialPyramidPooling(dspp_input):\n",
        "    dims = dspp_input.shape\n",
        "\n",
        "    # 1x1 Conv rate=1\n",
        "    out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1)\n",
        "    # 3x3 Conv rate=6\n",
        "    out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6)\n",
        "    # 3x3 Conv rate=12\n",
        "    out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12)\n",
        "    # 3x3 Conv rate=18\n",
        "    out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18)\n",
        "\n",
        "    # Image pooling\n",
        "    x = layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)\n",
        "    x = convolution_block(x, kernel_size=1, use_bias=True)\n",
        "    out_pool = layers.UpSampling2D(size = (dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation = \"bilinear\")(x)\n",
        "\n",
        "    # Concat\n",
        "    resultant = layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])\n",
        "\n",
        "    # 1x1 Conv\n",
        "    output = convolution_block(resultant, kernel_size=1)\n",
        "    return output\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ISzIfZnCIRKY"
      },
      "source": [
        "The encoder features are first bilinearly upsampled by a factor 4, and then concatenated with the corresponding low-level features from the network backbone that have the same spatial resolution. Here, we use a **ResNet50** pretrained on ImageNet as the backbone model, and we use\n",
        "the low-level features from the `conv4_block6_2_relu` block of the backbone.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "issjAdV78C2q"
      },
      "source": [
        "#### Exploring ResNet-50 architecture before using it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7Ofva4g8IlS",
        "outputId": "df568494-d353-4ae1-8ff7-0aa72199ad8a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94765736/94765736 [==============================] - 5s 0us/step\n",
            "Model: \"resnet50\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 128, 128, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " conv1_pad (ZeroPadding2D)   (None, 134, 134, 3)          0         ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " conv1_conv (Conv2D)         (None, 64, 64, 64)           9472      ['conv1_pad[0][0]']           \n",
            "                                                                                                  \n",
            " conv1_bn (BatchNormalizati  (None, 64, 64, 64)           256       ['conv1_conv[0][0]']          \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv1_relu (Activation)     (None, 64, 64, 64)           0         ['conv1_bn[0][0]']            \n",
            "                                                                                                  \n",
            " pool1_pad (ZeroPadding2D)   (None, 66, 66, 64)           0         ['conv1_relu[0][0]']          \n",
            "                                                                                                  \n",
            " pool1_pool (MaxPooling2D)   (None, 32, 32, 64)           0         ['pool1_pad[0][0]']           \n",
            "                                                                                                  \n",
            " conv2_block1_1_conv (Conv2  (None, 32, 32, 64)           4160      ['pool1_pool[0][0]']          \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_1_bn (BatchNo  (None, 32, 32, 64)           256       ['conv2_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_1_relu (Activ  (None, 32, 32, 64)           0         ['conv2_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block1_2_conv (Conv2  (None, 32, 32, 64)           36928     ['conv2_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_2_bn (BatchNo  (None, 32, 32, 64)           256       ['conv2_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_2_relu (Activ  (None, 32, 32, 64)           0         ['conv2_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block1_0_conv (Conv2  (None, 32, 32, 256)          16640     ['pool1_pool[0][0]']          \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_3_conv (Conv2  (None, 32, 32, 256)          16640     ['conv2_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_0_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv2_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_3_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv2_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_add (Add)      (None, 32, 32, 256)          0         ['conv2_block1_0_bn[0][0]',   \n",
            "                                                                     'conv2_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block1_out (Activati  (None, 32, 32, 256)          0         ['conv2_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv2_block2_1_conv (Conv2  (None, 32, 32, 64)           16448     ['conv2_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_1_bn (BatchNo  (None, 32, 32, 64)           256       ['conv2_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_1_relu (Activ  (None, 32, 32, 64)           0         ['conv2_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block2_2_conv (Conv2  (None, 32, 32, 64)           36928     ['conv2_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_2_bn (BatchNo  (None, 32, 32, 64)           256       ['conv2_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_2_relu (Activ  (None, 32, 32, 64)           0         ['conv2_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block2_3_conv (Conv2  (None, 32, 32, 256)          16640     ['conv2_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_3_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv2_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_add (Add)      (None, 32, 32, 256)          0         ['conv2_block1_out[0][0]',    \n",
            "                                                                     'conv2_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block2_out (Activati  (None, 32, 32, 256)          0         ['conv2_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv2_block3_1_conv (Conv2  (None, 32, 32, 64)           16448     ['conv2_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_1_bn (BatchNo  (None, 32, 32, 64)           256       ['conv2_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_1_relu (Activ  (None, 32, 32, 64)           0         ['conv2_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block3_2_conv (Conv2  (None, 32, 32, 64)           36928     ['conv2_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_2_bn (BatchNo  (None, 32, 32, 64)           256       ['conv2_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_2_relu (Activ  (None, 32, 32, 64)           0         ['conv2_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block3_3_conv (Conv2  (None, 32, 32, 256)          16640     ['conv2_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_3_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv2_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_add (Add)      (None, 32, 32, 256)          0         ['conv2_block2_out[0][0]',    \n",
