tutorial118_binary_semantic_segmentation_using_unet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "tutorial118_binary_semantic_segmentation_using_unet.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1xw32Pe6OToa6tNUR31cY-qF41qLbG6jU",
      "authorship_tag": "ABX9TyOjZUSPUL3ewoRUlDarrSGh",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bnsreenu/python_for_image_processing_APEER/blob/master/tutorial118_binary_semantic_segmentation_using_unet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BAywFlF3xZRP"
      },
      "source": [
        "https://youtu.be/oBIkr7CAE6g"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DWtdrQ4CIadK"
      },
      "source": [
        "Binary semantic segmentation using U-Net\n",
        "Dataset: https://www.epfl.ch/labs/cvlab/data/data-em/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGaY-5G4Ia6G"
      },
      "source": [
        "from tensorflow.keras.utils import normalize\n",
        "import os\n",
        "import cv2\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.optimizers import Adam\n",
        "import glob"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDVh0XT0IG4i"
      },
      "source": [
        "image_directory = '/content/drive/MyDrive/Colab Notebooks/data/mitochondria/mito256x256/images/'\n",
        "mask_directory = '/content/drive/MyDrive/Colab Notebooks/data/mitochondria/mito256x256/masks/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPCHFaI3MPNI"
      },
      "source": [
        "SIZE = 256\n",
        "num_images = 100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLeWPgkvRIyz"
      },
      "source": [
        "Load images and masks in order so they match"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nq3TsvM1Qwoj"
      },
      "source": [
        "image_names = glob.glob(\"/content/drive/MyDrive/Colab Notebooks/data/mitochondria/mito256x256/images/*.tif\")\n",
        "print(image_names)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-t7CqJ9pQ__G"
      },
      "source": [
        "image_names.sort()\n",
        "print(image_names)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-c3QKL8zR_es"
      },
      "source": [
        "image_names_subset = image_names[0:num_images]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBG0_PorQvFB"
      },
      "source": [
        "images = [cv2.imread(img, 0) for img in image_names_subset]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NojYOtGSUFN"
      },
      "source": [
        "image_dataset = np.array(images)\n",
        "image_dataset = np.expand_dims(image_dataset, axis = 3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HGqh1PaAS19H"
      },
      "source": [
        "Read masks the same way. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JRSBsa_QRg7d"
      },
      "source": [
        "mask_names = glob.glob(\"/content/drive/MyDrive/Colab Notebooks/data/mitochondria/mito256x256/masks/*.tif\")\n",
        "mask_names.sort()\n",
        "mask_names_subset = mask_names[0:num_images]\n",
        "masks = [cv2.imread(mask, 0) for mask in mask_names_subset]\n",
        "mask_dataset = np.array(masks)\n",
        "mask_dataset = np.expand_dims(mask_dataset, axis = 3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WPL2cpgwOMU4",
        "outputId": "7195dec7-1f46-4ca8-e655-b3942c82370a"
      },
      "source": [
        "print(\"Image data shape is: \", image_dataset.shape)\n",
        "print(\"Mask data shape is: \", mask_dataset.shape)\n",
        "print(\"Max pixel value in image is: \", image_dataset.max())\n",
        "print(\"Labels in the mask are : \", np.unique(mask_dataset))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image data shape is:  (100, 256, 256, 1)\n",
            "Mask data shape is:  (100, 256, 256, 1)\n",
            "Max pixel value in image is:  255\n",
            "Labels in the mask are :  [  0 255]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NeIv085mNXjC"
      },
      "source": [
        "#scaler = MinMaxScaler()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12JIfFgHPB4x"
      },
      "source": [
        "#test_image_data=scaler.fit_transform(image_dataset_uint8.reshape(-1, image_dataset_uint8.shape[-1])).reshape(image_dataset_uint8.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pEfm9pPEMmvL"
      },
      "source": [
        "#Normalize images\n",
        "image_dataset = image_dataset /255.  #Can also normalize or scale using MinMax scaler\n",
        "#Do not normalize masks, just rescale to 0 to 1.\n",