            "                                                                     'conv2_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block3_out (Activati  (None, 32, 32, 256)          0         ['conv2_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block1_1_conv (Conv2  (None, 16, 16, 128)          32896     ['conv2_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_1_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_1_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block1_2_conv (Conv2  (None, 16, 16, 128)          147584    ['conv3_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_2_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_2_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block1_0_conv (Conv2  (None, 16, 16, 512)          131584    ['conv2_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_3_conv (Conv2  (None, 16, 16, 512)          66048     ['conv3_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_0_bn (BatchNo  (None, 16, 16, 512)          2048      ['conv3_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_3_bn (BatchNo  (None, 16, 16, 512)          2048      ['conv3_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_add (Add)      (None, 16, 16, 512)          0         ['conv3_block1_0_bn[0][0]',   \n",
            "                                                                     'conv3_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block1_out (Activati  (None, 16, 16, 512)          0         ['conv3_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block2_1_conv (Conv2  (None, 16, 16, 128)          65664     ['conv3_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_1_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_1_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block2_2_conv (Conv2  (None, 16, 16, 128)          147584    ['conv3_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_2_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_2_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block2_3_conv (Conv2  (None, 16, 16, 512)          66048     ['conv3_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_3_bn (BatchNo  (None, 16, 16, 512)          2048      ['conv3_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_add (Add)      (None, 16, 16, 512)          0         ['conv3_block1_out[0][0]',    \n",
            "                                                                     'conv3_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block2_out (Activati  (None, 16, 16, 512)          0         ['conv3_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block3_1_conv (Conv2  (None, 16, 16, 128)          65664     ['conv3_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_1_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_1_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block3_2_conv (Conv2  (None, 16, 16, 128)          147584    ['conv3_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_2_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_2_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block3_3_conv (Conv2  (None, 16, 16, 512)          66048     ['conv3_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_3_bn (BatchNo  (None, 16, 16, 512)          2048      ['conv3_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_add (Add)      (None, 16, 16, 512)          0         ['conv3_block2_out[0][0]',    \n",
            "                                                                     'conv3_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block3_out (Activati  (None, 16, 16, 512)          0         ['conv3_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block4_1_conv (Conv2  (None, 16, 16, 128)          65664     ['conv3_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_1_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block4_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_1_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block4_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block4_2_conv (Conv2  (None, 16, 16, 128)          147584    ['conv3_block4_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_2_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block4_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_2_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block4_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block4_3_conv (Conv2  (None, 16, 16, 512)          66048     ['conv3_block4_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_3_bn (BatchNo  (None, 16, 16, 512)          2048      ['conv3_block4_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_add (Add)      (None, 16, 16, 512)          0         ['conv3_block3_out[0][0]',    \n",
            "                                                                     'conv3_block4_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block4_out (Activati  (None, 16, 16, 512)          0         ['conv3_block4_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block1_1_conv (Conv2  (None, 8, 8, 256)            131328    ['conv3_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_1_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_1_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block1_2_conv (Conv2  (None, 8, 8, 256)            590080    ['conv4_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_2_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_2_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block1_0_conv (Conv2  (None, 8, 8, 1024)           525312    ['conv3_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_3_conv (Conv2  (None, 8, 8, 1024)           263168    ['conv4_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_0_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_3_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_add (Add)      (None, 8, 8, 1024)           0         ['conv4_block1_0_bn[0][0]',   \n",
            "                                                                     'conv4_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block1_out (Activati  (None, 8, 8, 1024)           0         ['conv4_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block2_1_conv (Conv2  (None, 8, 8, 256)            262400    ['conv4_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_1_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_1_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block2_2_conv (Conv2  (None, 8, 8, 256)            590080    ['conv4_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_2_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_2_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block2_3_conv (Conv2  (None, 8, 8, 1024)           263168    ['conv4_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_3_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_add (Add)      (None, 8, 8, 1024)           0         ['conv4_block1_out[0][0]',    \n",
            "                                                                     'conv4_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block2_out (Activati  (None, 8, 8, 1024)           0         ['conv4_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block3_1_conv (Conv2  (None, 8, 8, 256)            262400    ['conv4_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_1_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_1_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block3_2_conv (Conv2  (None, 8, 8, 256)            590080    ['conv4_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_2_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_2_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block3_3_conv (Conv2  (None, 8, 8, 1024)           263168    ['conv4_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_3_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_add (Add)      (None, 8, 8, 1024)           0         ['conv4_block2_out[0][0]',    \n",
            "                                                                     'conv4_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block3_out (Activati  (None, 8, 8, 1024)           0         ['conv4_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block4_1_conv (Conv2  (None, 8, 8, 256)            262400    ['conv4_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_1_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block4_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_1_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block4_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block4_2_conv (Conv2  (None, 8, 8, 256)            590080    ['conv4_block4_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_2_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block4_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_2_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block4_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block4_3_conv (Conv2  (None, 8, 8, 1024)           263168    ['conv4_block4_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_3_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block4_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_add (Add)      (None, 8, 8, 1024)           0         ['conv4_block3_out[0][0]',    \n",
            "                                                                     'conv4_block4_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block4_out (Activati  (None, 8, 8, 1024)           0         ['conv4_block4_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block5_1_conv (Conv2  (None, 8, 8, 256)            262400    ['conv4_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_1_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block5_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_1_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block5_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block5_2_conv (Conv2  (None, 8, 8, 256)            590080    ['conv4_block5_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_2_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block5_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_2_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block5_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block5_3_conv (Conv2  (None, 8, 8, 1024)           263168    ['conv4_block5_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_3_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block5_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_add (Add)      (None, 8, 8, 1024)           0         ['conv4_block4_out[0][0]',    \n",
            "                                                                     'conv4_block5_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block5_out (Activati  (None, 8, 8, 1024)           0         ['conv4_block5_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block6_1_conv (Conv2  (None, 8, 8, 256)            262400    ['conv4_block5_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_1_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block6_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_1_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block6_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block6_2_conv (Conv2  (None, 8, 8, 256)            590080    ['conv4_block6_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_2_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block6_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_2_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block6_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block6_3_conv (Conv2  (None, 8, 8, 1024)           263168    ['conv4_block6_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_3_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block6_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_add (Add)      (None, 8, 8, 1024)           0         ['conv4_block5_out[0][0]',    \n",
            "                                                                     'conv4_block6_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block6_out (Activati  (None, 8, 8, 1024)           0         ['conv4_block6_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv5_block1_1_conv (Conv2  (None, 4, 4, 512)            524800    ['conv4_block6_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_1_bn (BatchNo  (None, 4, 4, 512)            2048      ['conv5_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_1_relu (Activ  (None, 4, 4, 512)            0         ['conv5_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block1_2_conv (Conv2  (None, 4, 4, 512)            2359808   ['conv5_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_2_bn (BatchNo  (None, 4, 4, 512)            2048      ['conv5_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_2_relu (Activ  (None, 4, 4, 512)            0         ['conv5_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block1_0_conv (Conv2  (None, 4, 4, 2048)           2099200   ['conv4_block6_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_3_conv (Conv2  (None, 4, 4, 2048)           1050624   ['conv5_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block1_0_bn (BatchNo  (None, 4, 4, 2048)           8192      ['conv5_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_3_bn (BatchNo  (None, 4, 4, 2048)           8192      ['conv5_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block1_add (Add)      (None, 4, 4, 2048)           0         ['conv5_block1_0_bn[0][0]',   \n",
            "                                                                     'conv5_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv5_block1_out (Activati  (None, 4, 4, 2048)           0         ['conv5_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv5_block2_1_conv (Conv2  (None, 4, 4, 512)            1049088   ['conv5_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_1_bn (BatchNo  (None, 4, 4, 512)            2048      ['conv5_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block2_1_relu (Activ  (None, 4, 4, 512)            0         ['conv5_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block2_2_conv (Conv2  (None, 4, 4, 512)            2359808   ['conv5_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_2_bn (BatchNo  (None, 4, 4, 512)            2048      ['conv5_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block2_2_relu (Activ  (None, 4, 4, 512)            0         ['conv5_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block2_3_conv (Conv2  (None, 4, 4, 2048)           1050624   ['conv5_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block2_3_bn (BatchNo  (None, 4, 4, 2048)           8192      ['conv5_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block2_add (Add)      (None, 4, 4, 2048)           0         ['conv5_block1_out[0][0]',    \n",
            "                                                                     'conv5_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv5_block2_out (Activati  (None, 4, 4, 2048)           0         ['conv5_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv5_block3_1_conv (Conv2  (None, 4, 4, 512)            1049088   ['conv5_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_1_bn (BatchNo  (None, 4, 4, 512)            2048      ['conv5_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block3_1_relu (Activ  (None, 4, 4, 512)            0         ['conv5_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block3_2_conv (Conv2  (None, 4, 4, 512)            2359808   ['conv5_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_2_bn (BatchNo  (None, 4, 4, 512)            2048      ['conv5_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block3_2_relu (Activ  (None, 4, 4, 512)            0         ['conv5_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv5_block3_3_conv (Conv2  (None, 4, 4, 2048)           1050624   ['conv5_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv5_block3_3_bn (BatchNo  (None, 4, 4, 2048)           8192      ['conv5_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv5_block3_add (Add)      (None, 4, 4, 2048)           0         ['conv5_block2_out[0][0]',    \n",