        "mask_dataset = mask_dataset /255.  #PIxel values will be 0 or 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "az5lOo48Msva"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(image_dataset, mask_dataset, test_size = 0.20, random_state = 42)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "tycUwzOoMtXZ",
        "outputId": "d9f05c72-e722-48be-f0d8-3877a83ea127"
      },
      "source": [
        "#Sanity check, view few mages\n",
        "import random\n",
        "\n",
        "image_number = random.randint(0, len(X_train)-1)\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(121)\n",
        "plt.imshow(X_train[image_number,:,:,0], cmap='gray')\n",
        "plt.subplot(122)\n",
        "plt.imshow(y_train[image_number,:,:,0], cmap='gray')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "do_6g3dqMGEw"
      },
      "source": [
        "# Building Unet by dividing encoder and decoder into blocks\n",
        "\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda\n",
        "from keras.optimizers import Adam\n",
        "from keras.layers import Activation, MaxPool2D, Concatenate\n",
        "\n",
        "\n",
        "def conv_block(input, num_filters):\n",
        "    x = Conv2D(num_filters, 3, padding=\"same\")(input)\n",
        "    x = BatchNormalization()(x)   #Not in the original network. \n",
        "    x = Activation(\"relu\")(x)\n",
        "\n",
        "    x = Conv2D(num_filters, 3, padding=\"same\")(x)\n",
        "    x = BatchNormalization()(x)  #Not in the original network\n",
        "    x = Activation(\"relu\")(x)\n",
        "\n",
        "    return x\n",
        "\n",
        "#Encoder block: Conv block followed by maxpooling\n",
        "\n",
        "\n",
        "def encoder_block(input, num_filters):\n",
        "    x = conv_block(input, num_filters)\n",
        "    p = MaxPool2D((2, 2))(x)\n",
        "    return x, p   \n",
        "\n",
        "#Decoder block\n",
        "#skip features gets input from encoder for concatenation\n",
        "\n",
        "def decoder_block(input, skip_features, num_filters):\n",
        "    x = Conv2DTranspose(num_filters, (2, 2), strides=2, padding=\"same\")(input)\n",
        "    x = Concatenate()([x, skip_features])\n",
        "    x = conv_block(x, num_filters)\n",
        "    return x\n",
        "\n",
        "#Build Unet using the blocks\n",
        "def build_unet(input_shape, n_classes):\n",
        "    inputs = Input(input_shape)\n",
        "\n",
        "    s1, p1 = encoder_block(inputs, 64)\n",
        "    s2, p2 = encoder_block(p1, 128)\n",
        "    s3, p3 = encoder_block(p2, 256)\n",
        "    s4, p4 = encoder_block(p3, 512)\n",
        "\n",
        "    b1 = conv_block(p4, 1024) #Bridge\n",
        "\n",
        "    d1 = decoder_block(b1, s4, 512)\n",
        "    d2 = decoder_block(d1, s3, 256)\n",
        "    d3 = decoder_block(d2, s2, 128)\n",
        "    d4 = decoder_block(d3, s1, 64)\n",
        "\n",
        "    if n_classes == 1:  #Binary\n",
        "      activation = 'sigmoid'\n",
        "    else:\n",
        "      activation = 'softmax'\n",
        "\n",
        "    outputs = Conv2D(n_classes, 1, padding=\"same\", activation=activation)(d4)  #Change the activation based on n_classes\n",
        "    print(activation)\n",
        "\n",
        "    model = Model(inputs, outputs, name=\"U-Net\")\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhNN7zEqMxaA"
      },
      "source": [
        "IMG_HEIGHT = image_dataset.shape[1]\n",
        "IMG_WIDTH  = image_dataset.shape[2]\n",
        "IMG_CHANNELS = image_dataset.shape[3]\n",
        "\n",
        "input_shape = (IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4mAlzJ5uM1nD",
        "outputId": "1be0368c-369a-4d85-f356-8c9d8a274621"
      },
      "source": [
        "model = build_unet(input_shape, n_classes=1)\n",
        "model.compile(optimizer=Adam(learning_rate = 1e-3), loss='binary_crossentropy', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sigmoid\n",