            "                                                                     'conv5_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv5_block3_out (Activati  (None, 4, 4, 2048)           0         ['conv5_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 23587712 (89.98 MB)\n",
            "Trainable params: 23534592 (89.78 MB)\n",
            "Non-trainable params: 53120 (207.50 KB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "res_input = keras.Input(shape=(128, 128, 3))\n",
        "resnet50 = keras.applications.ResNet50(weights=\"imagenet\", include_top=False, input_tensor = res_input)\n",
        "\n",
        "# Layers present in ResNet-50 network\n",
        "resnet50.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IdKRtggI8Y4E"
      },
      "source": [
        "From the above layers,\n",
        "\n",
        "- Use the low-level features from the `conv2_block3_2_relu` layer of the ResNet-50 network to fead in Decoder.\n",
        "\n",
        "- Use the features from the `conv4_block6_2_relu` layer of the ResNet-50 to fead in Dilated Spatial Pyramid Pooling module."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQIrjgPf84OQ"
      },
      "source": [
        "### Create Encoder\n",
        "\n",
        "Create a function to implement the architecture for Encoder block. Use **ResNet50** pretrained on ImageNet as the backbone network. Use the features from the **conv4_block6_2_relu** layer of the backbone to fead in Dilated Spatial Pyramid Pooling module. Then return the backbone network along with encoder output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EqL-GWCP82KP"
      },
      "outputs": [],
      "source": [
        "def Encoder(model_input):\n",
        "    # Backbone network\n",
        "    resnet50 = keras.applications.ResNet50(weights=\"imagenet\", include_top=False, input_tensor=model_input)\n",
        "    # Features from backbone network to fead in DSPP\n",
        "    x = resnet50.get_layer(\"conv4_block6_2_relu\").output\n",
        "    # DSPP module\n",
        "    concat_out = DilatedSpatialPyramidPooling(x)\n",
        "    # 1x1 Conv\n",
        "    output = convolution_block(concat_out, kernel_size=1)\n",
        "\n",
        "    return resnet50, output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SfKoDepj8_Tx"
      },
      "source": [
        "### Create Decoder\n",
        "\n",
        "Create a function to implement the architecture for Decoder block. The encoder features are first bilinearly upsampled by a factor 4, and then concatenated with the corresponding low-level features (the **conv2_block3_2_relu** layer) from the network backbone that have the same spatial resolution."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TSWbMKDR9ENp"
      },
      "outputs": [],
      "source": [
        "def Decoder(image_size, back_network, x):\n",
        "    # Output from Encoder, upsample by 4\n",
        "    input_a = layers.UpSampling2D(size = (image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),\n",
        "                                  interpolation = \"bilinear\")(x)\n",
        "    # Low-level features from backbone network\n",
        "    input_b = back_network.get_layer(\"conv2_block3_2_relu\").output\n",
        "    # Add 1x1 Conv on low-level features\n",
        "    input_b = convolution_block(input_b, num_filters=48, kernel_size=1)\n",
        "\n",
        "    # Concat\n",
        "    x = layers.Concatenate(axis=-1)([input_a, input_b])\n",
        "    # Add 3x3 Conv blocks\n",
        "    x = convolution_block(x)\n",
        "    x = convolution_block(x)\n",
        "\n",
        "    # Resultant upsample by 4\n",
        "    output = layers.UpSampling2D(size = (image_size // x.shape[1], image_size // x.shape[2]),\n",
        "                            interpolation = \"bilinear\")(x)\n",
        "    return output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rY70SsKa9Id6"
      },
      "source": [
        "#### Building full architecture of the Model\n",
        "\n",
        "Write a function that combines the encoder and decoder functions defined above to create & implement a complete  DeepLabV3+ architecture."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f1wzKmlM9IB5"
      },
      "outputs": [],
      "source": [
        "def DeeplabV3Plus(image_size, num_classes):\n",
        "    model_input = keras.Input(shape=(image_size, image_size, 3))\n",
        "    # Encoder part\n",
        "    back_network, x = Encoder(model_input)\n",
        "    # Decoder part\n",
        "    x = Decoder(image_size, back_network, x)\n",
        "\n",
        "    # Output/prediction layer\n",
        "    model_output = layers.Conv2D(num_classes, kernel_size=(1, 1), padding=\"same\")(x)\n",
        "\n",
        "    return keras.Model(inputs=model_input, outputs=model_output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3WPwmoG9IRKY",
        "outputId": "0581b157-c49b-456b-aef5-dbf3eb314185"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_2 (InputLayer)        [(None, 512, 512, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " conv1_pad (ZeroPadding2D)   (None, 518, 518, 3)          0         ['input_2[0][0]']             \n",
            "                                                                                                  \n",
            " conv1_conv (Conv2D)         (None, 256, 256, 64)         9472      ['conv1_pad[0][0]']           \n",
            "                                                                                                  \n",
            " conv1_bn (BatchNormalizati  (None, 256, 256, 64)         256       ['conv1_conv[0][0]']          \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv1_relu (Activation)     (None, 256, 256, 64)         0         ['conv1_bn[0][0]']            \n",
            "                                                                                                  \n",
            " pool1_pad (ZeroPadding2D)   (None, 258, 258, 64)         0         ['conv1_relu[0][0]']          \n",
            "                                                                                                  \n",
            " pool1_pool (MaxPooling2D)   (None, 128, 128, 64)         0         ['pool1_pad[0][0]']           \n",
            "                                                                                                  \n",
            " conv2_block1_1_conv (Conv2  (None, 128, 128, 64)         4160      ['pool1_pool[0][0]']          \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_1_bn (BatchNo  (None, 128, 128, 64)         256       ['conv2_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_1_relu (Activ  (None, 128, 128, 64)         0         ['conv2_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block1_2_conv (Conv2  (None, 128, 128, 64)         36928     ['conv2_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_2_bn (BatchNo  (None, 128, 128, 64)         256       ['conv2_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_2_relu (Activ  (None, 128, 128, 64)         0         ['conv2_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block1_0_conv (Conv2  (None, 128, 128, 256)        16640     ['pool1_pool[0][0]']          \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_3_conv (Conv2  (None, 128, 128, 256)        16640     ['conv2_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block1_0_bn (BatchNo  (None, 128, 128, 256)        1024      ['conv2_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_3_bn (BatchNo  (None, 128, 128, 256)        1024      ['conv2_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block1_add (Add)      (None, 128, 128, 256)        0         ['conv2_block1_0_bn[0][0]',   \n",
            "                                                                     'conv2_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block1_out (Activati  (None, 128, 128, 256)        0         ['conv2_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv2_block2_1_conv (Conv2  (None, 128, 128, 64)         16448     ['conv2_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_1_bn (BatchNo  (None, 128, 128, 64)         256       ['conv2_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_1_relu (Activ  (None, 128, 128, 64)         0         ['conv2_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block2_2_conv (Conv2  (None, 128, 128, 64)         36928     ['conv2_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_2_bn (BatchNo  (None, 128, 128, 64)         256       ['conv2_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_2_relu (Activ  (None, 128, 128, 64)         0         ['conv2_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block2_3_conv (Conv2  (None, 128, 128, 256)        16640     ['conv2_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block2_3_bn (BatchNo  (None, 128, 128, 256)        1024      ['conv2_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block2_add (Add)      (None, 128, 128, 256)        0         ['conv2_block1_out[0][0]',    \n",
            "                                                                     'conv2_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block2_out (Activati  (None, 128, 128, 256)        0         ['conv2_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv2_block3_1_conv (Conv2  (None, 128, 128, 64)         16448     ['conv2_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_1_bn (BatchNo  (None, 128, 128, 64)         256       ['conv2_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_1_relu (Activ  (None, 128, 128, 64)         0         ['conv2_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block3_2_conv (Conv2  (None, 128, 128, 64)         36928     ['conv2_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_2_bn (BatchNo  (None, 128, 128, 64)         256       ['conv2_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_2_relu (Activ  (None, 128, 128, 64)         0         ['conv2_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv2_block3_3_conv (Conv2  (None, 128, 128, 256)        16640     ['conv2_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2_block3_3_bn (BatchNo  (None, 128, 128, 256)        1024      ['conv2_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv2_block3_add (Add)      (None, 128, 128, 256)        0         ['conv2_block2_out[0][0]',    \n",
            "                                                                     'conv2_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv2_block3_out (Activati  (None, 128, 128, 256)        0         ['conv2_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block1_1_conv (Conv2  (None, 64, 64, 128)          32896     ['conv2_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_1_bn (BatchNo  (None, 64, 64, 128)          512       ['conv3_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_1_relu (Activ  (None, 64, 64, 128)          0         ['conv3_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block1_2_conv (Conv2  (None, 64, 64, 128)          147584    ['conv3_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_2_bn (BatchNo  (None, 64, 64, 128)          512       ['conv3_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_2_relu (Activ  (None, 64, 64, 128)          0         ['conv3_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block1_0_conv (Conv2  (None, 64, 64, 512)          131584    ['conv2_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_3_conv (Conv2  (None, 64, 64, 512)          66048     ['conv3_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block1_0_bn (BatchNo  (None, 64, 64, 512)          2048      ['conv3_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_3_bn (BatchNo  (None, 64, 64, 512)          2048      ['conv3_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block1_add (Add)      (None, 64, 64, 512)          0         ['conv3_block1_0_bn[0][0]',   \n",
            "                                                                     'conv3_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block1_out (Activati  (None, 64, 64, 512)          0         ['conv3_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block2_1_conv (Conv2  (None, 64, 64, 128)          65664     ['conv3_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_1_bn (BatchNo  (None, 64, 64, 128)          512       ['conv3_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_1_relu (Activ  (None, 64, 64, 128)          0         ['conv3_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block2_2_conv (Conv2  (None, 64, 64, 128)          147584    ['conv3_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_2_bn (BatchNo  (None, 64, 64, 128)          512       ['conv3_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_2_relu (Activ  (None, 64, 64, 128)          0         ['conv3_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block2_3_conv (Conv2  (None, 64, 64, 512)          66048     ['conv3_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block2_3_bn (BatchNo  (None, 64, 64, 512)          2048      ['conv3_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block2_add (Add)      (None, 64, 64, 512)          0         ['conv3_block1_out[0][0]',    \n",
            "                                                                     'conv3_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block2_out (Activati  (None, 64, 64, 512)          0         ['conv3_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block3_1_conv (Conv2  (None, 64, 64, 128)          65664     ['conv3_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_1_bn (BatchNo  (None, 64, 64, 128)          512       ['conv3_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_1_relu (Activ  (None, 64, 64, 128)          0         ['conv3_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block3_2_conv (Conv2  (None, 64, 64, 128)          147584    ['conv3_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_2_bn (BatchNo  (None, 64, 64, 128)          512       ['conv3_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_2_relu (Activ  (None, 64, 64, 128)          0         ['conv3_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block3_3_conv (Conv2  (None, 64, 64, 512)          66048     ['conv3_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block3_3_bn (BatchNo  (None, 64, 64, 512)          2048      ['conv3_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block3_add (Add)      (None, 64, 64, 512)          0         ['conv3_block2_out[0][0]',    \n",