            "Model: \"U-Net\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 256, 256, 1) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 256, 256, 64) 640         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 256, 256, 64) 256         conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation (Activation)         (None, 256, 256, 64) 0           batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 256, 256, 64) 36928       activation[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 256, 256, 64) 256         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 256, 256, 64) 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 128, 128, 64) 0           activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 128, 128, 128 73856       max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 128, 128, 128 512         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 128, 128, 128 0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 128, 128, 128 147584      activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 128, 128, 128 512         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 128, 128, 128 0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 64, 64, 128)  0           activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 64, 64, 256)  295168      max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 64, 64, 256)  1024        conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 64, 64, 256)  0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 64, 64, 256)  590080      activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 64, 64, 256)  1024        conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 64, 64, 256)  0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 32, 32, 256)  0           activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 32, 32, 512)  1180160     max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 32, 32, 512)  2048        conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 32, 32, 512)  0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 32, 32, 512)  2359808     activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 32, 32, 512)  2048        conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 32, 32, 512)  0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 16, 16, 512)  0           activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 16, 16, 1024) 4719616     max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 16, 16, 1024) 4096        conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 16, 16, 1024) 0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 16, 16, 1024) 9438208     activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 16, 16, 1024) 4096        conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 16, 16, 1024) 0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose (Conv2DTranspo (None, 32, 32, 512)  2097664     activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 32, 32, 1024) 0           conv2d_transpose[0][0]           \n",
            "                                                                 activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 32, 32, 512)  4719104     concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 32, 32, 512)  2048        conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 32, 32, 512)  0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 32, 32, 512)  2359808     activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 32, 32, 512)  2048        conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 32, 32, 512)  0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_1 (Conv2DTrans (None, 64, 64, 256)  524544      activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 64, 64, 512)  0           conv2d_transpose_1[0][0]         \n",
            "                                                                 activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 64, 64, 256)  1179904     concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 64, 64, 256)  1024        conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 64, 64, 256)  0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 64, 64, 256)  590080      activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 64, 64, 256)  1024        conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 64, 64, 256)  0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_2 (Conv2DTrans (None, 128, 128, 128 131200      activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 128, 128, 256 0           conv2d_transpose_2[0][0]         \n",
            "                                                                 activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 128, 128, 128 295040      concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 128, 128, 128 512         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 128, 128, 128 0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 128, 128, 128 147584      activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 128, 128, 128 512         conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 128, 128, 128 0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_3 (Conv2DTrans (None, 256, 256, 64) 32832       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_3 (Concatenate)     (None, 256, 256, 128 0           conv2d_transpose_3[0][0]         \n",