            "                                                                     'conv3_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block3_out (Activati  (None, 64, 64, 512)          0         ['conv3_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv3_block4_1_conv (Conv2  (None, 64, 64, 128)          65664     ['conv3_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_1_bn (BatchNo  (None, 64, 64, 128)          512       ['conv3_block4_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_1_relu (Activ  (None, 64, 64, 128)          0         ['conv3_block4_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block4_2_conv (Conv2  (None, 64, 64, 128)          147584    ['conv3_block4_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_2_bn (BatchNo  (None, 64, 64, 128)          512       ['conv3_block4_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_2_relu (Activ  (None, 64, 64, 128)          0         ['conv3_block4_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv3_block4_3_conv (Conv2  (None, 64, 64, 512)          66048     ['conv3_block4_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv3_block4_3_bn (BatchNo  (None, 64, 64, 512)          2048      ['conv3_block4_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv3_block4_add (Add)      (None, 64, 64, 512)          0         ['conv3_block3_out[0][0]',    \n",
            "                                                                     'conv3_block4_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv3_block4_out (Activati  (None, 64, 64, 512)          0         ['conv3_block4_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block1_1_conv (Conv2  (None, 32, 32, 256)          131328    ['conv3_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_1_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv4_block1_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_1_relu (Activ  (None, 32, 32, 256)          0         ['conv4_block1_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block1_2_conv (Conv2  (None, 32, 32, 256)          590080    ['conv4_block1_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_2_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv4_block1_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_2_relu (Activ  (None, 32, 32, 256)          0         ['conv4_block1_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block1_0_conv (Conv2  (None, 32, 32, 1024)         525312    ['conv3_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_3_conv (Conv2  (None, 32, 32, 1024)         263168    ['conv4_block1_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block1_0_bn (BatchNo  (None, 32, 32, 1024)         4096      ['conv4_block1_0_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_3_bn (BatchNo  (None, 32, 32, 1024)         4096      ['conv4_block1_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block1_add (Add)      (None, 32, 32, 1024)         0         ['conv4_block1_0_bn[0][0]',   \n",
            "                                                                     'conv4_block1_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block1_out (Activati  (None, 32, 32, 1024)         0         ['conv4_block1_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block2_1_conv (Conv2  (None, 32, 32, 256)          262400    ['conv4_block1_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_1_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv4_block2_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_1_relu (Activ  (None, 32, 32, 256)          0         ['conv4_block2_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block2_2_conv (Conv2  (None, 32, 32, 256)          590080    ['conv4_block2_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_2_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv4_block2_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_2_relu (Activ  (None, 32, 32, 256)          0         ['conv4_block2_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block2_3_conv (Conv2  (None, 32, 32, 1024)         263168    ['conv4_block2_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block2_3_bn (BatchNo  (None, 32, 32, 1024)         4096      ['conv4_block2_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block2_add (Add)      (None, 32, 32, 1024)         0         ['conv4_block1_out[0][0]',    \n",
            "                                                                     'conv4_block2_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block2_out (Activati  (None, 32, 32, 1024)         0         ['conv4_block2_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block3_1_conv (Conv2  (None, 32, 32, 256)          262400    ['conv4_block2_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_1_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv4_block3_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_1_relu (Activ  (None, 32, 32, 256)          0         ['conv4_block3_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block3_2_conv (Conv2  (None, 32, 32, 256)          590080    ['conv4_block3_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_2_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv4_block3_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_2_relu (Activ  (None, 32, 32, 256)          0         ['conv4_block3_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block3_3_conv (Conv2  (None, 32, 32, 1024)         263168    ['conv4_block3_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block3_3_bn (BatchNo  (None, 32, 32, 1024)         4096      ['conv4_block3_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block3_add (Add)      (None, 32, 32, 1024)         0         ['conv4_block2_out[0][0]',    \n",
            "                                                                     'conv4_block3_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block3_out (Activati  (None, 32, 32, 1024)         0         ['conv4_block3_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block4_1_conv (Conv2  (None, 32, 32, 256)          262400    ['conv4_block3_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_1_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv4_block4_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_1_relu (Activ  (None, 32, 32, 256)          0         ['conv4_block4_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block4_2_conv (Conv2  (None, 32, 32, 256)          590080    ['conv4_block4_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_2_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv4_block4_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_2_relu (Activ  (None, 32, 32, 256)          0         ['conv4_block4_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block4_3_conv (Conv2  (None, 32, 32, 1024)         263168    ['conv4_block4_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block4_3_bn (BatchNo  (None, 32, 32, 1024)         4096      ['conv4_block4_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block4_add (Add)      (None, 32, 32, 1024)         0         ['conv4_block3_out[0][0]',    \n",
            "                                                                     'conv4_block4_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block4_out (Activati  (None, 32, 32, 1024)         0         ['conv4_block4_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block5_1_conv (Conv2  (None, 32, 32, 256)          262400    ['conv4_block4_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_1_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv4_block5_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_1_relu (Activ  (None, 32, 32, 256)          0         ['conv4_block5_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block5_2_conv (Conv2  (None, 32, 32, 256)          590080    ['conv4_block5_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_2_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv4_block5_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_2_relu (Activ  (None, 32, 32, 256)          0         ['conv4_block5_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block5_3_conv (Conv2  (None, 32, 32, 1024)         263168    ['conv4_block5_2_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block5_3_bn (BatchNo  (None, 32, 32, 1024)         4096      ['conv4_block5_3_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block5_add (Add)      (None, 32, 32, 1024)         0         ['conv4_block4_out[0][0]',    \n",
            "                                                                     'conv4_block5_3_bn[0][0]']   \n",
            "                                                                                                  \n",
            " conv4_block5_out (Activati  (None, 32, 32, 1024)         0         ['conv4_block5_add[0][0]']    \n",
            " on)                                                                                              \n",
            "                                                                                                  \n",
            " conv4_block6_1_conv (Conv2  (None, 32, 32, 256)          262400    ['conv4_block5_out[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_1_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv4_block6_1_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_1_relu (Activ  (None, 32, 32, 256)          0         ['conv4_block6_1_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " conv4_block6_2_conv (Conv2  (None, 32, 32, 256)          590080    ['conv4_block6_1_relu[0][0]'] \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv4_block6_2_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv4_block6_2_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " conv4_block6_2_relu (Activ  (None, 32, 32, 256)          0         ['conv4_block6_2_bn[0][0]']   \n",
            " ation)                                                                                           \n",
            "                                                                                                  \n",
            " average_pooling2d (Average  (None, 1, 1, 256)            0         ['conv4_block6_2_relu[0][0]'] \n",
            " Pooling2D)                                                                                       \n",
            "                                                                                                  \n",
            " conv2d_4 (Conv2D)           (None, 1, 1, 256)            65792     ['average_pooling2d[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_4 (Bat  (None, 1, 1, 256)            1024      ['conv2d_4[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " conv2d (Conv2D)             (None, 32, 32, 256)          65536     ['conv4_block6_2_relu[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_1 (Conv2D)           (None, 32, 32, 256)          589824    ['conv4_block6_2_relu[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)           (None, 32, 32, 256)          589824    ['conv4_block6_2_relu[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_3 (Conv2D)           (None, 32, 32, 256)          589824    ['conv4_block6_2_relu[0][0]'] \n",
            "                                                                                                  \n",
            " tf.nn.relu_4 (TFOpLambda)   (None, 1, 1, 256)            0         ['batch_normalization_4[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " batch_normalization (Batch  (None, 32, 32, 256)          1024      ['conv2d[0][0]']              \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_1 (Bat  (None, 32, 32, 256)          1024      ['conv2d_1[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_2 (Bat  (None, 32, 32, 256)          1024      ['conv2d_2[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_3 (Bat  (None, 32, 32, 256)          1024      ['conv2d_3[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " up_sampling2d (UpSampling2  (None, 32, 32, 256)          0         ['tf.nn.relu_4[0][0]']        \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " tf.nn.relu (TFOpLambda)     (None, 32, 32, 256)          0         ['batch_normalization[0][0]'] \n",
            "                                                                                                  \n",
            " tf.nn.relu_1 (TFOpLambda)   (None, 32, 32, 256)          0         ['batch_normalization_1[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " tf.nn.relu_2 (TFOpLambda)   (None, 32, 32, 256)          0         ['batch_normalization_2[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " tf.nn.relu_3 (TFOpLambda)   (None, 32, 32, 256)          0         ['batch_normalization_3[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)   (None, 32, 32, 1280)         0         ['up_sampling2d[0][0]',       \n",
            "                                                                     'tf.nn.relu[0][0]',          \n",
            "                                                                     'tf.nn.relu_1[0][0]',        \n",
            "                                                                     'tf.nn.relu_2[0][0]',        \n",
            "                                                                     'tf.nn.relu_3[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_5 (Conv2D)           (None, 32, 32, 256)          327680    ['concatenate[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_5 (Bat  (None, 32, 32, 256)          1024      ['conv2d_5[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " tf.nn.relu_5 (TFOpLambda)   (None, 32, 32, 256)          0         ['batch_normalization_5[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)           (None, 32, 32, 256)          65536     ['tf.nn.relu_5[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_6 (Bat  (None, 32, 32, 256)          1024      ['conv2d_6[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)           (None, 128, 128, 48)         3072      ['conv2_block3_2_relu[0][0]'] \n",
            "                                                                                                  \n",
            " tf.nn.relu_6 (TFOpLambda)   (None, 32, 32, 256)          0         ['batch_normalization_6[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " batch_normalization_7 (Bat  (None, 128, 128, 48)         192       ['conv2d_7[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " up_sampling2d_1 (UpSamplin  (None, 128, 128, 256)        0         ['tf.nn.relu_6[0][0]']        \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " tf.nn.relu_7 (TFOpLambda)   (None, 128, 128, 48)         0         ['batch_normalization_7[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate  (None, 128, 128, 304)        0         ['up_sampling2d_1[0][0]',     \n",
            " )                                                                   'tf.nn.relu_7[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)           (None, 128, 128, 256)        700416    ['concatenate_1[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_8 (Bat  (None, 128, 128, 256)        1024      ['conv2d_8[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " tf.nn.relu_8 (TFOpLambda)   (None, 128, 128, 256)        0         ['batch_normalization_8[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)           (None, 128, 128, 256)        589824    ['tf.nn.relu_8[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_9 (Bat  (None, 128, 128, 256)        1024      ['conv2d_9[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " tf.nn.relu_9 (TFOpLambda)   (None, 128, 128, 256)        0         ['batch_normalization_9[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " up_sampling2d_2 (UpSamplin  (None, 512, 512, 256)        0         ['tf.nn.relu_9[0][0]']        \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)          (None, 512, 512, 2)          514       ['up_sampling2d_2[0][0]']     \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 11919170 (45.47 MB)\n",