            "                                                                 activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 256, 256, 64) 73792       concatenate_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 256, 256, 64) 256         conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 256, 256, 64) 0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 256, 256, 64) 36928       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 256, 256, 64) 256         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 256, 256, 64) 0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 256, 256, 1)  65          activation_17[0][0]              \n",
            "==================================================================================================\n",
            "Total params: 31,054,145\n",
            "Trainable params: 31,042,369\n",
            "Non-trainable params: 11,776\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbxgEXNDM4DY",
        "outputId": "557a1d80-6e90-4597-c6c4-aafc6052ebe0"
      },
      "source": [
        "history = model.fit(X_train, y_train, \n",
        "                    batch_size = 16, \n",
        "                    verbose=1, \n",
        "                    epochs=25, \n",
        "                    validation_data=(X_test, y_test), \n",
        "                    shuffle=False)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "50/50 [==============================] - 50s 905ms/step - loss: 0.3626 - accuracy: 0.8615 - val_loss: 76.7261 - val_accuracy: 0.0720\n",
            "Epoch 2/25\n",
            "50/50 [==============================] - 42s 837ms/step - loss: 0.0984 - accuracy: 0.9719 - val_loss: 0.3136 - val_accuracy: 0.9258\n",
            "Epoch 3/25\n",
            "50/50 [==============================] - 43s 860ms/step - loss: 0.0721 - accuracy: 0.9780 - val_loss: 0.2213 - val_accuracy: 0.9534\n",
            "Epoch 4/25\n",
            "50/50 [==============================] - 44s 880ms/step - loss: 0.0563 - accuracy: 0.9822 - val_loss: 0.8492 - val_accuracy: 0.8772\n",
            "Epoch 5/25\n",
            "50/50 [==============================] - 44s 877ms/step - loss: 0.0485 - accuracy: 0.9840 - val_loss: 1.1720 - val_accuracy: 0.8171\n",
            "Epoch 6/25\n",
            "50/50 [==============================] - 44s 879ms/step - loss: 0.0437 - accuracy: 0.9853 - val_loss: 0.3378 - val_accuracy: 0.9122\n",
            "Epoch 7/25\n",
            "50/50 [==============================] - 44s 882ms/step - loss: 0.0385 - accuracy: 0.9867 - val_loss: 0.2026 - val_accuracy: 0.9488\n",
            "Epoch 8/25\n",
            "50/50 [==============================] - 44s 882ms/step - loss: 0.0374 - accuracy: 0.9869 - val_loss: 0.2932 - val_accuracy: 0.9138\n",
            "Epoch 9/25\n",
            "50/50 [==============================] - 44s 881ms/step - loss: 0.0322 - accuracy: 0.9888 - val_loss: 0.2040 - val_accuracy: 0.9493\n",
            "Epoch 10/25\n",
            "50/50 [==============================] - 44s 881ms/step - loss: 0.0280 - accuracy: 0.9901 - val_loss: 0.1155 - val_accuracy: 0.9637\n",
            "Epoch 11/25\n",
            "50/50 [==============================] - 44s 880ms/step - loss: 0.0256 - accuracy: 0.9909 - val_loss: 0.0869 - val_accuracy: 0.9698\n",
            "Epoch 12/25\n",
            "50/50 [==============================] - 44s 881ms/step - loss: 0.0239 - accuracy: 0.9915 - val_loss: 0.0388 - val_accuracy: 0.9861\n",
            "Epoch 13/25\n",
            "50/50 [==============================] - 44s 882ms/step - loss: 0.0228 - accuracy: 0.9917 - val_loss: 0.0536 - val_accuracy: 0.9805\n",
            "Epoch 14/25\n",
            "50/50 [==============================] - 44s 882ms/step - loss: 0.0249 - accuracy: 0.9908 - val_loss: 0.0578 - val_accuracy: 0.9810\n",
            "Epoch 15/25\n",
            "50/50 [==============================] - 44s 882ms/step - loss: 0.0218 - accuracy: 0.9921 - val_loss: 0.0491 - val_accuracy: 0.9809\n",
            "Epoch 16/25\n",
            "50/50 [==============================] - 44s 882ms/step - loss: 0.0212 - accuracy: 0.9922 - val_loss: 0.0316 - val_accuracy: 0.9879\n",