            "Trainable params: 11885922 (45.34 MB)\n",
            "Non-trainable params: 33248 (129.88 KB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Create model\n",
        "model = DeeplabV3Plus(image_size = IMAGE_SIZE, num_classes = NUM_CLASSES)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t-wTaL2GIRKZ"
      },
      "source": [
        "### Training\n",
        "\n",
        "We train the model using sparse categorical crossentropy as the loss function, and\n",
        "Adam as the optimizer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lHOIcWvJJipS"
      },
      "outputs": [],
      "source": [
        "# Compile model\n",
        "loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "model.compile(optimizer = keras.optimizers.Adam(learning_rate=0.001),\n",
        "              loss = loss,\n",
        "              metrics = [\"accuracy\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4X5RJA-4IRKZ",
        "outputId": "a6feba1c-d8e2-42c1-9ae2-21d8014e0f2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "150/150 [==============================] - 102s 370ms/step - loss: 0.0796 - accuracy: 0.9704 - val_loss: 0.0919 - val_accuracy: 0.9738\n",
            "Epoch 2/10\n",
            "150/150 [==============================] - 54s 359ms/step - loss: 0.0536 - accuracy: 0.9804 - val_loss: 0.0767 - val_accuracy: 0.9737\n",
            "Epoch 3/10\n",
            "150/150 [==============================] - 54s 362ms/step - loss: 0.0489 - accuracy: 0.9816 - val_loss: 0.0908 - val_accuracy: 0.9698\n",
            "Epoch 4/10\n",
            "150/150 [==============================] - 54s 361ms/step - loss: 0.0466 - accuracy: 0.9822 - val_loss: 0.0652 - val_accuracy: 0.9765\n",
            "Epoch 5/10\n",
            "150/150 [==============================] - 54s 358ms/step - loss: 0.0451 - accuracy: 0.9828 - val_loss: 0.1205 - val_accuracy: 0.9632\n",
            "Epoch 6/10\n",
            "150/150 [==============================] - 55s 369ms/step - loss: 0.0449 - accuracy: 0.9827 - val_loss: 0.7847 - val_accuracy: 0.8587\n",
            "Epoch 7/10\n",
            "150/150 [==============================] - 55s 368ms/step - loss: 0.0430 - accuracy: 0.9833 - val_loss: 0.0845 - val_accuracy: 0.9725\n",
            "Epoch 8/10\n",
            "150/150 [==============================] - 54s 360ms/step - loss: 0.0429 - accuracy: 0.9834 - val_loss: 0.1094 - val_accuracy: 0.9687\n",
            "Epoch 9/10\n",
            "150/150 [==============================] - 54s 361ms/step - loss: 0.0407 - accuracy: 0.9839 - val_loss: 0.1356 - val_accuracy: 0.9584\n",
            "Epoch 10/10\n",
            "150/150 [==============================] - 56s 369ms/step - loss: 0.0409 - accuracy: 0.9840 - val_loss: 0.0967 - val_accuracy: 0.9664\n"
          ]
        }
      ],
      "source": [
        "# Model Training\n",
        "history = model.fit(train_dataset, validation_data = val_dataset, epochs=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EFI7dNzjls1n"
      },
      "source": [
        "We can plot the training and validation loss to see how the training went. This should show generally decreasing values per epoch."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZXQrtWilsys"
      },
      "source": [
        "###  Learning curve from model history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CK39p3vukEUB"
      },
      "outputs": [],
      "source": [
        "# Function to plot learning curves\n",
        "\n",
        "def display_learning_curves(model_history):\n",
        "    acc = model_history.history[\"accuracy\"]\n",
        "    val_acc = model_history.history[\"val_accuracy\"]\n",
        "    loss = model_history.history[\"loss\"]\n",
        "    val_loss = model_history.history[\"val_loss\"]\n",
        "    epochs_range = model_history.epoch\n",
        "\n",
        "    fig = plt.figure(figsize=(8,5))\n",
        "\n",
        "    plt.subplot(1,2,1)\n",
        "    plt.plot(epochs_range, acc, label=\"train accuracy\")\n",
        "    plt.plot(epochs_range, val_acc, label=\"validataion accuracy\")\n",
        "    plt.title(\"Accuracy\")\n",
        "    plt.xlabel(\"Epoch\")\n",
        "    plt.ylabel(\"Accuracy\")\n",
        "    plt.legend(loc=\"lower right\")\n",
        "\n",
        "    plt.subplot(1,2,2)\n",
        "    plt.plot(epochs_range, loss, label=\"train loss\")\n",
        "    plt.plot(epochs_range, val_loss, label=\"validataion loss\")\n",
        "    plt.title(\"Loss\")\n",
        "    plt.xlabel(\"Epoch\")\n",
        "    plt.ylabel(\"Loss\")\n",
        "    plt.legend(loc=\"upper right\")\n",
        "\n",
        "    fig.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "wKrXpv_fs5Rj",
        "outputId": "a62e6297-1c06-4277-e35f-3ce031effd32"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Display learning curves\n",
        "display_learning_curves(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xyE-Kbx2MT2-"
      },
      "source": [
        "### Visualize Predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PKjiu5Bih-pk"
      },
      "outputs": [],
      "source": [
        "# Inference from model\n",
        "\n",
        "def infer(model, image_tensor):\n",
        "    # predictions from model, output shape -> (1, 512, 512, 2)\n",
        "    predictions = model.predict(np.expand_dims((image_tensor), axis=0))\n",
        "    # Shape after squeeze -> (512, 512, 2)\n",
        "    predictions = np.squeeze(predictions)\n",
        "    # Select only maximum predicted value for every pixel, output shape -> (512, 512)\n",
        "    predictions = np.argmax(predictions, axis=2)\n",
        "    return predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j38LNzT3dREQ"
      },
      "source": [
        "#### Plot the predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VOUW1R60sh76"
      },
      "outputs": [],
      "source": [
        "def plot_predictions(test_img, test_mask, model = model):\n",
        "    pred = infer(image_tensor = read_image(test_img), model = model)\n",
        "\n",
        "    Image.open(test_img)\n",
        "\n",
        "    fig = plt.figure(figsize=(8,5))\n",
        "\n",
        "    plt.subplot(1,3,1)\n",
        "    plt.imshow(Image.open(test_img))\n",
        "    plt.title(\"Input image\")\n",
        "\n",
        "    plt.subplot(1,3,2)\n",
        "    plt.imshow(Image.open(test_mask), cmap=\"Greys_r\")\n",
        "    plt.title(\"Actual label\")\n",
        "\n",
        "    plt.subplot(1,3,3)\n",
        "    plt.imshow(pred, cmap=\"Greys_r\")\n",
        "    plt.title(\"Predicted label\")\n",
        "\n",
        "    fig.tight_layout()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qVLkMSsOtskC",
        "outputId": "4f895acd-0bbe-4377-b5ff-ec299e22af34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 27ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 25ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 28ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 23ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for i in range(10,15):\n",
        "    plot_predictions(test_images[i], test_masks[i])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}