            "Epoch 17/25\n",
            "50/50 [==============================] - 44s 882ms/step - loss: 0.0198 - accuracy: 0.9927 - val_loss: 0.1594 - val_accuracy: 0.9613\n",
            "Epoch 18/25\n",
            "50/50 [==============================] - 44s 881ms/step - loss: 0.0196 - accuracy: 0.9926 - val_loss: 0.1227 - val_accuracy: 0.9675\n",
            "Epoch 19/25\n",
            "50/50 [==============================] - 44s 878ms/step - loss: 0.0200 - accuracy: 0.9925 - val_loss: 0.1242 - val_accuracy: 0.9693\n",
            "Epoch 20/25\n",
            "50/50 [==============================] - 44s 879ms/step - loss: 0.0196 - accuracy: 0.9925 - val_loss: 0.0722 - val_accuracy: 0.9803\n",
            "Epoch 21/25\n",
            "50/50 [==============================] - 44s 879ms/step - loss: 0.0194 - accuracy: 0.9925 - val_loss: 0.1089 - val_accuracy: 0.9763\n",
            "Epoch 22/25\n",
            "50/50 [==============================] - 44s 880ms/step - loss: 0.0182 - accuracy: 0.9929 - val_loss: 0.0376 - val_accuracy: 0.9875\n",
            "Epoch 23/25\n",
            "50/50 [==============================] - 44s 881ms/step - loss: 0.0170 - accuracy: 0.9934 - val_loss: 0.0256 - val_accuracy: 0.9907\n",
            "Epoch 24/25\n",
            "50/50 [==============================] - 44s 882ms/step - loss: 0.0155 - accuracy: 0.9938 - val_loss: 0.0257 - val_accuracy: 0.9912\n",
            "Epoch 25/25\n",
            "50/50 [==============================] - 44s 882ms/step - loss: 0.0148 - accuracy: 0.9941 - val_loss: 0.0298 - val_accuracy: 0.9903\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNNYpeUJUz4Q"
      },
      "source": [
        "#Save the model for future use\n",
        "model.save('/content/drive/MyDrive/Colab Notebooks/saved_models/tutorial118_mitochondria_25epochs.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "Qyw0WoeVM8vw",
        "outputId": "f7ded1c4-dcb5-4073-8c05-61d29be6ffa8"
      },
      "source": [
        "#plot the training and validation accuracy and loss at each epoch\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, 'y', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "plt.plot(epochs, acc, 'y', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'r', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBmyuvr9U4MM"
      },
      "source": [
        "#Load previously saved model\n",
        "from keras.models import load_model\n",
        "model = load_model(\"/content/drive/MyDrive/Colab Notebooks/saved_models/tutorial118_mitochondria_25epochs.hdf5\", compile=False)\n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XflE1c9IM_16"
      },
      "source": [
        "#IOU\n",
        "y_pred=model.predict(X_test)\n",
        "y_pred_thresholded = y_pred > 0.5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NxwRS20eV05K"
      },
      "source": [
        "from tensorflow.keras.metrics import MeanIoU"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sz9lZ-oLVumx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46413096-2681-4fb5-d493-c2ed60363286"
      },
      "source": [
        "n_classes = 2\n",
        "IOU_keras = MeanIoU(num_classes=n_classes)  \n",
        "IOU_keras.update_state(y_pred_thresholded, y_test)\n",
        "print(\"Mean IoU =\", IOU_keras.result().numpy())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean IoU = 0.9221518\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIV3cDBWWeXH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "3bc0bb1c-ef76-4f8b-be26-389b0f48c580"
      },
      "source": [
        "threshold = 0.5\n",
        "test_img_number = random.randint(0, len(X_test)-1)\n",
        "test_img = X_test[test_img_number]\n",
        "ground_truth=y_test[test_img_number]\n",
        "test_img_input=np.expand_dims(test_img, 0)\n",
        "print(test_img_input.shape)\n",
        "prediction = (model.predict(test_img_input)[0,:,:,0] > 0.5).astype(np.uint8)\n",
        "print(prediction.shape)\n",
        "\n",
        "plt.figure(figsize=(16, 8))\n",
        "plt.subplot(231)\n",
        "plt.title('Testing Image')\n",
        "plt.imshow(test_img[:,:,0], cmap='gray')\n",
        "plt.subplot(232)\n",
        "plt.title('Testing Label')\n",
        "plt.imshow(ground_truth[:,:,0], cmap='gray')\n",
        "plt.subplot(233)\n",
        "plt.title('Prediction on test image')\n",
        "plt.imshow(prediction, cmap='gray')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 256, 256, 1)\n",
            "(256, 256)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}